Reviewerly/exHarmony huggingface dataset:
Obstruction Logic: A Strategic Temporal Logic to Reason About Dynamic Game Models

Games that are played in a dynamic model have been studied in several contexts, such as cybersecurity and planning. In this paper, we introduce a logic for reasoning about a particular class of games with temporal goals played in a dynamic model. In such games, the actions of a player can modify the game model itself. We show that the model-checking problem for our logic is decidable in polynomial-time. Then, using this logic, we show how to express interesting properties of cybersecurity games defined on attack graphs.
The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-Modal Distillation

Trends and opinion mining in social media increasingly focus on novel interactions involving visual media, like images and short videos, in addition to text. In this work, we tackle the problem of visual sentiment analysis of social media images – specifically, the prediction of image sentiment polarity. While previous work relied on manually labeled training sets, we propose an automated approach for building sentiment polarity classifiers based on a cross-modal distillation paradigm; starting from scraped multimodal (text + images) data, we train a student model on the visual modality based on the outputs of a textual teacher model that analyses the sentiment of the corresponding textual modality. We applied our method to randomly collected images crawled from Twitter over three months and produced, after automatic cleaning, a weakly-labeled dataset of ∼1.5 million images. Despite exploiting noisy labeled samples, our training pipeline produces classifiers showing strong generalization capabilities and outperforming the current state of the art on five manually labeled benchmarks for image sentiment polarity prediction.
Data-Driven Self-Supervised Graph Representation Learning

Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on heuristics commonly identified through trial and error and are effective only within some application domains. Also, it is not clear why one heuristic is better than another. Moreover, recent studies have argued against some techniques (e.g., dropout: that can change the properties of molecular graphs or destroy relevant signals for graph-based document classification tasks). In this study, we propose a novel data-driven SSGRL approach that automatically learns a suitable graph augmentation from the signal encoded in the graph (i.e., the nodes’ predictive feature and topological information). We propose two complementary approaches that produce learnable feature and topological augmentations. The former learns multi-view augmentation of node features, and the latter learns a high-order view of the topology. Moreover, the augmentations are jointly learned with the representation. Our approach is general that it can be applied to homogeneous and heterogeneous graphs. We perform extensive experiments on node classification (using nine homogeneous and heterogeneous datasets) and graph property prediction (using another eight datasets). The results show that the proposed method matches or outperforms the SOTA SSGRL baselines and performs similarly to semi-supervised methods. The anonymised source code is available at https://github.com/AhmedESamy/dsgrl/
Theoretical Remarks on Feudal Hierarchies and Reinforcement Learning

Hierarchical reinforcement learning is an increasingly demanded resource for learning to make sequential decisions towards long term goals. Feudal hierarchies are among the most deployed frameworks. However, there are few theoretical results for hierarchical structures. In this work, we formalize the common two-level feudal hierarchy as two Markov decision processes, with the one on the high level being dependent on the policy executed at the low level. Despite the non-stationarity raised by the dependency, we show that each of the processes presents stable behavior. We then build on the first result to show that, regardless of the convergent learning algorithm used for the low level, convergence of both prediction and control algorithms at the high-level is guaranteed. Our results contribute with theoretical support for the use of feudal hierarchies in combination with standard reinforcement learning methods at each level.
Improving the False Alarm Capability of the Extended Maximum Average Correlation Height Filter

The extended maximum average correlation height (EMACH) filter is a potent pattern-detection tool used in image processing and computer vision applications. This filter enhances the effectiveness of the maximum average correlation height (MACH) filter by adding more features and flexibility. Incorporating the benefits of wavelet decomposition, we updated the EMACH filter to enhance its performance. The updated filter offered improved accuracy, robustness, and flexibility in recognizing complex patterns and objects in images with varying lighting conditions, noise levels, and occlusions. To verify the results’ consistency and compare their performance with that of the MACH filter and EMACH filter, performance metrics like peak-to-correlation energy, peak-to-sidelobe ratio, signal-to-noise ratio, and discrimination ratio were computed. Through numerical and experimental studies, we found that the proposed filter enhances the identification rate and decreases the number of false alarms.
A novel approach to find feature selection using modified fuzzy C-mean

Feature selection, also known as dimensionality reduction, is a common preprocessing step in the fields of pattern recognition, data mining, and machine learning. This is a critical issue when mining high-dimensional, massive data sets. Preprocessing the data before analysis to acquire a smaller collection of representative features and keeping the optimal salient properties of the data leads to more compactness of the models trained and better generalization, as well as a decrease in processing time. Therefore, the standard for dimension reduction is to save just the information that is most important from the original data, as determined by certain optimality criteria. In this paper we are going to form a new framework include the combination of MFCM and APSO, to find the HIDS.
Applied Machine Learning: New Methods, Applications, and Achievements

