Reviewerly/exHarmony huggingface dataset:
Knowledge management in Yemeni universities between reality and expectations (an applied study on Albaydha University)

The study aimed to identify the reality of applying “knowledge management from the point of view of academic and administrative leaders at Albaydha University in the Republic of Yemen.” They are faculty deans, vice deans, faculty members, department heads. The department heads and directors are the Financial Affairs Department, the Personnel Affairs Department, Academic affairs, and student affairs. Their number reached (50) individuals, the study sample was Albaydha University, one of the public universities in the Republic of Yemen. The researcher used the descriptive and analytical approach to achieve the study objectives and test hypotheses. A special questionnaire was developed consisting of four axes and fields: diagnosis, knowledge acquisition, knowledge generation and distribution, knowledge application. , Storing and preserving knowledge, from the results summarized that Albaydha University in the Republic of Yemen “is not interested in acquiring and managing knowledge, nor is it implemented or stored in the university, as there are no centers at the university that encourage research in the field of knowledge, its management and use by providing an understanding of the importance of knowledge management in the era. Hadith, the study indicated a neglect of knowledge in the university despite its importance in the modern era, and the study also found that there are opportunities that contribute to a lack of interest in research, application and storage of knowledge, including the lack of financial resources, the lack of specialized cadres in the field of knowledge management and the lack of modern and advanced technology in the university.
Students’ Experiences on Learning Listening Through Youtube Media

YouTube has become a fabulous learning medium for teachers to enhance students’ language abilities and to boost students’ listening skills. But, for some students, learning listening through YouTube is their first experience, mainly 7th-grade students in junior high school. It picked the interest of the researcher’s present study to explore the student’s experiences of learning listening through YouTube using a phenomenological design at one of the junior high schools in Karangpucung, Cilacap, Central Java. The data were obtained from observation and close-ended questionnaires to 32 EFL students who have experienced learning listening using YouTube in listening class. The findings showed that learning listening using YouTube in class is not hard to understand for students and it makes them feel more eager, joyful, and enthusiastic. Furthermore, students became more self-confident to communicate in English after learning listening through Youtube.
Using E-Book Readers to Foster EFL Students’ Multi-Literacies (A Case Study at a Vocational High School in Ciamis)

This paper reports on the implementation of E-book Readers as a media to foster EFL students’ multi-literacies in one of vocational high school in Ciamis. This research is aimed at discovering how E-book Readers can foster EFL students’ multi-literacies, discovering kind of multi-literacy which will be indicated by students, and finding out students’ responses on E-book Readers. This research employed a case study with one class of the eleventh grade as research participant. This study also used observation, interview, and questionnaire to access the data. Those data from interview and classroom observation were analyzed by transcribing the data, while the questionnaires were analyzed by using percentage calculation. The findings of this research revealed the implementation of E-book Readers as a media in English learning process in the classroom. E-book Readers as a media to be applied in learning process can involve students in various kinds of literacy. Students can search their material and knowledge using E-book Readers. Students can also foster multi-literacies just only by using E-book Readers. The process of those learning activities enables students to be active in learning. The questionnaire results also revealed that most of students gave positive response on the use E-book Readers by the teacher in learning English. Some suggestions were presented for those who might benefit from this study. There is the advantage for the teacher who teaches English and also for the students who study English, E-book Readers can be helpful for them when they are used in teaching and learning process. Further researchers are suggested to search for more references about the media of E-book Readers. They are also suggested to carry out further research through E-book Readers media on the result of learning, in order to manifest national education goal.
EFL Teacher’s Strategies in Preparing Classroom Activity for Vocational High School Students

The research reported on teachers’ strategies in preparing classroom activities for vocational high school students. It was obvious that vocational school prepares students to work in particular industries. Thus, the teacher can provide classroom activities that were able to increase the student’s skills. A case study methodology was employed in this research and the data were obtained from classroom observation and non-structured interviews. The topic of this research is an English teacher at a vocational high school. The result found that the teacher prepared the learning materials based on the major that the student had taken and provided the classroom activity that adjusted to the student’s skill program. However, the study also showed that many students still seemed unmotivated and less focused. The teacher explained that it could be the students' less vocabulary mastery that made them hard to pay attention to the learning activity. The findings concluded that (1) the teacher can provide effective classroom activities even though it is still hard to manage the students, and (2) the teacher has provided the classroom activities based on the student’s needs, backgrounds, and skills.
Potential leukaemia classification using ISURF-DLCNN from blood cell image

