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Industrial energy efficiency is stated as a major means of reducing the threat of increased global warming, caused by human use of fossil fuels. Energy service companies (ESCOs) have been expected to play an important role in promoting energy efficiency in different sectors of the economy, including industry. Energy related outsourcing in the complex energy intensive pulp and paper industry, with a continuous production process, represents one of the more challenging types of industrial outsourcing. This paper studies the role of ESCOs as a method to promote energy efficiency in the Swedish pulp and paper industry. A questionnaire was used, complemented by in-depth interviews with mill executives in order to gain more knowledge of the issue. The main conclusion from this paper is that the utilization of ESCOs in the pulp and paper industry is higher in activities with a lower level of energy integration in the production process, and on the contrary, the utilization of ESCOs is lower with a higher level of energy integration in the production process. Since the PPI is a mature industry with a globally well-known and well-spread technique, results from this paper, may plausibly be generalized for PPIs in other countries as well., ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Patrik Thollander and Mikael Ottosson, Energy related outsourcing: - The case of ESCOs in the Swedish pulp and paper industry, 2011, IEEE International Technology Management Conference, 329-337. http://dx.doi.org/10.1109/ITMC.2011.5995962

Industrial energy efficiency is stated as a major means of reducing the threat of increased global warming, caused by human use of fossil fuels. Energy service companies (ESCOs) have been expected to play an important role in promoting energy efficiency in different sectors of the economy, including industry. Energy related outsourcing in the complex energy intensive pulp and paper industry, with a continuous production process, represents one of the more challenging types of industrial outsourcing. This paper studies the role of ESCOs as a method to promote energy efficiency in the Swedish pulp and paper industry. A questionnaire was used, complemented by in-depth interviews with mill executives in order to gain more knowledge of the issue. The main conclusion from this paper is that the utilization of ESCOs in the pulp and paper industry is higher in activities with a lower level of energy integration in the production process, and on the contrary, the utilization of ESCOs is lower with a higher level of energy integration in the production process. Since the PPI is a mature industry with a globally well-known and well-spread technique, results from this paper, may plausibly be generalized for PPIs in other countries as well., ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Patrik Thollander and Mikael Ottosson, Energy related outsourcing: - The case of ESCOs in the Swedish pulp and paper industry, 2011, IEEE International Technology Management Conference, 329-337. http://dx.doi.org/10.1109/ITMC.2011.5995962

Industrial energy efficiency is stated as a major means of reducing the threat of increased global warming, caused by human use of fossil fuels. Energy service companies (ESCOs) have been expected to play an important role in promoting energy efficiency in different sectors of the economy, including industry. Energy related outsourcing in the complex energy intensive pulp and paper industry, with a continuous production process, represents one of the more challenging types of industrial outsourcing. This paper studies the role of ESCOs as a method to promote energy efficiency in the Swedish pulp and paper industry. A questionnaire was used, complemented by in-depth interviews with mill executives in order to gain more knowledge of the issue. The main conclusion from this paper is that the utilization of ESCOs in the pulp and paper industry is higher in activities with a lower level of energy integration in the production process, and on the contrary, the utilization of ESCOs is lower with a higher level of energy integration in the production process. Since the PPI is a mature industry with a globally well-known and well-spread technique, results from this paper, may plausibly be generalized for PPIs in other countries as well., ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Patrik Thollander and Mikael Ottosson, Energy related outsourcing: - The case of ESCOs in the Swedish pulp and paper industry, 2011, IEEE International Technology Management Conference, 329-337. http://dx.doi.org/10.1109/ITMC.2011.5995962

Industrial energy efficiency is stated as a major means of reducing the threat of increased global warming, caused by human use of fossil fuels. Energy service companies (ESCOs) have been expected to play an important role in promoting energy efficiency in different sectors of the economy, including industry. Energy related outsourcing in the complex energy intensive pulp and paper industry, with a continuous production process, represents one of the more challenging types of industrial outsourcing. This paper studies the role of ESCOs as a method to promote energy efficiency in the Swedish pulp and paper industry. A questionnaire was used, complemented by in-depth interviews with mill executives in order to gain more knowledge of the issue. The main conclusion from this paper is that the utilization of ESCOs in the pulp and paper industry is higher in activities with a lower level of energy integration in the production process, and on the contrary, the utilization of ESCOs is lower with a higher level of energy integration in the production process. Since the PPI is a mature industry with a globally well-known and well-spread technique, results from this paper, may plausibly be generalized for PPIs in other countries as well., ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Patrik Thollander and Mikael Ottosson, Energy related outsourcing: - The case of ESCOs in the Swedish pulp and paper industry, 2011, IEEE International Technology Management Conference, 329-337. http://dx.doi.org/10.1109/ITMC.2011.5995962

