Reviewerly/exHarmony huggingface dataset:
Data flow refinement type inference

Refinement types enable lightweight verification of functional programs. Algorithms for statically inferring refinement types typically work by reduction to solving systems of constrained Horn clauses extracted from typing derivations. An example is Liquid type inference, which solves the extracted constraints using predicate abstraction. However, the reduction to constraint solving in itself already signifies an abstraction of the program semantics that affects the precision of the overall static analysis. To better understand this issue, we study the type inference problem in its entirety through the lens of abstract interpretation. We propose a new refinement type system that is parametric with the choice of the abstract domain of type refinements as well as the degree to which it tracks context-sensitive control flow information. We then derive an accompanying parametric inference algorithm as an abstract interpretation of a novel data flow semantics of functional programs. We further show that the type system is sound and complete with respect to the constructed abstract semantics. Our theoretical development reveals the key abstraction steps inherent in refinement type inference algorithms. The trade-off between precision and efficiency of these abstraction steps is controlled by the parameters of the type system. Existing refinement type systems and their respective inference algorithms, such as Liquid types, are captured by concrete parameter instantiations. We have implemented our framework in a prototype tool and evaluated it for a range of new parameter instantiations (e.g., using octagons and polyhedra for expressing type refinements). The tool compares favorably against other existing tools. Our evaluation indicates that our approach can be used to systematically construct new refinement type inference algorithms that are both robust and precise.
Cyclic proofs, system t, and the power of contraction

We study a cyclic proof system C over regular expression types, inspired by linear logic and non-wellfounded proof theory. Proofs in C can be seen as strongly typed goto programs. We show that they denote computable total functions and we analyse the relative strength of C and Gödel’s system T. In the general case, we prove that the two systems capture the same functions on natural numbers. In the affine case, i.e., when contraction is removed, we prove that they capture precisely the primitive recursive functions—providing an alternative and more general proof of a result by Dal Lago, about an affine version of system T. Without contraction, we manage to give a direct and uniform encoding of C into T, by analysing cycles and translating them into explicit recursions. Whether such a direct and uniform translation from C to T can be given in the presence of contraction remains open. We obtain the two upper bounds on the expressivity of C using a different technique: we formalise weak normalisation of a small step reduction semantics in subsystems of second-order arithmetic: ACA 0 and RCA 0 .
A lightweight epistemic logic and its application to planning

We study multiagent epistemic planning with a simple epistemic logic whose language is a restriction of that of standard epistemic logic. Its formulas are boolean combinations of observability atoms: sequences of ‘knowing whether’ operators followed by propositional variables. This compares favourably with other restricted languages where formulas are boolean combinations of epistemic literals: sequences of ‘knowing that’ epistemic operators and negations followed by propositional variables; or in other terms: epistemic formulas without conjunctions or disjunctions. The reason is that our language enables a richer theory of mind: we can express statements such as “I don't know whether p, but I know that you know whether p” which are important in communication and more generally in interaction and which cannot be expressed with epistemic literals. Going beyond previous work, we also introduce a ‘common knowledge whether’ operator. We show that satisfiability is nevertheless NP-complete. We then define simple epistemic planning tasks as generalisations of classical planning tasks: action descriptions have sets of observability atoms as add- and delete-lists, initial states are sets of observability atoms, and goals are boolean combinations of observability atoms. We show that simple epistemic planning tasks can be polynomially translated into classical planning tasks. It follows that checking solvability of simple epistemic planning tasks is PSpace-complete. We present some application examples such as the gossip problem and some experimental results and clarify the relationship with Dynamic Epistemic Logic-based planning.
Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge

Understanding context-dependent variation in word meanings is a key aspect of human language comprehension supported by the lexicon. Lexicographic resources (e.g., WordNet) capture only some of this context-dependent variation; for example, they often do not encode how closely senses, or discretized word meanings, are related to one another. Our work investigates whether recent advances in NLP, specifically contextualized word embeddings, capture human-like distinctions between English word senses, such as polysemy and homonymy. We collect data from a behavioral, web-based experiment, in which participants provide judgments of the relatedness of multiple WordNet senses of a word in a two-dimensional spatial arrangement task. We find that participants’ judgments of the relatedness between senses are correlated with distances between senses in the BERT embedding space. Specifically, homonymous senses (e.g., bat as mammal vs. bat as sports equipment) are reliably more distant from one another in the embedding space than polysemous ones (e.g., chicken as animal vs. chicken as meat). Our findings point towards the potential utility of continuous-space representations of sense meanings.
New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