There will be a total of 412,000 persons across the world who are diagnosed with leukaemia, with acute lymphoblastic leukaemia accounting for around 12% of all cases. As a consequence of this, leukaemia detection at an earlier stage has the potential to save the lives of millions of individuals. The identification of leukaemia using deep learning algorithms is the primary emphasis of this paper, along with blood cell counts. The photos are preprocessed using median filters, and then the K-means clustering (KMC) algorithm is used to split the data into its constituent parts. After that, the gathered features are fed into a deep learning convolutional neural network (DLCNN) in order to perform classification utilising an upgraded, speeded-up, and more robust feature descriptor (ISURF). The proposed technique achieved an accuracy rate of 99 percent while requiring a very low amount of effort, and it outperformed conventional approaches in terms of overall performance.
The role of self-regulated learning on foreign vocabulary learning: a multi-case study

In recent years, a considerable body of empirical research has acknowledged the role of self-regulated learning (henceforth SRL) in the domain of English as a foreign language (EFL). However, the study investigating the role and mechanism of SRL in vocabulary learning is still in scarcity particularly in Indonesian contexts. To address this noticeable gap, this multi-case study aims to explore the role of SRL on vocabulary learning strategies use and mechanism from the experience of undergraduate EFL learners. After signing the informed of consent form sheets, two undergraduate students from two different countries (Indonesia and Egypt) voluntarily participate in this study. By employing a series of semi-structured interviews and conducting narrative thematic analysis (Braun Clarke, 2006), the findings showcase that both participants employ a myriad of vocabulary learning strategies such as rehearsal, elaboration, and social strategy. Additionally, the empirical evidence indicates that they have higher level of motivation and persistence to maintain vocabulary-learning activity. The findings suggest the important practical implications for teachers, learners, and institution. This article also discusses limitations and recommendations.
Nearest Neighbours Graph Variational AutoEncoder

We present a Deep Learning generative model specialized to work with graphs with a regular geometry. It is build on a Variational Autoencoder framework and employs Graph convolutional layers in both encoding and decoding phases. We also introduce a pooling technique (ReNN-Pool), used in the encoder, that allows to downsample graph nodes in a spatially uniform and highly interpretable way. In the decoder, a symmetrical un-pooling technique is used to retrieve the original dimensionality of graphs. Performance of the model are tested on the standard Sprite benchmark dataset, a set of 2D images of video game characters, adequately transforming images data into graphs, and on the more realistic use-case of a dataset of cylindrical-shaped graph data that describe the distributions of the energy deposited by a particle beam in a medium.
Content based word image retrieval using multi-layer perception based convolutional neural networks

Content based image retrieval (CBIR) plays the major role in real time applications likes search engines, libraries. The conventional CBIR systems are implemented by using basic image processing and machine learning models. So, they resulted in the poor performance against various situations. Therefore, this article is focused on implementation of CBIR system using multi-layer perception based convolutional neural network (MLP-CNN). Initially, principal component analysis (PCA) applied on images to perform the dimensionality reduction operation, which also extracts the content specific features. Further, MLP-CNN model is used to train the system and generates the trained features. Finally, the testing operation is performed using MLP-CNN, which generates the output as content specific images. The simulation results shows that the proposed method resulted in superior performance as compared to state of art approaches.
Effects of Preemptive Empathy Interventions on Reply Toxicity among Highly Active Social Media Users

Can we reduce toxicity in online communication using behavioral interven- tions? Following the theoretical turn towards the role of empathy for demo- cratic discourse, this preregistered survey experiment tests various changes in an online discussion environments’ user interface (nudges) and an educa- tive intervention targeting users’ motivation to engage in empathy (boost) to reduce reply toxicity. Participants were recruited via Facebook Ads in the United States and Germany to acquire a sample of N = 2,154 highly active social media users. Based on their reported political attitudes, participants were asked to comment on social media statements they likely disagree with. Compared to the control group, neither the empathy or perspective taking nudges nor the friction placebo reduced reply toxicity. Boosting decreased reply toxicity to some degree but the effect was not robust against the inclu- sion of covariates. However, all nudging interventions significantly increased the length of replies. Overall, toxicity was highest among people with high online activity. While reducing toxicity online does not seem to work via simple changes in the user interface (nudging), more complex interventions (boosting) appear more promising to preemptively reduce toxicity before important voices are forced out of the public discourse online.
Overview of Adversarial Attack and Training in Computer Vision and Natural Language Processing

Pretrained language models (PLMs) has been widely used in various natural language processing (NLP) tasks, demonstrating outstanding performance across tasks and public benchmarks. However, despite the encouraging results in literature, applying pretrained language models to real world application stands as a great challenge due to various constrains. Especially, in natural language understanding (NLU) applications, such as voice assistant, it needs real-time response and has strict constrains on the accuracy, latency, cost and maintenance cycle. In this paper, we presents the challenges to utilize pretrained language models into large scale natural language understanding system and our strategies to customize the pretrained language models for production deployment given the practical constrains.
Relativity Theory, Quantum Theory and the Goedel Theorem

