Reviewerly/exHarmony huggingface dataset:
A Proof Assistant Based Formalisation of a Subset of Sequential Core Erlang

We present a proof-assistant-based formalisation of a subset of Erlang, intended to serve as a base for proving refactorings correct. After discussing how we reused concepts from related work, we show the syntax and semantics of our formal description, including the abstractions involved (e.g. the concept of a closure). We also present essential properties of the formalisation (e.g. determinism) along with the summary of their machine-checked proofs. Finally, we prove expression pattern equivalences which can be interpreted as simple local refactorings.
An Attribute Based Encryption Scheme with Dynamic Attributes Supporting in the Hybrid Cloud

Cloud computing is the flexible platform to outsource the data from local server to commercial cloud. However cloud provides tremendous benefits to user, data privacy and data leakage reduce the attention of cloud. For protecting data privacy and reduce data leakage various techniques has to be implemented in cloud. There are various types of cloud environment, but we concentrate on Hybrid cloud. Hybrid cloud is nothing but combination of more than two or more cloud. Where critical operations are performed in private cloud and non critical operations are performed in public cloud. So, it has numerous advantages and criticality too. In this paper, we focus on data security through encryption scheme over Hybrid Cloud. There are various encryption schemes are close to us but it also have data security issues. To overcome these issues, Attribute Based Encryption Scheme with Dynamic Attributes Supporting (ABE-DAS) has proposed. Attribute based Encryption Scheme with Dynamic Attributes Supporting technique enhance the security of the data in hybrid cloud.
Distributed synchronous cooperative tracking algorithm for ground moving target in urban by UAVs

In this paper, a cooperative path planning method is proposed to track the unknown ground moving target by multiple unmanned aerial vehicles (UAVs). The kinematic UAV model with dual-input including commanded speed and heading rate is adopted, and the unknown target state is forecasted by the Extended Kalman Filter (EKF) with probability estimate. The cooperative path planning with multiple objectives and priorities is designed, where the infrastructure occlusion to line of detection sight and energy consumption are considered. Three optimisation objectives are fuzzified, and the priority is modelled by the relaxed order of satisfactory degrees. With the synchronous communication, the collision avoidance constraints and compatibility constraints under communication delay are designed. Moreover, the obstacle avoidance constraints with high buildings are addressed. Based on the distributed receding horizon control (RHC), the fuzzy multi-objective synchronous cooperative tracking model is built, and the optimal path of UAVs can be obtained by regulating the parameter. The algorithm to find the bound of regulation parameter is developed based on Karush–Kuhn–Tucker (KKT) condition. Finally, simulation results verify that the proposed method is effective and reliable.
Real-Time Augmented Reality Application for Visualizing Variables in Industrial Processes

In recent years there has been an increase in the use of virtual tools in different areas such as: education, tourism or industry. The augmented reality (AR) enables the correlation of the real world with virtual objects so that users have additional information of the surrounding environment supplementing the learning and experience of the user. The present work shows the development of an AR application, which characterizes and sends information of the industrial sensors in real time; the recognition of objects utilized a three-dimensional analysis due to the holographic geometry of the MPS PA Compact Workstation, the mobile application combines various development platforms such as: Visual Studio, Android Studio Unity and Vuforia. The data of the sensors arrive to a public URL address which enables visualizing its status in real time, the Android application accesses the particle cloud server via WiFi enabling visualizing the information of the sensors in the smartphone.
Automatic Development of Deep Learning Architectures for Image Segmentation

Machine learning is a branch of artificial intelligence that has gained a lot of traction in the last years due to advances in deep neural networks. These algorithms can be used to process large quantities of data, which would be impossible to handle manually. Often, the algorithms and methods needed for solving these tasks are problem dependent. We propose an automatic method for creating new convolutional neural network architectures which are specifically designed to solve a given problem. We describe our method in detail and we explain its reduced carbon footprint, computation time and cost compared to a manual approach. Our method uses a rewarding mechanism for creating networks with good performance and so gradually improves its architecture proposals. The application for the algorithm that we chose for this paper is segmentation of eyeglasses from images, but our method is applicable, to a larger or lesser extent, to any image processing task. We present and discuss our results, including the architecture that obtained 0.9683 intersection-over-union (IOU) score on our most complex dataset.
Exploring Inter-Sensor Correlation for Missing Data Estimation