Industrial energy efficiency is stated as a major means of reducing the threat of increased global warming, caused by human use of fossil fuels. Energy service companies (ESCOs) have been expected to play an important role in promoting energy efficiency in different sectors of the economy, including industry. Energy related outsourcing in the complex energy intensive pulp and paper industry, with a continuous production process, represents one of the more challenging types of industrial outsourcing. This paper studies the role of ESCOs as a method to promote energy efficiency in the Swedish pulp and paper industry. A questionnaire was used, complemented by in-depth interviews with mill executives in order to gain more knowledge of the issue. The main conclusion from this paper is that the utilization of ESCOs in the pulp and paper industry is higher in activities with a lower level of energy integration in the production process, and on the contrary, the utilization of ESCOs is lower with a higher level of energy integration in the production process. Since the PPI is a mature industry with a globally well-known and well-spread technique, results from this paper, may plausibly be generalized for PPIs in other countries as well., ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Patrik Thollander and Mikael Ottosson, Energy related outsourcing: - The case of ESCOs in the Swedish pulp and paper industry, 2011, IEEE International Technology Management Conference, 329-337. http://dx.doi.org/10.1109/ITMC.2011.5995962

Maximum-likelihood (ML) decoding often reduces to solving an integer least-squares problem, which is NP hard in the worst-case. On the other hand, it has recently been shown that, over a wide range of dimensions and signal-to-noise ratios (SNR), the sphere decoding algorithm finds the exact solution with an expected complexity that is roughly cubic in the dimension of the problem. However, the computational complexity of sphere decoding becomes prohibitive if the SNR is too low and/or if the dimension of the problem is too large. In this paper, we target these two regimes and attempt to find faster algorithms by pruning the search tree beyond what is done in the standard sphere decoder. The search tree is pruned by computing lower bounds on the possible optimal solution as we proceed to go down the tree. Using ideas from H∞ estimation theory, we have developed a general framework to compute the lower bound on the integer least-squares. Several special cases of lower bounds were derived from this general framework. Clearly, the tighter the lower bound, the more branches can be eliminated from the tree. However, finding a tight lower bound requires significant computational effort that might diminish the savings obtained by additional pruning. In this paper, we propose the use of a lower bound which can be computed with only linear complexity. Its use for tree pruning results in significantly speeding up the basic sphere decoding algorithm.

Transformers are the vital section of transmission and distribution electrical network. In addition, they are the most expensive investment in the network. Therefore, they should be evaluated for proper working and monitor regularly to assess normal condition and life expectancy. Obviously, the health of transformer is strictly depending on transformer's insulation conditions, oil and paper, that both of them degrade along the time and are temperature dependent. There are some diagnostic methods to evaluate oil and paper conditions by considering of degradation byproducts. But after all, only furanic compounds are unique indicators of paper aging assessment of transformers. Hence, this article attempts to illustrate the paper thermal degradation mechanism and temperature depending of furans concentration in oil and specially relationship between furan concentration (in our purpose 2-furaldehyde or 2FAL caused by overheating) with degree of polymerization, paper strength and life expectancy of transformers.

In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about whom to interact with can be done soundly. Traditional trust models are based on modeling specific properties of agents, such as their expertise or reliability. Building those models requires too many prior interactions to be accurate. This paper proposes an approach that is based on keeping track of outcomes of agent's actions towards others rather than modeling other agents' performances explicitly. Contrary to existing modeling approaches that require domain knowledge to build models, our proposed approach can be effectively realized in multiagent systems when the agent's actions are clearly identified. Comparisons with other modeling approaches in various environments reveal that our proposed approach can create more precise models in short time and can adjust its behavior quickly when other agents' behaviors change.

In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about whom to interact with can be done soundly. Traditional trust models are based on modeling specific properties of agents, such as their expertise or reliability. Building those models requires too many prior interactions to be accurate. This paper proposes an approach that is based on keeping track of outcomes of agent's actions towards others rather than modeling other agents' performances explicitly. Contrary to existing modeling approaches that require domain knowledge to build models, our proposed approach can be effectively realized in multiagent systems when the agent's actions are clearly identified. Comparisons with other modeling approaches in various environments reveal that our proposed approach can create more precise models in short time and can adjust its behavior quickly when other agents' behaviors change.

Transformers are the vital section of transmission and distribution electrical network. In addition, they are the most expensive investment in the network. Therefore, they should be evaluated for proper working and monitor regularly to assess normal condition and life expectancy. Obviously, the health of transformer is strictly depending on transformer's insulation conditions, oil and paper, that both of them degrade along the time and are temperature dependent. There are some diagnostic methods to evaluate oil and paper conditions by considering of degradation byproducts. But after all, only furanic compounds are unique indicators of paper aging assessment of transformers. Hence, this article attempts to illustrate the paper thermal degradation mechanism and temperature depending of furans concentration in oil and specially relationship between furan concentration (in our purpose 2-furaldehyde or 2FAL caused by overheating) with degree of polymerization, paper strength and life expectancy of transformers.

In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about whom to interact with can be done soundly. Traditional trust models are based on modeling specific properties of agents, such as their expertise or reliability. Building those models requires too many prior interactions to be accurate. This paper proposes an approach that is based on keeping track of outcomes of agent's actions towards others rather than modeling other agents' performances explicitly. Contrary to existing modeling approaches that require domain knowledge to build models, our proposed approach can be effectively realized in multiagent systems when the agent's actions are clearly identified. Comparisons with other modeling approaches in various environments reveal that our proposed approach can create more precise models in short time and can adjust its behavior quickly when other agents' behaviors change.