We revisit N-version programming in the context of machine learning (ML). Generating N versions of an ML component does not require additional programming effort, but only extra computations. This opens up the possibility of executing hundreds of diverse replicas, which, if carefully deployed, can improve their overall reliability by a significant margin. We use mathematical modeling to evaluate these benefits.
Time-Sync Video Tag Extraction Using Semantic Association Graph

Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.
One-shot phase retrieval method for interferometry using a hypercolumns convolutional neural network

In three dimensional profilometry, phase retrieval technique plays a key role in signal processing stage. Fringe images need to be transformed into phase information to obtain the measurement result. In this paper, a new phase retrieval method based on deep learning technique is proposed for interferometry. Different from conventional multi-step phase shift methods, phase information can be extracted from only a single frame of an interferogram by this method. Here, the phase retrieval task is regarded as a regression problem and a hypercolumns convolutional neural network is constructed to solve it. Firstly, functions and each component of the network model are introduced in details; Then, four different mathematical functions are adopted to generate the training dataset; training and validation strategies are also designed subsequently; Finally, optimization processing is performed to eliminate local data defects in initial results with the help of polynomial fitting. In addition, hardware platform based on point diffraction interferometer is fabricated to support this method. Concluded from the experiment section, the proposed method possesses a desirable performance in terms of phase retrieval, denoising and time efficiency.
Problems of Mastering and Using Digital Learning Technology in the Context of a Pandemic

The article attempts to identify the difficulties associated with the use of digital learning technology at a technical university in the context of a pandemic. The research is based on the use of such methods as content analysis of modern publications on the problem under study, their logical analysis, generalization and systematization of published information, as well as understanding the experience of using digital technology at the technical university. A survey of polytechnic students was conducted regarding the timely identification of problems related to the use of digital learning technology. The findings revealed that awareness of the thoughtful use of digital learning technology, as well as thoughtful regulations in the access of students to digital equipment can open up new opportunities for their application. The relevance of digitalization for the technical university is shown. Prospects for further research of problems related to the use of digital technology of education at a technical university are outlined in order to identify its further directions and features of their development in domestic and foreign educational institutions. The novelty of the study consists in substantiating the relevance of analyzing the experience of using digital learning technology at the technical university, identifying problematic aspects of its use. The limitation of the study is that all the responses are from just one educational institution and, in future studies, it is necessary to take into account the experience of using digital learning technologies on a more global scale.
Efficient and Privacy-preserving Outsourced Image Retrieval in Public Clouds

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.
Exquisitor at the Video Browser Showdown 2021: Relationships Between Semantic Classifiers

Exquisitor is a scalable media exploration system based on interactive learning, which first took part in VBS in 2020. This paper presents an extension to Exquisitor, which supports operations on semantic classifiers to solve VBS tasks with temporal constraints. We outline the approach and present preliminary results, which indicate the potential of the approach.
Research in the Instagram Context: Approaches and Methods

Social media is not only an important component for mass communication but also a toolkit to provide people with various services. As the most representative visual-oriented social networking site in the world, Instagram enables users to take photos and videos, as well as share them on other platforms with its accurate target user positioning. There have been many researchers involving with studies on Instagram’s development from different perspectives. This paper conducts an extensive literature review of Instagram, focusing on the research methods that researchers use to collect and analyze data. Based on the review, it can be seen that most scholars collect Instagram data by capturing API data, designing questionnaires, and face-to-face interviews. Both quantitative methods such as data modeling and regression analysis and qualitative methods such as text analysis and context analysis are used to analyze Instagram features. Researchers also utilized advanced technical tools and analytical methods, such as face recognition, comparative analysis, and case studies, to gain a more insightful perception on Instagram.
Combining a Monte Carlo Tree Search and a Feasibility Database to Plan and Execute Rearranging Tasks

