Reviewerly/exHarmony huggingface dataset:
Reinforcement learning approach to implementation of individual controllers in data centre control system

Contemporary data centres consume electricity on an industrial scale and require control to improve energy efficiency and maintain high availability. The article proposes an idea and structure of the framework supporting development and validation of the multi-agent control for the energy-efficient data centre. The framework comprises two subsystems: the modelling toolbox and the controlling toolbox. This work focuses on such essential components of the controlling toolbox, as an individual controller. The reinforcement learning approach is applied to the controllers’ implementation. The server fan controller, named SF agent, is implemented based on the framework infrastructure and reinforcement learning approach. The agent’s capability of energy-saving is demonstrated.
Outlier Detection for Customs Post Clearance Audit Using Convex Space Representation

Audit plays an important customs role in which the past transactions of a company are investigated for non-conformance. Traditionally, audit is conducted based on a selection of a sample group of transactions for investigation which at times resulted on wrongful audit. Many resources were wasted in trying to prove the legitimacy of company activities. This process is also based on human perception and analytical capabilities without sufficient evidence of the reasons a company was selected for audit. This paper aims to build a solution that allows customs administrations to assess company’s behaviors in terms of trade legitimacy. This solution aims to enhance the efficiency in determining if a company shipment poses any risk. This study adopts the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and a convex-based approach is employed. This approach works by creating a relationship between shipments in order to clearly interpret the distances between these shipments. Accordingly, the shipments are represented as points in multi-dimensional space. This approach is able to significantly determine shipments that poses security issues. The performance results indicated the potential of adopting this method in customs administrations as it achieved an accuracy of 87%. This approach will significantly reduce or eliminate false audits and provide better resource management, thus reducing operating cost for both customs and the entity being audited.
Identification and prevention of online video (webcam) crimes

The article deals with the issues of both criminal and legal assessment of illegal activities related to the phenomenon of "webcam" and individual problems of identifying and documenting crimes in this sphere. The problems of qualification of criminal activity arising in the course of countering offenses related to this phenomenon are revealed. The peculiarities of conducting operational investigative measures aimed at uncovering the illegal activities of webcam studios (production and trafficking of pornographic materials, child pornography), and a special cluster of relations between subjects within the framework of modern Russian legislation are noted. The author pays special attention to the methodological training of law enforcement officers of the Russian Federation, as well as regular and effective interaction with the information systems of the national central bureaus of the International Criminal Police (Interpol). The novelty of the research lies in the fact that crimes arising in the sphere of limited regulation of the global Internet are a new trend in the transformation of the criminal world. The inability of the legislator and the law enforcement officer to predict the appearance and development of individual phenomena allowed the attackers to successfully build a "gray" webcam business industry. The phenomenon of "webcam" is a logical continuation of the development of public relations related to prostitution, the spread of pornography and sexual exploitation. The legalization of the webcam industry will create an ambiguous situation in which offenders will be able to disguise criminal activity as legitimate, which determines the relevance of the theoretical validity and practical implementation of effective regulatory tools for the activity in question. The improvement of the working methods of units engaged in operational investigative activities is a determining factor for solving the task. Based on the actual methods of committing crimes, the scientific work considers the features of documenting and qualifying the facts of illegal activity related to the phenomenon of Webcam.
3Rs: Data Augmentation Techniques Using Document Contexts For Low-Resource Chinese Named Entity Recognition

With recent advances of neural networks and pre-training techniques, Chinese Named Entity Recognition (NER) has achieved great progress in recent years. However, NER systems still have the problem of generalization ability issues due to lack of annotated data, and current NER models mostly consider input sentences individually, which prevent models from further exploiting cross-sentence document context in training. With regard of these problems, this paper present new insights into Chinese NER and propose 3Rs: three data augmentation methods incorporating document-level information for NER through random concatenating, random swapping and random erasing, which are inspired by some multi-sample data augmentation techniques in computer vision fields, aiming to reorganize the composition of training sentences, and generate more training examples with less human efforts. We conduct extensive experiments on two Chinese datasets, and introduce a two-level attacking method to audit robustness performance. Our experiment results show that even the best model can obtain a better accuracy and robustness, especially for smaller training sets, therefore alleviating performance bottlenecks on low-resource conditions.
SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs

In recent years, more and more exciting sources of data have been modeled as Knowledge Graphs (KGs). This modeling represents both structural relationships and the entity specific multi-modal data in KGs. In various data analytic pipelines and Machine Learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile content like categorical relations between classes or their attribute literals like strings, timestamp or numeric data. In this paper we propose SimE4KG framework as a resource providing generic open source modules that compute semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF-KG, including the experiment results, hyper-parameter setup, and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed semantic analytics stack SANSA.
Optimization of Experimental Teaching System based on ACSI Model

Based on the ACSI model, this paper constructs the latent variables such as perceived experimental teaching quality, perceived value of experimental teaching and experimental teaching satisfaction, establishes the satisfaction SEM model, and uses Amos to verify and analyze the data. The results verify the applicability of the model and the relationship between potential variables in the model.
Audio Embedding-Aware Dialogue Policy Learning

Following the success of Natural Language Processing (NLP) transformers pretrained via self-supervised learning, similar models have been proposed recently for speech processing such as Wav2Vec2, HuBERT and UniSpeech-SAT. An interesting yet unexplored area of application of these models is Spoken Dialogue Systems, where the users' audio signals are typically just mapped to word-level features derived from an Automatic Speech Recogniser (ASR), and then processed using NLP techniques to generate system responses. This paper reports a comprehensive comparison of dialogue policies trained using ASR-based transcriptions and extended with the aforementioned audio processing transformers in the DSTC2 task. Whilst our dialogue policies are trained with supervised and policy-based deep reinforcement learning, they are assessed using both automatic task completion metrics and a human evaluation. Our results reveal that using audio embeddings is more beneficial than detrimental in most of our trained dialogue policies, and that the benefits are stronger for supervised learning than reinforcement learning.
Improving SRN-Deblur Performance Based on Blur Direction-based Classification

With the development of neural networks in the field of image processing, deblurring is one of the important tasks of image processing, more and more excellent models have appeared one after another. We propose a classification-based strategy that assumes and verifies that the blurred images can be divided into different categories, and then feeds the blur images to the corresponding deblur models after classification. Our classification-based method can generally improve the performance of the current deblur models. After experimental verification, for the same dataset, our method improves PSNR from 30.26 to 31.15 and SSIM from 0.9342 to 0.9374.
DirectionQ: Continuous Mid-air Hand Input for Selecting Multiple Targets through Directional Visual Cues

We propose DirectionQ, an interaction technique using a continuous mid-air hand input that shows directional visual cues on objects to enable fast and accurate multiple target selection in dense environments. The user can select various targets in a row by moving their hand consecutively in the same direction with the visual cues of targeted objects. DirectionQ involves assigning directional visual cues to each object in the user's viewpoint, detecting the trajectory changes of hand movement, and inferring intended targets with directions of the segmented hand movement. Unlike traditional approaches for multiple target selection, DirectionQ does not need additional steps to disambiguate, providing less time for multiple target selection. While distant, small, or occluded objects often bring accuracy or time-consuming problems, DirectionQ shows similar performance regardless of target conditions. To prove the effectiveness of our approach, we conducted two controlled experiments comparing our system with two existing techniques, RayCasting and EyeGaze. The results show that DirectionQ is faster and more accurate than two techniques with a lower cognitive load on multiple target selection.
Multi-channel optical neuromorphic processing

We present the first optical neuromorphic processor for multi-channel signals. The design is demonstrated on wavelength-division multiplexed (WDM) signals. The technology is also suitable for processing orthogonal frequency-division multiplexed (OFDM) signals with applications in communications.
Skew Constacyclic Codes over $$F_q+vF_q$$

In this paper, we study v-skew constacyclic codes over the ring $$F_{q}+vF_{q}$$ , where $$v^2=1,~q=p^m$$ and p is an odd prime. We obtain the structural properties of v-skew constacyclic codes over $$F_{q}+vF_{q}$$ using decomposition method. The generator polynomials of v-skew constacyclic codes and their dual codes over R are obtained. Moreover, some examples of v-skew constacyclic codes over $$F_{q}+vF_{q}$$ have also been constructed.
ITE Law Enforcement Support through Detection Tools of Fake News, Hate Speech, and Insults in Digital Media

