Reviewerly/exHarmony huggingface dataset:
Spatially resolved transcriptomics in immersive environments

Abstract Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.
An area-efficient architecture for finite field inversion over GF(2m) using polynomial basis

Elliptic curve cryptography (ECC) is a powerful approach to cryptography , and its implementation in cryptography necessitates a smaller chip size, lower power consumption , and faster processing. Finite-field inversion is one of the most computational operations involved in Elliptic curve cryptography(ECC). Extended-Euclidean algorithm and Itoh–Tsujii’s algorithm (ITA) are the two commonly used algorithms for implementing the FF-inversion operation. In this paper, we present the Finite-field inversion architecture developed for the modified Itoh–Tsujii’s algorithm. The reduction in computational delay for the FF-inversion is achieved by using 4 k exponentiation in the realization of modified ITA. The hardware complexity of 4 k exponentiation module depends on k value and k value depends on the order of the field. In this work the best value of k for a specific irreducible trinomial is computed analytically to achieve reduction in area × time product of the proposed FF-inversion architecture. The area and time complexities of the proposed architecture over National Institute of Standard and Technology(NIST) irreducible trinomials GF( 2 193 ), GF( 2 233 ), and GF( 2 409 ) targeting virtex-4 FPGA are computed and compared with similar works in the literature.
Audio-Oriented Video Interpolation Using Key Pose

This paper describes a deep learning-based method for long-term video interpolation that generates intermediate frames between two music performance videos of a person playing a specific instrument. Recent advances in deep learning techniques have successfully generated realistic images with high-fidelity and high-resolution in short-term video interpolation. However, there is still room for improvement in long-term video interpolation due to lack of resolution and temporal consistency of the generated video. Particularly in music performance videos, the music and human performance motion need to be synchronized. We solved these problems by using human poses and music features essential for music performance in long-term video interpolation. By closely matching human poses with music and videos, it is possible to generate intermediate frames that synchronize with the music. Specifically, we obtain the human poses of the last frame of the first video and the first frame of the second video in the performance videos to be interpolated as key poses. Then, our encoder–decoder network estimates the human poses in the intermediate frames from the obtained key poses, with the music features as the condition. In order to construct an end-to-end network, we utilize a differentiable network that transforms the estimated human poses in vector form into the human pose in image form, such as human stick figures. Finally, a video-to-video synthesis network uses the stick figures to generate intermediate frames between two music performance videos. We found that the generated performance videos were of higher quality than the baseline method through quantitative experiments.
Machine Learning for Healthcare: A Survey &amp; its Algorithm for the Security of Medical Images

Recently Machine learning (ML) techniques shows a tremendous growth due to their superior achievements especially in the area of medical healthcare and has many applications like diagnosis and prediction of disease, tele surgery, monitoring of disease, medical image interpretation and segmentation, analysis of medical image etc. Due to the widespread application of ML, various security and privacy issues related to medical data comes into the picture. In this research paper we present a survey on ML and various applications of ML in healthcare. We also present few ML and DL techniques that gives the security and privacy of diagnostic images while transmitting in an open network. This paper closes with the current research problems for the security of the medical data, and promising challenges for future research directions.
Apologos: A Lightweight Design Method for Sociotechnical Inquiry

While scholars involved in studying the ethics and politics flowing from digital information and communication systems have sought to impact the design and deployment of digital technologies, the fast pace and iterative tempo of technical development in these contexts, and the lack of structured engagement with sociotechnical questions, have been major barriers to ensuring values are considered explicitly in the R&D process. Here I introduce Apologos, a lightweight design methodology informed by the author's experience of the challenges and opportunities of interdisciplinary collaboration between computational and social sciences over a five-year period. Apologos, which is inspired by “design apologetics”, is intended as an initial mechanism to introduce technologists to the process of considering how human values impact the digital design process.
Image Splicing Tamper Detection Based on Deep Learning and Attention Mechanism

