Reviewerly/exHarmony huggingface dataset:
Preparing Auditors for the Blockchain Oracle Problem

SUMMARY This article summarizes “Auditing the Blockchain Oracle Problem” (Sheldon 2021), which introduces auditors to the risks of having an irreversible business agreement codified on a blockchain using a short software program called a smart contract that relies on an oracle to provide information from outside the blockchain in order to execute correctly. The article begins with an explanation of the role that oracles play in the blockchain ecosystem and proposes a working definition of oracles. Next, the article highlights how the auditing standards from both the AICPA and PCAOB can be interpreted to classify an oracle as part of smart contract users' information systems, and that the oracle provides a robust set of services that qualify it as a service organization. Finally, the article describes the process used by an oracle to collect, store, transform, and transmit data, and highlights relevant risks and illustrative control objectives related to this process.
An Intuitive Framework to Segment the Fetal Brain Abnormalities using Improved Semantic Blend Segmentation Algorithm

Introduction:In this day and age, Machine Learning in clinical imaging introduces an energizing time with reengineered and rethought clinical abilities.Deep Learning aids deep further to make physicians feel like a walk in the park with a handful of more desirable resources. Aim and Objective:The Research focuses to classify and segment the abnormalities in the fetal brain MRI images.The normal and lesion tissue are identified with their location from the given raw images. Method:The model will perform localization, Segmentation, and Enhancement of the Fetal Brain and able to address the two significant abnormalities such as Encephalocele and Arteriovenous Malformation using the Improved Semantic Blend Segmentation Algorithm. Results:The model has been trained with the capability to segment the Region of Interest (ROI) on an average of 7.2 Seconds per input. Conclusion:The raw fetal brain images are segmented and enhanced with various classes of input and the results are analyzed which outperforms the existing techniques by saving time and achieving better accuracy.
Action Recognition Using Multi-Scale Temporal Shift Module and Temporal Feature Difference Extraction Based on 2D CNN

Convolutional neural networks, which have achieved outstanding performance in image recognition, have been extensively applied to action recognition.The mainstream approaches to video understanding can be categorized into two-dimensional and three-dimensional convolutional neural networks.Al-
Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net

Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication from the commit history of projects. Many works in this area studied networks of co-authorship of software artefacts, neglecting detailed information on code changes and code ownership available in software repositories. To address this issue, we introduce git2net, a scalable python software that facilitates the extraction of fine-grained co-editing networks in large git repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. We apply our tool in two case studies using GitHub repositories of multiple Open Source as well as a proprietary software project. Specifically, we use data on more than 1.2 million commits and more than 25,000 developers to test a hypothesis on the relation between developer productivity and co-editing patterns in software teams. We argue that git2net opens up an important new source of high-resolution data on human collaboration patterns that can be used to advance theory in empirical software engineering, computational social science, and organisational studies.The online version contains supplementary material available at (10.1007/s10664-020-09928-2).
Cryptocurrency as a factor for National Payment System Improvement in the process of Economy’s digitalization

Research develops the modern theory and methodology of cryptocurrency functioning as an integral part of modern money circulation, and the main directions of cryptocurrency progress as means of legal payment in the Republic of Indonesia. Based on the econometric model of the cryptocurrency main indicators influence the relationship between “bitcoin” attributes was assessed; the probable scenarios for the financial regulation system of the Republic of Indonesia (RI) effectivization based on the National Bank or special financial institutions competences were highlighted; the practical recommendations aimed at combating money laundering in cryptocurrency-based transactions have been developed from the position of priority for Indonesian payment system modernization.
Features of distance learning in the modern conditions of a Russian university: based on the materials of a survey of students

