Reviewerly/exHarmony huggingface dataset:
Automata Guided Semi-Decentralized Multi-Agent Reinforcement Learning

This paper investigates the problem of deploying a multi-robot team to satisfy a syntactically co-safe Truncated Linear Temporal Logic (scTLTL) task specification via multi-agent Reinforcement Learning (MARL). Due to the heterogeneous agents considered here, typical approaches cannot avoid the task assignment problem, which is inherently difficult and can sacrifice optimality (e.g., shortest path) through manual manipulation. scTLTL is exploited here to eliminate the task assignment as part of the learning process. MARL usually requires some direct or indirect coordination among agents to promote convergence and a tracker is needed to track the progress of satisfying the scTLTL specification given its temporal nature. We use the Finite State Automaton (FSA) to address these two issues. An FSA augmented Markov Decision Process (MDP) is constructed with each agent sharing the FSA state carrying the global information. Moreover, a metric called robustness degree is employed to replace the Boolean semantics and quantify the reward of gradually satisfying the scTLTL. Consequently, a language guided semi-decentralized Q-learning algorithm is proposed to maximize the return over the FSA augmented MDP. Simulation results demonstrate the effectiveness of the semi-decentralized multi-agent Q-learning while the complexity is significantly reduced.
A Survey of Differentially Private Regression for Clinical and Epidemiological Research

Summary Differential privacy is a framework for data analysis that provides rigorous privacy protections for database participants. It has increasingly been accepted as the gold standard for privacy in the analytics industry, yet there are few techniques suitable for statistical inference in the health sciences. This is notably the case for regression, one of the most widely used modelling tools in clinical and epidemiological studies. This paper provides an overview of differential privacy and surveys the literature on differentially private regression, highlighting the techniques that hold the most relevance for statistical inference as practiced in clinical and epidemiological research. Research gaps and opportunities for further inquiry are identified.
A New Loss Function for Traffic Classification Task on Dramatic Imbalanced Datasets

Traffic classification has always been a hot research topic, which can be used in network performance optimization, security management and some other scenarios. There have been a lot of high-performance classifiers in network classification domain, but nearly all these classifiers only focus on the overall accuracy. There exists tremendous traffic volume gaps among various network applications, which causes extreme imbalanced datasets when applying some artificial intelligence (AI) approaches to classify the traffic into categories or specific applications. The most intractable problem caused by training on imbalanced dataset is that even though the classifier misclassifies categories in rather small sample scale, the overall classification accuracy can be still quite high. This issue is intolerant if the minority category is vital but in small scale. To solve this problem mentioned above, we propose a self-defined loss function UniLoss, which greatly improves the classification accuracy of minority categories and maintains the performance of majorities meanwhile. VoIP traffic is representative for its imbalanced distribution, and thus VoIP traffic is chosen as the test instance. In addition, we design four deep neural networks and construct four test cases with different dramatic imbalanced category sample distributions, on which the results have verified the effectiveness of UniLoss.
A Deep Learning System for Sentiment Analysis of Service Calls

Sentiment analysis is crucial for the advancement of artificial intelligence (AI).Sentiment understanding can help AI to replicate human language and discourse.Studying the formation and response of sentiment state from well-trained Customer Service Representatives (CSRs) can help make the interaction between humans and AI more intelligent.In this paper, a sentiment analysis pipeline is first carried out with respect to real-world multi-party conversations -that is, service calls.Based on the acoustic and linguistic features extracted from the source information, a novel aggregated method for voice sentiment recognition framework is built.Each party's sentiment pattern during the communication is investigated along with the interaction sentiment pattern between all parties.* The accuracy on the test data decreases by 8% when including all the call segments in the training dataset.
Postponing Distillability Sudden Death in a Correlated Dephasing Channel

We investigated the dynamics of a two-qutrit system in a correlated quantum channel. The partial correlations between consecutive actions of the channel can effectively postpone the phenomenon of distillability sudden death (DSD) and broaden the range of the time cutoff that indicates entanglement of the positive partial transpose states. Particularly, the negativity of the system will revive and DSD will disappear in the fully correlated channel.
A comprehensive review on software comprehension models

Software comprehension is one of the most important among software development tasks since most developers do not start a brand new software every time they switch jobs or get transferred from one project to another but join long-running software projects.Every experienced and expert developer has their own established methods of understanding complex software systems.These methods might be different for everyone but they still have common aspects by which multiple well-defined code comprehension models can be constructed.Furthermore, the degree of understanding of a software can be categorized as well, according to the ability of the programmer to modify or develop a certain part of the software system.This paper is intended to provide a review of the cognitive software comprehension models established by extensive research in this topic as well as describe the dimensions of understanding software.It also determines the editor support of cognition models by examining common editor functionalities and categorizing code editors based on the availability of functionalities of each cognition approach.
Computation of Support and Confidence for Interval-Valued Fuzzy Association Rules

