Reviewerly/exHarmony huggingface dataset:
Resource-Tracking Concurrent Games

Abstract We present a framework for game semantics based on concurrent games, that keeps track of resources as data modified throughout execution but not affecting its control flow. Our leading example is time , yet the construction is in fact parametrized by a resource bimonoid $$\mathcal {R}$$ , an algebraic structure expressing resources and the effect of their consumption either sequentially or in parallel. Relying on our construction, we give a sound resource-sensitive denotation to $$\mathcal {R}$$ -IPA, an affine higher-order concurrent programming language with shared state and a primitive for resource consumption in $$\mathcal {R}$$ . Compared with general operational semantics parametrized by $$\mathcal {R}$$ , our resource analysis turns out to be finer, leading to non-adequacy. Yet, our model is not degenerate as adequacy holds for an operational semantics specialized to time. In regard to earlier semantic frameworks for tracking resources, the main novelty of our work is that it is based on a non-interleaving semantics, and as such accounts for parallel use of resources accurately.
Making sense of kernel spaces in neural learning

Kernel-based and Deep Learning methods are two of the most popular approaches in Computational Natural Language Learning. Although these models are rather different and characterized by distinct strong and weak aspects, they both had impressive impact on the accuracy of complex Natural Language Processing tasks. An advantage of kernel-based methods is their capability of exploiting structured information induced from examples. For instance, Sequence or Tree kernels operate over structures reflecting linguistic evidence, such as syntactic information encoded in syntactic parse trees. Deep Learning approaches are very effective as they can learn non-linear decision functions: however, general models require input instances to be explicitly modeled via vectors or tensors, and operating on structured data is made possible only by using ad-hoc architectures. In this work, we discuss a novel architecture that efficiently combines kernel methods and neural networks, in the attempt at squeezing the best from the two paradigms. The so-called Kernel-based Deep Architecture (KDA) adopts a Nyström-based projection function to approximate any valid kernel function and convert any structure they operate on (for instance, linguistic structures, such as trees) into dense linear embeddings. These can be used as input of a Deep Feed-forward Neural Network that exploits such embeddings to learn non-linear classification functions. KDA is a mathematically justified integration of expressive kernel functions and deep neural architectures, with several advantages: it (i) directly operates over complex non-tensor structures, e.g., trees, without ad hoc manual feature engineering or architectural design, (ii) achieves a drastic reduction of the computational cost w.r.t. pure kernel methods, and (iii) exploits the non-linearity of Deep Architectures to produce accurate models. We experimented the KDA in three rather different semantic inference tasks: Semantic Parsing, Question Classification, and Community Question Answering. Results show that the KDA achieves state-of-the-art accuracy, with a computational cost that is much lower than the one necessary to train and test a pure kernel-based method, such as the SVM algorithm.
Public security risk prevention and control support key technology research

Risk prevention and control is a process of taking measures to evade and resolve event risks based on risk source identification and risk assessment. Based on the relationship between risk prevention and control and emergency response, this research relies on modern information technology to propose a key technical system for marine environmental security risk prevention and control support including risk prevention and control knowledge base construction and risk prevention and control scheme generation technology. Through technical research, it provides technical support for the safe and efficient response of the marine environment.
Deploying Smart Program Understanding on a Large Code Base

Program understanding aims at discovering human-readable properties of a software project from the analysis of its source code. Recently, we proposed a smart approach based on hierarchical agglomerative clustering that extracts so-called program topoi from source code. These topoi are high-level observable properties of the project. Based on textual and structural representations of the source code, our multi-steps approach clusters program topoi in an effective and efficient way. In this paper, we depict novel exploitation tasks of this program understanding approach and report on its application to Software Heritage. Software Heritage is an ambitious project which aims at collecting and archiving the biggest corpus of publicly available software source code. One of the project goals is to provide a new scientific instrument for computer scientists to evaluate advanced machine learning and software engineering methods on a very large source code repository. Our in-depth experiments reveal that unsupervised learning is the appropriate tool to mine and understand the biggest corpus of software source code ever produced.
StyleExplorer: A Toolkit for Textual Writing Style Visualization

The analysis of textual writing styles is a well-studied problem with ongoing and active research in fields like authorship attribution, author profiling, text segmentation or plagiarism detection. While many features have been proposed and shown to be effective to characterize authors or document types in terms of high-dimensional feature vectors, an intuitive, human-friendly view on the computed data is often lacking. For example, machine learning algorithms are able to attribute previously unseen documents to a set of known authors by utilizing those features, but a visualization of the most discriminating features is usually not provided. To this end, we present StyleExplorer, a freely available web tool that is able to extract textual features from documents and to visualize them in multiple variants. Besides analyzing single documents intrinsically, it is also possible to visually compare multiple documents in single views with respect to selected metrics, making it a valuable analysis tool for various tasks in natural language processing as well as for areas in the humanities that work and analyze textual data.
[Invited papers] Supervised Nonparametric Multimodal Topic Models for Multi-class Video Classification

