Reviewerly/exHarmony huggingface dataset:
Virtual Reality as a Context for Adaptation

The COVID-19 pandemic has accelerated interest in virtual reality (VR) for education, entertainment, telerehabilitation, and skills training. As the frequency and duration of VR engagement increases—the number of people in the United States using VR at least once per month is forecasted to exceed 95 million—it is critical to understand how VR engagement influences brain and behavior. Here, we evaluate neurophysiological effects of sensory conflicts induced by VR engagement and posit an intriguing hypothesis: the brain processes VR as a unique “context” leading to the formation and maintenance of independent sensorimotor representations. We discuss known VR-induced sensorimotor adaptations to illustrate how VR might manifest as a context for learning and how technological and human factors might mediate the context-dependency of sensorimotor representations learned in VR.
A Secured Quantum Key Exchange Algorithm using Fermat Numbers and DNA Encoding

To address the concerns posed by certain security attacks on communication protocol, this paper proposes a Quantum Key Exchange algorithm coupled with an encoding scheme based on Fermat Numbers and DNA sequences. The concept of Watson-Crick’s transformation of DNA sequences and random property of the Fermat Numbers is applied for protection of the communication system by means of dual encryption. The key generation procedure is governed by a quantum bit rotation mechanism. The total process is illustrated with an example. Also, security analysis of the encryption and decryption process is also discussed.
Robot navigation path planning in power plant based on improved wolf pack algorithm

Substation is an important part of power system, and regular inspection is very important to ensure the safe operation of substation. Patrol robot can patrol power equipment in few or unattended substations. In order to obtain the global optimal path of the robot, this paper proposes a navigation path planning based on improved wolf pack algorithm (IWPA). Firstly, the path optimization in discrete domain is studied by introducing position-order coding in initialization stage. Secondly, improve the moving step of the attack behavior and improve the efficiency of the running behavior. Finally, the updating rules of IWPA increase the diversity of wolves to enhance the global optimization ability of the algorithm. Finally, both IWPA and Wolf Pack Algorithm (WPA) are simulated, and the results show that the improved algorithm is more effective in solving the navigation path planning problem of inspection robot.
Image difference metrics for high-resolution electron microscopy

Digital image comparison and matching brings many advantages over the traditional subjective human comparison, including speed and reproducibility. Despite the existence of an abundance of image difference metrics, most of them are not suited for high-resolution transmission electron microscopy (HRTEM) images. In this work we adopt two image difference metrics not widely used for TEM images. We compare them to subjective evaluation and to the mean squared error in regards to their behaviour regarding image noise pollution. Finally, the methods are applied to and tested by the task of determining precipitate sizes of a model material.
DoMoBOT: A Modelling Bot for Automated and Traceable Domain Modelling

In the initial phases of the software development cycle, domain modelling is typically performed to transform informal requirements expressed in natural language into concise and analyzable domain models. These models capture the key concepts of an application domain and their relationships in the form of class diagrams. Building domain models manually is often a time-consuming and labor-intensive task. The current approaches which aim to extract domain models automatically, are inadequate in providing insights into the modelling decisions taken by extractor systems. This inhibits modellers to quickly confirm the completeness and conciseness of extracted domain models. To address these challenges, we present DoMoBOT, a domain modelling bot that uses a traceability knowledge graph to enable traceability of modelling decisions from extracted domain model elements to requirements and vice-versa. In this tool demo paper, we showcase how the implementation and architecture of DoMoBOT facilitate modellers to extract domain models and gain insights into the modelling decisions taken by our bot.
An ADC Based Random Number Generator from a Discrete Time Chaotic Map

This paper introduces a robust random number generator that based on Bernoulli discrete chaotic map. An eight bit SAR ADC is used with discrete time chaotic map to generate random bit sequences. Compared to RNGs that use the continuous time chaotic map, sensitivity to process, voltage and temperature (PVT) variations are reduced. Thanks to utilizing switch capacitor circuits to implement Bernoulli chaotic map equations, power consumption decreased significantly. Proposed design that has a throughput of 500 Kbit/second is implemented in TSMC 180 nm process technology. Generated bit sequences has successfully passed all four primary tests of FIPS-140-2 test suite and all tests of NIST 820–22 test suite without post processing. Furthermore, data rate can be increased by sacrificing power consumption. Hence, proposed architecture could be utilized in high speed cryptography applications.
Physicochemical Studies for Risk Identification, Assessment, and Characterization of Artisanal Barite Mining in Nigeria

