Reviewerly/exHarmony huggingface dataset:
Hyper-parameters Tuning of Artificial Neural Networks: An Application in the Field of Recommender Systems

In this work, we carry out the hyper-parameters tuning of a Machine Learning (ML) Recommender Systems (RS) which utilizes an Artificial Neural Network (ANN), called CATA++. We have performed tuning of the activation function, weight initialization and training epochs of CATA++ in order to improve both training and performance. During the experiments, a variety of state-of-the-art activation functions have been tested: ReLU, LeakyReLU, ELU, SineReLU, GELU, Mish, Swish and Flatten-T Swish. Additionally, various weight initializers have been tested, such as: XavierGlorot, Orthogonal, He, Lecun. Moreover, we ran experiments with different epochs number from 10 to 150. We have used data from CiteULike and AMiner Citation Network. The recorded metrics (Recall, nDCG) indicate that hyper-parameters tuning can reduce notably the necessary training time, while the recommendation performance is significantly improved (up to +44.2% Recall).
Image tampering detection in image forensics using <scp>earthworm‐rider</scp> optimization

Summary This article introduces a novel earthworm‐rider optimization algorithm (EW‐ROA), by integrating the earthworm algorithm in the rider optimization algorithm for the detection of the tampered regions optimally. Initially, the individual binary maps are generated using the image blocks of the input image are fed to five forensic tools, and then they are concatenated into a single binary map. The features are extracted from the binary map moreover they are feed to classifier for the detection of the tampered image via the deep belief neural network, which is trained using the proposed earthworm‐rider optimization algorithm. The proposed system attains a highest accuracy of 0.9402, sensitivity of 0.98, and specificity of 0.98 that shows the superiority of the proposed system in effective tampering detection.
Digital Competence of Lecturers at the Universities of Education: In the Context of Education Digital Transformation Vietnam

This study aims to assess the digital proficiency of lecturers as well as the experiences and practices of digital technology in the teaching process in Vietnam Universities of Education. In this study, qualitative and quantitative survey methods were used. The study was conducted with 320 lecturers, 150 managers, training experts, and leaders at the top three Universities of Education in Vietnam. In the study, the digital competency questionnaire was used as a data collection tool. It is built based on the digital competence framework (DigComp 2.1 model), The European Framework for the Digital Competence of Educators (DigCompEdu), DigComp SAT. After the analysis and evaluation processes of the results, it is stated that the digital competence of lecturers at pedagogical schools in Vietnam is about the average and it varies by genders, academic degrees and awareness levels of digital capabilities. The results will help the leaders understand the needs, information about digital capabilities and things to focus on training so that lecturers at Vietnam pedagogical schools can meet these requirements.
Designing for serendipity: a means or an end?

Purpose This article aims to gain a better understanding of the reasons why serendipity is designed for in different kinds of environments. Understanding these design intents sheds light on the value such designs bring to designers, in contrast to the users of the environment. In this way, the article seeks to contribute to the literature on cultivating serendipity from a designers’ point of view. Design/methodology/approach An extensive review of the literature discussing designing for serendipity was conducted to elicit the different motivations to design for serendipity. Based on these findings and a thorough analysis, a typology of design intents for serendipity is presented. Findings The article puts forward four intents to design for serendipity: serendipity as an ideal, common good, mediator and feature. It also highlights that the current academic discourse puts a strong emphasis on two of them. It is argued that this academic abstraction could be problematic for how we deal with designs for serendipity, both in theory and practice. Originality/value The novelty of this article is that it addresses the question of why to design for serendipity from a designer’s point of view. By introducing the notion of directionality it opens up the opportunity to discuss serendipity from multiple perspectives, which contributes to gaining a firmer understanding of serendipity. It allows to more explicitly formulate the different functions of a design for serendipity and thereby expands our knowledge on the value of designing for serendipity. At the same time, it sheds light on the potential threats to designing for serendipity.
Compressed Sensing Technique for the Localization of Harmonic Distortions in Electrical Power Systems

