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The increasing request for space for private buildings was brought about by the utilization of the previous dumpsites. If the issue of leachate infiltration into the soil isn't legitimately controlled it'll lead to future harm in construction works. The objectives are to compare the geotechnical properties of soil of the contaminated and uncontaminated regions region of the dump site and evaluate if the effect of leachate on the geotechnical properties of soil changes with depth. Laboratory soil tests were conducted on the soil samples obtained and compared the effect of these leachates at the dump site. These methods are Natural water content, Bulk Density, Specific Gravity, Shear strength, and Consolidation tests. The soil samples were obtained from the contaminated region, and the uncontaminated region (i.e. at 100 m away from the dumpsite). All soil samples were obtained at depths 0.5m, 1.0m, and 1.5m below the ground level, to know the effect of leachate on the soil at the dumpsite and also to know if the effects of leachate changes with depth as it goes down the soil. The results obtained show that samples at 0.5m and 1m depth have been affected by leachates but the effects are not so significant at 1.5m depth, thereby making the soil at depths 0.5m and 1m unfit for construction purposes. This result was useful to check the land requirement in urban areas and guide the geotechnical engineers when designing and constructing foundations for buildings and other related structures on these types of soils.

The increasing request for space for private buildings was brought about by the utilization of the previous dumpsites. If the issue of leachate infiltration into the soil isn't legitimately controlled it'll lead to future harm in construction works. The objectives are to compare the geotechnical properties of soil of the contaminated and uncontaminated regions region of the dump site and evaluate if the effect of leachate on the geotechnical properties of soil changes with depth. Laboratory soil tests were conducted on the soil samples obtained and compared the effect of these leachates at the dump site. These methods are Natural water content, Bulk Density, Specific Gravity, Shear strength, and Consolidation tests. The soil samples were obtained from the contaminated region, and the uncontaminated region (i.e. at 100 m away from the dumpsite). All soil samples were obtained at depths 0.5m, 1.0m, and 1.5m below the ground level, to know the effect of leachate on the soil at the dumpsite and also to know if the effects of leachate changes with depth as it goes down the soil. The results obtained show that samples at 0.5m and 1m depth have been affected by leachates but the effects are not so significant at 1.5m depth, thereby making the soil at depths 0.5m and 1m unfit for construction purposes. This result was useful to check the land requirement in urban areas and guide the geotechnical engineers when designing and constructing foundations for buildings and other related structures on these types of soils.

The increasing request for space for private buildings was brought about by the utilization of the previous dumpsites. If the issue of leachate infiltration into the soil isn't legitimately controlled it'll lead to future harm in construction works. The objectives are to compare the geotechnical properties of soil of the contaminated and uncontaminated regions region of the dump site and evaluate if the effect of leachate on the geotechnical properties of soil changes with depth. Laboratory soil tests were conducted on the soil samples obtained and compared the effect of these leachates at the dump site. These methods are Natural water content, Bulk Density, Specific Gravity, Shear strength, and Consolidation tests. The soil samples were obtained from the contaminated region, and the uncontaminated region (i.e. at 100 m away from the dumpsite). All soil samples were obtained at depths 0.5m, 1.0m, and 1.5m below the ground level, to know the effect of leachate on the soil at the dumpsite and also to know if the effects of leachate changes with depth as it goes down the soil. The results obtained show that samples at 0.5m and 1m depth have been affected by leachates but the effects are not so significant at 1.5m depth, thereby making the soil at depths 0.5m and 1m unfit for construction purposes. This result was useful to check the land requirement in urban areas and guide the geotechnical engineers when designing and constructing foundations for buildings and other related structures on these types of soils.

The wide adoption of Docker containers for supporting agile and elastic enterprise applications has led to a broad proliferation of container images. The associated storage performance and capacity requirements place high pressure on the infrastructure of container registries that store and distribute images and container storage systems on the Docker client side that manage image layers and store ephemeral data generated at container runtime. The storage demand is worsened by the large amount of duplicate data in images. Moreover, container storage systems that use Copy-on-Write (CoW) file systems as storage drivers exacerbate the redundancy. Exploiting the high file redundancy in real-world images is a promising approach to drastically reduce the growing storage requirements of container registries and improve the space efficiency of container storage systems. However, existing deduplication techniques significantly degrade the performance of both registries and container storage systems because of data reconstruction overhead as well as the deduplication cost. We propose DupHunter, an end-to-end deduplication that deduplicates layers for both Docker registries and container storage systems while maintaining a high image distribution speed and container I/O performance. DupHunter is divided into 3 tiers: Docker registry tier, middle tier, and client tier. Specifically, we first build a high-performance deduplication engine at the Docker registry tier that not only natively deduplicates layers for space savings but also reduces layer restore overhead. Then, we use deduplication offloading at the middle tier that utilizes the deduplication engine to eliminate the redundant files from the client tier, which avoids introducing deduplication overhead to the Docker client side. To further reduce the data duplicates caused by CoW and improve the container I/O performance, we use a container-aware backing file system at the client tier that preallocates space for each con

Large Language Models (LLMs) like ChatGPT and Llama2 have revolutionized natural language processing and search engine dynamics. However, these models incur exceptionally high computational costs. For instance, GPT-3 consists of 175 billion parameters where inference demands billions of floating-point operations. Caching is a natural solution to reduce LLM inference costs on repeated queries which constitute about 31% of the total queries. However, existing caching methods are incapable of finding semantic similarities among LLM queries, leading to unacceptable false hit-and-miss rates. This paper introduces MeanCache, a user-centric semantic cache for LLMs that identifies semantically similar queries to determine cache hit or miss. Using MeanCache, the response to a user's semantically similar query can be retrieved from a local cache rather than re-querying the LLM, thus reducing costs, service provider load, and environmental impact. Existing caching solutions for LLMs raise privacy and scalability concerns and perform wasteful query requests. MeanCache leverages Federated Learning (FL) to collaboratively train a query similarity model across LLM users without violating privacy. By placing a local cache in each user's device and using FL, MeanCache reduces the latency and costs and enhances model performance, resulting in lower false hit rates. MeanCache compresses the embedding dimensions to minimize cache storage and also finds the optimal cosine similarity threshold. Our experiments benchmarked against the state-of-the-art caching method, reveal that MeanCache attains an approximately 17% higher F-score and a 20% increase in precision during semantic cache hit-and-miss decisions. It also reduces the storage requirement by 83% and accelerates semantic cache hit-and-miss decisions by 11%., Comment: This study presents the first privacy aware semantic cache for LLMs based on Federated Learning. Total pages 13

