Reviewerly/exHarmony huggingface dataset:
Hypergraph-based type theory for software development in a Cyber-Physical context

This chapter will explore the integration of several methodologies related to source code analysis and Requirements Engineering in a software development context. The chapter will review graph-based representations of source code, alongside applied type theory (for expressing programming languages’ type systems) and systematic accounts of foundational programming elements such as functions/procedures, function calls, and inter-procedure information flows. The new representational device described here involves a theory of “channels” which permits graph-based code models to be integrated with type theories and lambda calculi structured around modern programming paradigms. The proposed techniques support documentation and verification of procedural, data type, and holistic specifications—implementational assumptions on procedures and/or modeling assumptions on types, along with larger-scale inter-type relationships. This chapter will use real-world Cyber-Physical case studies to illustrate data models which call for advanced code-documentation techniques, insofar as Cyber-Physical software should prioritize safety and reliability. For concrete examples, an accompanying open-source dataset (at https://github.com/scignscape/ntxh) demonstrates code libraries concretizing techniques outlined here.
Robust Multi-View Feature Selection Method

Abstract Recently, Multi-view learning has attracted widely attention. Inspired by the idea of RFS [6], we propose to utilize the Multi-View features to develop a Multi-View feature selection method. In our work, the weight of each view can be adaptively adjusted. An efficient algorithm is designed to solve the proposed method. Numerical study confirms the effectiveness of our proposed method.
Estimating Approximation Errors of Elitist Evolutionary Algorithms

When evolutionary algorithms (EAs) are unlikely to locate precise global optimal solutions with satisfactory performances, it is important to substitute alternative theoretical routine for the analysis of hitting time/running time. In order to narrow the gap between theories and applications, this paper is dedicated to perform an analysis on approximation error of EAs. First, we proposed a general result on upper bound and lower bound of approximation errors. Then, several case studies are performed to present the routine of error analysis, and theoretical results show the close connections between approximation errors and eigenvalues of transition matrices. The analysis validates applicability of error analysis, demonstrates significance of estimation results, and then, exhibits its potential to be applied for theoretical analysis of elitist EAs.
Investigation of biases in identity linkage DataSets

In social networks, the problem of identity linkage is to find whether a pair of user identities on two social networks belong to the same individual or not. Prior works typically first collect ground truth datasets of user identities across social networks belonging to the same individuals and then build a machine learning model driven by features from user identities. User behaviors in different social networks drive the construction of these datasets, and as a consequence, behavioral biases get manifested in them. Our work performs a detailed investigation into these dataset biases, a work which has mostly remained under-explored in the identity linkage research. More specifically, we characterize, detect, and quantify behavioral biases in the dataset that manifest in the form of lexical differences in user-generated content, particularly in usernames and display names configured by users. We study these biases on more than 1 million user identity pairs obtained by leveraging two user behaviors, namely cross-posting and self-disclosure. We find that users who self-disclose their usernames and display names on different social networks show higher lexical similarity than users who cross-post. These behavioral biases lower down the performance (precision and recall) of learning models by 5-20%. Inspired by discrimination measurement metrics, we propose and implement a framework to quantify the extent of these biases and find that 15--20% of test data get affected.
Verifying Selective CPS Transformation for Shift and Reset

A selective CPS transformation enables us to execute a program with delimited control operators, \(\texttt {shift}\) and \(\texttt {reset}\), in a standard functional language without support for control operators. The selective CPS transformation dispatches not only on the structure of the input term but also its purity: it transforms only those parts that actually involve control effects. As such, the selective CPS transformation consists of many rules, each for one possible combination of the purity of subterms, making its verification tedious and error-prone. In this paper, we first formalize a monomorphic version of the selective CPS transformation in the Agda proof assistant. We use intrinsically typed term and context representations together with parameterized higher-order abstract syntax (PHOAS) to represent binding structures. We then prove the correctness of the transformation, i.e., the equality of terms is preserved by the CPS transformation. Through the formalization, we confirmed that all the rules of the selective CPS transformation in the previous work are correct, but found that one lemma on the behavior of \(\texttt {shift}\) was not precise.
Amalgamated Models for Detecting Duplicate Bug Reports