Two simple equations are given and we show their validity and consistency. Using model theory we apply central concepts of general relativity theory and the first law of the thermodynamics about energy conservation to that system. We then use Goedel Theorem to obtain new mathematical truth by transcending the original system and establish a new paradigm.
Determining Which Sine Wave Frequencies Correspond to Signal and Which Correspond to Noise in Eye-Tracking Time-Series

The Fourier theorem states that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some frequencies are important to reproduce key qualities of eye-movements ("signal") and some of frequencies are not important ("noise"). To investigate what is signal and what is noise, we analyzed our dataset in three ways: (1) visual inspection of plots of saccade, microsaccade and smooth pursuit exemplars; (2) analysis of the percentage of variance accounted for (PVAF) in 1,033 unfiltered saccade trajectories by each frequency band; (3) analyzing the main sequence relationship between saccade peak velocity and amplitude, based on a power law fit. Visual inspection suggested that frequencies up to 75 Hz are required to represent microsaccades. Our PVAF analysis indicated that signals in the 0-25 Hz band account for nearly 100% of the variance in saccade trajectories. Power law coefficients (a, b) return to unfiltered levels for signals low-pass filtered at 75 Hz or higher. We conclude that to maintain eye movement signal and reduce noise, a cutoff frequency of 75 Hz is appropriate. We explain why, given this finding, a minimum sampling rate of 750 Hz is suggested.
Reverse engineering adversarial attacks with fingerprints from adversarial examples

In spite of intense research efforts, deep neural networks remain vulnerable to adversarial examples: an input that forces the network to confidently produce incorrect outputs. Adversarial examples are typically generated by an attack algorithm that optimizes a perturbation added to a benign input. Many such algorithms have been developed. If it were possible to reverse engineer attack algorithms from adversarial examples, this could deter bad actors because of the possibility of attribution. Here we formulate reverse engineering as a supervised learning problem where the goal is to assign an adversarial example to a class that represents the algorithm and parameters used. To our knowledge it has not been previously shown whether this is even possible. We first test whether we can classify the perturbations added to images by attacks on undefended single-label image classification models. Taking a "fight fire with fire" approach, we leverage the sensitivity of deep neural networks to adversarial examples, training them to classify these perturbations. On a 17-class dataset (5 attacks, 4 bounded with 4 epsilon values each), we achieve an accuracy of 99.4% with a ResNet50 model trained on the perturbations. We then ask whether we can perform this task without access to the perturbations, obtaining an estimate of them with signal processing algorithms, an approach we call "fingerprinting". We find the JPEG algorithm serves as a simple yet effective fingerprinter (85.05% accuracy), providing a strong baseline for future work. We discuss how our approach can be extended to attack agnostic, learnable fingerprints, and to open-world scenarios with unknown attacks.
Alphazzle: Jigsaw Puzzle Solver with Deep Monte-Carlo Tree Search

Solving jigsaw puzzles requires to grasp the visual features of a sequence of patches and to explore efficiently a solution space that grows exponentially with the sequence length. Therefore, visual deep reinforcement learning (DRL) should answer this problem more efficiently than optimization solvers coupled with neural networks. Based on this assumption, we introduce Alphazzle, a reassembly algorithm based on single-player Monte Carlo Tree Search (MCTS). A major difference with DRL algorithms lies in the unavailability of game reward for MCTS, and we show how to estimate it from the visual input with neural networks. This constraint is induced by the puzzle-solving task and dramatically adds to the task complexity (and interest!). We perform an in-deep ablation study that shows the importance of MCTS and the neural networks working together. We achieve excellent results and get exciting insights into the combination of DRL and visual feature learning.
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes

Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management. Ideally these forecasts are equipped with error bars, so that downstream decisions can account for uncertainty. To produce predictions with error bars in this setting, we consider Gaussian processes (GPs) for modelling and predicting solar photovoltaic energy production in the UK. A standard application of GP regression on the PV timeseries data is infeasible due to the large data size and non-Gaussianity of PV readings. However, this is made possible by leveraging recent advances in scalable GP inference, in particular, by using the state-space form of GPs, combined with modern variational inference techniques. The resulting model is not only scalable to large datasets but can also handle continuous data streams via Kalman filtering.
Simplicity Bias in 1-Hidden Layer Neural Networks