This paper describes a transmission expansion planning method based on multi-objective optimization (MOOP). The method starts with constructing a candidate pool of feasible expansion plans, followed by selection of the best candidates through MOOP, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in the market environment. Subsequently, reliability assessment is performed to evaluate and reinforce the resultant expansion plan from MOOP. The proposed method has been tested with the IEEE 14-bus system and analyses and discussions have been presented.

The map is more important than ever, but what it can represent and how it is delivered is changing radically as new geospatial technologies emerge. In the field of geology, paper maps have always aimed to represent the complex threedimensional world beneath our feet and make it understandable to us at the surface. However, new smart phone mapping applications enable us to take the map with us more easily and to ask questions of it wherever we are, and to add our own observations on to it. Digital survey and modelling technologies will enable geologists to communicate geology in 3D for us, rather than having to translate it to 2D; everyone will then see the geology as the geologist does as we take visualisation from the lab to the street. As an interactive tool the geological map of the future will be very different.

The map is more important than ever, but what it can represent and how it is delivered is changing radically as new geospatial technologies emerge. In the field of geology, paper maps have always aimed to represent the complex threedimensional world beneath our feet and make it understandable to us at the surface. However, new smart phone mapping applications enable us to take the map with us more easily and to ask questions of it wherever we are, and to add our own observations on to it. Digital survey and modelling technologies will enable geologists to communicate geology in 3D for us, rather than having to translate it to 2D; everyone will then see the geology as the geologist does as we take visualisation from the lab to the street. As an interactive tool the geological map of the future will be very different.

As online social networks are becoming part of both social and work life, preserving privacy of their users is becoming tremendously difficult. While these social networks are promising privacy through privacy agreements, everyday new privacy leakages are emerging. Ideally, online social networks should be able to manage and maintain their agreements through well-founded methods. However, the dynamic nature of the online social networks is making it difficult to keep private information contained. We have developed PROTOSS, a run time tool for detecting privacy leakages in online social networks. PROTOSS captures relations among users, their privacy agreements with an online social network operator, and domain-based inference rules. It then uses model checking to detect if an online social network will leak private information.

As online social networks are becoming part of both social and work life, preserving privacy of their users is becoming tremendously difficult. While these social networks are promising privacy through privacy agreements, everyday new privacy leakages are emerging. Ideally, online social networks should be able to manage and maintain their agreements through well-founded methods. However, the dynamic nature of the online social networks is making it difficult to keep private information contained. We have developed PROTOSS, a run time tool for detecting privacy leakages in online social networks. PROTOSS captures relations among users, their privacy agreements with an online social network operator, and domain-based inference rules. It then uses model checking to detect if an online social network will leak private information.

As online social networks are becoming part of both social and work life, preserving privacy of their users is becoming tremendously difficult. While these social networks are promising privacy through privacy agreements, everyday new privacy leakages are emerging. Ideally, online social networks should be able to manage and maintain their agreements through well-founded methods. However, the dynamic nature of the online social networks is making it difficult to keep private information contained. We have developed PROTOSS, a run time tool for detecting privacy leakages in online social networks. PROTOSS captures relations among users, their privacy agreements with an online social network operator, and domain-based inference rules. It then uses model checking to detect if an online social network will leak private information.

The bidirectional broadcast channel denotes a broadcast channel with two receivers where each receiver knows the message intended for the other. We consider streaming sources where messages of each user arrive as bit-stream to the encoder. Thus our main quantity of interest is the bit error probability. By extending Sahai's argument to bidirectional broadcast channel, we show that under maximum-likelihood decoding the bit error probability decays exponentially in delay with positive exponent for all the rate pairs inside the capacity region. We also show existence of deterministic codes which achieve exponentially decaying bit error probability with delay., QC 20111216

The single-mode properties and design issues of three vertical-cavity surface-emitting laser (VCSEL) structures incorporating nano structures are rigorously investigated. Nano structuring enables to deliver selective pumping or loss to the fundamental mode as well as stabilizing the output polarization state. Comparison of three vectorial simulation methods reveals that the modal expansion method is suitable for treating the nano structured VCSEL designs.

Estimating software testability can crucially assist software managers to optimize test budgets and software quality. In this paper, we propose a new approach that radically differs from the traditional approach of pursuing testability measurements based on software metrics, e.g., the size of the code or the complexity of the designs. Our approach exploits automatic test generation and mutation analysis to quantify the evidence about the relative hardness of developing effective test cases. In the paper, we elaborate on the intuitions and the methodological choices that underlie our proposal for estimating testability, introduce a technique and a prototype that allows for concretely estimating testability accordingly, and discuss our findings out of a set of experiments in which we compare the performance of our estimations both against and in combination with traditional software metrics. The results show that our testability estimates capture a complementary dimension of testability that can be synergistically combined with approaches based on software metrics to improve the accuracy of predictions.

This paper addresses some of the challenges of video forgery detection in multimedia forensics. It presents a solution that enhances video verification by utilizing the non-alterable features of blockchain technology and video hashing algorithms. The proposed approach is applicable in different application areas and can be used to increase video credibility, identify manipulations, and improve the storage process of tracking changes to video data. The paper describes our experiments and results of the proposed solution for video integrity preservation and verification, providing an alternative way to the quality assurance and security of video content in different industries.

