Reviewerly/exHarmony huggingface dataset:
Word Level Sign Language Recognition via Handcrafted Features

The ability to be understood and convey feelings, requests or ideas through words (spoken or written) is one of the most undervalue by all the humans who have the privilege to do it. Deaf community faces this challenge every single day and, even though, sign languages exist as way to battle against this issue, not all in deaf community knows who to use them; in fact, hearing community knows in a smaller proportion how to interpret them. By this reason sign language recognition area becomes relevant as an effort to solve this issue and create new communication channels. This work aims a methodology for word level sign language recognition, as principal highlights a small set of handcrafted features are defined, between them non-manual features are explored deeply. Data augmentation and dimensionality reduction were performed to obtain a concise feature space. Two recognition models were used (Bidirection Long Term Memory and Transformer) in LIBRAS dataset, and the best result was an accuracy of 94.33%, which was obtained with the bidirectional long term memory network.
Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation

Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog. CTSs have become a key pillar in educational data mining research. A key challenge in CTSs is to engage the student in the conversation while exposing them to a diverse set of teaching strategies, akin to a human teacher, thereby, helping them learn in the process. Different from previous work that generates responses given the strategies as input, we propose to jointly predict teaching strategies and generate tutor responses accordingly, which fits a more realistic application scenario. We benchmark several competitive models on three dialog tutoring datasets and propose a unified framework that combines teaching response generation and pedagogical strategy prediction, where a self-distillation mechanism is adopted to guide the teaching strategy learning and facilitate tutor response generation. Our experiments and analyses shed light on how teaching strategies affect dialog tutoring.
Opportunities and Challenges in Neural Dialog Tutoring

Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors.Although there have been significant recent advances in neural conversational systems using large language models (LLMs) and growth in available dialog corpora, dialog tutoring has largely remained unaffected by these advances.In this paper, we rigorously analyze various generative language models on two dialog tutoring datasets for language learning using automatic and human evaluations to understand the new opportunities brought by these advances as well as the challenges we must overcome to build models that would be usable in real educational settings.We find that although current approaches can model tutoring in constrained learning scenarios when the number of concepts to be taught and possible teacher strategies are small, they perform poorly in less constrained scenarios.Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring, which measures learning opportunities for students and how engaging the dialog is.To understand the behavior of our models in a real tutoring setting, we conduct a user study using expert annotators and find a significantly large number of model reasoning errors in 45% of conversations.Finally, we connect our findings to outline future work.
A Potent Technique for Identifying Fake Accounts on Social Platforms

In this generation, online social media networks are rapidly growing in popularity and becoming more and more integrated into people's daily lives. These networks are used by users to exchange movies, read news articles, market products, and more. It has been simpler to add new friends and stay in touch with them and their updates. These online social networks have been the subject of research to see how they affect people. A significant amount of a user's data may attract attackers as these networks continue to develop, and these attackers may subsequently exchange incorrect information and disseminate dangerous falsehoods. Some fraudulent accounts are used to spread false information and further political agendas, for example. Finding a fraudulent account is important. Furthermore, these social networking platforms are increasingly being used by attackers to disseminate a vast amount of fake information. As a result, based on the categorization algorithms, researchers have started to investigate efficient strategies for spotting these sorts of actions and bogus accounts. In this study, various machine learning algorithms are investigated to successfully identify a phony account. To address this issue, several machine learning algorithms are utilized in conjunction with pre-processing methods to identify bogus accounts. The identification of bogus accounts uses the classification abilities of the algorithms Nave Bayes, Artificial Neural Network, Bagged Decision Tree, Radial Basis Function (RBF), Support Vector Machines, and Random Tree. The best features are used to compare the proposed model to other benchmark techniques on the dataset. The suggested Artificial Neural Network strategy outperforms the prior employed strategies to identify phony user accounts on major online social platforms, with a precision of 98.90%, when machine learning techniques are also compared.
SDS-Net: A lightweight 3D convolutional neural network with multi-branch attention for multimodal brain tumor accurate segmentation