Introduction. Distance learning is a forced and extreme condition due to the spread of COVID-19. The higher school is looking for ways to improve the process of education for young people and opens up new opportunities for the transition to distance learning through the introduction of innovative technologies. The purpose of the study is to find out the pros and cons of distance learning, using the analysis of data obtained through a sociological survey of students. Materials and methods. The material of the research was the answers of 122 students of the Stupino branch of the Moscow Aviation Institute (National Research University) from 1 to 3 courses of the full-time department by filling in Google forms. Results. 42.6% of teaching students positively assessed the importance of switching to the remote platform of the LMS MAI, although only a third of the respondents would like to study remotely next semester. Motivation and involvement in the learning process in the new environment is present in 44.3% of respondents, and 63.9 % of students believe that learning a foreign language at a distance makes it difficult to understand new material. In addition, 73.3% of respondents believe that humanities are easier to study remotely, as opposed to technical subjects. In the organization of the educational process through distance learning, 45.1% of the surveyed students discovered new opportunities to continue learning the language. It should be noted that a third of respondents (32.8%) believe that in the near future, higher education can be fully transferred to distance learning. The method of principal components revealed that the attitude of students to distance learning is not related to the motivation to learn. Conclusion. The drastic reformatting of the educational process with limited internal and external resources of the universities intensified their mobilization in an emergency implementation of distance learning technologies, but not in the systematic use of tools online training, require a much longer preparation time and testing on the students in order to develop a competitive and developing educational system, and natural is not always an orderly and systematic approach.
Statistical feature-based steganalysis for pixel-value differencing steganography

Abstract Pixel-value differencing (PVD) steganography is a popular spatial domain technology. Several PVD-based studies have proposed extended PVD steganography methods. The majority of these studies have verified their security against the regular-singular (RS) analysis. However, RS analysis is aimed at the feature of the least significant bit substitution method, which is relatively less significant for PVD steganography. The pixel difference histogram (PDH) is generally utilized to attack PVD steganography. If the embedding capacity is high, then the features on the PDH are evident; otherwise, the features are less obvious. In this paper, we propose a statistical feature-based steganalysis technique for the original PVD steganography. Experimental results demonstrate that, compared with existing steganalysis technique with weighted stego-image (WS) method, the proposed method effectively detects PVD steganography at low embedding ratios, such that there is no need of using the original embedding parameters. Furthermore, the accuracy and precision of the method are better than those of existing PVD steganalysis techniques. Therefore, the proposed method contributes to the security analysis of the original PVD steganography as an alternative to the commonly used RS, PDH and WS attack techniques.
Quantum repeater using three-level atomic states in the presence of dissipation: stability of entanglement

In this paper we want to investigate the possibility of transferring entanglement to two three-level separable atomic states over large distance using the quantum repeater protocol. In detail, our model consists of eight three-level atoms where only the pairs (1,2), (3,4), (5,6) and (7,8) are prepared in maximally entangled states. Performing suitable interaction between non-entangled three-level atoms (2,3) and (6,7) in two-mode cavities with photon leakage rates in the presence of spontaneous emission leads to producing entanglement between atoms (1,4) and (5,8), separately. Finally, the entanglement between atoms (1,8) is successfully produced by performing interaction between atoms (4,5) while spontaneous emission is considered in a dissipative cavity. In the continuation, the effects of detuning, dissipation and initial interaction time are considered on negativity and success probability of the processes. The maxima of negativity are decreased by increasing the detuning, in most cases. Also, the time evolution of negativity is non-periodic in the presence of dissipation. Increasing the initial interaction time has a constructive effect on negativity in all considered cases. The oscillations of negativity are destroyed as time goes on and the produced entanglement is stabled. The success probability of entangled state of atoms (1,8) is tunable by controlling the detuning and dissipation. We show that via justifying the involved parameters one can arrive at conditions in which the decoherence effects are fully disappeared; as a result an ideal quantum repeater can be achieved while atomic and field dissipations are taken into account.
Shared control methodology based on head positioning and vector fields for people with quadriplegia