In this paper, we address the problem of solving rearranging tasks using a robot. Rearranging tasks are challenging because they include many problems to solve at the same time, such as determining how to pick the items as well as planning how and where to place them. Solving a rearranging task usually consists of finding a set of pick-and-place instructions with a symbolic planner to perform the task. However, if the symbolic planner does not consider the robot's capability to execute the instructions, it will likely generate many infeasible instructions, which wastes time in multiple trials and failures. Therefore, we propose to combine symbolic and motion planning to confirm a sequence of instructions before its execution by the robot. To achieve this combination, we use a Motion Feasibility Checker (MFC), which selects a set of feasible poses for the robot from a feasibility database. The MFC verifies that the instructions of the symbolic planning are valid and searches for a pick-and-place pair of poses to execute the instructions. We use a Monte Carlo Tree Search (MCTS) as the symbolic planner, and we combine it with the MFC when creating or expanding the states in the tree. After the MCTS finds a set of instructions for the rearranging task, we execute those instructions with the robot. As these instructions were previously validated, the robot is able to execute them. The proposed method was tested in a simulation environment that reproduces the scenario of rearranging products on a shelf of a convenience store. The experiment results show that the proposed method outperforms the conventional method in various initial states of increasing levels of difficulty.
Hierarchical HMM for Eye Movement Classification

In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data. The efficient classification of these different types of eye movements helps to better analyze and utilize the eye tracking data. Different from the existing methods that detect eye movement by several pre-defined threshold values, we propose a hierarchical Hidden Markov Model (HMM) statistical algorithm for detecting fixations, saccades and smooth pursuits. The proposed algorithm leverages different features from the recorded raw eye tracking data with a hierarchical classification strategy, separating one type of eye movement each time. Experimental results demonstrate the effectiveness and robustness of the proposed method by achieving competitive or better performance compared to the state-of-the-art methods.
Comparative study on credit card fraud detection based on different support vector machines

Credit card fraud is the new financial fraud crime accompanied by the gradual development of the economy which causes billions of dollars of losses every year. Credit card fraud case not only seriously violated the cardholder benefits and financial institutions, but also undermined the credit management order. However, fraudsters keep exploring new crime strategies constantly which exacerbates the crime rate of fraud. Thus, a predictive model for credit card fraud detection is essential to minimize its losses. By distinguishing between fraud and non-fraud, machine learning is one of the most efficient solutions for detecting fraud. Support vector machines have proven to be a novel algorithm with excellent performance. Nevertheless, the performance of SVM depends largely on the correct choice of model parameters (C and g), which could cause that the false positive was very high if the kernel function type and parameter cannot be selected properly. In this paper, based on the real transaction data of the credit card business, firstly, it will find the optimal kernel function suitable for the data set. Secondly, this paper will propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. Last but not least, the Linear kernel function was found to be the best kernel function with an accuracy rate of 91.56%. Furthermore, the Radial basis function is used to optimize the kernel function, which can improve the accuracy from 42.86% to the highest accuracy rate of 98.05%. Compared with CS-SVM and GA-SVM, PSO-SVM has the best overall performance.
Dark Web Marketplaces and COVID-19: The vaccines

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A Trust Management System for Multi-agent System in Smart Grids using Blockchain Technology

In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks.
Experiencing Simulated Confrontations in Virtual Reality

The use of virtual reality (VR) to simulate confrontational human behaviour has significant potential for use in training, where the recreation of uncomfortable feelings may help users to prepare for challenging real-life situations. In this paper we present a user study (n=68) in which participants experienced simulated confrontational behaviour performed by a virtual character either in immersive VR, or on a 2D display. Participants reported a higher elevation in anxiety in VR, which correlated positively with a perceived sense of physical space. Character believability was influenced negatively by visual elements of the simulation, and positively by behavioural elements, which complements findings from previous work. We recommend the use of VR for simulations of confrontational behaviour, where a realistic emotional response is part of the intended experience. We also discuss incorporation of domain knowledge of human behaviours, and carefully crafted motion-captured sequences, to increase users’ sense of believability.
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.
Student Program Classification Using Gated Graph Attention Neural Network

Source code mining has received increasing attention, among which code classification plays a significant role in code understanding and automatic coding. Most source code mining efforts aim at the source code of projects, which are usually large and standardized, but less for student programs. There are two differences between project codes and student programs. On the one hand, some work on project codes is based on relatively single information, which is far from enough for student programs. Because student programs are relatively small, which makes them contain less information. Consequently, it is necessary to mine as much information as possible in student programs. On the other hand, the variable or function naming and the structure of the student programs are usually irregular, as compared with the source codes of projects. To learn from student programs, we proposed a Graph Neural Network (GNN) based model, which integrates data flow and function call information to the Abstract Syntax Tree (AST), and applies an improved GNN model to the integrated graph to achieve the state-of-art student program classification accuracy. The experiment results have shown that the proposed work can classify student programs with accuracy over 97%.