&lt;abstract&gt; &lt;p&gt;The accurate and fast segmentation method of tumor regions in brain Magnetic Resonance Imaging (MRI) is significant for clinical diagnosis, treatment and monitoring, given the aggressive and high mortality rate of brain tumors. However, due to the limitation of computational complexity, convolutional neural networks (CNNs) face challenges in being efficiently deployed on resource-limited devices, which restricts their popularity in practical medical applications. To address this issue, we propose a lightweight and efficient 3D convolutional neural network SDS-Net for multimodal brain tumor MRI image segmentation. SDS-Net combines depthwise separable convolution and traditional convolution to construct the 3D lightweight backbone blocks, lightweight feature extraction (LFE) and lightweight feature fusion (LFF) modules, which effectively utilizes the rich local features in multimodal images and enhances the segmentation performance of sub-tumor regions. In addition, 3D shuffle attention (SA) and 3D self-ensemble (SE) modules are incorporated into the encoder and decoder of the network. The SA helps to capture high-quality spatial and channel features from the modalities, and the SE acquires more refined edge features by gathering information from each layer. The proposed SDS-Net was validated on the BRATS datasets. The Dice coefficients were achieved 92.7, 80.0 and 88.9% for whole tumor (WT), enhancing tumor (ET) and tumor core (TC), respectively, on the BRTAS 2020 dataset. On the BRTAS 2021 dataset, the Dice coefficients were 91.8, 82.5 and 86.8% for WT, ET and TC, respectively. Compared with other state-of-the-art methods, SDS-Net achieved superior segmentation performance with fewer parameters and less computational cost, under the condition of 2.52 M counts and 68.18 G FLOPs.&lt;/p&gt; &lt;/abstract&gt;
The uncertainty analysis of bridge displacement in computer vision

Computer vision is commonly employed to measure the displacement of bridge structures. However, changes in environments often lead to poor data reproducibility. The uncertainty is an important parameter associated with the results, representing the dispersion of the data. This paper mainly discusses the uncertainty of measurement utilizing computer vision. The fundamental formula of measurement is derived from the principle of the pinhole camera model, followed by calculations of the uncertainty as well as a detailed analysis and deduction of the components that affect the uncertainty of measurement (e.g. pitch angle, deviation angle, observation distance, temperature, humidity). Both laboratory and on-site experiments have shown that the uncertainty model is more applicable than error estimation. In addition, the variation law of the uncertainty is revealed under the combination of different angles, laying a solid foundation for accurate measurement in the future.
Quantum Services Generation and Deployment Process: A Quality-Oriented Approach

Quantum computing technology is revolutionizing the current world and is enabling the creation of advanced applications in various fields, such as healthcare and economics. However, for the industry to carry out mass production of quantum software, it is crucial to ensure an adequate level of quality. However, as quantum technologies move towards practical applications, they face significant challenges. Depending heavily on specific platforms, developers have difficulty creating quantum applications that can run on different quantum providers. Additionally, the lack of mature tools makes the creation of quantum applications a slow and complex process that requires specialized knowledge of quantum mechanics and computer science, where the quality of quantum services can be compromised. This article addresses the need to ensure an adequate level of quality in quantum software, and proposes a process that allows for the improvement of the current generation and deployment of quantum services while evaluating the quality of the created quantum services, using an extension of the OpenAPI Specification and the SonarQube tool.
PLACE Dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization

Domain generalization (DG) aims to learn a generic model from multiple observed source domains that generalizes well to arbitrary unseen target domains without further training. The major challenge in DG is that the model inevitably faces a severe overfitting issue due to the domain gap between source and target domains. To mitigate this problem, some dropout-based methods have been proposed to resist overfitting by discarding part of the representation of the intermediate layers. However, we observe that most of these methods only conduct the dropout operation in some specific layers, leading to an insufficient regularization effect on the model. We argue that applying dropout at multiple layers can produce stronger regularization effects, which could alleviate the overfitting problem on source domains more adequately than previous layer-specific dropout methods. In this article, we develop a novel layer-wise and channel-wise dropout for DG, which randomly selects one layer and then randomly selects its channels to conduct dropout. Particularly, the proposed method can generate a variety of data variants to better deal with the overfitting issue. We also provide theoretical analysis for our dropout method and prove that it can effectively reduce the generalization error bound. Besides, we leverage the progressive scheme to increase the dropout ratio with the training progress, which can gradually boost the difficulty of training the model to enhance its robustness. Extensive experiments on three standard benchmark datasets have demonstrated that our method outperforms several state-of-the-art DG methods. Our code is available at https://github.com/lingeringlight/PLACEdropout .
Amazigh Spoken Digit Recognition using a Deep Learning Approach based on MFCC

