Reviewerly/exHarmony huggingface dataset:
Addressing Regulatory Requirements on Explanations for Automated Decisions with Provenance—A Case Study

AI-based automated decisions are increasingly used as part of new services being deployed to the general public. This approach to building services presents significant potential benefits, such as the reduced speed of execution, increased accuracy, lower cost, and ability to adapt to a wide variety of situations. However, equally significant concerns have been raised and are now well documented such as concerns about privacy, fairness, bias, and ethics. On the consumer side, more often than not, the users of those services are provided with no or inadequate explanations for decisions that may impact their lives. In this article, we report the experience of developing a socio-technical approach to constructing explanations for such decisions from their audit trails, or provenance, in an automated manner. The work has been carried out in collaboration with the UK Information Commissioner’s Office. In particular, we have implemented an automated Loan Decision scenario, instrumented its decision pipeline to record provenance, categorized relevant explanations according to their audience and their regulatory purposes, built an explanation-generation prototype, and deployed the whole system in an online demonstrator.
Effectiveness of Communication Techniques in Distance Education and its Impact on Learning Outcomes at Jordanian Universities (Northern Province)

The present study aimed at investigating the effectiveness of communication techniques in distance education programs. It aimed to explore the impact of such effectiveness on the student’s learning outcomes at Jordanian universities. The researchers adopted a descriptive analytical approach. They developed a questionnaire based on the relevant literature. Questionnaire forms were distributed to 300 students enrolled at Jordanian universities located in the Northern Province. Those students were selected randomly. All the questionnaire forms were retrieved and considered valid and the response rate is one hundred percent. The researchers found that the effectiveness of communication techniques in distance education programs at Jordanian universities is moderate. The communication techniques have moderate impact on students’ outcomes in distance education at Jordanian universities. Significant differences exist between the respondents’ attitudes which can be attributed to gender and faculties for the favor females and the ones enrolled in human sciences faculties. There isn’t any statistically significant difference between the respondents’ attitudes which can be attributed to university year. The researchers recommend developing distance education.
Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification

We describe the system submitted to SemEval-2020 Task 6, Subtask 1.The aim of this subtask is to predict whether a given sentence contains a definition or not.Unsurprisingly, we found that strong results can be achieved by fine-tuning a pre-trained BERT language model.In this paper, we analyze the performance of this strategy.Among others, we show that results can be improved by using a two-step fine-tuning process, in which the BERT model is first fine-tuned on the full training set, and then further specialized towards a target domain.1. fine-tuning a single BERT model based on all the available training data; 2. fine-tuning a separate BERT model for each of the 7 domains, each time only relying on the training data that is available for that domain.The first strategy has the advantage that all training data can be exploited.However, our hypothesis is that this strategy may struggle to optimally capture the different definition styles that are used in different
Model-View-Update-Communicate: Session Types meet the Elm Architecture

Session types are a type discipline for communication channel endpoints which allow conformance to protocols to be checked statically. Safely implementing session types requires linearity, usually in the form of a linear type system. Unfortunately, linear typing is difficult to integrate with graphical user interfaces (GUIs), and to date most programs using session types are command line applications. In this paper, we propose the first principled integration of session typing and GUI development by building upon the Model-View-Update (MVU) architecture, pioneered by the Elm programming language. We introduce λMVU, the first formal model of the MVU architecture, and prove it sound. By extending λMVU with commands as found in Elm, along with linearity and model transitions, we show the first formal integration of session typing and GUI programming. We implement our approach in the Links web programming language, and show examples including a two-factor authentication workflow and multi-room chat server.
A novel method for development of post-quantum digital signature schemes

Introduction: Development of post-quantum digital signature standards represents a current challenge in the area of cryptography. Recently, the signature schemes based on the hidden discrete logarithm problem had been proposed. Further development of this approach represents significant practical interest, since it provides possibility of designing practical signature schemes possessing small size of public key and signature. Purpose: Development of the method for designing post-quantum signature schemes and new forms of the hidden discrete logarithm problem, corresponding to the method. Results: A method for designing post-quantum signature schemes is proposed. The method consists in setting the dependence of the publickey elements on masking multipliers that eliminates the periodicity connected with the value of discrete logarithm of periodic functions constructed on the base of the public parameters of the cryptoscheme. Two novel forms for defining the hidden discrete logarithm problem in finite associative algebras are proposed. The first (second) form has allowed to use the finite commutative (non-commutative) algebra as algebraic support of the developed signature schemes. Practical relevance: Due to significantly smaller size of public key and signature and approximately equal performance in comparison with the known analogues, the developed signature algorithms represent interest as candidates for practical post-quantum cryptoschemes.
Employer as an Actor of Student-Centered Education: Implementation Experience

