Reviewerly/exHarmony huggingface dataset:
A qualitative study of developers’ discussions of their problems and joys during the early COVID-19 months

Many software developers started to work from home on a short notice during the early periods of COVID-19. A number of previous papers have studied the wellbeing and productivity of software developers during COVID-19. The studies mainly use surveys based on predefined questionnaires. In this paper, we investigate the problems and joys that software developers experienced during the early months of COVID-19 by analyzing their discussions in online forum devRant, where discussions can be open and not bound by predefined survey questionnaires. The devRant platform is designed for developers to share their joys and frustrations of life. We manually analyze 825 devRant posts between January and April 12, 2020 that developers created to discuss their situation during COVID-19. WHO declared COVID-19 as pandemic on March 11, 2020. As such, our data offers us insights in the early months of COVID-19. We manually label each post along two dimensions: the topics of the discussion and the expressed sentiment polarity (positive, negative, neutral). We observed 19 topics that we group into six categories: Workplace & Professional aspects, Personal & Family well-being, Technical Aspects, Lockdown preparedness, Financial concerns, and Societal and Educational concerns. Around 49% of the discussions are negative and 26% are positive. We find evidence of developers' struggles with lack of documentation to work remotely and with their loneliness while working from home. We find stories of their job loss with little or no savings to fallback to. The analysis of developer discussions in the early months of a pandemic will help various stakeholders (e.g., software companies) make important decision early to alleviate developer problems if such a pandemic or similar emergency situation occurs in near future. Software engineering research can make further efforts to develop automated tools for remote work (e.g., automated documentation).
Privacy-Preserving Data Mining on Blockchain-Based WSNs

Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementation avoids the single point of failure problem that typically applies to centralized approaches, it is subject to other threats, such as external monitoring, and new challenges, such as the complexity of providing decentralized implementations for data mining algorithms. In this paper, we present a solution for privacy-aware distributed data mining on wireless sensor networks. Our solution uses a permissioned blockchain to avoid a single point of failure in the system. Contracts are used to construct an onion-like structure encompassing the Hoeffding trees and a route. The onion-routed query conceals the network identity of the sensors from external adversaries, and obfuscates the actual computation to hide it from internally compromised nodes. We validate our solution on a use case related to an air quality-monitoring sensor network. We compare the quality of our model against traditional models to support the feasibility and viability of the solution.
Medical Image Authentication Method Based on the Wavelet Packet and Energy Entropy

The transmission of digital medical information is affected by data compression, noise, scaling, labeling, and other factors. At the same time, medical data may be illegally copied and maliciously tampered with without authorization. Therefore, the copyright protection and integrity authentication of medical information are worthy of attention. In this paper, based on the wavelet packet and energy entropy, a new method of medical image authentication is designed. The proposed method uses the sliding window to measure the energy of the detail information. In the time-frequency data distribution, the local details of the data are mined. The complexity of energy is quantitatively described to highlight the valuable information. Based on the energy weight, the local energy entropy is constructed and normalized. The adjusted entropy value is used as the feature vector of the authentication information. A series of experiments show that the authentication method has good robustness against shearing attacks, median filtering, contrast enhancement, brightness enhancement, salt-and-pepper noise, Gaussian noise, multiplicative noise, image rotation, scaling attacks, sharpening, JPEG compression, and other attacks.
A Framework for Covid-19 Vaccine Management System Using Blockchain Technology

The SARS-CoV-2 first surfaced in 2019 in China and later spread across the globe causing a pandemic. Immunisation has thus far been considered to be mankind’s weapon of choice in the frontline fight against the virus defined as Covid-19. Mass vaccination programmes carried out by nations are closely related to public health information, data safety and data security. As countries roll out the immunisation efforts, cyber offenders try to exploit people’s personal health data and inoculation records while citizens can also be exposed to fake vaccine certificates issued by hackers. To prevent such any data breach or data exploitation, an effective system is urgently required to be in place that ensures the maximum security at a time of the unprecedented global crisis. Blockchain can be the perfect solution in this case thanks to its transparency, trustworthiness, and decentralised operations. We have proposed a blockchain based framework for covid 19 vaccination process to provide data immutability, transparency and correctness of beneficiary registration for vaccination, eliminating identity thefts and impersonation, tamper proof self-reporting of side effects, person identification and vaccine certification.
V-Doc : Visual questions answers with Documents