The realm of machine learning (ML) is one of the most dynamic and compelling domains within the computing landscape today [...]
PENERAPAN MODEL PEMBELAJARAN CONTEXTUAL TEACHING AND LEARNING TIPE LEARNING COMMUNITY UNTUK MENINGKATKAN PRESTASI BELAJAR SISWA PADA MATA PELAJARAN BAHASA INDONESIA TENTANG SURAT

This research is Classroom Action Research (PTK). Carried out collaboratively between researchers, observers and the subjects studied. The main aim of this research is to improve the learning achievement of Indonesian language regarding letters for class VII students. E MTsN 5 Brass. The subjects of this research were class VII students. E MTsN 5 Brass. This classroom action research process was carried out in two cycles, each cycle consisting of one meeting consisting of four actions, namely planning, implementation, action, observation and reflection. Learning activities in the first cycle obtained an average score of 68, while the second cycle obtained an average score of 85. This research concludes that through the contextual teaching and learning type learning community learning model, it can improve Indonesian language learning achievement regarding letters for class VII students. E MTsN 5 Brass. Apart from that, the contextual teaching and learning type learning community model can increase students' learning motivation in the learning process. ABSTRAKPenelitian ini merupakan Penelitian Tindakan Kelas (PTK). Dilaksanakan secara kolaboratif antara peneliti, observer, dan subyek yang diteliti. Tujuan utama dalam penetian ini adalah untuk meningkatkan prestasi belajar bahasa indonesia tentang surat siswa kelas VII. E MTsN 5 Kuningan. Subyek penelitian ini adalah siswa kelas VII. E MTsN 5 Kuningan. Proses penelitian tindakan kelas ini dilakukan dua siklus, setiap siklus satu kali peretemuan terdiri empat tindakan yaitu perencanaan, pelaksanaan, tindakan, observasi dan refleksi. Kegitan pembelajaran pada siklus pertama dengan memperoleh nilai rata-rata 68, sedangkan siklus kedua memperoleh nilai rata-rata 85. Penelitian ini berkesimpulan bahwa melalui model pembelajaran contextual teaching and learning type learning community dapat meningkatkan prestasi belajar bahasa indonesia tentang surat siswa kelas VII. E MTsN 5 Kuningan. Selain itu model pembelajaran contextual teaching and learning type learning community dapat meningkatkan motivasi belajar siswa dalam proses pembelajaran.
Can Artificial Intelligence Beat Humans in Detecting Breast Malignancy on Mammograms?

Background: The study was aimed at identifying how useful Computer-Aided Detection (CAD) could be in reducing false-negative reporting in mammography and early detection of breast cancer at an early stage as the best protection is early detection. Materials and methods: This retrospective study was conducted in a tertiary care setup of Atomic Energy Cancer Hospital, Nuclear Medicine, Oncology and Radiotherapy Institute (AECH-NORI), where 33 patients with suspicious findings on mammography and subsequent biopsy-proven malignancy were included. The findings of mammography including the lesion type, breast parenchymal density, and sensitivity of CAD detection, as well as the final biopsy results, were recorded. A second group of 40 normal screening mammograms was also included who had no symptoms, had Breast Imaging-Reporting and Data System category I (BI-RADS I) mammograms, and had no pathology identified on correlative sonomammography as well. Results: A total of 35 masses, 11 pleomorphic clusters of microcalcification, five clustered foci of macrocalcification, and nine lesions with pleomorphic clusters of microcalcification and two with pleomorphic clusters of microcalcification only were included. The CAD system was able to identify 26 masses (74%), eight lesions with pleomorphic clusters of microcalcification (72%), five foci of macrocalcification (100%), six lesions with pleomorphic clusters of microcalcification (66%), and two pleomorphic clusters of microcalcification without formed mass (100%). The overall sensitivity of the CAD system was 75.8%. CAD was able to identify 13 out of 16 masses with invasive ductal carcinoma (81.3%), eight out of nine lesions proven as invasive ductal carcinoma with ductal carcinoma in situ (DCIS) (88.9%), two out of five masses with invasive lobular carcinoma (40%), four out of four masses with invasive mammary carcinoma (100%), and zero out of one lesion identified as medullary carcinoma (0%). There was 100% detection for pleomorphic clusters of microcalcification without formed mass with CAD marking two out of two mammograms. Conclusion: CAD performed better with combined lesions, accurately marked pleomorphic clusters of microcalcification, and identified small lesions in predominant fibrofatty parenchymal density but was not reliable in dense breast, areas of asymmetric increased density, summation artifacts, edematous breast parenchyma, and retroareolar lesions. It also performed poorly with ill-defined lesions of invasive lobular carcinoma. Human intelligence hence beats CAD for the diagnosis of breast malignancy in mammograms as per our experience.
Relating Optimal Repairs in Ontology Engineering with Contraction Operations in Belief Change