With the widespread application of intelligent transportation systems and navigation software, traffic prediction should be modeled in finer granularity to facilitate lane-changing guidance and congestion mitigation. However, existing studies divide the road network into continuous segments which assumes different lanes share the same spatio-temporal patterns. This paper proposes a novel lightweight, attention-based, fully convolutional model, named the Spatio-Temporal Attention- Based Convolutional network (ST-ABC), where lane segments are treated as graph nodes and dynamically models the adjacent spatial dependencies using local attention graph convolution. The attention-based dilated convolutions can process longer sequence periods in parallel, and a global attention layer allows individual nodes to be associated with the global context. By setting a target window, it can further reduce unnecessary computations and improve the prediction effect for the targeted area. Further-more, the ST-ABC model facilitates the simultaneous integration of spatio-temporal information and relational distance metrics among lane segments, enriching the granularity of multi-scaled spatial prediction. Empirical evaluations conducted on two real-world datasets substantiate the augmented efficacy of the STABC model in comparison to established models, with a marked prominence in long-term prediction scenarios.

Owing to the nature of privacy protection, feder-ated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most existing FedRecs only allow participating clients to collaboratively train a recommendation model of the same public parameter size. Training a model of the same size for all clients can lead to suboptimal performance since clients possess varying resources. For example, clients with limited training data may prefer to train a smaller recommendation model to avoid excessive data consumption, while clients with sufficient data would benefit from a larger model to achieve higher recommendation accuracy. To address the above challenge, this paper introduces HeteFedRec, a novel FedRec framework that enables the assignment of personalized model sizes to partici-pants. Specifically, we present a heterogeneous recommendation model aggregation strategy, including a unified dual-task learning mechanism and a dimensional decorrelation regularization, to allow knowledge aggregation among recommender models of different sizes. Additionally, a relation-based ensemble knowledge distillation method is proposed to effectively distil knowledge from heterogeneous item embeddings. Extensive experiments conducted on three real-world recommendation datasets demonstrate the effectiveness and efficiency of HeteFedRec in training federated recommender systems under heterogeneous settings.

Recently, graph contrastive learning proposes to learn node representations from the unlabeled graph to alleviate the heavy reliance on node labels in graph neural networks (GNNs). The core idea is to generate diverse positive views and negative views according to local subgraphs. Then, GNNs take these views as supervised signals and train the model by maximizing the similarity between positive view pairs of each node and minimizing the similarity between positive and negative views. Regardless of the fruitful progress, existing graph contrastive learning approaches still suffer from low-efficiency, insufficient-expressivity, and unpreserved-locality issues. First, they train GNNs by all nodes, reducing the efficiency due to similar and redundant nodes. Second, they only use limited operations (e.g., edge deletion and feature masking) to generate positive views, thereby restricting their expressivity. Third, they uniformly delete edges and mask node features and may modify important edges and features, thereby damaging the important locality information of nodes. In this paper, we propose an efficient and expressive contrastive learning framework for GNNs, namely E 2 GCL. Specifically, given a limited node budget, we select a set of representative nodes instead of all nodes to accelerate the GNNs training. Besides, we use three general operations (edge deletion, edge addition, and feature perturbation) to generate expressive and locality-preserved positive views based on edge and feature importance. Extensive experiments on various real-world datasets demonstrate the superior effectiveness and efficiency of our proposed E 2 GCL.

Recently, continual learning (CL) has attracted much attention due to its widespread applications in the real world. Given a set of data sets sequentially, continual learning aims to achieve good performance on the new data sets while avoiding deterioration in performance on the old data sets. Despite the success, most CL models follow the supervised setting, which limits their potential in data scarcity cases. Thus, some pioneering works study unsupervised CL (UCL) to discuss what CL tricks suit the unsupervised setting. However, their advancements lack in-depth analysis of the characteristics of UCL, especially the lack of attention to the use of old data. We identify that using old data sets is essential for improving the UCL model performance while existing works ignore them. Unfortunately, given a limited data storage budget, it is a nontrivial task to select representative data and effectively replay them without label assistance. To further improve the UCL performance, we present a new method in this paper, named Effective Data Selection and Replay (EDSR) for UCL. Specifi-cally, we analyze that entropy can be an effective data selection metric, where representative data usually exhibit the highest entropy in the representation space. Then, to balance the model stability for old data and the plasticity for new data, we adopt a strategy of replaying those stored representative data with a noise-enhanced knowledge distillation process. The empirical study demonstrates the outstanding performance of EDSR on benchmark computer vision data sets. Especially, EDSR shows strong resistance to forgetting old data knowledge while maintaining high accuracy. The implementation is publicly available at https://github.com/LeeJarvis996/edsr_project/tree/main/EDSR.

