Reviewerly/exHarmony huggingface dataset:
Arabic Authorship Attribution Using Synthetic Minority Over-Sampling Technique and Principal Components Analysis for Imbalanced Documents

Nowadays, dealing with imbalanced data represents a great challenge in data mining as well as in machine learning task. In this investigation, we are interested in the problem of class imbalance in Authorship Attribution (AA) task, with specific application on Arabic text data. This article proposes a new hybrid approach based on Principal Components Analysis (PCA) and Synthetic Minority Over-sampling Technique (SMOTE), which considerably improve the performances of authorship attribution on imbalanced data. The used dataset contains 7 Arabic books written by 7 different scholars, which are segmented into text segments of the same size, with an average length of 2900 words per text. The obtained results of our experiments show that the proposed approach using the SMO-SVM classifier, presents high performance in terms of authorship attribution accuracy (100%), especially with starting character-bigrams. In addition, the proposed method appears quite interesting by improving the AA performances in imbalanced datasets, mainly with function words.
HOW TO DEVELOP DIGITAL COMPETENCE IN PRE-SERVICE FL TEACHERS AT UNIVERSITY LEVEL

The study aims to investigate pre-service teachers’ readiness to use digital resources for foreign language teaching; to find out the pre-service teachers’ attitudes to the development of their digital competence; to outline the ways of the development of the digital competence. A mixed research design was employed which involved 56 pre-service teachers of Kyiv National Linguistic University in 2019-2020. The quantitative research method was employed to assess the level of digital competence in the pre-service foreign language (FL) teachers using the Fisher Criterion. The qualitative research method allowed analysing and interpreting data of the experimental learning. The results showed that the pre-service FL teachers who were consulted by in-service teachers and used checklists for systematical assessment of their digital skills in progress had higher results than those who were only consulted by in-service teachers. Also, the qualitative method aimed to gain data about the pre-service teachers’ attitudes to using digital resources for teaching foreign languages. For that purpose, a closed-ended questionnaire for the pre-service teachers was suggested as a data collection tool. It was determined that the digital competence is a necessary component of the teacher’s professional competence which affects the success of the learning process. The digital competence can be developed in two areas: the Digital Resources Managing Area and Methodological Managing of Digital Resources Area. The criteria for critical selection, analysis and assessment of digital resources were singled out.
Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.
Domain-based Latent Personal Analysis and its use for impersonation detection in social media

Zipf’s law defines an inverse proportion between a word’s ranking in a given corpus and its frequency in it, roughly dividing the vocabulary into frequent words and infrequent ones. Here, we stipulate that within a domain an author’s signature can be derived from, in loose terms, the author’s missing popular words and frequently used infrequent words. We devise a method, termed Latent Personal Analysis (LPA), for finding domain-based attributes for entities in a domain: their distance from the domain and their signature, which determines how they most differ from a domain. We identify the most suitable distance metric for the method among several and construct the distances and personal signatures for authors, the domain’s entities. The signature consists of both over-used terms (compared to the average) and missing popular terms. We validate the correctness and power of the signatures in identifying users and set existence conditions. We test LPA in several domains, both textual and non-textual. We then demonstrate the use of the method in explainable authorship attribution: we define algorithms that utilize LPA to identify two types of impersonation in social media: (1) authors with sockpuppets (multiple) accounts and (2) front-users accounts, operated by several authors. We validate the algorithms and employ them over a large-scale dataset obtained from a social media site with over 4000 users. We corroborate these results using temporal rate analysis. LPA can further be used to devise personal attributes in a wide range of scientific domains in which the constituents have a long-tail distribution of elements.
Digital pathology in academia: Implementation and impact

Yava Jones-Hall discusses the promise and pain points of setting up a digital pathology program at a new academic institution.
On Lexicographic Proof Rules for Probabilistic Termination

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in LexRSM not existing even for simple terminating programs. Our contributions are twofold: First, we introduce a generalization of LexRSMs which allows for some components to be negative. This standard feature of non-probabilistic termination proofs was hitherto not known to be sound in the probabilistic setting, as the soundness proof requires a careful analysis of the underlying stochastic process. Second, we present polynomial-time algorithms using our generalized LexRSMs for proving a.s. termination in broad classes of linear-arithmetic programs.
ricu: R’s interface to intensive care data

