Reviewerly/exHarmony huggingface dataset:
An Improved Multi-Source Data Fusion Method Based on a New Divergence Measure

Nowadays, due to its efficiency in processing conflict and uncertainty information, multi-sensor data fusion method has received extensive attention in practical applications. Dempster-Shafer evidence theory (DSET) plays a vital role in solving complex and imprecise information problems. However, when dealing with highly conflicting evidence, DSET will produce an unreasonable result between distinct basic probability assignments (BPAs). Recent researches indicate that divergence measure is a valid method with similarity degree and information volume to confirm the weight of evidence. As a result, how to solve the counterintuitive result with the weight model has become an open question. In this article, an improved multi-source data fusion method model with a novel divergence measure was proposed. Within the framework of DSET, the proposed divergence measure method has been demonstrated to satisfy various expected characteristics of divergence measure. In addition, we combine the proposed fusion model and the divergence measure to preprocess the data to obtain the final weights. Compared with the existing experimental results, the proposed fusion model outperforms other correlation models, giving the highest belief value 99.38% to the right target.
Foresight of promising scientific fields in scientific social networks

Abstract Currently, the problem of developing methods for determining promising directions, trends in scientific research and technology is the most important component of the process of forming a national science policy. The problem of finding priority or breakthrough areas of development is reduced to sorting out in one form or another and assessing possible existing areas of scientific activity. It is proposed to use a semantic network of communicative information space, in which the structuring of the subject area is based on the selection of objects and identifying features, which include not only features-properties, but also features-relationships. A module for traversing elements of a scientific social network by reference graph has been implemented, as well as an algorithm for determining the level of importance of a scientific direction according to multiple criteria.
Multi-Task Semantic Matching model for Small Noisy Data Set

The dominant semantic matching models are based on deep neural networks, but the performance of these models can be limited by the quality of data. In this paper, we propose a Multi-Task Semantic Matching model(MTSM) for small noisy data collected from a specific domain. The MTSM uses the pre-trained language model RoBERTa as the shared layer of multitask learning and sets different networks at the task-specific layer for sentence pairs with different literal matching degrees in the data set to fit the matching patterns of data precisely. The MTSM also introduces semantic matching tasks from different but relative domains as auxiliary tasks so that the main task can benefit from large amounts of data and a regularization effect of other tasks. Experiments show that the MTSM is superior in dealing with small noisy data compared to the RoBERTa model and the traditional machine learning model XGBoost. We also demonstrate that the MTSM generalizes well when using limited training data to overcome the impact of label errors.
Application of Simulation in the Teaching of Electronic Technology

The engineering, application and practice of electronic technology determines that the experimental training has a special important position in the electronic technology course. Simulation teaching mode as a means of information teaching has solved the problem of shortage of teaching equipment and slow replacement of the curriculum. It is helpful for students to understand and analyze the composition and working principle of complex circuit and it is conducive to the cultivation of students' innovative ability. Simulation teaching mode brings great convenience to our teaching.
Asynchronous Decentralized Distributed Training of Acoustic Models

Large-scale distributed training of deep acoustic models plays an important role in today's high-performance automatic speech recognition (ASR). In this paper we investigate a variety of asynchronous decentralized distributed training strategies based on data parallel stochastic gradient descent (SGD) to show their superior performance over the commonly-used synchronous distributed training via allreduce, especially when dealing with large batch sizes. Specifically, we study three variants of asynchronous decentralized parallel SGD (ADPSGD), namely, fixed and randomized communication patterns on a ring as well as a delay-by-one scheme. We introduce a mathematical model of ADPSGD, give its theoretical convergence rate, and compare the empirical convergence behavior and straggler resilience properties of the three variants. Experiments are carried out on an IBM supercomputer for training deep long short-term memory (LSTM) acoustic models on the 2000-hour Switchboard dataset. Recognition and speedup performance of the proposed strategies are evaluated under various training configurations. We show that ADPSGD with fixed and randomized communication patterns cope well with slow learners. When learners are equally fast, ADPSGD with the delay-by-one strategy has the fastest convergence with large batches. In particular, using the delay-by-one strategy, we can train the acoustic model in less than 2 hours using 128 V100 GPUs with competitive word error rates.
Pruning Technology Based on Gaussian Mixture Model