The field of speech recognition has made human-machine voice interaction more convenient. Recognizing spoken digits is particularly useful for communication that involves numbers, such as providing a registration code, cellphone number, score, or account number. This article discusses our experience with Amazigh's Automatic Speech Recognition (ASR) using a deep learning- based approach. Our method involves using a convolutional neural network (CNN) with Mel-Frequency Cepstral Coefficients (MFCC) to analyze audio samples and generate spectrograms. We gathered a database of numerals from zero to nine spoken by 42 native Amazigh speakers, consisting of men and women between the ages of 20 and 40, to recognize Amazigh numerals. Our experimental results demonstrate that spoken digits in Amazigh can be recognized with an accuracy of 91.75%, 93% precision, and 92% recall. The preliminary outcomes we have achieved show great satisfaction when compared to the size of the training database. This motivates us to further enhance the system's performance in order to attain a higher rate of recognition. Our findings align with those reported in the existing literature.
An Ensemble Classification Method Based on Deep Neural Networks for Breast Cancer Diagnosis

Advances in technology have led to advances in breast cancer screening by detecting symptoms that doctors have overlooked. In this paper, an automatic detection system for breast cancer cases based on Internet of Things (IoT) is proposed. First, using IoT technology, direct medical images are sent to the data repository after the suspicious person's visit through medical equipment equipped with IoT. Then, in order to help radiologists, interpret medical images as best as possible, we use four pre-trained convolutional neural network models including InceptionResNetV2, InceptionV3, VGG19 and ResNet152. These models are combined by an ensemble classifier. Also, these models are used to accurately predict cases with breast cancer, healthy people, and cases with pneumonia by using two datasets of X-RAY and CT-scan in a three-class classification. Finally, the best result obtained for CT-scan images belongs to InceptionResNetV2 architecture with 99.36% accuracy and for X-RAY images belongs to InceptionV3 architecture with 96.94% accuracy. The results show that this method leads to a reduction in daily visits to medical centers and thus reduces the pressure on the medical care system. It also helps radiologists and medical staff to detect breast cancer in its early stages.
SINGLE AXIS SOLAR TRACKING SYSTEM

In this study, the layout and implementation of a PV converter panel solar tracking system are discussed. Generally, the amount of solar radiation and the ambient temperature will affect the electricity production of photovoltaic panels. As they will create their most electricity when the amount of solar radiation they receive is at its highest, PVs should be controlled to keep them always perpendicular to the sun. The proposed single-axis solar tracking system offers an ideal solar energy conversion process by correctly aligning the photovoltaic panels with the actual location of the sun. The experiment's mechanism depends on a DC motor that intelligently moves the prototype in response to input from an LDR sensor using a fuzzy logic controller. An experimental study was carried out to study the performance of the solar tracking system. There is a 47% increase in strength between the design system and the fixed system.
Modelling and verification of post-quantum key encapsulation mechanisms using Maude

Communication and information technologies shape the world's systems of today, and those systems shape our society. The security of those systems relies on mathematical problems that are hard to solve for classical computers, that is, the available current computers. Recent advances in quantum computing threaten the security of our systems and the communications we use. In order to face this threat, multiple solutions and protocols have been proposed in the Post-Quantum Cryptography project carried on by the National Institute of Standards and Technologies. The presented work focuses on defining a formal framework in Maude for the security analysis of different post-quantum key encapsulation mechanisms under assumptions given under the Dolev-Yao model. Through the use of our framework, we construct a symbolic model to represent the behaviour of each of the participants of the protocol in a network. We then conduct reachability analysis and find a man-in-the-middle attack in each of them and a design vulnerability in Bit Flipping Key Encapsulation. For both cases, we provide some insights on possible solutions. Then, we use the Maude Linear Temporal Logic model checker to extend the analysis of the symbolic system regarding security, liveness and fairness properties. Liveness and fairness properties hold while the security property does not due to the man-in-the-middle attack and the design vulnerability in Bit Flipping Key Encapsulation.
Personalized Repository Recommendation Service for Developers with Multi-modal Features Learning

Nowadays an increasing number of software developers have joined in open-source software development communities such as GitHub, and develop and share softwares in these communities. Those online communities contain a huge volume of open-source repositories. Developers commonly search from existing repositories and intend to find suitable repositories to their development requirements. However, it is time-and energy-consuming to discover suitable repositories from such a large number of candidates and it may be also hard for developers to choose accurate keywords. So an effective repository recommendation service becomes an indispensable tool for developers. There have been some solutions for repository recommendation, but existing solutions have several defects such as mediocre accuracy and ignorance of useful features. In this paper, we develop a new personalized repository recommendation service with multi-modal features learning. We propose to mine two modes of features and jointly utilize the mined multimodal features. One of the features is the developers’ sequential behavior features and the other is text features of repositories. We design novel features learning mechanisms for the two modes of features. We performed sufficient experiments on a real-world dataset and the experimental results demonstrate that our model generates superior recommendation results and produces an improvement of 15.3% and 14.5% in Precision and Recall compared to well-known existing methods.
Micromotion compensation of trapped ions by qubit transition and direct scanning of dc voltages

