Reviewerly/exHarmony huggingface dataset:
ICT in Facilitating Tertiary-level English: A Note on the Prospect of BSMRSTU

Bangladesh has three basic levels of education: primary, secondary, and tertiary. English is taught as a mandatory subject/course at all levels. This is the only language through which people can get information from one part of the world to another. The 21st century has witnessed tremendous growth in information technology. Technology is used in medicine, business, agriculture, airlines, education, and what not. The government of Bangladesh enacted a policy in 2009 to use ICT for multivariate purposes. The policy makers aspire to impart education at all levels through the use of modern technology. This is why the curriculum has been updated. CALL (Computer-assisted language learning) and MALL (Mobile-assisted language learning) have become generic approaches in teaching and learning. To teach English, several computer-based programs and mobile-based applications can be utilized. This paper investigates the prospects of such measures in the context of BSMRSTU (Bangabandhu Sheikh Mujibur Rahman Science and Technology University). Participants were selected from the students of foundational English courses who are taught using the traditional classroom set-up. Through interviews and focused group discussions, the study diagnoses some pressing pedagogical problems that hinder effective teaching and learning and proposes that technology can be a probable solution in this regard.
Projection based lower bounds of concurrence for multipartite quantum systems

We study the concurrence of arbitrary-dimensional multipartite quantum states. Analytical lower bounds of concurrence for tripartite quantum states are derived by projecting high-dimensional states to 2 ⊗ 2 ⊗ 2 substates. The results are then generalized to arbitrary multipartite quantum systems. Furthermore, the scheme enables us obtain lower bounds of concurrence for arbitrary four-partite quantum states by projecting high-dimensional states to arbitrary given lower dimensional substates. By detailed examples we show that our results improve the existing lower bounds of concurrence.
The Impact of Training and Development on Organizational Overall Performance in IT Industry

An excellent performance of employees is a key factor in an organization's growth.Employees are the foundation of the company.Organization is investing more money on preparation and advancement of workers to improve their productivity and effectiveness.The study has interviewed workers to examine the factors that impacts on the performance of the employees.Upon interviewing the IT Industry employees, we can say that the IT industry employees are happy with their respective training and development programmes.It has also been concluded in this study that employee satisfaction, work security, and financial benefits are the main factors in employee performance growth.Management with a focus on improving employees will strengthen the interest and commitment of employees to the job.
Comparison of Denoising Algorithms for Demosacing Low Lighting Images Using CFA 2.0

In modern digital cameras, the Bayer color filter array (CFA) has been widely used. It is also widely known as CFA 1.0. However, Bayer pattern is inferior to the red-green-blue-white (RGBW) pattern, which is also known as CFA 2.0, in low lighting conditions in which Poisson noise is present. It is well known that demosaicing algorithms cannot effectively deal with Poisson noise and additional denoising is needed in order to improve the image quality. In this paper, we propose to evaluate various conventional and deep learning based denoising algorithms for CFA 2.0 in low lighting conditions. We will also investigate the impact of the location of denoising, which refers to whether the denoising is done before or after a critical step of demosaicing. Extensive experiments show that some denoising algorithms can indeed improve the image quality in low lighting conditions. We also noticed that the location of denoising plays an important role in the overall demosaicing performance.
Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution

Hard cases of pronoun resolution have been used as a long-standing benchmark for commonsense reasoning.In the recent literature, pre-trained language models have been used to obtain state-of-the-art results on pronoun resolution.Overall, four categories of training and evaluation objectives have been introduced.The variety of training datasets and pretrained language models used in these works makes it unclear whether the choice of training objective is critical.In this work, we make a fair comparison of the performance and seedwise stability of four models that represent the four categories of objectives.Our experiments show that the objective of sequence ranking performs the best in-domain, while the objective of semantic similarity between candidates and pronoun performs the best out-of-domain.We also observe a seed-wise instability of the model using sequence ranking, which is not the case when the other objectives are used.
Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification

This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: "Identification of Informative COVID-19 English Tweet".Our system explores three recent Transformerbased deep learning models pretrained on largescale data to encode documents.Moreover, we developed two feature enrichment methods to enhance document embeddings by integrating emoji embeddings and syntactic features into deep learning models.Our system achieved F 1 -score of 0.897 and accuracy of 90.1% on the test set, and ranked in the top-third of all 55 teams.
Constructing Picard curves with complex multiplication using the Chinese remainder theorem