Abstract Mobile robotic systems are used in a wide range of applications. Especially in the assistive field, they can enhance the mobility of the elderly and disable people. Modern robotic technologies have been implemented in wheelchairs to give them intelligence. Thus, by equipping wheelchairs with intelligent algorithms, controllers, and sensors, it is possible to share the wheelchair control between the user and the autonomous system. The present research proposes a methodology for intelligent wheelchairs based on head movements and vector fields. In this work, the user indicates where to go, and the system performs obstacle avoidance and planning. The focus is developing an assistive technology for people with quadriplegia that presents partial movements, such as the shoulder and neck musculature. The developed system uses shared control of velocity. It employs a depth camera to recognize obstacles in the environment and an inertial measurement unit (IMU) sensor to recognize the desired movement pattern measuring the user’s head inclination. The proposed methodology computes a repulsive vector field and works to increase maneuverability and safety. Thus, global localization and mapping are unnecessary. The results were evaluated by simulated models and practical tests using a Pioneer-P3DX differential robot to show the system’s applicability.
A Novel Patient-to-Image Surface Registration Technique for ENT- and Neuro-Navigation Systems: Proper Point Set in Patient Space

Patient-to-medical image registration is a crucial factor that affects the accuracy of image-guided ENT- and neurosurgery systems. In this study, a novel registration protocol that extracts the point cloud in the patient space using the contact approach was proposed. To extract the optimal point cloud in patient space, we propose a multi-step registration protocol consisting of augmentation of the point cloud and creation of an optimal point cloud in patient space that satisfies the minimum distance from the point cloud in the medical image space. A hemisphere mathematical model and plastic facial phantom were used to validate the proposed registration protocol. An optical and electromagnetic tracking system, of the type that is commonly used in clinical practice, was used to acquire the point cloud in the patient space and evaluate the accuracy of the proposed registration protocol. The SRE and TRE of the proposed protocol were improved by about 30% and 50%, respectively, compared to those of a conventional registration protocol. In addition, TRE was reduced to about 28% and 21% in the optical and electromagnetic methods, respectively, thus showing improved accuracy. The new algorithm proposed in this study is expected to be applied to surgical navigation systems in the near future, which could increase the success rate of otolaryngological and neurological surgery.
Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI

Abstract Reasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH &amp; DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.
Decoupling Value and Policy for Generalization in Reinforcement Learning

Standard deep reinforcement learning algorithms use a shared representation for the policy and value function. However, we argue that more information is needed to accurately estimate the value function than to learn the optimal policy. Consequently, the use of a shared representation for the policy and value function can lead to overfitting. To alleviate this problem, we propose two approaches which are combined to create IDAAC: Invariant Decoupled Advantage Actor-Critic. First, IDAAC decouples the optimization of the policy and value function, using separate networks to model them. Second, it introduces an auxiliary loss which encourages the representation to be invariant to task-irrelevant properties of the environment. IDAAC shows good generalization to unseen environments, achieving a new state-of-the-art on the Procgen benchmark and outperforming popular methods on DeepMind Control tasks with distractors. Moreover, IDAAC learns representations, value predictions, and policies that are more robust to aesthetic changes in the observations that do not change the underlying state of the environment.
Using Color Difference Compensation Method to Balance and Repair the Image of Art Design

In the case of complex illumination background, the image has fuzziness and chromatic aberration, which can improve the imaging effect of the image by white balance chromatic aberration compensation. The traditional method uses the contour feature fusion adaptive matching method to compensate the white balance under the complex illumination background, which leads to the good color difference compensation effect when the image pixel is low. A white balance color compensation algorithm for fuzzy chromatic aberration based on wavelet packet decomposition is proposed. The original image was denoised and filtered. The white balance characteristics of the image were analyzed and extracted based on the wavelet packet decomposition method, and the adaptive balance design was carried out to realize the white balance and color difference compensation of the art design chromatic aberration image. By using the known pixel information of the image block to be repaired, the statistical properties of the image block to be repaired are predicted, and the matching cost of the image block to be matched that meets the restriction conditions is calculated. By introducing the objective factor, the matching cost function is improved to balance the restoration process, and the unnatural problem caused by the repeated appearance of some image details in the restoration results is solved. The simulation results show that the method can effectively balance the color difference of the image, improve the aesthetic feeling of the image, and improve the performance of the detail features of the image. The peak signal-to-noise ratio is high.
Knowledge Graph Entity Similarity Calculation under Active Learning

