Reviewerly/exHarmony huggingface dataset:
The arrangement of on-line training of master students, majoring in Mathematics for internship in technical universities

Abstract This article looks into the issue of online-training of master students, majoring in Mathematics for internship in technical universities. This study is focused on arranging students' learning activities with the help of an on-line course “Methods for teaching Mathematics to students in technical universities”. The study considers the issues that students face during the internship in technical universities and debates a possibility to factor in the students’ needs while designing the course content. The present study gives a description of the activities that students do while working with the course materials, requirements, and recommendations on facilitating the learning process through this course. Active participation of students and their contribution to discussing the course, its content and the facilitation of the learning process presume that introduction of the on-line course and its integration into the program of training master students enhances their readiness for internship.
Detection and Localization of Facial Expression Manipulations

Concerns regarding the wide-spread use of forged images and videos in social media necessitate precise detection of such fraud. Facial manipulations can be created by Identity swap (DeepFake) or Expression swap. Contrary to the identity swap, which can easily be detected with novel deepfake detection methods, expression swap detection has not yet been addressed extensively. The importance of facial expressions in inter-person communication is known. Consequently, it is important to develop methods that can detect and localize manipulations in facial expressions.To this end, we present a novel framework to exploit the underlying feature representations of facial expressions learned from expression recognition models to identify the manipulated features. Using discriminative feature maps extracted from a facial expression recognition framework, our manipulation detector is able to localize the manipulated regions of input images and videos. On the Face2Face dataset, (abundant expression manipulation), and Neural-Textures dataset (facial expressions manipulation corresponding to the mouth regions), our method achieves higher accuracy for both classification and localization of manipulations compared to state-of-the-art methods. Furthermore, we demonstrate that our method performs at-par with the state-of-the-art methods in cases where the expression is not manipulated, but rather the identity is changed, leading to a generalized approach for facial manipulation detection.
On producing energy-efficient and contrast-enhanced images for OLED-based mobile devices

Organic Light-Emitting Diode (OLED) display panels have become increasingly popular in recent years due to their numerous advantages over traditional LCDs. The power consumption in OLED based displays highly depends on the displayed contents. This paper utilizes the image-dependent power-consuming quality of OLED displays to produce energy-efficient and contrast-enhanced images that consume less power and have better visual quality compared to their original form when displayed on an OLED device. The goal of reducing the power consumption in images is achieved by lowering the RGB intensity levels in color images. This idea is inspired by the “Land-Effect”, which reveals that a full-color image can be generated by using only two color components instead of three. However, in this work, a modified Land-Effect has been proposed to yield better results as compared to the original Land-Effect. The proposed method also uses some image enhancement algorithms such as white balance and retinex filter to produce quality enhanced images that have visually better color contrasts as compared to the original images. The real power consumption of the modified images was measured on Samsung Galaxy A50, an OLED-based mobile device. The experimental results demonstrated that the proposed method reduced the power consumption by 13.16% on average. Moreover, to assess the visual quality of images and to understand the user acceptability of the modified images, a subjective evaluation was also performed. The subjective test results showed a higher preference rate for the modified images as compared to the original images. The proposed system in its current form is suitable for server-side implementation where an image would be modified once and the modified image could be used indefinitely by millions of users on social media platforms.
On the Social Shaping of Quantum Technologies: An Analysis of Emerging Expectations Through Grant Proposals from 2002–2020

Abstract The term ‘quantum technology’ was first popularised by an Australian physicist in the mid-1990s. These technologies make use of the properties of quantum physics and are being developed and invested across the world, yet this emerging technology is understudied in science and technology studies. This article investigates the emergence of the notion of ‘quantum technologies’ and examines the expectations shaping this field through an analysis of research grants funded by a national research funder, the Australian Research Council between 2002 and 2020. I examine how ‘quantum technology’ and ‘quantum computing’ have come to dominate claims and expectations surrounding research in quantum science. These expectations do more than inform the scientific goals of the field. They also provide an overarching, uniting rhetoric for individual projects and people and shape the uses imagined for quantum technologies. This analysis shows how claims for this emerging technology draw on ‘breakthrough’ metaphors to engage researchers and marshal investment and concludes by highlighting the need for increased clarity regarding expectations for quantum technologies.
Solving Non-image Learning Problems by Mapping to Images

