Reviewerly/exHarmony huggingface dataset:
A cluster and content analysis of data mining studies in library and information science

This study examines the use of data mining strategies in library and information science research journals, including the types of studies that use the strategy as well as most popular journals for publishing article that employ the strategies. A content and cluster analysis was performed with articles published in major LIS journals during the years 2006, 2012, and 2018. The Journal of the Association for Information Science and Technology had the most such articles with 42, with Information Processing and Management following close behind with 32. A cluster analysis performed based on word frequency in these articles’ abstracts identified three unique clusters associated with the topics of Publications/Citations, Consumer Behavior, and Information/Media Use. This analysis indicates a shift away from Publications/Citations towards more Consumer Behavior-based data mining studies. The findings of this study may be significant for current researchers in preparing and publishing their own data mining-based studies and determining avenues for publishing their work.
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models.

Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as self-supervised refinement of a pre-trained language model. In contrast to previous studies that rely on fine-tuning annotated datasets, we seek to boost conceptualization via loss landscape refinement. To this end, we propose a novel self-supervised learning approach that refines the language model utilizing a set of linguistic perturbations of similar concept relationships. Empirical analysis of our conceptually simple framework demonstrates the viability of zero-shot commonsense reasoning on multiple benchmarks.
A Novel AHRS Estimation Algorithm of Small UAV

Aimed at nonlinear attitude and heading reference and system(AHRS) solution accuracy, this paper presents a novel attitude model of the Singular Value Decomposition (SVD) Cubature Kalman Filter (SVDCKF) fusion algorithm. Firstly, this paper designs the attitude integrated model of small UAV, and uses quaternion to update attitude. Then, in order to solve the nonlinear attitude model, this paper present the SVDCKF to improve the attitude solution accuracy. Finally, the results of numerical simulation and experimental test verify the proposed algorithm has the high attitude accuracy and can meet the flying attitude requirements of small UAV.
Cross-Policy Compliance Detection via Question Answering.

Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines). This task has been previously instantiated as a form of textual entailment, which results in poor accuracy due to the complexity of the policies. In this paper we propose to address policy compliance detection via decomposing it into question answering, where questions check whether the conditions stated in the policy apply to the scenario, and an expression tree combines the answers to obtain the label. Despite the initial upfront annotation cost, we demonstrate that this approach results in better accuracy, especially in the cross-policy setup where the policies during testing are unseen in training. In addition, it allows us to use existing question answering models pre-trained on existing large datasets. Finally, it explicitly identifies the information missing from a scenario in case policy compliance cannot be determined. We conduct our experiments using a recent dataset consisting of government policies, which we augment with expert annotations and find that the cost of annotating question answering decomposition is largely offset by improved inter-annotator agreement and speed.
Undergraduate student assessment as a factor contributing to HE quality

The forced transition to distant education in 2019 accounted for numerous discussions on HE as there was an urgent need for measures to secure smooth educational process. Amid pandemic the quality of education depends mainly on infrastructure though the assessment in remote learning gets more complicated due to an increasing academic dishonesty. Therefore, the academic results may be challenged, threatening all the stakeholders: HE institutions, students, employers. Amid high demand on HE, diplomas are loosing credibility. The author assumes, that well designed assessment should help to regain trust in academic results and serve as a factor contributing to the quality of higher education. Two recent trends in undergraduate student assessment are noted: development of academic performance ratings and complication of a student assessment system by rankings of participation in social, cultural, scientific and sports events. Pros and cons of Summative and Formative assessment are briefly addressed. The following suggestions to improve student assessment are made and discussed: Focusing on Assessment for learning; Building Summative assessment on Formative assessment; Changing the role of a lecturer; Introducing new assessment techniques; Reducing the subjectivity of assessment.
Climate projections over orographic regions of Africa under the 1.5°C and 2°C warming scenarios