We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Data and demo video: https://github.com/usydnlp/vdoc
A systematic mapping study of source code representation for deep learning in software engineering

The usage of deep learning (DL) approaches for software engineering has attracted much attention, particularly in source code modelling and analysis. However, in order to use DL, source code needs to be formatted to fit the expected input form of DL models. This problem is known as source code representation. Source code can be represented via different approaches, most importantly, the tree-based, token-based, and graph-based approaches. We use a systematic mapping study to investigate i detail the representation approaches adopted in 103 studies that use DL in the context of software engineering. Thus, studies are collected from 2014 to 2021 from 14 different journals and 27 conferences. We show that each way of representing source code can provide a different, yet orthogonal view of the same source code. Thus, different software engineering tasks might require different (combinations of) code representation approaches, depending on the nature and complexity of the task. Particularly, we show that it is crucial to define whether the DL approach requires lexical, syntactical, or semantic code information. Our analysis shows that a wide range of different representations and combinations of representations (hybrid representations) are used to solve a wide range of common software engineering problems. However, we also observe that current research does not generally attempt to transfer existing representations or models to other studies even though there are other contexts in which these representations and models may also be useful. We believe that there is potential for more reuse and the application of transfer learning when applying DL to software engineering tasks.
Precise Fault Injection to Enable DFIA for Attacking AES in Remote FPGAs

Differential Fault Intensity Analysis (DFIA) is a class of biased-fault attacks that aim to recover secret keys from block ciphers such as Advanced Encryption Standard (AES). In DFIA an attacker collects a set of ciphertexts generated while carefully controlling the fault intensity, and then performs an analysis on the results that reveals the secret encryption key. In AES, DFIA requires injecting varied intensity faults during exactly the 9th round of encryption, which could be accomplished using clock or supply voltage glitching, although previous works give scant consideration to shaping the fault within a realistic scenario.In this work, we demonstrate DFIA against an FPGA implementation of AES without assuming arbitrary external control of clock or supply voltage. Instead we use on-chip ring oscillators (ROs) to create a precise and controllable voltage drop in the vicinity of the AES circuit, which causes timing faults to occur. The fault intensity is finely controlled by changing the number of activated ROs, and we explore how to optimize the timing of the RO activation to cause a fault in the 9th round as is required in DFIA. We use this approach to perform DFIA against AES on Xilinx Spartan-7 FPGA, show that it successfully extracts AES key bytes, and discuss its performance.
Authenticated-Encryption in the Quantum Key Distribution Classical Channel Using Post-Quantum Cryptography

Quantum computers are much faster than traditional computers and can easily break all Asymmetric Cryptographic Algorithms like RSA, Diffie-Hellman, elliptic curve, etc. For the secure transition of data in the future, we should use Quantum Cryptography, which is an emerging cryptographic technique that can be used for the secure transmission of data in a quantum world. Quantum Cryptography provides unconditional security to the data which were transmitted. Quantum Cryptography uses two channels for communication - a classical channel and a quantum channel. However, authentication in the classical channel is currently done by the classical cryptographic algorithms that are not efficient and quantum-safe. In addition to that, the existing QKD schemes do not ensure confidentiality to the data transmitted through the classical channel. PostQuantum Cryptography(PQC) is a computational-security based cryptographic technique that is quantum-resistant. So, as a solution to the authentication and encryption inefficiencies in the classical channel, PQC can be used. Here we are proposing an Authenticated-Encryption Scheme in the classical channel using the PQC algorithms Falcon and NTRU that are Quantum safe.
Resistance spot welding nugget cross-sectional contour extraction and geometric modeling based on the improved Gabor filter

In resistance spot welding (RSW), the accurate, rapid extraction of the nugget cross-sectional contour and geometric modeling enables the quantification of the cross-section’s characteristic parameters. In this paper, an improved Gabor filter–based method for the contour extraction of an RSW nugget cross-section is proposed that combines the characteristics of the nugget cross-section with complex textures in multiple directions, the high overlap of gray values with the base material, and high noise. First, the shape of the filter kernel is reconstructed and the multi-directional texture features obtained. Then, a hybrid contour extraction strategy is designed. Finally, the geometric model of the nugget contour is established. Experiments showed that the improved Gabor filtering algorithm reduced the runtime by an order of magnitude and improved the performance of the nugget cross-sectional contour extraction method, with an accuracy of 98.61% for a root-mean-square-error threshold of 5 pixels.
Specialized document embeddings for aspect-based similarity of research papers