The aim of this paper is to provide an algorithm for the computation of support and confidence of the association rules on interval-valued fuzzy sets.Each element of the interval-valued fuzzy set has a membership degree defined as an interval.In other words, the membership intervals may be interpreted as partial knowledge when the precise value is not known.The computations of the support and the confidence are discussed with respect to the three most common triangular norms (minimum, product and Łukasiewicz), which act as conjunction in support and confidence definitions.
Efficient and High-Quality Neural Machine Translation with OpenNMT

This paper describes the OpenNMT submissions to the WNGT 2020 efficiency shared task.We explore training and acceleration of Transformer models with various sizes that are trained in a teacher-student setup.We also present a custom and optimized C++ inference engine that enables fast CPU and GPU decoding with few dependencies.By combining additional optimizations and parallelization techniques, we create small, efficient, and highquality neural machine translation models.
Discrete Correlation of Order 2 of Generalized Rudin-Shapiro Sequences on Alphabets of Arbitrary Size

Abstract In 2009, Grant, Shallit, and Stoll [Acta Arith. 140 (2009), [345–368] constructed a large family of pseudorandom sequences, called generalized Rudin--Shapiro sequences, for which they established some results about the average of discrete correlation coefficients of order 2 in cases where the size of the alphabet is a prime number or a squarefree product of primes. We establish similar results for an even larger family of pseudorandom sequences, constructed via difference matrices, in the case of an alphabet of any size. The constructions generalize those from Grant et al. In the case where the size of the alphabet is squarefree and where there are at least two prime factors, we obtain an improvement in the error term by comparison with the result of Grant et al.
Detecting and understanding moral biases in news

We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing.First we draw connections between issue-specific frames and moral frames that apply to all issues.Then we analyze the connection between moral frame presence and news source political leaning.We develop and test a simple classification model for detecting the presence of a moral frame, highlighting the need for more sophisticated models.We also discuss some of the annotation and frame detection challenges that can inform future research in this area.
Colour recognition using colour histogram feature extraction and K-nearest neighbour classifier

K-nearest neighbours (KNN) is a widely used neural network and machine learning classification algorithm. Recently, it has been used in the neural network and digital image processing fields. In this study, the KNN classifier is used to distinguish 12 different colours. These colours are black, blue, brown, forest green, green, navy, orange, pink, red, violet, white and yellow. Using colour histogram feature extraction, which is one of the image processing techniques, the features that distinguish these colours are determined. These features increase the effectiveness of the KNN classifier. The training data consist of saved frames and the test data are obtained from the video camera in real-time. The video consists of consecutive frames. The frames are 100 × 70 in size. Each frame is tested with K = 3,5,7,9 and the obtained results are recorded. In general, the best results are obtained when used K = 5.&#x0D; &#x0D; Keywords: KNN algorithm, classifier, application, neural network, image processing, developed, colour, dataset, colour recognition.
AN ANALYSIS OF STUDENTS’ DIFFICULTIES IN READING COMPREHENSION AT SMA NEGERI 1 SUKODADI LAMONGAN

Reading is one of the four language skills taught in the English language learning process besides listening, speaking, and writing. This research aims to know the students’ difficulties in reading comprehension at SMA Negeri 1 Sukodadi and to know the factors of students’ difficulties in reading comprehension at SMA Negeri 1 Sukodadi. This research is descriptive qualitative research. The research was conducted in march 2020. The subject of research was the tenth-grade MIPA 5 of SMA Negeri 1 Sukodadi who were detected that they had difficulties in reading comprehension. The techniques for collecting the data were interviews and documents. the researcher collected the data such as students’ worksheet. The second was data reduction. The researcher classified and reduced the data based on focus. The researcher deleted several data that did not relate to the difficulties in reading comprehension. The third was the data display, the researcher arranged the information to conclude. The researcher presented the data from the difficulties in reading comprehension, and the last is the conclusion. The result of the research showed that some students had difficulties in reading comprehension and they had difficulty in different levels, namely literal comprehension, inferential comprehension, critical comprehension, and creative comprehension. The most difficulties experienced by students were at the level of creative comprehension. The factor of the students’ difficulties in comprehension at tenth grade, there was an inadequate instruction presented by the teacher, lack of pupils’ interest, and the vocabulary difficulties. Keywords: Students’ Difficulties, Reading Comprehension
Multi-modal meaning: An empirically-founded process algebra approach