The objective of this study is to estimate the potential changes in extremes rainfall over three orographic regions of Africa (Guinea, Nigeria and Cameroon) under the 1.5°C and 2°C warming scenarios. The results project an increase of the number of rainy days over Guinea under both scenarios by 2059. Over Nigeria, this index will decrease in the north of the country under both scenarios. Over Cameroon, this index will decrease over the whole country under the 1.5°C warming and will increase in the southeast and northeast parts of the country under the 2°C warming. Rainfall intensity will increase over the northern and southwestern parts of Guinea under both scenarios. The maximum one-day rainfall accumulation will increase in almost all three considered countries in the near future. However, compared to Cameroon, the maximum one-day rainfall accumulation will be more important in the northern and central parts of Guinea and Nigeria. There will be a decrease of the number of consecutive wet days in the southwest and central parts of Guinea under the 1.5°C scenario and over most parts of Guinea under the 2°C scenario. Over Nigeria, this index will decrease in the south and center of the country. The number of consecutive wet days will decrease in the southwest and northern Cameroon and increase slightly in the southeast of this country under both warming scenarios. The number of consecutive dry days will decrease over the whole Guinea, over the northern and central parts of Nigeria under both scenarios. The increase noted in extreme events could cause flooding. This work can be considered as a support for the Guinean, Nigerian and Cameroon policymakers for implementing sustainable mitigation and good adaptation measures to limit and combat the adverse effects of climate change (floods, heat waves and drought). Finally, this study shows that the three considered regions will need additional irrigation as an adaptation strategy for the protection of the agricultural sector by 2059 under both scenarios due to the decrease of the number of consecutive wet days.
Fine-grained Entity Typing via Label Reasoning

Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.
Event Graph based Sentence Fusion.

Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence. The importance of sentence fusion has long been recognized by communities in natural language generation, especially in text summarization. It remains challenging for a state-of-the-art neural abstractive summarization model to generate a well-integrated summary sentence. In this paper, we explore the effective sentence fusion method in the context of text summarization. We propose to build an event graph from the input sentences to effectively capture and organize related events in a structured way and use the constructed event graph to guide sentence fusion. In addition to make use of the attention over the content of sentences and graph nodes, we further develop a graph flow attention mechanism to control the fusion process via the graph structure. When evaluated on sentence fusion data built from two summarization datasets, CNN/DaliyMail and Multi-News, our model shows to achieve state-of-the-art performance in terms of Rouge and other metrics like fusion rate and faithfulness.
DWT and SVD-Based Robust Watermarking Using Differential Evolution with Adaptive Optimization

Digital technologies have been rapidly developing and low copy-cost and convenient temper make a huge impact. Digital watermarking plays a crucial role in copyright protection, even in IoT applications. It provides a good solution for copyright problems and content authentication to achieve data integrity. The objective behind this chapter is to obtain optimal, robust, and invisible image watermarking using differential evolution. A good watermark is robust against attacks, is invisible, and carries a good amount of information related to the owner. This chapter presents a digital watermarking algorithm that makes use of differential evolution along with adaptive optimization for choosing the most suitable scaling factors. It can be inferred from the results after experimentation that the proposed algorithm performs well with respect to invisibility and robustness.
STUDENTS PERSPECTIVE ON ANXIETY IN LEARNING ENGLISH

Learning English as a foreign language is currently very important in the world of education, because English is very popular in the world, almost all Indonesian students are interested in English after all it is to add insight. In fact, when students learn English, some students often get into trouble, especially in controlling emotions. One of the emotional states that makes this process difficult is anxiety. anxiety is classified as a psychological problem that occurs to everyone. not all studentshave the same factors as anxiety, but some people are different. in order to reduce anxiety, students need appropiate strategies. The statement of the research problems are: 1) what factors do believe to contribute to their anxiety in learning English? 2) How do the students' strategies to reduce their anxiety in learning English? The analysis approach used in this thesis is a qualitative and descriptive method. The subject of the study is three students selected through a distribution questionnaire. The data of this study is the information selected by the students of psychology department IAIN Tulungagung, as well as the description of anxiety factors and strategies to reduce anxiety. Collect data through in-depth interviews. The data in this study is in the form of statements and descriptions, which are qualitative data. The process of data analysis is: 1) Researchers collect data through in-depth interviews; 2) Researchers make notes by referring to research questions, select and pay attention to the data; 3) Researchers display the selected data on the screen. The form of narration or description 4) Draw conclusions based on analysis data. The findings showed that the factors caused anxiety in learning English were: over self-prediction toward fear, irrational faith, over-sensitivity toward threat, the sensitivity of anxiety, wrong atribute body signal. Meanwhile,.the strategies.which.were.used.by students.in reducing their anxiety were: preparation, relaxation, positive thinking, peer seeking, resignation. The findings of this analysis may be useful to teachers, to English lecturers and to future researchers.
Discrete representations in neural models of spoken language

