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We investigate three tutorials on Android development to elicit design choices related to their organization and content. We analyze organization styles related to the intended reading sequence, use of code fragments, and reliance on external resources. We survey design variations in the choice of topics covered by tutorials, for example deprecated or convenience APIs, and concerns unrelated to the functional features such as development methodologies and best practices. We provide insights about the impact of different design decisions on readers. For example, a clear reading order provides an easier introduction to the technology for beginners, but focused, independent sections can be more effective for an intermediate-level audience. We describe these decision points and their trade-offs and propose guidelines to assist tutorial authors in making explicit and informed decisions about the design of tutorials.

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.

At present, the traditional geographical study is somewhat shifted to virtual web-datacentric geographical study and it was started almost around last decade of the twentieth century concerning remotely sensed earth surface, digitisation, virtual data storing and their artificial intelligence based machine learning for spatio-temporal analysis, prediction and thematic mapping. Now in the age of webdom—a global digital kingdom of internet and web services, when internet services flooded everything relating to continuous up-gradation of computer-related science and technologies along with increasing number of internet users and GPS enabled smart phones, geographical data are generated by exaflood leading to bigdata. Many online data are now easily available to everyone from anywhere at any time. The objective is to assess the webdom and to understand the scope of contemporary virtual web-data-centric geographical study involving geographical bigdata, machine learning and WebGIS, when virtual world abundantly expands gradually during this period of webdom. Each data in bigdata contains geospatial attributes. Worldwide under the exponential growth and advancement of data science like other scientific disciplines geography is also predicting different phenomenon based on artificial intelligence and machine learning. The three main facets of virtual web-data-centric geographical study are geographical bigdata, geographical machine learning, and WebGIS. It is centrally concerned with digital data and web services. Here data are analysed through machine learning methods. Sometimes reliability of web-data is in question without field verification which is considered as the primary requisition of traditional geography.

Machine learning teaches computers to think in a similar way to how humans do. An ML models work by exploring data and identifying patterns with minimal human intervention. A supervised ML model learns by mapping an input to an output based on labeled examples of input-output (X, y) pairs. Moreover, an unsupervised ML model works by discovering patterns and information that was previously undetected from unlabelled data. As an ML project is an extensively iterative process, there is always a need to change the ML code/model and datasets. However, when an ML model achieves 70-75% of accuracy, then the code or algorithm most probably works fine. Nevertheless, in many cases, e.g., medical or spam detection models, 75% accuracy is too low to deploy in production. A medical model used in susceptible tasks such as detecting certain diseases must have an accuracy label of 98-99%. Furthermore, that is a big challenge to achieve. In that scenario, we may have a good working model, so a model-centric approach may not help much achieve the desired accuracy threshold. However, improving the dataset will improve the overall performance of the model. Improving the dataset does not always require bringing more and more data into the dataset. Improving the quality of the data by establishing a reasonable baseline level of performance, labeler consistency, error analysis, and performance auditing will thoroughly improve the model's accuracy. This review paper focuses on the data-centric approach to improve the performance of a production machine learning model.

Peer evaluations are critical for assessing teams, but are susceptible to bias and other factors that undermine their reliability. At the same time, collaborative tools that teams commonly use to perform their work are increasingly capable of logging activity that can signal useful information about individual contributions and teamwork. To investigate current and potential uses for activity traces in peer evaluation tools, we interviewed (N=11) and surveyed (N=242) students and interviewed (N=10) instructors at a single university. We found that nearly all of the students surveyed considered specific contributions to the team outcomes when evaluating their teammates, but also reported relying on memory and subjective experiences to make the assessment. Instructors desired objective sources of data to address challenges with administering and interpreting peer evaluations, and have already begun incorporating activity traces from collaborative tools into their evaluations of teams. However, both students and instructors expressed concern about using activity traces due to the diverse ecosystem of tools and platforms used by teams and the limited view into the context of the contributions. Based on our findings, we contribute recommendations and a speculative design for a data-centric peer evaluation tool.

Digital supply chains (SCs) and data-centric collaborations have boosted data exchange between companies and, combined with recent advancements in data science, have brought a new type of knowledge...

