Reviewerly/exHarmony huggingface dataset:
Positive Impact of Red Soil on Albedo and the Annual Yield of Bifacial Photovoltaic Systems in Ghana

The annual yield of bifacial photovoltaic systems is highly dependent on the albedo of the underlying soil. There are currently no published data about the albedo of red soil in western Africa. In this study, the impact of the albedo of red soil in Ghana on the energy yield of bifacial photovoltaic systems is analysed. A bifacial photovoltaic simulation model is created by combining the optical view factor matrix with an electrical output simulation. For an exact simulation, the albedo of red soil at three different locations in Ghana is measured for the first time. The average albedo of every red soil is clearly determined, as well as the measurement span including instrumentation uncertainty; values between 0.175 and 0.335 were measured. Considering these data, a state-of-the-art bifacial photovoltaic system with an average of 19.8% efficient modules in northern Ghana can achieve an annual energy yield of 508.8 kWh/m2 and a bifacial gain of up to 18.3% in comparison with monofacial photovoltaic panels. To summarise, red soil in two out of three locations in Ghana shows higher albedo values than most natural ground surfaces and therefore positively impacts the annual yield of bifacial photovoltaic systems.
Distilling Monolingual Models from Large Multilingual Transformers

Although language modeling has been trending upwards steadily, models available for low-resourced languages are limited to large multilingual models such as mBERT and XLM-RoBERTa, which come with significant overheads for deployment vis-à-vis their model size, inference speeds, etc. We attempt to tackle this problem by proposing a novel methodology to apply knowledge distillation techniques to filter language-specific information from a large multilingual model into a small, fast monolingual model that can often outperform the teacher model. We demonstrate the viability of this methodology on two downstream tasks each for six languages. We further dive into the possible modifications to the basic setup for low-resourced languages by exploring ideas to tune the final vocabulary of the distilled models. Lastly, we perform a detailed ablation study to understand the different components of the setup better and find out what works best for the two under-resourced languages, Swahili and Slovene.
Touch Matters: The Impact of Physical Contact on Haptic Product Perception in Virtual Reality

Nowadays, the presentation of products through virtual reality and other online media coexists with traditional means. However, while some products may be perceived correctly in digital media, others may need physical contact. In this scenario, this work analyses how presenting a product highlighted for its haptic properties and the presence or absence of physical contact during the presentation can influence the perception of its attributes and stimulate purchase intention. To this end, an experiment was designed in which each participant viewed and interacted with a chair presented in five different means that elicited a greater or lesser sense of presence. Participants evaluated the product’s attributes on a semantic scale with bipolar pairs. No relation was found between the presentation means and users’ purchase intention. However, results showed significant differences in the evaluation of some physical characteristics depending on the presentation means, and the product was generally more liked when presented in means in which it could be touched. We conclude that choosing means that allow a product to be touched and elicit a greater sense of presence may impact more positively on evaluations of haptic features when presenting a product with high haptic importance.
Dataset of Public Objects in Uncontrolled Environment for Navigation Aiding

Computer vision is a new approach to navigation aiding that assists visually impaired people to travel independently. A deep learning-based solution implemented on a portable device that uses a monocular camera to capture public objects could be a low-cost and handy navigation aid. By recognizing public objects in the street and estimating their distance from the user, visually impaired people are able to avoid obstacles in the outdoor environment and walk safely. In this paper, we created a dataset of public objects in an uncontrolled environment for navigation aiding. The dataset contains three classes of objects which commonly exist on pavements in the city. It was verified that the dataset was of high quality for object detection and distance estimation, and was ultimately utilized as a navigation aid solution.
Multi-view contrastive learning for multilayer network embedding

