Reviewerly/exHarmony huggingface dataset:
Motion Capture Modeling of Dexterous Hand for Intelligent Sensing

Motion capture technology is an emerging comprehensive multidisciplinary field cross technology involving graphics, engineering and communication technology. In order to acquire the posture of dexterous hand, a motion capture model for intelligent perception of dexterous hand was proposed in this paper. Sensing system, which is composed of 16 six axis inertial sensors is used to obtain the acceleration and angular velocity of dexterous hand movement. The Kalman filter algorithm was used to calculate the angle after sensor calibration and fusion. The sensor data was sent and received through an acquisition board using a serial port and the collected sensor angles were transmitted through multiple threads with Socket. A buffer was written to store a large amount of data, which map the fused angle with the hand model on the Unity 3D platform, and transmit the angle information to the corresponding virtual hand joint according to the port number to achieve real-time transmission. This article mapped 15 knuckles one by one, resulting in more accurate mapping. Socket was used for data transfer, offering a faster speed compared to USB. Experimental results show that the proposed method could capture the movements of dexterous hands, and realize the interaction between virtual hands and dexterous hands in Unity 3D. Besides, the angle information is saved by the Json file.
ESTPE: An efficient and stable thumbnail-preserving encryption scheme

Thumbnail-preserving encryption (TPE) can balance the usability and the privacy of images in the cloud. Most ideal TPE schemes obtain thumbnails by dividing the plaintext image into multiple blocks, and then perform substitution and permutation operations in each block. The size of partitioned blocks affect the usability and the privacy of the encrypted images. The size of the pixel groups in the blocks, i.e., the number of encrypted pixels per time, has experienced the development from 2-pixel to n-pixel in recent years. Existing solutions cannot promote the real implementation and commercial realization because of inefficiency and instability. In this paper, we propose an efficient and stable TPE scheme, which can select intelligently different sizes of pixel groups in each block. Theoretical analysis indicates that the encryption time is influenced by the sum and the number of pixels in a pixel group. The length of the pixel group and the sum of pixel values in a group influence each other. We try to determine the optimal combination of these two factors. Through the analysis of Markov model, the proposed scheme has the nonce-respecting security. The experimental results of comparison show that, for the images with size of 480 × 320, the average encryption time of the proposed scheme is 61.7% less than that of 3-pixel flexible TPE, which is the most effective scheme up to now. And the fluctuation value decreases by 34.8%.
Characteristics Verification of the Luggage Transportation Problem Using Relative Vectors in Multi-agent Reinforcement Learning

In recent years, researchers have developed multi-agent reinforcement learning systems to automate luggage transfer. However, these systems often struggle to learn effectively in partially observable environments, such as POMDPs. This paper presents a novel learning approach that leverages relative vectors to address this limitation. The proposed method is compared to conventional approaches, and the results show that it can achieve better performance in POMDPs.
Machine learning class numbers of real quadratic fields

We implement and interpret various supervised learning experiments involving real quadratic fields with class numbers 1, 2 and 3. We quantify the relative difficulties in separating class numbers of matching/different parity from a data-scientific perspective, apply the methodology of feature analysis and principal component analysis, and use symbolic classification to develop machine-learned formulas for class numbers 1, 2 and 3 that apply to our dataset.
Horizontal crossover and co-operative hunting-based Whale Optimization Algorithm for feature selection

The Whale Optimization Algorithm (WOA) is a widely-used approach for problem-solving, but it has some inherent limitations such as poor exploration capabilities, susceptibility to local optima, and reduced solution accuracy. To address these drawbacks, this study introduces a novel approach known as Horizontal Crossover and Co-operative-hunting-based WOA (HCCWOA). This enhanced algorithm incorporates a weight, co-operative learning techniques, and a horizontal crossover strategy into the WOA framework. The introduction of horizontal crossover bolsters the exploration capabilities of WOA, while the integration of co-operative learning techniques and an inertia weight enhances its exploitation abilities. Recognizing the significance of feature selection in this context, the proposed algorithm is applied in a wrapper mode with the K-Nearest Neighbor (KNN) classifier to select relevant features. The effectiveness of the HCCWOA is rigorously evaluated on twelve classical datasets sourced from the UCI repository. A comprehensive comparison is conducted with eight well-established metaheuristic algorithms and five recent variations of WOA. Performance metrics, including maximum accuracy, minimum fitness, and minimum feature count, are considered in this comparative analysis. The simulation results affirm that the HCCWOA outperforms other algorithms in at least six out of the twelve datasets. This enhanced performance is further substantiated through statistical analyses, including Friedman's rank test, paired-sample Wilcoxon signed-rank test, two-way ANOVA test, T-test, and boxplot analysis. The combination of empirical results and statistical validation supports the superior effectiveness of the proposed HCCWOA approach, highlighting its ability to effectively explore feature spaces and select the most relevant characteristics for classification tasks.
Color image reversible data hiding based on multi-channel synchronized histogram