Automatic identification of duplicate bug reports is a critical research problem in the software repositories' mining area. The aim of this paper is to propose and compare amalgamated models for detecting duplicate bug reports using textual and non-textual information of bug reports. The algorithmic models viz. LDA, TF-IDF, GloVe, Word2Vec, and their amalgamation are used to rank bug reports according to their similarity with each other. The amalgamated score is generated by aggregating the ranks generated by models. The empirical evaluation has been performed on the open datasets from large open source software projects. The metrics used for evaluation are mean average precision (MAP), mean reciprocal rank (MRR) and recall rate. The experimental results show that amalgamated model (TF-IDF + Word2Vec + LDA) outperforms other amalgamated models for duplicate bug recommendations. It is also concluded that amalgamation of Word2Vec with TF-IDF models works better than TF-IDF with GloVe. The future scope of current work is to develop a python package that allows the user to select the individual models and their amalgamation with other models on a given dataset.
Formation of competencies in the field of project management for future informatics teachers

The implementation of project management in the education system is impossible without the development of new types of competencies by teachers, which will allow the use of project management methods in professional activities. The article substantiates the need for training teachers who are competent both in managing student projects and in organizing their own project activities, as well as in project activities and project management of an educational institution. The article describes the experience of building competencies in the field of project management among students — future informatics teachers studying in the field of training 44.04.01 “Pedagogical Education” (master’s program “Information Technologies in Education”) at Nosov Magnitogorsk State Technical University. Based on the requirements of the professional standard 06.016 “Project Manager in the Field of Information Technologies”, the requirements for the competence of specialists in the field of project activities, etc., the author examined indicators for the formation of competency UK-2 “Able to manage a project at all stages of its life cycle” for academic discipline “Project Management in Education”. The methodology of forming competencies in the field of project management in education for masters of teacher education using the digital educational resource developed by the author is highlighted.
The Use of Unreal Engine to Create Virtual Realty Fitness and Gaming Application

Virtual Reality (VR) has stolen the technology spotlight in the new era of innovation. It provides excitement and a thrilling sense of presence. VR headsets have emerged from being a concept into a reality people look forward to experience. While gaming is one of the primary uses for VR technology, other forms of entertainment exist. Theater audiences will be able to watch movies on a large virtual screen that is more like their own theater. VR also serves as a costeffective and attractive tool for rehabilitation. Virtual exercises help in motivating patients to practice everyday activities. Beside the health care industry, developers have turned to the fitness industry as well, where they promote exercising with VR. Combining VR and exercising will attain remarkable results. In this paper we introduce the VR-Bike; a system that combines the fun of VR gaming with the fitness of exercising on a bicycle. This is achieved by connecting a microcontroller with sensors on an exercise bike to a VR game developed in Unreal Engine.
Exploring the Cross-Species Experience and the Coevolutionary Capacity: Sensorial Transcoding and Critical Play Design of Bio-Sonic Sense

This paper investigates the concept and development of Bio-Sonic Sense, an artistic interpretation of bio sonars, as an attempt to create a cross-species experience.It examines the potential of sensory transformation through technology-specifically, transcoding visual to audio-with the purpose of communicating the mechanisms of ultrasonic communication employed by marine mammals.Bio-Sonic sense is the result of using artistic practice and critical play, in order to disseminate the effects of noise pollution on marine life.This paper proposes that those practices should be explored as methods that can design for the use of technics as tools that can expand the human senses, thus allowing the exploration of non-human "worlds".
High Dynamic GNSS Anti-jamming Algorithms Based on Nulling Widening and Deepening

According to the motion characteristics of GNSS receiver carrier under high dynamic conditions, a signal statistical model under high dynamic conditions is established. In order to solve the problem that the depth of the traditional CMT algorithm will become shallow while the nulling is widened, the nulling widening and deepening algorithm based on Gauss distribution is proposed. The algorithm preprocesses the data by projection transformation, extracts the interference components from the sampled signal, enhances the interference components by weighting the coefficients, widens and deepens the nulling, constructs a new covariance matrix, and finally completes the interference suppression by using the power inversion algorithm. The simulation results show that under high dynamic conditions, the proposed algorithm achieves the increase of nulling depth on the basis of CMT algorithm, and improves the robustness of interference suppression under high dynamic conditions.
A conditional learnability argument for constraints on underlying representations

We explore the implications of a particular approach to learning for an architectural question in phonology. The learning approach follows the principle of Minimum Description Length (MDL), which has recently been used for learning in both constraint-based and rule-based phonology. The architectural question on which we focus is whether the grammar allows language-specific statements to be made at the level of the lexicon, as was assumed in early generative phonology, or whether such statements are prohibited, as is commonly assumed within more recent work. We show that under MDL, the architectural question has real empirical implications: across a range of seemingly natural representational schemes, an ability to make language-specific statements about the lexicon is needed to ensure the learnability of an important aspect of phonological knowledge.
Target tracking algorithm for pedestrians movement based on kernel-correlation filtering