The methodological problem of student-centered education and the person-oriented paradigm is revealed. The article considers the experience of Norilsk State Industrial Institute on the creation of a dialogue atmosphere «teacher – student – employer», which is characterized by respectful attitude of a student towards a teacher and employer. Our approach implies that student accepts a guiding influence of teacher and employer on his/her personal and professional development. The authors dwell on the model that has been introduced in the educational process to develop hard and soft skills in students demanded by the employer. The employer compiles competency models for every training area. According to these models, the University forms competency maps. A rapid response of the university to the formation of the relevant hard and soft skills is possible due to introducing them into the system of supplementary education, as well as the active participation of the employer at all stages of the educational process. The educational program «Formation of soft skills based on the practice of mindfulness» on the portal of the Norilsk State Industrial Institute is available for free and free training for everyone.
Impact of lossy compression of nanopore raw signal data on basecalling and consensus accuracy

Abstract Motivation Nanopore sequencing provides a real-time and portable solution to genomic sequencing, enabling better assembly, structural variant discovery and modified base detection than second generation technologies. The sequencing process generates a huge amount of data in the form of raw signal contained in fast5 files, which must be compressed to enable efficient storage and transfer. Since the raw data is inherently noisy, lossy compression has potential to significantly reduce space requirements without adversely impacting performance of downstream applications. Results We explore the use of lossy compression for nanopore raw data using two state-of-the-art lossy time-series compressors, and evaluate the tradeoff between compressed size and basecalling/consensus accuracy. We test several basecallers and consensus tools on a variety of datasets at varying depths of coverage, and conclude that lossy compression can provide 35–50% further reduction in compressed size of raw data over the state-of-the-art lossless compressor with negligible impact on basecalling accuracy (≲0.2% reduction) and consensus accuracy (≲0.002% reduction). In addition, we evaluate the impact of lossy compression on methylation calling accuracy and observe that this impact is minimal for similar reductions in compressed size, although further evaluation with improved benchmark datasets is required for reaching a definite conclusion. The results suggest the possibility of using lossy compression, potentially on the nanopore sequencing device itself, to achieve significant reductions in storage and transmission costs while preserving the accuracy of downstream applications. Availabilityand implementation The code is available at https://github.com/shubhamchandak94/lossy_compression_evaluation. Supplementary information Supplementary data are available at Bioinformatics online.
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints

Convolutional neural networks (CNNs) have achieved state-of-the-art performance on various tasks in computer vision. However, recent studies demonstrate that these models are vulnerable to carefully crafted adversarial samples and suffer from a significant performance drop when predicting them. Many methods have been proposed to improve adversarial robustness (e.g., adversarial training and new loss functions to learn adversarially robust feature representations). Here we offer a unique insight into the predictive behavior of CNNs that they tend to misclassify adversarial samples into the most probable false classes. This inspires us to propose a new Probabilistically Compact (PC) loss with logit constraints which can be used as a drop-in replacement for cross-entropy (CE) loss to improve CNN's adversarial robustness. Specifically, PC loss enlarges the probability gaps between true class and false classes meanwhile the logit constraints prevent the gaps from being melted by a small perturbation. We extensively compare our method with the state-of-the-art using large scale datasets under both white-box and black-box attacks to demonstrate its effectiveness. The source codes are available at https://github.com/xinli0928/PC-LC.
Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper)

