Reviewerly/exHarmony huggingface dataset:
Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media

SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020).The task was subdivided into multiple languages and datasets were provided for each one.The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification.I have participated in the task-C, that is, offense target identification.For preparing the proposed system, I have made use of Deep Learning networks like LSTMs and frameworks like Keras which combine the bag of words model with automatically generated sequence based features and manually extracted features from the given dataset.My system on training on 25% of the whole dataset achieves macro averaged f1 score of 47.763%.
KIPoS @ EVALITA2020: Overview of the Task on KIParla Part of Speech Tagging

The paper describes the first task on Part of Speech tagging of spoken language held at the Evalita evaluation campaign, KIPoS. Benefiting from the availability of a resource of transcribed spoken Italian (i.e. the KIParla corpus), which has been newly annotated and released for KIPoS, the task includes three evaluation exercises focused on formal versus informal spoken texts. The datasets and the results achieved by participants are presented, and the insights gained from the experience are discussed.
Full-immersion virtual reality for experiential education: An exploratory user experience analysis

Experiential education is widely considered an effective pedagogy to foster learning for our rapidly changing world. Despite this, residential fieldtrips are on the decline. Recent advances in full-immersion virtual reality (VR) technology offer great potential to make situated experiential education, such as fieldtrips, more accessible to educational institutions, however, research on VR technology’s effectiveness in this context is lacking. This article documents exploratory action research which examines the effectiveness of full-immersion VR technology for experiential education by adopting a user experience (UX) analytical frame. Six university staff and five students who participated in a trial of a virtual environment developed for sustainable tourism education, discussed their user experience through semi-structured interviews. The UX lens which distinguished between three UX facets during the analysis, beyond the instrumental, emotion and affect, experiential, serves to identify research areas requiring attention and assists in the technology’s improvement prioritisation. Interviews revealed many positive perspectives thus lending support to VR technology’s suitability to foster experiential education, however, several negative experiences were also identified; principally motion sickness. The exploratory findings suggest that more research is warranted to more comprehensively examine VR technology’s capacity to foster experiential education and locate VR’s place in the education landscape.
Identification of the English Accent Spoken in Different Countries by the k-Nearest Neighbor Method

Sound is the pressure wave created by an object vibrating with a certain frequency.3 organs are needed for the formation of voice in humans.These are lungs, vocal cords and mouth.Due to the structure of these organs and the similarity of the person with their current language, they can speak another language with different accent.A language can be spoken in different parts of the same country and in different countries.The second most widely used language in the world is English, has numerous accents around the world.In this study, it is aimed to determine which country the English accent spoken in different regions belongs to.In the dataset used, there are 330 sound samples including English accents spoken in Spain, France, Germany, Italy, England and America.Classification has been made with 12 features obtained by Mel Frequency Cepstrum Coefficients feature extraction method.k-Nearest Neighbor (kNN) were used in the classification and 87.2% success was achieved.
Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models

This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media.Our strategy is two-fold.First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens.Then, we propose stacking a trainable distribution fusion (DISTFUSE) system to fuse the predictions of the fine-tuned models.Experimental results show that DISTFUSE is comparable or better when compared with a naive average ensemble.As a result, we were ranked 2nd amongst 31 teams.
REVIEW PAPER: THE MEDIATING ROLE OF CREATIVITY ON THE RELATIONSHIP BETWEEN KNOWLEDGE MANAGEMENT AND HUMAN RESOURCE MANAGEMENT TOWARD INNOVATION PERFORMANCE IN THE JORDANIAN SMES

This study aims to review around 100 previous studies in the SMEs in Jordan. The economic revolution at the current moment is relying on the SMEs industry to handle the crucial business in the countries. The main concern of this study is the innovation performance in the SMEs in Jordan, which is a need to compete in the current competitive business world nowadays. This study found the innovation performance is needed in the SMEs in Jordan. The study found human resource management, knowledge management, and creativity are among the variables that must be included toward the prosperity of the industry to be able to assist the country’s economy. Moreover, there is a relationship found between activating the role of human resource management on the intellectual capital, creativity, and innovation in the Jordanian SMEs.
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension

Weakly supervised machine reading comprehension (MRC) task is practical and promising for its easily available and massive training data, but inevitablely introduces noise.Existing related methods usually incorporate extra submodels to help filter noise before the noisy data is input to main models.However, these multistage methods often make training difficult, and the qualities of submodels are hard to be controlled.In this paper, we first explore and analyze the essential characteristics of noise from the perspective of loss distribution, and find that in the early stage of training, noisy samples usually lead to significantly larger loss values than clean ones.Based on the observation, we propose a hierarchical loss correction strategy to avoid fitting noise and enhance clean supervision signals, including using an unsupervisedly fitted Gaussian mixture model to calculate the weight factors for all losses to correct the loss distribution, and employ a hard bootstrapping loss to modify loss function.Experimental results on different weakly supervised MRC datasets show that the proposed methods can help improve models significantly.
Towards Estimation of Emotions From Eye Pupillometry With Low-Cost Devices