Objective To reduce the effects of light changes, scale changes, local occlusion and other factors during target tracking, a kernel-correlation filtering (KCF) target tracking algorithm is introduced, which introduces the target block model.Results Comparative experiments of multiple mainstream algorithms on multiple data sets. Experimental results show that the algorithm has the highest accuracy and success rate, which are 11.89% and 15.24% higher than the KCF algorithm, respectively, indicating that the algorithm proposed in this paper possesses a more sensitive response to changes in illumination. Among them, factors such as scale change and local occlusion are more robust.
Challenges and Countermeasures of Education in the Era of Big Data

The advent of the era of big data provides some new ideas for the reform and development of higher education. In this paper, the importance of educational big data is analyzed from six aspects: improving the effectiveness of teaching and learning, promoting scientific decision-making in education, completing the quality monitoring system, facilitating comprehensive evaluation of education quality, promoting the popularization and personalization of education, and improving personalized teaching. Then, some challenges faced in the application process of education big data are analyzed, including thinking mode, data sharing, data technology, talent support, and data security and privacy. Based on them, several feasible countermeasures are proposed from the aspects of enhancing data awareness, realizing open sharing, accelerating professional talent training and strengthening privacy protection. At last, some research directions of educational big data are put forward.
Local Fourier Slice Photography

Light field cameras provide intriguing possibilities, such as post-capture refocus or the ability to synthesize images from novel viewpoints. This comes, however, at the price of significant storage requirements. Compression techniques can be used to reduce these, but refocusing and reconstruction require so far, again, a dense pixel representation. To avoid this, we introduce local Fourier slice photography that allows for refocused image reconstruction directly from a sparse wavelet representation of a light field, either to obtain an image or a compressed representation of it. The result is made possible by wavelets that respect the “slicing’s” intrinsic structure and enable us to derive exact reconstruction filters for the refocused image in closed-form. Image reconstruction then amounts to applying these filters to the light field’s wavelet coefficients; hence, no reconstruction of a dense pixel representation is required. We demonstrate that this can reduce storage requirements and also computation times. We furthermore analyze the computational complexity of our algorithm and show that it scales linearly with the size of the reconstructed region and the non-negligible wavelet coefficients, i.e., with the visual complexity.
Advanced Functions for Lowering Nuisance Alerts in a DAA System: Implementation and Performance Evaluation in Real-Time Human-in-the-Loop Testing

Over the past several decades, Remotely Piloted Aircraft Systems (RPAS) have experienced a large spreading into a number of applications, however, the current operational limitations imposed by the Aviation Authorities concerning safety, make their integration in the controlled airspace a great challenge. To this aim, the detect and avoid (DAA) systems play a fundamental role giving to the RPAS the capability to detect conflicting traffic or other hazards and take appropriate actions in order to guarantee the airspace safety. In this framework, the present work describes the DAA module, named ADAPT, that the Italian Aerospace Research Centre (CIRA) is developing. Such system merges several aspects of DAA state-of-the-art introducing some improvements to decrease the nuisance alerts and increase the situational awareness of the remote pilot through the customization of the graphical appearance of the human machine interface. The effectiveness of the developed DAA system has been demonstrated through the execution of a real-time human-in-the-loop test campaign of which some remarkable results are reported.
NONBINARY DELSARTE–GOETHALS CODES AND FINITE SEMIFIELDS

Abstract Symplectic finite semifields can be used to construct nonlinear binary codes of Kerdock type (i.e., with the same parameters of the Kerdock codes, a subclass of Delsarte–Goethals codes). In this paper, we introduce nonbinary Delsarte–Goethals codes of parameters $(q^{m+1}\ ,\ q^{m(r+2)+2}\ ,\ {\frac{q-1}{q}(q^{m+1}-q^{\frac{m+1}{2}+r})})$ over a Galois field of order $q=2^l$ , for all $0\le r\le\frac{m-1}{2}$ , with m ≥ 3 odd, and show the connection of this construction to finite semifields.
Total Variation Filter via Multiquadric Radial Basis Function Approximation Scheme for Additive Noise Removal