To address the objectives of the adaptive learning platform, the requirements of the system in terms of business, functionality, and performance are mainly analysed, and the design of functions and database is completed; then, an updatable learner model is constructed based on the cognitive diagnosis model and resource preference attributes; then, the construction of the knowledge map is completed based on embedding to achieve knowledge point alignment, and based on this, the target knowledge points of learners are located with the help of deep learning; at the same time, the target knowledge points are taken as the starting point to generate the best learning path by traversing the knowledge map, and the corresponding learning resources and test questions are recommended for them with the help of the architecture; finally, the adaptive learning platform is developed in the environment using the architecture. Also, the target knowledge point is used as the starting point to traverse the knowledge map to generate the best learning path, and the corresponding learning resources and test questions are recommended for the learner in combination with the learner model; finally, this study adopts an architecture for the development of an adaptive learning platform in the environment to realize online tests, score analysis, resource recommendation, and other functions. A knowledge graph fusion system supporting interactive facilitation between entity alignment and attribute alignment is implemented. Under a unified conceptual layer, this system can combine entity alignment and attribute alignment to promote each other and truly achieve the final fusion of the two graphs. Our experimental results on real datasets show that the entity alignment algorithm proposed in this paper has a great improvement in accuracy compared with the previous mainstream alignment algorithms. Also, the attribute alignment algorithm proposed in this paper, which calculates the similarity based on associated entities, outperforms the traditional methods in terms of accuracy and recall.
The Covid-19 shutdown: when studying turns digital, students want more structure

Abstract In March 2020, universities in Norway and many other countries shut down due to the Covid-19 pandemic. The students lost access to classrooms, libraries, study halls, and laboratories. Studying turned digital. Because it is unclear when this pandemic will cease to affect students and because we cannot know whether or when a new pandemic occurs, we need to find ways to improve digital study-life for students. An important step in this direction is to understand the students’ experiences and perspectives regarding how the digitalization affected their study-life both in structured learning arenas and their self-study. Therefore, we interviewed 12 students in an introductory mechanics course at a Norwegian university in June of 2020. Through a thematic analysis, we identified four broad categories in the students’ different experiences and reflections, namely that digitalization: (a) provides benefits, e.g. the flexibility inherent in online video lectures; (b) incurs learning costs, e.g. students reducing their study effort; (c) incurs social costs, e.g. missing being around other students; and (d) increases the need for structure, e.g. wanting to be arranged in digital groups to solve mandatory tasks. We also found that the 2019 students on average scored significantly better on the final exam than the 2020 students, d = 0.31, but we discuss why this result should be interpreted with caution. We provide suggestions for how to adapt courses to make students’ digital studying more socially stimulating and effective. Furthermore, this study is a contribution to the historical documentation of the Covid-19 pandemic.
LLVM-Based Circuit Compilation for Practical Secure Computation

Multi-party computation (MPC) allows two or more parties to jointly and securely compute functions over private inputs. Cryptographic protocols that realize MPC require functions to be expressed as Boolean or arithmetic circuits. Deriving such circuits is either done manually, or with hardware synthesis tools and specialized MPC compilers. Unfortunately, such existing tools compile only from a single front-end language and neglect decades of research for optimizing regular compilers. In this paper, we make MPC practical for developers by automating circuit compilation based on the compiler toolchain LLVM. For this, we develop an LLVM optimizer suite consisting of multiple transform passes that operate on the LLVM intermediate representation (IR) and gradually lower functions to circuit level. Our approach supports various front-end languages (currently C, C++, and Fortran) and takes advantage of powerful source code optimizations built into LLVM. We furthermore make sure to produce circuits that are optimized for MPC, and even offer fully automated post-processing for efficient post-quantum MPC. We empirically measure the quality of our compilation results and compare them to the state-of-the-art specialized MPC compiler HyCC (Büscher et al. CCS’2018). For all benchmarked HyCC example applications (e.g., biomatch and linear equation solving), our highly generalizable approach achieves similar quality in terms of gate count and composition.