Reviewerly/exHarmony huggingface dataset:
Experimental Study of The Effectiveness of Gamification Module for Arabic Language in Primary School

The teaching and learning of Arabic in primary schools was officially introduced in the Malaysian education system through the j-QAF program (Jawi, al-Quran, Arabic, and fardhu ain) beginning in 2005.Therefore, teachers need to use a centralized approach and strategy.Gamification is an alternative approach to strengthening students' knowledge, cultivating attitudes, increasing motivation as well as improving their achievement and language skills.The implementation of gamification can be through online applications, card games, board games, or even hands-on without using technology.This Gamification Modelling in Arabic Language Learning (MGdPBA) module was guided by GaML (Gamification Modelling Language) elements and applied hands-on in the classroom.The design of this study uses quasi-experimental and SPSS software for quantitative data analysis.The t-test was used to evaluate the effectiveness of MGdPBA between the control and treatment groups involving 60 students through pre-and post-tests.The results of the study showed a positive impact between the achievement of students in the treatment group and the control group.It is hoped that this study will be a motivation for teachers to use a hand on gamification in teaching and learning sessions in the classroom.
Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network

In this paper, through data analysis of multimodal knowledge graph optimized neural network and visual analysis of college students' sports performance, we use huge graph, a graph database supporting distributed storage, to store domain knowledge in the form of the knowledge graph, use Spring Boot to build a server-side framework, use Vue framework combined with vis.js to visualize relational network graphs, and design and implement a knowledge-oriented. This paper proposes a visual analytics system based on the theory of visual analytics. Based on the idea of visual analytics, this paper presents a visual analytics framework combining predictive models. This framework combines the automated analysis capability of predictive models with interactive visualization as a new idea to explore the visual analysis of student behavior and performance changes. Using relevant predictive algorithms in machine learning, corresponding models are built to refine the importance of features for visual analysis and correlate behavioral data with achievement data. In this process, multiple prediction algorithms are used to build prediction models. The model effects are analyzed and compared to select the optimal model for use in the visual analytics framework. The graphical analytic view is integrated. EduRedar, an optical analytical system for sports data based on the performance prediction model, is designed and implemented to support multidimensional and multiangle data analysis and visualize the changes in college students' sports and performance based on accurate campus exercise data.
Human-annotated dataset for social media sentiment analysis for Albanian language

Social media was a heavily used platform by people in different countries to express their opinions about different crises, especially during the Covid-19 pandemics. This dataset is created through collecting people's comments in the news items on the official Facebook site of the National Institute of Public Health of Kosovo. The dataset contains a total of 10,132 comments that are human-annotated in the Albanian language as a low-resource language. The dataset was collected from March 12, 2020, and this coincides with the emergence of the first confirmed Covid-19 case in Kosovo until August 31, 2020, when the second wave started. Due to the scarcity of labeled data for low-resource languages, the dataset can be used by the research community in the field of machine learning, information retrieval, affective computing, as well as by the public agencies and decision makers.
The Development of College English Teachers in the New Era

The rapid development of science and technology and the global integration have put forward new requirements for talents. In the new era, colleges and universities are facing unprecedented challenges in talents cultivation. As educators of the talents, college teachers must take the initiative to meet the challenges and improve the quality of talent training to contribute for the development of the country and society. College English teachers, as the guiders in language and cultural learning, thinking development and the formation of outlook on life, world outlook and values, must have enough ability to fulfill their responsibilities. This requires every college English teacher to keep pace with the times and constantly improve themselves from various aspects, and better meet the new demand for talents in the new era.
Looking for the good shape in the Poly-Universe material

This communication reports a set of activities carried out with a three-year-old male child in residential care, using the Poly-Universe material. This material involves sets of 24 pieces of geometric shapes and basic colours (square, circle and triangle), which allow puzzle-like constructions, transforming into a colourful geometric skill development game based on the symmetry of scale change, created by the artist János Szasz Saxon. In cooperation with the child, the educator made the material available, introducing circles, then squares and finally triangles, letting the child explore the material, which led to the construction of creative patterns and playful solutions, associated with other objects (eg the car track and the box to be used as a garage). Through participant observation, it was possible to record, in addition to the knowledge of geometric shapes and colours, through their naming, the development of curiosity, motivation, attention and visual perception. It was registered the placement of series, based on pieces of the same shape, geometric and enlarged forms of a wider set. In this way, the child’s curiosity and desire to learn gave way to intentional processes of exploring the material and understanding different mathematical concepts. It was possible to observe a gradual evolution in terms of material handling, showing an increasing willingness to make/discover new connections. In an autonomous way, he sorted the pieces by colour, created imaginary shapes or shapes that he could play with, put together equal sizes and was able to create new geometric figures.
Determining the regional tectonic stress field by remote sensing in the Bou Azzer inlier, Central Anti-Atlas, Morocco

