Reviewerly/exHarmony huggingface dataset:
An Intelligent Computer System for Assessing Student Performance

The purpose of the study is to identify and compare the influence of formative and summative assessment approaches based on an intelligent computer system that provides automatic feedback; the assessment is carried out in paper format, but obtaining feedback requires an appointment with a teacher. The study was conducted among 50 students in I.M. Sechenov First Moscow State Medical University (Russia) and Wuxi Institute of Technology (China). The assessment was carried out based on online tools and an intelligent learning system (ASP.NET web applications and MCQ tests). It was found that the average score of the formative test of students who passed an assessment test in the electronic format is higher than the score of those who passed the test in the classroom [t (165 = 5.334, p &lt;0.05]. Pearson's correlation coefficient in the experimental (r2 = +0.329; p = 0.009) and control (r2 = +0.176; p = 0.076) groups confirmed a sig-nificant positive correlation. The solution can be integrated into the educational process as an additional student tool that will reduce the burden of teacher work-load and increase the assessment objectivity along with the overall performance of students.
Model Partition Defense against GAN Attacks on Collaborative Learning via Mobile Edge Computing

With growing concerns about privacy issues of machine learning, collaborative learning (CL) is developed to offer on-device training. However, adversarial behaviors of model inversion (MI) are undermining privacy of training data. Specifically, adversaries act as ordinary participants in CL and reproduce private data of a class in training data by training generative adversarial networks (GAN) on the fly, unknowingly. To this end, we design a novel model partition defense, PAMPAS, over user devices and trustworthy edge server to resist GAN attack, and formulate a new optimization problem, TENSOR, to optimize training time. To address the challenges that come with PAMPAS, we propose an algorithm TESLA that yields the optimal solution. Experiment and simulation results manifest that PAMPAS effectively defend GAN attack and TESLA reduces training time by 50% compared with other solutions.
WorldView-3 mapping of Tarmat deposits of the Ras Rakan Island, Northern Coast of Qatar: Environmental perspective

This study characterizes the spectral behavior of tarmats and maps the tarmat deposits found along the coast of Ras Rakan Island off Qatar using WorldView-3 (WV-3) sensor data. The laboratory spectra of tar materials showed diagnostic absorptions features at 0.6 and 1.1 μm in the visible and near-infrared (VNIR) and 1.52, 1.73, 2.04, and 2.31 μm in the short wave infrared (SWIR) region. The panchromatic grayscale image and FCC showed the tarmat deposit as a linear warp feature between beach and water. The mapping of deposits using WV-3 data by decorrelation stretch and Linear Spectral Unmixing (LSU) methods discriminated the tarmats from the sandy soil, vegetation and sabkha features in a different tone. The capability of WV-3 sensor and the potential of image processing methods were verified by mapping the tar distribution of the Ras Ushayriq and NE of Al Ruwais.
Learning image-based Receding Horizon Planning for manipulation in clutter

The manipulation of an object into a desired location in a cluttered and restricted environment requires reasoning over the long-term consequences of an action while reacting locally to the multiple physics-based interactions. We present Visual Receding Horizon Planning (VisualRHP) in a framework which interleaves real-world execution with look-ahead planning to efficiently solve a short-horizon approximation to a multi-step sequential decision making problem. VisualRHP is guided by a learned heuristic that acts on an abstract colour-labelled image-based representation of the state. With this representation, the robot can generalize its behaviours to different environment setups, that is, different number and shape of objects, while also having transferable manipulation skills that can be applied to a multitude of real-world objects. We train the heuristic with imitation and reinforcement learning in discrete and continuous actions spaces. We detail our heuristic learning process for environments with sparse rewards, and non-linear, non-continuous, dynamics. In particular, we introduce necessary changes for improving the stability of existing reinforcement learning algorithms that use neural networks with shared parameters. In a series of simulation and real-world experiments, we show the robot performing prehensile and non-prehensile actions in synergy to successfully manipulate a variety of real-world objects in real-time.
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

