Reviewerly/exHarmony huggingface dataset:
Recruitment Search Engines for Screening Resumes through AI by Using Boolean Search Functions

The days are gone when hard copies of resumes were sent to recruiters and they used to screen and set aside non relevant resumes. Artificial Intelligence has taken up mundane tasks of recruiters by simplifying search algorithms and human computer interaction. Job Boards provide recruiters with a database of candidates powered by a search tool with lot of filters. The demonstration paper actually carries out searches on recruitment database access tools to filter the relevant applicants from a wide pool of data with the help of search engine tools in resume databases of various jobboards. It shows how Boolean operators get through better search results in few minutes. Recruiter job to get to the right candidate is sorted out with Artificial Intelligence so that they can focus more on strategic tasks. Future of AI in recruitment is immense. Already companies are empowering recruiters with 360 degree tools which help with all Human Resource Aspects.
Syntactic Complexity Measures and Academic Writing Proficiency : A Corpus-based Study of Professional and Students’ Prose

This study examines large-grained, medium-grained, and fine-grained measures, as different dimensions of syntactic complexity, and their relationship with academic writing proficiency in the abstract sections of academic prose.We sampled 237 texts written by expert academic writers of research articles, PhD level L1 Persian writers, and MA level L1 Persian writers.Using one-way multiple analysis of variance (MANOVA) and ordinal logistic regression, we compared the abstracts in terms of their syntactic complexity and checked the predictability of large-grained, medium-grained, and fine-grained measures in academic writing proficiency.The findings revealed that the differences among the groups were not large enough for clausal and sentential measures of dependent clauses per T-unit, and clauses per sentence to show statistical significance.However, the differences among the groups on length-based measures of mean length of T-unit, and mean length of clause along with noun phrase measures of clauses per nominals, noun premodifiers, and prepositional phrases reached significance.Analysis of fine-grained measures of noun phrase complexity shows that four indices that measured the number and variation of dependents in the texts made unique statistically significant contribution to the prediction of academic writing proficiency.These findings suggest the importance of incorporating complementary measures of syntactic complexity (i.e., large-grained measures) as integral parts of L2 writing instruction practices.The implications of the findings in relation to L2 academic writing pedagogy are discussed.
Negative Impoliteness and Reconstruction of Identity: Cyberpragmatics Analysis of Palestinian Conflict News Comments on Arab Youtube Channel

This study aims to explain the negative impoliteness in the comments on the news of the Palestinian conflict on the Arab Youtube channel. This descriptive qualitative research took the source of data in the form of 5 news of the attack on the Al-Aqsa mosque complex by the Israeli military on the Al Jazeera youtube channel as a data source. The internet archive documentation technique and free-of-conversation listening technique were used at the data collection stage. Meanwhile, the identity method by referring to the stages of qualitative analysis was used as a guide in data analysis. The researchers found 310 negative impoliteness speeches consisting of 5 types: frighten found at 17 speeches (6%); condescend, scorn or ridicule at 113 speeches (36%); invade the other’s space at 72 speeches (23%); explicitly associate the others with negative aspect at 97 speeches (31%); put the other’s indebtedness on record at 11 speeches (4%). The negative impoliteness has a context in the form of criticism of the political policies of Arab countries in responding to the Palestinian conflict. Speakers seek to construct a new community identity for Arab countries in the context of fighting against the Israeli occupation of Palestine.
Efficient Use of Audio-Visual Equipment in Teaching and Learning: It’s Implication to School Supervision

This paper aimed at determining the possible effectiveness of the use of audio-visual equipment in English, Math and Science subjects to the academic performance of grade 8 learners in Talangan Integrated National High School.Three sections of grade 8 and their teachers in English, Math and Science became part of this action research.At the end of the study, implication to school supervision was lined-up to improve teaching strategy among teachers.The three sections of Grade 8 (Courteous, Caring and Charity), with a total number of 102 became part of the population for this study.This paper is a qualitative, experimental research which took note on the performance of the learners in three subjects -English, Science and Mathematics.Three quizzes were given on all subjects after each session without the use of audio-visual material.After that, audio-visual materials are used and another three quizzes were given after each session.Without the use of audio-visual material, the mean level of learners are 5.48, 6.91 and 4.27 in English, Mathematics and Science, respectively, in quizzes which are out of 10.On the other hand, the mean level of learners in a quiz with 10 items and using audio-visual material are 9.00, 8.48 and 4.45 in the said three subjects, respectively.Using ttest for paired sample mean, it was found out (with the assurance of the p-values 0.01, 0.00 and 0.22 for English, Science and Mathematics, respectively) that the use of audio-visual materials significantly improved learner's performance only in English and Science.Recommendations to teachers, school heads, district and division officials, and to future researchers were given at the end of the study.Implications to school supervision, especially on classroom observation, are also mentioned to guide school heads and master teachers in improving teaching strategy among teachers.
312-Avoiding reduced valid hook configurations and duck words