Over 90% of barite mining activities in Nigeria are carried out by artisanal and small-scale miners (ASMs), and up to 75% of these miners operate informally, without mining license and rights. Such mining activities endanger human lives through the uncontrolled release of toxic heavy metals and other pollutants which are major causes and consequences of severe health hazards in children and adults living close to the mining sites. This paper is in two parts. The first section assesses the extent of heavy metal contamination of Cd, Cu, Fe, Pb, Ba, and Zn in drinking water by the mine water and tailing effluents. The second section estimates heavy metals’ toxicity and exposure level and analyzes and characterizes other human health risks in tailing effluents selected from three barite mining sites in Nigeria. Inductively coupled plasma mass spectroscopy (ICP-MS) results show that the concentrations of Ba and Pb among other heavy metals are above the allowable limits for drinking water. Index of geo-accumulation (Igeo) and contamination factor (CF) for Ba, Fe, and Pb were classified as moderately to extremely polluted (based on the Igeo) and highly contaminated (based on the CF). The calculated non-carcinogenic risk for Ba is 0.87 and 0.99 for Pb. HQ/HI (health quotient/health index) for Zn, Cu, and Fe is ≤0.005. The results indicate that some precautionary measures should be taken to avert the non-carcinogenic risk of Ba and Pb. It is important that barite mining is carried out in a responsible manner, respecting local and national mining laws and global environmental standards.
Automatic Symptom Extraction from Unstructured Web Data for Designing Healthcare Systems

The Web pages provide unlimited source of information. The information available online is helpful in designing various systems, especially in medical domain. The unstructured form of data has to be extracted and processed to suit it to our requirements. The proposed system aims to extract the symptoms for diseases from medical websites. The symptom information extracted for diseases forms the basis of designing a Clinical Decision Support System. Popular medical websites are crawled to extract symptoms for the pre-defined diseases. The html pages are converted into a tree-like structure for efficient parsing and identification of the data-rich region. NLP Techniques are applied on the extracted data for processing. The novel approach in the proposed system is the validation of the symptoms extracted from Web pages with the medical vocabulary available in SNOMED-CT. The initial work of the proposed system has been tested for some common diseases information crawled from some popular websites (framework for many diseases).
Age, Gender and Ethnicity Classification from Face Images with CNN-Based Features

The problem of estimating age, gender and ethnicity from human images is encountered in many areas today, where human-computer interaction has increased considerably. These problems can be quite challenging since labels containing personal data are not found in many data sets. By using the feature maps extracted with the convolutional neural networks (CNN) model we prepared in this study, hybrid models were created with CNN, support vector machines (SVM), random forests (RF), and eXtreme gradient boosting (XGBoost) algorithms; Age, gender, and ethnicity classifications were made and the results were compared. In the experimental results, close results were obtained from the hybrid models and it was observed that they outperformed the pure CNN classifier.
Predictive Privacy: Towards an Applied Ethics of Data Analytics

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective mat- ter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.
Neural coreference resolution for Slovene language

Coreference resolution systems aim to recognize and cluster together mentions of the same underlying entity. While there exist large amounts of research on broadly spoken languages such as English and Chinese, research on coreference in other languages is comparably scarce. In this work we first present SentiCoref 1.0 - a coreference resolution dataset for Slovene language that is comparable to English-based corpora. Further, we conduct a series of analyses using various complex models that range from simple linear models to current state-of-the-art deep neural coreference approaches leveraging pre-trained contextual embeddings. Apart from SentiCoref, we evaluate models also on a smaller coref149 Slovene dataset to justify the creation of a new corpus. We investigate robustness of the models using cross-domain data and data augmentations. Models using contextual embeddings achieve the best results - up to 0.92 average F1 score for the SentiCoref dataset. Cross-domain experiments indicate that SentiCoref allows the models to learn more general patterns, which enables them to outperform models, learned on coref149 only.
Equivalence of Rainfall Type and Maximum Discharge to the Drainage Channel Capacity

