Reviewerly/exHarmony huggingface dataset:
The Role and Place of Covid-19: An Opportunistic Avenue for Exponential World’s Upsurge in Cyber Crime

An evidence-based analysis of COVID-19 suggests that the ailment is a bio-medically inclined natural mystic blowing through the world. To this end, this study focuses solely on the role the pandemic plays as an outbreak of cybercrime vector. The study presents a number of the world’s most recent cyber insecurity cases that accompanied the onset of the pandemic and findings were discussed within the context of situational opportunity theory of crime. It provides a framework for emergency management approach to protect global citizens and institutions from cyberattacks, as well as, mitigating the outbreak of the crime being propagated by the presence of the novel virus. Global sensitization and awareness programmes across various communities on the potential dangers of cyber insecurity accompanying the COVID-19 pandemic should be helpful. Of most significance, the fight against the invisible warfare should continue with high spirits of relentlessness until absolute peace, relief, resilience and normalcy are able to take root in the global communities.
Posterior contraction rates for non-parametric state and drift estimation

<p style='text-indent:20px;'>We consider a combined state and drift estimation problem for the linear stochastic heat equation. The infinite-dimensional Bayesian inference problem is formulated in terms of the Kalman–Bucy filter over an extended state space, and its long-time asymptotic properties are studied. Asymptotic posterior contraction rates in the unknown drift function are the main contribution of this paper. Such rates have been studied before for stationary non-parametric Bayesian inverse problems, and here we demonstrate the consistency of our time-dependent formulation with these previous results building upon scale separation and a slow manifold approximation.
Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models

Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying, fake news and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now publicly available for sentiment detection. Users can easily incorporate these APIs in their applications. While combining inputs from multiple modalities or black-box models for increasing accuracy is commonly studied in multimedia computing literature, there has been little work on combining different modalities for increasingfairness of the resulting decision. In this work, we audit multiple commercial sentiment detection APIs for the gender bias in two-actor news headlines settings and report on the level of bias observed. Next, we propose a "Flexible Fair Regression" approach, which ensures satisfactory accuracy and fairness by jointly learning from multiple black-box models. The results pave way for fair yet accurate sentiment detectors for multiple applications.
Seeking artificial common sense

The long-standing goal of providing artificial intelligence some measure of common sense remains elusive.
Graph-Based Universal Dependency Parsing in the Age of the Transformer: What Works, and What Doesn't.

Current state-of-the-art graph-based dependency parsers differ on various dimensions. Among others, these include (a) the choice of pre-trained word embeddings or language models used for representing token, (b) training setups performing only parsing or additional tasks such as part-of-speech-tagging, and (c) their mechanism of constructing trees or graphs from edge scores. Because of this, it is difficult to estimate the impact of these architectural decisions when comparing parsers. In this paper, we perform a series of experiments on STEPS, a new modular graph-based parser for basic and enhanced Universal Dependencies, analyzing the effects of architectural configurations. We find that pre-trained embeddings have by far the greatest and most clear-cut impact on parser performance. The choice of factorized vs. unfactorized architectures and a multi-task training setup affect parsing accuracy in more subtle ways, depending on target language and output representation (trees vs. graphs). Our parser achieves new state-of-the-art results for a wide range of languages on both basic as well as enhanced Universal Dependencies, using a unified and comparatively simple architecture for both parsing tasks.
Consistency-preserving evolution planning on feature models

A software product line (SPL) enables large-scale reuse in a family of related software systems through configurable features. SPLs represent a long-term investment so that their ongoing evolution becomes paramount and requires careful planning. While existing approaches enable to create an evolution plan for an SPL on feature-model (FM) level, they assume the plan to be rigid and do not support retroactive changes. In this paper, we present a method that enables to create and retroactively adapt an FM evolution plan while preventing undesired impacts on its structural and logical consistency. This method is founded in structural operational semantics and linear temporal logic. We implement our method using rewriting logic, integrate it within an FM tool suite and perform an evaluation using a collection of existing FM evolution scenarios.
Mutually unbiased unextendible maximally entangled bases in Cd ⊗ Cq(d+1)