Document embeddings and similarity measures underpin content-based recommender systems, whereby a document is commonly represented as a single generic embedding. However, similarity computed on single vector representations provides only one perspective on document similarity that ignores which aspects make two documents alike. To address this limitation, aspect-based similarity measures have been developed using document segmentation or pairwise multi-class document classification. While segmentation harms the document coherence, the pairwise classification approach scales poorly to large scale corpora. In this paper, we treat aspect-based similarity as a classical vector similarity problem in aspect-specific embedding spaces. We represent a document not as a single generic embedding but as multiple specialized embeddings. Our approach avoids document segmentation and scales linearly w.r.t. the corpus size. In an empirical study, we use the Papers with Code corpus containing 157, 606 research papers and consider the task, method, and dataset of the respective research papers as their aspects. We compare and analyze three generic document embeddings, six specialized document embeddings and a pairwise classification baseline in the context of research paper recommendations. As generic document embeddings, we consider FastText, SciBERT, and SPECTER. To compute the specialized document embeddings, we compare three alternative methods inspired by retrofitting, fine-tuning, and Siamese networks. In our experiments, Siamese SciBERT achieved the highest scores. Additional analyses indicate an implicit bias of the generic document embeddings towards the dataset aspect and against the method aspect of each research paper. Our approach of aspect-based document embeddings mitigates potential risks arising from implicit biases by making them explicit. This can, for example, be used for more diverse and explainable recommendations.
Evaluation of an online text simplification editor using manual and automated metrics for perceived and actual text difficulty

Simplifying healthcare text to improve understanding is difficult but critical to improve health literacy. Unfortunately, few tools exist that have been shown objectively to improve text and understanding. We developed an online editor that integrates simplification algorithms that suggest concrete simplifications, all of which have been shown individually to affect text difficulty.The editor was used by a health educator at a local community health center to simplify 4 texts. A controlled experiment was conducted with community center members to measure perceived and actual difficulty of the original and simplified texts. Perceived difficulty was measured using a Likert scale; actual difficulty with multiple-choice questions and with free recall of information evaluated by the educator and 2 sets of automated metrics.The results show that perceived difficulty improved with simplification. Several multiple-choice questions, measuring actual difficulty, were answered more correctly with the simplified text. Free recall of information showed no improvement based on the educator evaluation but was better for simplified texts when measured with automated metrics. Two follow-up analyses showed that self-reported education level and the amount of English spoken at home positively correlated with question accuracy for original texts and the effect disappears with simplified text.Simplifying text is difficult and the results are subtle. However, using a variety of different metrics helps quantify the effects of changes.Text simplification can be supported by algorithmic tools. Without requiring tool training or linguistic knowledge, our simplification editor helped simplify healthcare related texts.
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A new robust fuzzy clustering framework considering different data weights in different clusters

In conventional fuzzy C-means clustering algorithms, each data and each feature are treated equally, the clustering performance is sensitive to the noise points; in existing weighting clustering algorithms, few studies have focus on data weighting and feature weighting simultaneously, besides, the same data in different clusters is treated equally. To address this issue, in this paper, taking the different data weights in different clusters and the different feature weights in different clusters into consideration, we present a new robust fuzzy C-means clustering framework. For the first time, we propose a whole new idea that the same data in different clusters should have different importance, the different data in a cluster should have different importance, as well; By the new data weighting method, the proposed clustering algorithm can weaken the impact of noise points on the formation of each clustering center, which could enhance the robustness of clustering; to stimulate more data and more features to take part in the process of clustering and to avoid overfitting, we add l2-norm regularization of the data weights and l2-norm regularization of the feature weights to the objective function. Then, based on the presented objective function, we get the scientific update rules of the different data weights in different clusters, the different feature weights in different clusters, the membership degrees, and the cluster centers, during each iteration. To assess the performance of the new fuzzy C-means framework, experimental verifications on synthetic dataset and real-world datasets are conducted, experimental results have shown that the new algorithm can achieve better clustering performances in comparison to other related clustering methods.
Toward Better Understanding Older Adults: A Biography Brief Timeline Extraction Approach

