Reviewerly/exHarmony huggingface dataset:
A Proposal for Single-Photon Detection in Millimeter-Wave and THz Regions

Quantum key distribution (QKD) allows secure communication between spatially distant users. It has been successfully implemented in fiber-based and free space optical networks. However, QKD has not been implemented in other important regions of the electromagnetic spectrum: millimeter- wave and THz regions. One of the reasons for this is that single- photon detectors in these regions are not easily available. In the present work, a proposal of a simple and cheap single-photon detector working in the mmWave and THz region, using glow discharge detector, is presented and its parameters are discussed.
Research of Light Field Technology

Abstract Light field technology is a relatively new theory and technical direction. At present, it has played a significant role in scientific research, production and life, national defense and military, but there are still many problems to be solved in terms of theoretical description, technical implementation, software and hardware processing capabilities, commercialization costs, and ease of use. Therefore, research the light field technology, explore the principle and application of light field imaging, and put forward some enlightenments for its application in industrial machine vision.
On the Geometric Featureless Visual Velocity of UGV in an Agriculture Scale

The paper discusses a camera-based velocity estimation for unmanned ground vehicles in an agriculture scale. The proposed concept-based method does not require any geometric feature and focuses on a mapping between the captured images only. The paper provides three pilot experiments. First, we check an assumption of the proposed concept by a field experiment. Second, we check the verification by a set of numerical and laboratory experiments. Third, we check the verification by the field experiment. In the sense that the existence and sensitivity of a representation of the mapping are verified experimentally, the feasibility of the proposed concept is confirmed.
Rain and Snow Removal

Raining and snowing have negative effects on the visual quality and degrade the performance of various vision algorithms in outdoor environment. In this chapter, we propose three rain and snow removal algorithms including two for videos and one for single images. We first introduce an algorithm to remove snowflakes in videos by using the global and local low-rank property; i.e., assume the snowflake is sparse in snowflake-removed scenes. The proposed framework contains two steps of low-rank decomposition. The first step is to extract stationary background with the whole frames, and the second step focuses on local blocks obtained by patch-matching for removing snowflakes in front of moving foreground. Then, we introduce a rain and snow removal algorithm based on matrix decomposition no longer with the assumption that all the snowflakes/rain streaks are sparse in snow/rain scenes. We divide snowflakes and rain streaks into two categories: sparse ones and dense ones. With background fluctuations and optical flow information, the detection of moving objects and sparse snowflakes/rain streaks is formulated as a multi-label Markov Random Fields (MRFs), making the rain and snow removal model adaptable to different rain and snow scenes. Finally, we propose an encoder–decoder deraining net for single-image rain removal in heavy scenes, which is only supervised by MSE loss and adopts dually skip connections for rain removal in one stage. The dual connections, skip sum connection and skip concatenation connection, enable the deraining net to promote information flow between layers, and thus can help to localize rain streaks. In addition, the learnable pixel-wise attention can adaptively recalibrate the decoded rain features to preserve more image details.
Automated Refactoring of Unbounded Queries in Software Automation Platforms

Software Automation Platforms (SAPs) enable faster development and reduce the need to use code to construct applications. SAPs provide abstraction and automation, resulting in a low-entry barrier for users with lower programming skills to become proficient developers. An unfortunate of using SAPs is to produce code with a higher technical-debt, since such developers are less familiar with the software development best practices. Hence, SAPs should aim to produce a simpler software construction and evolution pipeline beyond providing a rapid software development environment.An example of such high technical debt is the Unlimited Records anti-pattern, which occurs whenever queries are unbounded, i.e. the maximum number of records to be fetched is not explicitly limited. Limiting the number of records retrieved may, in many cases, improve the performance of applications by reducing screen loading time, thus making applications faster and more responsive, which is a top priority for developers. The OutSystems development environment signals the existence of unbounded queries as potential efficiency pitfall, as a development warning. There are scenarios where the limit was not defined because the query returns only one record by construction (e.g. get by primary key). The profusion of false positives in this analysis hinders the handling of true problems. Thus, we focus on the identification of such spurious warnings, and automatically fixing some straightforward occurrences, thus contributing to a more effective software evolution process. The total number of real-world findings reported in the OutSystems analysis tools, as of January 2021, consists of +1,3M unbounded queries.The state-of-the-art solution reports and recommends that developers solve these findings by hand. However, up to to this day, developers only solved 15% ($\approx$200K) of the reports. In this work, we propose an innovative approach for automated refactoring of the Unlimited Records anti-pattern by automatically limiting the number of records returned by simply following Relational Databases query size rules. We have developed detection and refactoring algorithms for two different subcases of the Unlimited Records anti-pattern. We experimentally evaluated our approach in a sample containing a wide range of OutSystems applications (+737K queries). Our results showed a 28% ($\approx$140K) reduction of extra Unlimited Records occurrences ($\approx$515K), a percentage of almost twice as much as the ones solved by hand. The combined effort of manual and automatic fixing of warnings of spurious unbounded queries amounts to solving 30% of the total findings. The main goal of our detecting and automated refactoring mechanisms is the reduction of notorious occurrences of the Unlimited Records anti-pattern which we achieve by substantially solving the two subcases.
Trajectory-based Split Hindsight Reverse Curriculum Learning