Learning from visual data opens the potential to accrue a large range of manipulation behaviors by leveraging human demonstrations without specifying each of them mathe-matically, but rather through natural task specification. In this paper, we present Learning by Watching (LbW), an algorithmic framework for policy learning through imitation from a single video specifying the task. The key insights of our method are two-fold. First, since the human arms may not have the same morphology as robot arms, our framework learns unsupervised human to robot translation to overcome the morphology mis-match issue. Second, to capture the details in salient regions that are crucial for learning state representations, our model performs unsupervised keypoint detection on the translated robot videos. The detected keypoints form a structured representation that contains semantically meaningful information and can be used directly for computing reward and policy learning. We evaluate the effectiveness of our LbW framework on five robot manipulation tasks, including reaching, pushing, sliding, coffee making, and drawer closing. Extensive experimental evaluations demonstrate that our method performs favorably against the state-of-the-art approaches. More results and analysis are available at pair.toronto.edu/lbw-kp/.
Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

Abstract Data preservation is the mechanism of protecting and safeguarding the confidentiality and integrity of data. Data stored in huge databases may contain metadata, elements that may be imprecise and unstable, It may include sensitive data, personal profiles and so on, which is vulnerable to third parties such as hackers or attackers. They may misuse the data and as a consequence of this the confidentiality and privacy of the data gets lost. There is a need to conserve the data and make it available for reuse when needed. Hence, it needs a proficient method to maintain and protect individuals' data privacy regarding confidentiality and reliability. This paper intends to develop an advanced model for privacy preservation of huge data with the accomplishment of two stages, namely data sanitization and data restoration. Data sanitization process preserves the safety of sensitive data stored in huge databases, by means of hiding those sensitive data from unauthorized users. Data restoration is the process of recovering or restoring of data that is sanitized at the sender side. Concerning the secrecy, there is a need for an optimal key to hide the sensitive data at sender as well as receiver side. Subsequent to the data sanitization, it requires the same key to restore the sanitized data. Thus, the optimal key generation plays a vital role to maintain privacy preservation. In order to choose an optimal key, a modified Rider optimization Algorithm (ROA) named as Randomized ROA (RROA) model is implemented in this work. Furthermore, the efficiency of the proposed work is compared over the state‐of‐the‐arts models by concerning the sanitization as well as restoration efficiency.
Satellite‐based distribution of hybrid entanglement

Heterogeneous quantum networks consisting of mixed-technologies - Continuous Variable (CV) and Discrete Variable (DV) - will become ubiquitous as global quantum communication matures. Hybrid quantum-entanglement between CV and DV modes will be a critical resource in such networks. A leading candidate for such hybrid quantum entanglement is that between Schr\"odinger-cat states and photon-number states. In this work, we explore the use of Two-Mode Squeezed Vacuum (TMSV) states, distributed from satellites, as a teleportation resource for the re-distribution of our candidate hybrid entanglement pre-stored within terrestrial quantum networks. We determine the loss conditions under which teleportation via the TMSV resource outperforms direct-satellite distribution of the hybrid entanglement, in addition to quantifying the advantage of teleporting the DV mode relative to the CV mode. Our detailed calculations show that under the loss conditions anticipated from Low-Earth-Orbit, DV teleportation via the TMSV resource will always provide for significantly improved outcomes, relative to other means for distributing hybrid entanglement within heterogeneous quantum networks.
Effects of good governance, sustainable development and aid on quality of life: Evidence from sub‐saharan Africa

Abstract The paper empirically investigates the effects of governance (GGov), official development assistance (ODA), sustainability [adjusted net savings (ANS)], and macroeconomic variables on the quality of life [human development index (HDI)] for selected sub‐Saharan African (SSA) countries using the most recent data from 2000 to 2017. The study employed different panel techniques. The findings provide insightful and interesting empirical results that resonate with the magnitude of a significant of the role of GGov on ANS and HDI. Our study shows that GGov is important to improve HDI. Additionally, ANS has important implications on the well‐being of human existence in SSA. In addition to these, this study found that macroeconomic variables such as trade openness and economic growth, wealth, and opportunity creation factors like urbanization and electrification rate are essential. Furthermore, empirical results revealed that ODA has a negative and significant association with HDI, which is in line with some of the existing literature. Our findings have several implications for organizations such as the World Bank, International Monetary Fund, and African Development Bank. This study serves as a policy instrument and guides in coordinating SSA on promoting HDI.
The Battle between NoSQL Databases and RDBMS