The present work proposes to locate harmonic frequencies that distort the fundamental voltage and current waves in electrical systems using the compressed sensing (CS) technique. With the compressed sensing algorithm, data compression is revolutionized, a few samples are taken randomly, a measurement matrix is formed, and according to a linear transformation, the signal is taken from the time domain to the frequency domain in a compressed form. Then, the inverse linear transformation is used to reconstruct the signal with a few sensed samples of an electrical signal. Therefore, to demonstrate the benefits of CS in the detection of harmonics in the electrical network of this work, power quality analyzer equipment (commercial) is used. It measures the current of a nonlinear load and issues its results of harmonic current distortion (THD-I) on its screen and the number of harmonics detected in the network; this equipment acquires the data based on the Shannon-Nyquist theorem taken as a standard of measurement. At the same time, an electronic prototype senses the current signal of the nonlinear load. The prototype takes data from the current signal of the nonlinear load randomly and incoherently, so it takes fewer samples than the power quality analyzer equipment used as a measurement standard. The data taken by the prototype are entered into the Matlab software via USB, and the CS algorithm run and delivers, as a result, the harmonic distortions of the current signal THD-I and the number of harmonics. The results obtained with the compressed sensing algorithm versus the standard measurement equipment are analyzed, the error is calculated, and the number of samples taken by the standard equipment and the prototype, the machine time, and the maximum sampling frequency are analyzed.
Online Self-Calibration of 3D Measurement Sensors Using a Voxel-Based Network

Multi-sensor fusion is important in the field of autonomous driving. A basic prerequisite for multi-sensor fusion is calibration between sensors. Such calibrations must be accurate and need to be performed online. Traditional calibration methods have strict rules. In contrast, the latest online calibration methods based on convolutional neural networks (CNNs) have gone beyond the limits of the conventional methods. We propose a novel algorithm for online self-calibration between sensors using voxels and three-dimensional (3D) convolution kernels. The proposed approach has the following features: (1) it is intended for calibration between sensors that measure 3D space; (2) the proposed network is capable of end-to-end learning; (3) the input 3D point cloud is converted to voxel information; (4) it uses five networks that process voxel information, and it improves calibration accuracy through iterative refinement of the output of the five networks and temporal filtering. We use the KITTI and Oxford datasets to evaluate the calibration performance of the proposed method. The proposed method achieves a rotation error of less than 0.1° and a translation error of less than 1 cm on both the KITTI and Oxford datasets.
Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection on Ultrasound Images

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound images (BUI) are commonly employed for the detection and classification of abnormalities that exist in the breast. The ultrasound images are necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving the detection performance, Computer Aided Diagnosis (CAD) models are useful for breast cancer detection and classification. The current advancement of the deep learning (DL) model enables the detection and classification of breast cancer with the use of biomedical images. With this motivation, this article presents an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN algorithm will be utilized to allocate appropriate class labels to the input images. Finally, the AO technique was exploited to fine-tune the parameters related to the BNN method so that the classification performance is improved. To validate the enhanced performance of the AOBNN-BDNN method, a wide experimental study is executed on benchmark datasets. A wide-ranging experimental analysis specified the enhancements of the AOBNN-BDNN method in recent techniques.
Heterogeneous Graph Neural Network for Short Text Classification

Aiming at the sparsity of short text features, lack of context, and the inability of word embedding and external knowledge bases to supplement short text information, this paper proposes a text, word and POS tag-based graph convolutional network (TWPGCN) performs short text classification. This paper builds a T-W graph of text and words, a W-W graph of words and words, and a W-P graph of words and POS tags, and uses Graph Convolutional Network (GCN) to learn its feature and performs feature fusion. TWPGCN only focuses on the structural information of text graph, and does not require pre-training word embedding as initial node features, which improves classification accuracy, increases computational efficiency, and reduces computational difficulty. Experimental results show that TWPGCN outperforms state-of-the-art models on five publicly available benchmark datasets. The TWPGCN model is suitable for short text or ultra-short text, and the composition method in the model can also be extended to more fields.
Comparative Performance Study of CNN-based Algorithms and YOLO

Tasks such as image classification, object detection, to mention a few, play an important role in computer vision. Numerous algorithms have been developed to improve the performance of such tasks for benchmark datasets. Although advanced algorithms offer state-of-the-art performance on such tasks, it is also important to analyze their algorithmic feasibility over the time to make it practical for end-user applications. This paper analyzes two such groups of algorithms, namely, Convolutional Neural Networks (CNN) based algorithms with You Only Look Once (YOLO) in terms of speed and accuracy.
Enhanced k-Anonymity model based on clustering to overcome Temporal attack in Privacy Preserving Data Publishing