Data mining techniques have been widely applied to various fields including industrial, business, and governmental applications. Missing data is a common occurrence in a number of real-world databases, which may substantially affect the accuracy of data processing. In this paper, we propose a novel approach for missing data estimation by efficiently exploring inter-sensor correlation. Namely, given multiple sensors for data collection, we attempt to recover the missing data of a few sensors by using the measurement data from other sensors. Towards this goal, we develop an iterative solver for missing data estimation. Our numerical experiments on two industrial datasets demonstrate that the proposed method can reduce the imputation error by up to 7.25× compared to a conventional method in the literature.
Integration of Hybrid Intelligence Components Based on the Quadrant Enabled Delphi Method While Diversifying XBML Capabilities

The report proposes a new approach to integrating hybrid intelligence components in purpose to create a virtual semantic environment. The idea is mainly focused on providing monitoring of activity and selective interaction based on "Commonsense Knowledge." The basis for their applicability is the integration components of protocols through the "Blackboard System" for organizing multi-sided communication of eventual subjects with each other.
Quantum speed-up in global optimization of binary neural nets

Abstract The performance of a neural network (NN) for a given task is largely determined by the initial calibration of the network parameters. Yet, it has been shown that the calibration, also referred to as training, is generally NP-complete. This includes networks with binary weights, an important class of networks due to their practical hardware implementations. We therefore suggest an alternative approach to training binary NNs. It utilizes a quantum superposition of weight configurations. We show that the quantum training guarantees with high probability convergence towards the globally optimal set of network parameters. This resolves two prominent issues of classical training: (1) the vanishing gradient problem and (2) common convergence to sub-optimal network parameters. We prove that a solution is found after approximately <?CDATA $4{n}^{2}\enspace \mathrm{log}\left(\frac{n}{\delta }\right)\sqrt{\tilde {N}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>4</mml:mn> <mml:msup> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mspace width="0.17em" /> <mml:mi>log</mml:mi> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mfrac> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>δ</mml:mi> </mml:mrow> </mml:mfrac> </mml:mrow> </mml:mfenced> <mml:msqrt> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mo>˜</mml:mo> </mml:mover> </mml:mrow> </mml:msqrt> </mml:math> calls to a comparing oracle, where δ represents a precision, n is the number of training inputs and <?CDATA $\tilde {N}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mo>˜</mml:mo> </mml:mover> </mml:mrow> </mml:math> is the number of weight configurations. We give the explicit algorithm and implement it in numerical simulations.
A study of Secure cryptographic based Hardware security module in a cloud environment

With the expansion of distributed computing, security of sensitive client information is emerging as a significant challenge. This paper proposes a secured cloud engineering with an equipment security module that separates cloud client information from conceivably malignant special areas or cloud chairmen. Further, the equipment security module gives basic security usefulness within a safely disconnected execution condition with just limited interfaces presented to weak administration frameworks or then again to cloud directors. Such limitation forestalls cloud directors from influencing the security of visitor instances [7]. The proposed building not simply makes preparations for wide attack vectors yet furthermore achieves a hardware security module [12]. This paper talks about the equipment and programming of the proposed cloud design along with its security and presents its exhibition results.
A Two-Dimensional Architecture for Fast Large-Scale Trapped-Ion Quantum Computing

Building blocks of quantum computers have been demonstrated in small to intermediate-scale systems. As one of the leading platforms, the trapped ion system has attracted wide attention. A significant challenge in this system is to combine fast high-fidelity gates with scalability and convenience in ion trap fabrication. Here we propose an architecture for large-scale quantum computing with a two-dimensional array of atomic ions trapped at such large distance which is convenient for ion-trap fabrication but usually believed to be unsuitable for quantum computing as the conventional gates would be too slow. Using gate operations far outside of the Lamb–Dicke region, we show that fast and robust entangling gates can be realized in any large ion arrays. The gate operations are intrinsically parallel and robust to thermal noise, which, together with their high speed and scalability of the proposed architecture, makes this approach an attractive one for large-scale quantum computing.
MUTAMA: An Automated Multi-label Tagging Approach for Software Libraries on Maven