Graph embedding aims to learn low-dimension node representations which can be applied to various downstream tasks, such as node clustering and node classification. When it comes to heterogeneous graphs, things get more challenging as the graph contains richer semantic and structural information. Most existing heterogeneous graph embedding methods usually convert the graph into several homogenous graphs, and then use the manual label information to train the model. However, Graph neural networks (GNNs) mostly require task-dependent labels to learn representations, which is even more costly for heterogeneous graphs. Furthermore, these models treat each converted homogenous graph independently while ignoring the inter-layer informative relational clues among the multi-graphs. Inspired by recent advances of Deep Graph Infomax and multi-view learning in homogeneous graph embedding, we propose an unsupervised graph neural network called CL-MNE that adopts multi-view contrastive learning and multilayer information fusion for heterogeneous graph embedding. The proposed model learns rich local and global graph information by maximizing the local-global mutual information from adjacency matrix and diffusion matrix two different views. Moreover, a variant model CL-MNEreg, which provides embedding information of all graph layers, is constructed by addingg a consensus regularization to the CL-MNE. Experiments of node clustering and classification tasks on mainstream datasets demonstrate the effectiveness of the proposed two models.
Fractal video compression for IOT-based smart cities applications using motion vector estimation

Recently, Internet of the things (IoTs) devices have been used highly in surveillance applications in smart environments, cities, and buildings. Most of the surveillance data recorded by the IoT devices is in the form of video and needs to be transmitted to the admin or server by video streaming technology. Fractal video compression uses a self-similarity concept for video compression, meaning the fractal image contains self-similarity of itself that is explained and denoted by change. Computational cost is more in fractal video coding; different methods have been developed to reduce this computational cost. The proposed work's main objective is to implement a video streaming approach by combining the Diamond-search-pattern- block-matching motion estimation algorithm and hash-based fractal video compression algorithm to reduce the encoding time. It improves both the delay and security of the block matching system and the developed approach measures based on evaluation parameters: Peak signal-to-noise ratio (PSNR), encoding time, decoding time, Mean squared error (MSE), and Compression ratio for Smart City Applications. Compared to other algorithms, the IoTs video data is streamed efficiently in a smart city to analyse the abnormalities.
Helper objective-based multifactorial evolutionary algorithm for continuous optimization

Searching for global optima is challenging for complex optimization problems since multi-modality commonly exists. To address this issue, researchers have made many attempts, including high-performance initializing methods, novel variant operators and multimodal-based environmental selection strategies. However, such approaches are highly sensitive to the parameters or their implementation is complex. To this end, in this work, a helper problem (objective function) is proposed to assist evolutionary algorithms (EAs) in dealing with complex optimization problems, where the multitask optimization framework is adopted to transfer knowledge between the helper and the original problems. Specifically, the helper objective is a smoother fit of the original problem, which leads EAs to concentrate on the global optima of the original problem. Then, the helper objective-assisted multifactorial differential evolutionary algorithm is proposed, termed h-MFDE. Experimental results show that h-MFDE is competitive in finding the global optimal on complex optimization problems comparing with other state-of-the-art EAs.
Photovoltaic Power Prediction Based on EMD-BLS Model

Abstract While the energy consumption structure is changing in this world, the consumption rate of renewable energy is gradually increasing. This paper takes photovoltaic power as the research object, analyses the power curve characteristics in different climatic change situation and the correlation between different weather factors and photovoltaic(PV) power output, and then puts forward a photovoltaic power prediction model based on empirical mode decomposition-broad learning system (EMD-BLS). First, the PV power historical sequence needs to be reconstructed after pretreatment, then the EMD method is used to decompose the reconstructed output sequence. The broad learning system model is established for each subsequence. Finally, the prediction results of each sub-series are superimposed to get the prediction of photovoltaic generation. The model is tested with a genuine data of photovoltaic power generation in a region of our country. Compared with the traditional broad learning system (BLS), extreme learning machine (ELM), LSTM, and other prediction models, The prediction error of the proposed model is lower, which can effectively improve the prediction accuracy of photovoltaic power generation.
Algebraic and quantum attacks on two digital signature schemes

Abstract In this article, we analyze two digital signature schemes, proposed in Moldovyan et al., that use finite noncommutative associative algebras as underlying platforms. We prove that these schemes do not possess the claimed property of being quantum safe. We also show that in many cases these schemes are, in fact, vulnerable to “classical” algebraic cryptanalysis.
A Study on Threat Analysis and Risk Assessment Based on the “Asset Container” Method and CWSS