Emotional care is important for some patients and their caregivers. Within a clinical or home care situation, technology can be employed to remotely monitor the emotional response of such people. This paper considers pupillometry as a non-invasive way of classifying an individual's emotions. Standardized audio signals were used to emotionally stimulate the test subjects. Eye pupil images of up to 32 subjects of different genders were captured as video images by low-cost, infrared, Raspberry Pi board cameras. By processing of the images, a dataset of pupil diameters according to gender and age characteristics was established. Appropriate statistical tests for inference of the emotional state were applied to that dataset to establish the subjects' emotional states in response to the audio stimuli. Results showed agreement between the test subjects' opinions of their emotional state and the classification of emotions according to the range of pupil diameters found using the described method.
Personal Reflection Development as Means of Forming Culture of Scientific Text Perception by Humanitarians

The paper presents the structure, contents, principles of the organization of the forming experiment on personal reflection development, the results, the interpretation and conclusions of the research. The program of development of creative abilities during the process of understanding the scientific text is described and tested, and it testifies about the significance of creative interpretation for the success of educational and professional activity of students of Humanities of higher educational institutions. The purpose of the paper is to prove that the process of creative interpretation promotes text understanding, determines the formation of adaptive strategies of behavior and reduces the probability of occurrence of stereotyped and maladaptation strategies. The forming activities include training for developing creative abilities, in which group discussion, psychotechnical and psychotherapeutic exercises, elements of psychoanalysis (psychoanalysis, symbol-drama) are used. The paper explores and discusses a concept of personal reflection, which has been used as a means of forming culture of perception of the scientific text during 2019-2020 among the students of the Humanities Faculty at Khmelnytsky National University, Ukraine. In conclusion, the author emphasizes the guided questions for the development of personal reflection as a means of forming culture of perception of the scientific text by humanitarians. Besides, the paper lists and explores benefits and drawbacks of interpretation levels (scientific, emotional-sensitive, creative) and their relations due to the components of intellect, strategies of interpreted behavior, success of educational and professional activity and creativity.
Dissipative failure of adiabatic quantum transport as a dynamical phase transition

Entanglement is the central resource in adiabatic quantum transport. Dephasing affects the availability of that resource by biasing trajectories, driving transitions between success and failure. This depletion of entanglement is important for the practical implementation of quantum technologies. We present a new perspective on the failure of adiabatic computation by understanding the failure of adiabatic transport as a dynamical phase transition. These ideas are demonstrated in a toy model of adiabatic quantum transport in a two spin system.
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analysis.We introduce GPolS, a neural model for political speech stance analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members.Through experiments on real-world English data, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction.
KAFK at SemEval-2020 Task 12: Checkpoint Ensemble of Transformers for Hate Speech Classification

This paper presents the approach of Team KAFK for the English edition of SemEval-2020 Task 12.We use checkpoint ensembling to create ensembles of BERT-based transformers and show that it can improve the performance of classification systems.We explore attention mask dropout to mitigate for the poor constructs of social media texts.Our classifiers scored macro-f1 of 0.909, 0.551 and 0.616 for subtasks A, B and C respectively.
Classification of mammograms based on features extraction techniques using support vector machine

Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a Hybrid Bounding Box and Region Growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM), Finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.
The Influence of Coronavirus (COVID-19) Outbreaks on Tourists Destination Choice

Little information about the impact of pandemic/epidemic on tourist's choice of destination. The current study aims to investigate the impact of six factors following the COVID-19 pandemic. Drawing on a sample of 504 Saudis who had a domestic /international travel experience was utilized to collect the data throughout the online survey. While respondents of the research display a high self-efficacy on travel, they demonstrate a moderate perception of tourism services, perception of susceptibility, perception of travel risk, a panic of crisis in destination, and perception of tourism activities in the destination. The finding of the study shows some significant differences between genders on perception of these six dimensions. The results express that even after the ban is lifted, Saudi tourists perceive domestic travel as risky as international travel. Females appear to be more travel avoidance than males because of the outbreak of (COVID-19) pandemic. In addition, females appear to have a higher panic of dying because of having coronavirus disease (COVID-19) during international and domestic travel.
Exploring the Language of Data

We set out to uncover the unique grammatical properties of an important yet so far underresearched type of natural language text: that of short labels typically found within structured datasets.We show that such labels obey a specific type of abbreviated grammar that we call the Language of Data, with properties significantly different from the kinds of text typically addressed in computational linguistics and NLP, such as 'standard' written language or social media messages.We analyse orthography, parts of speech, and syntax over a large, bilingual, hand-annotated corpus of data labels collected from a variety of domains.We perform experiments on tokenisation, part-of-speech tagging, and named entity recognition over real-world structured data, demonstrating that models adapted to the Language of Data outperform those trained on standard text.These observations point in a new direction to be explored as future research, in order to develop new NLP tools and models dedicated to the Language of Data.
The UCD-Net System at SemEval-2020 Task 1: Temporal Referencing with Semantic Network Distances