As the number of pre-trained machine learning (ML) models is growing exponentially, data reduction tools are not catching up. Existing data reduction techniques are not specifically designed for pre-trained model (PTM) dataset files. This is largely due to a lack of understanding of the patterns and characteristics of these datasets, especially those relevant to data reduction and compressibility. This paper presents the first, exhaustive analysis to date of PTM datasets on storage compressibility. Our analysis spans different types of data reduction and compression techniques, from hash-based data deduplication, data similarity detection, to dictionary-coding compression. Our analysis explores these techniques at three data granularity levels, from model layers, model chunks, to model parameters. We draw new observations that indicate that modern data reduction tools are not effective when handling PTM datasets. There is a pressing need for new compression methods that take into account PTMs' data characteristics for effective storage reduction. Motivated by our findings, we design ELF, a simple yet effective, error-bounded, lossy floating-point compression method. ELF transforms floating-point parameters in such a way that the common exponent field of the transformed parameters can be completely eliminated to save storage space. We develop Elves, a compression framework that integrates ELF along with several other data reduction methods. Elves uses the most effective method to compress PTMs that exhibit different patterns. Evaluation shows that Elves achieves an overall compression ratio of $1.52\times$, which is $1.31\times$, $1.32\times$ and $1.29\times$ higher than a general-purpose compressor (zstd), an error-bounded lossy compressor (SZ3), and the uniform model quantization, respectively, with negligible model accuracy loss., Comment: This paper presents the first, exhaustive analysis to date of PTM datasets on storage compressibility. Motivated by our findings, we design ELF, a simple yet effective, error-bounded, lossy floating-point compression method

Federated Learning (FL) has emerged as a powerful approach that enables collaborative distributed model training without the need for data sharing. However, FL grapples with inherent heterogeneity challenges leading to issues such as stragglers, dropouts, and performance variations. Selection of clients to run an FL instance is crucial, but existing strategies introduce biases and participation issues and do not consider resource efficiency. Communication and training acceleration solutions proposed to increase client participation also fall short due to the dynamic nature of system resources. We address these challenges in this paper by designing FLOAT, a novel framework designed to boost FL client resource awareness. FLOAT optimizes resource utilization dynamically for meeting training deadlines, and mitigates stragglers and dropouts through various optimization techniques; leading to enhanced model convergence and improved performance. FLOAT leverages multi-objective Reinforcement Learning with Human Feedback (RLHF) to automate the selection of the optimization techniques and their configurations, tailoring them to individual client resource conditions. Moreover, FLOAT seamlessly integrates into existing FL systems, maintaining non-intrusiveness and versatility for both asynchronous and synchronous FL settings. As per our evaluations, FLOAT increases accuracy by up to 53%, reduces client dropouts by up to 78×, and improves communication, computation, and memory utilization by up to 81×, 44×, and 20× respectively.

A primary function of back-propagation is to compute both the gradient of hidden representations and parameters for optimization with gradient descent. Training large models requires high computational costs due to their vast parameter sizes. While Parameter-Efficient Fine-Tuning (PEFT) methods aim to train smaller auxiliary models to save computational space, they still present computational overheads, especially in Fine-Tuning as a Service (FTaaS) for numerous users. We introduce Collaborative Adaptation (ColA) with Gradient Learning (GL), a parameter-free, model-agnostic fine-tuning approach that decouples the computation of the gradient of hidden representations and parameters. In comparison to PEFT methods, ColA facilitates more cost-effective FTaaS by offloading the computation of the gradient to low-cost devices. We also provide a theoretical analysis of ColA and experimentally demonstrate that ColA can perform on par or better than existing PEFT methods on various benchmarks.

Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively. In this work, we propose FedDefender, a defense mechanism against targeted poisoning attacks in FL by leveraging differential testing. FedDefender first applies differential testing on clients’ models using a synthetic input. Instead of comparing the output (predicted label), which is unavailable for synthetic input, FedDefender fingerprints the neuron activations of clients’ models to identify a potentially malicious client containing a backdoor. We evaluate FedDefender using MNIST and FashionMNIST datasets with 20 and 30 clients, and our results demonstrate that FedDefender effectively mitigates such attacks, reducing the attack success rate (ASR) to 10% without deteriorating the global model performance.

Present study was carried out to observe the economic importance of backyard poultry birds in rural areas of Taluka Jhando Mari district Tando Allahyar during the 2022. Objective: To determine the profitability of backyard poultry production in rural areas of Tando Allahyar. Methods: Our study was divided into two different parts (i) primary and (ii) secondary part. Primary category was based on collection of data by interview from respondents by filling questionnaire. Results: The results revealed that 70% of household poultry kept by female and 30% were males. The cost function was estimated using the OLS method. The model of study suggested that price of egg and price of per bird were recorded statistically significant whereas per bird cost was observed non-significant statistically. It was concluded that high egg producing poultry breeds such RIR, Playmouth, White leghorn must be raised in order to increase the production of household backyard poultry birds. Conclusions: It is also concluded that improvement in rural poultry germplasm will be highly effective in rural poultry production system in future and will be major business for completing the food security and nutritional diet in middle class population of our country. Government of Sindh should also encourage private sector poultry by providing loan and poultry feed on cheapest prices to improve the production of backyard poultry farmers and their livelihood.

Climate-smart agriculture (CSA) is an approach to developing the technical, policy and investment conditions to achieve sustainable agricultural development for food security under climate change. multiple cropping systems using crop rotations or intercropping, growing of nitrate catch crops, growing of deep rooting crops following harvest of short duration and shallow rooting crops like vegetables,. winter wheat with deep rooting nature, high n demand and high n harvest index, crop livestock integration system, climate hardy crops such as pigeon pea, chickpea,inetnsive integrated farming systems, relay mixed cropping and lots of more systems have been time tested to be climate smart practices. Adoption of such cropping system strategy could substantially reduce offset the ill effects of climate vagaries. Pulse crops are also popular for their suitability in different cropping systems, owing to their short–life cycle. Development of large number of high yielding cultivars of crops with varying maturity duration and resistance to biotic and abiotic stresses, have made it possible to include them in irrigated crop sequence as well. Legume–based cropping systems can transform the rural livelihoods by overcoming the production constraints largely experienced over the decades due to cereal–based production systems and bring farmers I the league of defending boldily any vagaries of the nature.. Several conferences deliberated on such issues and number of lead papers were submitted in some of the conferences such as PRAGATI,ICFA,DISHA,ARISE,RAINFED etc. conducted during 2015 to 2018. The idea to bring out the entire edited chapters in the form a book was long felt and stressed upon by the eminent speakers and contributors.