Implementing reliable end-to-end transmission and reduced congestion in a single transport layer protocol is a critical issue for wireless sensor networks (WSNs). These issues degrade the performance of the WSN; since the devices used to configure the networks are resource-constrained ones. If the protocol manages without any one of the above-said problems which will not be a complete solution for transport layer issues in WSN. Some of the data-centric applications of WSNs need the reliable transmission of data packets with reduced congestion. To addresses the same, this paper proposed the data-centric transport layer protocol (DCTP). It effectively addresses problems like reliable delivery and congestion-free transmission. One of the major problems of WSNs is cluster head (CH) election and channel assignment to the members of CH. In this paper, the modified black widow optimization is used to select the CH which leads to reduced congestion and balanced energy utilization among the nodes. In a separate phase, the reliability of the event-to-sink transmission has also been addressed. Hence this proposed DCTP will be ultimately suitable for the data-centric applications of WSNs and ensures reliability and energy efficiency (i.e., 6.2315 mJ and 7.825 mJ) in the case of varying data rate and the number of nodes.

Body movements, from a short smile to a marathon run, are driven by muscle activity. Despite the fact that measuring muscle activity with electromyography (EMG) is technically well established, it is highly complex and its use in interfaces has been limited. Easy access to muscle sensing can offer new opportunities to Human-Computer Interaction (HCI) research. Off-the-shelf sensors often only provide low-level access, hence requiring expertise in signal processing and widening the gulf of execution for users without engineering skills. To address this challenge, we introduce EMBody, a data-centric toolkit for EMG-based interface prototyping and experimentation. EMBody offers multiple levels of prototyping fidelity for EMG sensing, signal processing, and data interpretation. Our data-centric toolkit encapsulates the different data representation stages, offering a wide range of customization opportunities to experts while also allowing non-technical designers to focus on creating new interaction techniques. EMBody features a lightweight form factor and wireless connectivity. Additionally, the system leverages an exploration-centered workflow by allowing rapid access to measurement data via the accompanying software. Users define a set of motions to be recognized and interactively provide example data points. The toolkit then handles signal processing and classification. The recognized movements are streamed on the local network, ready to be used by interactive applications. This paper reports on how to use EMBody and its implementation. We iteratively developed the toolkit in a series of workshops and example applications. Users who had none or very limited knowledge of EMG could rapidly create engaging functional prototypes, while experts appreciated the modularity of the software component allowing for a high degree of customization. We contribute the software and hardware components of EMBody as a tool for the research community to stimulate creative exploration of EMG systems.

Our emotional intelligence (EI) indicates our capability for learning the various practical skills that are based on its components: self-awareness, managing emotions, maturity, empathy, and social skills & social competencies. Most of the emotions and feelings are well-formed in raising up in the family. Family plays an active role to build constructive emotions. An emotional person is capable to set his/her own goal according to his/her strengths and weaknesses. S/he can also critically evaluate himself/herself. Family helps individuals to maintain a healthy balance between emotions and logic. Generally, children who belong to a joint family, get more opportunities to be familiar with the different emotions. Se/he tries to behave according to their family members. Se/he has to realise others’ point of view too that motivates them to live together.

The Internet of Things (IoT) boosts road safety, efficiency, and infotainment by connecting vehicles to form the Internet of Vehicles (IoV). Specifically to safety, IoV complements autonomous cars beyond sensors’ line-of-sight, facilitating vehicle-to-vehicle (V2V) communications in a smart transportation environment. The correctness of data exchanged among vehicles is paramount to ensure vehicles behave as per norms. Traditional misbehavior detection methods hardly defend vehicular security effectively due to rapid dynamics and location privacy. In particular, those node-centric classifiers become ill-fit in IoV. This work proposes a data-centric misbehavior detection model based on supervised machine learning (ML). The work also integrates plausibility checks with ML techniques and instantiates the model with six algorithms to demonstrate their comparative effectiveness. In addition to misbehavior detection, the model classifies attack types to support validating countermeasures. Specifically, the work analyzes the supervised learning algorithms for detecting misbehavior in IoV, compares their performance, and identifies their limitations. VeReMi, a vehicle-to-everything (V2X) position forgery attack built-in simulated road traffic data set, is used to test the effectiveness of the proposed model. The performance metrics include precision–recall (PR) and receiver operating characteristic (ROC) curves. The results demonstrate the effectiveness and significance of ML to detect misbehavior in IoV. The addition of plausibility checks improves the precision and recall by 5% and 2%, respectively.