There are a lot of redundant parameters in the deep learning network model from the convolutional layer to the full connection layer, and the activation value of a large number of neurons approaches 0. After these neurons are removed, the same model expression ability can be shown. This situation is called over-parameterization, and the corresponding technology is called model pruning. Pruning is an important research area aimed at reducing the computational cost of neural networks. It can efficiently generate models with smaller scale, higher memory utilization, lower energy consumption, faster inference speed, and minimal loss of inference accuracy. Model pruning, as a model compression technology with a long history, has made great progress and development at present. With the deeper development of the technology, there are more requirements in the society about this new method. Generally speaking, conventional pruning methods follow fixed steps, first training a large and redundant network, and then identifying less important neurons or channels that can be deleted. There are two of traditional pruning methods technically: structured pruning and unstructured pruning. Among the two, the latter one is mainly direct pruning weight. The pruning method of this work is to first classify the weights by size through the clustering method based on GaussianMixture, then remove certain types of channels with low weights, and finally use the weights before pruning as initialization to retrain the network. Experiments on VGG-19, Resnet and Densenet show that the network can obtain basically the same or even better accuracy with fewer channels.
Evaluating Relevance Judgments with Pairwise Discriminative Power

Relevance judgments play an essential role in the evaluation of information retrieval systems. As many different relevance judgment settings have been proposed in recent years, an evaluation metric to compare relevance judgments in different annotation settings has become a necessity. Traditional metrics, such as ĸ, Krippendorff's α and Φ have mainly focused on the inter-assessor consistency to evaluate the quality of relevance judgments. They encounter "reliable but useless" problem when employed to compare different annotation settings (e.g. binary judgment v.s. 4-grade judgment). Meanwhile, other existing popular metrics such as discriminative power (DP) are not designed to compare relevance judgments across different annotation settings, they therefore suffer from limitations, such as the requirement of result ranking lists from different systems. Therefore, how to design an evaluation metric to compare relevance judgments under different grade settings needs further investigation. In this work, we propose a novel metric named pairwise discriminative power (PDP) to evaluate the quality of relevance judgment collections. By leveraging a small amount of document-level preference tests, PDP estimates the discriminative ability of relevance judgments on separating ranking lists with various qualities. With comprehensive experiments on both synthetic and real-world datasets, we show that PDP maintains a high degree of consistency with annotation quality in various grade settings. Compared with existing metrics (e.g., Krippendorff's α, Φ, DP, etc), it provides reliable evaluation results with affordable additional annotation efforts.
An Empirical Investigation of Blockchain Scalability

Blockchains have emerged as one of the most disruptive technologies of the previous decade with applications in diverse domains such as e-government, finance, and healthcare. However, scalability remains a significant challenge within blockchains affecting its wide-spread adoption. As such, scalable blockchains can enhance performance with respect to factors such as transaction processing speed when the system is expanded and brought under stress. In this chapter, the challenge of blockchain scalability has been investigated to enable the reader to develop an in-depth understanding of this aspect of blockchain as well as facilitating the development of scalable blockchains and their adoption in diverse application domains. This chapter introduces scalability along with its different dimensions (vertical and horizontal) and identifies specific parameters that can affect blockchain scalability. The empirical investigation includes diverse scenarios highlighting the role of parameters such as block size, block generation rate, and transaction processing speed to achieve scalable blockchains.
Life-long learning skills as a basis for individual learning style of university students

In a current situation of society development, each person should have life-long learning skills, which allow him to be flexible, autonomous, able to adapt to different scenarios of todays unstable reality. These skills can be referred to the category of soft skills, the development of which give the students the opportunity to organize their learning process effectively, and it will also help in forming students individual learning style. Life-long learning skills formed at the university can also be considered as skills oriented to the prospect of further personal and professional development perspective life-long learning skills. Thus, the purpose of this study is to find mechanisms of the development of students life-long learning skills that also help in forming their individual learning style. Using the methods of theoretical, structural-content and comparative analysis, as well as the synthesis of ideas, it was possible to find out that life-long learning skills belong to the category of soft methodological skills, the development mechanism of which is the strategies of independent learning. Moreover, studying the relationship between such concepts as life-long learning skills, universal competencies, individual learning style showed that the development of soft methodological skills of life-long education is the basis for the formation of students individual learning style. As part of the study, it was developed a methodology for teaching students cognitive and metacognitive strategies for independent learning in a foreign language class while working on listening. As the expected results of the application of this methodology, it is assumed that having mastered the strategies and techniques of independent learning, students will be able to deal with the learning material better, which in turn will help to master the competences more successfully, to increase learning motivation and to form students subjective position. As a prospect for further research, we see the development and testing of technology for teaching students the skills of life-long education, with a detailed description of the stages and guidelines for students and teachers.
Leveraging Domain Information to Classify Financial Documents via Unsupervised Graph Momentum Contrast

