Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Brokerage Platform for Fog Computing Resource Federation

This demonstration aims at showcasing an initial version of the DECENTER Brokerage Platform, which leverages an Ethereum blockchain to enable resource federation among different Fog Computing infrastructures. We consider a scenario where an Italian Infrastructure Provider wants to seamlessly extend its pool of resources to get access to an IP camera located in Korea, so that it can deploy an application to locally perform text recognition from a live video stream.
Smart Anti-Theft Door locking System

Privacy and security are two pivotal rights in day-to-day life. At present, keys, passwords and PIN's are used to secure the confidential data. However the above mentioned methods can be compromised and thus propose threats to security. This paper provides an advanced method to enhance the security system using face detection and recognition algorithms integrated with raspberry pi that is used to control the access to the door. Since face is indubitably related to an individual, it cannot be duplicated. This paper consists of three subsystems-Face detection, Feature extraction and Face recognition for door access. Initially the system is trained with authorized persons features, stored in the database. Firstly, the process is started by capturing the image of an object using raspberry pi camera followed by face detection done using Viola Jones algorithm as it provides a greater accuracy in real-time object detection. Next the feature extraction and face detection is done using Local Binary Pattern (LBP) algorithm that can extract local neighboring texture information of grey scale image and can efficiently differentiate between object and background. The extracted features are dimensionally reduced using Principal Component Analysis (PCA) algorithm .The detected face is compared against the stored features and if there is a match the access is provided to the authorized person. If not, the access to the door is denied and an alarm is raised alerting the admin.
DIGITIZATION OF HIGHER EDUCATION INSTITUTION

The article discusses the process of digitization of higher education institution on the example of Borys Grinchenko Kyiv University and analyzes the main aspects of digital campus implementation. The perspective world educational technologies are described which development influences changes of the concept of management in modern educational establishments and in the educational process as a whole. Also described the main legislative decisions that may serve as a basis for transforming a higher education institution into a «digital campus». Regulatory documents of Borys Grinchenko Kyiv University identified. The existing IT infrastructure and the solutions of Grinchenko University is analyzed that can be the basis for the introduction of a «digital campus». The main components of the «digital campus» considered, which correspond to the implemented best solutions in the European educational space and at the same time satisfy the needs of the university. Analyzed the innovative solutions for the implementation of «digital campus», such as Almus Net, Classter, LMS 365, TESLA EDU, SMART HCM&amp;LMS, ZEROBIT, and EXAMUS. The comparative analysis of the components of these solutions displayed for the construction of «digital campus». The main stages of implementation of the «digital campus» identified. Attention is paid to the fact that the construction of the digital campus should integrate the existing solutions as much as possible to ensure the comfort and convenience of participants in the educational process, implement new required solutions, each of which, thanks to unification into a single system, will ensure the functioning of a modern higher education institution, will work on its development and competitiveness. The created «digital campus» will ensure the functioning of the educational institution, the automation of all business processes, provide access to educational resources and facilitate successful management decisions.
Decision Support System for Ranking of Students in Learning Management System (LMS) Activities using Analytical Hierarchy Process (AHP) Method

Abstract Learning Management System (LMS) is part of E-Learning which is used as a means of online learning. However, the use of LMS is not in accordance with Minister of Education and Culture Regulation (PERMENDIKBUD) No 23 of 2016 which contains education assessment standards. This is because the LMS only focuses on the content that is used as a medium for student learning without any assessment or ranking given by the teacher. The purpose of this study is to provide an evaluation of the student learning process in the form of ranking students’ activities in LMS using the AHP (Analitycal Hierarchy Process) method so that student learning media is in accordance with PERMENDIKBUD No. 23 of 2016. The AHP method was chosen to assist teachers in ranking students so that it is not subjective. The method used can help teachers to determine student ranking from student activities that are ranked criteria in LMS. From the ranking criteria that are processed using the AHP method, the results obtained in the form of a ranking obtained by each student.
The potential effects of cryptocurrencies on monetary policy

