Reviewerly/exHarmony huggingface dataset:
INFuse: Towards Efficient Context Consistency by Incremental-Concurrent Check Fusion

Nowadays applications are getting increasingly attractive by being capable of adapting their behaviors based on their understanding to running environments (a.k.a. contexts). However, such capability can be subject to illness or even unexpected crash, when contexts, for suffering environmental noises, become inaccurate or even conflict with each other. Fortunately, various constraint checking techniques have been proposed to validate contexts against consistency constraints, in order to guard context consistency for applications in a timely manner. However, with the growth of environmental dynamics and context volume, it is getting more and more challenging to check context consistency in time. In this paper, we propose a novel approach, INFuse, to soundly fuse together two lines of techniques, namely, incremental checking and concurrent checking, for efficient constraint checking. Realizing such check fusion has to address the challenges rising from the gap between the micro analysis for reusable elements in incremental checking and the macro collection of parallel tasks in concurrent checking. INFuse solves the challenges by automatically deciding maximal concurrent boundaries for context changes under checking (i.e., what-correctness problem), and soundly fusing incremental and concurrent checking for context consistency (i.e., how-correctness problem), with theoretical guarantees. Our experimental evaluation with real-world data shows that INFuse could improve constraint checking efficiency by 18.6x–171.1x, as compared with existing state-of-the-art techniques.
E-textiles Assisting Healthcare, Rehabilitation, and Well-being - To whom, for What, and How?

The use of e-textiles in healthcare, rehabilitation, and well-being (referred here as assistive e-textiles) is spreading for its obvious benefits, such as monitoring of physiological signals and vital signs. Although there are versatile studies on individual applications of assistive e-textiles, there are not many that include in the design process a wide variety of stakeholders who have roles in development or use of assistive technology. To provide stakeholder-oriented design knowledge regarding the development of assistive e-textiles, we organized five multidisciplinary ideation workshops for 50 participants with different backgrounds and roles. Many distinct ideas were created that targets a diverse set of users from different age groups, ability levels and even for non-human actors. Participants came up with ideas related to work environment, rehabilitation, healthcare, and daily life. This article presents those findings and discusses how those can help designers and researchers in the field.
Strong Baseline with Auto-encoder for Scale-Invariant Person Re-identification

Person Re-identification associates the same person images captured by disjoint cameras. The different training strategies and precise hyper-parameter selection are required for the construction of an effective person re-identification. However, in the current work, the tests are carried out by trial and error, which takes a long time to reach an optimal solution. We experiment with various training strategies with the metric learning model serving as the baseline to address this issue. Existing person re-identification benchmarks have insufficient samples for training the ReID model. Also, the previous approaches fail when the same individual appears in varied sizes. To solve this issue, an auto-encoder module is used to create a single image in three distinct scales to enhance the sample size and tackle the scale variation problem of the person reidentification. In addition, a performance comparison is made between the baseline and the baseline with auto-encoder to demonstrate the influence of an auto-encoder as a data augmentation for the person re-identification. The adoption of auto-encoder module and bestperforming training techniques to a baseline have enhanced the rank1 and mAP on Market1501, DukeMTMC-reID datasets.
Deep Learning-Based Approach for Object Detection in Robot Football Competition

Robot football competition is a complex and emerging field of artificial intelligence research involving object detection technology, robotics, intelligent control, and other technologies. However, object detection is one of the most core technologies, supporting robots to realize tactical cooperation and real-time actions such as shooting, passing, and obstacle avoidance behavior. With precise accuracy and detection speed requirements, fast-moving robots and footballs must be recognized accurately by object detection algorithms under environments of changing backgrounds and lighting conditions. In this paper, to propose a reliable detection method for robot football competition, an end-to-end training approach is applied based on the YOLOv3 algorithm. K-means reclustering is used to calculate more appropriate bounding box priors to adapt to the size of detected objects. Besides, the smooth L1 loss function is adopted for the loss of the bounding box instead of MSE loss to reduce the model's sensitivity to outliers. With the framework of Pytorch, the proposed method can reach the mAP up to 96.5%, recognizing specific targets under the Standard Platform League(SPL) of Robocup. Accurate object detection algorithms can improve the capabilities of robot behavioral decision-making and positioning. In the future, superior lightweight algorithms can also be deployed on edge devices to meet the visual needs of real-time intelligent services.
A Memory Enhancement Adjustment Method Based on Stochastic Gradients