Financial documents often contain rich domain information, such as named entities, which could be used to indicate the documents' classification categories. Existing classification methods either ignore such contained financial domain information, achieving less optimal performances, or train document representations in supervised ways, with expensive data labeling costs. In this paper, we propose to leverage domain information to improve classification performance for financial documents, via a graph representation learning model, namely G-MoCo, based on unsupervised graph momentum contrast. With G-MoCo, we could extract latent features from massive unlabeled raw data, and then further use the learned representations for document classification. Compared with the state-of-the-art baselines, representations learned by our method could improve performances by significant margins on a financial document dataset and three non-financial public graph datasets.
Implication of Budgeting on Contemporary Project Management

It is the total sum of money allocated for the specific project for a specific period of time. The goal of the project budget is to cost control. The objective of this chapter is to give the implication of budgeting on contemporary project management. The main focus of this chapter is to discuss the introduction, history, agile approach, etc. It has a direct impact on the company's financial feasibility. The research would discuss the approaches for the projects budget estimation and various steps for cost control. There are two types of reserves against risks: one is the contingency reserve for identified risk, and the other is the management reserve for unidentified risks. The project manager needs to be aware of existing resources acquisition policies, guidelines, and procedures. The author discusses the procedures for project budgeting and methods for evaluation of project budgeting.
Effect Algebras, Girard Quantales and Complementation in Separation Logic

We study convolution and residual operations within convolution quantales of maps from partial abelian semigroups and effect algebras into value quantales, thus generalising separating conjunction and implication of separation logic to quantitative settings. We show that effect algebras lift to Girard convolution quantales, but not the standard partial abelian monoids used in separation logic. It follows that the standard assertion quantales of separation logic do not admit a linear negation relating convolution and its right adjoint. We consider alternative dualities for these operations on convolution quantales using boolean negations, some old, some new, relate them with properties of the underlying partial abelian semigroups and outline potential uses.
Speech-Based Selective Labeling of Objects in an Inventory Setting

Object detection has been extensively used and is considered as one of the prerequisites for various vision-based applications like object recognition, instance segmentation, pose estimation, and so on. This field has attracted much research attention in recent years due to its close relationship with scene analysis and image understanding. Traditional methods extract features and use shallow machine learning architectures to detect objects. These methods faced many difficulties in combining the extracted low-level features with the high-level context of the detector and classifier. Though the recent developments in deep learning architectures helped visual scene analysis and detection methods to perform remarkably, a lot more focus is required in performing voice-based scene analysis and object detection methods. In this paper, a voice-based scene analysis and object detection methods are proposed which aims in detecting a specific object through voice input in inventory storage. An automatic speech recognizer based on deep learning architecture is used to perform speech-to-text conversion. The converted text is used to identify which object is to be located in the scene. A dictionary-based search algorithm is used for reducing the search time of the class of interest. Object detection is performed using Faster RCNN architecture. Experimental results are tested on the retail product checkout dataset for evaluating the system performance. Experimental results show that this approach makes it possible for the model to segment only the specific object while ignoring all the other classes.
Distillation-Based Multi-exit Fully Convolutional Network for Visual Tracking

Obtaining a trade-off between accuracy and efficiency for a convolutional neural network is highly desired in the deep classification-based trackers. However, it is observed that existing methods make the predictions with the latest exits strategy for all the samples, making such strategy a time-consuming solution. Motivated by this, we propose a multi-exit architecture based on the principle of knowledge distillation to improve the speed of prediction by encouraging early exits to imitate later and more accurate exits. Specifically, we propose a distillation-based multi-exit fully convolutional network (FCN), named DMENet, for visual tracking. In DMENet, different types of attention mechanisms are embedded into different representation levels of FCN to capture more discriminative information. Then, three exits augment at different levels of FCN to handle the processing of a frame to stop early. The DMENet is trained offline with knowledge distillation to improve the accuracy of early exits. The confidence score of an exit is utilized to decide whether to locate the target with high confidence on this exit or continue processing the next exit. The extensive evaluation performed on OTB-100, UAV123, LaSOT and VOT2018 benchmarks demonstrate the proposed tracker outperforms state-of-the-art approaches with a high speed (36 FPS).
Decision List Compression by Mild Random Restrictions