Inefficient utilization of licensed spectrum bands and overcrowding of unlicensed bands are caused due to the spectrum shortage and growing demand for wireless communication. The wireless spectrum is burdened due to the host centric traditional approaches for data detection and recovery in the IP-based networks that. The service or data is retrieved from the service provider through a new routing path every time the mobile service requester initiates a request. The vacant licensed channels are utilized appropriately enabling opportunistic and efficient band usage of the spectrum using Cognitive Radio (CR) technology. Wireless communication with low cost, compact antenna element, high gain, wideband and low profile can be performed using patch antenna. Patch is a significant aspect of antenna design. The antenna design parameters are understood by varying the patch. A good return loss can be achieved by enhancing the radiation pattern on changing the patch dimensions. High Frequency Structure Simulator (HFSS) is used for simulation and analysis of the circular patch antenna. The return loss, radiation efficiency, Voltage Standing Wave Ratio (VSWR) and radiation pattern of the antenna are analyzed.

Smart health is bringing vast and promising possibilities on the road to comprehensive health management. Smart health applications are strongly data-centric and, thus, empowered by two key factors: information sensing and information learning. In a smart health system, it is crucial to effectively sense individuals’ health information and intelligently learn from its high-level health insights. These two factors are also closely coupled. For example, to enhance the signal quality, a sensing array requires advanced information learning techniques to fuse the information, and to enrich medical insights in mobile health monitoring, we need to combine “multimodal signal processing and machine learning techniques” and “nonintrusive multimodality sensing methods.” In new smart health application exploration, challenges arise in both information sensing and learning, especially their areas of interaction.

Convolutional neural network (CNN) has been widely used in the medical domain, but how to protect privacy is still a challenge. In addition, traditional differential privacy protection methods are very sensitive to noise; hyper-scale noise has a serious effect on the validity of the network. In this article, therefore, a novel framework named improved private aggregation of teacher ensembles (IPATE) is proposed. IPATE aims to protect the privacy of trained models and medical data by adding hyperscale Laplace noise to the extended voting difference of many CNN models. An experimental example including 12 leads data of 148 myocardial infarction patients and 52 normal subjects was used in the study. Experimental results show that with the increase of noise and under the condition of limited data, IPATE can still achieve about 90 percent accuracy, and it is not sensitive to noise.

Special Section on Parallel, Distributed, and Reconfigurable Computing, and Networking Graphics processing units (GPUs) are highly efficient architectures for parallel stencil code; however, the small device (i.e., GPU) memory capacity (several tens of GBs) necessitates the use of out-of-core computation to process excess data. Great programming effort is needed to manually implement efficient out-of-core stencil code. To relieve such programming burdens, directive-based frameworks emerged, such as the pipelined accelerator (PACC); however, they usually lack specific optimizations to reduce data transfer. In this paper, we extend PACC with two data-centric optimizations to address data transfer problems. The first is a direct-mapping scheme that eliminates host (i.e., CPU) buffers, which intermediate between the original data and device buffers. The second is a region-sharing scheme that significantly reduces host-to-device data transfer. The extended PACC was applied to an acoustic wave propagator, automatically extending the length of original serial code 2.3-fold to obtain the out-of-core code. Experimental results revealed that on a Tesla V100 GPU, the generated code ran 41.0, 22.1, and 3.6 times as fast as implementations based on Open Multi-Processing (OpenMP), Unified Memory, and the previous PACC, respectively. The generated code also demonstrated usefulness with small datasets that fit in the device capacity, running 1.3 times as fast as an in-core implementation. This study was supported in part by the Japan Society for the Promotion of Science KAKENHI Grant Numbers JP15H01687, JP16H02801, and JP20K21794.

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% accuracy is trained on the corrected dataset, which improve the baseline from 83.3% to 91.7%.