In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and collaborative training make FL appealing for applications with data-privacy concerns, such as medical imaging. However, these FL characteristics pose unprecedented challenges for debugging. When a global model's performance deteriorates, identifying the responsible rounds and clients is a major pain point. Developers resort to trial-and-error debugging with subsets of clients, hoping to increase the global model's accuracy or let future FL rounds retune the model, which are time-consuming and costly. We design a systematic fault localization framework, FedDebug, that advances the FL debugging on two novel fronts. First, FedDebug enables interactive debugging of realtime collaborative training in FL by leveraging record and replay techniques to construct a simulation that mirrors live FL. FedDebug's breakpoint can help inspect an FL state (round, client, and global model) and move between rounds and clients' models seamlessly, enabling a fine-grained step-by-step inspection. Second, FedDebug automatically identifies the client(s) responsible for lowering the global model's performance without any testing data and labels--both are essential for existing debugging techniques. FedDebug's strengths come from adapting differential testing in conjunction with neuron activations to determine the client(s) deviating from normal behavior. FedDebug achieves 100% accuracy in finding a single faulty client and 90.3% accuracy in finding multiple faulty clients. FedDebug's interactive debugging incurs 1.2% overhead during training, while it localizes a faulty client in only 2.1% of a round's training time., Comment: Published at ICSE 2023. Link https://ieeexplore.ieee.org/document/10172839

Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that cannot be accomplished by a single entity alone. This is likely due to factors such as security constraints, privacy concerns, and limitations in computation resources. As a result, collaborative learning (CL) research has been gaining momentum. However, a significant challenge in practical applications of CL is how to effectively incentivize multiple entities to collaborate before any collaboration occurs. In this study, we propose ICL, a general framework for incentivized collaborative learning, and provide insights into the critical issue of when and why incentives can improve collaboration performance. Furthermore, we show the broad applicability of ICL to specific cases in federated learning, assisted learning, and multi-armed bandit with both theory and experimental results.

In the domain of intelligent transportation systems (ITS), collaborative perception has emerged as a promising approach to overcome the limitations of individual perception by enabling multiple agents to exchange information, thus enhancing their situational awareness. Collaborative perception overcomes the limitations of individual sensors, allowing connected agents to perceive environments beyond their line-of-sight and field of view. However, the reliability of collaborative perception heavily depends on the data aggregation strategy and communication bandwidth, which must overcome the challenges posed by limited network resources. To improve the precision of object detection and alleviate limited network resources, we propose an intermediate collaborative perception solution in the form of a graph attention network (GAT). The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision. We propose a feature fusion scheme using attention-based architectures and evaluate the results quantitatively in comparison to other state-of-the-art collaborative perception approaches. Our proposed approach is validated using the V2XSim dataset. The results of this work demonstrate the efficacy of the proposed approach for intermediate collaborative perception in improving object detection average precision while reducing network resource usage.

Personalized FL has been widely used to cater to heterogeneity challenges with non-IID data. A primary obstacle is considering the personalization process from the client's perspective to preserve their autonomy. Allowing the clients to participate in personalized FL decisions becomes significant due to privacy and security concerns, where the clients may not be at liberty to share private information necessary for producing good quality personalized models. Moreover, clients with high-quality data and resources are reluctant to participate in the FL process without reasonable incentive. In this paper, we propose PI-FL, a one-shot personalization solution complemented by a token-based incentive mechanism that rewards personalized training. PI-FL outperforms other state-of-the-art approaches and can generate good-quality personalized models while respecting clients' privacy.

This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, including fully-supervised, self-supervised, multi-task or proxy depth. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. This includes complex natural environments, e.g. forests or fields, which are greatly underrepresented in current benchmarks. The challenge received eight unique submissions that outperformed the provided SotA baseline on any of the pointcloud- or image-based metrics. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%. Supervised submissions generally leveraged large collections of datasets to improve data diversity. Self-supervised submissions instead updated the network architecture and pretrained backbones. These results represent a significant progress in the field, while highlighting avenues for future research, such as reducing interpolation artifacts at depth boundaries, improving self-supervised indoor performance and overall natural image accuracy., Comment: Published at CVPRW2023

Federated Learning (FL) trains a collaborative machine learning model by aggregating multiple privately trained clients' models over several training rounds. Such a long, continuous action of model aggregations poses significant challenges in reasoning about the origin and composition of such a global model. Regardless of the quality of the global model or if it has a fault, understanding the model's origin is equally important for debugging, interpretability, and explainability in federated learning. FL application developers often question: (1) what clients contributed towards a global model and (2) if a global model predicts a label, which clients are responsible for it? We introduce, neuron provenance, a fine-grained lineage capturing mechanism that tracks the flow of information between the individual participating clients in FL and the final global model. We operationalize this concept in ProvFL that functions on two key principles. First, recognizing that monitoring every neuron of every client's model statically is ineffective and noisy due to the uninterpretable nature of individual neurons, ProvFL dynamically isolates influential and sensitive neurons in the global model, significantly reducing the search space. Second, as multiple clients' models are fused in each round to form a global model, tracking each client's contribution becomes challenging. ProvFL leverages the invertible nature of fusion algorithms to precisely isolate each client's contribution derived from selected neurons. When asked to localize the clients responsible for the given behavior (i.e., prediction) of the global model, ProvFL successfully localizes them with an average provenance accuracy of 97%. Additionally, ProvFL outperforms the state-of-the-art FL fault localization approach by an average margin of 50%., Comment: 22 pages. For access to the source code used in this study, please contact the authors directly

Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by countries such as Belgium and the Netherlands as an essential step towards a sustainable future. These environments pose unique challenges, since they can contain dynamic obstacles that do not broadcast their location, such as small vessels, kayaks or buoys. Therefore, this research proposes a navigational algorithm which can navigate an inland vessel in a wide variety of complex port scenarios using ranging sensors to observe the environment. The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning. By randomizing the port environments during training, the trained model can navigate in scenarios that it never encountered during training. Furthermore, results show that our approach outperforms the commonly used dynamic window approach and a benchmark model-free reinforcement learning algorithm. This work is therefore a significant step towards vessels that can navigate autonomously in complex port scenarios., Comment: Presented at 42nd International Conference on Ocean, Offshore & Arctic Engineering. June 11 - 16, 2023. Melbourne, Australia