With the popularization of mobile applications and the timely acquisition of fresh data, real-time clustering and its evolution analysis have become the primary operations for data processing and knowledge discovery. Such continuous queries on massive objects are computation-intensive tasks in dynamic scenarios. However, existing clustering techniques are incompetent to achieve decent performance when computation-intensive operations frequently occur in streaming scenarios, which is caused by two challenges: (i) uncertainty of the clustering frequency; (ii) unpredictable distribution evolution. Hence, it is critical to find a lightweight model that can cluster the high-speed dynamic instances while exploiting the evolution amid different clustering results. This paper focuses on the problem of real-time clustering on streaming data in computation-intensive and high-dynamics tasks, through a framework Ocean, consisting of the Online clustering algorithm and evolution analysis. Particularly, the framework conceives a flexible composite window to augment the knowledge mining, achieving a proper real-time response in various scenarios. The evolution analysis supports full life-cycle detection, improving the adaptability to dynamic concept drifts and multiple patterns. Inspired by the grid partition strategy, this framework adopts grid feature vectors to capture the significant changes in streaming data. Furthermore, we propose an optimization that removes sparse grids timely and performs the online clustering adaptively for space and time efficiency. It is proven to be effective both theoretically and experimentally. This strategy enables real-time clustering for dynamic streaming data without degrading the clustering quality or increasing the computation cost. Experiments on real datasets and synthetic datasets verify the accuracy and effectiveness of Ocean compared to the state-of-the-art approaches, as well as the superior ability to perform clustering in a real-time

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available. https.//anonymous.4open.science/status/Meta-SGCL-05B5

The Constrained Shortest Path (CSP) problem seeks to identify the shortest path between two vertices in a road network while adhering to a specific constraint on another criterion. Solving the CSP problem frequently entails navigating the two-criteria skyline path problem, which incurs a substantial computational expense in large road networks. The primary challenge lies in handling a vast quantity of partial skyline paths, which often hinders index-based solutions from accurately determining the skyline paths. This paper introduces a-FHL, a practical approximation method designed to circumvent the costly skyline path search and hasten computation on skyline path indexing. a-FHL uses tree decomposition to hierarchically assign approximation ratios, thereby facilitating effective pruning within the labelling index. Moreover, we devise various strategies to allocate approximation ratios and an efficient approximation concatenation method to respond to the approximate CSP queries via the a-FHL index. Our method culminates in swift index construction and efficient query response. Comprehensive exper-iments conducted on real-world road networks substantiate the superiority of our approach over contemporary solutions

Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually complicated rather than pairwise only. Hypergraphs offer a flexible approach to modeling these complex high-order correlations (not just pairwise connections), since hypergraphs can leverage hyperedeges to link more than two nodes. However, most hypergraph-based methods are generic and cannot be well applied to the trust prediction task. In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations. AHNTP utilizes Motif-based PageRank to capture high-order social influence information. In addition, it constructs hypergroups from both node-level and structure-level attributes to incorporate complex correlation information. Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network (GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user embeddings, facilitating trust relationship prediction. To enhance model generalization and robustness, we introduce a novel supervised contrastive learning loss for optimization. Extensive experiments demonstrate the superiority of our model over the state-of-the-art approaches in terms of trust prediction accuracy.

This paper considers cell-free multiple-input multiple-output (MIMO) systems with multi-antenna access points (APs) and multi-antenna users in constrained spaces. Two main effects emerge in such a space-constrained design: spatial correlation and mutual coupling. We analytically study the resulting performance with conjugate beamforming and reducing inter-antenna distance. We derive the closed-form expression for the spectral efficiency (SE) as a function of the spatial correlation and mutual coupling matrices. Additionally, we consider a practical power consumption model to investigate the energy efficiency (EE) with power control coefficients. The obtained ergodic and analytical results will show essential insights into the system performance. The decreasing inter-antenna distance introduces performance loss, while an appropriate number of APs could balance the SE and EE trade-off. The number of users improves the SE performance, while an optimal number of antennas per user can be selected to achieve maximum SE performance. Moreover, more users will introduce a smaller optimal number of antennas per user.

The maximum inner product search (MIPS) problem in high-dimensional vector spaces has various applications, primarily driven by the success of deep neural network-based embedding models. Existing MIPS methods designed for dense vectors using approximate techniques like locality-sensitive hashing (LSH) have been well studied, but they are not efficient and effective for searching sparse vectors due to the near-orthogonality among the sparse vectors. The solutions to MIPS over sparse vectors rely heavily on inverted lists, resulting in poor query efficiency, particularly when dealing with large-scale sparse datasets. In this paper, we introduce SOSIA, a novel framework specifically tailored to address these limitations. To handle sparsity, we propose the SOS transformation, which converts sparse vectors into a binary space while providing an unbiased estimator of the inner product between any two vectors. Additionally, we develop a minHash-based index to enhance query efficiency. We provide a theoretical analysis on the query quality of SOSIA and present extensive experiments on real-world sparse datasets to validate its effectiveness. The experimental results demonstrate its superior performance in terms of query efficiency and accuracy compared to existing methods.

Deep reinforcement learning (RL) has emerged as a promising approach for portfolio management due to its ability to make sequential decisions. However, applying RL techniques to this domain is still challenging due to the non-stationary nature of financial markets. Existing RL-based solutions fail to consider the intrinsic causes behind this non-stationary, which primarily stem from the involvement of diverse traders with distinct investment horizons and their varied investment strategies. In this paper, we tackle the non-stationary problem by examining its intrinsic causes and propose cross-insight trader, a novel two-step RL-based approach that integrates multiple trading policies with different investment horizons to adapt to the changing market conditions. In the first step, we learn multiple horizon-specific policies by providing each policy with tailored information specific to its investment horizon. This allows each policy to recognize dynamic patterns within its respective horizon and make insightful pre-decisions. In the second step, we learn a cross-insight policy to make the final trade decision by considering the investment pre-decisions made by multiple horizon-specific policies in the first step. To enable effective learning of two types of policies, our approach employs a centralized critic to evaluate the actions performed by both horizon-specific and cross-insight policies. By incorporating multiple insights from different investment horizons into the decision-making process, our approach enhances its adaptability to changing market conditions. Experimental results conducted on three stock markets demonstrate the superiority of our framework.