Building an open global sensing layer is critical for the Internet of Things (IoT). In this article, we present a Sensory Data-Centric Networking (SDCN) architecture for inter-networking two main networked sensing systems in IoT—wireless sensor networks and mobile sensing networks. Specifically, the proposed SDCN is a systematic solution including NDNs for sensor nodes in the Zigbee network, NDNm for mobilephones in the Wi-Fi network, and NDNg for gateways. Considering the sensing requirement of IoT, we first design a novel Spatio-Temporal 16 Tree (ST16T) naming scheme associated with the scope-matching method. Based on the naming scheme, we further propose the related discovery methods, network switching mechanism, forwarding, and routing strategies according to the features of large-scale sensing and resource-constrained environment. A proof-of-concept prototype is implemented and further is deployed on our campus (BUPT) and the Great Wall (Shaanxi, China) for Environment Monitoring Project. Several experiments are conducted on the deployed platform. The experimental results show that SDCN outperforms the state-of-the-arts and gains a great performance improvement in terms of energy consumption, data collection efficiency, memory footprint, and time delay.

In this work, we describe an urban Internet of Things (IoT) architecture, grounded in big data patterns and focused on the needs of cities and their key stakeholders. First, the architecture of the dedicated platform USE4IoT (Urban Service Environment for the Internet of Things), which gathers and processes urban big data and extends the Lambda architecture, is proposed. We describe how the platform was used to make IoT an enabling technology for intelligent transport planning. Moreover, key data processing components vital to provide high-quality IoT data streams in a near-real-time manner are defined. Furthermore, tests showing how the IoT platform described in this study provides a low-latency analytical environment for smart cities are included.

In this present era, internet and cyber technologies have revolutionized and improved people's lifestyles completely at a remarkable rate. Having significant benefits, there are certain challenges faced by current technologies. In this perspective, one of the main challenges is not the production and acquisition of data; rather, it is the proper management, of it, including storage and analysis of data, has become a major concern. In this article, we conduct a study to discuss the two most widely used data centric technologies, i.e., Cyber Physical Systems (CPSs) and Internet of Things (IoT). The goal is to identify the architectural similarities and differences to achieve an interoperable solution that can manage data more efficiently. Our analysis revealed that a possible transformation of IoT to CPS and vice versa is possible when certain transformation steps are performed.

The efficiency of an accelerator depends on three factors-mapping, deep neural network (DNN) layers, and hardware-constructing extremely complicated design space of DNN accelerators. To demystify such complicated design space and guide the DNN accelerator design for better efficiency, we propose an analytical cost model, MAESTRO. MAESTRO receives DNN model description and hardware resources information as a list, and mapping described in a data-centric representation we propose as inputs. The data-centric representation consists of three directives that enable concise description of mappings in a compiler-friendly form. MAESTRO analyzes various forms of data reuse in an accelerator based on inputs quickly and generates more than 20 statistics including total latency, energy, throughput, etc., as outputs. MAESTRO's fast analysis enables various optimization tools for DNN accelerators such as hardware design exploration tool we present as an example.

In the context of highly data-centric applications, close reconciliation of computation and storage should significantly reduce the energy-consuming process of data movement. This letter proposes a computational SRAM (C-SRAM) combining in- and near-memory computing (IMC/NMC) approaches to be used by a scalar processor as an energy-efficient vector processing unit. Parallel computing is thus performed on vectorized integer data on large words using usual logic and arithmetic operators. Furthermore, multiple rows can be advantageously activated simultaneously to increase this parallelism. The proposed C-SRAM is designed with a two-port pushed-rule foundry bitcell, available in most existing design platforms, and an adjustable form factor to facilitate physical implementation in a SoC. The 4-kB C-SRAM testchip of 128-b words manufactured in 22-nm FD-SOI process technology displays a subarray efficiency of 72% as well as an additional computing area of less than 5%. The measurements averaged on 10 dies at 0.85 V and 1 GHz demonstrate an energy efficiency per unit area of 35.6 and 1.48 TOPS/W/mm2 for 8-b additions and multiplications with 3- and 24-ns computing latency, respectively. Compared to a 128-b SIMD processor architecture, up to $2\times $ energy reduction and $1.8\times $ speed-up gains are achievable for a representative set of highly data-centric application kernels.