Recent works have demonstrated that neural networks exhibit extreme simplicity bias(SB). That is, they learn only the simplest features to solve a task at hand, even in the presence of other, more robust but more complex features. Due to the lack of a general and rigorous definition of features, these works showcase SB on semi-synthetic datasets such as Color-MNIST, MNIST-CIFAR where defining features is relatively easier. In this work, we rigorously define as well as thoroughly establish SB for one hidden layer neural networks. More concretely, (i) we define SB as the network essentially being a function of a low dimensional projection of the inputs (ii) theoretically, we show that when the data is linearly separable, the network primarily depends on only the linearly separable ($1$-dimensional) subspace even in the presence of an arbitrarily large number of other, more complex features which could have led to a significantly more robust classifier, (iii) empirically, we show that models trained on real datasets such as Imagenette and Waterbirds-Landbirds indeed depend on a low dimensional projection of the inputs, thereby demonstrating SB on these datasets, iv) finally, we present a natural ensemble approach that encourages diversity in models by training successive models on features not used by earlier models, and demonstrate that it yields models that are significantly more robust to Gaussian noise.
Parameterized Complexity of Weighted Team Definability

In this article, we study the complexity of weighted team definability for logics with team semantics. This problem is a natural analogue of one of the most studied problems in parameterized complexity, the notion of weighted Fagin-definability, which is formulated in terms of satisfaction of first-order formulas with free relation variables. We focus on the parameterized complexity of weighted team definability for a fixed formula phi of central team-based logics. Given a first-order structure A and the parameter value k as input, the question is to determine whether A,T models phi for some team T of size k. We show several results on the complexity of this problem for dependence, independence, and inclusion logic formulas. Moreover, we also relate the complexity of weighted team definability to the complexity classes in the well-known W-hierarchy as well as paraNP.
DoCoFL: Downlink Compression for Cross-Device Federated Learning

Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing model updates, which are expected to decay throughout training. As a result, such methods are inapplicable to downlink (i.e., from the parameter server to clients) compression in the cross-device setting, where heterogeneous clients $\textit{may appear only once}$ during training and thus must download the model parameters. Accordingly, we propose $\textsf{DoCoFL}$ -- a new framework for downlink compression in the cross-device setting. Importantly, $\textsf{DoCoFL}$ can be seamlessly combined with many uplink compression schemes, rendering it suitable for bi-directional compression. Through extensive evaluation, we show that $\textsf{DoCoFL}$ offers significant bi-directional bandwidth reduction while achieving competitive accuracy to that of a baseline without any compression.
Content-based Recommender System for Answering Web Based User Queries

Abstract: Online the idea of this paper is to give insights about a web-based project that aims to resolve the users query using machine learning technique of content-based filtering. The crux is to define the scope of the website. It becomes crucial to understand the business prospects of the project along with the relevance in the current technical world. As the paper goes about, various other papers and ideas will also be discussed that have been previously published and implemented. This paper will also break down the concept of content-based filtering for any individual who is a beginner in the field of machine learning.
Parceling Ordinal Items in Latent Interaction Modeling

"Parceling Ordinal Items in Latent Interaction Modeling." Multivariate Behavioral Research, ahead-of-print(ahead-of-print), pp. 1–2
A Study of the Occurrence of &amp;lt;i&amp;gt;de&amp;lt;/i&amp;gt; in Multiple Modifier-head Constructions: A Minimalist Approach

摘要: 本文在生成语言学的理论框架下对汉语多项定中结构中"的
Data Forecasting Model to Know the Social Impact of Poverty in the Era of Globalization in West Java Province, Indonesia

Globalization has a social impact in the form of poverty. Meanwhile, poverty data in West Java Province, Indonesia, will increase in 2021 by 999,960 people. In addition to education, a country's poverty level shows its citizens' welfare. Therefore the poverty level in that country must be considered. In the Sustainable Development Goals, poverty is the priority scale to be considered. Therefore, forecasting is quite crucial in planning to know in advance what will happen. ARIMA (Auto Regressive Integrative Moving Average) is a modeling approach that can calculate the probability of a future value between two specified limits. This study predicts the number of poor people in West Java Province, Indonesia, from 2022 to 2025. The data used are 15 years from 2007 to 2021 and are processed with the Eviews computer program to see patterns and results in the ARIMA model. The modeling stage starts from data stationarity testing, model identification, model estimation, and model verification to forecasting. Based on the results of this study, the prediction results of the number of poor people in 2022 are 3,618,866; in 2023, it will be 3,512,758; in 2024, there will be 3,406,651, and in 2025 it will be 3,300,543 people. This forecasting uses the ARIMA (Auto Regressive Integrative Moving Average) model (1, 2, 1) as the most accurate method with MAD (Mean Absolute Deviation) error parameters of 1,751,747, MSE (Mean Square Error) of 6,977,202,252. 995 and MAPE (Mean Absolute Percentage Error) of 8%.