Transforming non-image Machine Learning (ML) problems into the image recognition problems, opens an opportunity to solve these problems, by powerful deep learning algorithms. This paper proposes a new CPC-R algorithm, which converts non-image data into images, by visualizing non-image data. Then several deep learning CNN algorithms are exploited to solve the learning problems. The CPC-R algorithm preserves high-dimensional information in 2-D. It splits the attributes of an n-D point into pairs of its values and visualizes pairs as 2-D points, in the same 2-D Cartesian coordinates. Next, it maps pairs to grey scale or color intensity values to encode the order of pairs producing a heatmap. This paper reports the results of computational experiments with CPC-R for different CNN architectures, and methods to optimize the CPC-R images. These results show that the combined CPC-R and deep learning CNN algorithms can solve non-image ML problems, at the state-of-the-art level of accuracy for the benchmark datasets.
MuMIA: Multimodal Interactions to Better Understand Art Contexts

Cultural heritage is a challenging domain of application for novel interactive technologies, where varying aspects in the way that cultural assets are delivered play a major role in enhancing the visitor experience, either onsite or online. Technology-supported natural human–computer interaction that is based on multimodalities is a key factor in enabling wider and enriched access to cultural heritage assets. In this paper, we present the design and evaluation of an interactive system that aims to support visitors towards a better understanding of art contexts through the use of a multimodal interface, based on visual and audio interactions. The results of the evaluation study shed light on the dimensions of evoking natural interactions within cultural heritage environments, using micro-narratives for self-exploration and understanding of cultural content, and the intersection between human–computer interaction and artificial intelligence within cultural heritage. We expect our findings to provide useful insights for practitioners and researchers of the broad human–computer interaction and cultural heritage communities on designing and evaluating multimodal interfaces to better support visitor experiences.
A comparative study of language transformers for video question answering

With the goal of correctly answering questions about images or videos, visual question answering (VQA) has quickly developed in recent years. However, current VQA systems mainly focus on answering questions about a single image and face many challenges in answering video-based questions. VQA in video not only has to understand the evolution between video frames but also requires a certain understanding of corresponding subtitles. In this paper, we propose a language Transformer-based video question answering model to encode the complex semantics from video clips. Different from previous models which represent visual features by recurrent neural networks, our model encodes visual concept sequences with a pre-trained language Transformer. We investigate the performance of our model using four language Transformers over two different datasets. The results demonstrate outstanding improvements compared to previous work.
Gaps Identification for User Experience for Model Driven Engineering

Since ages, Model-Driven Engineering (MDE) has been a very important part of software engineering. It focuses mainly on technical models that simplify the pattern of study and understanding of a topic. The models used in MDE are based on languages and logic. The one unknown fact is the interconnection among UX (user experience), UI (user interaction), and MDE. Feedbacks from users and different industries indicate the necessity of this interconnection in the technical industry. UX and UI are not only responsible for the development of the software industry but are also the future of the technical world due to the increasing need for fulfilling the demands of customers. The designing as well as the functions of models in software engineering can be improved with the help of user experience. UI helps MDE models to be deployed on different user platforms. It involves taking one complete MDE model as input and producing an output that is suitable for both android and web environments. So in this paper, we will see how these three terms are inter-related to each other and how they complement each other very well.
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment

Recent research has demonstrated that deep learning architectures are vulnerable to adversarial attacks, high-lighting the vital need for defensive techniques to detect and mitigate these attacks before they occur. We present UnMask, an adversarial detection and defense framework based on robust feature alignment. UnMask combats adversarial attacks by extracting robust features (e.g., beak, wings, eyes) from an image (e.g., "bird") and comparing them to the expected features of the classification. For example, if the extracted features for a "bird" image are wheel, saddle and frame, the model may be under attack. UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its robust features. Our extensive evaluation shows that UnMask detects up to 96.75% of attacks, and defends the model by correctly classifying up to 93% of adversarial images produced by the current strongest attack, Projected Gradient Descent, in the gray-box setting. UnMask provides significantly better protection than adversarial training across 8 attack vectors, averaging 31.18% higher accuracy. We open source the code repository and data with this paper: https://github.com/safreita1/nmask.
Application of Deep Learning for Crowd Anomaly Detection from Surveillance Videos