Stochastic gradient descent methods and its variants have been widely used to learn the parameters of a neural network by solving an associated non-convex minimization problem. We propose a new momentum method and adaptive method (MEA/AdaMEA) based on memory enhancement adjustment, which is different from the traditional momentum methods and adaptive methods. At the theoretical level, we prove the convergence of the MEA method for solving a non-convex minimization problem, and then analyze the generalization error of the MEA method from the perspective of consistent stability, showing that the MEA method can improve the stability of the learned model and enhance the generalization performance. At the experimental level, we compare the empirical results of the MEA/AdaMEA method and common optimizers for deep learning on the datasets CIFAR10 and CIFAR100 under the network architectures of the convolutional neural networks ResNet18 and ResNet34. Experiments demonstrate the effectiveness of our proposed MEA/AdaMEA.
Neuro-Evolution in Flappy Bird Game

This paper presents an implementation of an Artificial Intelligence (AI) using a Genetic Algorithm (GA) and Deep Neural Network (DNN), with the goal of creating an AI that can win in the game of Flappy Bird. The neural network is a straightforward dense neural network with a single layer of concealment. The application of GA proves to be essential to the achievement of the AI's goals, particularly the mutation of the weight vector. The game starts off with a thousand birds, and a neural network determines whether each bird needs to jump at the beginning of each round. Each bird starts off with its own network already established. The genetic algorithm oversees both the mutation process and the generation of the subsequent population. After a certain number of repetitions, the network, with the help of mutation, eventually figures out how to win the game. The p5.js JavaScript graphics package is also utilized in this work. This was a wonderful and comprehensive introduction to the process of making visuals in a smooth manner on the front end.
An empirical study on quality issues of eBay's big data SQL analytics platform

Big data SQL analytics platform has evolved as the key infrastructure for business data analysis. Compared with traditional costly commercial RDBMS, scalable solutions with open-source projects, such as SQL-on-Hadoop, are more popular and attractive to enterprises. In eBay, we build Carmel, a company-wide interactive SQL analytics platform based on Apache Spark. Carmel has been serving thousands of customers from hundreds of teams globally for more than 3 years. Meanwhile, despite the popularity of open-source based big data SQL analytics platforms, few empirical studies on service quality issues (e.g., job failure) were carried out for them. However, a deep understanding of service quality issues and taking right mitigation are significant to the ease of manual maintenance efforts. To fill this gap, we conduct a comprehensive empirical study on 1,884 real-word service quality issues from Carmel. We summarize the common symptoms and identify the root causes with typical cases. Stakeholders including system developers, researchers, and platform maintainers can benefit from our findings and implications. Furthermore, we also present lessons learned from critical cases in our daily practice, as well as insights to motivate automatic tool support and future research directions.
Tencent AVS: A Holistic Ads Video Dataset for Multi-Modal Scene Segmentation

Temporal video segmentation and classification have been advanced greatly by public benchmarks in recent years. However, such research still mainly focuses on human actions, failing to describe videos in a holistic view. In addition, previous research tends to pay much attention to visual information yet ignores the multi-modal nature of videos. To fill this gap, we construct an ‘Ads Video Segmentation’ dataset (AVS) in the ads domain to escalate multi-modal video analysis to a new level. AVS describes videos from three independent perspectives as ‘presentation form’, ‘place’, and ‘style’, and contains rich multi-modal information such as video, audio, and text. AVS is organized hierarchically in semantic aspects for comprehensive temporal video segmentation with three levels of categories for multi-label classification, e.g., ‘place’ - ‘working place’ - ‘office’. Therefore, AVS is distinguished from previous temporal segmentation datasets due to its multi-modal information, holistic view of categories, and hierarchical granularities. It includes 12,000 videos, 82 classes, 33,900 segments, 121,100 shots, and 168,500 labels. Accompanied with AVS, we also present a strong multi-modal video segmentation baseline coupled with multi-label class prediction. Extensive experiments are conducted to evaluate our proposed method as well as existing representative methods to reveal key challenges of our dataset AVS.
A method for low-carbon dispatch of PEDF (photovoltaic, energy storage, direct current and flexibility) microgrid considering indirect carbon emissions