In recent years, legislation and standardization of cyber security management for cyber-physical systems such as automotive systems have been progressing steadily. ISO/SAE 21434, published in 2021, addresses the management and analysis of electrical systems within road vehicles from a cybersecurity perspective. It also recommends some methods for the threat analysis and risk assessment (TARA) process. However, there are two problems in the evaluation methods derived from conventional security analysis approaches. One problem is related to the insufficient evaluation of attack feasibilities for cyber-physical systems by the CVSS-based approach. Another problem is the unclear relationship between damage factors in analyzing the impact of damage to each asset. In this paper, we focus on the TARA process, and apply an “asset container” method for threat classification, proposed by the authors at DECSoS 2017, and a CWSS-based risk quantification method. Moreover, we can also add some perspective to improve risk evaluation suitable for automotive systems. Following our past studies on methodologies to evaluate the risk of such special cyber-physical systems, we can quantify risks limited to some cyber-physical systems, such as direct access attacks to in-vehicle networks.
Analyses of Tabular AlphaZero on Strongly-Solved Stochastic Games

The AlphaZero algorithm achieved superhuman levels of play in chess, shogi, and Go by learning without domain-specific knowledge except for game rules. This paper targets stochastic games and investigates whether AlphaZero can learn theoretical values and optimal play. Since the theoretical values of stochastic games are expected win rates, not a simple win, loss, or draw, it is worth investigating the ability of AlphaZero to approximate expected win rates of positions. This paper also thoroughly studies how AlphaZero is influenced by hyper-parameters and some implementation details. The analyses are mainly based on AlphaZero learning with lookup tables. Deep neural networks (DNNs) like the ones in the original AlphaZero are also experimented and compared. The tested stochastic games include reduced and strongly-solved variants of Chinese dark chess and EinStein würfelt nicht!. The experiments showed that AlphaZero could learn policies that play almost optimally against the optimal player and could learn values accurately. In more detail, such good results were achieved by different hyper-parameter settings in a wide range, though it was observed that games on larger scales tended to have a little narrower range of proper hyper-parameters. In addition, the results of learning with DNNs were similar to lookup tables.
About the experience of using gamification in higher education on the example of teaching mathematical disciplines

The article deals with the issue of improving the quality of education at the university through the use of gamification methods in the process of teaching mathematical disciplines. The principles of gaming activity, its structural components, cases of gamification application are highlighted, the classification of games is given, pedagogic conditions are formulated, as well as a number of requirements for conducting a didactic game. The article presents the authors’ educational and methodological kit of the game, tested on students of the training direction of Tourism at Kostroma State University.
A Medical Assistant for the Visually Impaired

A visually impaired individual loses vision when a part of the eye or the brain that processes images becomes diseased or damaged. Visually impaired people face many problems during their daily activities such as walking around places, identifying objects, identifying people's feelings or emotions, detecting obstacles, etc. Several solutions work around these problems. The area that needs the attention of researchers is helping the visually impaired people in reading the medical reports, a product's name, and a device's reading. To make them independent, this paper proposed a medical assistant “M.A.V.I” which helps a visually impaired person to read medical blood reports, products, normal reports, and LED screens of inaccessible medical devices like a glucose monitor, BP machine, and weighing machine, etc.
Dual-server certificateless public key encryption with authorized equality test for outsourced IoT data

The Internet of Things (IoT) devices with restricted storage and computing sources are unable to store and handle massive data at local. A promising method is to engage some cloud server for maintaining outsourced encrypted IoT data and responding user request, in the meantime, the privacy of outsourced data can be protected. Existing encryption techniques with equality test allow the authorized cloud server to compare encrypted data and perform data classification. However, these schemes in the single server model cannot resist internal keyword guessing attacks. Also, the existing schemes in public key setting confront the burden of maintaining public key certificates, and the ones in identity-based setting face the key escrow problem. To address these issues, this paper proposes a dual-server certificateless public key encryption scheme supporting authorized equality test (DS-CL-AET) in bilinear groups. Security analysis indicates that the proposed DS-CL-AET scheme offers confidentiality protection for outsourced ciphertext and authentication tokens. Performance analysis demonstrates the practicality of our DS-CL-AET scheme in securing outsourced IoT data in clouds.
Computer-Assisted Corpus Analysis: An Introduction to Concepts, Processes, and Decisions