In the era of technological advancement, the relatively low literacy rate of the people in the digital world will result in many behaviors that have negative impacts, such as quickly spreading fake news and unhealthy communication. The government has taken steps to regulate digital information transactions through the ITE Law to overcome this. However, people's understanding of this matter still needs improvement. An automatic detection device for inappropriate speech in the digital media needs to be built to overcome this. An utterance entering this detector will be identified into one of 4 categories: normal speech, fake news, hate speech, and offensive. This detector is built by training a neural network model on a compilation of various digital speech data sources. We select several architectural models by comparing the results obtained from the same data. We also build a classification model with three categories considering that hateful and offensive utterances are often quite similar and difficult to distinguish. The model obtains good final results, with the best performance obtained by the LSTM model with three categories.
A Driving Position-Sensitive Neural Network for Driver Fixation Prediction

Driver fixation prediction has recently received increasing attention in the field of autonomous and assisted driving, and is becoming an essential research problem in advanced driver assistance systems (ADAS). Since many information sources attract driver's attention in complex traffic scenarios, the model needs to predict multiple salient regions or objects that the driver must pay close attention to. However, there is still a lack of driver fixation prediction models that can precisely predict drivers' multiple fixation regions. In this work, we propose a driving position-sensitive neural network (DPSNN) with coordinate attention module (CAM) for more accurately predicting drivers' multiple fixation areas or objects. In DPSNN, the CAM filters irrelevant information and redundant information from multi-scale feature maps, and generates driving position-sensitive feature maps containing precise positional information. Combining the driving position-sensitive feature maps with the upsampled outputs in expansive path helps the network to identify and locate objects of interest completely and precisely. The experimental results indicate that the proposed DPSNN outperforms the state-of-the-art fixation prediction models and can predict driver's multiple fixation locations more accurately.
Question Classification from Thai Sentences by Considering Word Context to Question Generation

The potential of automated question generation is role play in the multi-fields and multi-applications such as question and answering systems, examination systems, and information retrieval. Before learning the question generated, one should understand how to classify questions. This research aims to generate possible questions considering the possible question categories from question classification based on Natural Language Processing. In this research, we compared the results on Logistic Regression, Support Vector Machine, and Multinomial Naï ve Bayes, which were traditional classification models. The deep learning techniques were Convolutional Neural Networks, Bidirectional Long Short-Term Memory, combined CNN and BiLSTM models, and BERT models. The experimental results show that the preprocessing phase using Natural Language Processing could enhance question classification. The classification of the sentence to question classification attained an average micro $F_{1} -$ score of 91.40% when applied BERT model by pre-trained WangchanBERTa on simple sentences. In contrast, the satisfying score with an average micro $F_{1} -$ score of 82.07% (from 80.37% on original input) when applied to add all POS tags unigram + bigram TF-IDF by using the SVM model. The experimental results when the CNN model with GloVe on adding focusing POS tags is a satisfactory score with an average micro $F_{1} -$ score of 79.79%.
Contrastive Learning with Latent Augmentation

Contrastive learning with well-designed data augmentation has recently greatly improved the quality of self-supervised representation learning. However, existing works only concentrate on various forms of hand-crafted data augmentation on raw image pixels. We explore data augmentation in feature space and find that it can achieve a similar effect with augmentation on image pixels in contrastive learning. Based on this finding, we present Contrastive Learning with Latent Augmentation (CLLA), which is a new automatic data augmentation strategy that augments intermediate features for contrastive learning. Specifically, aiming at automatic data augmentation, CLLA generates adversarial examples of intermediate features and then leverages them as feature augmentations. Moreover, we extend CLLA to the multi-level data augmentation framework M-CLLA which regards data augmentation on image pixels as a special case. Extensive experiments demonstrate that CLLA and M-CLLA achieve better performance over strong baselines on multiple visual benchmarks.
Semantic Approach to User Answer Verification in Intelligent Tutoring Systems

A semantic approach to develop a problem solver for automatic verification of user answers in intelligent tutoring systems built on OSTIS Technology is proposed in this article. In the framework of OSTIS Technology, the problem solvers are developed based on multi-agent technology. The developed problem solver allows to automatically verify the completeness and correctness of user answers based on semantics by calling the corresponding sc-agent according to the type of test questions (judgement questions, multiple-choice questions, questions of definition interpretation, etc.). For some questions of definition interpretation with multiple standard answers, the problem solver allows to pre-screen a semantic fragment of the standard answer that best matches the user answer according to the semantic fragment of the user answer, and then verify the completeness and correctness between them. At the same time, since the standard answers and user answers to some questions do not match on the surface, but the logical formulas used to formalize them satisfy logical equivalence, the developed problem solver can also determine whether the standard answer and user answer satisfy logical equivalence.
Adversarial Bidirectional Feature Generation for Generalized Zero-Shot Learning Under Unreliable Semantics