Recently, there has been an upsurge in the research on maritime vision, where a lot of works are influenced by the application of computer vision for Unmanned Surface Vehicles (USVs). Various sensor modalities such as camera, radar, and lidar have been used to perform tasks such as object detection, segmentation, object tracking, and motion planning. A large subset of this research is focused on the video analysis, since most of the current vessel fleets contain the camera's onboard for various surveillance tasks. Due to the vast abundance of the video data, video scene change detection is an initial and crucial stage for scene understanding of USVs. This paper outlines our approach to detect dynamic scene changes in USVs. To the best of our understanding, this work represents the first investigation of scene change detection in the maritime vision application. Our objective is to identify significant changes in the dynamic scenes of maritime video data, particularly those scenes that exhibit a high degree of resemblance. In our system for dynamic scene change detection, we propose completely unsupervised learning method. In contrast to earlier studies, we utilize a modified cutting-edge generative picture model called VQ-VAE-2 to train on multiple marine datasets, aiming to enhance the feature extraction. Next, we introduce our innovative similarity scoring technique for directly calculating the level of similarity in a sequence of consecutive frames by utilizing grid calculation on retrieved features. The experiments were conducted using a nautical video dataset called RoboWhaler to showcase the efficient performance of our technique., Comment: WACV 2024 conference

In recent years, interest in autonomous shipping in urban waterways has increased significantly due to the trend of keeping cars and trucks out of city centers. Classical approaches such as Frenet frame based planning and potential field navigation often require tuning of many configuration parameters and sometimes even require a different configuration depending on the situation. In this paper, we propose a novel path planning approach based on reinforcement learning called Model Predictive Reinforcement Learning (MPRL). MPRL calculates a series of waypoints for the vessel to follow. The environment is represented as an occupancy grid map, allowing us to deal with any shape of waterway and any number and shape of obstacles. We demonstrate our approach on two scenarios and compare the resulting path with path planning using a Frenet frame and path planning based on a proximal policy optimization (PPO) agent. Our results show that MPRL outperforms both baselines in both test scenarios. The PPO based approach was not able to reach the goal in either scenario while the Frenet frame approach failed in the scenario consisting of a corner with obstacles. MPRL was able to safely (collision free) navigate to the goal in both of the test scenarios., Comment: \c{opyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Present study was carried out to observe the economic importance of backyard poultry birds in rural areas of Taluka Jhando Mari district Tando Allahyar during the 2022. Objective: To determine the profitability of backyard poultry production in rural areas of Tando Allahyar. Methods: Our study was divided into two different parts (i) primary and (ii) secondary part. Primary category was based on collection of data by interview from respondents by filling questionnaire. Results: The results revealed that 70% of household poultry kept by female and 30% were males. The cost function was estimated using the OLS method. The model of study suggested that price of egg and price of per bird were recorded statistically significant whereas per bird cost was observed non-significant statistically. It was concluded that high egg producing poultry breeds such RIR, Playmouth, White leghorn must be raised in order to increase the production of household backyard poultry birds. Conclusions: It is also concluded that improvement in rural poultry germplasm will be highly effective in rural poultry production system in future and will be major business for completing the food security and nutritional diet in middle class population of our country. Government of Sindh should also encourage private sector poultry by providing loan and poultry feed on cheapest prices to improve the production of backyard poultry farmers and their livelihood.

Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively. In this work, we propose FedDefender, a defense mechanism against targeted poisoning attacks in FL by leveraging differential testing. Our proposed method fingerprints the neuron activations of clients' models on the same input and uses differential testing to identify a potentially malicious client containing a backdoor. We evaluate FedDefender using MNIST and FashionMNIST datasets with 20 and 30 clients, and our results demonstrate that FedDefender effectively mitigates such attacks, reducing the attack success rate (ASR) to 10\% without deteriorating the global model performance., Comment: Published in SE4SafeML 2023 (co-located with FSE 2023). See https://dl.acm.org/doi/abs/10.1145/3617574.3617858

Present study was carried out to observe the economic importance of backyard poultry birds in rural areas of Taluka Jhando Mari district Tando Allahyar during the 2022. Objective: To determine the profitability of backyard poultry production in rural areas of Tando Allahyar. Methods: Our study was divided into two different parts (i) primary and (ii) secondary part. Primary category was based on collection of data by interview from respondents by filling questionnaire. Results: The results revealed that 70% of household poultry kept by female and 30% were males. The cost function was estimated using the OLS method. The model of study suggested that price of egg and price of per bird were recorded statistically significant whereas per bird cost was observed non-significant statistically. It was concluded that high egg producing poultry breeds such RIR, Playmouth, White leghorn must be raised in order to increase the production of household backyard poultry birds. Conclusions: It is also concluded that improvement in rural poultry germplasm will be highly effective in rural poultry production system in future and will be major business for completing the food security and nutritional diet in middle class population of our country. Government of Sindh should also encourage private sector poultry by providing loan and poultry feed on cheapest prices to improve the production of backyard poultry farmers and their livelihood.

The widespread use of synthetic insecticides has led to many negative consequences, resulting in increasing attention to natural products. The aim of this study was to evaluate the bio-insecticidal capacity of Datura stramonium leaves and flowers using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Datura leaves contained saponnins, alkaloids, tannins but the flowers contained also flavonoids and steroids. 14 compounds were detected from Datura leaves ethanol extracts of which Butanol, 3-methyl is the main component (79.76%) followed by Toluene (6.14%) and Phytol (3.9%), while that of the flowers detected also 14 compounds of which formic acid, 3-methylbut-2-yl ester is the main component (82.22%). The ethanol extract of Datura leaves reflected LC50 of: 844.43 mg/L, against Cx. quiquefasciatus larvae, while that of the flowers was 636.62 mg/L. The ethanol extract of Datura leaves, after one week, left no survived Anopheles or Culex larvae, whereas, 10% of Aedes larvae were survived. The ethanol extract of Datura flowers, after one week, left no survived Anopheles or Culex or Aedes larvae, but some were developed to pupae. Field assessment should be run to evaluate the impact on the aquatic predators.