NoSQL is a free and open-source, scattered, extensive column store database management system intended to handle large amounts of data across many product servers, providing high obtainability and accessibility with no single point of failure. It is the easiest truly big-data database that can scale and replicate data globally in a master-less configuration. A NoSQL database delivers a mechanism for storage and recovery of data that is demonstrated in means other than the tabular relations used in relational databases. NoSQL databases usually understood by engineers as ‘not only SQL databases’ neither ‘no SQL’, it is an alternative to the most widely used relational databases. As the given name proposed, it is a substitute for SQL that uses in such a way that the SQL is co-existed. A relational database management system (RDBMS) is a database management system based on the relational model of data and it is a completely structured way of storing data. But NoSQL is an unstructured way of storing data. Is NoSQL an RDBMS? This is an article we will discuss various features of NoSQL, various advantages over RDBMS and the future of NoSQL. This article also reviews the basics of NoSQL and its usages.
From Passive Observers to Active Participants: Using Interactive Remote Demonstrations to Increase Student Involvement in Online Chemistry Instruction

The remote screen control feature in Zoom was used to remotely control instrumentation during an online summer chemistry enrichment session for high school students. This feature was used to draw students into actively participating in chemistry demonstrations where instrumentation was used to gather and analyze data. It can be a challenge to keep students engaged while they are learning in an online environment. Due to being physically absent from the classroom and distant from the teacher, a sense of disconnectedness can be present. We aimed to combat this sense by drawing students into activities by giving several students during any given lesson a chance to interact with the instructor and with each other while remotely controlling digital sensors. Students were surveyed at the conclusion of the summer session and expressed very positive reactions to this technique of enhancing student involvement.
Ethics of Digital Contact Tracing and COVID-19: Who Is (Not) Free to Go?

Digital tracing technologies are heralded as an effective way of containing SARS-CoV-2 faster than it is spreading, thereby allowing the possibility of easing draconic measures of population-wide quarantine.But existing technological proposals risk addressing the wrong problem.The proper objective is not solely to maximise the ratio of people freed from quarantine but to also ensure that the composition of the freed group is fair.We identify several factors that pose a risk for fair group composition along with an analysis of general lessons for a philosophy of technology.Policymakers, epidemiologists, and developers can use these risk factors to benchmark proposal technologies, curb the pandemic, and keep public trust.
Politics of Adversarial Machine Learning

In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights. In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems. To make this concrete, we use real-world examples of how attacks such as perturbation, model inversion, or membership inference can be used for socially desirable ends. Although the predictions of this analysis may seem dire, there is hope. Efforts to address human rights concerns in the commercial spyware industry provide guidance for similar measures to ensure ML systems serve democratic, not authoritarian ends.
Review on Quantum Walk Algorithm

Abstract Quantum walk is the quantum version of classical random walk, which has shown great advantage over classical algorithms. In this article, main discoveries and breakthroughs along with several applications in the last two decades are reviewed and discussed. Reducing hitting time and diminishing mixing time, as two hot research fields, are addressed. Possibility of universal computation with more generic case of quantum walk is also mentioned. In the future, we can expect new methods and models being feasible ways to approach the theoretical lower bounds of related problems, as well as more efficient applications on specific scene.
Handwriting based Personality Identification using Textural Features

Nowadays, personality identification based on handwriting processing is becoming a very active field of research and experimentation, for the reason of its high demand in domains such as resources management, criminal investigations and mental health diagnostics. The implicit information includes attributes like writer identity, gender, age group and handedness etc. Furthermore, handwriting has also been employed as indicator of neurodegenerative disorders, cognitive and emotional development and the personality attributes of writers. Several works have revealed the existence of a significant relationship between people's psychological traits and their manuscript writings. This paper aim to present a technique that exploits textural features extracted from handwriting images for the purpose of predicting the writer's personality traits. Mainly, the work is based on the HWxPI Dataset which was built as a part of the multimedia information processing for personality & social networks analysis (MIPPSNA) contest. The Five Factor Model (FFM) was employed to assign binary labels to individuals on five personality traits and the writing samples of the same individuals were employed for prediction of the same using automated techniques. We adopt a global approach and represent the handwriting images using the Oriented Basic Image Features Column (oBIFC) scheme. Being a multi-label classification problem, we employ RankSVM to predict the labels (0 or 1) for each of FFM traits. Experimental study of the system was carried out using the protocol of the MIPPSNA contest as well as using a k-fold cross validations. The realized results validated the effectiveness of the proposed technique in characterizing personality traits from the visual information in handwriting.
Era or error of transformation? Assessing afrocentric attributes to digitalization