Quantum measurements based on mutually unbiased base (MUB) are commonly used in quantum information processing. Firstly, we construct two completed unextendible maximally entangled bases (UMEBs) in bipartite systems [Formula: see text]. Then, the sufficient and necessary conditions of these two UMEBs develop into MUB can be obtained. Finally, we present some UMEBs that are mutually unbiased in concrete bipartite systems. These results provide a method to construct the best measurement bases.
A Competent and Accurate BlockChain based E-Voting System on Liquid Democracy

In order to avoid the demerits of Direct Democracy (DD) and Representative Democracy (RD), recently a democratic reform called Liquid Democracy (LD) was introduced which is a combination of DD and RD that gives the people full decisional control either by voting directly or by entrusting their voting power to a representative for some political ideologies or bills. Our work attempts to study the democratic models based on accuracy and competence parameters. Further a liquid democracy voting protocol is proposed and implemented on a block chain based distributed ledger which is capable of fighting attacks of integrity and non-repudiation while ensuring authenticity of the votes. This system would be transparent thus ensuring people about the legitimacy of their delegation of votes and direct votes. This is a big step up from the current existing system of EVM’s that do not guarantee this level of security.
Problems of teaching mathematics in primary grades and some ways to solve them

The article is devoted to a retrospective analysis of the formation and development of methods of teaching mathematics at school. In the direction of the development of methodology and methodological training, there can be the integration of subject methodologies, as a dialogue of methods on the functional-target, organizational-activity and content aspects.
CareHub: Smart Screen VUI and Home Appliances Control for Older Adults

While voice user interfaces (VUIs) are becoming promising tools for users to control home appliances, the VUIs pose great challenges to the older adults. In this paper, I explored how to use multimedia interactions (i.e., voice+visual output) to enhance the VUI for older adults to better control home appliances. I conducted two preliminary studies with six older adults to understand the current usability problems. Given the results, I designed a smart screen prototype with visual-enhanced VUI. The evaluation showed that the participants were highly receptive to the prototype and considered its features effective and accessible.
Designing Mathematics Learning Media Based on Mobile Learning for Ten Graders of Vocational High School

The development of information technology plays an essential role in supporting learning activities in all subjects, including mathematics.In mathematics learning, teachers usually use conventional media, such as printed textbooks and paper-based students' worksheets.Regarding this issue, an innovation of mathematics learning media based on information technology is needed to be developed.Therefore, this study aims to design learning media based on mobile learning.This study is a research and development (R&D) study applying the 4D development model (Define, Design, Develop, Disseminate).This study's subjects are students in the tenth grade of a private vocational high school in Central Java, Indonesia.The results of this study can be described as follows: 1) the rating of media experts with an average value of 67.5 is included in the excellent category; 2) the material experts' assessment with an average score of 75 is included in the excellent category; 3) the students' evaluation with an average grade of 70,119 is included in the excellent category.The assessment was obtained from the SUS (System Usability Scale) questionnaire.Moreover, based on the analysis of a pre-experimental design, it can be concluded the media improve students' scores in the topic of matrix operations.
RECOApy: Data Recording, Pre-Processing and Phonetic Transcription for End-to-End Speech-Based Applications

Deep learning enables the development of efficient end-to-end speech processing applications while bypassing the need for expert linguistic and signal processing features. Yet, recent studies show that good quality speech resources and phonetic transcription of the training data can enhance the results of these applications. In this paper, the RECOApy tool is introduced. RECOApy streamlines the steps of data recording and pre-processing required in end-to-end speech-based applications. The tool implements an easy-to-use interface for prompted speech recording, spectrogram and waveform analysis, utterance-level normalisation and silence trimming, as well grapheme-to-phoneme conversion of the prompts in eight languages: Czech, English, French, German, Italian, Polish, Romanian and Spanish. The grapheme-to-phoneme (G2P) converters are deep neural network (DNN) based architectures trained on lexicons extracted from the Wiktionary online collaborative resource. With the different degree of orthographic transparency, as well as the varying amount of phonetic entries across the languages, the DNN's hyperparameters are optimised with an evolution strategy. The phoneme and word error rates of the resulting G2P converters are presented and discussed. The tool, the processed phonetic lexicons and trained G2P models are made freely available.
ANIMATED IMAGES AS A MEANS OF DIGITAL TECHNOLOGIES IN TEACHING MATHEMATICS