The question of how a given knowledge base can be modified such that certain unwanted consequences are removed has been investigated in the area of ontology engineering under the name of repair and in the area of belief change under the name of contraction. Whereas in the former area the emphasis was more on designing and implementing concrete repair algorithms, the latter area concentrated on characterizing classes of contraction operations by certain postulates they satisfy. In the classical setting, repairs and contractions are subsets of the knowledge base that no longer have the unwanted consequence. This makes these approaches syntax-dependent and may result in removal of more consequences than necessary. To alleviate this problem, gentle repairs and pseudo-constractions have been introduced in the respective research areas, and their connections have been investigated in recent work. Optimal repairs preserve a maximal amount of consequences, but they may not always exist. We show that, if they exist, then they can be obtained by certain pseudo-contraction operations, and thus they comply with the postulates that these operations satisfy. Conversely, under certain conditions, pseudo-contractions are guaranteed to produce optimal repairs. Recently, contraction operations have also been defined for concepts rather than for whole knowledge bases. We show that there is again a close connection between such operations and optimal repairs of a restricted form of knowledge bases.
Enhancing Company Productivity through Information Sharing in Supply Chain Implementation

The lack of accuracy and slow information flow within the supply chain poses significant challenges to the sustainability of the manufacturing process. Effective information management is essential for enhancing supply chain competitiveness, encompassing increased product yield, improved product quality, cost reduction, and enhanced productivity for quicker product distribution. This research investigates the impact of information sharing on supply chain management to bolster company productivity. Additionally, it delves into the detrimental consequences of insufficient information sharing and offers solutions to ensure efficient information exchange within the organization. To achieve these research objectives, we employ a quantitative approach utilizing the Structural Equation Model (SEM)-Amos and six questionnaire variables: information sharing, information quality, information technology, supply chain management implementation, productivity, and customer satisfaction. Our findings underscore the adverse effects of limited information sharing on supply chain management performance, leading to reduced productivity. The enhancement of supply chain management hinges on effective and transparent information sharing, facilitated by disseminating clear and sustainable information throughout the company. Furthermore, ensuring employees understand supply chain management practices comprehensively is crucial. These measures are poised to contribute to increased productivity and the achievement of customer satisfaction goals.
Elliptic Curve Scalar Multiplication Operation: a Survey Study

Scalar multiplication is the fundamental operation in the elliptic curve cryptosystem. It involves calculating the integer multiple of a specific elliptic curve point. It involves three levels: field, point, and scalar arithmetic. Scalar multiplication will be significantly more efficient overall if the final level is improved. By reducing the hamming weight or the number of operations in the scalar representation, one can raise the level of scalar arithmetic. This paper reviews some of the algorithms and techniques that improve the elliptic curve scalar multiplication in terms of the third level.
Sentiment Analysis of Electric Cars Using Naive Bayes Classifier Method

In recent years, electric cars have become increasingly popular as an alternative to environmentally friendly vehicles in the automotive industry. These vehicles use electric power as an energy source that can reduce dependence on fossil fuels so as to contribute to efforts to reduce greenhouse gas emissions and air pollution. However, the presence of electric cars raises pro and con opinions from the public. Where, the conversation about electric cars has become one of the hot conversations on social media twitter. Twitter is a microblogging-based social media that facilitates its users to write short messages and share them easily and quickly. These opinions require sentiment analysis. The purpose of conducting sentiment analysis is to find out how people's perceptions and opinions on electric cars are leading in a positive direction or in a negative direction. Thus, sentiment analysis can help companies in designing marketing strategies, product development, and making better business decisions. Then the opinions will be classified based on positive and negative categories. This research uses the naive bayes classifier method to generate positive and negative sentiment towards electric cars on Twitter. The accuracy results of naive bayes obtained by using a confusion matrix in this research are 78.57% with a dataset split composition of 80%:20%.
An Optical Tracking Approach to Computer-Assisted Surgical Navigation via Stereoscopic Vision