Studies have shown that life stories can help caregivers better understand older adults, leading to better care. The original life stories are often redundant and disordered, hindering the discovery of valuable information. There has been little research on organizing older adults' life stories automatically. This article proposes the ALBERT Based Text Extraction Network (ABTE-NET) to generate valuable event timelines to address this problem. To evaluate the proposed method, we created an Older Adults' Life Story dataset with 80 older adults' life stories. In experiments, we verify that the timelines generated by ABTE-NET have good readability and summarization for life stories. A survey of 33 caregivers from two nursing homes shows that timelines can help caregivers understand older adults and build positive relationships, just like life stories. More importantly, timelines are better organized and more concise than original life stories, reducing the cognitive load and helping caregivers form a preliminary understanding of the older adult quickly.
SynchronEyes: A Novel, Paired Data Set of Eye Movements Recorded Simultaneously with Remote and Wearable Eye-Tracking Devices

Comparing the performance of new eye-tracking devices against an established benchmark is vital for identifying differences in the way eye movements are reported by each device. This paper introduces a new paired data set comprised of eye movement recordings captured simultaneously with both the EyeLink 1000—considered the "gold standard" in eye-tracking research studies—and the recently released AdHawk MindLink eye tracker. Our work presents a methodology for simultaneous data collection and a comparison of the resulting eye-tracking signal quality achieved by each device.
Analysis of the performance of Islamic gold-backed cryptocurrencies during the bear market of 2020

This study examines the performance of Islamic gold-backed cryptocurrencies during the bear market of 2020. Price data is collected for three Islamic gold-backed cryptocurrencies, OneGram, HelloGold and X8X, as well as the conventional gold-backed cryptocurrency, PaxGold, and the conventional fiat-backed cryptocurrency, Bitcoin, from December 2019 to November 2020. Analysis via ARMA-EGARCH models show that returns on all cryptocurrencies are lower during the bear market but the decrease is only significant for the Islamic gold-backed cryptocurrencies. Volatility is found to be higher for all five cryptocurrencies but the effect of the bear market on the volatility is only significant for the conventional cryptocurrencies.
Blockchain system in the higher education: Storing academical students' records and achievements accumulated in the educational process

Abstract The significant interest in using of the blockchain technology in higher education for keeping secure student's records storage and retrieval is noted. This paper describes a new system based on blockchain approach to store students' records accumulated in the educational process at the higher institution in extended format. Every block of the records contains four types of information about student: academic activities, research participation, cultural activities and achievements. The system suggests participation of educational institution, student and potential employers in usage of integrated machine learning and blockchain technologies. The novelty of our paper is that the system allows university and student to keep complete and accurate data about the entire student learning process, avoid forgeries of educational records, e‐portfolio inaccuracies and fabricated degrees. Additionally, it could be used by employers to find qualifying potential employees, using large amount of verified and systematically accumulated data.
Self-Regulated Learning and Academic Performance in Chilean University Students in Virtual Mode During the Pandemic: Effect of the 4Planning App

Research on the use of smartphone apps with the aim of developing self-regulated learning (SRL) and increasing academic performance of university students in virtual mode, as a result of the COVID-19 pandemic, is recent and scarce. The present article shows the results of a study that analyzed the effect of using the 4Planning app with an intra-curricular approach on SRL and on the academic performance of 119 1st-year psychology students in virtual mode, at a Chilean university (MAge = 22.81, SDAge = 6.52). The research was conducted with quantitative methodology and a quasi-experimental design, with pre- and posttest measurements of an experimental group and comparisons with three control groups. The results show that students who used the app during 10 weeks of the first academic semester 2021: (1) increased SRL self-report, (2) increased academic self-efficacy, and (3) obtained higher academic performance, compared to those who did not use it. The above occurs because the 4Planning app activates functions of the self-regulatory system of goal-directed behavior, which allows exercising the capacity of self-direction and self-influence on this type of behavior in the particular context of academic performance, which produces SRL. It is concluded that the 4Planning app is effective in developing self-regulation and improving the academic performance of 1st-year university students, in virtual learning mode.