Plant-based pesticides are gaining attention as safe, effective, eco-friendly alternatives to synthetic pesticides. The aim of this study was to evaluate the bio-insecticidal capacity of Cannabis (Cannabis sativa) plants using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Cannabis seeds contained flavonoids, alkaloids and steroids, and separated 5 spots through TLC. The GC-MS tests of Cannabis seeds detected Dronabinol (41.46%) as the main component, followed by Cannabinol (10.38%), Phytol (2.86%), Vitamin E (2.27%) and Caryophylene (2.07%). The ethanol extract of Cannabis seeds reflected LC50 of 218.1 mg/L, after 24. The more potent spot of Cannabis seeds was (3) against Anopheles larvae, but only spot (4) was more potent against Culex larvae and spot (5) against Aedes larvae. After one week of submission to Cannabis seed ethanol extract, the survived Anopheles larvae was 6.7%, while no survived Culex larvae, whereas 13.3% of Aedes larvae survived. Field assessment should be run to evaluate the sustainability of this products.

The widespread use of synthetic insecticides has led to many negative consequences, resulting in increasing attention to natural products. The aim of this study was to evaluate the bio-insecticidal capacity of Datura stramonium leaves and flowers using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Datura leaves contained saponnins, alkaloids, tannins but the flowers contained also flavonoids and steroids. 14 compounds were detected from Datura leaves ethanol extracts of which Butanol, 3-methyl is the main component (79.76%) followed by Toluene (6.14%) and Phytol (3.9%), while that of the flowers detected also 14 compounds of which formic acid, 3-methylbut-2-yl ester is the main component (82.22%). The ethanol extract of Datura leaves reflected LC50 of: 844.43 mg/L, against Cx. quiquefasciatus larvae, while that of the flowers was 636.62 mg/L. The ethanol extract of Datura leaves, after one week, left no survived Anopheles or Culex larvae, whereas, 10% of Aedes larvae were survived. The ethanol extract of Datura flowers, after one week, left no survived Anopheles or Culex or Aedes larvae, but some were developed to pupae. Field assessment should be run to evaluate the impact on the aquatic predators.

Plant-based pesticides are gaining attention as safe, effective, eco-friendly alternatives to synthetic pesticides. The aim of this study was to evaluate the bio-insecticidal capacity of Cannabis (Cannabis sativa) plants using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Cannabis seeds contained flavonoids, alkaloids and steroids, and separated 5 spots through TLC. The GC-MS tests of Cannabis seeds detected Dronabinol (41.46%) as the main component, followed by Cannabinol (10.38%), Phytol (2.86%), Vitamin E (2.27%) and Caryophylene (2.07%). The ethanol extract of Cannabis seeds reflected LC50 of 218.1 mg/L, after 24. The more potent spot of Cannabis seeds was (3) against Anopheles larvae, but only spot (4) was more potent against Culex larvae and spot (5) against Aedes larvae. After one week of submission to Cannabis seed ethanol extract, the survived Anopheles larvae was 6.7%, while no survived Culex larvae, whereas 13.3% of Aedes larvae survived. Field assessment should be run to evaluate the sustainability of this products.

Plant-based pesticides are gaining attention as safe, effective, eco-friendly alternatives to synthetic pesticides. The aim of this study was to evaluate the bio-insecticidal capacity of Cannabis (Cannabis sativa) plants using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Cannabis seeds contained flavonoids, alkaloids and steroids, and separated 5 spots through TLC. The GC-MS tests of Cannabis seeds detected Dronabinol (41.46%) as the main component, followed by Cannabinol (10.38%), Phytol (2.86%), Vitamin E (2.27%) and Caryophylene (2.07%). The ethanol extract of Cannabis seeds reflected LC50 of 218.1 mg/L, after 24. The more potent spot of Cannabis seeds was (3) against Anopheles larvae, but only spot (4) was more potent against Culex larvae and spot (5) against Aedes larvae. After one week of submission to Cannabis seed ethanol extract, the survived Anopheles larvae was 6.7%, while no survived Culex larvae, whereas 13.3% of Aedes larvae survived. Field assessment should be run to evaluate the sustainability of this products.

Friction stir welding (FSW) of high-strength materials and thicker plates has a number of challenges that are preventing widespread industry application such as (i) premature tool failure, (ii) selection of tool material and design, and (iii) weld productivity. A finite element (FE) based analysis for Laser-FSW hybrid welding technology is carried out in ABAQUS, commercially accessible FE software, to solve these typical difficulties. The thermal analysis of Laser-FSW hybrid technique is done using two moving heat sources for the case of 3.2 mm thick S45C steel plate. A negative moving heat source is placed as a replacement of the backing plate material in the high contact pressure region under the FSW tool. It was shown that the laser heat source needs to be 10 mm ahead of the FSW tool for achieving the duplex phase (Ferrite + Bainite/Martensite) microstructure. © 2022

Friction stir welding (FSW) of high-strength materials and thicker plates has a number of challenges that are preventing widespread industry application such as (i) premature tool failure, (ii) selection of tool material and design, and (iii) weld productivity. A finite element (FE) based analysis for Laser-FSW hybrid welding technology is carried out in ABAQUS, commercially accessible FE software, to solve these typical difficulties. The thermal analysis of Laser-FSW hybrid technique is done using two moving heat sources for the case of 3.2 mm thick S45C steel plate. A negative moving heat source is placed as a replacement of the backing plate material in the high contact pressure region under the FSW tool. It was shown that the laser heat source needs to be 10 mm ahead of the FSW tool for achieving the duplex phase (Ferrite + Bainite/Martensite) microstructure. © 2022

Friction stir welding (FSW) of high-strength materials and thicker plates has a number of challenges that are preventing widespread industry application such as (i) premature tool failure, (ii) selection of tool material and design, and (iii) weld productivity. A finite element (FE) based analysis for Laser-FSW hybrid welding technology is carried out in ABAQUS, commercially accessible FE software, to solve these typical difficulties. The thermal analysis of Laser-FSW hybrid technique is done using two moving heat sources for the case of 3.2 mm thick S45C steel plate. A negative moving heat source is placed as a replacement of the backing plate material in the high contact pressure region under the FSW tool. It was shown that the laser heat source needs to be 10 mm ahead of the FSW tool for achieving the duplex phase (Ferrite + Bainite/Martensite) microstructure. © 2022