Humans communicate with different modalities. We offer an account of multi-modal meaning coordination, taking speech-gesture meaning coordination as a prototypical case. We argue that temporal synchrony (plus prosody) does not determine how to coordinate speech meaning and gesture meaning. Challenging cases are asynchrony and broadcasting cases, which are illustrated with empirical data. We propose that a process algebra account satisfies the desiderata. It models gesture and speech as independent but concurrent processes that can communicate flexibly with each other and exchange the same information more than once. The account utilizes the ψ-calculus, allowing for agents, input-output-channels, concurrent processes, and data transport of typed λ-terms. A multi-modal meaning is produced integrating speech meaning and gesture meaning into one semantic package. Two cases of meaning coordination are handled in some detail: the asynchrony between gesture and speech, and the broadcasting of gesture meaning across several dialogue contributions. This account can be generalized to other cases of multi-modal meaning. BibTeX info
Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections

Abstract In many fields of science and engineering, we frequently encounter experiments or simulations datasets that describe the behavior of complex systems and uncovering human interpretable patterns between their inputs and outputs via exploratory data analysis is essential for building intuition and facilitating discovery. Often, we resort to 2D embeddings for examining these high-dimensional relationships (e.g. dimensionality reduction). However, most existing embedding methods treat the dimensions as coordinates for samples in a high-dimensional space, which fail to capture the potential functional relationships, and the few methods that do take function into consideration either only focus on linear patterns or produce non-linear embeddings that are hard to interpret. To address these challenges, we proposed function preserving projections (FPP), which construct 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns between the domain and the range of a high-dimensional function. The intuition here is that humans are good at understanding potentially non-linear patterns in 2D but unable to interpret non-linear mapping from high-dimensional space to 2D. Therefore, we should restrict the projection to linear but not the pattern we are seeking. Using FPP on real-world datasets, one can obtain fundamentally new insights about high-dimensional relationships in extremely large datasets that could not be processed with existing dimension reduction methods.
Partially Coherent Direct Sum Channels

We introduce <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">Partially Coherent Direct Sum</mml:mtext></mml:mrow></mml:math> (PCDS) quantum channels, as a generalization of the already known <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">Direct Sum</mml:mtext></mml:mrow></mml:math> quantum channels. We derive necessary and sufficient conditions to identify the subset of those maps which are degradable, and provide a simplified expression for their quantum capacities. Interestingly, the special structure of PCDS allows us to extend the computation of the quantum capacity formula also for quantum channels which are explicitly not degradable (nor antidegradable). We show instances of applications of the results to dephasing channels, amplitude damping channels and combinations of the two.
SAT Heritage: A Community-Driven Effort for Archiving, Building and Running More Than Thousand SAT Solvers

SAT research has a long history of source code and binary releases, thanks to competitions organized every year. However, since every cycle of competitions has its own set of rules and an adhoc way of publishing source code and binaries, compiling or even running any solver may be harder than what it seems. Moreover, there has been more than a thousand solvers published so far, some of them released in the early 90’s. If the SAT community wants to archive and be able to keep track of all the solvers that made its history, it urgently needs to deploy an important effort. We propose to initiate a community-driven effort to archive and to allow easy compilation and running of all SAT solvers that have been released so far. We rely on the best tools for archiving and building binaries (thanks to Docker, GitHub and Zenodo) and provide a consistent and easy way for this. Thanks to our tool, building (or running) a solver from its source (or from its binary) can be done in one line.
The Science of Science

The rapid development of digital libraries and the proliferation of scholarly big data have created an unprecedented opportunity to explore scientific production and reward at scale. Fueled by the data exploration and computational advances in digital libraries, the science of science is an emerging multidisciplinary field that aims to quantify patterns for scientific relationships and dependencies, and how scientific progress emerges from the scholarly big data. In this tutorial, we will provide an overview of the science of science, including major topics on scientific careers, scientific collaborations and scientific ideas. We will also discuss its historical context, the state-of-art models and exciting discoveries, and promising future directions for participants interested in mining scholarly big data.
Shuffle block SRGAN for face image super-resolution reconstruction