Purpose. This paper deals with the determination of the regional stress field direction of the Bou Azzer inlier using the remote sensing tool. Methods. CIn this study, we use an approach to digital mapping by remote sensing, including the steps of pre-processing and processing of Landsat-8 OLI images. Then, an automatic extraction of lineaments based on directional filtering has been performed. To determine the main directions of major mean fractures, these results have been supplemented and confirmed by an integrated model, including a synthesis of bibliographic works and field studies. Findings. The directional rosette analysis results show four systems of major directions namely, N0°, N45°, N90° and N135°. The regional stress field in the study area, according to tectonic history, is characterized by a horizontal compression tectonic regime, as indicated by several systems of strike-slip faults with a high tendency to deformation. Thus, the abundance of brittle and ductile microtectonic indicators confirms the direction of the main compressive stress N°30. The direction of the three-dimensional stress field: σ1: N°30, σ2: N°120, σ3: Vertical component. Originality. The present study allows to determine the regional stress field direction of the Bou Azzer inlier, in particular, in areas affected by complex tectonics of various scales, as well as in hard-to-reach areas. Practical implications. In mining practice, the study of stability using 2D and 3D geotechnical numerical modeling of underground mine workings is essential. The stress field direction is an important input parameter to develop more realistic decision support models, as well as to ensure the safety of people and materials at the Bou Azzer mine.
Learning Mixed Strategies in Trajectory Games

In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior.Trajectory games capture these complex effects by design.In competitive settings, this makes them a more faithful interaction model than traditional "predict then plan" approaches.However, current game-theoretic planning methods have important limitations.In this work, we propose two main contributions.First, we introduce an offline training phase which reduces the online computational burden of solving trajectory games.Second, we formulate a lifted game which allows players to optimize multiple candidate trajectories in unison and thereby construct more competitive "mixed" strategies.We validate our approach on a number of experiments using the pursuit-evasion game "tag."
Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)

We introduce a model-agnostic algorithm for manipulating SHapley Additive exPlanations (SHAP) with perturbation of tabular data. It is evaluated on predictive tasks from healthcare and financial domains to illustrate how crucial is the context of data distribution in interpreting machine learning models. Our method supports checking the stability of the explanations used by various stakeholders apparent in the domain of responsible AI; moreover, the result highlights the explanations' vulnerability that can be exploited by an adversary.
Using Random Perturbations to Mitigate Adversarial Attacks on NLP Models

Deep learning models have excelled in solving many problems in Natural Language Processing, but are susceptible to extensive vulnerabilities. We offer a solution to this vulnerability by using random perturbations such as spelling correction, synonym substitution, or dropping the word. These perturbations are applied to random words in random sentences to defend NLP models against adversarial attacks. Our defense methods are successful in returning attacked models to their original accuracy within statistical significance.
The Effect of Manifold Entanglement and Intrinsic Dimensionality on Learning

We empirically investigate the effect of class manifold entanglement and the intrinsic and extrinsic dimensionality of the data distribution on the sample complexity of supervised classification with deep ReLU networks. We separate the effect of entanglement and intrinsic dimensionality and show statistically for artificial and real-world image datasets that the intrinsic dimensionality and the entanglement have an interdependent effect on the sample complexity. Low levels of entanglement lead to low increases of the sample complexity when the intrinsic dimensionality is increased, while for high levels of entanglement the impact of the intrinsic dimensionality increases as well. Further, we show that in general the sample complexity is primarily due to the entanglement and only secondarily due to the intrinsic dimensionality of the data distribution.
Inference and Learning with Model Uncertainty in Probabilistic Logic Programs