We give a new algorithm for constructing Picard curves over a finite field with a given endomorphism ring. This has important applications in cryptography since curves of genus 3 allow for smaller key sizes than elliptic curves. For a sextic CM-field $K$ containing the cube roots of unity, we define and compute certain class polynomials modulo small primes and then use the Chinese Remainder Theorem to construct the class polynomials over the rationals. We also give some examples.
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering

Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability.However, annotating rationales requires much effort and only few datasets contain such labeled rationales, making supervised learning for rationalization difficult.In this paper, we propose a novel approach that leverages the benefits of both multi-task learning and transfer learning for generating rationales through question answering in a zeroshot fashion.For two benchmark rationalization datasets, the proposed method achieves comparable or even better performance of rationalization without any supervised signal, demonstrating the great potential of zero-shot rationalization for better interpretability. 1
From Linear Logic to Cyclic Sharing

We present a translation from Multiplicative Exponential Linear Logic to a simply-typed lambda calculus with cyclic sharing.This translation is derived from a simple observation on the Intconstruction on traced monoidal categories.It turns out that the translation is a mixture of the call-by-name CPS translation and the Geometry of Interaction-based interpretation.
SENTIMENT FRAMES FOR ATTITUDE EXTRACTION IN RUSSIAN

Texts can convey several types of inter-related information concerning opinions and attitudes. Such information includes the author’s attitude towards mentioned entities, attitudes of the entities towards each other, positive and negative effects on the entities in the described situations. In this paper, we described the lexicon RuSentiFrames for Russian, where predicate words and expressions are collected and linked to so-called sentiment frames conveying several types of presupposed information on attitudes and effects. We applied the created frames in the task of extracting attitudes from a large news collection.
Freezing dynamics of entanglement and nonlocality for qutrit-qutrit (3 ⊗ 3) quantum systems

We examine the possibilities of nontrivial phenomena of time-invariant entanglement and freezing dynamics of entanglement for qutrit-qutrit quantum systems. We find no evidence for time-invariant entanglement, however, we do observe that certain quantum states freeze their entanglement after decaying for some time. It is interesting that quantum states are changing whereas their entanglement remains constant. We find that the combined action of decoherence free subspaces and subspaces where quantum states decay, facilitate this phenomenon. This study is an extension of similar phenomena observed for qubit-qubit systems, qubit-qutrit, and multipartite quantum systems. We examine nonlocality of a specific family of states and find the certain instances where the states still remain entangled, however, they can either loose their nonlocality at a finite time or remain nonlocal for all times.
Information Literacy Experience of Undergraduate Students during Their Involvement in the University Creativity Program

Indonesian universities have a long tradition of supporting their students in developing their innovative and creative ways of thinking during their times in university.One of the programs being continuously offered to the students is named student creativity program.It was initiated in 2001 to showcase students' creative and innovative works.This study presented undergraduate students' information literacy experience during their involvement in this program conducted by Diponegoro University, Indonesia.This research was based on the relational perspective of information literacy that emphasizes the relationship between people and information.Qualitative data were collected through semi-structured interviews with 15 undergraduate students from 8 different faculties.Thematic analysis was conducted to interpret various related aspects.Results revealed that the identified themes contributed to communities, brainstorming, and Googlization.All the themes illustrated the information literacy experience of undergraduate students as they contributed to the university creative program.This study provided new insights into students' creativity programs and information literacy as a basis for designing future research.Although the majority of studies in Indonesia have focused on the conception of information literacy from a behavioral perspective, this research offered different ways on understanding the conception of information literacy.This research would be beneficial to university libraries in terms of enhancing their information literacy programs and faculties in terms of understanding how undergraduate students interact with information during their academic activities.
Design of a Hybrid Digital Watermarking Algorithm with High Robustness

With the development of the Internet, storage and transmission technologies such as printers and scanners, digital multimedia products are rapidly transmitted through the Internet broadcasting, multimedia works becoming easy to obtain and illegally tampering and copying. The copyright of media works urgently needs to be protected. As an important information security scheme, digital watermarking technology provides a powerful solution to the protection of multimedia works. In this paper, we propose an image digital watermarking algorithm combining discrete wavelet transform, discrete cosine transform and matrix singular value decomposition and new scrambling technique. Furthermore, to improve the robustness of the algorithm, grayscale scrambling and pseudo magic square transform are used. To evaluate our proposed algorithm, we realize the simulation based on Python 3.7. All the simulation results show that our proposed algorithm has strong imperceptibility and robustness.
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization

Physical common sense plays an essential role in the cognition abilities of robots for humanrobot interaction.Machine learning methods have shown promising results on physical commonsense learning in natural language processing but still suffer from model generalization.In this paper, we formulate physical commonsense learning as a knowledge graph completion problem to better use the latent relationships among training samples.Compared with completing general knowledge graphs, completing a physical commonsense knowledge graph has three unique characteristics: training data are scarce, not all facts can be mined from existing texts, and the number of relationships is small.To deal with these problems, we first use a pre-training language model BERT to augment training data, and then employ constrained tucker factorization to model complex relationships by constraining types and adding negative relationships.We compare our method with existing state-ofthe-art knowledge graph embedding methods and show its superior performance.
Implementing Virtual Reality Headsets in Design Education

While Virtual Reality (VR) is now widely used as a final design presentation tool, little research focuses on the role of VR during design development processes in design education. However, properly positioned and pedagogically researched VR holds great potential for helping students make better design decisions to support end-users with diverse needs. This research seeks to connect the value of VR as not only a presentation medium but also as a “perspective taking” tool to help students develop better design solutions. Undergraduate Interior Design students (n=15) were recruited to experience their already completed studio projects of a retail store design as a VR character in a virtual wheelchair. Each participant was asked to assess the effectiveness of their design solution for wheelchair users before and after the intervention. The interactions students had during their VR sessions were recorded and content analyzed for emerging themes. Although the sample size was small to achieve statistical power, qualitative findings revealed numerous perceptual shifts as students identified problems for wheelchair users in their design solutions.
The Role of the Jordanian Media in Addressing the Issues of Persons with Disabilities from the Perspective of their Families

The purpose of the study is to explore the role of Jordanian media in dealing with persons with Disabilities and adopting their problems as perceived by their families. A random sampling totaling (656) parents of disabled participated in the study to achieve its goal. A scale to measure the role of media in dealing with Persons with Disabilities issues was developed consisting of (50) items with (6) domains within Likert scale, then calculated validity and reliability. The findings of the study showed that the means of the items ranged from average to high degrees. Further, there were no significant statistical differences attributed to gender on the role of Jordanian media in dealing with Persons with disabilities issues; there were significant statistical differences attributed to working parents; there were significant statistical differences between families of children with severe disability and families of children with mild and moderate disability in favor of the first. Moreover, there were significant statistical differences attributed to scientific qualification in favor of master’s degree and above. Based on the findings, the researchers called upon media representatives to use the services of experts and specialized people in preparing programs that aim to improve the stereotype image in the minds of the audience; directing media to spread the public awareness of the issues of Persons with.
Personal Information Leakage Detection in Conversations

The global market size of conversational assistants (chatbots) is expected to grow to USD 9.4 billion by 2024, according to Marketsand-Markets.Despite the wide use of chatbots, leakage of personal information through chatbots poses serious privacy concerns for their users.In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants.The detection task is formulated as an alignment optimization problem and a new dataset PERSONA-LEAKAGE is collected for evaluation.In this paper, we propose two novel constrained alignment models, which consistently outperform baseline methods on PERSONA-LEAKAGE 1 .Moreover, we conduct analysis on the behavior of recently proposed personalized chit-chat dialogue systems.The empirical results show that those systems suffer more from personal information disclosure than the widely used Seq2Seq model and the language model.In those cases, a significant number of information leaking utterances can be detected by our models with high precision.
EXAMINING THE FINANCE- GROWTH NEXUS IN CAMEROON USING COINTEGRATION APPROACH