Grasping is one of the most fundamental problems in robotic manipulation. In recent years, with the development of data-driven methods, reinforcement learning has been used in solving robotic grasping problems. However, grasping is a long-horizon and sparse reward task, whose natural reward only appears when the task is successfully achieved. Therefore, it brings great challenges to the deployment of reinforcement learning methods. To tackle this difficulty, we propose a new method called Trajectory-based Split Hindsight Reverse Curriculum Learning. This method of reverse learning from the goal can greatly improve the learning efficiency and the final performance of the tasks. Specifically, based on referred trajectories, the agent starts to learn in a small state space near the goal and then gradually in larger state spaces until covering the entire state space. Through split hindsight experience replay, the sampled trajectory is divided into segments that match the current subspace's size; then, they are modified to successful trajectories to enable more efficient learning. In both simulation and real-world experiments, our method surpasses the existing methods and achieves the goal-oriented grasping tasks with higher success rates and better data efficiencies. The detailed experimental results can be viewed at https://youtu.be/7uNRzmRZhDk.
A personalized attractions recommendation model based on tourism knowledge graph

Knowledge graph is a structured organization form of data, and it will be useful to introduce it into the tourism recommendation system as a kind of auxiliary information. The research proposes a model of attractions recommendation based on knowledge graph, which aims to use the semantic information and network structure of knowledge graph to encode the potential interests of tourists. Specifically, the model uses graph convolutional neural network to spread the embedding representation of attractions, and considers the importance of relationship and entity similarity in the convolution process to reflect the difference in preference of tourists. In addition, we also use the attention network to encode the sequential movement of tourists. The study developed a Beijing tourism knowledge graph to organize and share travel information, and used travel notes data to verify the model’s performance. Experimental results show that the recommendation model based on tourism knowledge graph can effectively overcome the problem of data sparsity and achieve better performance than state-of-the-art models.
Pulling a Fast More-Than-One: Milton Babbitt’s Time-point Practice

Milton Babbitt’s employment of the rhythmic techniques known as the time-point system appears well-designed to obscure the premise on which the system is built, namely the maintenance of a steady pulse in modular organisation. In a variety of ways, Babbitt’s music brings any such pulse into contact, even conflict, with other pulses; and the timepoints of the system come more consistently to delineate spans filled with other activity than themselves to specify the times of events. Curiously, Babbitt’s theoretical presentation of the system comes in an article about the resources of the electronic medium, resources that appear to include the ease with which the machine can contribute to this obscuring of it. All this suggests some new thoughts about the relation of construction and result in Babbitt: that the alignment of the two may be even less close in rhythm than in pitch; that the usual definition of time-point technique may emphasise aspects of it that are not most important in Babbitt’s use of it, including its putative analogy to the pitch-class system; that Babbitt’s working method may in these respects have involved resisting the system to a degree not previously accounted for; and that the implications of this practice for listening are thus far very little articulated.
A framework for unbiased explainable pairwise ranking for recommendation

Recent research in recommender systems has demonstrated the advantages of pairwise ranking in recommendation. In this work, we focus on the state-of-the-art pairwise ranking loss function, Bayesian Personalized Ranking (BPR), and aim to address two of its limitations, namely: (1) the lack of explainability and (2) exposure bias. We propose a recommendation framework that encompasses various loss functions that are based on BPR and which aim to mitigate the aforementioned limitations. Our open-source framework includes code to train and tune state-of-the-art pairwise ranking recommender systems on benchmark datasets and evaluate them based on the three criteria of ranking accuracy, explainability, and popularity debiasing.
Analysis of Utilization of UGTV as A Distance Learning Media during the Covid 19 Pandemic