An issue that has so far received only limited attention in probabilistic logic programming (PLP) is the modelling of so-called epistemic uncertainty, the uncertainty about the model itself. Accurately quantifying this model uncertainty is paramount to robust inference, learning and ultimately decision making. We introduce BetaProbLog, a PLP language that can model epistemic uncertainty. BetaProbLog has sound semantics, an effective inference algorithm that combines Monte Carlo techniques with knowledge compilation, and a parameter learning algorithm. We empirically outperform state-of-the-art methods on probabilistic inference tasks in second-order Bayesian networks, digit classification and discriminative learning in the presence of epistemic uncertainty.
Learning from the Tangram to Solve Mini Visual Tasks

Current pre-training methods in computer vision focus on natural images in the daily-life context. However, abstract diagrams such as icons and symbols are common and important in the real world. We are inspired by Tangram, a game that requires replicating an abstract pattern from seven dissected shapes. By recording human experience in solving tangram puzzles, we present the Tangram dataset and show that a pre-trained neural model on the Tangram helps solve some mini visual tasks based on low-resolution vision. Extensive experiments demonstrate that our proposed method generates intelligent solutions for aesthetic tasks such as folding clothes and evaluating room layouts. The pre-trained feature extractor can facilitate the convergence of few-shot learning tasks on human handwriting and improve the accuracy in identifying icons by their contours. The Tangram dataset is available at https://github.com/yizhouzhao/Tangram.
Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications

This book contains the successful invited submissions [...]
An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity

Clustering algorithms are often evaluated using metrics which compare with ground-truth cluster assignments, such as Rand index and NMI. Algorithm performance may vary widely for different hyperparameters, however, and thus model selection based on optimal performance for these metrics is discordant with how these algorithms are applied in practice, where labels are unavailable and tuning is often more art than science. It is therefore desirable to compare clustering algorithms not only on their optimally tuned performance, but also some notion of how realistic it would be to obtain this performance in practice. We propose an evaluation of clustering methods capturing this ease-of-tuning by modeling the expected best clustering score under a given computation budget. To encourage the adoption of the proposed metric alongside classic clustering evaluations, we provide an extensible benchmarking framework. We perform an extensive empirical evaluation of our proposed metric on popular clustering algorithms over a large collection of datasets from different domains, and observe that our new metric leads to several noteworthy observations.
A Unique Three-Step Weather Data Approach in Solar Energy Prediction Using Machine Learning

The importance of renewable energy sources like solar energy in reducing carbon emissions and other greenhouse gases has contributed to an increase in grid integration.However, the intermittent nature of solar power causes reliability issues and a loss of energy balance in the system, which are barriers to solar energy penetration.This study proposes a unique three-step approach that identifies weather parameters with moderate to strong correlation to solar radiation and uses them to predict solar energy generation.The combination of an on-site weather station and a reliable local weather station produces relevant data that increases the accuracy of the forecasting model irrespective of the machine learning algorithm used.This data source combination is tested, along with two other scenarios, using the exponential Gaussian Process Regression machine learning algorithm in MATLAB.It was found to be the most effective algorithm with a Normalized Root Mean Square Error of 1.1922, and an R 2 value of 0.66.
Memotion Analysis through the Lens of Joint Embedding (Student Abstract)

Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.
Conditional Abstract Dialectical Frameworks

dialectical frameworks (in short, ADFs) are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. This idea is generalized by letting acceptance conditions being assigned to complex formulas, resulting in conditional abstract dialectical frameworks (in short, cADFs). We define the semantics of cADFs in terms of a non-truth-functional four-valued logic, and study the semantics in-depth, by showing existence results and proving that all semantics are generalizations of the corresponding semantics for ADFs.
Multi-Dimension Attention for Multi-Turn Dialog Generation (Student Abstract)

We present a generative neural model for open and multi-turn dialog response generation that relies on a multi-dimension attention process to account for the semantic interdependence between the generated words and the conversational history, so as to identify all the words and utterances that influence each generated response. The performance of the model is evaluated on the wide scope DailyDialog corpus and a comparison is made with two other generative neural architectures, using several machine metrics. The results show that the proposed model improves the state of the art for generation accuracy, and its multi-dimension attention allows for a more detailed tracking of the influential words and utterances in the dialog history for response explainability by the dialog history.
Renovate Yourself: Calibrating Feature Representation of Misclassified Pixels for Semantic Segmentation