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Problem:</b> This tutorial aims to guide readers through key concepts, basic processes, and common decision points that inform computer-assisted corpus-based research in technical, professional, and scientific communication (TPSC). <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Key concepts:</b> Based on our collaborative experiences and an example developed for this tutorial, key concepts of corpus analysis useful to TPSC researchers and practitioners include the following: corpus location, text preparation, and programming language and software selection. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Key lessons:</b> These key concepts can be used to establish basic processes and decision points that, in turn, yield lessons related to the usefulness of lexicogrammatical language models and the significance of multidisciplinarity. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Implications:</b> Although corpus research is a growing and important part of the field of TPSC, challenges remain in terms of language model variety and ethical considerations. At least in part, these challenges can be met, respectively, by alignment between corpus and analytic tools and reference to the Common Rule and related international standards.
VISAS - Detecting GPS spoofing attacks against drones by analyzing camera's video stream

In this study, we propose an innovative method for the real-time detection of GPS spoofing attacks targeting drones, based on the video stream captured by a drone's camera.The proposed method collects frames from the video stream and their location (GPS); by calculating the correlation between each frame, our method can detect a GPS spoofing on a drone.We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed.Then, we analyze its performance in the real world using a DJI drone.Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at an altitude of 50-100 meters over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1-4 meters (an average of 2.5 meters) from the real location.
Utilizing Voice Assistance and Wearable Interactive Mixed Reality Solutions for Industrial Training Workflows

Field workers under training often need assistance locating, recognizing, and familiarizing themselves with different machines and equipment. Whenever further guidance is needed, an expert is expected to assist using visual and auditory means. Thus, we employed an intelligent voice assistant system by integrating Augmented Reality (AR) and Natural Language Processing (NLP) methods to alleviate workflows and construct an interactive approach for field workers in training or practice. Microsoft HoloLens 2 was used as the Head Mounted Display (HMDs) to overlay necessary AR visuals and speech synthesis for user assistance. for its implementation, the multinomial Naive Bayes classification algorithm was performed at different sequential run-times throughout a standard operating procedure. This resulted in preserving training speeds and maintaining high accuracy when each and any unique attribute of a mechanical part is uttered. As the objective is to design an interactive training solution, further development in this research can be made by enhancing the user interface, interaction, and overall experience.
Rehabilitation Training Evaluation and Correction System Based on BlazePose

We apply computer vision to rehabilitation training in medicine. According to the survey, many diseases require rehabilitation training after treatment. However, due to reasons such as a lack of relevant knowledge of patients and large differences in patients’ physical condition, it is difficult for patients to perform rehabilitation training correctly. Improper rehabilitation training prolongs the recovery time and causes secondary injury or even leaves sequelae in serious cases. Rehabilitation training at this stage relies on the doctor’s instruction, ignoring the problem that the patient’s movements are deformed and unguided during the rehabilitation training process. The proposed system corrects the user’s movements during the rehabilitation training process, monitors the user’s heart rate in real-time, and suggests a way of improvement and training. It also provides users with professional and personalized rehabilitation training assistance.
Solving NP-hard Problems with Quantum Annealing

Quadratic unconstrained binary optimization (QUBO) formulas of quantum annealing (QA) algorithms are classified into four categories. QA algorithms using different QUBO formulas solve specific NP-hard problems as examples of the classification. The NP-hard problems solved are the subset sum, the vertex cover, the graph coloring, and the traveling salesperson problems. The QA algorithms are compared with their classical counterparts in terms of the quality of the solution and the time to the solution. Based on the comparison results, observations and suggestions are given for each QUBO formula category, so that proper actions can be adopted to improve the performance of QA algorithms. Compared with classical algorithms, QA algorithms are competitive in the current noisy intermediate-scale quantum (NISQ) era and beyond.
Quantum Circuit Based on Grover Algorithm to Solve Hamiltonian Cycle Problem

We propose the concepts of the explicit oracle and the implicit oracle for realizing quantum algorithms. Then, the quantum circuit of the well-known Grover algorithm is constructed with the explicit oracle to solve the Hamiltonian cycle problem for the complete graph. The quantum circuit has a quadratic speedup over the classical unstructured search algorithm for solving the same problem. The IBM quantum computer simulator is used to run the quantum circuit to validate that it can indeed derive the Hamiltonian cycle of the complete graph.