This research will identify the equivalence of the serial rainfall to the design flood. The equivalence of serial rainfall data is very necessary to solve the flooding problem. A case study is in the University of Brawijaya-Malang-East Java Province-Indonesia. The methodology consists of analyzing the design flood using the Nakayasu Synthetic Unit Hydrograph and then analyzing the equivalence of cumulative serial rainfall to the design flood. The equivalence of rainfall in this research discusses two items: the rainfall equivalence to the hydraulic and hydrologic conditions. Based on the hydraulic condition, the capacity of the drainage channel can store the rainfall for 85.77 mm for the return period of 2 years; 105.86 mm for the return period of 5 years; and 119.26 mm for the return period of 10 years. However, based on the hydrologic condition, for the design flood with the return period of 2 years is 382.25 m3/s, and it has the equivalence close to the discharged recording of AWLR Gadang that is 386.76 m3/s which is due to the rainfall for 11 mm (the category of heavy rain); for the return period of 5 years, the design flood is 471.07 m3/s, and it is equivalent with the flood discharge of 463.73 m3/s that is caused by the rainfall of 12.1 mm (the category of heavy rain); for the return period of 10 years, the design flood is 589.99 m3/s, and it is caused by the rainfall of 13.4 mm (the category of heavy rain). Based on the hydraulic and hydrologic condition, the capacity of the drainage channel that is installed in the campus region of Brawijaya University, it can be concluded that for the return period of 2 years, the drainage channel is only able to reduce 41% of flood volume; for the return period of 5 years, the drainage channel is only able to reduce 26% of flood volume; however, for the return period of 10 years, the drainage channel is only able to reduce 23% of flood volume.
COVID-19 Face Mask Detection Using Deep Learning

Corona Virus Disease (COVID-19) pandemic is causing a health crisis all over the world. One of the key weapons against Corona virus is wearing a face mask. In this paper, the automatic face mask detection system is presented using deep learning approach. It is used by authorities to check and monitor face mask wearing by civilians in various places like traffic signals, parks, cinema theatres and in other embedded systems. This system assists them to evaluate, track, mitigate and prevent COVID-19 spread. The face mask detection model is developed with a pre-trained Convolutional Neural network (CNN) model. Initially model is trained for detecting human face and in the later stage it will detect the mask. Exhaustive experimentation is carried out with the collection of datasets from Bing search API, Kaggle and RMFD and achieved a training accuracy of 98.6% and validation accuracy of 95.7%. Thus, the proposed system has successfully detected face mask and otherwise too.
The Object According State Prediction to Diagnostic Data

Abstract The predicting methodology the state of the object based on diagnostic data is considered. With the selected parameter that determines the state of the object, it is measured in real time at a fixed sampling step. According to the measurement data, the value of this parameter is predicted in the future. This operation is implemented by an extrapolator of the l order - a l degree polynomial, built using the least squares method based on the previous measurements results. The changing process model of the diagnosed parameter is a random time function described by the stationary centered random component sum and a mathematical expectation deterministic change. The estimating prediction error method and the extrapolator parameters influence on its value are presented.
Performance Comparison of Convolutional Neural Network Models for Object Detection in Tethered Balloon Imagery

The growing field of remote sensing requires object detection solutions. This work compares the performance of different neural networks for detecting vehicle, person, and boat objects in tethered balloon imagery. Four state-of-the-art architectures were chosen – Faster R-CNN, SSD, Retinanet, and YOLO. For training, the algorithms apply transfer learning, backbone variation, and hyper-parameter tuning. The evaluation utilizes the mean average precision and the inference time as metrics. The YOLOv4 416x model achieved the best trade-off between speed and accuracy with a mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> of 90.3%.
Blockchain Tokenisation in Grant Management

Grant management is a vital process that enables organisations to successfully deliver programmes that address social, economic, environmental, and other challenges. Globally, grant transfers represent significant sums, and their management is fraught with deficiencies that undermine the impact of the donated funds. Among the most urgent challenges in grant management are to improve transparency and reduce high administration costs. Digital transformation has done much to enhance efficiency, transparency, and accountability in grant administration; however, numerous shortcomings still exist. Blockchain, smart contracts, and tokenisation can be combined to achieve greater levels of efficiency and control in grant management than is currently possible. Through the study of fundamental blockchain concepts, the authors propose a grant-management solution that tokenises every expenditure item and uses smart contracts to automate payments, reporting, and budgetary compliance.
The iTHRIV Commons: a cross-institution information and health research data sharing architecture and web application