Friction stir welding (FSW) of high-strength materials and thicker plates has a number of challenges that are preventing widespread industry application such as (i) premature tool failure, (ii) selection of tool material and design, and (iii) weld productivity. A finite element (FE) based analysis for Laser-FSW hybrid welding technology is carried out in ABAQUS, commercially accessible FE software, to solve these typical difficulties. The thermal analysis of Laser-FSW hybrid technique is done using two moving heat sources for the case of 3.2 mm thick S45C steel plate. A negative moving heat source is placed as a replacement of the backing plate material in the high contact pressure region under the FSW tool. It was shown that the laser heat source needs to be 10 mm ahead of the FSW tool for achieving the duplex phase (Ferrite + Bainite/Martensite) microstructure. © 2022

Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: the personalization problem that the RSs trained traditionally may not be customized for individual users, and the privacy problem that directly sharing user data is not encouraged. We propose Personalized Federated Recommender Systems (PersonalFR), which introduces a personalized autoencoder-based recommendation model with Federated Learning (FL) to address these challenges. PersonalFR guarantees that each user can learn a personal model from the local dataset and other participating users' data without sharing local data, data embeddings, or models. PersonalFR consists of three main components, including AutoEncoder-based RSs (ARSs) that learn the user-item interactions, Partially Federated Learning (PFL) that updates the encoder locally and aggregates the decoder on the server-side, and Partial Compression (PC) that only computes and transmits active model parameters. Extensive experiments on two real-world datasets demonstrate that PersonalFR can achieve private and personalized performance comparable to that trained by centralizing all users' data. Moreover, PersonalFR requires significantly less computation and communication overhead than standard FL baselines.

Cloud object storage such as AWS S3 is cost-effective and highly elastic but relatively slow, while high-performance cloud storage such as AWS ElastiCache is expensive and provides limited elasticity. We present a new cloud storage service called ServerlessMemory, which stores data using the memory of serverless functions. ServerlessMemory employs a sliding-window-based memory management strategy inspired by the garbage collection mechanisms used in the programming language to effectively segregate hot/cold data and provides fine-grained elasticity, good performance, and a pay-per-access cost model with extremely low cost. We then design and implement InfiniStore, a persistent and elastic cloud storage system, which seamlessly couples the function-based ServerlessMemory layer with a persistent, inexpensive cloud object store layer. InfiniStore enables durability despite function failures using a fast parallel recovery scheme built on the autoscaling functionality of a FaaS (Function-as-a-Service) platform. We evaluate InfiniStore extensively using both microbenchmarking and two real-world applications. Results show that InfiniStore has more performance benefits for objects larger than 10 MB compared to AWS ElastiCache and Anna, and InfiniStore achieves 26.25% and 97.24% tenant-side cost reduction compared to InfiniCache and ElastiCache, respectively., Comment: An extensive report of the paper accepted by VLDB 2023

Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated learning typically requires the utilization of multi-party computation techniques to provide strong privacy guarantees by ensuring that an untrusted or curious aggregator cannot obtain isolated replies from parties involved in the training process, thereby preventing potential inference attacks. Until recently, it was thought that some of these secure aggregation techniques were sufficient to fully protect against inference attacks coming from a curious aggregator. However, recent research has demonstrated that a curious aggregator can successfully launch a disaggregation attack to learn information about model updates of a target party. This paper presents DeTrust-FL, an efficient privacy-preserving federated learning framework for addressing the lack of transparency that enables isolation attacks, such as disaggregation attacks, during secure aggregation by assuring that parties' model updates are included in the aggregated model in a private and secure manner. DeTrust-FL proposes a decentralized trust consensus mechanism and incorporates a recently proposed decentralized functional encryption (FE) scheme in which all parties agree on a participation matrix before collaboratively generating decryption key fragments, thereby gaining control and trust over the secure aggregation process in a decentralized setting. Our experimental evaluation demonstrates that DeTrust-FL outperforms state-of-the-art FE-based secure multi-party aggregation solutions in terms of training time and reduces the volume of data transferred. In contrast to existing approaches, this is achieved without creating any trust dependency on external trusted entities.

Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4X, time efficiency by 8X, and reduces costs by more than 2X compared to extant cloud-based static methodologies., Comment: 10 pages, 12 figures, 4 tables This paper has been accepted at the 2023 IEEE International Conference on Big Data (BigData)

Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data. With respect to ethical guidelines, FL is promising regarding privacy, but needs to excel vis-\`a-vis transparency and trustworthiness. In particular, FL has to address the accountability of the parties involved and their adherence to rules, law and principles. We introduce AF^2 Framework, where we instrument FL with accountability by fusing verifiable claims with tamper-evident facts, into reproducible arguments. We build on AI FactSheets for instilling transparency and trustworthiness into the AI lifecycle and expand it to incorporate dynamic and nested facts, as well as complex model compositions in FL. Based on our approach, an auditor can validate, reproduce and certify a FL process. This can be directly applied in practice to address the challenges of AI engineering and ethics., Comment: 16 pages, 4 figures, 2 tables

The widespread use of synthetic insecticides has led to many negative consequences, resulting in increasing attention to natural products. The aim of this study was to evaluate the bio-insecticidal capacity of Datura stramonium leaves and flowers using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Datura leaves contained saponnins, alkaloids, tannins but the flowers contained also flavonoids and steroids. 14 compounds were detected from Datura leaves ethanol extracts of which Butanol, 3-methyl is the main component (79.76%) followed by Toluene (6.14%) and Phytol (3.9%), while that of the flowers detected also 14 compounds of which formic acid, 3-methylbut-2-yl ester is the main component (82.22%). The ethanol extract of Datura leaves reflected LC50 of: 844.43 mg/L, against Cx. quiquefasciatus larvae, while that of the flowers was 636.62 mg/L. The ethanol extract of Datura leaves, after one week, left no survived Anopheles or Culex larvae, whereas, 10% of Aedes larvae were survived. The ethanol extract of Datura flowers, after one week, left no survived Anopheles or Culex or Aedes larvae, but some were developed to pupae. Field assessment should be run to evaluate the impact on the aquatic predators.

Plant-based pesticides are gaining attention as safe, effective, eco-friendly alternatives to synthetic pesticides. The aim of this study was to evaluate the bio-insecticidal capacity of Cannabis (Cannabis sativa) plants using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Cannabis seeds contained flavonoids, alkaloids and steroids, and separated 5 spots through TLC. The GC-MS tests of Cannabis seeds detected Dronabinol (41.46%) as the main component, followed by Cannabinol (10.38%), Phytol (2.86%), Vitamin E (2.27%) and Caryophylene (2.07%). The ethanol extract of Cannabis seeds reflected LC50 of 218.1 mg/L, after 24. The more potent spot of Cannabis seeds was (3) against Anopheles larvae, but only spot (4) was more potent against Culex larvae and spot (5) against Aedes larvae. After one week of submission to Cannabis seed ethanol extract, the survived Anopheles larvae was 6.7%, while no survived Culex larvae, whereas 13.3% of Aedes larvae survived. Field assessment should be run to evaluate the sustainability of this products.