Among the many changes brought about by the COVID-19 pandemic, one of the most pressing for scientific research concerns user testing.For the researchers who conduct studies with human participants, the requirements for social distancing have created a need for reflecting on methodologies that previously seemed relatively straightforward.It has become clear from the emerging literature on the topic and from first-hand experiences of researchers that the restrictions due to the pandemic affect every aspect of the research pipeline.The current paper offers an initial reflection on user-based research, drawing on the authors' own experiences and on the results of a survey that was conducted among researchers in different disciplines, primarily psychology, human-computer interaction (HCI), and visualization communities.While this sampling of researchers is by no means comprehensive, the multi-disciplinary approach and the consideration of different aspects of the research pipeline allow us to examine current and future challenges for user-based research.Through an exploration of these issues, this paper also invites others in the VIS-as well as in the wider-research community, to reflect on and discuss the ways in which the current crisis might also present new and previously unexplored opportunities.
Objective image quality assessment based on image color appearance and gradient features

With the rapid development of color image contents and imaging devices in various kinds of multimedia communication systems, conventional grayscale counterparts are replaced by chromatic ones. Under such a transition, the image quality assessment (IQA) model needs to be built by subjective visual measurement, designed in accordance with the results, and applied to the related practical problems. Based on the visual perception characteristics, chromaticity and the structure feature information are quantified, and an objective IQA model combining the color appearance and the gradient image features is proposed in this paper, namely color appearance and gradient similarity(CAGS) model. Two new color appearance indices, vividness and depth, are selected to build the chromatic similarity map. The structure information is characterized by gradient similarity map. Vividness map plays two roles in the proposed model. One is utilized as feature extractor to compute the local quality of distorted image, and the other is as a weight part to reflect the importance of local domain. To quantify the specific parameters of CAGS, Taguchi method is used and four main parameters, i.e., &lt;i&gt;K&lt;/i&gt;&lt;sub&gt;&lt;i&gt;V&lt;/i&gt;&lt;/sub&gt;, &lt;i&gt;K&lt;/i&gt;&lt;sub&gt;&lt;i&gt;D&lt;/i&gt;&lt;/sub&gt;, &lt;i&gt;K&lt;/i&gt;&lt;sub&gt;&lt;i&gt;G&lt;/i&gt;&lt;/sub&gt; and &lt;i&gt;α&lt;/i&gt;, of this model are determined based on the statistical correlation indices. The optimal parameters of CAGS are &lt;i&gt;K&lt;/i&gt;&lt;sub&gt;&lt;i&gt;V&lt;/i&gt;&lt;/sub&gt; = &lt;i&gt;K&lt;/i&gt;&lt;sub&gt;&lt;i&gt;D&lt;/i&gt;&lt;/sub&gt; = 0.02, &lt;i&gt;K&lt;/i&gt;&lt;sub&gt;&lt;i&gt;G&lt;/i&gt;&lt;/sub&gt; = 50, and &lt;i&gt;α&lt;/i&gt; = 0.1. Furthermore, the CAGS is tested by utilizing 94 reference images and 4830 distorted images from the four open image databases (LIVE, CSIQ, TID2013 and IVC). Additionally, the influences of the 35 distortion types on IQA are analyzed. Massive experiments are performed on four publicly available benchmark databases between CAGS and other 10 state-of-the-art and recently published IQA models, for the accuracy, complexity and generalization performance of IQA. The experimental results show that the accuracy PLCC of the CAGS model can achieve 0.8455 at lowest and 0.9640 at most in the four databases, and the results about commonly evaluation criteria prove that the CAGS performs higher consistency with the subjective evaluations. Among the 35 distortion types, the two distortion types, namely contrast change and change of color saturation, CAGS and mostly IQA models have the worst influence on IQA, and the CAGS yields the highest top three rank number. Moreover, the SROCC values of CAGS for other distortion types are all larger than 0.6 and the number of SROCC value larger than 0.95 is 14 times. Besides, the CAGS maintains a moderate computational complexity. These results of test and comparison above show that the CAGS model is effective and feasible, and the corresponding model has an excellent performance.
Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options

Large-scale natural language inference (NLI) datasets such as SNLI or MNLI have been created by asking crowdworkers to read a premise and write three new hypotheses, one for each possible semantic relationships (entailment, contradiction, and neutral). While this protocol has been used to create useful benchmark data, it remains unclear whether the writing-based annotation protocol is optimal for any purpose, since it has not been evaluated directly. Furthermore, there is ample evidence that crowdworker writing can introduce artifacts in the data. We investigate two alternative protocols which automatically create candidate (premise, hypothesis) pairs for annotators to label. Using these protocols and a writing-based baseline, we collect several new English NLI datasets of over 3k examples each, each using a fixed amount of annotator time, but a varying number of examples to fit that time budget. Our experiments on NLI and transfer learning show negative results: None of the alternative protocols outperforms the baseline in evaluations of generalization within NLI or on transfer to outside target tasks. We conclude that crowdworker writing still the best known option for entailment data, highlighting the need for further data collection work to focus on improving writing-based annotation processes.
Application of Data-Oriented Design in Game Development