Valid hook configurations are combinatorial objects used to understand West's stack sorting map as well as cumulants in noncommutative probability theory.We show a bijection between reduced valid hook configurations on 312-avoiding permutations with the maximal allowed number of points and 3D-Dyck words, proving a conjecture of Sankar's.We extend this to a bijection between all 312-avoiding reduced valid hook configurations and 3D-Dyck words with specified modifications.We show how these can be counted in terms of the number of 3D-Dyck words of length 3k in which exactly i Y's do not have an X immediately before them, the (k, i)-Duck words, and use this relationship to prove several properties about sums of 312-avoiding reduced valid hook configurations, including two more of Sankar's conjectures.We also show that the number of (k, 1)-Duck words is given by a variant of the tennis ball numbers, giving the first combinatorial interpretation for these numbers that is purely counting objects.
Chaotic African Buffalo Optimization Based Efficient Key Mechanism in Categorized Sensor Networks

In recent days, Wireless Sensor Network (WSN) can be used to monitor the circumstances of various movable objects and several processes such as friendly forces monitoring, biological attack detection, fire detection and so on its applications are extended. To make all these applications reliable and secure, it is necessary to use cryptographic processes. The security of the whole network is depending on the strength of a generated key, the algorithm decides the size of the key handling and processing. In this research, a secure efficient key management scheme is proposed with the help of Chaotic African Buffalo Optimization (CABO) for wireless sensor networks. The evaluation of this method is made with the objective to improve the security strength and reduce the cost of resource. The traditional methods called genetic algorithm and an evolutionary algorithm named as Particle Swarm Optimization (PSO) is compared to verify the proposed scheme.
The effect of electronic learning strategy on the quality of educational service from the economic point of view: the case of Iraq

The massive technological revolution of communication and information imposes a need to adopt new strategies based on methodological foundations that are compatible with contemporary technologies, where the use of technology in economy has become an urgent necessity, since traditional economy is no longer suitable for the new generation. The purpose of the research was to answer the following question: what is the effect of the e-learning strategy on the quality of educational services of technical institutes in Iraq? In order to conduct the research, the author applied the descriptive analytical method. The relevant data were obtained from 60 students of the accounting department of Kut Technical Institute. The variables studied are the department management quality, the professor quality and the curriculum quality. The author of the research has made a number of conclusions, including the proof of the presence of a statistically significant effect of e-learning on the quality of educational services. The research stresses the need to raise awareness of the importance of e-learning among students and its positive impact on achieving the quality of services in education.
Chained Digital Signature for the Improved Video Integrity Verification

The recorded videos from the surveillance cameras can be used as potential evidence in forensic applications. These videos can be easily manipulated or tampered with video editing tools without leaving visible clues. Hence integrity verification is essential before using the videos as evidence. Existing methods mostly depend on the analysis of video data stream and video container for tampering detection. This paper discusses an active video integrity verification method using Elliptic Curve Cryptography and blockchain. The method uses Elliptic Curve Digital Signature Algorithm for calculating digital signature for video content and previous block. The digital signature of the encoded video segment (video content with predetermined size) and that of previous block are kept in each block to form an unbreakable chain. Our method does not consider any coding or compression artifacts of the video file and can be used on any video type and is tested on public-available standard videos with varying sizes and types. The proposed integrity verification scheme has better detection capabilities towards different types of alterations like insertion, copy-paste and deletion and can detect any type of forgery. This method is faster and more resistant to brute force and collision attacks in comparison to existing recent blockchain method.
The Effect of Facilitating Conditions on Implementing A Regional Financial Management Information System (SIPKD) at Padang City Hall Goverment