Statement of the problem. Universal digitalization of economy and public relations is a reality of today, and a school teacher is faced with the task of forming a personality ready to reveal his/her creative potential in the digital economy. The problem is to outline the ways of digitalization of teaching mathematics that meets the needs of society, and contribute to the understanding and assimilation of mathematical knowledge based on digital educational resources using the animation capabilities of computer environments. The purpose of the article is to present animated images in the GeoGebra environment as new means of digital technologies for teaching mathematics. Research methodology. Analysis of scientific, educational and methodological literature, synthesis of the authors’ work experience at school and university in approbation of elements of the mathematics teaching system using animated images. Research results. Specific types of animated images are presented and the expediency of their use as effective means of digital technologies for teaching mathematics in the lessons of arithmetic, algebra, trigonometry and the beginnings of mathematical analysis is substantiated. Conclusion. The use of new technology with the use of animated images in mathematics lessons at school increases the level of understanding and assimilation of mathematical knowledge by providing clarity of mathematical concepts and statements. In addition, animated images are used to eliminate computational difficulties, to generate tasks of the same type, and to organize self-testing of students’ knowledge. The creation of digital educational content using the animation capabilities of computer environments increases the technological equipment of a modern mathematics teacher, which will allow him/her to achieve better educational results.
Neural Language Modeling with Implicit Cache Pointers

A cache-inspired approach is proposed for neural language models (LMs) to improve long-range dependency and better predict rare words from long contexts. This approach is a simpler alternative to attention-based pointer mechanism that enables neural LMs to reproduce words from recent history. Without using attention and mixture structure, the method only involves appending extra tokens that represent words in history to the output layer of a neural LM and modifying training supervisions accordingly. A memory-augmentation unit is introduced to learn words that are particularly likely to repeat. We experiment with both recurrent neural network- and Transformer-based LMs. Perplexity evaluation on Penn Treebank and WikiText-2 shows the proposed model outperforms both LSTM and LSTM with attention-based pointer mechanism and is more effective on rare words. N-best rescoring experiments on Switchboard indicate that it benefits both very rare and frequent words. However, it is challenging for the proposed model as well as two other models with attention-based pointer mechanism to obtain good overall WER reductions.
Multimedia Information System (MIS) for Knowledge Generation and ICT Policy Framework in Education

The digital characteristics of the 21st century business and academic environment had become increasingly dependent on the software system and ICT policy configuration to maintain smart operations as the global world is increasingly becoming internet-driven and computer database centred. The role of the information system otherwise regarded as the federated multimedia digital library, ICT policy framework implementation, and e-learning education in the academic sustainability of the developing nations such Nigeria and Africa is to ensure free circulation of knowledge within digital information ecosystem. As the global world evolve into digital economy with ever increasing possibility of software technology (information system) taking greater percentage of the entire world existence in the near future for educational automation. This current paper is of the opinion that educational investment in the 21st century should embrace technological innovation, software consolidation, and ICT infrastructure in transforming education and making it flexible for easy management.
Integral Cryptanalysis of Reduced-Round Tweakable TWINE

Tweakable TWINE (T-TWINE) is the first lightweight dedicated tweakable block cipher family built on Generalized Feistel Structure (GFS). T-TWINE family is an extension of the conventional block cipher TWINE with minimal modification by adding a simple tweak based on the SKINNY’s tweakey schedule. Similar to TWINE, T-TWINE has two variants, namely T-TWINE-80 and T-TWINE-128. The two variants have the same block size of 64 bits and a variable key length of 80 and 128 bits. In this paper, we study the implications for adding the tweak on the security of T-TWINE against the integral cryptanalysis. In particular, we first utilize the bit-based division property to search for the longest integral distinguisher. As a result, we are able to perform a distinguishing attack against 19 rounds using $$2^{6} \times 2^{63} = 2^{69}$$ chosen tweak-plaintext combinations. We then convert this attack to key recovery attacks against 26 and 27 rounds (out of 36) of T-TWINE-80 and T-TWINE-128, respectively. By prepending one round before the distinguisher and using dynamically chosen plaintexts, we manage to extend the attack one more round without using the full codebook of the plaintext. Therefore, we are able to attack 27 and 28 rounds of T-TWINE-80 and T-TWINE-128, respectively.
Insight into Diverse Keyphrase Extraction Techniques from Text Documents

