Reviewerly/exHarmony huggingface dataset:
Research on Edge Computing-oriented Storm Edge Node Scheduling Optimization Strategy

Edge computing is characterized by interactive processing of big data and high real-time performance. However, when using edge computing, it also shows that the scheduling time between edge heterogeneous nodes is too long and the communication has high delay and so on. Relying on traditional cloud computing platforms will make it difficult to meet the new requirements. With the development of energy Internet, all kinds of data stream processing based on real-time sensing data has gradually become the key to the construction of current energy Internet application. In this paper, according to the data acquisition content of the terminal, marginal calculation will be carried out to improve the data processing capacity, computing capacity and intelligence level of the acquisition terminal.
Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM

Abstract In this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models.
Drone Image Stitching Using Local Least Square Alignment

This paper proposes a strategy for drone image stitching using local least square alignment, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using simple homography cannot handle the situation that the input drone images have parallax effect, and the mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following two steps, namely, local least square alignment and global similarity constraint. Starting from initial feature sets obtained by traditional feature extraction methods, we construct a robust alignment energy based on parallax errors to adaptively eliminate parallax effects. The energy can be efficiently minimized used least square estimate. Combined with global similarity constraint, our proposed strategy can flexibly improve the naturalness of the results. Experiments show that our stitching strategy can more effectively eliminate parallax effects and achieve natural-looking results compared to other state-of-the-art methods.
Implementing a solution for monitoring SLA violations in Cloud

Over time, the increased use of the Internet has led to the widespread adoption of web and cloud applications. More and more applications are coming to the aid of persons or companies to make their work easier. A Service Level Agreement (SLA) gathers information about all contracted services and their expected reliability in a single document and plays a very important role when it comes to IT (Information Technology) sales. This paper examines the extent to which the SLA is complied with through a series of specific reports and graphs. The usefulness of this application is to get an overview of web services to know the frequency of situations unforeseen and the moments when they deviated from the operation in good conditions.
An End-to-End Review-Based Aspect-Level Neural Model for Sequential Recommendation

Users’ reviews of items contain a lot of semantic information about their preferences for items. This paper models users’ long-term and short-term preferences through aspect-level reviews using a sequential neural recommendation model. Specifically, the model is devised to encode users and items with the aspect-aware representations extracted globally and locally from the user-related and item-related reviews. Given a sequence of neighbor users of a user, we design a hierarchical attention model to capture union-level preferences on sequential patterns, a pointer model to capture individual-level preferences, and a traditional attention model to balance the effects of both union-level and individual-level preferences. Finally, the long-term and short-term preferences are combined into a representation of the user and item profiles. Extensive experiments demonstrate that the model substantially outperforms many other state-of-the-art baselines substantially.
The Impact of the Detector on the Performances of a Multi-Person Tracking System

Background: Pedestrian detection and tracking are an important area of study in realworld applications, such as mobile robots, human-computer interaction, video surveillance, pedestrian protection systems, etc. As a result, it has attracted the interest of the scientific community. Objective: Certainly, tracking people is critical for numerous utility areas which cover unusual situations detection, like vicinity evaluation, and sometimes change direction in human gait and partial occlusions. Researchers' primary focus is to develop a surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. So, it has become a significant issue and challenge to design a tracking system that can be more suitable for such situations. To this end, this paper presents a comparative evaluation of the tracking-by-detection system along with the publicly available pedestrian benchmark databases. Method: Unlike recent works where person detection and tracking are usually treated separately, our work explores the joint use of the popular Simple Online and Real-time Tracking (SORT) method and the relevant visual detectors. Consequently, the choice of the detector is an important factor in the evaluation of the system's performance. Results: Experimental results demonstrate that the performance of the tracking-by-detection system is closely related to the optimal selection of the detector and should be required prior to a rigorous evaluation. Conclusion: The study demonstrates how sensitive the system performance as a whole is to the challenges of the dataset. Furthermore, the efficiency of the detector and the detector-tracker combination is also depending on the dataset.
A Collaborative BSN-Enabled Architecture for Multi-user Activity Recognition