When low-resolution face images are used for face recognition, the model accuracy is substantially decreased. How to recover high-resolution face features from low-resolution images precisely and efficiently is an essential subtask in face recognition. In this study, we introduce shuffle block SRGAN, a new image super-resolution network inspired by the SRGAN structure. By replacing the residual blocks with shuffle blocks, we can achieve efficient super-resolution reconstruction. Furthermore, by considering the generated image quality in the loss function, we can obtain more realistic super-resolution images. We train and test SB-SRGAN in three public face image datasets and use transfer learning strategy during the training process. The experimental results show that shuffle block SRGAN can achieve desirable image super-resolution performance with respect to visual effect as well as the peak signal-to-noise ratio and structure similarity index method metrics, compared with the performance attained by the other chosen deep-leaning models.
Using Load Forecasting to Control Domestic Battery Energy Storage Systems

The profitability of domestic battery energy storage systems has been poor and this is the main barrier to their general use. It is possible to increase profitability by using multiple control targets. Market price-based electricity contracts and power-based distribution tariffs alongside storage of surplus photovoltaic energy make it possible to have multiple control targets in domestic use. The battery control system needs accurate load forecasting so that its capacity can be utilized in an optimally economical way. This study shows how the accuracy of short-term load forecasting affects cost savings by using batteries. The study was conducted by simulating actual customers’ load profiles with batteries utilized for different control targets. The results of the study show that knowledge of customers’ load profiles (i.e., when high and low peaks happen) is more important that actual forecast accuracy, as measured by error criteria. In many cases, the load forecast based on customers’ historical load data and the outdoor temperature is sufficient to be used in the control system, but in some cases a more accurate forecast can give better cost savings.
Novel fully convolutional network for cryptanalysis of cryptosystem by equal modulus decomposition

The optical cryptosystem based on equal modulus decomposition (EMD) has attracted wide attention due to its remarkable anti-attack characteristics. In this paper, we propose a novel fully convolutional network model, which is an end-to-end deep learning method, to attack the EMD-based cryptosystem. The trained network model can retrieve plaintext after inputting many ciphertext-plaintext pairs and optimizing parameters. Numerical simulation results and analysis show that EMD-based cryptosystems by Fourier and Fresnel transforms are both vulnerable to our proposed method. Furthermore, the proposed network model can also successfully attack the interference-based cryptosystem. Compared with other methods, the proposed attack method has the advantages of shorter training time and stronger generalization ability. The proposed method provides a new approach for cryptoanalysis of cryptosystem based on EMD.
TAM: Targeted Analysis Model With Reinforcement Learning on Short Texts

Mining topics on social media (e.g., Twitter and Facebook) is an important task for various applications, such as hot topic discovery, advertising, and promotion activities. Topic modeling techniques are helpful to find out topics that people are talking about. However, current full-analysis models cannot perform well on a focused analysis task-find out all topics related to one particular area in short documents. One reason is that the targeted topic is usually sparse in the corpus of short texts. Another one is, during clustering, even minor errors may compound and render the model useless. This article studies these problems and proposes a targeted analysis model (TAM) with reinforcement learning (RL) to extract any specific topic in a given corpus and perform fine-grained topic generation. In this work, we design a reward function of RL to prevent the false propagation problem induced by Gibbs sampling during the clustering. We amend the targeted topic modeling techniques to the case of RL and use policy search combined with the Gibbs EM algorithm for parameter estimation. Metrics of F1 score and the proposed normalized mutual information-F1 are exploited for the evaluation of clustering and topic generation, respectively. Our experiments have demonstrated that TAM can outperform state-of-the-art models-specifically achieving 25.7% improvement on the F1 score for binary clustering on average.
Challenges and limitations in secure software development adoption - A qualitative analysis in Malaysian software industry prospect

Background/Objectives Inclusion of security in software development from the initial design phase has not been consistently addressed by the software developers.As a result there is an abundance of software systems with weak security.The objective of this study is to find out factors influencing developer's intention to adopt secure software development practices.Methodology: This study is based on qualitative research methodology.Interviews were conducted from the professionals working at senior positions at Malaysian software development organization.All the interviews were first transcribed, as they were digitally recorded.Then transcribed data was analyzed in a way that all frequent words or repetitive concepts were highlighted, after which many similar or relevant concepts were grouped together and categorized as themes and sub themes.Findings: The data was analyzed using the thematic analysis method.The results revealed five main themes, whereas each main theme has subthemes.These subthemes are parameters to justify the main theme.Main themes were identified in the light of the interviewee's response.The main results include interviewee's demographic characteristics, and then the main themes identified include, Adoption of SSD practices, Influencing authorities, Motivating Factors, Attitude towards SSD, Hindrances / Issues towards SSD Adoption.Sub themes included: