Reviewerly/exHarmony huggingface dataset:
A Modified Whale Optimization Algorithm for Enhancing the Features Selection Process in Machine Learning

In recent years, when there is an abundance of large datasets in various fields, the importance of feature selection problem has become critical for researchers. The real world applications rely on large datasets, which implies that datasets have hundreds of instances and attributes. Finding a better way of optimum feature selection could significantly improve the machine learning predictions. Recently, metaheuristics have gained momentous popularity for solving feature selection problem. Whale Optimization Algorithm has gained significant attention by the researcher community searching to solve the feature selection problem. However, the exploration problem in whale optimization algorithm still exists and remains to be researched as various parameters within the whale algorithm have been ignored. This paper proposes a new and improved version of the whale algorithm entitled Modified Whale Optimization Algorithm (MWOA) that hybrid with the machine learning models such as logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, na&#x00EF;ve Bayes model. To test this new approach and the performance, the breast cancer dataset was used for MWOA evaluation. The test results revealed the superiority of this model when compared to the results obtained by machine learning models.
Integration of University Foreign Language Teaching and Multimedia Resources in Artificial Intelligence Vision

With the development of science and technology, the application of multimedia assisted teaching in practical teaching is becoming more and more important. Combining multimedia technology with teaching is not only the need of current teaching, but also the need of current educational development and reform. This paper mainly studies the integration of multimedia resources in the field of college foreign language teaching from the perspective of artificial intelligence. Firstly, the teaching characteristics under the background of artificial intelligence are analyzed, and the application of artificial intelligence in university teaching is summarized. Based on the theories of previous studies, this paper investigates and analyzes the situation of multimedia assisted teaching in a certain university, aiming to understand the overall situation of university multimedia assisted teaching in TCSL classroom teaching. Multimedia teaching as a new teaching method in the process of the national new curriculum reform can play a very key role, it will be recognized by all educators and educators, become a kind of basic English teaching means.
What Matters for Ad-hoc Video Search? A Large-scale Evaluation on TRECVID

For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an important international evaluation. Solutions submitted by the task participants vary in terms of their choices of cross-modal matching models, visual features and training data. As such, what one may conclude from the evaluation is at a high level that is insufficient to reveal the influence of the individual components. In order to bridge the gap between the current solution-level comparison and the desired component-wise comparison, we propose in this paper a large-scale and systematic evaluation on TRECVID. By selected combinations of state-of-the-art matching models, visual features and (pre-)training data, we construct a set of 25 different solutions and evaluate them on the TRECVID AVS tasks 2016–2020. The presented evaluation helps answer the key question of what matters for AVS. The resultant observations and learned lessons are also instructive for developing novel AVS solutions.
Towards an Arabic Sign Language (ArSL) <i>corpus</i> for deaf drivers

Sign language is a common language that deaf people around the world use to communicate with others. However, normal people are generally not familiar with sign language (SL) and they do not need to learn their language to communicate with them in everyday life. Several technologies offer possibilities for overcoming these barriers to assisting deaf people and facilitating their active lives, including natural language processing (NLP), text understanding, machine translation, and sign language simulation. In this paper, we mainly focus on the problem faced by the deaf community in Saudi Arabia as an important member of the society that needs assistance in communicating with others, especially in the field of work as a driver. Therefore, this community needs a system that facilitates the mechanism of communication with the users using NLP that allows translating Arabic Sign Language (ArSL) into voice and vice versa . Thus, this paper aims to purplish our created dataset dictionary and ArSL corpus videos that were done in our previous work. Furthermore, we illustrate our corpus , data determination (deaf driver terminologies), dataset creation and processing in order to implement the proposed future system. Therefore, the evaluation of the dataset will be presented and simulated using two methods. First, using the evaluation of four expert signers, where the result was 10.23% WER. The second method, using Cohen’s Kappa in order to evaluate the corpus of ArSL videos that was made by three signers from different regions of Saudi Arabia. We found that the agreement between signer 2 and signer 3 is 61%, which is a good agreement. In our future direction, we will use the ArSL video corpus of signer 2 and signer 3 to implement ML techniques for our deaf driver system.
Regularized Hardmining loss for face recognition

For face recognition using deep learning architectures, loss functions have become a topic of research these days. This is because of the fact that when the discriminative ability of the loss function increases, then the face recognition accuracy increases. Hardmining loss is one such generic loss function that can be used with any basic loss function and has the ability to enhance the face recognition accuracy of the given basic loss function. Hardmining loss achieves the improvement by introducing greater penalty for hard examples. However, the problem with Hardmining loss is that the easy examples are allocated loss value near to zero. This limits the contribution of the easy examples specially in the later training stages. We therefore propose an improved Hardmining loss called the Regularized Hardmining loss. The Regularized Hardmining loss allocates a reasonable loss value to the easier examples as well. It thus helps easy examples maintain their contribution in the later training stage while still giving relatively greater penalty for hard examples thus preserving the property of the Hardmining loss. The Regularized Hardmining loss fine-tunes the behavior of Hardmining loss for better performance. The results of Regularized Hardmining loss when applied with Cross Entropy loss show an increased accuracy of face recognition from 93.78% to 95.55% on LFW dataset.
A Comprehensive Survey on Blockchain and Cryptocurrency Technologies: Approaches, Challenges, and Opportunities

Blockchain and cryptocurrency are some of the buzzwords in recent years. This chapter provides an organized review of literature on blockchain-based applications in various domains. The main purpose of this study is to explore the latest trends of blockchain technology and its applications and to focus on certain features of this disruptive technology, which can transform traditional business practices in the near future. There are many researchers working in this field for more than 10 years, and they have published their articles in highly reputed journals. Also, many reports and white papers are available. Therefore, a systematic collection of those articles and reviews of this literature will be very much helpful for the upcoming researchers. In this paper, a detailed classification of blockchain-based applications from different domains such as land registration, healthcare, Internet of things, security, and the latest areas for research are presented. The challenges and issues that were identified from various works of literature are presented for deeper understanding. Also, based on the review, the identified gaps in the research and the potential future work directions are given in predicting the importance of value for the researchers and academicians.
Exact simulation of coupled Wright–Fisher diffusions

Abstract In this paper an exact rejection algorithm for simulating paths of the coupled Wright–Fisher diffusion is introduced. The coupled Wright–Fisher diffusion is a family of multivariate Wright–Fisher diffusions that have drifts depending on each other through a coupling term and that find applications in the study of networks of interacting genes. The proposed rejection algorithm uses independent neutral Wright–Fisher diffusions as candidate proposals, which are only needed at a finite number of points. Once a candidate is accepted, the remainder of the path can be recovered by sampling from neutral multivariate Wright–Fisher bridges, for which an exact sampling strategy is also provided. Finally, the algorithm’s complexity is derived and its performance demonstrated in a simulation study.
Design Smells in Deep Learning Programs: An Empirical Study

Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural network (DNN) and then training it on a dataset. This process requires that developers make multiple architectural (e.g., type, size, number, and order of layers) and configuration (e.g., optimizer, regularization methods, and activation functions) choices that affect the quality of the DL models, and consequently software quality. An under-specified or poorly-designed DL model may train successfully but is likely to perform poorly when deployed in production. Design smells in DL programs are poor design and-or configuration decisions taken during the development of DL components, that are likely to have a negative impact on the performance (i.e., prediction accuracy) and then quality of DL based software systems. In this paper, we present a catalogue of 8 design smells for a popular DL architecture, namely deep Feedforward Neural Networks which is widely employed in industrial applications. The design smells were identified through a review of the existing literature on DL design and a manual inspection of 659 DL programs with performance issues and design inefficiencies. The smells are specified by describing their context, consequences, and recommended refactorings. To provide empirical evidence on the relevance and perceived impact of the proposed design smells, we conducted a survey with 81 DL developers. In general, the developers perceived the proposed design smells as reflective of design or implementation problems, with agreement levels varying between 47% and 68%.
Dimensional Optimization of 4-DOF Robot Manipulator Using Artificial Bee Colony Algorithm

Kinematic synthesis has become an essential issue in robot design, and as a result, much research in recent years has focused on the optimal dimensional synthesis of robot manipulators. However, for particular application area, such as robots in the agriculture field, the robot design is more challenging and need to consider the unstructured structure and working environment. For such an application, the robot manipulator with an open-source design has a high potential to be utilized and optimized. This paper uses an artificial bee colony algorithm (ABC) to optimize the structural dimensions of a four degrees-of-freedom robot manipulator. It was found that the robot manipulator with optimized dimensions effectively reaches the desired end-effector position. For example, for the target position of (1.5, 0.5, 2.0), the optimal link lengths generated an actual end-effector position of (1.4990, 0.4995, 2.000), with a mean position error of 0.62174 mm. This outcome shows that the optimal dimensions of the robot manipulator can be accurately obtained using the ABC algorithm.
Quantum Computing for Undergraduate Engineering Students: Report of an Experience

This paper presents the experience of a Quantum Computer course addressed for the first time for undergraduate engineering students. Students’ levels varied from year two to five of their careers and had previous knowledge on Calculus, Linear Algebra and Programming, but not on Quantum Mechanics or on Modern Physics. The main objective of the course was that students acquire programming skills in Quantum Computing, so it had a practical approach. Instead of beginning by introducing the physical phenomenology under the field, the course started directly with presenting the logic of quantum computing from an abstract point of view. The language used was Q#. The curricula was based on the one available for the Microsoft Quantum Network.
Understanding the role of technological platforms in schools

This research paper evaluates how schools have embraced technological platforms as they shift from traditional learning methods to online learning methods, prior and during the covid-19 pandemic. Technological platforms are a suite of online tools that permit collaboration between teachers and students, as well as provide parents/carer visibility of their child's school work in real-time. Such platforms permit equality, the creation of authentic learning material and collaboration that previously was inaccessible. Professional development and student boot camps were found to play a considerable role in all school stakeholders to use the technology.
Character-Aware Sampling and Rectification for Scene Text Recognition

Curved scene text recognition is a challenging task in multimedia society due to large shape and texture variance. Previous methods address this challenge by extracting and rectifying text line with equidistantly sampling, which ignore character level information and lead to distorted characters. To address this issue, this paper proposes a Character-Aware Sampling and Rectification (CASR) module, which rectifies irregular text instance according to the location and orientation information of each individual character. Specifically, CASR regards each character as a basic unit and predicts the character-level attributes for sampling and rectification. Our module not only exploits detailed character information to obtain better rectification of text line, but also employs character-level supervision in training process. In addition, CASR provides a plug-and-play module which can be easily incorporated to existing text recognition pipeline. Extensive experiments on several benchmarks demonstrate that our method obtains more accurate rectified text instances and achieves promising performance. We will release our code and models in the future.
HoloLens 2 for Maxillofacial Surgery: A Preliminary Study

Since custom-made 3D printed surgical guides for maxillofacial surgery are usually expensive, Augmented Reality (AR) can be efficiently employed to overcome the high costs. The proposed work aims to develop and test an AR application for different maxillofacial surgeries. The application consists in overlaying the cutting lines on the patient's mandible to guide the clinician during the procedure. It has been realized in Unity and preliminary tested with HoloLens 2 and a 3D printed mandible. Seven participants performed two consecutive trials. The mandible with the obtained surgical lines has been scanned after each test to digitally reconstruct the traced lines and compare them with the surgical lines previously designed. The results allowed the preliminary analysis of the developed AR system's accuracy and precision. Mean distances from the designed surgical guides showed good accuracy for the genioplasty (deviation error around 1.03 mm) and orthognathic surgery (deviation error around 1.27 mm), suggesting the applicability of HoloLens 2 for these kinds of surgery. On the contrary, the application was not suitable for the mandibular angle osteotomy (deviation error over 2.50 mm).
University Classroom Teaching Model Based on Decision Tree Analysis and Machine Learning

The existing teaching evaluation system partially reflects the teaching effect and other related conditions through statistical reports, but it is difficult to find the useful knowledge hidden in the database, and it cannot effectively assist the decision-making support. In order to improve the evaluation effect of college classroom teaching mode, this paper mainly uses decision tree algorithm and data mining technology of association rules to construct the effectiveness evaluation system of college classroom teaching mode based on decision tree analysis. Moreover, this paper analyzes the teaching information and evaluation data to extract the potentially useful knowledge contained in it, which can help decision-makers find the rules and explore various factors that affect the teaching effect of teachers, thereby improving teaching management and optimizing resource allocation. In addition, this paper uses experimental teaching methods to verify the performance of the system model constructed in this paper. The research results show that the system constructed in this paper has certain reliability and practicability.
Machine learning-based adaptive CSI feedback interval

The channel state information (CSI) is essential for the base station (BS) to schedule user equipments (UEs) and efficiently manage the radio resources. Hence, the BS requests UEs to regularly feed back the CSI. However, frequent CSI reporting causes large signaling overhead. To reduce the feedback overhead, we propose two machine learning-based approaches to adjust the CSI feedback interval. We use a deep neural network and reinforcement learning (RL) to decide whether an UE feeds back the CSI. Simulation results show that the RL-based approach achieves the lowest mean squared error while reducing the number of CSI feedback transmissions.
A population-based algorithm with the selection of evaluation precision and size of the population

In this paper, a new nature-inspired hybrid population-based algorithm is proposed. Firstly, during its operation, it changes the size of the population to reduce the number of processed individuals. For this purpose, dedicated functions that determine the size of population for each algorithm step are used. Secondly, for each individual of the population, the algorithm selects and changes an operator for its modification. This provides a balance between searching for new solutions and fine-tuning of those already found. Thirdly, the algorithm can control the sampling period of the optimized (dynamic) systems, reducing the complexity of the fitness function for individuals. This makes it easier to use the algorithm to optimize even complex systems, which is of great practical importance. Finally, the algorithm allows to solve problems consisting in choosing the structure of the solution and the parameters of this structure. The control problems considered in the simulations, where both the parameters and the structure of the PID-based controller have to be selected, are exactly this type of problem. The results obtained for the proposed algorithm are significantly better than the results obtained with the use of other methods.
Comprehensive Analysis of Privacy in Black-Box and White-Box Inference Attacks Against Generative Adversarial Network

Nowadays, deep learning models have many applications in social life. Specifically, the generative adversarial network (GAN) has many applications such as multimodal image-to-image translation, text to image, image filter, image editing, stylized images, data augmentation. However, deep neural networks are vulnerable to inference attacks as they memorize information about their training data. In this study, we set up black-box and white-box attacks to comprehensively evaluate the privacy of generalization models on the LFW dataset and CIFAR dataset. In addition, we measured the leakage of private information through the parameters of the fully trained model as well as the parameter updates of the model during training. In a white box attack setup, we evaluated inference attacks against GAN by monitoring their training data samples. In the black box attack setup, we divided it into two types of black box attacks with supporting information and without supporting information. We assumed that the attacker had about 10% to 20% of the target model training dataset in the black box attack with supporting information. Finally, we concluded the relationship between the number of training epochs and the GAN properties with information leakage.
UAV Path Planning in 3D Complex Environments Using Genetic Algorithms

This paper studies the path planning problem of an unmanned aerial vehicle (UAV) flying in a mountainous region, in which obstacles both on the ground and in the air are included. Two objectives, i.e., minimizing total length and minimizing radar detection threat, are considered in path planning. Two genetic algorithms (GAs) with different population initialization methods and genetic operations are proposed to solve the problem. Computational results show that both algorithms can find safe and flyable paths in three dimensional (3D) complex environments. Furthermore, one of them generally leads to a better performance.
Visual aspect-oriented modeling of explorable extended reality environments

Abstract The availability of various extended reality (XR) systems for tracking users’ and objects’ behavior opens new opportunities for analyzing users’ and objects’ interactions and autonomous actions. Such analysis can be especially useful and attainable to domain experts when it is based on domain knowledge related to a particular application, liberating the analysts from going into technical details of 3D content. Analysis of XR users’ and objects’ behavior can provide knowledge about the users’ experience, interests and preferences, as well as objects’ features, which may be valuable in various domains, e.g., training, design and marketing. However, the available methods and tools for building XR focus on 3D modeling and programming rather than knowledge representation, making them unsuitable for domain-oriented analysis. In this paper, a new visual approach to modeling explorable XR environments is proposed. It is based on a semantic representation of aspects, which extend the primary code of XR environments to register their behavior in a form explorable with reasoning and queries, appropriate for high-level analysis in arbitrary domains. It permits domain experts to comprehend and analyze what happened in an XR environment regarding users’ and objects’ actions and interactions. The approach has been implemented as an extension to MS Visual Studio and demonstrated in an explorable immersive service guide for household appliances. The evaluation results show that the approach enables efficient development of explorable XR and may be useful for people with limited technical skills.
Objective criteria for explanations of machine learning models

Abstract Objective criteria to evaluate the performance of machine learning (ML) model explanations are a critical ingredient in bringing greater rigor to the field of explainable artificial intelligence. In this article, we survey three of our proposed criteria that each target different classes of explanations. In the first, targeted at real‐valued feature importance explanations, we define a class of “infidelity” measures that capture how well the explanations match the ML models. We show that instances of such infidelity minimizing explanations correspond to many popular recently proposed explanations and, moreover, can be shown to satisfy well‐known game‐theoretic axiomatic properties. In the second, targeted to feature set explanations, we define a robustness analysis‐based criterion and show that deriving explainable feature sets based on the robustness criterion yields more qualitatively impressive explanations. Lastly, for sample explanations, we provide a decomposition‐based criterion that allows us to provide very scalable and compelling classes of sample‐based explanations.