Due to immense need for implementing security measures and control ongoing activities, intelligent video analytics is regarded as one of the outstanding and challenging research domains in Computer Vision. Assigning video operator to manually monitor the surveillance videos 24×7 to identify occurrence of interesting and anomalous events like robberies, wrong U-turns, violence, accidents is cumbersome and error- prone. Therefore, to address the issue of continuously monitoring surveillance videos and detect the anomalies from them, a deep learning approach based on pipelined sequence of convolutional autoencoder and sequence to sequence long short-term memory autoencoder has been proposed. Specifically, unsupervised learning approach encompassing one-class classification paradigm has been proposed for detection of anomalies in videos. The effectiveness of the propped model is demonstrated on benchmarked anomaly detection dataset and significant results in terms of equal error rate, area under curve and time required for detection have been achieved.
LSV-ANet: Deep Learning on Local Structure Visualization for Feature Matching

Feature matching is a fundamental and important task in many applications of remote sensing and photogrammetry. Remote sensing images often involve complex spatial relationships due to the ground relief variations and imaging viewpoint changes. Therefore, using a pre-defined geometrical model will probably lead to inferior matching accuracy. In order to find good correspondences, we propose a simple yet efficient deep learning network, which we term the “local structure visualization-attention” network (LSV-ANet). Our main aim is to transform outlier detection into a dynamic visual similarity evaluation. Specifically, we first map the local spatial distribution into a regular grid as descriptor LSV, and then customized a spatial SCale Attention (SCA) module and a spatial STructure Attention (STA) module, which explicitly allows structure manipulation and scale selection of LSV within the network. Finally, the embedded SCA and STA deduce optimal LSV for solving feature matching task by training the LSV-ANet end-to-end. In order to demonstrate the robustness and universality of our LSV-ANet, extensive experiments on various real image pairs for general feature matching are conducted and compared against eight state-of-the-art methods. The experiment results demonstrate the superiority of our method over state of the art.
Approaches, models, methods and means of training of future IT-specialists with the use of elements of dual education

Abstract The article substantiates the necessity and expediency of using the dual form of education in training specialists in the field of information technology in technical universities of Ukraine, interprets the concept of “dual education” from various sources, including UNESCO documents and the Law of Ukraine “On Education”, analyzes some international experience of using dual study in higher education, in particular in Germany, considers the tasks to be solved for successful implementation of the dual form of higher education in Ukraine, and the main stages of this implementation for the period up to 2023. The paper considers some existing models of dual education that can be used in domestic universities. Also considered one of the approaches to the formation of methodological and information support for training of future specialists in information technology in the dual form of education based on distance learning support system developed on the basis of Moodle system, presented experience in implementing elements of dual education at the Faculty of Information Technology and Systems of Cherkasy State Technological University.
Near-Realtime Face Mask Wearing Recognition Based on Deep Learning

COVID-19 pandemic has led to serious economic and life losses. Face Masks serve as first infection barrier when used in public spaces. In this paper, we propose a new near-realtime method to automatically recognize face mask wearing that combines human posture recognition with convolutional neural network (CNN). We use the power of human posture recognition to perform background filtering and spatial reduction in the original images. The outcome is then used by a trained CNN model to identify if the subject is wearing a mask. We exploit Openpose to identify the skeleton of human body and locate the facial region thus spatially reducing the area to be processed by the CNN framework. We then adopt supervised learning approach to detect if a face mask is present. The CNN is trained using images, cropped to the supposed face mask covered region. This approach led to a substantial reduction in neural network complexity yet improving the recognition accuracy. The system has been evaluated in a multitude of scenarios using images taken in public places at different time of day and with different angles. Overall, our system achieves a recognition accuracy of 95.8% and 94.6% in daytime and nighttime respectively.
Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences

Physical samples are foundational entities for research across biological, Earth, and environmental sciences. Data generated from sample-based analyses are not only the basis of individual studies, but can also be integrated with other data to answer new and broader-scale questions. Ecosystem studies increasingly rely on multidisciplinary team-science to study climate and environmental changes. While there are widely adopted conventions within certain domains to describe sample data, these have gaps when applied in a multidisciplinary context. In this study, we reviewed existing practices for identifying, characterizing, and linking related environmental samples. We then tested practicalities of assigning persistent identifiers to samples, with standardized metadata, in a pilot field test involving eight United States Department of Energy projects. Participants collected a variety of sample types, with analyses conducted across multiple facilities. We address terminology gaps for multidisciplinary research and make recommendations for assigning identifiers and metadata that supports sample tracking, integration, and reuse. Our goal is to provide a practical approach to sample management, geared towards ecosystem scientists who contribute and reuse sample data.
“It Makes You Feel That You Are There”: Exploring the Acceptability of Virtual Reality Nature Environments for People with Memory Loss