Abstract Objective To develop a unified framework for analyzing data from 5 large publicly available intensive care unit (ICU) datasets. Findings Using 3 American (Medical Information Mart for Intensive Care III, Medical Information Mart for Intensive Care IV, electronic ICU) and 2 European (Amsterdam University Medical Center Database, High Time Resolution ICU Dataset) databases, we constructed a mapping for each database to a set of clinically relevant concepts, which are grounded in the Observational Medical Outcomes Partnership Vocabulary wherever possible. Furthermore, we performed synchronization in the units of measurement and data type representation. On top of this, we built functionality, which allows the user to download, set up, and load data from all of the 5 databases, through a unified Application Programming Interface. The resulting ricu R-package represents the computational infrastructure for handling publicly available ICU datasets, and its latest release allows the user to load 119 existing clinical concepts from the 5 data sources. Conclusion The ricu R-package (available on GitHub and CRAN) is the first tool that enables users to analyze publicly available ICU datasets simultaneously (datasets are available upon request from respective owners). Such an interface saves researchers time when analyzing ICU data and helps reproducibility. We hope that ricu can become a community-wide effort, so that data harmonization is not repeated by each research group separately. One current limitation is that concepts were added on a case-to-case basis, and therefore the resulting dictionary of concepts is not comprehensive. Further work is needed to make the dictionary comprehensive.
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning

Federated Learning (FL) has become an active and promising distributed machine learning paradigm. As a result of statistical heterogeneity, recent studies clearly show that the performance of popular FL methods (e.g., FedAvg) deteriorates dramatically due to the client drift caused by local updates. This paper proposes a novel Federated Learning algorithm (called IGFL), which leverages both Individual and Group behaviors to mimic distribution, thereby improving the ability to deal with heterogeneity. Unlike existing FL methods, our IGFL can be applied to both client and server optimization. As a by-product, we propose a new attention-based federated learning in the server optimization of IGFL. To the best of our knowledge, this is the first time to incorporate attention mechanisms into federated optimization. We conduct extensive experiments and show that IGFL can significantly improve the performance of existing federated learning methods. Especially when the distributions of data among individuals are diverse, IGFL can improve the classification accuracy by about 13% compared with prior baselines.
New Model of Food Supply Chain Finance Based on the Internet of Things and Blockchain

The grain supply chain not only broadens the financing channels for small and medium-sized enterprises in the supply chain and strengthens the cohesion of the supply chain but also opens up new business models and sources of profit for commercial banks. This article aims to study the use of advanced information technologies such as mobile Internet, Internet of Things (IoT) transmission, BT, and other advanced information technologies to design intelligent grain depot integrated systems based on the IoT technology, in accordance with the principles of practicality and advancement, to realize the informatization of the daily management of the grain depot and provide construction experience for the informatization construction of the food industry. This article proposes a detailed summary of several models of agricultural supply chain finance from the perspective of supply chain management and analyzes the applicable conditions and basic processes of these models. It also analyzes the structure of the agricultural supply chain through typical cases, introduces the operation of supply chain finance, and evaluates and analyzes its effectiveness and problems. The experimental results in this paper show that, compared with other storage methods, this solution has obvious advantages in data query efficiency and data storage cost. Without a central server, the entire distributed system will have a certain degree of efficiency, reliability, and cost-effectiveness. When the amount of grain data reaches 100,000, 1 million, and 10 million, the system data integrity check time is 0.0263 seconds, 0.3251 seconds, and 1.5032 seconds, respectively. The program seems very useful.
User-Generated Content-Consumer Buying Intentions Nexus: The Mediating Role of Brand Image

This research paper investigates the effect of user-generated content (UGC) on consumer intentions to buy cosmetic products in Jordan taking into consideration the mediating role of brand image on this effect. A conceptual research model was developed based on an in-depth review of the extant literature and in accordance with the stimulus-organism-response (SOR) theory. To test the model, an online questionnaire survey was distributed to a convenient sample of cosmetic products buyers in Jordan. A dataset of 316 usable questionnaires were subjected to data analysis using Smart PLS 3.0. The main findings showed that consumer buying intentions are positively affected by both UGC and brand image. Brand image was also found to be significantly predicted by UGC and to mediate its effect on consumer buying intentions. The results demonstrate the significance of UGC as a powerful marketing tool in crafting brand image and driving consumer buying intentions in the context of cosmetics. Findings' discussion, conclusion, research limitations and areas for future research are also provided.
Value-Based Retweet Prediction on Twitter

Retweeting is an online activity done on the twitter social network.This activity leads to sharing of opinions and ideas from one person to another.Predicting retweet decision has been an interesting and challenging task since the past decade.Past studies have shown that emotions, sentiments and topic specific emotions can influence the retweet decision of the user.However, value systems of an individual can also be an important and crucial aspect in predicting the decision of user.Hence, through our work, we propose to study retweet prediction as a function of value systems.Our work also presents an experimental comparative study with the features used in previous studies.The experimental results using the different machine learning algorithms shows that value-systems have a higher performance in predicting retweet decision of the user as compared to emotions, sentiments and topic-specific emotions.Povzetek: Z metodami
I Will Have Order! Optimizing Orders for Fair Reviewer Assignment