The application of PEDF (photovoltaic, energy storage, direct current and flexibility) microgrids can bring considerable gain effect for social energy saving, distributed photovoltaic consumption and building carbon emission reduction. However, the current economic dispatch methods implemented by most microgrids cannot reflect the carbon emission responsibility of users, which limits the potential of PEDF microgrids in energy conservation and emission reduction to a certain extent. Based on the carbon emission flow theory, this paper investigates the dispatching of typical PEDF microgrids, and takes the minimum daily indirect carbon emissions of microgrids as the objective function to construct a dynamic dispatching model of energy storage in microgrids with low carbon concept. The model combines the idea of deep learning neural network modeling and dynamic programming method. The “Gree Photovoltaic Future House-DC Community” PEDF microgrid system is taken as an example to verify the effectiveness of the proposed method. By introducing carbon trading, the economic benefits of microgrid under low-carbon dispatch are analyzed. In conclusion, the low-carbon dispatching method of PEDF microgrids can effectively reduce indirect carbon emissions, and the resulting economic losses can be reduced by selling carbon assets accrued from photovoltaic power generation.
DST-based Video Watermarking Robust to Lossy Channel Compression

It is increasingly common for people to share videos on social platforms such as TikTok, YouTube and bilibili. The shared videos, however, is usually affected by lossy channel compression, thus a robust video watermarking based on discrete sine transform (DST) is proposed. Firstly, We divide each video frame into blocks and perform DST in embedding region, then the high-frequency component of DST is selected for watermark embedding. For further strengthening the robustness of lossy channel compression, a signed odd-even interval quantization method is designed by improving the odd-even quantization method. As a consequence of uncomplicated feature of DST, the computational efficiency of this algorithm is efficient, making it ideal for application in real-time scenarios such as video live broadcast and video conference. Moreover, the watermark is extracted without the host video. Experimental results show that the proposed algorithm not only ensures excellent video quality, but also significantly improves the robustness of lossy channel compression, and the robustness to different video contents is stable.
Efficient Distributed Consensus Algorithm For Swarm Robotic

Swarm robotics is a network based multi-device system designed to achieve shared objectives in a synchronized way. This system is widely used in industries like farming, manufacturing, and defense applications. In recent implementations, swarm robotics is integrated with Blockchain based networks to enhance communication, security, and decentralized decision-making capabilities. As most of the current blockchain applications are based on complex consensus algorithms, every individual robot in the swarm network requires high computing power to run these complex algorithms. Thus, it is a challenging task to achieve consensus between the robots in the network. This paper will discuss the details of designing an effective consensus algorithm that meets the requirements of swarm robotics network.
A New Distributed Echo State Network Integrated With an Auto-Encoder for Dynamic Soft Sensing

As dynamic industrial processes become increasingly complicated, it tends to be difficult to develop accurate soft sensors. Echo state networks (ESNs) as dynamic neural network (NN) models have been broadly used in developing dynamic soft sensors. However, when facing high-dimensional data, the dimension disaster problem might occur in the input space of ESNs. Furthermore, in ESNs, the number of reservoir nodes is large, causing collinearity between the reservoir outputs. To solve these problems, in this article, a new distributed ESN model integrated with auto-encoder (AE-DESNm) is proposed. In particular, AE-DESNm has distributed and independent input subnets. The dimensionality of each input subnet has been reduced, eliminating the dimension disaster problem. To obtain the distributed and independent input subnets of AE-DESNm, the first integer neighbor clustering hierarchy (FINCH) algorithm is used to cluster the input attributes of high-dimensional data. Each cluster corresponds to one input subnet. In addition, seeking to handle the collinearity issue in the reservoir outputs, auto-encoder (AE) is used to extract features from the reservoir outputs. Finally, the extracted features are modeled by an extreme learning machine (ELM) to estimate key variables. To verify the performance of AE-DESNm, a numerical example and a dataset from one real industrial process named purified terephthalic acid (PTA) production are used. Compared with other methods, the experimental results show that the proposed AE-DESNm can achieve much higher accuracy in soft sensor modeling for dynamic industrial processes.
Improving Documentation Agility in Safety-Critical Software Systems Development For Aerospace

Despite documentation being considered the pri-mary challenge to agile methods in safety-critical software systems development [1], agile would be of particular interest to improve changeability while providing efficiency and effective-ness to all the phases of software development. In this work, we created mechanisms for automating document processing and management to improve the efficiency and effectiveness of documentation activities of safety-critical software systems development, most concretely in the aerospace domain. The implemented tools were co-designed and validated iteratively in the concrete industrial context of Critical Software (CSW) projects, within a wider research work towards continuous certification [3]. We interviewed Critical Software professionals to validate our solution, collected feedback on the implemented tools and got insights for future work. The tools were also the target of synthetic tests that allowed us to conclude that document automation is possible in the critical-safety software development industry and carries several benefits. The developed tools are not yet qualified in compliance with the DO-330 standard (Tools Qualification).
Quotient Approximation Modular Reduction