The rapid development of Information Communication Technology-ICT in the current era of globalization cannot be avoided its influence on the world of education anymore. Global demands require the world of education to consistently and constantly adapt technological developments to improve the quality of education. Universitas Gunadarma has an educational broadcasting medium, Universitas Gunadarma Television (UGTV), used as a distance learning medium. This study aimed to analyze the use of digital UGTV as a student distance learning media during the pandemic. The method used is UGTV live broadcasting collaboration with zoom cloud meeting. The application of blended learning methods, both synchronously and asynchronously, continues to be carried out. The results of this study, UGTV developed many literacy programs, team teaching, UGTalk, and all learning shows can be watched again on the official UGTV YouTube.
Research on WeChat Service Contents of Public Libraries Based on Co-occurrence Relationship of Items

WeChat public account has become an important part of microservice system of public libraries, and its service content setting is the key to affect the WeChat service quality of public libraries. This paper takes provincial public libraries as research objects, combining content analysis and social network analysis methods to analyze the collected data. The classification system of WeChat service content is summarized on the basic of content analysis, and the association network is constructed to explore service contents' relative importance and correlation based on their co-occurrence relationship. The results show that basic service contents' relative importance is higher, while featured service contents' relative importance is lower. At last, suggestions on service content setting are proposed.
Multi-Agent Reinforcement Learning Policy Transfer by Buffer

The learning complexity of learning from scratch in a new environment is getting higher because of the existence of multi-agent, which costs enormous calculating resources and time. In our work, to accelerate the training of multi-agent reinforcement learning in the new environment, we propose a method to transfer knowledge by measuring the differences between environments. The method mainly includes two aspects: (1) policy and transition model learning in the source environment;(2) Accelerate training by using knowledge from the source environment. We validated our approach in The StarCraft Multi-Agent Challenge (SMAC) [1]. Experimental results show that this method can effectively transfer knowledge and speed up the training algorithm.
The Comparison of Rice Codes and Even-Rodeh Codes Algorithms for Text Compression

Compression is the process of encoding using fewer bits to remove redundant information, resulting in smaller data size. There are two types of data compression techniques that are used to compress different data formats, namely lossless and lossy compression techniques. In this research, we use a lossless data compression methodology to compare both Rice Code and Even-Rodeh Code algorithms with Ratio of Compression, Space Savings, Time Process, and the Bit Rate parameters. This research was conducted by the Canterbury Corpus files and shows that the Rice Code algorithm is more efficient to compress text files.
Structural Intellectual Techniques for Texture Image Analysis

Current state of the problem of texture analysis and synthesis involves a variety of proposed methods. This paper is devoted to the description and application of some intellectual methods of structural analysis of texture images. In this paper, gradient methods of texture analysis are described, in particular, a special method based on the two-parameter Weibull distribution. Also, the applications of this method to various practical problems are described and its effectiveness is shown. The paper gives some examples of applying of texture features. Additionally, methods of local segmentation and semantic image analysis are described. The results of the conducted experiments prove the efficiency of proposed methods.
Categorization of facial expressions using rearranged landmarks

As corona virus infections are prolonged, the number of people complaining of anxiety, sleep disorders, suicidal thoughts, stress and depression is increasing. As this mental stress increases, there are many accidents that occur. Thus, in order to predict this psychological health condition in advance, this paper proposes a facial expression technique using rearranged landmarks. The proposed algorithm was first extracted only from the face image through pretreatment to detect landmarks, then rearranged to classify facial expressions through deep learning networks. The database was experimented with a self-built facial dataset. As a result, it was confirmed that 15.2% better classification performance was achieved by using only existing face images and 3.46% better classification performance than algorithms that categorized facial expressions using only landmark feature information.
Interval Fuzzy Models Based on Evolving Gaussian Clustering—eGauss+

In this paper a new approach to interval fuzzy model identification is given. It is based on evolving Gaussian clustering algorithm, eGauss+. This algorithm clusters the data form the stream into small clusters called granules. These granules are then merged together if they fulfill all necessary criteria. This means that the cluster partitions are learned incrementally online from data streams and after that they are merged together in bigger structures. The proposed approach is not limited to the use in the data stream clustering, but can be used also in the case of classical batch clustering methods, especially for big data problems. The idea of interval fuzzy model is to find a lower and upper bound of the data set and describe these bounds by fuzzy model. The band or confidence interval should contain the prescribed number of samples. The interval fuzzy model is described and shown on simple examples.
The Challenges of Autonomous Unmanned Aerial System Test and Evaluation