Human activity plays a significant role in various fields, such as manufacturing, healthcare, and public safety; therefore, recognizing human activity is crucial to enable smart innovative services. The development of ubiquitous sensing and pervasive computing allows studying what humans perform in real time and mobility. Single- and multi-user activity recognition (AR) differ by the number of involved users. With recent developments of multi-sensor and multi-information fusion, multi-user activity recognition is gradually becoming an emerging and relevant research frontier. In this paper, we propose a software architecture which combines cloud and edge computing with collaborative body sensor networks (CBSNs) to support the development of CBSNs-enabled services and in particular we provide its case-study in the context of multi-user AR.
Developing MCQA Framework for Basic Science Subjects using Distributed Similarity Model and Classification Based Approaches

In this paper, we proposed a novel approach to improve the performance of multiple choice question answering (MCQA) system using distributed semantic similarity and classification approach. We mainly focus on science-based MCQ which is really difficult to handle. Our proposed method is based on the hypothesis that the relation between question and answer of that question will be high in distributional semantic model rather than other options of that question. We are using IJCNLP shared Task 5 and SciQ dataset for our experiments. We have built three Models (i.e., Model 1, Model 2, Model 3) based on the dataset format. The basic difference between IJCNLP Task 5 and SciQ datasets is that SciQ dataset contains supporting text with questions whereas IJCNLP Task 5 dataset does not contain supporting text. Model 1 and Model 2 are mainly built to deal with IJCNLP Task 5 dataset whereas Model 3 is mainly built for SciQ dataset. Model 2 is mainly built to deal with the dependencies between options (i.e., all of these, two of them, none of them) whereas Model 1 is the basic model for MCQA and it cannot capture the dependencies between options. We also compare the result of SciQ dataset with supporting text (i.e., using Model 3) and without supporting text (i.e., using Model 1). We also compared our system with other existing methods. Though in some cases the performance of our proposed method is not satisfactory, we have noted that our submission is simple and robust that allows it to be more easily integrated into complex applications. This work investigates different techniques for choosing the correct answer of a given question in MCQA system. These experiments may therefore be useful to improve the performance of current science-based question answering (QA) systems. For IJCNLP Task 5 dataset, we achieved 44.5% using Model 2 and PubMed Dataset. Similarly for SciQ dataset we achieved 82.25% using Model 3 and PubMed dataset.
Design of Multi-Channel Integrated Platform for Electric Power Marketing Based on Microsoft Service Architecture

With the continuous advancement of power system reform and the vigorous development of the Internet industry, power marketing services are also facing higher challenges. In some scenarios, business applications using traditional monolithic architecture will become less flexible and less capable of continuous delivery, which makes business application maintenance difficult. In order to expand the multichannel service of power marketing and establish a comprehensive service platform system, this paper takes spring-boot and spring-cloud as the technical framework, divides the traditional single application into several fine-grained ones. Based on the business application characteristics of micro-service architecture, it builds a domain-driven model for the whole process of scenario, and builds a multi-channel integrated application platform for power marketing, which solves the problems of distributed data interaction caused by large business concurrency or cross-specialty in power marketing. Business applications can achieve gray-scale publishing, frequent deployment, and strong sustained delivery ability, flexible and fast to adapt to the effect of business sustainable development and change.
Construction of long MDS self-dual codes from short codes

In this paper, we propose a mechanism on how to construct long MDS self-dual codes from short ones. These codes are special types of generalized Reed-Solomon (GRS) codes or extended generalized Reed-Solomon codes. The main tool is utilizing additive structure or multiplicative structure on finite fields. By applying this method, more MDS self-dual codes can be constructed.
Hybrid Recommendation System for Scientific Literature

This paper aims to create a recommendation system for scientific articles. The system can help both researchers and students in finding suitable articles related to their fields of study in a time-efficient manner. This paper proposes a model that uses the DBLP-Citation-network V10 Dataset and a second model that uses a custom dataset created by combining an existing CORA dataset with web scraped information. The core idea of this paper is to use the latent relationships found in the citation data to find more relevant papers. An additional feature provided by this system is that it can scale to any number of new scientific articles. The proposed approach uses various pre-processing techniques and algorithms to calculate scores such as PageRank, TextRank, cosine similarity, author score, etc. Since the content-based and collaborative methods are used to calculate the scores, the proposed system is a hybrid recommendation system. The final recommendations are made by normalizing the scores and selecting the top 10 scores. The system was found to provide suitable recommendations to the user achieving accuracies of 76.4% and 81.4% for the two models, respectively.
Dear Diary: Conducting Diary Studies with Participants with Visual Impairments