Smart infrastructure has the potential to revolutionise how infrastructure is delivered, managed and automatically controlled. Data and digital twins offer an opportunity for this purpose. However, before using data to aid stakeholders for their decisions, one should have a data plan, collect useful data, assess the quality of the data, have the language for enabling communication related to data, manage the data and have or acquire the skills that are needed to integrate and analyse data. To this end, this paper aims to provide an explanation and description of several challenges related to the data. The article concludes by suggesting that the industry addresses the challenges related to the availability, accessibility, quality, volume, variety and longevity of data by considering user-centric approaches, blending methodologies and identifying the organizational needs. The industry should plan and act on these challenges as part of its data analytics strategies of today to expedite the artificial intelligence applications of the future.

In recent years, the number of financial technology players and users have increased at a significant rate due to the rapid technological advancement in financial technology. While smart devices are providing more useful features to users, they have also made it possible for cyber threats to migrate from desktops to smart devices. Thus, it is important for smart device users to be aware that their device could be exposed to cyber threats and that users could protect their devices by employing data-centric cyber security measures. This study reveals how financial technology business model responded to the breach phenomenon by employing data-centric protection approaches. The result is very interesting. Data-centric security is very needed as it is capable of protecting data as a whole. It provides a gapless protection, meaning to say, the data are encrypted and classified wherever it moves. With persistent protection and cross-platform operability, data-centric security will eliminate gaps and keep data protected.

The cyber-physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards a data-centric Internet where the human user becomes more central than ever. We argue that this will profoundly impact primarily on the way data should be handled in the Next Generation Internet. It will require a radical change of the Internet data-management paradigm, from the current platform-centric to a human-centric model. In this paper we present a new paradigm for Internet data management that we name Internet of People (IoP) because it embeds human behavior models in its algorithms. To this end, IoP algorithms exploit quantitative models of the humans’ individual and social behavior, from sociology, anthropology, psychology, economics, physics. IoP is not a replacement of the current Internet networking infrastructure, but it exploits legacy Internet services as (reliable) primitives to achieve end-to-end connectivity on a global-scale. In this opinion paper, we first discuss the key features of the IoP paradigm along with the underlying research issues and challenges. Then, we present emerging data-management paradigms that are anticipating IoP.

With the next generation immersive video applications, network capacity is becoming a growing bottleneck to deliver a high quality video to end-users. Recent advances to tackle this challenge introduced super-resolution (SR) for video quality enhancement through neural computations by leveraging client-side compute capacity. However, the existing SR models are bulky, compute-, and memory-expensive, which makes it difficult to deploy them in practice. In this work, we present dcSR, a lightweight data-centric SR approach that enables a practical neural quality enhancement for videos. On the server-side, dcSR constructs micro SR models trained on a few selected frames from each video through a data-centric paradigm by employing a long term video scene understanding mechanism. On the client-side, dcSR integrates the micro SR models into the regular video decoder and enhances the video quality in real-time without compromising on quality enhancement. We evaluate dcSR and show its benefits by comparing it with previous methods.

Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with significant interest and investment from commercial stakeholders. Mechanistic models, based on physical equations, and purely data-driven statistical approaches represent two ends of the modelling spectrum. New hybrid, data-centric engineering approaches, leveraging the best of both worlds and integrating both simulations and data, are emerging as a powerful tool with a transformative impact on the physical disciplines. We review the key research trends and application scenarios in the emerging field of integrating simulations, machine learning, and statistics. We highlight the opportunities that such an integrated vision can unlock and outline the key challenges holding back its realisation. We also discuss the bottlenecks in the translational aspects of the field and the long-term upskilling requirements of the existing workforce and future university graduates., 20 pages, 2 figures

The process of testing conventional programs is quite easy as compared to the programs using Deep Learning approach. The term Deep learning (DL) is used for a novel programming approach that is highly data centric and where the governing rules and logic are primarily dependent on the data used for training. Conventionally, Deep Learning models are evaluated by using a test dataset to evaluate their performance against set parameters. The difference in data and logic handling between programs using conventional methods and programs using the DL approach makes it difficult to apply the traditional approaches of testing directly to DL based programs. The accuracy of test data is currently the best measure of the adequacy of testing in the DL based systems. This poses a problem because of the difficulty in availability of test data that is of sufficient quality. This in turn restricts the level of confidence that can be established on the adequacy of testing of DL based systems. Unlike conventional applications, using the conventional programming approaches the lack of quality test data and the lack of interpretability makes the system analysis and detection of defects a difficult task in DL based systems. So testing of DL based models can be done automatically with a different approach compared to normal software.