Nonparametric topic models such as hierarchical Dirichlet processes (HDP) have been attracting more and more attentions for multimedia data analysis. However, the existing models for multimedia data are unsupervised ones that purely cluster semantically or characteristically related features into a specific latent topic without considering side information such as class information. In this paper, we present a novel supervised sequential symmetric correspondence HDP (Sup-SSC-HDP) model for multi-class video classification, where the empirical topic frequencies learned from multimodal video data are modeled as a predictor of video class. Qualitative and quantitative assessments demonstrate the effectiveness of Sup-SSC-HDP.
Video-Based Vietnamese Sign Language Recognition Using Local Descriptors

Sign Language is one of the method for non-verbal communication. It is most commonly used by deaf or dumb people who have hearing or speech problems to communicate among themselves or with normal people. Vietnamese Sign Language (VSL) is a sign language system used in the community of Vietnamese hearing impaired individuals. VSL recognition aims to develop algorithms and methods to correctly identify a sequence of produced signs and to understand their meaning in Vietnamese. However, automatic VSL recognition in video has many challenges due to the orientation of camera, hand position and movement, inter hand relation, etc. In this paper, we present some feature extraction approaches for VSL recognition includes spatial feature, scene-based feature, and especially motion-based feature. Instead of relying on a static image, we specifically capture motion information between frames in a video sequence. We evaluated the proposed framework on our acquired VSL dataset including 23 alphabets, 3 diacritic marks and 5 tones in Vietnamese language with 2D camera. Additionally, in order to gain more information of hand movement and hand position, we also used the data augmentation technique. All these helpful information would contribute to an effective VSL recognition system. The experiments achieved the satisfactory results with 86.61%. It indicates that data augmentation technique provides more information about the orientation of hand. Moreover, the combination of spatial, scene and especially motion information could help the system to be able to capture information from both single frame and from multiple frames, and thus the performance of VSL recognition system could be improved.
Continuous Estimation of Speed and Torque of Induction Motors Using the Unscented Kalman Filter under Voltage Sag

Due to sensor limitations in some applications, induction motors state estimators are widely used in industries. One of the most powerful tools available for estimation is the Kalman filter. In this paper, unscented Kalman filter (UKF) and extended Kalman filter (EKF) is used to estimate the speed and torque of an induction motor. In the UKF algorithm, three types of unscented transformation (UT): basic, general and spherical types are presented and compared. It will be shown that the spherical UKF presents good estimation performance. Speed and torque Estimation approach is applied at both steady state conditions and at the time of sudden and rapid change in the motor input voltage. It will be shown that, EKF cannot trace the motor speed at the time of a large disturbance. Finally, experimental validation is presented to show the effectiveness of UKF for continuous estimation of torque and speed of induction motors.
Scalable local features and hybrid classifiers for improving action recognition

In recent years, action recognition techniques have played an increasingly important role in autonomous systems. However, the computational costs and precision of action recognition algorithms are still major challenges. Recently, a deep learning approach was proposed to obtain a higher accuracy, but large and deep neural networks have high computational costs. This paper presents a new approach that allows for a significant reduction in computational time while slightly increasing the accuracy. The contribution consists of two parts: a scalable feature extraction method (SFE) and a hybrid model of different classifiers. First, the SFE method is proposed for application to histogram orientation-based feature descriptors, such as the histogram of orientated gradient (HOG), histogram of optical flow (HOF), and the motion boundary histogram (MBH). An advantage of SFE is its ability to quickly compute features. Scalable feature extraction enables accurate approximation of features extracted from traditional image pyramids by efficiently using only the original image. Our method is inspired by a special data structure used for storing basic information of optical flow and image gradients, which are computed from the original image and then used to extract features across multiple scales of the feature region without recomputing the image gradients and optical flow. Second, we focus on a hybrid classification method based on a linear support vector machine (SVM) and hidden conditional random field (HCRF) model that improves the recognition precision. This effort shows that a combination of SVM and HCRF models provides a better accuracy than the traditional approaches. Experimental results illustrate that the proposed approach allows for both a significant reduction in computational time and an improved accuracy.
Graphical Programming Language Design for Decentralized Building Intelligent System