Aim: To report on the acceptability of virtual reality (VR) nature environments for people with memory loss at memory cafes, and explore the experiences and perceptions of carers and staff. Methods: A qualitative study was conducted between January and March 2019. Ten adults with memory loss, eight carers and six volunteer staff were recruited from two memory cafes, located in Cornwall, UK. There were 19 VR sessions which were audio recorded and all participants were interviewed at the end of the sessions. Framework analysis was used to identify patterns and themes in the data. Results: During the VR experience, participants were engaged to varying degrees, with engagement facilitated by the researcher, and in some cases, with the help of a carer. Participants responded positively to the nature scenes, finding them soothing and evoking memories. The VR experience was positive; many felt immersed in nature and saw it as an opportunity to ‘go somewhere’. However, it was not always positive and for a few, it could be ‘strange’. Participants reflected on their experience of the VR equipment, and volunteer staff and carers also shared their perceptions of VR for people with dementia in long-term care settings. Conclusions: The VR nature experience was an opportunity for people with memory loss to be immersed in nature and offered the potential to enhance their quality of life. Future work should build on lessons learned and continue to work with people with dementia in developing and implementing VR technology in long-term care settings.
Probability Distribution Model to Analyze the Trade-off between Scalability and Security of Sharding-Based Blockchain Networks

Sharding is considered to be the most promising solution to overcome and to improve the scalability limitations of blockchain networks. By doing this, the transaction throughput increases, at the same time compromises the security of blockchain networks. In this paper, a probability distribution model is proposed to analyze this trade-off between scalability and security of sharding-based blockchain networks. For this purpose hypergeometric distribution and Chebyshev's Inequality are mainly used. The upper bounds of hypergeometric distributed transaction processing and failure probabilities for shards are mainly evaluated. The model validation is accomplished with Class A (Omniledger, Elastico, Harmony, and Zilliqa), and Class B (RapidChain) sharding protocols. This validation shows that Class B protocols have a better performance compared to Class A protocols. The proposed model observes the transaction processing and failure probabilities are increased when shard size is reduced or the number of shards increased in sharding-based blockchain networks. This trade-off between the scalability and the security decides on the shard size of the blockchain network based on the real-world application and the blockchain platform. This explains the scalability trilemma in blockchain networks claiming that decentralization, scalability, and security cannot be met at primary grounds. In conclusion, this paper presents a comprehensive analysis providing essential directions to develop sharding protocols in the future to enhance the performance and the best-cost benefit of sharing-based blockchains by improving the scalability and the security at the same time.
Binary thresholding defense against adversarial attacks

Convolutional neural networks are always vulnerable to adversarial attacks. In recent research, Projected Gradient Descent (PGD) has been recognized as the most effective attack method, and adversarial training on adversarial examples generated by PGD attack is the most reliable defense method. However, adversarial training requires a large amount of computation time. In this paper, we propose a fast, simple and strong defense method that achieves the best speed-accuracy trade-off. We first compare the feature maps of naturally trained model with adversarially trained model in same architecture, then we find the key of adversarially trained model lies on the binary thresholding the convolutional layers perform. Inspired by this, we perform binary thresholding to preprocess the input image and defend against PGD attack. On MNIST, our defense achieves 99.0% accuracy on clean images and 91.2% on white-box adversarial images. This performance is slightly better than adversarial training, and our method largely saves the computation time for retraining. On Fashion-MNIST and CIFAR-10, we train a new model on binarized images and use this model to defend against attack. Though its performance is not as good as adversarial training, it gains the best speed-accuracy trade-off.
Citizen-centered, auditable and privacy-preserving population genomics

The growing number of health-data breaches, the use of genomic databases for law enforcement purposes and the lack of transparency of personal genomics companies are raising unprecedented privacy concerns. To enable a secure exploration of genomic datasets with controlled and transparent data access, we propose a citizen-centric approach that combines cryptographic privacy-preserving technologies, such as homomorphic encryption and secure multi-party computation, with the auditability of blockchains. Our open-source implementation supports queries on the encrypted genomic data of hundreds of thousands of individuals, with minimal overhead. We show that real-world adoption of our system alleviates widespread privacy concerns and encourages data access sharing with researchers. Sharing of personal genomics data raises privacy concerns due to the sensitive nature of the data. A citizen-centric approach involving cryptographic privacy-preserving technologies and blockchains is proposed to address this problem.