Purpose: This study explores the empirical relationship between growth rate of real GDP and financial development using Cameroons time series data spanning from 1978 to 2017. &#x0D; Methodology: After shedding light to the evolution of financial development in Cameroon and exploring some relevant literature, the study assesses the finance-growth linkages in Cameroon by specifying and estimating the long run and short run functions for financial development using cointegration and Error Correction modeling (ECM) techniques in addition to Engle and Granger causality testing. &#x0D; Findings: Growth of real GDP used in this paper to capture economic growth was reported to have a positive and highly significant relationship with the variable for financial development and the relation was more significant in the short run than in the long run after controlling for other variables. Bidirectional causality was also noticed between the two set of variables.&#x0D; Unique contribution to theory, practice and policy: Results of this paper suggests that financial sector of Cameroon can efficiently allocate credit to the private sector as an indicator of financial development by stimulating economic activities with the aim of raising gross domestic product of the country in both short and long run.&#x0D; Keywords: Economic growth, financial development, cointegration, Cameroon
BlockSim-Net: a network-based blockchain simulator

Since its proposal by Eyal and Sirer (CACM '13), selfish mining attacks on proof-of-work blockchains have been studied extensively.The main body of this research aims at both studying the extent of its impact and defending against it.Yet, before any practical defense is deployed in a real world blockchain system, it needs to be tested for security and dependability.However, real blockchain systems are too complex to conduct any test on or benchmark the developed protocols.Instead, some simulation environments have been proposed recently, such as BlockSim (Maher et al., SIGMETRICS Perform.Eval.Rev. '19), which is a modular and easy-to-use blockchain simulator.However, BlockSim's structure is insufficient to capture the essence of a real blockchain network, as the simulation of an entire network happens over a single CPU.Such a lack of decentralization can cause network issues such as propagation delays being simulated in an unrealistic manner.In this work, we propose BlockSim-Net, a modular, efficient, high performance, distributed, network-based blockchain simulator that is parallelized to better reflect reality in a blockchain simulation environment.
The Mechanism Analysis of the Accelerator for Support Vector Regression Based on Data Partition

Support vector regression is an important algorithm in machine learning, and it is widely used in real life for its good performance, such as house price forecast, disease prediction, weather forecast, and so on. However, it cannot efficiently process large-scale data, because it has a high time complexity in the training process. Data partition as an important solution to solve the large-scale learning problem mainly focuses on the classification task, it trains the classifiers over the divided subsets produced by data partition and obtain the final classifier by combining those classifiers. Meanwhile, the most existing method rarely study the influence of data partition on the regressor performance, so that it is difficult to keep its generation ability. To solve this problem, we obtain the estimation of the difference in objective function before and after the data partition. Mini-Batch K-Means clustering is adopted to largely reduce this difference, and an improved algorithm is proposed. This proposed algorithm includes training stage and prediction stage. In training stag, it uses Mini-Batch K-Means clustering to divide the input space into some disjoint sub-regions of equal sample size, then it trains the regressor on each divided sub-region using support vector regression algorithm. In the prediction stage, the regressor merely offers the predicted label for the unlabeled instances that are in the same sub-region. Experiment results on real datasets illustrate that the proposed algorithm obtains the similar generation ability as the original algorithm, but it has less execution time than other acceleration algorithms.
Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts

Graph visualization has been successfully applied in a wide range of problems and applications. Although different approaches are available to create visual representations, most of them suffer from clutter when faced with many nodes and/or edges. Among the techniques that address this problem, edge bundling has attained relative success in improving node-link layouts by bending and aggregating edges. Despite their success, most approaches perform the bundling based only on visual space information. There is no explicit connection between the produced bundled visual representation and the underlying data (edges or vertices attributes). In this paper, we present a novel edge bundling technique, called Similarity-Driven Edge Bundling (SDEB), to address this issue. Our method creates a similarity hierarchy based on a multilevel partition of the data, grouping edges considering the similarity between nodes to guide the bundling. The novel features introduced by SDEB are explored in different application scenarios, from dynamic graph visualization to multilevel exploration. Our results attest that SDEB produces layouts that consistently follow the similarity relationships found in the graph data, resulting in semantically richer presentations that are less cluttered than the state-of-the-art.
#Turki$hTweets: A Benchmark Dataset for Turkish Text Correction

Turki$hTweets is a benchmark dataset for the task of correcting the user misspellings, with the purpose of introducing the first public Turkish dataset in this area.#Turki$hTweets provides correct/incorrect word annotations with a detailed misspelling category formulation based on the real user data.We evaluated four state-of-the-art approaches on our dataset to present a preliminary analysis for the sake of reproducibility.