Aiming at the low detection accuracy and poor positioning effect of traditional image Splicing tampering detection methods, an image Splicing tampering detection method based on deep learning and attention mechanism was proposed. Firstly, by convolution neural network (CNN) to extract the image features, the features extracted from CNN were input into the channel attention mechanism module to generate attention characteristic figure; Then, the convolution of 1*1 is used to replace the original full connection layer in the classification, realizing tampering image classification. Finally, the method of adversarial complementary learning (ACOL) is used to automatically locate the tamper area of semantic interest, which is mapped to the original image in the form of heat map to locate the tampering area. The experimental results show that, on CASIA1.0 and CASIA2.0 data sets, the algorithm proposed in this paper can effectively improve the accuracy of Mosaic image tamper detection and locate the tamper area. As for Accuracy, the model in this paper is superior to the existing image Mosaic tamper detection algorithm, and the prediction Accuracy of the two data sets reaches 99.60% and 99.92% respectively.
Information Technology in Forming Engineering Competencies in Technological University Students During Pandemic

The paper deals with the experience of information technology in forming engineering competencies in technological university students during pandemic. Despite working in conditions of uncertainty and the need to make quick decisions, Kazan National Research Technological University managed to keep students engaged in educational activities. The university had already formed a legal framework regulating distance education, there had been the appropriate technologies, and staff capable of quick and efficient deployment of necessary technologies in the proposed conditions. The transition to remote work forms required the teaching staff to completely restructure the educational process, change teaching methods and current knowledge control, including the organization of final attestations, defense of graduation theses, and so on. Teaching the vast majority of subjects switched to the distant format and conducted according to the curriculum. Introducing modern ICTs in education allowed students to interact with teachers, participate in the educational process, perform educational tasks and projects.
Blockchain-Enabled Smart Contract Optimization for Healthcare Monitoring Systems

Blockchain technology is gaining popularity day by day in every field. This technology has many advantages over traditional data storing approaches such as transparency, security, and immutability. This can be a boom in various healthcare applications. The core part of the technology is a smart contract which implies contract policies and conditions between two signing parties without any intermediary. In this chapter, a smart contract is proposed for the Healthcare system. Ethereum platform has been chosen for developing smart contracts. Remix Ethereum Integrated Development Environment (IDE) has been used for this purpose. Besides this, practical considerations have been addressed while building a smart contract. Further, the smart contract was tested for its functionality. Finally, optimization techniques have been applied to the developed smart contract to reduce the gas cost.
AN ANALYSIS ON CRYPTOCURRENCIES AND MACROECONOMIC VARIABLES USING VECTOR ERROR CORRECTION MODEL (VECM)

Cryptocurrency symbolizes of a new development in the financial sector since it is the world's first entirely decentralized digital payment system. The cryptocurrency known as virtual money is one of the most important innovations brought on by digitalization. The purpose of this study is to analyze the relationship between the cryptocurrency (Bitcoin, Monero, and Stellar) with macroeconomics variables known as stock price index (Dow Jones dan Nikkei), oil price (Brent Oil dan WTI), and exchange rates (Australian Dollar, Euro, and Pound Sterling). The data was obtained from investing.com on monthly basis for the period between January 2016 untuil December 2020. The analysis were conducted based on unit root test, co-integration and vector error correction model (VECM) in order to identify the relationship between the three selected cryptocurrencis with macroeconomic variables. The findings of this paper showed that there is cointegration between the variables. The Vector Error Correction Model (VECM) indicates that the Bitcoin model and Stellar model did not have a long-run relationship. While for the second model, Monero found to have a long-run relationship with the variables. This research contributes to the growing study on cryptocurrency while extend and complement the literature by sourcing the latest research paper on this related field.
Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach

Information hiding can be seen everywhere in our daily life, and this technology improves the security of information. The requirements for information security are becoming higher and higher. The coverless information hiding with the help of mapping relationship has high capacity, but there is still a problem in which the secret message cannot find the mapping relationship and the process requires extra storage burden during the transmission. Therefore, on the basis of symmetric reversible watermarking, the paper introduces the two-level mechanism and novel arrangements to solve the problem of sufficient diversity of features and has better capacity and image quality as a whole. Besides, for the security of secret message, this paper designs a new encryption model based on Logistic mapping. This method only employs coverless information hiding of one carrier image to transmit secret message with the help of the two-level mechanism and look-up table. Reversible information hiding is applied to embed the generated location table on the original image so that ensures storage and security. The experiment certifies that the diversity of hash code is increased by using the two-level image mechanism and the quality of the image is excellent, which proves the advantages of the proposed symmetric method over the previous algorithm.
Adversarial Machine Learning, Research Trends and Applications