All current cryptocurrencies are controlled by private entities, so that the issue of impact on monetary system becomes very important.Autonomous decisions by private entities concerning the money supply could diminish the ability of central banks to implement monetary policy effectively.The subject of this paper is the influence of alternative cryptocurrency forms on the monetary system.The aim of this paper is to determine the ability of central banks to conduct monetary policy successfully in conditions of widespread use of cryptocurrencies in payment transactions.The situation in the cryptocurrency market is compared with the phases of Internet development and the current situation on other markets of electronic payment systems.It is concluded that cryptocurrencies do not have the capacity to endanger the traditional monetary system at the current level.Bearing in mind the early maturity of this market, certain predictions of possible future trends have been made.In the case of private cryptocurrency usage growth, central banks could partially or completely lose influence over monetary policy.The proposed solution is the development of national cryptocurrencies that would ensure the retention of seigniorage to central banks and the prevention of further use of private cryptocurrencies in criminal activities.
Deep Text Summarization using Generative Adversarial Networks in Indian Languages

Abstractive Text Summarization (ATS) is a task of capturing information from different sources and condense it such that, content is represented well and there is no loss of information. It has been an active area of research for quiet sometime now. ATS is more closer to human generated summaries and have the capability of representing and combining multiple information. With advent of deep learning architectures, many tasks relating to natural language processing have achieved persistent and comparable high performances. It has proven advantageous and showed promising results in machine translation, speech recognition, image captioning and many others using sequence to sequence models. Language tools such as Part of Speech taggers, Named Entity Recognizer for Indian languages are not very competitive and hence, language specific techniques do not perform very well for Indian languages. Deep learning techniques are language agnostic and hence can overcome these shortcomings. In this paper, Generative Adversarial Networks(GAN(s)) are assimilated to create gist for longer piece of text in conjunction to paraphrase detection.
TR-GAN: thermal to RGB face synthesis with generative adversarial network for cross-modal face recognition

Unlike RBG cameras, thermal cameras perform well under very low lighting conditions and can capture information beyond the human visible spectrum. This provides many advantages for security and surveillance applications. However, performing face recognition tasks in the thermal domain is very challenging given the limited visual information embedded in thermal images and the inherent similarities among facial heat maps. Attempting to perform recognition across modalities, such as recognizing a face captured in the thermal domain given the corresponding visible light domain ground truth database or vice versa is also a challenge. In this paper, a Thermal to RGB Generative Adversarial Network (TRGAN) to automatically synthesize face images captured in the thermal domain, to their RBG counterparts, with a goal of reducing current inter-domain gaps and significantly improving cross-modal facial recognition capabilities is proposed. Experimental results on the TUFTS Face Dataset using the VGG-Face recognition model without retraining, demonstrates that performing image translation with the proposed TR-GAN model almost doubles the cross-modal recognition accuracy and also performs better than other state-of-the-art GAN models on the same task. The generator in our network uses a UNET like architecture with cascaded-in-cascaded blocks to reuse features from earlier convolutions, which helps generate high quality images. To further guide the generator to synthesize images with fine details, we optimize a training loss as the weighted sum of the perceptual, adversarial, and cycle-consistent loss. Simulation results demonstrate that the proposed model generates more realistic and more visually appealing images, with finer details and better reconstruction of intricate details such sunglasses and facial emotions, than similar GAN models.
A Neural Scaling Law from the Dimension of the Data Manifold

When data is plentiful, the loss achieved by well-trained neural networks scales as a power-law $L \propto N^{-α}$ in the number of network parameters $N$. This empirical scaling law holds for a wide variety of data modalities, and may persist over many orders of magnitude. The scaling law can be explained if neural models are effectively just performing regression on a data manifold of intrinsic dimension $d$. This simple theory predicts that the scaling exponents $α\approx 4/d$ for cross-entropy and mean-squared error losses. We confirm the theory by independently measuring the intrinsic dimension and the scaling exponents in a teacher/student framework, where we can study a variety of $d$ and $α$ by dialing the properties of random teacher networks. We also test the theory with CNN image classifiers on several datasets and with GPT-type language models.
Platinum: Reusing Constraint Solutions in Bounded Analysis of Relational Logic

Alloy is a lightweight specification language based on relational logic, with an analysis engine that relies on SAT solvers to automate bounded verification of specifications. In spite of its strengths, the reliance of the Alloy Analyzer on computationally heavy solvers means that it can take a significant amount of time to verify software properties, even within limited bounds. This challenge is exacerbated by the ever-evolving nature of complex software systems. This paper presents Platinum, a technique for efficient analysis of evolving Alloy specifications, that recognizes opportunities for constraint reduction and reuse of previously identified constraint solutions. The insight behind Platinum is that formula constraints recur often during the analysis of a single specification and across its revisions, and constraint solutions can be reused over sequences of analyses performed on evolving specifications. Our empirical results show that Platinum substantially reduces (by 66.4% on average) the analysis time required on specifications extracted from real-world software systems.
Analyzing CVE Database Using Unsupervised Topic Modelling