Existing image semantic segmentation methods favor learning consistent representations by extracting long-range contextual features with the attention, multi-scale, or graph aggregation strategies. These methods usually treat the misclassified and correctly classified pixels equally, hence misleading the optimization process and causing inconsistent intra-class pixel feature representations in the embedding space during learning. In this paper, we propose the auxiliary representation calibration head (RCH), which consists of the image decoupling, prototype clustering, error calibration modules and a metric loss function, to calibrate these error-prone feature representations for better intra-class consistency and segmentation performance. RCH could be incorporated into the hidden layers, trained together with the segmentation networks, and decoupled in the inference stage without additional parameters. Experimental results show that our method could significantly boost the performance of current segmentation methods on multiple datasets (e.g., we outperform the original HRNet and OCRNet by 1.1% and 0.9% mIoU on the Cityscapes test set). Codes are available at https://github.com/VipaiLab/RCH.
Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability

Investigating the reasoning abilities of transformer models, and discovering new challenging tasks for them, has been a topic of much interest. Recent studies have found these models to be surprisingly strong at performing deductive reasoning over formal logical theories expressed in natural language. A shortcoming of these studies, however, is that they do not take into account that logical theories, when sampled uniformly at random, do not necessarily lead to hard instances. We propose a new methodology for creating challenging algorithmic reasoning datasets that focus on natural language satisfiability (NLSat) problems. The key idea is to draw insights from empirical sampling of hard propositional SAT problems and from complexity-theoretic studies of language. This methodology allows us to distinguish easy from hard instances, and to systematically increase the complexity of existing reasoning benchmarks such as RuleTaker. We find that current transformers, given sufficient training data, are surprisingly robust at solving the resulting NLSat problems of substantially increased difficulty. They also exhibit some degree of scale-invariance—the ability to generalize to problems of larger size and scope. Our results, however, reveal important limitations too: careful sampling of training data is crucial for building models that generalize to larger problems, and transformer models’ limited scale-invariance suggests they are far from learning robust deductive reasoning algorithms.
‘Beach’ to ‘Bitch’: Inadvertent Unsafe Transcription of Kids’ Content on YouTube

Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment. Consequently, YouTube Kids' content should receive an additional level of scrutiny to ensure children's safety. While research on detecting offensive or inappropriate content for kids is gaining momentum, little or no current work exists that investigates to what extent AI applications can (accidentally) introduce content that is inappropriate for kids. In this paper, we present a novel (and troubling) finding that well-known automatic speech recognition (ASR) systems may produce text content highly inappropriate for kids while transcribing YouTube Kids' videos. We dub this phenomenon as inappropriate content hallucination. Our analyses suggest that such hallucinations are far from occasional, and the ASR systems often produce them with high confidence. We release a first-of-its-kind data set of audios for which the existing state-of-the-art ASR systems hallucinate inappropriate content for kids. In addition, we demonstrate that some of these errors can be fixed using language models.
Implementation of Forward Chaining for Diagnosis of Dengue Hemorrhagic Fever

Insects in the form of mosquitoes are transmitting vectors of several types of diseases that are quite dangerous and need to be watched out for because they can be deadly, such as dengue fever mosquitoes, malaria, elephantiasis and the like. Patients suffering from mosquito-borne diseases must be treated immediately to find out the type of disease they are suffering from and their treatment solutions. The era of digitalization that is growing rapidly, including in the field of medical science, makes the use of information technology to help improve quality services for the community. The analysis used in this research is using the forward chaining method, as a comparison of the overall process of the problems obtained in the investigation and developed using the waterfall paradigm. Forward chaining is optimized because the disease diagnosis process begins by observing the symptoms experienced by a patient and then continues with a tracing process that results in conclusions about the dengue fever suffered. While the waterfall method is used in the development of this expert system because the method can accommodate all stages of the system starting from the analysis of functional and non-functional system requirements, system design, coding, expert system testing and the last stage is expert system maintenance. The data used in this study used data at the Maos Health Center, Cilacap Regency. Testing is carried out using the black box testing method where the expert system is tested on end-users to determine the level of success and usability of the developed system. The questionnaire was given to 76 respondents with the results of the questionnaire: 91.9% indicating the level of user satisfaction, 89.6% indicating the level of effectiveness and 92.2% indicating the level of efficiency of the expert system. The output of this study is the conclusion of the disease suffered by the patient and the solution for handling it.