The intelligence extracted through machine learning algorithms (MLAs) plays an important role in most of the smart applications and systems around us. Those MLAs make intelligence decisions on behalf of humans based on knowledge extracted from historical and current data. With such growth of MLA roles in human lives, the rise of adversarial attempts to manipulate those MLAs and influence their choices is not a surprise. The main goal of this paper is to present recent approaches, models and progresses in AMLs. Additionally, our goal is to focus on AML research trends and challenges.
Explainable Human--AI Interaction: A Planning Perspective

From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), and be ready to provide customized explanations when needed.
Secure Data Sharing in UAV-assisted Crowdsensing: Integration of Blockchain and Reputation Incentive

Unmanned aerial vehicles (UAVs) combining with crowdsensing technology has been viewed as a promising paradigm for performing sensing tasks in extreme scenarios such as earthquakes, etc. However, potential security issues could incur on data sharing between UAVs and task publishers owing to the vulnerability of central nodes and selfishness of distrusted UAVs. To cope with these problems, we propose a novel blockchain-based crowdsensing framework with reputation incentive (BCFR) in UAV-assisted mobile crowdsensing. Specifically, we first propose a novel reputation incentive scheme to choose UAVs with a high reputation to perform sensing tasks, thereby protecting data sharing between UAVs and task publishers from internal attack (i.e., some UAVs with insufficient resources may turn into malicious UAVs to provide wrong sensory data to the task publishers). Then, we design a blockchain-based secure data transmission scheme to securely record data transactions of UAVs. Furthermore, since UAVs with limited resources are difficult to perform compute-intensive mining tasks, edge computing is incorporated to increase the success probability of block creation. The interactions between UAVs and edge computing provider (ECP) are modeled as a two-stage Stackelberg game to motivate UAVs participating in the block creation process while providing high-quality services. Finally, we conduct extensive simulations to demonstrate that the proposed BCFR scheme can effectively improve successful mining probabilities and utilities of UAVs, and ensure the security of data sharing among UAVs and task publishers.
Solving Colored Traveling Salesman Problem via Multi-neighborhood Simulated Annealing Search

A colored traveling salesman problem (CTSP) is an important variant of the well-known multiple traveling salesman problem, which uses colors to differentiate salesmen&#x2019;s accessibility to individual cities to be visited. As a highly useful model for some complex scheduling problems, CTSP is NP-hard. A Multi-neighborhood Simulated Annealing Search (MSAS) approach is proposed to solve it in this paper. Starting from an initial solution, it iterates through two complementary neighborhoods: intra-route and inter-route neighborhoods. Experiments on three groups of 60 widely-used benchmark instances show that it achieves highly competitive performance compared to state-of-the-art algorithms. Moreover, MSAS can be integrated into other search methods to further improve performance, which is demonstrated by using a recently proposed iterated two-phase local search.
Detection of Abnormalities in Mammograms Using Deep Convolutional Neural Networks

Suganya Devi, K. Sekar, K. Singh, Nivedita Baroi, Subhra Jyoti Sah, Dhiraj Kr. Borahi, KaushikDeep learning is a category of machine learning algorithms and has sparked a great deal of interest in its applicability to radiography challenges due to its tremendous improvement. It is using an end-to-end learning methodology, which effectively utilizes training datasets with comprehensive clinical annotations. In this paper, we propose modified CNN methods that can effectively identify breast cancer in the investigation of mammography. In this work, tumor descriptions are required through the early training phase, and later phases require only image-level descriptors, removing the need for hardly accessible tumor descriptions. In comparison with earlier approaches, all of our modified CNNs for categorizing the mammograms with promising result in accuracies analyzed through various color schemes.
Virtual Reality

Virtual Reality (VR) is an emerging technology that provides K–12 students with unique experiences for robust science learning by transporting them to a virtual world where they may engage directly with scientific phenomena. This is because VR creates lifelike three-dimensional spaces where students can manipulate objects; hear, see, and sometimes feel the environment; and explore places that mimic attributes of the real world. VR holds great utility in science education by engaging students in science topics that may be otherwise inaccessible to them in the real world. This inaccessibility may stem from the content (being too small, large, or abstract), safety issues (too hazardous or dangerous), not having access to the materials in their context, possessing physical or cognitive disabilities where they need to do the activity repeatedly or differently, or having cultural, religious, or ethical concerns related to conducting specific science experiments. This commentary discusses how three key types of VR hardware (VR viewers, desktop VR systems, and head-mounted displays) can be incorporated into science standards, curriculum, and instruction by delineating the pros and cons of each. The commentary concludes with specific, stepwise guidance in ideating, designing, and implementing VR-based experiences for K–12 students in the science classroom.
The Solar Resource