This paper describes our study of the vulnerability reports in the Common Vulnerability and Exposures (CVE) database by using topic modeling on the description texts of the vulnerabilities. Prevalent vulnerability types were found, and new trends of vulnerabilities were discovered by studying the 121,716 unique CVE entries that are reported from January 1999 to July 2019. The topics found through topic modeling were mapped to OWASP Top 10 vulnerabilities. It was found that the OWASP vulnerabilities A2: 2017-Broken Authentication, A4:2017-XML External Entities (XXE), and A5:2017-Broken Access Control increased, yet the vulnerability A7:2017-Cross-Site Scripting (XSS) had a steep decrease over the period of 20 years.
A Primer on Private Statistics

Differentially private statistical estimation has seen a flurry of developments over the last several years. Study has been divided into two schools of thought, focusing on empirical statistics versus population statistics. We suggest that these two lines of work are more similar than different by giving examples of methods that were initially framed for empirical statistics, but can be applied just as well to population statistics. We also provide a thorough coverage of recent work in this area.
Mesh-Structure-Enabled Programmable Multitask Photonic Signal Processor on a Silicon Chip

Photonic integrated circuits (PICs) have recently attracted extensive attention in advanced photonic signal processing to meet the ever-increasing demands on high-speed and ultracompact data signal management. However, programmable and multitask photonic signal processing is still full of challenges, especially the scalable photonic integration solution. Here, we design, fabricate, and demonstrate a mesh-structure-enabled programmable multitask photonic signal processor on a silicon chip. It relies on a scalable 2D mesh structure network with a number of hexagonal unit cells formed by building blocks of tunable Mach–Zehnder interferometers (MZIs). We study several simple and complex optical filtering functions using configured single ring resonator, cascaded ring resonators, ring-assisted MZI, cascaded MZIs, reconfigurable and tunable comb filter and (de)interleaver, and double-injection ring resonators. For the proof-of-concept demonstration of on-chip programmable multitask photonic signal processing, a monolithically integrated silicon chip with four hexagonal unit cells is fabricated with greatly reduced geometric dimension. By appropriately adjusting the thermo-optic phase shifters of MZIs, versatile programmable multitask photonic signal processing functions are demonstrated in the experiment with impressive performance, including single ring resonator, cascaded ring resonators, asymmetric MZI, ring-assisted MZI, optical delay line, multiport router, and N × N optical switch. In particular, we also demonstrate the optical interference unit (OIU)-enabled self-configurable router and switch and their practical applications in fiber-optic communication systems. The demonstrations may open up new perspectives for on-chip solutions to ultracompact, reconfigurable, programmable, and multifunctional data signal management in advanced optical communication networks.
Addressing Data Bias Problems for Chest X-ray Image Report Generation

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using two different word LSTMs in a hierarchical LSTM model. We conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation.
Quantum teleportation of single-electron states

We consider a scheme for on-demand teleportation of a dual-rail electron qubit state, based on single-electron sources and detectors. The scheme has a maximal efficiency of 25%, which is limited both by the shared entangled state as well as the Bell-state measurement. We consider two experimental implementations, realizable with current technology. The first relies on surface acoustic waves, where all the ingredients are readily available. The second is based on Lorentzian voltage pulses in quantum Hall edge channels. As single-electron detection is not yet experimentally established in these systems, we consider a tomographic detection of teleportation using current correlators up to (and including) third order. For both implementations, we take into account environmental effects.
BlockTrack-L: A Lightweight Blockchain-based Provenance Message Tracking in IoT