Reversible data hiding (RDH) has been generally utilized in copyright protection and content integrity authentication for multimedia. In general, many typical RDH schemes are based on grayscale images. Considering that color images are widely employed in daily life, color image based RDH should be significantly concerned. Recently, color image based RDH is rare and those schemes are mostly empirically designed, which means that embedding capacity is manually allocated between different channels (such as RGB channels). In addition, most existing algorithms only focus on designing effective prediction methods to utilize the correlation in each channel, which means that the correlation between different channels is insufficiently employed and thus the algorithm performance is limited. Therefore, in this paper, the correlation between those different channels is sufficiently considered and an adaptive capacity allocation framework is proposed instead of empirical schemes. Moreover, the construction of a new synchronous histogram associated with the above-mentioned capacity allocation algorithm is proposed to further improve its performance. Experimental results demonstrate the superiority of the proposed scheme compared with other state-of-the-art ones.
No-go theorems for photon state transformations in quantum linear optics

We give a necessary condition for photon state transformations in linear optical setups preserving the total number of photons. From an analysis of the algebra describing the quantum evolution, we find a conserved quantity that appears in all allowed optical transformations. We comment some examples and numerical applications, with example code, and give three general no-go results. These include (i) the impossibility of deterministic transformations which redistribute the photons from one to two different modes, (ii) a proof that it is impossible to generate a perfect Bell state in heralded schemes with a separable input for any number of ancillary photons and modes and a fixed herald and (iii) a restriction for the conversion between different types of entanglement (converting GHZ to W states). These tools and results can help in the design of experiments for optical quantum state generation.
Pro-IDD: Pareto-based ensemble for imbalanced and drifting data streams

Concept drifts and class imbalance are two primary challenges in supervised data stream classification, whereas their co-occurrence presents a more complicated learning problem. To tackle these challenges, this paper proposes Pro-IDD, a Pareto-based ensemble for imbalanced and drifting data streams. As part of Pro-IDD, Min++ module resolves the class imbalance issue by improving the minority class visibility such that class overlaps are reduced and small disjuncts in minority space are enlarged. Additionally, the ProEns module is designed to construct the ensemble pool by taking concept drifts and class imbalance into account. ProEns prunes the ensemble pool by using Pareto-based multi-objective learning for two measures: time-decayed recall-based weight and ensemble diversity. Experiments are conducted on 20 data streams with concept drifts and class imbalance and comparisons are reported against 10 state-of-the-art methods. Results show that improving the minority class visibility and using time-decayed recall-based weight and diversity for ensemble selection through Pareto-based multi-objective learning could improve the classification performance of data streams learners in the presence of concept drifts and class imbalance.
Design and realization of data mining simulation and methodological models

Data mining in education is steadily gaining momentum to explore the learning analytics because of its easy to use with any programming knowledge. This emerging method primarily focus on developing methods for handling and exploring voluminous information represented in distinct types of data. Deployment of data mining in education helps to better understand the synergy between the learning and educational settings established by the institutions to foster their learning activity. This work presents a data mining based virtual simulation experimental teaching system which provisions personalized interactive learning, real-world scene experimental training and comprehensive assessment of the learning effect. The proposed system employs interactive programming, automatic evaluation of experimental results, assessing the impact of teaching effectiveness with other modules. The work also provides a series of comprehensive cases and programming experiments apart from presenting the execution pages. The results indicate that this system can intuitively help students to improve their learning process, student participation and foster independent learning ability. The application of data mining alleviates the dependency on complex programming paradigms to make learning analytics, which is beneficial to both the teaching and learning communities.
PIXELS AND POLICY: THE SYMBIOTIC RELATIONSHIP BETWEEN DIGITALIZATION AND INTERNATIONAL TRADE LEGISLATION