The integrated Translational Health Research Institute of Virginia (iTHRIV) aims to develop an information architecture to support data workflows throughout the research lifecycle for cross-state teams of translational researchers.The iTHRIV Commons is a cross-state harmonized infrastructure supporting resource discovery, targeted consultations, and research data workflows. As the front end to the iTHRIV Commons, the iTHRIV Research Concierge Portal supports federated login, personalized views, and secure interactions with objects in the ITHRIV Commons federation. The canonical use-case for the iTHRIV Commons involves an authenticated user connected to their respective high-security institutional network, accessing the iTHRIV Research Concierge Portal web application on their browser, and interfacing with multi-component iTHRIV Commons Landing Services installed behind the firewall at each participating institution.The iTHRIV Commons provides a technical framework, including both hardware and software resources located in the cloud and across partner institutions, that establishes standard representation of research objects, and applies local data governance rules to enable access to resources from a variety of stakeholders, both contributing and consuming.The launch of the Commons API service at partner sites and the addition of a public view of nonrestricted objects will remove barriers to data access for cross-state research teams while supporting compliance and the secure use of data.The secure architecture, distributed APIs, and harmonized metadata of the iTHRIV Commons provide a methodology for compliant information and data sharing that can advance research productivity at Hub sites across the CTSA network.
An Overview of 51% Attack Over Bitcoin Network

Cryptocurrencies are a new paradigm in terms of digital currency due to various features but mostly because of secure in nature and provide anonymity. Bitcoins are the most prominent example of cryptocurrency based on the proof of work (PoW) consensus mechanism. Bitcoins have been initiated as the exchangeable currency in some parts of the world, but the question arises that what are the major challenges that Bitcoins face even if it is secure in nature. The paper highlights the important attributes of a Bitcoin network, how the mining of the Bitcoin is done, and the consensus protocol on which Bitcoins are based on. It also gives insight into the impact of the “51%” attack on the Bitcoin network and the countermeasures that can be applied as a remedial solution to prevent such an attack.
Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networks

Despite the recent successes in computer vision, there remain new avenues to explore. In this work, we propose a new dataset to investigate the effect of self-occlusion on deep neural networks. With TEOS (The Effect of Self-Occlusion), we propose a 3D blocks world dataset that focuses on the geometric shape of 3D objects and their omnipresent self-occlusion. We designed TEOS to investigate the role of self-occlusion in the context of object classification. In the real-world, self-occlusion of 3D objects still presents significant challenges for deep learning approaches. However, humans deal with this by deploying complex strategies, for instance, by changing the viewpoint or manipulating the scene to gather necessary information. With TEOS, we present a dataset with two subsets (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ), containing 36 and 12 objects, respectively. We provide 768 uniformly sampled views of each object, their mask, object and camera position, orientation, amount of self-occlusion, as well as the CAD model of each object. We present baseline evaluations with five well-known classification deep neural networks and show that TEOS poses a significant challenge for all of them. The dataset, as well as the pre-trained models, are made publicly available for the scientific community under https: //data.nvision.eecs.yorku.ca/TEOS.
Formulation and Solution of the Multi-agent Concurrent Search and Rescue Problem

In this paper we formulate and solve the concurrent multi-agent search and rescue problem (C-SARP), where a multi-agent system is to concurrently search an area and assist the victims found during the search. It is widely believed that a UAV-system can help saving lives by locating and assisting victims over large inaccessible areas in the initial stages after a disaster, such as an earthquake, flood, or plane crash. In such a scenario, a natural objective is to minimize the loss of lives. Therefore, two types of uncertainties needs to be taken into account, the uncertainty in position of the victims, and the uncertainty in health over time. It is rational to start looking where victims are most likely to be found, such as the reported position of a victim in a life boat with access to a radio, but it is also rational to start looking where loss of lives is most likely to occur, such as the uncertain position of victims swimming in cold water. We show that the proposed C-SARP is NP-hard, and that the two elements of search and rescue should not be decoupled, making C-SARP substantially different from previously studied multi agent problems, including coverage, multi agent travelling salesmen problems and earlier studies of decoupled search and rescue. Finally, we provide an experimental comparison between the most promising algorithms used in the literature to address similar problems, and find that the solutions to the C-SARP reproduce the trajectories recommended in search and rescue manuals for simple problems, but outperform those trajectories in terms of expected survivability for more complex scenarios.