Modular reduction is a core operation in public-key cryptography. While a standard modular re-duction is often required, a partial reduction limiting the growth of the coefficients is enough for several usecases. Knowing the quotient of the Euclidean division of an integer by the modulus allows to easily recover the remainder. We propose a way to compute efficiently, without divisions, an approximation of this quotient. From this approximation, both full and partial reductions are deduced. The resulting algorithms are modulus specific: the sequence of operations to perform in order to get a reduction depends on the modulus and the size of the input. We analyse the cost of our algorithms for a usecase coming from post-quantum cryptography. We show that with this modulus, our method gives an algorithm faster than prior art algorithms.
Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures

Federated learning is driving the development of new techniques to efficiently and securely use data across multiple sites while using diverse resources. One of these techniques is the use of the serverless computing paradigm to abstract away resource specific configurations, allowing federated learning across heterogeneous environments. However, deploying federated learning across edge resources, the cloud, and traditional HPC sites will require specialized approaches in order to best account for the weaknesses and strengths of each resource. In this work, we explore the new tradeoffs presented by managing a federated learning task across heterogeneous resources and demonstrate these tradeoffs with experiments using a serverless federated learning framework.
Medicine recommender system: A machine learning approach

Recommendation systems (RS) suggest the right item to the right user. It predicts the user’s rating to an item and based on this rating RS provides the suggestion to users. In today’s world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable Medicineaccording to diseasesby using library “recommenderlab” in R. This study proposed a system that gives the best medicine available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable medicine from available database
How to Explain It to Energy Engineers?

Research in the area of explainable AI (XAI) has made some progress. Research papers [9, 17] report that explainability cannot be built into technology without understanding the needs, goals, and tasks of the target user group. Little research has been done to provide evidence that explanations should be user role specific. The research results reported in this paper intents to provide data points that explanations need to be user role specific. The research addresses two research questions: RQ 1 Is a one-explanation fits all approach acceptable. To better understand explainability, the paper assumes three explanation qualities: trustworthiness (contributing to acceptability), understandability (contributing to effectiveness), and actionability (contributing to efficiency). The paper hypothesis that trustworthiness is a pre-requisite for understandability which is a pre-requisite for actionability. A user-centered design approach is performed to elicit explanation needs to validate them with representatives of the target user group of energy engineers, professionals that maintain buildings and their building services (providing a comfortable environment for occupants while optimizing cost and other goals). The research found that even for one user group (energy engineers), different explanations are needed for different user steps. The hypothesis of one-explanation fits-all had to be rejected. Based on the results, the hypothetical relationship between trustworthiness, understandability, and actionability had to be rejected. A new hypothetical relationship is formulated: understandability (contribution to effectiveness) and actionability (contributing to efficiency) are pre-requisites for trustworthiness (contributing to acceptability).
Evolution of Locality on Ethereum Transaction Network

With a large market capitalization, Ethereum is one of the most famous blockchain platforms supporting smart contracts nowadays. To better understand Ethereum, previous researches have performed numerous analyses on Ethereum via complex network theory, while many of them merely focus on a single perspective of scale or time series. This motivates us to investigate the evolution of Ethereum from a local point of view. We concentrate our study on some key accounts labeled by the community. Then we crawl and extract their transaction subgraphs, and conduct an analysis based on several basic network properties consisting of network scale and triadic tendencies by sliding window. Furthermore, we provide an insight into the local structure of these subgraphs by counting the graphlets induced from the label nodes. Subsequently, we observe diverse similarities and differences between various label nodes in the changing trend and subgraph patterns in the locality of Ethereum networks. The results and findings from this study may help us to understand the activity of Ethereum more comprehensively.
Academic writing and publishing beyond documents

Research on writing tools stopped in the late 1980s when Microsoft Word had achieved monopoly status. However, the development of the Web and the advent of mobile devices are increasingly rendering static print-like documents obsolete. In this vision paper we reflect on the impact of this development on scholarly writing and publishing. Academic publications increasingly include dynamic elements, e.g., code, data plots, and other visualizations, which clearly requires other tools for document production than traditional word processors. When the printed page no longer is the desired final product, content and form can be addressed explicitly and separately, thus emphasizing the structure of texts rather than the structure of documents. The resulting challenges have not yet been fully addressed by document engineering.