The article deals with all fundamentals οf solar radiation. It consists of 16 sections and 4 appendices; a special section is devoted to the bibliography used. Section 3.02.1 is an introduction to the article. Section 3.02.2 gives the necessary astronomical relationships for positioning the Sun in the sky for any place on Earth. Section 3.02.3 refers to the solar constant, while Section 3.02.4 talks about the solar spectrum. The interference of solar radiation as electromagnetic waves with the constituents of the Earth׳s atmosphere is discussed in Section 3.02.5. The most commonly used broadband solar radiation models for horizontal and tilted surfaces are given in Section 3.02.6 and the basics about solar spectral modeling in Section 3.02.8. The methods for testing the broadband solar models are mentioned in Section 3.02.7. All basics on net solar radiation are given in Section 3.02.9. The need for the formation of networks of solar radiation stations and the generation of Solar Atlases are discussed in Section 3.02.10. Section 3.02.11 gives an account of the utility tools for easy calculations of solar radiation. Section 3.02.12 gives an account of the instruments for measuring the solar radiation components with associated errors and uncertainties. Section 3.02.13 refers to the availability of solar radiation data sets worldwide, while Section 3.02.14 talks about the use of solar radiation values from satellite observations. 3.02.15 Solar radiation climate/climatology, 3.02.16 The global dimming/brightening effect deal with the solar radiation climate/climatology of a location, and the effects of the global dimming and global brightening, respectively. Appendix A provides tabulated values of the solar constant at all wavelengths. Appendix B gives an account of all terms used in solar radiometry. Appendix C assumes the Sun as black body and gives the maximum wavelength of the solar spectrum of Appendix A by applying Wien׳s law. There is also mentioned the atmosphere as a grey body. Appendix D gives a list of the most common physical constants and conversion factors of the units used in solar radiometry.
Mining SQL workloads for learning analysis behavior

This paper presents a set of analyses aiming at better understanding the SQLShare workload Jain et al. (2016) and learning users’ analysis behavior. SQLShare is a database-as-a-service platform targeting scientists and data scientists with minimal database experience, whose workload was made available to the research community. According to the authors of Jain et al. (2016) , this workload is the only one containing primarily ad-hoc hand-written queries over user-uploaded datasets. In this paper we analyze this workload, by comparing users’ explorations (sequences of queries), looking for common SQL operations performed by the users during data analysis and studying query complexity. We use a clustering algorithm to retrieve groups of similar explorations and we analyze the obtained clusters through many statistical and visual indicators for explaining analysis patterns inside clusters. To our knowledge, this is the first attempt to characterize human analysis behavior in SQL workloads.
Automatic Spoken Language Identification using Convolutional Neural Networks

Automatic Spoken Language Identification systems classify the spoken language automatically and can be used in many tasks, for example, to support Automatic Speech Recognition or Video Recommendation systems. In this work, we propose an automatic language identification model obtained through a Convolutional Neural Network trained over audio spectrograms on Portuguese, English and Spanish languages. The audio for the model training was obtained through audiobooks and different corpora for speech recognition systems. The audios were used to generate instances having five seconds each. We addressed the limitation of having few speakers in our dataset with simple data augmentation techniques such as speed and pitch changing on the original instances to increase the size of the dataset. The proposed model was optimized with a random hyperparameter search which provided a final model able to identify the proposed languages with 83% of accuracy on a new, unseen test data, made with audios from different sources.
Backdoor DNFs

We introduce backdoor DNFs, as a tool to measure the theoretical hardness of CNF formulas. Like backdoor sets and backdoor trees, backdoor DNFs are defined relative to a tractable class of CNF formulas. Each conjunctive term of a backdoor DNF defines a partial assignment that moves the input CNF formula into the base class. Backdoor DNFs are more expressive and potentially smaller than their predecessors backdoor sets and backdoor trees. We establish the fixed-parameter tractability of the backdoor DNF detection problem. Our results hold for the fundamental base classes Horn and 2CNF, and their combination. We complement our theoretical findings by an empirical study. Our experiments show that backdoor DNFs provide a significant improvement over their predecessors.