The distributed and continuous representations used by neural networks are at odds with representations employed in linguistics, which are typically symbolic. Vector quantization has been proposed as a way to induce discrete neural representations that are closer in nature to their linguistic counterparts. However, it is not clear which metrics are the best-suited to analyze such discrete representations. We compare the merits of four commonly used metrics in the context of weakly supervised models of spoken language. We compare the results they show when applied to two different models, while systematically studying the effect of the placement and size of the discretization layer. We find that different evaluation regimes can give inconsistent results. While we can attribute them to the properties of the different metrics in most cases, one point of concern remains: the use of minimal pairs of phoneme triples as stimuli disadvantages larger discrete unit inventories, unlike metrics applied to complete utterances. Furthermore, while in general vector quantization induces representations that correlate with units posited in linguistics, the strength of this correlation is only moderate.
Discernment of Digital Video Manipulation

We propose emblem-new additives for the reliable discovery similarly to localization of video clip duplicate-skip imitations. Uncovering properly crafted video clip duplicate-actions might be rather tough, especially whilst some uniform records are copied to occlude foreground objects. To reliably spot each additive similarly to occlusive duplicate relocations we appoint a dense-area technique, with normal features that guarantee effectiveness to numerous positioned up-processing operations. To restrict intricacy, the suitable video-orientated version of Spot Suit is made use of, with a multiresolution are attempting to find method, similarly to a cope with portions of ardour. Efficiency evaluation is based upon a emblem-new dataset, created ad hoc, with sensible duplicate-actions and a huge kind of difficult times. Speculative consequences show the encouraged approach to discover further to localize video clip duplicate-movements with super precision even in bad troubles.
What training reveals about neural network complexity

This work explores the hypothesis that the complexity of the function a deep neural network (NN) is learning can be deduced by how fast its weights change during training. Our analysis provides evidence for this supposition by relating the network's distribution of Lipschitz constants (i.e., the norm of the gradient at different regions of the input space) during different training intervals with the behavior of the stochastic training procedure. We first observe that the average Lipschitz constant close to the training data affects various aspects of the parameter trajectory, with more complex networks having a longer trajectory, bigger variance, and often veering further from their initialization. We then show that NNs whose biases are trained more steadily have bounded complexity even in regions of the input space that are far from any training point. Finally, we find that steady training with Dropout implies a training- and data-dependent generalization bound that grows poly-logarithmically with the number of parameters. Overall, our results support the hypothesis that good training behavior can be a useful bias towards good generalization.
Designing a Tourist Attractions Information System Using the Design Thinking Method (Case Study: Maimoon Palace)

Maimoon Palace is one of the tourist attractions and cultural heritages that has high potential, both in Medan and Indonesia. However, in the digital era, Maimoon Palace does not have an Information System which can give information about its availability, while it does not provide online ticketing as well. This underlies the writer to create Web Based Tourism Information System of Maimoon Palace. This research uses direct observation method, interview with related parties, and literature studies related to the issues. Web Based Tourism Information System of Maimoon Palace is generated by PHP and HTML programming language and MySQL database. This system uses design thinking system development method. Design Thinking is the method utilized by the writer to solve the problem with user-centered approach to define the problem, thenceforth to conceptualize ideas as a solution to produce a system. This information systems can be used to give the information about Maimoon Palace availability to the visitors and can be used to book the ticket online. This system is expected to be useful as information and media promotion for tourism potential in Medan. The more tourists visit Maimoon Palace, the more income will be gained by the people surround Maimoon Palace.Keyword: Information System, Tourist Attractions, Design Thinking, Maimoon Palace
Syntax and Themes: How Context Free Grammar Rules and SemanticWord Association Influence Book Success