Abstract Game development is a big, human resource intensive industry. Object-Orientated Design (OOD) is the most popular software design paradigm in game development. However, it is not the optimal choice whet it comes to performance and resource utilization. To solve this problem, Data-Oriented Design (DOD) can serve as an alternative for resource-intensive games where performance is the highest priority. The latter approach is often perceived as something exclusive to highly qualified specialists. Thus, a question arises whether DOD can suit and be useful for smaller companies and projects, and what are the trade-offs of using the approach compared to more wide-spread object-orientation. This paper examines the experience of a small game development team implementing two similar case study projects, one using OOD and another using DOD. After an overview of both implementations, the authors analyze and compare the two projects in regards to performance and maintainability. Recommendations are given on when each of the two design approaches should be applied and what are the corresponding skill sets for developers to use them.
DeepClone: Modeling Clones to Generate Code Predictions

Programmers often reuse code from source code repositories to reduce the development effort. Code clones are candidates for reuse in exploratory or rapid development, as they represent often repeated functionality in software systems. To facilitate code clone reuse, we propose DeepClone, a novel approach utilizing a deep learning algorithm for modeling code clones to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The predicted tokens require minimal customization to fit the context. DeepClone applies natural language processing techniques to learn from a large code corpus, and generates code tokens using the model learned. We have quantitatively evaluated our solution to assess (1) our model’s quality and its accuracy in token prediction, and (2) its performance and effectiveness in clone method prediction. We also discuss various application scenarios for our approach.
Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.
Unsatisfiable Core Analysis and Aggregates for Optimum Stable Model Search

Many efficient algorithms for the computation of optimum stable models in the context of Answer Set Programming (ASP) are based on unsatisfiable core analysis. Among them, algorithm OLL was the first introduced in the context of ASP, whereas algorithms ONE and PMRES were first introduced for solving the Maximum Satisfiability problem (MaxSAT) and later on adapted to ASP. In this paper, we present the porting to ASP of another state-of-the-art algorithm introduced for MaxSAT, namely K, which generalizes ONE and PMRES. Moreover, we present a new algorithm called OLL-IN-ONE that compactly encodes all aggregates of OLL by taking advantage of shared aggregate sets propagators. The performance of the algorithms have been empirically compared on instances taken from the latest ASP Competition.
A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements

Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majority of the proposed methods are based on existing algorithms and have only attempted to identify human or simple data mining methods that have high overhead and are also costly. The data mining methods presented so far have had high computational overhead or low accuracy. The purpose of this study is to present a model in which an improved ID3 decision tree with a support vector machine is used as a hybrid approach and also to improve the performance and accuracy, genetic algorithm and multilayer perceptron neural networks are applied. More efficient feature selection has been used to reduce computational overhead. The tree proposed in the proposed method has the lowest depth possible and therefore has high velocity and low computational overhead. For this purpose, the financial statements of 151 listed companies in Tehran Stock Exchange during 2014-2015 were surveyed and 125 financial ratios were extracted using ANOVA test, 23 fraud related ratios were selected as model input data. The proposed model has a high accuracy of about 80% of prediction accuracy compared to similar models.
Few-Shot Learning With Class Imbalance

Few-shot learning (FSL) algorithms are commonly trained through meta-learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood that class imbalance harms the performance of supervised methods, limited research examines the impact of imbalance on the FSL evaluation task. Our analysis compares ten state-of-the-art ML and FSL methods on different imbalance distributions and rebalancing techniques. Our results reveal that: 1) some FSL methods display a natural disposition against imbalance while most other approaches produce a performance drop by up to 17% compared to the balanced task without the appropriate mitigation; 2) many ML algorithms will not automatically learn to balance from exposure to imbalanced training tasks; 3) classical rebalancing strategies, such as random oversampling, can still be very effective, leading to state-of-the-art performances and should not be overlooked.