Keyphrase refers to a set of terms which best present the document content in a brief way. As the world is already approached toward digital documents, it is more crucial to find out a particular document accurately and efficiently. This can be achieved by keyphrase extraction as it describes the core information of documents which will be helpful to find target documents for the users. The keyphrase extraction is one of the challenging research areas in the natural language processing field because of the involvement of the voluminous unstructured and unorganized data. This paper surveys various keyphrase extraction technologies and their respective families. A systematic review of this paper provides an overview of the existing technologies with their pros and cons, and explores the direction for future development and research in this field.
Blockchain Network Model to Improve Supply Chain Visibility based on Smart Contract

Due to the increasing complexity of supply chains over the past years, many factors significantly contribute to lowering the supply chains performance.Poor visibility is one of the major challenging factors that lowers supply chains performance.This paper proposes a Blockchain-based supply chain network model to improve the supply chain visibility.The model focuses in improving the visibility measurements properties: information sharing, traceability, and inventory visibility.The proposed model consists of information sharing, traceability, and inventory visibility platforms based on Blockchain technology smart contract.The model built with Hyperledger platform and extend the Hyperledger Composer Supply Chain Network (HCSC) model.The research is designed to three main phases.First phase: the preliminary phase which is the literature review phase to identify the existing challenges in the domain.The second phase: the design and implementation phase which is the development steps of the proposed research model.The third phase: the evaluation phase which represent the performance evaluation of the proposed model and the comparisons between the proposed model and the existing models.In the evaluation performance, the common performance metrics Lead time and average inventory levels will be compared in the proposed model, Cloud-based information system, and the traditional supply chain.These proposed platforms offer an end-to-end visibility of products, orders, and stock levels for supply chain practitioners and customers within supply chain networks.Which helps managers' access key information that support critical business decisions and offers essential criteria for competitiveness and therefore, enhance supply chain performance.
EVALUASI KETERGUNAAN WEBSITE PERPUSTAKAAN UNIVERSITAS INDONESIA MENGGUNAKAN SYSTEM USABILITY SCALE

The University of Indonesia (UI) Library has a website that is accessed by students to meet their information needs.Evaluation of usability on the university library website is important so that users remain loyal to visit it and the results can be used as a basis for further development.This study aims to evaluate the usability on the University Indonesia Library website between the batch of 2016 and 2019 and compare its results.The method used in this study is descriptive quantitative using a questionnaire based on the System Usability Scale.Questionnaires are distributed online through instant messaging applications and social media.The research data were analyzed using the System Usability Scale score calculation technique, then the results were compared between the two batches.The results of the analysis showed that the batch of 2016 had a better website usability value than the batch of 2019.The two batches also have good user experience based on the five components of Nielsen's usability, namely learnability, efficiency, memorability, errors, and satisfaction.The usability of the Indonesian Library's website has not been well accepted by students of batch 2016 and 2019, although there are differences in scores between the two batches.Because the SUS score is below average and below acceptable, further research can be conducted using different method or instrument to identify the existing problems.
Morphosyntactic predictability of translationese

Abstract It is often assumed that translated texts are easier to process than original ones. However, it has also been shown that translated texts contain evident traces of source-language morphosyntax, which should presumably make them less predictable and harder to process. We test these competing observations by measuring morphosyntactic entropies of original and translated texts in several languages and show that there may exist a categorical distinction between translations made from structurally-similar languages (which are more predictable than original texts) and those made from structurally-divergent languages (which are often non-idiomatic, involve structural transfer, and therefore are more entropic).
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning.

Sample efficiency has been one of the major challenges for deep reinforcement learning. Recently, model-based reinforcement learning has been proposed to address this challenge by performing planning on imaginary trajectories with a learned world model. However, world model learning may suffer from overfitting to training trajectories, and thus model-based value estimation and policy search will be pone to be sucked in an inferior local policy. In this paper, we propose a novel model-based reinforcement learning algorithm, called BrIdging Reality and Dream (BIRD). It maximizes the mutual information between imaginary and real trajectories so that the policy improvement learned from imaginary trajectories can be easily generalized to real trajectories. We demonstrate that our approach improves sample efficiency of model-based planning, and achieves state-of-the-art performance on challenging visual control benchmarks.