Recent studies show that the Maven ecosystem alone already contains over 2 million library artefacts including their source code, byte code, and documentation.To help developers cope with this information, several websites overlay configurable views on the ecosystem.For instance, views in which similar libraries are grouped into categories or views showing all libraries that have been tagged with tags corresponding to coarse-grained library features.The MVNRepository overlay website offers both category-based and tag-based views.Unfortunately, several libraries have not been categorised or are missing relevant tags.Some initial approaches to the automated categorisation of Maven libraries have already been proposed.However, no such approach exists for the problem of tagging of libraries in a multi-label setting.This paper proposes MUTAMA, a multi-label classification approach to the Maven library tagging problem based on information extracted from the byte code of each library.We analysed 4 088 randomly selected libraries from the Maven software ecosystem.MUTAMA trains and deploys five multi-label classifiers using feature vectors obtained from class and method names of the tagged libraries.Our results indicate that classifiers based on ensemble methods achieve the best performances.Finally, we propose directions to follow in this area.
How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset

Reading comprehension style questionanswering (QA) based on patient-specific documents represents a growing area in clinical NLP with plentiful applications.Bidirectional Encoder Representations from Transformers (BERT) and its derivatives lead the state-of-the-art accuracy on the task, but most evaluation has treated the data as a pre-mixture without systematically looking into the potential effect of imperfect train/test questions.The current study seeks to address this gap by experimenting with full versus partial train/test data consisting of paraphrastic questions.Our key findings include 1) training with all pooled question variants yielded best accuracy, 2) the accuracy varied widely, from 0.74 to 0.80, when trained with each single question variant, and 3) questions of similar lexical/syntactic structure tended to induce identical answers.The results suggest that how you ask questions matters in BERTbased QA, especially at the training stage.
Occlusion Boundary: A Formal Definition &amp; Its Detection via Deep Exploration of Context

Occlusion boundaries contain rich perceptual information about the underlying scene structure and provide important cues in many visual perception-related tasks such as object recognition, segmentation, motion estimation, scene understanding, and autonomous navigation. However, there is no formal definition of occlusion boundaries in the literature, and state-of-the-art occlusion boundary detection is still suboptimal. With this in mind, in this paper we propose a formal definition of occlusion boundaries for related studies. Further, based on a novel idea, we develop two concrete approaches with different characteristics to detect occlusion boundaries in video sequences via enhanced exploration of contextual information (e.g, local structural boundary patterns, observations from surrounding regions, and temporal context) with deep models and conditional random fields. Experimental evaluations of our methods on two challenging occlusion boundary benchmarks (CMU and VSB100) demonstrate that our detectors significantly outperform the current state-of-the-art. Finally, we empirically assess the roles of several important components of the proposed detectors to validate the rationale behind these approaches.
Organization of Research Activities as a Factor in Increasing the Efficiency of Training International Students in Host Russian Universities

This article summarizes the practices of organizing research activities that can improve the training of international students in Russian universities. The authors analyzed and identified the correlation between the internal costs of research and development (R&amp;D), the equipment in university research laboratories, and the number of foreigners studying at Russian universities. The authors used the methods of analysis and synthesis and a systematic approach to explore the experience of Russian universities in the training of international students. To identify the impact of internal R&amp;D costs and the equipment of university research laboratories on the number of foreigners studying in Russian universities, the authors applied correlation and regression analysis, which included building a regression equation, calculating the correlation coefficient, the t-test, the coefficient of elasticity, and the coefficient of determination. This research paper revealed a strong correlation between the number of international students in the Russian Federation on the internal R&amp;D costs and the cost of fixed assets and the equipment of Russian universities, which was proven by the calculated correlation coefficients, elasticity coefficients, and determination coefficients. The authors concluded that universities could influence the number of international students. This article proposes some methods for organizing the research activities of international students that increase their academic mobility and form the most relevant scientific and professional competencies. Higher educational institutions of any major can implement these recommendations for managing the research activities of international students. The novelty of this study lies in the fact that the authors performed the correlation and regression analysis and revealed the dependence of the number of international students in the Russian Federation on internal R&amp;D costs, the cost of fixed assets, and the equipment of Russian universities. The authors illustrated the analysis results with the trend predictive values of factorial features and the value of the effective feature estimated according to the regression equations built. Using the calculated MAPE and Forecast Accuracy indicators for these predicted values, the authors concluded that the level of factorial features and the effective feature were predicted with high accuracy. Thus, host Russian universities can increase the number of international students through effective organization of research activities.
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation

Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain.Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain.The transferred knowledge, however, may unintendedly leak private information of the source domain.For example, an attacker can accurately infer user demographics from their historical purchase provided by a source domain data owner.This paper addresses the above privacy-preserving issue by learning a privacyaware neural representation by improving target performance while protecting source privacy.The key idea is to simulate the attacks during the training for protecting unseen users' privacy in the future, modeled by an adversarial game, so that the transfer learning model becomes robust to attacks.Experiments show that the proposed PrivNet model can successfully disentangle the knowledge benefitting the transfer from leaking the privacy.
Description model of programming style ecosystem

In the process of developing and maintaining a software product, many things are created and used that are called software artefacts. Software artifacts are changed, reused, and change relationships in the development and maintenance processes of a software product. The complexity and variety of software artifact relationships require adequate means of description and management. They may be a software artifact ecosystem. In the article, for the first time, a concept of a software artifact ecosystem is proposed. The concept describes a generalized model of the software artifact ecosystem, which is the Cornerstone ecosystem type and consists of three actors – the platform, the software, and the artifact. The roles of actors in the ecosystem are indicated, the relationships between actors are described. As an example, based on the generalized model of the software artifact ecosystem, a declarative model of the programming style ecosystem has been developed.
A Comparison of Authentication Methods via Keystroke Dynamics

Authentication systems based on keystroke dynamics analyze the typical typing pattern of a user when interacting with an input device, such as the keyboard of a computer. In the literature, three major approaches on keystroke dynamics can be found: distance-based, statistical-based and machine learning-based approaches, which are often used to solve the problem. Nevertheless, in the literature there are several works which results are obtained from different comparison methodologies; this represents a great problem for future researchers who seek to improve or advance with prior works. Furthermore, by using proprietary databases, researchers do not provide a good overview of the overall performance of their methods, but rather an overview in a specific case: that represented by their database. In this investigation, we proposed to evaluate the performance of the most representative classifiers in two of the three most common approaches used in keystroke dynamics using the public Greyc dataset. The experimental results, reveal that machine-learning based approaches outperformed the distance-based techniques. Moreover, the Random Forest classifier, provided encouraging results.
IoT based Secure Digital Forensic Process according to Process Management

The implementation of Secure FI process by digitally by adapting the IoT based connecting many wireless smart devices may expose security vulnerabilities. The DF related to IoT based can be characterized as a field of study however ought to be utilized in defensive condition which is absence of the present world and the use of innovative and constant techniques for collect, preserve, validate, analyze, interpret and present the evidences extracted using the forensic tools and carefully separated from various sources and presenting official courtroom for justice and fair judicial processor legal cycle. The IoT smart devices will require appropriate procedural and ensured remote condition with safer and secure information reinforcement in place from secure home control to cutting edge city the executives connected networks. The smart gadgets will detect their condition with interconnected one another to shape canny brilliant spaces and any suspected or unapproved occasion will be put away and set off to the requirement office to heighten the issue or Forensic examination [1], [2]. These smart devices all things considered produce and store enormous sum delicate criminological information. The paper clarifies the current Process by zeroing in and improving on pre and post examination measure with more solid and secure cycle with back up Data stockpiling with proof. The information stream from Pre-examination to post examination and capacity or cloud-based focusing on key zones of character on irregular action registering impressions to the worker (pre-examination) when recognized and continue to post examination with assessment and sparing the information in to make sure storage.
Hardware Implementation of SHA Algorithms on Different FPGA and Speed Comparison

This paper presents a comparison of hardware efficiency of the current Secure Hash Algorithm (SHA) SHA-2 standard algorithm and Blake SHA-3 candidate realized on actual Intel field-programmable gate array (FPGA). The research methods are based on various platforms, which are based on pipelined methods, and folding and unrolling processes. Research is being conducted into bandwidth based on the speed and the overall usage of the resource of microcircuits. When creating hardware implementations of algorithms, overall performance is extremely necessary, and this parameter take into the system-wide comparative analysis.
Organization of Remote Education for Higher Mathematics. Challenges and Solutions

The paper considers the organization of distance learning in higher mathematics during the period of quarantine measures. It is told about the means already available and their further development. The issue of the possibilities of intermediate certification in the remote mode is discussed, as well as the experience of conducting a colloquium with proctoring elements is highlighted. Interim students’ results on studying higher mathematics of one of the streams are given. The results showed that remote learning, which, of course, cannot completely replace personal communication with lecturers, can and should be used not only during the period of self-isolation, but also in the normal educational process.