The emergence of additive manufacture (AM) for metallic material enables components of near arbitrary complexity to be produced. This has potential to disrupt traditional engineering approaches. However, metallic AM components exhibit greater levels of variation in their geometric and mechanical properties compared to standard components, which is not yet well understood. This uncertainty poses a fundamental barrier to potential users of the material, since extensive post-manufacture testing is currently required to ensure safety standards are met. Taking an interdisciplinary approach that combines probabilistic mechanics and uncertainty quantification, we demonstrate that intrinsic variation in AM steel can be well described by a generative statistical model that enables the quality of a design to be predicted before manufacture. Specifically, the geometric variation in the material can be described by an anisotropic spatial random field with oscillatory covariance structure, and the mechanical behaviour by a stochastic anisotropic elasto-plastic material model. The fitted generative model is validated on a held-out experimental dataset and our results underscore the need to combine both statistical and physics-based modelling in the characterization of new AM steel products.

Pandey et al. (2021) demonstrate the importance of diversifying training data to make balanced predictions of thermodynamic properties for inorganic crystals.

Data­-centric (Dc) approaches are being used for data processing in several application domains, such as distributed systems, natural language processing, and others. There are different data processing frameworks that ease the task of parallel and distributed data processing. However, there are few research approaches studying on how to execute model manipulation operations, as model transformations models on such frameworks. In addition, it is often necessary to provide extraction of XMI­-based formats into possibly distributed models. In this paper, we present a Model2GraphFrame operation to extract a model in a modeling technical space into the Apache Spark framework and its GraphFrame supported format. It generates GraphFrame from the input models, which can be used for partitioning and processing model operations. We used two model partitioning strategies: based on sub­graphs, and clustering. The approach allows to perform model analysis applying operations on the generated graphs, as well as Model Transformations (MT). The proof of concept results such as model2GraphFrame, GraphFrame partitioning, GraphFrame connectivity, and GraphFrame model transformations indicate that our Model Extraction can be used in various application domains, since it enables the specification of analytical expressions on graphs. Furthermore, its model graph elements are used in model transformations on a scalable platform.

Adapting applications’ data rates in multi-hop wireless ad-hoc networks is inherently challenging. Packet collision, channel contention, and queue buildup contribute to packet loss but are difficult to manage in conventional TCP/IP architecture. This work explores a data-centric approach based on Name Data Networking (NDN) architecture, which is considered more suitable for wireless ad-hoc networks. We show that the default NDN transport offers better performance in linear topologies but struggles in more extensive networks due to high collision and contention caused by excessive Interests from out-of-order data retrieval and redundant data transmission from improper Interest lifetime setting as well as in-network caching. To fix these, we use round-trip hop count to limit Interest rate and Dynamic Interest Lifetime to minimize the negative effect of improper Interest lifetime. Finally, we analyze the effect of in-network caching on transport performance and which scenarios may benefit or suffer from it.

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

The recent development of IOT emphasizes on the connection of most of the devices by means of smarter and intelligence way. These devices communicate with each other via internet without or/very less communication link of human being. The network of IoT is highly heterogeneous, uncontrolled, broad and perspicacious. All the data centric activities are done by 3 vital steps i.e sensing, analyzing and processing. The Internet of Things (IoT) is a cooperative system which connects a large number of computing devices which includes sensors, actuators, processors to a single IP address and the computing devices of IoT are provided with Unique Identifiers (UIDs). The major goal of IoT is to create a real world environment where frequently used agile objects coherently linked with internet. The growth rate of IoT devices has been increasing at a very high speed, so IOT can find its application in every field. All most all areas are using IoT to improve efficiency, customer service and value of the business, but in all respect IOT devices are still requires security and privacy optimization for future sustainable development.