In this paper we introduce GS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance between efficiency and accuracy. Compared to recent SLAM methods employing neural implicit representations our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Specifically we propose an adaptive expansion strategy that adds new or deletes noisy 3D Gaussians in order to efficiently reconstruct new observed scene geometry and improve the mapping of previously observed areas. This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods. Moreover in the pose tracking process an effective coarse-to-fine technique is designed to select reliable 3D Gaussian representations to optimize camera pose resulting in runtime reduction and robust estimation. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica TUM-RGBD datasets. Project page: \href https://gs-slam.github.io/ https://gs-slam.github.io/ .

Pervasive Augmented Reality (AR) requires accurate pose registration of the device in real-time at a neighbourhood-to-city scale. At such a scale, most pose registration techniques suffer from exponential computational and storage costs and a significant data collection burden. This paper introduces AnchorLoc, a framework that relies on visual anchors (stable and highly recognisable visual elements in a scene) to perform fast and accurate pose registration. Anchorloc automatically identifies these anchors from large image sequences to optimise the search space in later image retrieval and pose registration. As such, it significantly improves the computational efficiency of existing hierarchical localisation pipelines without compromising accuracy. We collect a large-scale localisation dataset consisting of image sequences and 3D reconstruction of a university campus. AnchorLoc reduces localisation runtime by 83% on our campus dataset and 69% on the Cambridge Landmarks dataset without significantly increasing mean pose estimation errors. It is also more accurate and faster than SLD, a localisation algorithm that takes a comparable approach at the keypoint level. This work informs the development of more efficient pervasive AR applications that rely on both absolute and relative camera pose tracking on image sequences. © 2024 IEEE.

Existing open-vocabulary object detectors typically require a predefined set of categories from users significantly confining their application scenarios. In this paper we introduce DetCLIPv3 a high-performing detector that excels not only at both open-vocabulary object detection but also generating hierarchical labels for detected objects. DetCLIPv3 is characterized by three core designs: 1. Versatile model architecture: we derive a robust open-set detection framework which is further empowered with generation ability via the integration of a caption head. 2. High information density data: we develop an auto-annotation pipeline leveraging visual large language model to refine captions for large-scale image-text pairs providing rich multi-granular object labels to enhance the training. 3. Efficient training strategy: we employ a pre-training stage with low-resolution inputs that enables the object captioner to efficiently learn a broad spectrum of visual concepts from extensive image-text paired data. This is followed by a fine-tuning stage that leverages a small number of high-resolution samples to further enhance detection performance. With these effective designs DetCLIPv3 demonstrates superior open-vocabulary detection performance e.g. our Swin-T backbone model achieves a notable 47.0 zero-shot fixed AP on the LVIS minival benchmark outperforming GLIPv2 GroundingDINO and DetCLIPv2 by 18.0/19.6/6.6 AP respectively. DetCLIPv3 also achieves a state-of-the-art 19.7 AP in dense captioning task on VG dataset showcasing its strong generative capability.

Annotation through crowdsourcing draws incremental attention, which relies on an effective selection scheme given a pool of workers. Existing methods propose to select workers based on their performance on tasks with ground truth, while two important points are missed. 1) The historical performances of workers in other tasks. In real-world scenarios, workers need to solve a new task whose correlation with previous tasks is not well-known before the training, which is called cross-domain. 2) The dynamic worker performance as workers will learn from the ground truth. In this paper, we consider both factors in designing an allocation scheme named cross-domain-aware worker selection with training approach. Our approach proposes two estimation modules to both statistically analyze the cross-domain correlation and simulate the learning gain of workers dynamically. A framework with a theoretical analysis of the worker elimination process is given. To validate the effectiveness of our methods, we collect two novel real-world datasets and generate synthetic datasets. The experiment results show that our method outperforms the baselines on both real-world and synthetic datasets.

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Few-shot RAW Image Denoising track on MIPI 2024. In total, 165 participants were successfully registered, and 7 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Few-shot RAW Image Denoising. More details of this challenge and the link to the dataset can be found at https://mipichallenge.org/MIPI2024.

Much progress has been made in dynamic scene reconstruction by neural rendering techniques. Even if existing methods based on Neural Radiance Field (NeRF) achieve marvelous fidelity, they suffer from either complex camera settings, dense training timestamps, or limited ability to inter- and extrapolate. In this paper, a novel paradigm is presented to address these issues. Our method, called Dynamic Ode NErf (DONE), comprises two main modules. The first one trains a deformable neural representation in a static scene to store neural features. The second module establishes a deterministic neural ordinary differential equation to model the dynamics of the scene object. We also present a new 360-degree dynamic dataset for the research of entire 360-degree dynamic scene reconstruction. Experiments visually and quantitatively illustrate the effectiveness of the proposed model. © 2024 IEEE.