Excess micromotion is detrimental to accurate qubit control of trapped ions, thus measuring and minimizing it is crucial. In this paper, we present a simple approach for measuring and suppressing excess micromotion of trapped ions by leveraging the existing laser-driven qubit transition scheme combined with direct scanning of dc voltages. The compensation voltage is deduced by analyzing the Bessel expansion of a scanned qubit transition rate. The method provides a fair level of sensitivity for practical quantum computing applications, while demanding minimal deviation of trap condition. By accomplishing compensation of excess micromotion in the qubit momentum-excitation direction, the scheme offers an additional avenue for excess micromotion compensation, complementing existing compensation schemes.
Homography matrix based trajectory planning method for robot uncalibrated visual servoing

In view of the classical visual servoing trajectory planning method which only considers the camera trajectory, this paper proposes one homography matrix based trajectory planning method for robot uncalibrated visual servoing. Taking the robot-end-effector frame as one generic case, eigenvalue decomposition is utilized to calculate the infinite homography matrix of the robot-end-effector trajectory, and then the image feature-point trajectories corresponding to the camera rotation is obtained, while the image feature-point trajectories corresponding to the camera translation is obtained by the homography matrix. According to the additional image corresponding to the robot-end-effector rotation, the relationship between the robot-end-effector rotation and the variation of the image feature-points is obtained, and then the expression of the image trajectories corresponding to the optimal robot end-effector trajectories (the rotation trajectory of the minimum geodesic and the linear translation trajectory) are obtained. Finally, the optimal image trajectories of the uncalibrated visual servoing controller is modified to track the image trajectories. Simulation experiments show that, compared with the classical IBUVS method, the proposed trajectory planning method can obtain the shortest path of any frame and complete the robot visual servoing task with large initial pose deviation.
Associative memory by virtual oscillator network based on single spin-torque oscillator

A coupled oscillator network may be able to perform an energy-efficient associative memory operation. However, its realization has been difficult because inhomogeneities unavoidably arise among the oscillators during fabrication and lead to an unreliable operation. This issue could be resolved if the oscillator network were able to be formed from a single oscillator. Here, we performed numerical simulations and theoretical analyses on an associative memory operation that uses a virtual oscillator network based on a spin-torque oscillator. The virtual network combines the concept of coupled oscillators with that of feedforward neural networks. Numerical experiments demonstrate successful associations of 60-pixel patterns with various memorized patterns. Moreover, the origin of the associative memory is shown to be forced synchronization driven by feedforward input, where phase differences among oscillators are fixed and correspond to the colors of the pixels in the pattern.
Polygamy Relation of Quantum Correlations with Equality

Abstract This study provides a generalized definition of polygamy relations for any quantum correlation measures. Instead of the usual polygamy inequality, a polygamy relation with equality is given by introducing the polygamy weight. From the polygamy relation with equality, it presents polygamy inequalities satisfied by the βth power of the quantum correlation measures. Taking concurrence of assistance as an example, it further illustrates the significance and advantages of these relations. It also obtains a polygamy relation with equality by considering the one‐to‐group entanglements for any quantum entanglement measures that do not satisfy the polygamy relations. It demonstrates that such relations for tripartite states can be generalized to multipartite systems.
Prevention in Healthcare: An Explainable AI Approach

Intrusion prevention is a critical aspect of maintaining the security of healthcare systems, especially in the context of sensitive patient data. Explainable AI can provide a way to improve the effectiveness of intrusion prevention by using machine learning algorithms to detect and prevent security breaches in healthcare systems. This approach not only helps ensure the confidentiality, integrity, and availability of patient data but also supports regulatory compliance. By providing clear and interpretable explanations for its decisions, explainable AI can enable healthcare professionals to understand the reasoning behind the intrusion detection system's alerts and take appropriate action. This paper explores the application of explainable AI for intrusion prevention in healthcare and its potential benefits for maintaining the security of healthcare systems.