With the development of artificial intelligence, computing and sensor technology, the autonomy of Unmanned Aerial System (UAS) has improved continually, but the autonomous capability levels vary. Hence, how to describe and evaluate autonomy of UAS scientifically and reasonably has become a hot topic in UAS test and evaluation area. In this paper, the developing status of research about autonomous UAS test and evaluation was reviewed. Then the concept meaning of UAS autonomy and the basic features of autonomy were analyzed comprehensively and systematically. Aiming at characteristics of UAS autonomy, the challenges of autonomous UAS test and evaluation were analyzed in depth from three aspects, autonomy levels, testing complexity and uncertainty and man-machine trust problem. Finally, according to current UAS testing methods and characteristics of autonomous UAS, the architecture of UAS autonomy test and evaluation, testing methods and contents were proposed, which would provide technical support for autonomous UAS test and evaluation.
Inference-based Hierarchical Reinforcement Learning for Cooperative Multi-agent Navigation

This work aims to address the multi-agent cooperative navigation problem (MCNP), where multiple agents work together to occupy the landmarks in an environment without collision and with minimum time consumption. To this end, we propose an inference-based hierarchical reinforcement learning (IHRL) model, in which the high-level component infers the target allocation scheme among the agents and landmarks using a local message-passing algorithm, while the low-level component trains the sub-policy corresponding to the target assigned by the high-level component using traditional RL algorithms. The highlight of our model lies in the interplay of high-level inference based on the knowledge from learning and low-level learning with the results from inference. In this way, the overall learning efficiency can be improved by integrating more indicative information into the agents’ coordinated learning process. Extensive experiments demonstrate the effectiveness of the proposed model.
Genetic Fuzzy Systems: Genetic Fuzzy Based Tetris Player

Tetris is a single player game, the objective being to place four-piece blocks and clear as many rows of blocks as possible. The game requires quickness and flexibility in its decision making, which makes it a good candidate for Fuzzy Logic decision making. To train the Fuzzy Logic Tetris Player (FLTP), a genetic algorithm is used. The genetic algorithm trains the input and output membership functions, along with the rules of the fuzzy logic system. Previously the FLTP was created by playing the game repeatedly, creating input functions and rules and iterating upon these parameters by identifying faulty decisions from previous runs. The genetic algorithm is able to search a bigger solution space, exploring this complex problem and the many parameters to tune; therefore, achieving higher game scores than this previous FLTP not trained by a genetic algorithm.
Introduction

We introduce in this chapter some of the areas in computer science on which the book is based. We provide some background and discussion of the literature in areas such as the specific class of formal languages used for describing systems with the mobility of structures, known as calculi for mobility, a specific approach for defining the meaning of such languages, known as denotational semantics and finally, we give some background on the method of static program analysis, and particularly, abstract interpretation-based analysis. We also give an overview of the literature related to the three modern protocols used as our case studies.
Photovoltaic Power Prediction Based on Optimized Back Propagation Neural Network Model

With the development of society and the progress of science and technology, the market of photovoltaic power generation is gradually turning to grid-connected power generation system. However, the output power of photovoltaic power generation is vulnerable to the interference of the external environment, and its volatility and intermittent shortcomings will impact the main power grid. Therefore, it is of great significance for the prediction research of photovoltaic power generation. In this paper, the photovoltaic of smart microgrid project of shanghai electric power university is taken as the research object, a back propagation neural network (BP) model based on particle swarm optimization (PSO) and momentum method is proposed, and the weight and bias of BP neural network are optimized by PSO algorithm and momentum method. In the early stage, the data were firstly normalized, and then the important features were selected through the gray correlation degree analysis. Finally, the photovoltaic power prediction experiment was conducted. The results show that the algorithm model in this paper has better prediction performance for photovoltaic power generation.
Cloud Based Syncope Detection Framework using Wearable ECG Device and Smartphone Sensors

The modern-day world is fast paced and an unhealthy lifestyle has caused a sharp rise in cardiovascular conditions that can take someone’s life in a second, if prior detection or timely first aid and quick hospitalization is not provided. A system which can detect and alarm the person a few minutes prior to such attacks can be life-saving. The demand for such wearable healthcare devices has led to advancements in research and of Biomedical Engineering and IoT. We propose a Cloud-based novel tech stack that can alarm the user and the closest family and friends on their respective smartphones about a possible syncope attack by obtaining data from wireless ECG devices and integrating it with orientation-based sensor data obtained from smartphones and using these data to design a fall detection algorithm-based framework.