Abstract Computer-assisted navigation has become a popular solution in surgical procedures where a high amount of precision is required. Current state-of-the-art methods of surgical navigation involve tracking reflective 3D marker spheres using IR stereo-scopic cameras. However, the cost of implementing such systems may not be affordable for smaller healthcare systems. In this paper, we propose that fully optical navigation has the potential to be a viable alternative to state-of-the-art reflective marker navigation. We use fiducial ArUco markers to facilitate the tracking of real-time position. Using two inexpensive cameras, we design and calibrate a stereoscopic camera to record the 3D position of an ArUco marker moving through space along a positioning platform. Additionally, we explore the possibility of using different color spaces and physical marker colors to improve the detection percentage and accuracy of markers. We identified that black-and-white ArUco markers using the Hue, Saturation, and Lightness (HSL) color space gave a positional mean error of 5.38 mm. Using the Red, Green, and Blue (RGB) color space gave the highest detection percentage for the same ArUco markers. In the future, the mean error can be reduced by increasing camera quality and by using a multi-stereoscopic camera setup.
Research on Hybrid A<sup>*</sup> and Genetic Algorithm for Path Planning

The field of intelligent robotics is progressing rapidly in today's rapid development of intelligence. Robots based on intelligent algorithms are being used in large numbers in various complex jobs to replace manual work, reduce human labor, and accomplish tasks more efficiently. In this paper, the real-life application of a warehouse robot walking is taken, avoiding obstacles and carrying items in a complex warehouse as the background. The job of a vehicle beginning from the starting point in a map with barriers and eventually reaching the end point is then realized in MATLAB using the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and genetic combination methods. The outcome of whether A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> is coupled with genetic algorithm in many instances can be determined through MATLAB operation and horizontal comparison. The results show that the algorithm can indeed carry out efficient global path planning, which can meet the practical application needs, and has certain application value and practicability. In the near future, path planning algorithms and intelligent robots will have rapid development, and both algorithmic control and hardware materials will be updated and iterated rapidly. The development trend of production intelligence and industrial wisdom all foretell that robots will become more and more intelligent and have more application scenarios in more fields to help humans or replace them in labor production. Path planning algorithms and intelligent robots will make people's lives more convenient, faster and better.
K-means text clustering based on improved particle swarm optimization

To address the influence of initial cluster center selection on K-means, we propose IPSOK-means, a text clustering method that utilizes improved particle swarm optimization (PSO). IPSOK-means incorporates a self-adjusting inertia weight mechanism to enhance population diversity and exploration capabilities. Experimental results demonstrate that IPSOK-means achieves superior text clustering effectiveness when compared to K-means, SKM, and PSOK-means.
PENTINGNYA UPAYA MENINGKATKAN MINAT BELAJAR IPS DENGAN MENGGUNAKAN MEDIA AUDIOVISUAL PADA SISWA SD KELAS RENDAH DI SD NEGERI KUNIR 1 DEMPET DEMAK

The purpose of this study was to determine the extent to which learning activities using audio-visual media increased interest in learning social studies. Local facts show that when learning social studies, students are more concerned with themselves, do not pay attention to the teacher and are indifferent to other friends. This shows that students' interest in IPS is still low. Audio-visual media is expected to increase interest in learning Social Sciences because the application of audio-visual media can increase. So that the character of students can change habits and care for the environment. The research method uses quantitative research methods, which are carried out by looking for material in written form. The purpose of this research is to increase interest in learning social studies through audiovisual media. The results of this study indicate that the use of audiovisual media is very important to increase elementary school students' interest in social studies learning.
Partial Face Identification using Local Feature Extraction Algorithm on Different Classifiers

Face identification is a complex problem with a long history and is currently the subject of much research in unconstrained conditions, including occlusion, facial expression, position change, and lighting. Nevertheless, the occlusion issue receives less attention in current studies. Many sophisticated deep learning and machine learning algorithms have been developed to enhance the identification of occlusion. A new face recognition method: Local Features Extraction with Logistic Classifier, designed to identify someone from the area of their face that is not covered. This proposed work extracts the features by taking the difference of patches and then generating the patch sequence using the k-means clustering algorithm and classifying them using different classifiers. This paper used five classifiers to check their effectiveness in matching the train and test patch sequence. Two public datasets, AR for natural occlusion and ORL for synthetic occlusion are used for experimental work.