The decentralized building intelligent system is a new type of building intelligent control system, which forms a decentralized network through intelligent nodes. It solves the difficulties in the configuration, modification, and information sharing of traditional centralized building control systems. However, because of the characteristics of no-center and parallelism, the decentralized system still lacks effective programming language support. In this regard, this paper designs a dedicated graphical programming language for the decentralized building intelligent system, then describes and explains from the static definition and dynamic interaction of graphics element two aspects. Finally, combining a case of control, this paper draws the connection diagram with the graphical programming language and makes an explanation, which verifies the feasibility of the graphical programming language for the decentralized system.
An Empirical Study to Classify Website Using Thresholds from Data Characteristics

The advent of web had resulted in a plethora of information and data. However, its volume heterogeneity and unstructured organization makes information retrieval difficult. To the existing practice where website categorization is largely based on style rather than text, addition of an extra dimension in form of genre is expected to significantly improve the search outcome. Keeping this in view, we attempt to build a novel classification model to categorize websites into genres using thresholds of the web metrics. Statistical measures of central tendency are assumed to render a value that distinguish websites from a sample space containing News, Travel and Tourism, Entertainment and Social media. Through the statistical analysis of the data we find that the data distribution of all metrics which constitute the website properties are highly skewed. Hence, conventional analysis based on normal distribution statistics fails to apply. Adopting to a systematic empirical approach, we find that the classification performance measure identified through the Area Under the Curve is maximized around a threshold value which is twice the value of the “median-absolute-deviation” of the web metrics.
RELIGIOUS ONLINE DISCOURSE: SOCIOLINGUISTIC ASPECT

The article deals with researching the phenomenon of the religious internet-discourse through the lens of socially determined factors influencing speech communicative behaviour of the net user’s language identity. The author explains definition of the hypertext material as a basic unit providing successful functioning of the researched discourse type. The analysis of the definite strategies, tactics and ways used by the producent proves cognitive pragmatic power of the hypertext to influence the language identity of the religious internet-discourse participant. Practical significance of the article consists in using methods of network navigation and browsing from the position of the user’s language identity with the aim of studying particularities of realizing information distributive and sociocommunicative functions of the hypertext material. Basing on the research results the author comes to the conclusion that functioning of the studied discourse type is mediated with the hypertext space. The researcher manages to renovate the topic of the religious discourse in the sphere of network discourse practices by means of analyzing the particularities of realizing hypertext features and functions in the religious internet-discourse space.
A Survey on Object Tracking in Aerial Surveillance

Nowadays the Unmanned Aerial Vehicle (UAV) has been widely used due to its low-cost and unique flexibility. Specifically, the high-altitude operational capability makes it the ideal tool in military and civilian surveillance system, in which object tracking based on computer vision is the core ingredient. In this paper, we presented a survey on object tracking methods in aerial surveillance. After briefly reviewing the development history and current research institutions, we summarized frequently used sensors in aerial platform. Then we focused on UAV-based tracking methods by providing detailed descriptions of its common framework (ego motion compensation, object detection, object tracking) and representative tracking algorithms. Through discussing the requirement of a good tracking system and deficiency of current technologies, future directions for aerial surveillance were proposed.
Cryptographic methods and development stages used throughout history

Throughout history the need for secrecy has been important. This need for secrecy brought about the invention and the art of concealment, coding and code making [1]. As early as 1900 B.C., Egyptian scribes used hieroglyphs in a non-standard fashion, presumably to hide the meaning from those who did not know the meaning [2]. Cryptography is the effect of designing privacy requirements systems in communication [3]. From the past to the present, many classical encryption methods such as Caesar, Vigenere, and Enigma have been replaced by more powerful modern new encryption methods such as DES, RSA, PGP, AES, and SHA-3.This study aims to present chronologically together about major cryptography methods and developmental stages in the history of cryptology.
Codes over non-abelian groups

In this paper we consider the group algebra FG, where G is a non-abelian group and characteristic of the field F does not divides o(G), then FG is semisimple, and hence decomposes into a direct sum of minimal ideals generated by the idempotents we give the explicit expressions for the linear and non linear idempotents in the group algebra of nonabelian groups: generalized quaternion group Q4n of order 4n for every n, V8n of order 8n where n is odd, and the group U6n. We also describe (4n, 4n−1, 2), (4n, 4n−2, 2), (4n, 4n−3, 2), (4n, 4n−4, 2) group codes. Codes over Q12 and V24 are also obtained.
Identification of Possible Common Causes by Intrinsic Dimension Estimation