The infrastructure required for data storage and processing has become increasingly feasible, and hence, there has been a massive growth in the field of data acquisition and analysis. This acquired data is published, empowering organizations to make informed data-driven decisions based on previous trends. However, data publishing has led to the compromise of privacy as a result of the release of entity-specific information. Privacy-Preserving Data Publishing [1] can be accomplished by methods such as Data Swapping, Differential Privacy, and the likes of k-Anonymity. k-Anonymity is a well-established method used to protect the privacy of the data published. We propose a clustering-based novel algorithm named SAC or the Score, Arrange, and Cluster Algorithm to preserve privacy based on k-Anonymity. This method outperforms existing methods such as the Mondrian Algorithm by K. LeFevre and the One-pass K-means Algorithm by Jun-Lin Lin from a data quality perspective. SAC can be used to overcome temporal attack across subsequent releases of published data. To measure data quality post anonymization we present a metric that takes into account the relative loss in the information, that occurs while generalizing attribute values.
Cooee: An Artificial Intelligence Chatbot for Complex Energy Environments

Contemporary energy platforms are leveraging advanced data management and Artificial Intelligence (AI) capabilities in response to the increasing complexity of energy systems and grids. Despite these advances, it is a non-trivial and challenging task to support the decision-making needs of the human operators of such complex energy-related implementations. Conversational agents or chatbots are a potential emerging technology that can be utilized to address this challenge. Although there is a large body of literature on chatbots in general, they are not robust as they rely on predefined conversational pathways that are inadequate to efficiently address the complexities of dynamic data spaces in energy platforms. The capability of generating answers in real-time by communicating with the dynamic dataspace is crucial as energy management decisions are real-time and time sensitive. In this paper, we present the design and development of Cooee, a chatbot for conversational engagement with the dynamic data spaces of complex energy environments. Cooee leverages state-of-art language models along with rule-based language processing methods for a conversational interaction with dynamic data spaces, which consequently supports and enables decision-making by human experts. We have developed Cooee as a standalone application and then integrated into a real-world energy AI platform deployed within a multi-campus tertiary education institution setting. Cooee was empirically evaluated in this setting and compared with several state-of-the-art Q&A approaches.
A New Adoption of Cloud Computing Model for Saudi Arabian SMEs (ACCM-SME)

The significant developments and great contributions that Cloud computing technology has achieved recently has made them widespread and the focus of attention by many of the Small and Medium Enterprises (SMEs) around the world. This has helped them to take advantage of this technology to facilitate and improve business. In the last decade, many SMEs have utilised Cloud computing technology in an effective manner and benefit from its services. This has contributed to accelerating the success and sustainability of the business sector. However, the adoption rate of Cloud computing services by Saudi SMEs is still low due to some influencing factors. This paper examines the four main influencing factors that impact on Cloud computing adoption by SMEs in Saudi Arabia which are the technological context (data confidentiality, data integrity, data security, relative advantages, service cost, and service quality), the organisational context (organisation regulations, organisation size, technology readiness, and top management support), the environmental context (technology infrastructure, Cloud provider, legal framework, and enterprise competitiveness), and the social context (culture, awareness, and trust). This paper will use both quantitative (survey) and qualitative (semi-structured interviews) methods to collect the data and evaluate the proposed model. This paper proposes a new comprehensive model called the adoption of Cloud computing model by Saudi Arabian SMEs (ACCM-SME). This was the framework created to explore the factors influencing the adoption of Cloud computing services by Saudi SMEs which is the contribution of this paper. Therefore, this is position paper which will be followed by collecting data, then analysing them and evaluating the proposed model in order to increase the Saudi SMEs’ intention to adopt Cloud computing services.
When, Where and How Does it Fail? A Spatial-Temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving

Arguably the most representative application of artificial intelligence, autonomous driving systems usually rely on computer vision techniques to detect the situations of the external environment. Object detection underpins the ability of scene understanding in such systems. However, existing object detection algorithms often behave as a black box, so when a model fails, no information is available on When, Where and How the failure happened. In this paper, we propose a visual analytics approach to help model developers interpret the model failures. The system includes the micro- and macro-interpreting modules to address the interpretability problem of object detection in autonomous driving. The micro-interpreting module extracts and visualizes the features of a convolutional neural network (CNN) algorithm with density maps, while the macro-interpreting module provides spatial-temporal information of an autonomous driving vehicle and its environment. With the situation awareness of the spatial, temporal and neural network information, our system facilitates the understanding of the results of object detection algorithms, and helps the model developers better understand, tune and develop the models. We use real-world autonomous driving data to perform case studies by involving domain experts in computer vision and autonomous driving to evaluate our system. The results from our interviews with them show the effectiveness of our approach.
Parameter-Free Similarity-Aware Attention Module for Medical Image Classification and Segmentation

Automatic classification and segmentation of medical images play essential roles in computer-aided diagnosis. Deep convolutional neural networks (DCNNs) have shown their advantages on image classification and segmentation. However, they have not achieved the same success on medical images as they have done on natural images. In this paper, two challenges are exploited for DCNNs on medical images, including 1) lack of feature diversity; 2) neglect of small lesions. These two issues heavily influence the classification and segmentation performances. To improve the performance of DCNN on medical images, similarity-aware attention (simi-attention) module is proposed, including a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-similarity-aware Channel Attention</i> (FCA) and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Region-similarity-aware Spatial Attention</i> (RSA). Our simi-attention provides three advantages: 1) higher accuracy can be achieved since it extracts both diverse and discriminant features from medical images via our FCA and RSA; 2) the lesions can be exactly focused and located by it even for the data with low intensity contrast and small lesions; 3) it does not increase the complexity of backbone models due to NO trainable parameters in its module. The experimental results are conducted on both classification and segmentation tasks under four public medical classification datasets and two public medical segmentation datasets. The visualization results show that our simi-attention can accurately focuses on the lesions for classification and generate fine segmentation results even for small objects. The overall performances show that our simi-attention can significantly improve the performances of backbone models and outperforms compared attention models on most of datasets for both classification and segmentation.
Improved Transformer With Multi-Head Dense Collaboration

Recently, the attention mechanism boosts the performance of many neural network models in Natural Language Processing (NLP). Among the various attention mechanisms, Multi-Head Attention (MHA) is a powerful and popular variant. MHA helps the model to attend to different feature subspaces independently which is an essential component of Transformer. Despite its success, we conjecture that the different heads of the existing MHA may not collaborate properly. To validate this assumption and further improve the performance of Transformer, we study the collaboration problem for MHA in this paper. First, we propose the Single-Layer Collaboration (SLC) mechanism to help each attention head improve its attention distribution based on the feedback of other heads. Furthermore, we extend SLC to the cross-layer Multi-Head Dense Collaboration (MHDC) mechanism. MHDC helps each MHA layer learn the attention distributions considering the knowledge from the other MHA layers. Both SLC and MHDC are implemented as lightweight modules with very few additional parameters. When equipped with these modules, our new framework, i.e., Collaborative TransFormer (<i>CollFormer</i>), significantly outperforms the vanilla Transformer on a range of NLP tasks, including machine translation, sentence semantic relatedness, natural language inference, sentence classification, and reading comprehension. Besides, we also carry out extensive quantitative experiments to analyze the properties of the MHDC in different settings. The experimental results validate the effectiveness and universality of MHDC as well as <i>CollFormer</i>.
Optimization of Innovation and Entrepreneurship Education and Training System in Colleges and Universities Based on OpenStack Cloud Computing

With economic globalization and rapid development of science and technology, many colleges and universities pay more and more attention to the cultivation of students’ innovative thinking and creativity, and innovation and entrepreneurship education has also become an important part of the education system. Due to the current unevenness of teachers in innovation and entrepreneurship education in colleges and universities, high training cost, and lack of strong atmosphere, this paper optimizes the innovation and entrepreneurship education and training system in colleges and universities through OpenStack cloud computing. This paper optimizes the cloud computing platform according to the OpenStack virtual machine and the multiobjective ant colony improvement algorithm and then designs the innovation and entrepreneurship education and training system. The multiobjective ant colony improvement algorithm uses the way ants find food to find the best information resource route from the traces left by the information trend in the cloud platform. In order to test the effectiveness of these methods, this paper uses the simulation method to test. The results show that, sometimes, the load utilization rate of the innovation and entrepreneurship education and training system in colleges and universities exceeds 80%, which is in line with the expected settings. Through the OpenStack cloud computing platform, it can provide a good innovation and entrepreneurship training environment for more users at low cost and low risk and promote the development of innovation and entrepreneurship education.