The government is trying to realize the excellent service to the community by building information technology in finance or accounting in relation to the financial management area. The purpose of this study to determine the influence of facilitating conditions on implementing a regional financial management information system (SIPKD). This research was conducted at Padang City Hall government. One hundred questionnaires have sent to companies, and only 80 questionnaires can be used. The data collection techniques uses aquestionnaire prepared by using the UTAUT konstuk. Quantitative analysis techniques and simple linear regression analysis is used as a data analysis technique with the help of SPSS 25. The analysis shows the facilitating conditions positive influence on the implementing a regional financial management information system (SIPKD).
EmoCNN: Encoding Emotional Expression from Text to Word Vector and Classifying Emotions—A Case Study in Thai Social Network Conversation

We present EmoCNN, a collection of specially-trained word embedding layer and convolutional neural network model for the classification of conversational texts into 4 types of emotion.This model is part of a chatbot for depression evaluation.The difficulty in classifying emotion from conversational text is that most word embeddings are trained with emotionally-neutral corpus such as Wikipedia or news articles, where emotional words do not appear very often or at all, and the language style is formal writing.We trained a new word embedding based on the word2vec architecture in an unsupervised manner and then fine-tuned it on soft-labelled data.The data was obtained from mining Twitter using emotion keywords.We show that this emotion word embedding can differentiate between words which have the same polarity and words which have opposite polarity, as well as find similar words with the same polarity, while the standard word embedding cannot.We then used this new embedding as the first layer of EmoCNN that classifies conversational text into the 4 emotions.EmoCNN achieved macro-averaged f1score of 0.76 over the test set.We compared EmoCNN against three different models: a shallow fully-connected neural network, fine-tuning RoBERTa, and ULMFit.These got the best macro-averaged f1-score of 0.5556, 0.6402 and 0.7386 respectively.
Development of 3D convolutional neural network to recognize human activities using moderate computation machine

Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.
Block-wise Word Embedding Compression Revisited: Better Weighting and Structuring

Word embedding is essential for neural network models for various natural language processing tasks.Since the word embedding usually has a considerable size, in order to deploy a neural network model having it on edge devices, it should be effectively compressed.There was a study for proposing a block-wise low-rank approximation method for word embedding, called GroupReduce.Even if their structure is effective, the properties behind the concept of the block-wise word embedding compression were not sufficiently explored.Motivated by this, we improve GroupReduce in terms of word weighting and structuring.For word weighting, we propose a simple yet effective method inspired by the term frequency-inverse document frequency method and a novel differentiable method.Based on them, we construct a discriminative word embedding compression algorithm.In the experiments, we demonstrate that the proposed algorithm more effectively finds word weights than competitors in most cases.In addition, we show that the proposed algorithm can act like a framework through successful cooperation with quantization.
Examining Heterogeneity Structured on a Large Data Volume with Minimal Incompleteness

While Big Data analytics can provide a variety of benefits, processing heterogeneous data comes with its own set of limitations. A transaction pattern must be studied independently while working with Bitcoin data, this study examines twitter data related to Bitcoin and investigate communications pattern on bitcoin transactional tweet. Using the hashtags #Bitcoin or #BTC on Twitter, a vast amount of data was gathered, which was mined to uncover a pattern that everyone either (speculators, teaches, or the stakeholders) uses on Twitter to discuss Bitcoin transactions. This aim is to determine the direction of Bitcoin transaction tweets based on historical data. As a result, this research proposes using Big Data analytics to track Bitcoin transaction communications in tweets in order to discover a pattern. Hadoop platform MapReduce was used. The finding indicate that In the map step of the procedure, Hadoop's tokenize the dataset and parse them to the mapper where thirteen patterns were established and reduced to three patterns using the attributes previously stored data in the Hadoop context, one of which is the Emoji data that was left out in previous research discussions, but the text is only one piece of the puzzle on bitcoin transaction interaction, and the key part of it is “No certainty, only possibilities” in Bitcoin transactions
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing

Thanks to the strong representation learning capability of deep learning, especially pretraining techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains.However, the parsing community has to face the more realistic setting where the parsing performance drops drastically when labeled data only exists for several fixed outdomains.In this work, we propose a novel model for multi-source cross-domain dependency parsing.The model consists of two components, i.e., a parameter generation network for distinguishing domain-specific features, and an adversarial network for learning domain-invariant representations.Experiments on a recently released dataset for multidomain dependency parsing show that our model can consistently improve cross-domain parsing performance by about 2 points in averaged labeled attachment accuracy (LAS) over strong BERT-enhanced baselines.Detailed analysis is conducted to gain more insights on contributions of the two components.
Educating Adolescents about Social Behavior using Information and Communications Technology

The media has a wide range of applications. Information and Communications Technology (ICT) plays a vital role to educate the young generation. For over three decades, we have observed a very good involvement of media in spreading awareness on various issues pertaining to the society. The aim of this research study to seek the relationship between teaching multimedia (animation) at school level to educate adolescents about the social behavior. A survey has been carried out in five different levels of educational institution in Sultanate of Oman. 873 samples were collected, correlation analysis was performed on hypotheses to test the linear association. Linear regression has been performed to test hypothesis for comparing regression constants. The analysis discovered that there is adequate evidence to support the claim at the alpha level of significance that social behavior learning at school level on adolescents, multimedia effect on educating social behavior and technology aided support on learning of social behavior have a positive and significant effect having α=0.85, significance F = 0.0 and p value less than 0.05 proving to be significant.&#x0D;
Exploring the Impacts of Peer Interaction in ESL University Students’ Argumentative Writing Activities via WhatsApp

This study explores whether peer interaction in WhatsApp discussion affect participants' writing achievement.The initial phase of this study is to improve the participants' ability to share ideas and discussions on the eight argumentative topics in the WhatsApp group through peer interaction.Eight undergraduate students from University Sains Malaysia were chosen purposely based on their low proficiency in MUET results of Band 2. In this qualitative study, twenty open-ended interview questions were added to understand the participants' views and experiences on peer interaction in WhatsApp.The open-ended interview questions were validated by three language experts from three local universities in Malaysia.The participants' writing scores were also recorded to measure their writing achievement in order to answer the research questions.The writing achievement was on the argumentative writing essays that the participants sent every week for eight weeks after three days of peer interaction in WhatsApp.The study showed significant improvement that the participants' perceived a strong peer interaction in their discussion via WhatsApp.The participants were able to interact with their peers and used the ideas shared in supporting their arguments in the essay writing.Relatedly, these promising improvements in the students' participation and the findings found that the participants' peer interaction had created a positive community environment and increased their writing achievement.
Automatic Discrimination between Inherited and Borrowed Latin Words in Romance Languages

In this paper, we address the problem of automatically discriminating between inherited and borrowed Latin words.We introduce a new dataset and investigate the case of Romance languages (Romanian, Italian, French, Spanish, Portuguese and Catalan), where words directly inherited from Latin coexist with words borrowed from Latin, and explore whether automatic discrimination between them is possible.Having entered the language at a later stage, borrowed words are no longer subject to historical sound shift rules, hence they are presumably less eroded, which is why we expect them to have a different intrinsic structure distinguishable by computational means.We employ several machine learning models to automatically discriminate between inherited and borrowed words and compare their performance with various feature sets.We analyze the models' predictive power on two versions of the datasets, orthographic and phonetic.We also investigate whether prior knowledge of the etymon provides better results, employing n-gram character features extracted from the word-etymon pairs and from their alignment.
Serbian NER&Beyond: The Archaic and the Modern Intertwinned

In this work, we present a Serbian literary corpus that is being developed under the umbrella of the "Distant Reading for European Literary History" COST Action CA16204.Using this corpus of novels written more than a century ago, we have developed and made publicly available a Named Entity Recognizer (NER) trained to recognize 7 different named entity types, with a Convolutional Neural Network (CNN) architecture, having F 1 score of ≈91% on the test dataset.This model has been further assessed on a separate evaluation dataset.We wrap up with comparison of the developed model with the existing one, followed by a discussion of pros and cons of the both models.
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations

We present UMR-Writer, a web-based application for annotating Uniform Meaning Representations (UMR), a graph-based, crosslinguistically applicable semantic representation developed recently to support the development of interpretable natural language applications that require deep semantic analysis of texts.We present the functionalities of UMR-Writer and discuss the challenges in developing such a tool and how they are addressed.