We study mechanisms that allocate reviewers to papers in a fair and efficient manner. We model reviewer assignment as an instance of a fair allocation problem, presenting an extension of the classic round-robin mechanism, called Reviewer Round Robin (RRR). Round-robin mechanisms are a standard tool to ensure envy-free up to one item (EF1) allocations. However, fairness often comes at the cost of decreased efficiency. To overcome this challenge, we carefully select an approximately optimal round-robin order. Applying a relaxation of submodularity, γ-weak submodularity, we show that greedily inserting papers into an order yields a (1+γ²)-approximation to the maximum welfare attainable by our round-robin mechanism under any order. Our Greedy Reviewer Round Robin (GRRR) approach outputs highly efficient EF1 allocations for three real conference datasets, offering comparable performance to state-of-the-art paper assignment methods in fairness, efficiency, and runtime, while providing the only EF1 guarantee.
Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data.
Consistent Inference for Dialogue Relation Extraction

Relation Extraction is key to many downstream tasks. Dialogue relation extraction aims at discovering entity relations from multi-turn dialogue scenario. There exist utterance, topic and relation discrepancy mainly due to multi-speakers, utterances, and relations. In this paper, we propose a consistent learning and inference method to minimize possible contradictions from those distinctions. First, we design mask mechanisms to refine utterance-aware and speaker-aware representations respectively from the global dialogue representation for the utterance distinction. Then a gate mechanism is proposed to aggregate such bi-grained representations. Next, mutual attention mechanism is introduced to obtain the entity representation for various relation specific topic structures. Finally, the relational inference is performed through first order logic constraints over the labeled data to decrease logically contradictory predicted relations. Experimental results on two benchmark datasets show that the F1 performance improvement of the proposed method is at least 3.3% compared with SOTA.
How Blockchain Technology Changes Government

It is widely argued that Blockchain Technology (BCT) is one of the most promising trends nowadays. The most prominent characteristic of this technology is the improved sense of trust to the shared information provided by BCT applications as well as the ubiquitous access to the data ledger. At the same time governments pursue amplified trust from their citizens increasing transparency through shared information and open data. Since BCT supports this strategic goal of governments worldwide, numerous governments try to capitalize on the advances of this technology through testing the results of pilot applications in different vertical governmental sectors. Even though there are several implementations in the Government sector, there is no comprehensive study towards the analysis of the major characteristics of these developments. This paper moves towards the fulfillment of this gap by conducting a thorough analysis of e-Government pilot applications of BCT at a European level providing information to policymakers and practitioners about the grey areas of this technology.
ConvLogMiner: A Real-Time Conversational Lifelog Miner

This paper presents a conversational lifelog mining system, ConvLogMiner, which detects personal life events from the human online conversation in real-time. Given a daily conversation of two speakers, ConvLogMiner identifies the new life events specific to each speaker that occur in the latest utterances. The lifelogs mined by our system are useful to provide complementary information to support lifestyle analysis and memory assistance service.
Multistability of saxophone oscillation regimes and its influence on sound production

The lowest fingerings of the saxophone can lead to several different regimes, depending on the musician’s control and the characteristics of the instrument. This is explored in this paper through a physical model of saxophone. The harmonic balance method shows that for many combinations of musician control parameters, several regimes are stable. Time-domain synthesis is used to show how different regimes can be selected through initial conditions and the initial evolution (rising time) of the blowing pressure, which is explained by studying the attraction basin of each stable regime. These considerations are then applied to study how the produced regimes are affected by properties of the resonator. The inharmonicity between the first two resonances is varied in order to find the value leading to the best suppression of unwanted overblowing. Overlooking multistability in this description can lead to biased conclusions. Results for all the lowest fingerings show that a slightly positive inharmonicity, close to that measured on a saxophone, leads to first register oscillations for the greatest range of control parameters. A perfect harmonicity (integer ratio between the first two resonances) decreases first register production, which adds nuance to one of Benade’s guidelines for understanding sound production. Thus, this study provides some a posteriori insight into empirical design choices relative to the saxophone.
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning

In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures.Twenty years later this division feels far more ephemeral, both in terms of assigning individuals to camps, and in terms of intellectual boundaries.We argue that this is largely due to the "data modelers" incorporating algorithmic methods into their toolbox, particularly driven by recent developments in the statistical understanding of Breiman's own Random Forest methods.While this can be simplistically described as "Breiman won", these same developments also expose the limitations of the prediction-first philosophy that he espoused, making careful statistical analysis all the more important.This paper outlines these exciting recent developments in the random forest literature which, in our view, occurred as a result of a necessary blending of the two ways of thinking Breiman originally described.We also ask what areas statistics and statisticians might currently overlook.
An identity privacy scheme for blockchain‐based on edge computing

Abstract Blockchain has decentralization characteristics and requires more targeted security schemes to protect user privacy. In contrast, existing signature schemes have many high‐complexity operations and impose an enormous computational burden on wireless nodes. This article proposes a light‐weighted identity privacy scheme for blockchain‐based on edge computing. We construct linkable identity privacy and non‐linkable identity privacy, which can resist collusion attacks while virtually guaranteeing blockchain nodes' identity privacy. Since edge computing offloads heavily, the proposed scheme has lower computational complexity than the existing techniques.