A decision list is an ordered list of rules. Each rule is specified by a term, which is a conjunction of literals, and a value. Given an input, the output of a decision list is the value corresponding to the first rule whose term is satisfied by the input. Decision lists generalize both CNFs and DNFs and have been studied both in complexity theory and in learning theory. The size of a decision list is the number of rules, and its width is the maximal number of variables in a term. We prove that decision lists of small width can always be approximated by decision lists of small size, where we obtain sharp bounds for such approximation. This also resolves a conjecture of Gopalan, Meka, and Reingold (Computational Complexity, 2013) on DNF sparsification. An ingredient in our proof is a new random restriction lemma, which allows to analyze how DNFs (and more generally, decision lists) simplify if a small fraction of the variables are fixed. This is in contrast to the more commonly used switching lemma, which requires most of the variables to be fixed.
Multiposition phase triangulation for measure three-dimensional geometry of convex and extended objects

A method of multipositional phase triangulation for measure the three-dimensional geometry of convex and extended objects is proposed. The method is universal for the light-scattering properties of the measured object and can be used to measure objects with any geometric structure. The method improves the accuracy of measurements and makes it possible to measure the full three-dimensional geometry of convex and extended objects in an optical manner based on the triangulation principle and structured lighting. phase triangulation, 3D geometry, stitching fragments
Patterns in Malware Attacks Leveraging COVID-19 Pandemic Fears

Malware authors routinely exploit global events to entice unsuspecting users into downloading malicious code. As COVID-19 spread around the globe over the past year and a half, it is no surprise that hackers have used pandemic hysteria to their advantage. Increasingly, COVID-19 themed malware has been distributed in many forms including emails, text messages, applications, and malicious sites. This research attempts to find common trends in malware phishing attempts that leverage COVID-19 pandemic fears. The analysis focuses on similarities and differences concerning the authors, types of attacks, mechanisms of attack, motivations, strategies, and domains. Similarly, a sample of metadata is extracted and analyzed from PCAP files of known malware. Identifying patterns and common characteristics in malware is vital to the overall cybersecurity effort and can be used to educate, train, and create a safety net for protection against future attacks.
Guaranteed Cost Control of Networked Inverted Pendulum Visual Servo System with Computational Errors and Multiple Time−Varying Delays

Computational errors and multiple time−varying delays will inevitably affect control performance of networked inverted pendulum visual servo system (NIPVSS). This paper investigates guaranteed cost control of NIPVSS. Firstly, computational errors is converted into parameter uncertainties and NIPVSS is then modeled as a closed-loop system with parameter uncertainties and multiple time−varying delays. Secondly, a guarantee cost controller is designed to maintain the asymptotic stability of NIPVSS and meanwhile minimize the performance index. Finally, experimental results demonstrate the feasibility of the proposed method while the effectiveness of parameter uncertainties and multiple time−varying delays on the system stability and performance index are analyzed.
A Fully Autonomous Person Re-Identification System

The problem of re-identification involves the association of the appearances of a person caught with one or more surveillance cameras. This task is especially challenging in very crowded areas, where possible occlusions of people can drastically reduce visibility. In this paper, we aim to obtain a fully automatic re-identification system containing a stage of detection of persons before the stage of re-identification. Both stages are based on a general-purpose DNN (Deep Neural Network) object detector - the YOLO (You Only Look Once) model. The primary purpose and novelty of the proposed method are to obtain an autonomous re-identification system, starting from a simple detection model. Thus, with minimal computational and hardware resources, the proposed method leads to comparable results with other existing methods, even when running in real-time on multiple security cameras.
Network Analysis for Learners’ Concept Maps While Using Mobile Augmented Reality Gaming

Using mobile augmented reality games in education combines situated and active learning with pleasure. The aim of this research is to analyze the responses expressed by young, middle-aged, and elderly adults about the location-based mobile augmented reality (MAR) games using methods of content analysis, concept maps, and social network analysis (SNA). The responses to questions related to MAR game Ingress were collected from 36 adult players, aged 20–60, from Greece, and subsequently analyzed by means of content analysis, concept maps, and social network analysis. Our findings show that for question 1 (How do you feel when you endow the geographical space with personal preferences?), there was a differentiation of the answers between age groups with age groups agreeing in pairs, the first two and the last two, while for question 2 (Do you think that the game offers opportunities for learning and teaching geography, building on your previous geographical knowledge?), there was an overlap in responses of participants among age groups. It was also revealed that the MAR games foster a constructivism approach of learning, as their use learning becomes an active, socially supported process of knowledge construction.