This paper describes the UCD-Net system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection.We propose a novel method based on distance between temporally referenced lexical nodes in a semantic network constructed from a combination of the time specific corpora.We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an application of an interactive semantic network exploration tool for interpreting specific aspects of subtle changes in conceptual meaning.
Explorative Visualization for Traffic Safety using Adaptive Study Areas

The pressing need to improve trafﬁc safety has become a societal concern in many cities around the world. Many trafﬁc accidents are not occurring as stand-alone events but as consequences of other road incidents and hazards. To capture the trafﬁc safety indications from a holistic aspect, this paper presents a suite of visualization techniques to explore large trafﬁc safety datasets collected from different sources using adaptive study areas which include the whole region (Hamilton County, Ohio, U.S.) as well as smaller sub-areas. In the present study, these data source include (1) Hamilton County’s 911 emergency response data, which includes trafﬁc incidents as well as other types of incidents throughout the county, and (2) Tennessee crash data, which contains only vehicle crashes with more detail on the circumstances of each crash. Both abstract and spatial visualization techniques are used to derive a better understanding of trafﬁc safety patterns for different trafﬁc participants in various urban environments. In addition to the entire region of Hamilton County, safety is examined on the highways, in the downtown area, and in a shopping district east of the city center. It is possible to characterize incidents in the different areas, gain a better understanding of common incident patterns, and identify outliers in the data. Finally, a textured tile calendar is presented to compare spatiotemporal patterns.
DBTagger

Translating Natural Language Queries (NLQs) to Structured Query Language (SQL) in interfaces deployed in relational databases is a challenging task, which has been widely studied in database community recently. Conventional rule based systems utilize series of solutions as a pipeline to deal with each step of this task, namely stop word filtering, tokenization, stemming/lemmatization, parsing, tagging, and translation. Recent works have mostly focused on the translation step overlooking the earlier steps by using adhoc solutions. In the pipeline, one of the most critical and challenging problems is keyword mapping; constructing a mapping between tokens in the query and relational database elements (tables, attributes, values, etc.). We define the keyword mapping problem as a sequence tagging problem, and propose a novel deep learning based supervised approach that utilizes POS tags of NLQs. Our proposed approach, called DBTagger (DataBase Tagger), is an end-to-end and schema independent solution, which makes it practical for various relational databases. We evaluate our approach on eight different datasets, and report new state-of-the-art accuracy results, 92.4% on the average. Our results also indicate that DBTagger is faster than its counterparts up to 10000 times and scalable for bigger databases.
Solving Common-Payoff Games with Approximate Policy Iteration

For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, decentralized control is difficult---computing even an epsilon-optimal joint policy is a NEXP complete problem. Nevertheless, a recently rediscovered insight---that a team of agents can coordinate via common knowledge---has given rise to algorithms capable of finding optimal joint policies in small common-payoff games. The Bayesian action decoder (BAD) leverages this insight and deep reinforcement learning to scale to games as large as two-player Hanabi. However, the approximations it uses to do so prevent it from discovering optimal joint policies even in games small enough to brute force optimal solutions. This work proposes CAPI, a novel algorithm which, like BAD, combines common knowledge with deep reinforcement learning. However, unlike BAD, CAPI prioritizes the propensity to discover optimal joint policies over scalability. While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so.
PERSEPSI MAHASISWA UNIVERSITAS PAHLAWAN TUANKU TAMBUSAI TERHADAP PERKULIAHAN DARING SEBAGAI SARANA PEMBELAJARAN SELAMA MASA KARANTINA COVID-19

Online lectures is one form of internet utilization that can enhance the role of students in the learning process. Descriptive analysis in this study examines students' perceptions of the University Pahlawan Tuanku Tambusai on online learning models related to the use of media, learning styles, and certain types of communication that are popular with students to help them get better output from online learning activities. The number of samples in this study were 806 students of the University Pahlawan Tuanku Tambusai who had been involved in online learning during the Covid-19 quarantine period. The results found that the most popular online learning media are Google Classroom. As many as 89,1% of University Pahlawan Tuanku Tambusai students are familiar with various online learning media before online lectures begin. In addition, the communication pattern that is most popular by students is the semi-two-way pattern. Further research is needed on online learning research with problem- based, collaborative, and other models.
Supervoxel Attention Graphs for Long-Range Video Modeling

A significant challenge in video understanding is posed by the high dimensionality of the input, which induces large computational cost and high memory footprints. Deep convolutional models operating on video apply pooling and striding to reduce feature dimensionality and to increase the receptive field. However, despite these strategies, modern approaches cannot effectively leverage spatiotemporal structure over long temporal extents. In this paper we introduce an approach that reduces a video of 10 seconds to a sparse graph of only 160 feature nodes such that efficient inference in this graph produces state-of-the-art accuracy on challenging action recognition datasets. The nodes of our graph are semantic supervoxels that capture the spatiotemporal structure of objects and motion cues in the video, while edges between nodes encode spatiotemporal relations and feature similarity. We demonstrate that a shallow network that interleaves graph convolution and graph pooling on this compact representation implements an effective mechanism of relational reasoning yielding strong recognition results on both Charades and Something-Something.