The Digital Total Variation (DTV) scheme is a digitized energy regularization scheme used for image denoising. This technique takes advantage of being applied to arbitrarily located data points and also has the edge detective property. This article aims to introduce a novel meshless scheme using DTV filtering and Radial Basis Functions (RBFs) to solve the associated equation with the DTV model numerically which results in the image denoising to remove additive noise from image information. This meshless algorithm based on local collocation and Multiquadric Radial Basis Function. These appearances allow this algorithm not only to remove the additive noise from images but also to resolve the discontinuities sharply. It is also noticed that the proposed meshless scheme is simple, fast, computationally effective, requires simply post-processing, and can be easily implemented mathematically. Experimental results confirm that the peak signal-to-noise ratio, the structural similarity, signal-to-noise ratio, the visual effect, and the computational performance of this new meshless scheme are improved compared with state-of-the-art denoising schemes. Furthermore, the proposed scheme can be applied to colour images as well.
Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.
Two-tier garbage collection for persistent object

An object allocated in NVM is persisted permanently even when the power is turned off. When the persistent object becomes garbage, it causes fatal space overhead in the system, and previous studies on NVM management schemes did not address an effective solution to this problem. In this paper, we propose a Two-Tier Garbage Collection (TTGC) scheme for fast and stable garbage collection in C language based persistent heap system, and validate the algorithm by implementing the technique in HEAPO, a persistent heap library for NVMs. We also verify that this scheme works more effectively than the other garbage collection techniques using other persistent heaps. Proposed technique shows about 4 to 11 times better performance in garbage collection complete time than the techniques.
A Semantic Driven CNN – LSTM Architecture for Personalised Image Caption Generation

Image Captioning is generating a human-readable textual description or a sentence about an image. The proposed semantic driven CNN-LSTM architecture comprises of the feature extraction process, semantic keywords extraction, facial recognition, and encoder-decoder LSTM networks. A pre-trained CNN is used to extract features from an image. A semantic keywords extraction module is used to identify the objects present in the image. The objects identified are labeled as the semantic tags present in the image. It increases the efficiency of captions in describing the objects and inclusion of these semantic labels in the captions. The LSTM based language model generates the captions by producing one word at a time. The facial recognition system identifies and recognizes the celebrity faces in the images, we have collected faces dataset which has facial images 232 celebrities. The instances of the person in the sentence were replaced with their names and personalized captions were generated. The Bilingual evaluation understudy (BLEU) and METEOR scores were generated to calculate the precision of generated captions.
Reconfigurable Routing Paths As Noise Generators Using NoC Platform for Hardware Security Applications

We propose a multicore power balancing technique by configuring data routing paths in the 9-core Network-on-Chip (NoC) based platform for hardware security applications, particularly for differential power analysis (DPA) countermeasures. There are three degrees of configurability to hide the leaked data. First, the cores are flexibly-programmable to perform AES operation and dummy operations to moderate the power dissipation. Second, the NoC routers are also flexibly-programmable, further enhancing the power moderation. Third, the cores and their associated routers feature individual controllable clock frequencies to moderate the time that leaks data. These three configurable features collectively balance the power dissipation and enable the time moderation to resist DPA. The 9-core platform IC occupies ~1.9 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , operates up to 100MHz @1.2V and dissipates 2.46mW @100MHz power. When running Advanced Encryption Standard (AES) operation, the normalized energy deviation (NED) is undesirable high at 0.21 for power-on (1 core + 1 router). By applying the multicore power balancing technique, the NED reduces by -52% when increasing the number of operating routers from 1 to 5, i.e. (1 core + 5 routers), and reduces by ~73% when increasing the number of operating cores from 1 to 4, i.e. (4 cores + 4 routers).
Hierarchical Identity-Based Signature in Polynomial Rings

Abstract Hierarchical identity-based signature (HIBS) plays a core role in a large community as it significantly reduces the workload of the root private key generator. To make HIBS still available and secure in post-quantum era, constructing lattice-based schemes is a promising option. In this paper, we present an efficient HIBS scheme in polynomial rings. Although there are many lattice-based signatures proposed in recent years, to the best of our knowledge, our HIBS scheme is the first ring-based construction. In the center of our construction are two new algorithms to extend lattice trapdoors to higher dimensions, which are non-trivial and of independent interest. With these techniques, the security of the new scheme can be proved, assuming the hardness of the Ring-SIS problem. Since operations in the ring setting are much faster than those over integers and the new construction is the first ring-base HIBS scheme, our scheme is more efficient and practical in terms of computation and storage cost when comparing to the previous constructions.