In an era where digital frontier continually reshapes our jurisprudential landscape, this research embarks upon an odyssey to decipher its complex tapestry. Employing a qualitative approach, it delves deep into interplay between burgeoning data realms and legal scaffolds, drawing insights from extensive array of scholarly literature. The investigation unveils that confluence of data and law transcends mere operational shifts; it signals a profound transformation in our societal ethos. Findings underline the dual-edged nature of this evolution: while legal tech offers unparalleled chances for enhanced oversight and efficiency, it concurrently provokes challenges related to privacy &amp; surveillance. In conclusion, as we stand at cusp of this digital jurisprudential metamorphosis, it becomes imperious to cultivate a balance, imbuing legal structures with prowess of technology yet anchored by enduring tenets of justice and human rights. The suggestions include fostering interdisciplinary dialogues &amp; proactive regulatory frameworks, ensuring that our march into the digital future remains both the innovative and ethically grounded.
Pengaruh Metode Team Game Tournament (TGT) Dalam Meningkatkan Minat Belajar Siswa Kelas VII di SMP Swasta Nur Adia Medan

The purpose of the study was to determine the effect of the Team Games Tournament (TGT) method in increasing student interest in learning at Nur Adia Private Junior High School Medan, especially for class VII, which consisted of 30 students. The type of research used is qualitative research. In this study, the researchers used the principal, tutor teachers and students as resource persons. Observations and interviews were conducted to obtain results about the Team Games Tournament method which aims to increase student interest in learning. This type of cooperative learning model TGT (Team Games Tournamnet) proves that with this method students' interest in learning increases rapidly, 1) students are more active in learning because the teacher promises rewards or awards, 2) students can learn more fun and entertained, 3) foster a sense of mutual respect and responsibility. With this method, it can encourage progress in the quality of education and increase student interest in learning in the world of education.
Perlocution vs Illocution: How Different Interpretations of the Act of Explaining Impact on the Evaluation of Explanations and XAI

This article discusses the concepts of illocutionary, perlocutionary, and locutionary acts, and their role in understanding explanations. Illocutionary acts concern the speaker’s intended meaning, perlocutionary acts refer to the listener’s reaction, and locutionary acts are about the speech act itself. We suggest a new way to categorise established definitions of explanation based on these speech act principles. This method enhances our grasp of how explanations work. We found that if you define explanation as a perlocutionary act, it requires subjective judgements. This makes it hard to assess an explanation objectively before the listener receives it. On the other hand, we claim that existing legal systems prefer explanations based on illocutionary acts. We propose that the exact meaning of explanation depends on the situation. Some kinds of definitions suit specific circumstances better. For example, in educational settings, a perlocutionary approach often works best, while legal settings call for an illocutionary approach. Additionally, we show how current measures of explainability can be grouped based on their theoretical support and the speech act they rely on. This categorisation helps us pinpoint which measures are best for assessing the results of Explainable AI (XAI) tools in legal or other settings. In simpler terms, we are explaining how to evaluate and improve XAI and explanations in different situations, such as in education and law. By understanding where and when to apply different explainability measures, we can make better and more specialised XAI tools. This will lead to significant improvements in AI explainability.
The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers

Interpretable part-prototype models are computer vision models that are explainable by design. The models learn prototypical parts and recognise these components in an image, thereby combining classification and explanation. Despite the recent attention for intrinsically interpretable models, there is no comprehensive overview on evaluating the explanation quality of interpretable part-prototype models. Based on the Co-12 properties for explanation quality as introduced in [42] (e.g., correctness, completeness, compactness), we review existing work that evaluates part-prototype models, reveal research gaps and outline future approaches for evaluation of the explanation quality of part-prototype models. This paper, therefore, contributes to the progression and maturity of this relatively new research field on interpretable part-prototype models. We additionally provide a “Co-12 cheat sheet” that acts as a concise summary of our findings on evaluating part-prototype models.
Using Generative Adversarial Network to Forecast Stock Price

Stock price forecasting is one of the hottest research fields in recent years because it can help policymakers and investors make appropriate decisions. The method of deep learning can make the prediction more accurate. Therefore, in this paper, a Generative Adversarial Network (GAN)model is proposed for the prediction of stock price. In this model, the Gated Recurrent Units (GRU) are used as the generator to predict the future stock price by training historical data. Convolutional neural networks (CNN) are used as discriminators to distinguish real data from predicted data. We selected Apple's stock price as the research object, extracted the highest price, lowest price, opening price, S&P 500 index, and other indicators as characteristics, and added the feature of the news index into the research, which can increase the accuracy of the forecast. Finally, the accuracy of the prediction results was determined by calculating the value of RMSE.
Explainable Machine Learning for Bag of Words-Based Phishing Detection