Data tracking is of great significance and a central part in digital forensics.In today's complex network design, Internet of Things (IoT) devices communicate with each other and require strong security mechanisms.In maintaining an audit trail of IoT devices or provenance of IoT device data, it is important to know the origins of requests to ensure certain level of trust in IoT data.Blockchain can provide traceability of records generated from IoT devices in a sensitive environment.In this paper, we present an application layer data provenance model that works on execute-order architecture for cloud based IoT networks.It supports high throughput of transactions on the blockchain network with lightweight security overhead by using outsourced encryption on edge nodes.All communications among the IoT devices are connected to a blockchain network and stored on permissioned blockchain peers.The proposed system is evaluated to have less cryptographic load by offloading the IoT nodes with Edge nodes.
Boundaries of quantum supremacy via random circuit sampling

Abstract Google’s quantum supremacy experiment heralded a transition point where quantum computers can evaluate a computational task, random circuit sampling, faster than classical supercomputers. We examine the constraints on the region of quantum advantage for quantum circuits with a larger number of qubits and gates than experimentally implemented. At near-term gate fidelities, we demonstrate that quantum supremacy is limited to circuits with a qubit count and circuit depth of a few hundred. Larger circuits encounter two distinct boundaries: a return of a classical advantage and practically infeasible quantum runtimes. Decreasing error rates cause the region of a quantum advantage to grow rapidly. At error rates required for early implementations of the surface code, the largest circuit size within the quantum supremacy regime coincides approximately with the smallest circuit size needed to implement error correction. Thus, the boundaries of quantum supremacy may fortuitously coincide with the advent of scalable, error-corrected quantum computing.
Development history and prospect of remote sensing technology in coal geology of China

Abstract Coal remote sensing technology was founded in the period of coal resources survey after the founding of People’s Republic of China. Aerophoto Grammetry &amp; Remote Sensing Bureau of China National Administration of Coal Geology was established, specializing in the application and promotion of coal remote sensing technology. With the rapid development of coal geological exploration in China, coal remote sensing technology has evolved from coal geology based survey to comprehensive survey that factors in resources, environment, ecology and so on. This paper summarizes the general situation, theories, development process, key research and future of remote sensing technology for coal mining in China. Spanning over 50 years, the history of China’s coal remote sensing technology can be divided into five stages: aero-geological mapping, coal remote sensing theory experimental research, application research and promotion, architecture planning and productionisation, “3S” technology integration and application. This paper expounds the main technical progress, application fields, major projects and major achievements in various historical periods, and points out that the coal remote sensing has entered a unified development stage of “Aviation, Aerospace and Ground”, with a focus on high-resolution remote sensing, hyperspectral remote sensing, radar remote sensing, “3S” technology integration and multi-means comprehensive exploration and evaluation. In the future, coal remote sensing technology will develop rapidly in data mass, technology integration, evaluation intelligence, integration application programming, system visualization, etc. Coal remote sensing technology has entered the industrial development from technology application.
Describing the public perception of chemistry on twitter

The public image of chemistry is a relevant issue for chemical stakeholders. It has been studied throughout history by means of document analysis and more recently through surveys. Twitter, a worldwide online social network, is based on spontaneous opinions. We tried to identify the public perception of chemistry on Twitter, what it explains, and which sentiments are perceived. We gathered 256 833 tweets between 1st January 2015 and 30th June 2015 containing the words “chemistry”, “chemical” or “chem”. We cleaned and filtered them down to 50 725 tweets with textual information in English and clustered them using spherical k-means. The resulting clusters were categorised according to six topics by 18 chemistry experts. The prevailing topics were the learning environment topic, related to activities and tasks in chemistry courses, and the human activity topic, referring to facts and news about the chemical industry. The scientific knowledge topic, concerning communication of chemistry knowledge, only accounted for a small percentage of the tweets. We classified the tweets of most relevant topics based on their sentiment values and obtained more positive than negative perceptions. Nevertheless, the analysis of the unigrams and bigrams word clouds revealed a significant presence of chemophobia-related terms in the human activity topic, both in positive and negative classified tweets. It also revealed specific elements of chemistry courses negatively perceived in the learning environment topic.
Foundations of Reversible Computation

Reversible computation allows computation to proceed not only in the standard, forward direction, but also backward, recovering past states. While reversible computation has attracted interest for its multiple applications, covering areas as different as low-power computing, simulation, robotics and debugging, such applications need to be supported by a clear understanding of the foundations of reversible computation. We report below on many threads of research in the area of foundations of reversible computing, giving particular emphasis to the results obtained in the framework of the European COST Action IC1405, entitled “Reversible Computation - Extending Horizons of Computing”, which took place in the years 2015–2019.