The widespread use of synthetic insecticides has led to many negative consequences, resulting in increasing attention to natural products. The aim of this study was to evaluate the bio-insecticidal capacity of Datura stramonium leaves and flowers using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Datura leaves contained saponnins, alkaloids, tannins but the flowers contained also flavonoids and steroids. 14 compounds were detected from Datura leaves ethanol extracts of which Butanol, 3-methyl is the main component (79.76%) followed by Toluene (6.14%) and Phytol (3.9%), while that of the flowers detected also 14 compounds of which formic acid, 3-methylbut-2-yl ester is the main component (82.22%). The ethanol extract of Datura leaves reflected LC50 of: 844.43 mg/L, against Cx. quiquefasciatus larvae, while that of the flowers was 636.62 mg/L. The ethanol extract of Datura leaves, after one week, left no survived Anopheles or Culex larvae, whereas, 10% of Aedes larvae were survived. The ethanol extract of Datura flowers, after one week, left no survived Anopheles or Culex or Aedes larvae, but some were developed to pupae. Field assessment should be run to evaluate the impact on the aquatic predators.

Plant-based pesticides are gaining attention as safe, effective, eco-friendly alternatives to synthetic pesticides. The aim of this study was to evaluate the bio-insecticidal capacity of Cannabis (Cannabis sativa) plants using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Cannabis seeds contained flavonoids, alkaloids and steroids, and separated 5 spots through TLC. The GC-MS tests of Cannabis seeds detected Dronabinol (41.46%) as the main component, followed by Cannabinol (10.38%), Phytol (2.86%), Vitamin E (2.27%) and Caryophylene (2.07%). The ethanol extract of Cannabis seeds reflected LC50 of 218.1 mg/L, after 24. The more potent spot of Cannabis seeds was (3) against Anopheles larvae, but only spot (4) was more potent against Culex larvae and spot (5) against Aedes larvae. After one week of submission to Cannabis seed ethanol extract, the survived Anopheles larvae was 6.7%, while no survived Culex larvae, whereas 13.3% of Aedes larvae survived. Field assessment should be run to evaluate the sustainability of this products.

The widespread use of synthetic insecticides has led to many negative consequences, resulting in increasing attention to natural products. The aim of this study was to evaluate the bio-insecticidal capacity of Datura stramonium leaves and flowers using GC-MS and phytochemical techniques and also mosquito's larvae as bioindicators. The phytochemical screening and the biological effect tests were run at Basic Sciences Laboratories, whereas GC-MS applications were run at the Central Laboratory, University of Gezira. The results showed that, Datura leaves contained saponnins, alkaloids, tannins but the flowers contained also flavonoids and steroids. 14 compounds were detected from Datura leaves ethanol extracts of which Butanol, 3-methyl is the main component (79.76%) followed by Toluene (6.14%) and Phytol (3.9%), while that of the flowers detected also 14 compounds of which formic acid, 3-methylbut-2-yl ester is the main component (82.22%). The ethanol extract of Datura leaves reflected LC50 of: 844.43 mg/L, against Cx. quiquefasciatus larvae, while that of the flowers was 636.62 mg/L. The ethanol extract of Datura leaves, after one week, left no survived Anopheles or Culex larvae, whereas, 10% of Aedes larvae were survived. The ethanol extract of Datura flowers, after one week, left no survived Anopheles or Culex or Aedes larvae, but some were developed to pupae. Field assessment should be run to evaluate the impact on the aquatic predators.

In the present work, a thermal treatment technique is applied for the synthesis of CexSn1−xO2 nanoparticles. Using this method has developed understanding of how lower and higher precursor values affect the morphology, structure, and optical properties of CexSn1−xO2 nanoparticles. CexSn1−xO2 nanoparticle synthesis involves a reaction between cerium and tin sources, namely, cerium nitrate hexahydrate and tin (II) chloride dihydrate, respectively, and the capping agent, polyvinylpyrrolidone (PVP). The findings indicate that lower x values yield smaller particle size with a higher energy band gap, while higher x values yield a larger particle size with a smaller energy band gap. Thus, products with lower x values may be suitable for antibacterial activity applications as smaller particles can diffuse through the cell wall faster, while products with higher x values may be suitable for solar cell energy applications as more electrons can be generated at larger particle sizes. The synthesized samples were profiled via a number of methods, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). As revealed by the XRD pattern analysis, the CexSn1−xO2 nanoparticles formed after calcination reflect the cubic fluorite structure and cassiterite-type tetragonal structure of CexSn1−xO2 nanoparticles. Meanwhile, using FT-IR analysis, Ce-O and Sn-O were confirmed as the primary bonds of ready CexSn1−xO2 nanoparticle samples, whilst TEM analysis highlighted that the average particle size was in the range 6−21 nm as the precursor concentration (Ce(NO3)3·6H2O) increased from 0.00 to 1.00. Moreover, the diffuse UV-visible reflectance spectra used to determine the optical band gap based on the Kubelka–Munk equation showed that an increase in x value has caused a decrease in the energy band gap and vice versa.

In the present work, a thermal treatment technique is applied for the synthesis of CexSn1−xO2 nanoparticles. Using this method has developed understanding of how lower and higher precursor values affect the morphology, structure, and optical properties of CexSn1−xO2 nanoparticles. CexSn1−xO2 nanoparticle synthesis involves a reaction between cerium and tin sources, namely, cerium nitrate hexahydrate and tin (II) chloride dihydrate, respectively, and the capping agent, polyvinylpyrrolidone (PVP). The findings indicate that lower x values yield smaller particle size with a higher energy band gap, while higher x values yield a larger particle size with a smaller energy band gap. Thus, products with lower x values may be suitable for antibacterial activity applications as smaller particles can diffuse through the cell wall faster, while products with higher x values may be suitable for solar cell energy applications as more electrons can be generated at larger particle sizes. The synthesized samples were profiled via a number of methods, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). As revealed by the XRD pattern analysis, the CexSn1−xO2 nanoparticles formed after calcination reflect the cubic fluorite structure and cassiterite-type tetragonal structure of CexSn1−xO2 nanoparticles. Meanwhile, using FT-IR analysis, Ce-O and Sn-O were confirmed as the primary bonds of ready CexSn1−xO2 nanoparticle samples, whilst TEM analysis highlighted that the average particle size was in the range 6−21 nm as the precursor concentration (Ce(NO3)3·6H2O) increased from 0.00 to 1.00. Moreover, the diffuse UV-visible reflectance spectra used to determine the optical band gap based on the Kubelka–Munk equation showed that an increase in x value has caused a decrease in the energy band gap and vice versa.

Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive communication costs, limited resources, and data heterogeneity. In this paper, we propose SPATL, an FL method that addresses these issues by: (a) introducing a salient parameter selection agent and communicating selected parameters only; (b) splitting a model into a shared encoder and a local predictor, and transferring its knowledge to heterogeneous clients via the locally customized predictor. Additionally, we leverage a gradient control mechanism to further speed up model convergence and increase robustness of training processes. Experiments demonstrate that SPATL reduces communication overhead, accelerates model inference, and enables stable training processes with better results compared to state-of-the-art methods. Our approach reduces communication cost by up to $86.45\%$, accelerates local inference by reducing up to $39.7\%$ FLOPs on VGG-11, and requires $7.4 \times$ less communication overhead when training ResNet-20., Comment: Accepted at The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 22)

Mobile commerce (m-commerce) has become increasingly important for organizations attempting to grow revenue by expanding into international markets. However, for multinational mobile retailers (m-retailers), one of the greatest challenges lies in carefully managing their websites across multiple national markets. This work advances cross-national research on m-retailing by (1) examining how value dimensions shape m-shoppers’ motivations, (2) analyzing differential effects of hedonic and utilitarian motivations on intention and habit, and (3) examining the competing roles of conscious (intentional) and unconscious (habitual) m-commerce use drivers across developed and developing countries. This research also examines the moderating role of m-commerce readiness at the country level on the effect of motivation on intention and habit, along with their impact on m-commerce use. Based on data from 1,975 m-shoppers in nine countries (Australia, Bangladesh, Brazil, India, Pakistan, Singapore, the United Kingdom, the United States, and Vietnam) across four continents, the results demonstrate differential relationships: consumers at an advanced (early) readiness stage are more likely to be hedonism-motivated (utility-motivated) when using m-commerce and tend to use it intentionally/consciously (habitually/unconsciously). In addition to advancing knowledge about m-commerce from a scientific perspective, the findings can help multinational firms decide whether to standardize or adapt m-shopping experiences when internationalizing.

Image copy detection is challenging and appealing topic in computer vision and signal processing. Recent advancements in multimedia have made distribution of image across the global easy and fast: that leads to many other issues such as forgery and image copy retrieval. Local keypoint descriptors such as SIFT are used to represent the images, and based on those descriptors matching, images are matched and retrieved. Features are quantized so that searching/matching may be made feasible for large databases at the cost of accuracy loss. In this paper, we propose binary feature that is obtained by quantizing the SIFT into binary, and rank list is re-examined to remove the false positives. Experiments on challenging dataset shows the gain in accuracy and time.

Federated learning has arisen as a mechanism to allow multiple participants to collaboratively train a model without sharing their data. In these settings, participants (workers) may not trust each other fully; for instance, a set of competitors may collaboratively train a machine learning model to detect fraud. The workers provide local gradients that a central server uses to update a global model. This global model can be corrupted when Byzantine workers send malicious gradients, which necessitates robust methods for aggregating gradients that mitigate the adverse effects of Byzantine inputs. Existing robust aggregation algorithms are often computationally expensive and only effective under strict assumptions. In this paper, we introduce LayerwisE Gradient AggregatTiOn (LEGATO), an aggregation algorithm that is, by contrast, scalable and generalizable. Informed by a study of layer-specific responses of gradients to Byzantine attacks, LEGATO employs a dynamic gradient reweighing scheme that is novel in its treatment of gradients based on layer-specific robustness. We show that LEGATO is more computationally efficient than multiple state-of-the-art techniques and more generally robust across a variety of attack settings in practice. We also demonstrate LEGATO's benefits for gradient descent convergence in the absence of an attack.

Federated Learning (FL) has emerged as a new paradigm for training machine learning models distributively without sacrificing data security and privacy. Learning models on edge devices such as mobile phones is one of the most common use cases for FL. However, Non-identical independent distributed~(non-IID) data in edge devices easily leads to training failures. Especially, over-parameterized machine learning models can easily be over-fitted on such data, hence, resulting in inefficient federated learning and poor model performance. To overcome the over-fitting issue, we proposed an adaptive dynamic pruning approach for FL, which can dynamically slim the model by dropping out unimportant parameters, hence, preventing over-fittings. Since the machine learning model's parameters react differently for different training samples, adaptive dynamic pruning will evaluate the salience of the model's parameter according to the input training sample, and only retain the salient parameter's gradients when doing back-propagation. We performed comprehensive experiments to evaluate our approach. The results show that our approach by removing the redundant parameters in neural networks can significantly reduce the over-fitting issue and greatly improves the training efficiency. In particular, when training the ResNet-32 on CIFAR-10, our approach reduces the communication cost by 57\%. We further demonstrate the inference acceleration capability of the proposed algorithm. Our approach reduces up to 50\% FLOPs inference of DNNs on edge devices while maintaining the model's quality., Comment: Preprint of the CCGrid 2023 Submission

Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this paradigm, only model updates, such as model weights or gradients, are shared. Many existing approaches have focused on horizontal FL, where each party has the entire feature set and labels in the training data set. However, many real scenarios follow a vertically-partitioned FL setup, where a complete feature set is formed only when all the datasets from the parties are combined, and the labels are only available to a single party. Privacy-preserving vertical FL is challenging because complete sets of labels and features are not owned by one entity. Existing approaches for vertical FL require multiple peer-to-peer communications among parties, leading to lengthy training times, and are restricted to (approximated) linear models and just two parties. To close this gap, we propose FedV, a framework for secure gradient computation in vertical settings for several widely used ML models such as linear models, logistic regression, and support vector machines. FedV removes the need for peer-to-peer communication among parties by using functional encryption schemes; this allows FedV to achieve faster training times. It also works for larger and changing sets of parties. We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.