The effect of confounding factors cannot be ignored in real world causal discovery tasks. A common cause is a general confounder between two variables. In this paper, we propose using intrinsic dimension estimation as a necessary condition to determine a possible common cause for two variables. Simulated application showed that the proposed method worked well for both linear and non-linear functions. Testing using different types of noise showed that it generally worked well for different types of added noise. In particular, it worked better than a kernel-based conditional independence test for Poisson noise. Testing of how the estimated intrinsic dimension is affected by different types of distributions showed that the estimated dimension is nearly not affected by the type of distribution. Simulation of mixed pattern showed that the proposed method can still tell a possible common cause when it is mixed with causal relationship. Finally, experiments using variables from the CauseEffectPairs dataset showed that the proposed method can give correct inferred results for real world data.
Design of the Vocabulary Query System for Computer-Aided English Translation Teaching

The design of the vocabulary query system for the computer-aided English translation teaching is mainly aimed at the students’ use, hoping to improve the efficiency of the students’ English vocabulary learning. Experiments show that this system can effectively and accurately query the vocabulary in the English translation teaching activities. It has the advantages of the fast speed, the strong stability and the high practicability. It can promote the learning of the English vocabulary and realize the initial expectation of the system design. The application of the computer-aided translation in our translation teaching has improved the traditional translation teaching methods. For students, the computer-aided translation not only provides a living dictionary, but also provides the rich translation examples, the complete contexts and the sufficient background information.
Fuzzy Rules Based Solution for System Administration Security Management via a Blockchain

Digital transformation has led to the fact that almost all organizations and companies are provided with internal private networks and manage sensitive data and applications. In this context, system administrators are superusers who can access all this sensitive material. As it is known that many frauds are caused by internal actions, we argue that it is important to be provided with strong automated logging systems even for superusers. For this purpose, blockchains are an efficient solution as they cannot be overwritten by the system administrators. However, as it is not efficient to store all the actions, we introduce in this paper a novel system based on fuzzy rules in order to efficiently manage the system logging system in a blockchain.
Research on 2D digital image correlation measurement based on smartphones

Digital image correlation (DIC) is a non-contact, full-field optical measurement method that has been extensively used in various applications like structural health monitoring, material characterization, high temperature testing etc. However, most of the above applications are used in the laboratory, and rarely employed in the actual structure. This is mainly because of, firstly, DIC instruments are expensive and cannot be afforded by ordinary people. Secondly, the corresponding equipment is complicated to operate and cannot be widely used. The built-in camera of the smartphone is becoming more and more high-definition so that it has brought about an opportunity to solve this problem. From this point of view, this study monitored the deformation of compressive concrete blocks by smartphone and DIC technology, and analyzed the changes of displacement and strain field on the surface of concrete blocks under different loads. The location and shape of cracks in the displacement results were compared with those in the actual image as well. The results show the feasibility of using smartphones to monitor the strain of structures.
Image shadow removal using cycle generative adversarial networks

A shadow-removal algorithm based on cycle generative adversarial network is proposed. There are two networks in the proposed method. To increase the diversity of shadow images for improving the robustness in the shadow-removal process, the first network is used to add shadows in nonshadow images to increase the variation of training data. Then, the second network is trained for the shadow-removal task. Six different losses are calculated and combined in the loss function to increase the performance. Ablation experiments show that the resulting images suffer from some artifacts without any of the six losses in the loss function. The proposed method presents lower value of root-mean-squared error and the superior visual quality compared to the state-of-the-art image removal algorithms.
Decentralized Differential Evolutionary Algorithm for Large-Scale Networked Systems

The optimization of a complex system with multiple subsystems is a tough problem. In this paper, a Decentralized differential evolutionary algorithm (DDEA) is proposed. The simulations for both DDEA and centralized DE on three benchmark functions are carried out. The numerical results show that DDEA is efficient to solve decentralized optimization problems. On these problems, the proposed DDEA outperforms centralized DE in convergence.
EXPERIENCE OF USING THE PROJECT METHOD WHILE STUDYING THE THEME “ALGORITHMIZATION AND PROGRAMMING” IN THE CONDITIONS IMPLEMENTING THE FSES

The Federal State Educational Standards of general education defines the system-activity approach as the main approach to the formation of the universal educational actions. The article presents the experience of implementing the FSES using the most effective method — the project activity. Projects are considered, each of which involves the use of a whole complex of software tools: Microsoft Publisher, Microsoft PowerPoint, PascalABC.net, KuMir, HTML language, which is especially important when organizing training in the information technology lyceum.