Phishing is a fraudulent practice aimed at convincing individuals to reveal sensitive information, such as account credentials or credit card details, by clicking the links of malicious websites. To reduce the impacts of phishing, the timely identification of these websites is essential. For this purpose, machine learning models are often devised. In this paper, we address the problem of website phishing detection by proposing an explainable machine learning model based on bag of words features extracted from the content of the webpages. To select the most important features to be used in the model, we propose to employ the Lorenz Zonoid, the multidimensional generalization of the Gini coefficient. The resulting model is characterized by a good accuracy and it provides explanations of which words are most likely associated with phishing websites. In addition, the number of features retained is significantly reduced, thus making the model parsimonious and easier to interpret.
Understanding Unsupervised Learning Explanations Using Contextual Importance and Utility

While the concept of Explainability has advanced significantly in the past decade, many areas remain unexplored. Although XAI implementations have historically been employed in attempting to ‘open’ the traditional black-box model of supervised learning implementation aiming to extract human-understandable information, there are no successful attempts at tackling unsupervised learning. This paper aims to tackle the challenge of using an XAI approach, specifically Contextual Importance and Utility (CIU), in order to provide an explainability layer for unsupervised learning models. The paper introduces the current XAI approaches of CIU as well as the other state-of-the-art implementations such as Lime or Shapley. The challenges posed by the unsupervised learning problem are explored and discussed, both on a conceptual and technical level. A relatively novel approach using a CIU implementation on unsupervised clustering techniques is presented along with the brief comparison with another state-of-the-art method called LIME.
Evaluating Explanations of an Alzheimer’s Disease 18F-FDG Brain PET Black-Box Classifier

eXplainable Artificial Intelligence (XAI) has been increasingly applied to interpret Deep Neural Networks (DNN) in medical imaging applications, but a general consensus about the best interpretation strategy is missing. This is also due to the absence of a validated framework to assess the quality of the explanations/interpretations produced by different XAI methods. This work aims to quantify the ability of interpretation techniques of producing good explanations and non-misleading representations of what a black-box model has learned. We selected a DNN which classifies 18F-FDG PET images according to the cognitive decline in Alzheimer’s disease, and we applied two different interpretability methods commonly employed in bioimaging: attribution maps (Backpropagation, GradCAM++, Layerwise Relevance Propagation), and latent space interpretation (t-SNE, UMAP, TriMAP, PaCMAP). We evaluated the interpretations using different literature frameworks: evaluation of attribution maps with imaging biomarkers and region perturbation, and preservation of data local and global structure of the latent space. Results suggested that we are not able to observe a clear relationship between the PET signal and attribution maps, highlighting the importance of not assuming that XAI explanations should reflect the human’s reasoning. Layerwise Relevance Propagation best explains the classifier’s decisions according to the region-perturbation evaluation, confirming literature results. Finally, the UMAP and the TriMAP embedding respectively reported the best result for the preservation of the local and the global data structure, which is, to the best of our knowledge, the first systematic assessment in the medical imaging domain, and in line with theoretical background of the methods employed.
Counterfactual Explanations for Graph Classification Through the Lenses of Density

Counterfactual examples have emerged as an effective approach to produce simple and understandable post-hoc explanations. In the context of graph classification, previous work has focused on generating counterfactual explanations by manipulating the most elementary units of a graph, i.e., removing an existing edge, or adding a non-existing one. In this paper, we claim that such language of explanation might be too fine-grained, and turn our attention to some of the main characterizing features of real-world complex networks, such as the tendency to close triangles, the existence of recurring motifs, and the organization into dense modules. We thus define a general density-based counterfactual search framework to generate instance-level counterfactual explanations for graph classifiers, which can be instantiated with different notions of dense substructures. In particular, we show two specific instantiations of this general framework: a method that searches for counterfactual graphs by opening or closing triangles, and a method driven by maximal cliques. We also discuss how the general method can be instantiated to exploit any other notion of dense substructures, including, for instance, a given taxonomy of nodes. We evaluate the effectiveness of our approaches in 7 brain network datasets and compare the counterfactual statements generated according to several widely-used metrics. Results confirm that adopting a semantic-relevant unit of change like density is essential to define versatile and interpretable counterfactual explanation methods.
The Xi Method: Unlocking the Mysteries of Regression with Statistics

As machine learning models become increasingly complex, the ability to provide explanations for their predictions becomes more important. In this work, we propose the Xi method, a novel technique for providing pre hoc and post hoc explanations of black box regression models. The Xi method is based on distances between probability distributions, which can be interpreted as variable importance measures. Notably, the explanations provided by this method are backed by theoretical guarantees, as they are obtained through statistical estimators that are proven to be asymptotically consistent. Additionally, the Xi method is model and data agnostic, meaning that it can be applied to a wide range of machine learning models and data types. To demonstrate the potential of the Xi method, we provide applications that encompass three different data types: tabular, image, and text.