Time-varying graphical LASSO (TVGL) aims to infer a sequence of graphs from time series data and has been widely used in many statistical inference problems. The existing algorithms usually suffer from high computational cost when solving large-scale TVGL problems. In this paper, we develop an efficient and scalable hierarchical block coordinate descent (HBCD) method for solving TVGL with smooth temporal difference prior. The proposed HBCD method contains both outer-loop and inner-loop BCD iterations. The outer loops seperate the original TVGL problem into a sequence of subproblems, which are variants of the static graphical LASSO problems. Then, we propose an efficient BCD method to solve the inner-loop subproblems. We provide theoretical analysis that indicates the linear convergence of our proposed method. Furthermore, numerical experiments on both synthetic and real datasets show that our method significantly outperforms the state-of-the-art algorithm in terms of both the required iterations and CPU time to reach the target precision. © 2024 IEEE.

This paper uses multimodal haptic feedback to enhance users' body ownership in virtual reality through wearable devices. In this case, the human is transformed into a bird, which belongs to the beyondreal transformations category in virtual reality interactions. For body transformation, wearable retractable straps can help people mimic the movement mechanism of avian bodies; for space transformation, the inflatable cushions and blowers can simulate the air resistance and lift, oxygen deprivation, and temperature decrease during the take-off process of avian avatars. The system aims to establish a realistic fidelity of the haptic feedback to enhance the user's body ownership. © 2024 IEEE.

Large language models (LLMs) offer impressive performance across diverse fields, but their increasing complexity raises both design costs and the need for specialized expertise. These challenges are intensified for Neural Architecture Search (NAS) methods reliant on weight-sharing techniques. This paper introduces GNAS, a new NAS method that boosts the search process with the aid of LLMs for efficient model discovery. With insights from existing architectures, GNAS swiftly identifies superior models that can adapt to changing resource constraints. We provide a mathematical framework to facilitate the transfer of knowledge across different model sizes, thereby improving search efficiency. Our experiments conducted on ImageNet, NAS-Bench-Macro, and Channel-Bench-Macro confirm the effectiveness of GNAS across both CNN and Transformer architectures. © 2024 IEEE.

The purpose of this paper is to use an advanced spiking neural network algorithm, and accelerate the algorithm with a variety of acceleration methods based on the high-level synthesis platform. Its target is to solve the problem of insufficient performance of the traditional computing platform in real-time electrocardiogram data processing. The experiment shows that the performance is improved significantly, which proves that the proposed method is effective in real-time data processing. © 2024 IEEE.

Augmented Reality (AR) teleconferencing aims to enable remotely separated users to meet with each other in their own physical spaces as if they are face-to-face. Among all solutions, the video-avatar-based approach has the advantage of balancing fidelity and the sense of co-presence using easy-to-setup devices, including only a camera and an AR Head-Mounted Display (HMD). However, non-verbal cues indicating 'who is looking at whom' are always lost or misdelivered in multiparty teleconferencing experiences. To make users aware of such non-verbal cues, existing solutions explore screen-based visualizations, incorporate additional hardware, or alter to use a virtual avatar representation. However, they lack immersion, are less feasible for everyday usage due to complex installations, or lose the fidelity of remote users' authentic appearances. In this paper, we decompose such attention awareness into the awareness of being looked at and the awareness of attention between other users and address them in a decoupled process. Specifically, through a user study, we first find an unobtrusive and reasonable layout 'Attention Circle' to retarget a looker's head gaze to the one being looked at. We then conduct the second user study to find an effective and intuitive 'rotatable 2.5D video avatar with attention thumbnail' visualization to aid users in being aware of other users' attention. With the design choice distilled from the studies, we implement A3RT, a proof-of-concept prototype system that empowers attention-aware 2.5D-video-avatar-based multiparty AR teleconferencing in an easy, everyday setup. Ablation and usability studies on the prototype verify the effectiveness of our proposed components and the full system.

Ambient radio frequency (RF) powered Internet of Things (IoT) is a promising batteryless system for collecting environmental information for data exchange among devices. Ambient RF energy harvesting technology is most suitable for use in places that are hidden from light sources and are in difficult to reach places for battery replacement. In this paper, a demonstration of IoT sensors powered by ambient RF energy harvesting is presented. The system we have developed provides all key components from antenna, rectifier, power management unit (PMU), microcontroller unit (MCU) to sensing unit. In addition a novel 3-port antenna element for the systems is also described. The experimental results confirm that ambient RF signals can be regarded as an alternative to power supply in the next generation of wireless communications. © 2024 IEEE.

In this paper, we introduce a novel micro-transfer printing method for photoresist transfer. This approach utilizes the thermo-responsive adhesion modulation properties of a shape memory polymer (SMP) stamp, to facilitate the efficient transfer of patterned photoresist from a donor substrate to various unconventional receiver, such as water-soluble, stretchable, and curved surfaces, which typically present challenges in direct photolithography processes. Our study successfully demonstrates the effective transfer of various photoresists (PR), including AZ 5214E and SU-8 2010, onto diverse substrates, such as polyvinyl alcohol (PVA), silicone and curved glass substrates, highlighting the versatility of the technology. This technology paves the way for the development of advanced wearable electronics, biomedical sensors, and implantable medical devices, representing a progression in the high-resolution microfabrication of innovative electronics for a wide range of applications. © 2024 IEEE.