This special issue is indeed special. When the idea to guest edit this issue was mooted, none of us had heard of COVID-19. After organizing what turned out to be a successful pre-conference, buoyed...
Finding ethereum smart contracts security issues by comparing history versions

Smart contracts are Turing-complete programs running on the blockchain. They cannot be modified, even when bugs are detected. The Selfdestruct function is the only way to destroy a contract on the blockchain system and transfer all the Ethers on the contract balance. Thus, many developers use this function to destroy a contract and redeploy a new one when bugs are detected. In this paper, we propose a deep learning-based method to find security issues of Ethereum smart contracts by finding the updated version of a destructed contract. After finding the updated versions, we use open card sorting to find security issues.
Comparative analysis of image projection-based descriptors in Siamese neural networks

Low-level object matching can be done using projection signatures. In case of a large number of projections, the matching algorithm has to deal with less significant slices. A trivial approach would be to do statistical analysis or apply machine learning to determine the significant features. To take adjacent values of the projection matrices into account, a convolutional neural network should be used. To compare two matrices, a Siamese-structure of convolutional heads can be applied. In this paper, an experiment is designed and implemented to analyze the object matching performance of Siamese Convolutional Neural Networks based on multi-directional image projection data. A backtracking search-based Neural Architecture Generation method is used to create convolutional architectures, and a Master/Worker structured distributed processing with highly efficient scheduling based on the Longest Processing Times-heuristics is used for parallel training and evaluation of the models. Results show that the projection-based methods are Pareto optimal in terms of one-shot classification accuracy and memory consumption.
Is Bug Severity in Line with Bug Fixing Change Complexity?

Both complexity of code change for bug fixing and bug severity play an important role in release planning when considering which bugs should be fixed in a specific release under certain constraints. This work investigates whether there are significant differences between bugs of different severity levels regarding the complexity of code change for fixing the bugs. Code change complexity is measured by the number of modified lines of code, source files, and packages, as well as the entropy of code change. We performed a case study on 20 Apache open source software (OSS) projects using commit records and bug reports. The study results show that (1) for bugs of high severity levels (i.e. Blocker, Critical and Major in JIRA), there is no significant difference on the complexity of code change for fixing bugs of different severity levels for most projects, while (2) for bugs of low severity levels (i.e. Major, Minor and Trivial in JIRA), fixing bugs of a higher severity level needs significantly more complex code change than fixing bugs of a lower severity level for most projects. These findings provide useful and practical insights for effort estimation and release planning of OSS development.
A Study on Analysing the impact of Feature Selection on Predictive Machine Learning Algorithms

In recent times, one of the most used scenarios in many industry domains is enhancing the bids or tenders made by suppliers. In this paper, we will be analyzing one such use case for studying the effects of mixed feature selection to optimize the Learning model. The use case is to target and build a predictive clustering model in such a way that the scheduler receives the suggestions based on the most optimal options. There are few feature selection, enhancement, and scaling methodologies which this paper aims to explore with real-time data. Based on the analysis, the most important feature derived would be used to predict the optimal suggestion. The results will then be compared to understand the shortfalls and strong points of this new approach based on the accuracy of prediction. A clustering model will not just help reduce the hours of manual effort put into selecting the right source but will also provide an authentic and optimal option for a scheduler's consideration.
<i>Men will be Men?</i>: Masculinities on display in the Facebook communication practices of Pakistani men

In the past decade, the popularization of social media in Pakistan has greatly influenced the way in which people communicate and interact with each other. The rapidly evolving nature of online social media communication in the country when viewed against the backdrop of the country's socio-cultural characteristics and religion is particularly significant. Facebook, being the prime social media platform in Pakistan has been revolutionary and liberating because while acting as a medium of communication, which transgresses the traditional manner of gendered social interactions, it has at the same time enabled the users to perpetuate and reinforce the existing gender ideology. As a result, Facebook in Pakistan has evolved into a space where individuals construct gender identities discursively. Using Multimodal Discourse Analysis, I investigate the ways in which Pakistani men multimodally construct their gender identities on Facebook. I show how Pakistani men are not only upholding the existing socio-cultural norms and discourse but also there are subtle signs of digression from the established models of masculinity.