Although extensive methods have been proposed for Generalized Zero-shot Learning (GZSL), studies are still scarce for GZSL with unreliable data, which is a common issue in real circumstances. In this paper, we introduce a new problem of Generalized Zero-shot Learning with Unreliable Semantics (GZSL-US), based on which we study the GZSL under the partially ambiguous or even missing semantics. To address such a problem, we present a unified generative framework named Adversarial Bidirectional Feature Generation (ABFG), which introduces two extra operations, namely Bidirectional Matching (BM) and Adversary Injection (AI), to the basic generation process. The BM is to guarantee the consistency of the visual and semantic spaces by matching them with a learned metric, which alleviates the domain bias problem in GZSL. Meanwhile, AI is not only introduced for further exploiting the sampling space, but also for endowing the model with a strong resistance ability to semantic interference. The experimental results of Generative Adversarial Network (GAN)-based and Variational AutoEncoder (VAE)-based ABFG instances on four popular benchmarks not only prove the superior robustness of ABFG to the unreliable semantics but also demonstrate the encouraging GZSL performances in comparison with the state-of-the-arts.
MQ-OFL: Multi-sensitive Queue-based Online Fair Learning

Recently, there has been growing interest in fairness considerations in Artificial Intelligence (AI) and AI-based systems, as the decisions made by AI applications may negatively impact individuals and communities with ethical or legal consequences. Indeed, it is crucial to ensure that decisions based on AI-based systems do not reflect discriminatory behavior toward certain individuals or groups. The development of approaches to handle these concerns is an active area of research. However, most existing methods process the data in offline settings and are not directly suitable for online learning from evolving data streams. Further, these techniques fail to take the effects of data skew, or so-called class imbalance, on fairness-aware learning into account. In addition, recent fairness-aware online learning supervised learning approaches focus on one sensitive attribute only, which may lead to subgroup discrimination. In a fair classification, the equality of fairness metrics across multiple overlapping groups must be considered simultaneously. In this paper, we address the combined problem of fairness-aware online learning from imbalanced evolving streams, while considering multiple sensitive attributes. We introduce the Multi-Sensitive Queue-based Online Fair Learning (MQ-OFL) algorithm, an online fairness-aware approach, which maintains valid and fair models over evolving stream. MQ-OFL changes the training distribution in an online fashion based on both stream imbalance and discriminatory behavior of the model evaluated over the historical stream. We compare our MQ-OFL method with state-of-art studies on real-world data sets, and present comparative insights on the performance.
Augmented Reality in Education: what XR the Moroccan Association’s Experts Think

Due to its proven learning and teaching effectiveness, augmented reality (AR) is becoming increasingly popular in educational settings. Numerous papers have outlined the developments, advantages, potential, challenges, and effects of trend technology on the educational domain, however, earlier studies didn’t accurately evaluate those elements. This study aims primarily to identify and rank the important AR’s characteristics as a useful educational tool. In this inquiry, A’WOT, an hybrid multi-criteria decision-making technique, is employed. This approach combines the strengths, weaknesses, opportunities, and threats (SWOT) and Analytic Hierarchy Process (AHP) approaches. The components that have an impact on a system’s performance may be identified and prioritized using this method. This study based on the A’WOT approach led to the determination and prioritization of the core elements of AR technology in education.
Learner Profile based Knowledge Tracing

In recent years, knowledge tracing has gradually become a core technology for online education, which can evaluate learners' knowledge states and provide a personalized learning path. Most of the existing knowledge tracing methods mainly considered the interaction sequence of learners, but they normally ignored the individual differences among learners. For example, learners with various levels of comprehension will behave differently when faced with new questions, which indicates that individual differences affect prediction accuracy. In addition, most learners learn only part of the concept, which leads to data sparsity. However, the existing methods do not solve the data sparsity well. In this paper, we are motivated to propose a Learner Profile-based Knowledge Tracing (LPKT) model, which uses learners' unique id and the features extracted from historical interaction sequences as learners' representation to model individual differences among learners. In addition, we establish relationships between concepts and utilize related concepts to augment the concept's representation to address the data sparsity. We conducted experiments on several benchmark datasets, and the results show that our proposed LPKT model outperforms existing KT methods (with the highest AUC improvement of up to 8%).