Spiking neural networks (SNNs) have ultra-low energy consumption and high biological plausibility due to their binary and bio-driven nature compared with artificial neural networks (ANNs). While previous research has primarily focused on enhancing the performance of SNNs in classification tasks, the generative potential of SNNs remains relatively unexplored. In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality. To fully exploit the energy efficiency of SNNs, we propose a purely Spiking U-Net architecture, which achieves comparable performance to its ANN counterpart using only 4 time steps, resulting in significantly reduced energy consumption. Extensive experimental results reveal that our approach achieves state-of-the-art on the generative tasks and substantially outperforms other SNN-based generative models, achieving up to 12× and 6× improvement on the CIFAR-10 and the CelebA datasets, respectively. Moreover, we propose a threshold-guided strategy that can further improve the performances by 2.69% in a training-free manner. The SDDPM symbolizes a significant advancement in the field of SNN generation, injecting new perspectives and potential avenues of exploration. Our code is available at https://github.com/AndyCao1125/SDDPM. © 2024 IEEE.

Virtual reality (VR) is an immersive medium that offers users a unique opportunity to experience a digital environment realistically. As the demand for VR content continues to grow, the importance of effective VR editing techniques becomes increasingly apparent. This paper is a pioneering work investigating the effects of jump cuts on the viewer's sense of presence, viewing experience, and edit quality in cinematic VR. Specifically, this work focuses on using the 30-degree and 180-degree rules in VR editing to minimize the adverse effects of jump cuts. We conducted a user study with thirteen participants, who watched nine different VR edits and completed a survey for each edited video. Our results indicate that employing the 30-degree and 180-degree rules in VR editing can significantly improve the sense of presence, viewing experience, and edit quality while mitigating the negative effects of jump cuts. We provide valuable insights for VR content creators and editors to achieve more effective and immersive VR experiences. © 2024 IEEE.

Multi-task learning for dense prediction has emerged as a pivotal area in computer vision enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks. However the substantial computational demands of state-of-the-art (SoTA) models often limit their widespread deployment. This paper addresses this challenge by introducing network binarization to compress resource-intensive multi-task dense predictors. Specifically our goal is to significantly accelerate multi-task dense prediction models via Binary Neural Networks (BNNs) while maintaining and even improving model performance at the same time. To reach this goal we propose a Binary Multi-task Dense Predictor Bi-MTDP and several variants of \bimtdp in which a multi-task dense predictor is constructed via specified binarized modules. Our systematical analysis of this predictor reveals that performance drop from binarization is primarily caused by severe information degradation. To address this issue we introduce a deep information bottleneck layer that enforces representations for downstream tasks satisfying Gaussian distribution in forward propagation. Moreover we introduce a knowledge distillation mechanism to correct the direction of information flow in backward propagation. Intriguingly one variant of Bi-MTDP outperforms full-precision (FP) multi-task dense prediction SoTAs ARTC (CNN-based) and InvPT (ViT-based). This result indicates that Bi-MTDP is not merely a naive trade-off between performance and efficiency but is rather a benefit of the redundant information flow thanks to the multi-task architecture.

Orthogonal time frequency space (OTFS) modulation is strongly considered as a promising solution for high-mobility communications. In contrast to conventional modulation techniques, wherein information symbols are multiplexed in a one-dimensional time or frequency domain, OTFS employs a two-dimensional modulation scheme by multiplexing information symbols in the delay-Doppler domain. This paper presents a comprehensive survey of OTFS. It starts with an overview of OTFS, its advantages over conventional air interface techniques, general block diagrams, and implementations. Subsequently, the paper explores the potential integration of multiple-input multiple-output and OTFS techniques. The paper further discusses the feasibility of integrating OTFS into multiple access techniques as a solution for maintaining acceptable performance in high-mobility scenarios. Then, widespread applications of OTFS in satellite communications are highlighted. Also, the potential utilization of OTFS modulation in integrated sensing and communications paradigm is thoroughly treated. In addition, the survey covers further applications of OTFS in deep learning, index modulation, underwater acoustic, and unmanned aerial vehicle communications. The paper concludes by pointing out numerous challenging and promising directions for future OTFS research.

In real-world scenarios, gas sensor responses to mixtures of different compositions can be costly to determine a-priori, posing difficulties in identifying the presence of target analytes. In this paper, we propose the use of graph convolutional networks (GCN) to handle gas mixtures with few labelled data. We transform sensor responses into a graph structure using manifold learning and clustering, and then apply GCN for semisupervised node classification. Our approach does not require extensive training data of gas mixtures like many competing approaches, but it outperforms classical semi-supervised learning methods and achieves classification accuracy exceeding 88.5% and over 0.85 Cohen's kappa score given only 5% labelled data for training. This result demonstrates the potential towards realistic gas identification when varied mixtures are present., This work is supported by the project SP13 'Monitoring of airflow and airborne particles, to provide early warning of irrespirable atmospheric conditions' under the academic program Sustainable Underground Mining (SUM), jointly financed by LKAB and the Swedish Energy Agency.