Within this report we critically reflect on our experiences using a diary study used to understand the use of social networking sites (SNS) by persons with a visual impairment. We elaborate on the development of the data collection process and the challenges involved in retrieving the data from the participants. Feedback from the participants during post-study interviews revealed that most participants were indifferent to the methods for submitting the diary entries and did not find it a challenge. Most of the participants found the process to be a positive experience for self-awareness of their social media use while reporting little to no adverse effects of carrying out the study. Lastly, we provide reflections and lessons learned from the process.
Sparse quantum Gaussian processes to counter the curse of dimensionality

Abstract Gaussian processes are well-established Bayesian machine learning algorithms with significant merits, despite a strong limitation: lack of scalability. Clever solutions address this issue by inducing sparsity through low-rank approximations, often based on the Nystrom method. Here, we propose a different method to achieve better scalability and higher accuracy using quantum computing, outperforming classical Bayesian neural networks for large datasets significantly. Unlike other approaches to quantum machine learning, the computationally expensive linear algebra operations are not just replaced with their quantum counterparts. Instead, we start from a recent study that proposed a quantum circuit for implementing quantum Gaussian processes and then we use quantum phase estimation to induce a low-rank approximation analogous to that in classical sparse Gaussian processes. We provide evidence through numerical tests, mathematical error bound estimation, and complexity analysis that the method can address the “curse of dimensionality,” where each additional input parameter no longer leads to an exponential growth of the computational cost. This is also demonstrated by applying the algorithm in a practical setting and using it in the data-driven design of a recently proposed metamaterial. The algorithm, however, requires significant quantum computing hardware improvements before quantum advantage can be achieved.
Companion Guided Soft Margin for Face Recognition

Face recognition has achieved remarkable improvements with the help of the angular margin based softmax losses. However, the margin is usually manually set and kept constant during the training process, which neglects both the optimization difficulty and the informative similarity structures among different instances. Although some works have been proposed to tackle this issue, they adopt similar methods by simply changing the margin for different classes, leading to limited performance improvements. In this paper, we propose a novel sample-wise adaptive margin loss function from the perspective of the hypersphere manifold structure, which we call companion guided soft margin (CGSM). CGSM introduces the information of distribution in the feature space, and conducts teacher-student optimization within each mini-batch. Samples of better convergence are considered as teachers, while students are optimized with extra soft penalties, so that the intra-class distances of inferior samples can be further compacted. Moreover, CGSM does not require sophisticated mining techniques, which makes it easy to implement. Extensive experiments and analysis on MegaFace, LFW, CALFW, IJB-B and IJB-C demonstrate that our approach outperforms state-of-the-art methods using the same network architecture and training dataset.
Fooling Edge Computation Offloading via Stealthy Interference Attack

There is a growing interest in developing deep learning methods to solve many resource management problems in wireless edge computing systems where model-based designs are infeasible. While deep learning is known to be vulnerable to adversarial example attacks, the security risk of learningbased designs in the context of edge computing is not well understood. In this paper, we propose and study a new adversarial example attack, called stealthy interference attack (SIA), in deep reinforcement learning (DRL)-based edge computation offloading systems. In SIA, the attacker exerts a carefully determined level of interference signal to change the input states of the DRL-based policy, thereby fooling the mobile device in selecting a target and compromised edge server for computation offloading while evading detection. Simulation results demonstrate the effectiveness of SIA, and show that our algorithm outperforms existing adversarial machine learning algorithms in terms of a higher attack success probability and a lower power consumption.
Transformation of Voice Signals to Spatial Domain for Code Optimization in Digital Image Processing