Achieving the customer demand in manufacturing industries during Covid-19 situation is a challenging task since the industry needs to stick on to the local government policies such as high safety, work from home, man power reduction for the purpose of social distance, travelling in transport etc. If the machines produce quality products without any interruption, minor stoppages or breakdown, then the industry can achieve its production demand satisfying the customer need along with Covid-19 restrictions. By implementing autonomous maintenance called Jisu Hazen (JH) pillar of Total Productive Maintenance, around 70% of breakdown caused due to forced deterioration can be eliminated. Data centric digitalization through Industry 4.0 and Industrial Internet of Things, manpower and manmade errors are reduced which intern eliminate breakdown, defects and increase productivity. Using smart sensors, the signature parameters related to Clean Lubricate Inspection and Tightening CLIT are digitalized and a smart JH is followed. Through smart JH, operators working hours, number of employees are reduced, work from home situation is made for maintenance engineers to maintain Covid-19 rules.

The world is rapidly shifting towards data-centric computing model with significant growth in compute, storage and networking. Moore’s law scaling to reduce power, improve performance and reduce area and cost per transistor to support data centric growth places significant challenges for photomask process, control, and metrology. DUV lithography continues to be workhorse technology and continues to drive innovation. Multiple solutions are being developed to solve DUV mask challenges associated with a big die requirement. While DUV production is being projected to be strong for next few years, EUV is picking up pace and being deployed in high volume manufacturing. EUV is an opportunity-rich environment for improvements and innovations that will continue to advance the journey. EUV HiNA is a major step in the lithography paradigm where 0.55 NA EUV optics will enable a shift to sub-10nm resolution. The HiNA mask ecosystem will adapt by growing the newly created low NA EUV supply chain into new directions. The photomask environment will continue to bring creativity, drive innovations and gain commercial rewards.

Subsea field development planning can be a complicated undertaking requiring the coordination and collaboration of multiple engineering and commercial disciplines with competing objectives. Thus, finding the optimal development solution can be challenging. To combat this, a data-centric omnichannel digital platform for configuring subsea field developments has been created. The study workflow orchestrated by the digital platform is detailed along with an overview of the data model, functionality, and deliverables. A case study is presented to demonstrate the value delivered using this digital platform. The digital platform is inherently collaborative as it orchestrates specialist engineering tools and their workflows around the same data for study teams to configure subsea development solutions. The platform is composed of: A web-based graphical user interface that allows discipline and product engineers to collaboratively configure the system, products, planning and costing for an entire subsea field development scenario, leveraging the same base data i.e., a single source of truth. A proprietary data model covering system, product (e.g., hardware or equipment), activity planning and costing breakdowns, and; Microservices that directly attach engineering tools and their workflows to the digital platform to automate product design and analysis. A case study is presented to demonstrate the use of the digital platform on a subsea field development prospect and a qualitative comparison with the conventional way of working is made. The case study illustrates the use of a digital hardware configurator (subsea tree system configuration) and the automated planning workflow for an EPCI (Engineering, Procurement, Construction, and Installation) prospect enabled by the digital platform. The results of the case study demonstrate the platform values and benefits the digital platform delivers. The benefits are underpinned by the automated data transfer, the versioning functionality, software logic, and the common base data used by the microservices. The benefits that have been found when compared with the conventional way of working include: Faster validation of alternative development scenarios, meaning that more concepts and sensitivities can be investigated in the same length of time; A reduction in the overall lead time and person hours required to configure and optimize a field development solution; Design risk reduction, and; Efficient and consistent transition of data via virtual handovers. This paper demonstrates a new approach for subsea field development planning using a data-centric omnichannel digital platform called Subsea Studio™ FD, which is shown to deliver benefits over the conventional document-centric way of working. The digital platform brings multiple engineering disciplines together to configure optimal development solutions, accounting for competing objectives. It initiates the digital thread through the project lifecycle and will ultimately culminate in a digital twin during project execution, which can be leveraged throughout the life-of-field to optimize operations.