In this paper, we attempt to improve upon the state-of-the-art in predicting a novel's success by modeling the lexical semantic relationships of its contents.We created the largest dataset used in such a project containing lexical data from 17,962 books from Project Gutenberg.We utilized domain specific feature reduction techniques to implement the most accurate models to date for predicting book success, with our best model achieving an average accuracy of 94.0%.By analyzing the model parameters, we extracted the successful semantic relationships from books of 12 different genres.We finally mapped those semantic relations to a set of themes, as defined in Roget's Thesaurus and discovered the themes that successful books of a given genre prioritize.At the end of the paper, we further showed that our model demonstrate similar performance for book success prediction even when Goodreads rating was used instead of download count to measure success.
We Need to Talk About train-dev-test Splits

Standard train-dev-test splits used to benchmark multiple models against each other are ubiquitously used in Natural Language Processing (NLP). In this setup, the train data is used for training the model, the development set for evaluating different versions of the proposed model(s) during development, and the test set to confirm the answers to the main research question(s). However, the introduction of neural networks in NLP has led to a different use of these standard splits; the development set is now often used for model selection during the training procedure. Because of this, comparing multiple versions of the same model during development leads to overestimation on the development data. As an effect, people have started to compare an increasing amount of models on the test data, leading to faster overfitting and “expiration” of our test sets. We propose to use a tune-set when developing neural network methods, which can be used for model picking so that comparing the different versions of a new model can safely be done on the development data.
When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute

Large language models have become increasingly difficult to train because of the growing computation time and cost. In this work, we present SRU++, a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. SRU++ exhibits strong modeling capacity and training efficiency. On standard language modeling tasks such as Enwik8, Wiki-103 and Billion Word datasets, our model obtains better bits-per-character and perplexity while using 3x-10x less training cost compared to top-performing Transformer models. For instance, our model achieves a state-of-the-art result on the Enwik8 dataset using 1.6 days of training on an 8-GPU machine. We further demonstrate that SRU++ requires minimal attention for near state-of-the-art performance. Our results suggest jointly leveraging fast recurrence with little attention as a promising direction for accelerating model training and inference.
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel

We revisit on-average algorithmic stability of Gradient Descent (GD) fortraining overparameterised shallow neural networks and prove new generalisationand excess risk bounds without the Neural Tangent Kernel (NTK) orPolyak-{\L}ojasiewicz (PL) assumptions. In particular, we show oracle typebounds which reveal that the generalisation and excess risk of GD is controlledby an interpolating network with the shortest GD path from initialisation (in asense, an interpolating network with the smallest relative norm). While thiswas known for kernelised interpolants, our proof applies directly to networkstrained by GD without intermediate kernelisation. At the same time, by relaxingoracle inequalities developed here we recover existing NTK-based risk bounds ina straightforward way, which demonstrates that our analysis is tighter.Finally, unlike most of the NTK-based analyses we focus on regression withlabel noise and show that GD with early stopping is consistent.
Requirements to Enhance the Effectiveness of E-Learning Platforms and their Influence on the Quality of Education at Jordanian Universities' Faculties of Finance and Administrative Sciences and Colleges of Hospitality in Light of the Corona Pandemic

The research seeks to examine the needs for improving the efficiency of e-learning platforms in light of the Corona Pandemic as well as their impact on the quality of education in Jordanian institutions.In order to establish the data's eligibility for factor analysis, we used version 20 of the SPSS to analyze 37 elements on the Likert scale before doing axial components analytics. It has been evaluated.Incentives are lowered by early hypotheses that ELPs are more efficient and acceptable. incentives. When the incentive effect was 0.031 (-0.248), the likelihood of increasing the efficiency of e-learners was increased. Researchers do not think that the incentives can benefit as they initially thought.
Visual signal device (VSD) for deaf sports athletics Malaysia

Previous study revealed that the advancement of technology has widen the potential for all individual including the disable to participate in any sport competition. Deaflympicand Malaysia Deaf Games,SOPMArespectively provide the opportunity for the deaf people to compete in sports event internationally and locally. From the literature, it is shown that deaf individuals have some visual-spatial advantages relative to hearing individuals. Hence, in this study, Visual Signal Device (VSD)beingdeveloped as a sight-based visual starting system for deaf sports athletes in track and field event. VSD system consist of a RGB LED panel, controller and the SEIKO brand electronic gun. The VSD system were exploredto see its effectiveness during the sports for deaf people. The results show that, VSD successfully employed during the recent SOPMA2018 in Perak, Malaysia