The paper is intended to transform the voice-signal from the frequency domain into a spatial domain in form of grayscale image and applied the image processing techniques. To satisfy our hypothesis, two models of signal processing were carried out in this research: Speaker Recognition and Signal Segmentation. For applying the image processing techniques on the voice-signal, two methodologies were proposed to convert the signal into grayscale-image: signal-range based and fuzzy-based. The signal-range based is to convert the signal range from (−1 ↔ 1) into (0 ↔ 256). The second method of conversion, Fuzzy Gaussian Membership Function is applied to convert the signal range into (0 ↔ 1), then multiply them by 255 to be in the range of grayscale image. In the Speaker Recognition, the LBP is used as pre-processing for filtering the intensity of the signal image. The HOG is used to extract the features of signal-image. So, the total length of features-vector is 324. The classification learner tool in MATLAB was used for classifying the feature-vectors and the results were found to be satisfactory. The automatic word segmentation was proposed based on thresholding and morphology operators. The segmentation accuracy is 93.67% in the Marathi-language. The highest recognition rate in speaker identification system is 96.9%.
Metric Learning for Categorical and Ambiguous Features: An Adversarial Method

Metric learning learns a distance metric from data and has significantly improved the classification accuracy of distance-based classifiers such as k-nearest neighbors. However, metric learning has rarely been applied to categorical data, which are prevalent in health and social sciences, but inherently difficult to classify due to high feature ambiguity and small sample size. More specifically, ambiguity arises as the boundaries between ordinal or nominal levels are not always sharply defined. In this paper, we mitigate the impact of feature ambiguity by considering the worst-case perturbation of each instance and propose to learn the Mahalanobis distance through adversarial training. The geometric interpretation shows that our method dynamically divides the instance space into three regions and exploits the information on the "adversarially vulnerable" region. This information, which has not been considered in previous methods, makes our method more suitable than them for small-sized data. Moreover, we establish the generalization bound for a general form of adversarial training. It suggests that the sample complexity rate remains at the same order as that of standard training only if the Mahalanobis distance is regularized with the elementwise 1-norm. Experiments on ordinal and mixed ordinal-and-nominal datasets demonstrate the effectiveness of the proposed method when encountering the problems of high feature ambiguity and small sample size.
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning

Communication is an important factor that en-ables agents to work cooperatively in multi-agent reinforcement learning (MARL) contexts. Prior work used continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete emergent message communication protocols can increase the interpretability for human designers and other agents. This paper proposes a method to generate discrete messages analogous to human languages. Discrete message communication is achieved by a broadcast-and-listen mecha-nism based on self-attention. We show that discrete message communication has performance comparable to continuous message communication but with a much smaller vocabulary size. Discrete message communication protocols can potentially be used for human-agent interaction.
Industry-wide linguistic instruments: The role in information support of scientific research in Russia’s agricultural industry

The studies were accomplished at the Central Scientific Agricultural Library (CSAL) to update the industry-wide linguistic instruments used in the Library’s Information Retrieval System and union catalog of the research institutions’ libraries of the Agroindustrial Complex (AIC) of Russia. The set of linguistic tools is briefly described; the two-level model of the linguistic support structure is introduced. The significance and advantages of using information retrieval languages in the Automated Information-Retrieval System are discussed. The purpose of the study was to improve the information support of research in the AIC in terms of actualization of the database (BD) of the Information Retrieval Thesaurus in Agriculture and Food and the database “Authority File (AF) of Scientific Research Institutions (SRI) in the AIC”. Brief descriptions of these information retrieval languages and their functions are included. The functions of the Information-Search Thesaurus are: to index documents and inquiries; to reflect the paradigmatic relations between lexical units; to control and normalize lexis in agriculture and food; to provide unified and formalized information representation in AGROS database; to expand automatically document search profiles; it also functions as a terminological guide in agriculture and food. As the guide, it retrieves the names of institutions since the foundations date; it can be also used for constructing the bibliographic record element (organization’s name. or corporate author); and retrieves information from information resources. The procedures for adding new lexical items and revising the previously included data are described.
Web-based information system sales

Abstract This research aims to make it easier for consumers in buying and selling transactions as well as for sellers to promote various kinds of products that can be reached by consumers without visiting the store. This study used descriptive analysis method by conducting interviews directly with the PD. XYZ store as the case study. The results of this study indicate the design of website sales increase the sale and purchase transactions at the store. A media campaign is needed to increase the attractiveness of buyers. In conclusion, the design of a sales information system can help sellers in promoting products in an effort to increase revenue from the store.