There have been numerous calls for O&G companies to rethink the way they carry out capital projects. Various studies have highlighted that opportunities to enhance productivity exist from early development through to construction and handover to operations. Companies that are can realize these opportunities can complete projects safer and faster, reduce costs and quality issues, and improve schedule predictability. This paper presents data centric verification as a key enabler for such change, as it focusses on delivery of desired outcomes – the key to successful projects. This paper presents the rapid benefits that can be harvested, if the effort to put data centric verification in place is made. The following aspects are detailed to illustrate the benefits that are achievable: The wide variety of checking and confirmation activities on projects that frequently overlap, happen too late or don't focus on the correct aspects can be streamlined through a unified verification approach. Using a data centric approach to the set up and tracking of verification activities can support analytics to enable better planning and early identification of issues. Traceability in the data centric approach will enable quicker identification of root causes and remediations. Handover to operations moves from a document accumulation exercise to a data custody transfer. A typical O&G project is executed by a consortium of companies from owner/operators, engineering, procurement and construction companies, various suppliers and equipment manufacturers, etc. Data centric verification, if achieved, can only be through collaboration on a commonly agreed approach. Objectives/Scope Using data centric verification as an enabler to enhance project productivity from early development through handover. Methods, Procedures, Process This paper presents data centric verification as a key enabler for such change, as it focusses on delivery of desired outcomes – the key to successful projects. This paper presents the rapid benefits that can be harvested, if the effort to put data centric verification in place is made. The following aspects are detailed to illustrate the benefits that are achievable: The wide variety of checking and confirmation activities on projects that frequently overlap, happen too late or don't focus on the correct aspects can be streamlined through a unified verification approach. Using a data centric approach to the set up and tracking of verification activities can support analytics to enable better planning and early identification of issues. Traceability in the data centric approach will enable quicker identification of root causes and remediations. Handover to operations moves from a document accumulation exercise to a data custody transfer. Results, Observations, Conclusions The paper demonstrates the resulting requirements management and verification data models that can be established to enable the efficiency gains during project execution. Several example implementations of this data centric approach will be shared to validate the observed improvements. Novel/Additive Information Data centric verification is new to the Oil and Gas Industry. These Shell example projects are the first to implement this new approach within our supply chain.

Recent years have witnessed the rapid deployment of smart homes; most of them are controlled by remote servers in the cloud. Such designs raise security and privacy concerns for end users. In this paper, we describe the design of Sovereign, a home IoT system framework that provides end users complete control of their home IoT systems. Sovereign lets home IoT devices and applications communicate via application-named data and secures data directly. This enables direct, secure, one-to-one and one-to-many device-to-device communication over wireless broadcast media. Sovereign utilizes semantic names to construct usable security solutions. We implement Sovereign as a publish-subscribe-based development platform together with a prototype home IoT controller. Our preliminary evaluation shows that Sovereign provides a systematic, easy-to-use solution to user-controlled, self-contained smart homes running on existing IoT hardware without imposing noticeable overhead.

Adoption of AI-equipped systems and their societal benefits are heavily dependent on human understanding of the rationale behind the systems’ outputs. Such systems’ widespread inability to explain their outputs causes human mistrust and doubts regarding their regulatory compliance. Research in psychology points to the amenability of argumentation as a paradigm for human reasoning, advocating that humans developed reasoning in order to argue. We here overview a number of approaches using computational argumentation frameworks as the scaffolding for explanations for human consumption. Our argumentation frameworks are automatically mined from data and data-centric methods. We define explanations as graphs obtained from these argumentation frameworks, which are customisable by means of properties. We illustrate our methods with various consumer-oriented tasks in the media and entertainment industry, providing reasoning outputs that can be explained to consumers, and that consumers can directly interact with to give rise to improved recommendations.

Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. In this work, we present a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes.

Data-Centric Networking (DCN) is a research project based on Named Data Networking (NDN), which focuses on the high-performance name-based forwarder, distributed pub/sub data distribution platform, distributed network storage, in-network processing platform, and blockchain-based data trading platform. In this paper, we present a design of real-time public Internet of Things (IoT) data distribution platform which is based on a Data-Centric Networking (DCN) distributed pub/sub data distribution platform.

Recent advances in sensor technology and wireless communications have led to many new data dissemination routing protocols, especially designed for wireless sensor networks, where energy awareness is the most important consideration. The focus of this thesis is on the area of routing protocols for wireless sensor networks, especially for those applications where, the whole sensor field need to be taken under observation to detect available different types of moving objects. Besides the efficient use of limited energy, reliability is another important issue in sensor communication, where the network is susceptible to environmental factors. In this thesis, the design of a new energy-efficient data-centric routing protocol, named Reliable and Energy-Efficient Protocol (REEP), is proposed. We have used MATLAB 7.4 for our implementation. The performance of REEP has been compared with Directed Diffusion (DD) for the aforementioned sensor network application. Our simulations and experimental results show that REEP performs better than DD.