Reviewerly/exHarmony huggingface dataset:
A Feature Selection Algorithm Integrating Maximum Classification Information and Minimum Interaction Feature Dependency Information

Feature selection is the key step in the analysis of high-dimensional small sample data. The core of feature selection is to analyse and quantify the correlation between features and class labels and the redundancy between features. However, most of the existing feature selection algorithms only consider the classification contribution of individual features and ignore the influence of interfeature redundancy and correlation. Therefore, this paper proposes a feature selection algorithm for nonlinear dynamic conditional relevance (NDCRFS) through the study and analysis of the existing feature selection algorithm ideas and method. Firstly, redundancy and relevance between features and between features and class labels are discriminated by mutual information, conditional mutual information, and interactive mutual information. Secondly, the selected features and candidate features are dynamically weighted utilizing information gain factors. Finally, to evaluate the performance of this feature selection algorithm, NDCRFS was validated against 6 other feature selection algorithms on three classifiers, using 12 different data sets, for variability and classification metrics between the different algorithms. The experimental results show that the NDCRFS method can improve the quality of the feature subsets and obtain better classification results.
Consistent or not? An investigation of using Pull Request Template in GitHub

The arbitrary usage of pull requests in GitHub may bring many issues such as incomplete, verbose, and duplicated descriptions, which hinder the development and maintenance of the project. Thus, GitHub proposed the Pull Request Template (PRT) in 2016 so that developers could edit the pull request in a relevant consistent manner. However, whether the PRT has been widely applied to GitHub and what impact it might bring remain little known. Such uninformed cases may affect the efficiency of cooperative development. In this work, we conduct an empirical study on large-scale repositories to explore whether the PRT has been widely applied and what impact it can bring to the GitHub community. This work aims to answer four research questions. The first is a statistical experiment with the aim of analyzing the current status of PRTs. The second is an explored experiment, which aims at probing which repositories are suitable to adopt the PRT. The third is the measurement evaluation experiment, focusing on discussing what impact the PRT can bring. The last is an online survey to explain why few PRTs have been adopted. Notably, both the second and third questions are conducted a mixed quantitative and qualitative analysis. In this work, we find that only 1.2% of repositories contain the PRT in GitHub, and such repositories are mostly in high popularity and contain a large number of PRs. Besides, contributors are willing to accept the PRT that requires pivotal information, including description, test, and check_list. Meanwhile, the PRT can assist developers to manage repositories, reflecting in less time for reviewing, fewer duplicated pull requests, and almost non-existentially invalid comments. Finally, we survey 527 well-reputed developers to explain why few repositories adopt the PRT, and further provide some actionable suggestions.
Soft Compression for Lossless Image Coding Based on Shape Recognition

Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.
Towards Optimizing Computational Costs of Federated Learning in Clouds

Federated Learning is a strategy where distributed training datasets are processed by several clients coordinated by a central server that keeps the global learning model. This approach is very attractive in the biomedical domain because it allows the use of private datasets from multiple institutions to train a model without the need of sharing the data. Moreover, Machine Learning approaches often require larger training samples than what can be afforded by a single institution. In this work, we are interested in analyzing the performance of a Tumor-Infiltrating Lymphocytes Classification problem when solved by a federated learning approach deployed in a commercial cloud. In the presented evaluation, we consider executions of a federated learning implementation on accelerated instance types of the AWS EC2, on on-demand and spot markets, while varying the number of clients. The obtained results showed the improvement of the accuracy and execution times when the number of clients increases. They also revealed that, although the spot instances suffered from revocations, their use could significantly reduce the financial costs compared to the on-demand one.
Overcoming Knowledge-Sharing Barriers that Affect Software Quality

There are several barriers to the knowledge management process in the current technological market environment that can affect software quality, especially in a distributed environment. One of them relates to the knowledge sharing needed to fulfill required tasks to ensure the projects’ deliverables. This paper is an experience report that assesses the gaps of knowledge (GoK) in the project leaders (PL) team. To accomplish this, it was carried out documental analysis and focus group sessions to review essential activities developed by the PL team during the software development process. Afterward, we carried out a survey to verify the GoK of PL team members related to their ability to perform essential activities to develop Security Maintenance Release projects. This paper also shows analyzes of three ways to share knowledge and a training program guidelines to eliminate existing GoK in the PL team. The findings reveal that although many factors affect the success rates of the knowledge sharing process, a training plan considering the combination of four factors can shorten the learning curve of PL team members. We hope this paper can contribute helping other knowledge-based companies and scientific community to increase their competitive advantages related to the knowledge sharing process and to minimize software quality problems such as rework effort and defective product delivery.
FedRC: A Federated Learning-Based Roadside Computing Paradigm Through the Facilitation of Internet of Drones

The modern era is filled with smart entities (e.g., smart vehicles) that have both sense and actuate capabilities. These entities can collect lots of data during their functional period and these data can be utilized for the wellbeing of citizens. However, these data are very sensitive raising issues like privacy. Moreover, network scarcity, bandwidth consumption, etc. can worsen the circumstance. Federated learning (FL), internet of drones (IoD), and dew computing (DC) are revolutionary technologies that can be engaged to mitigate the aforementioned challenges. An FL-based computing paradigm is initiated over the dew computing to process road-related data to bring efficiency in the applications (e.g., finding parking locations) utilizing IoD. An experimental environment is established containing a traffic dataset as a proof of concept. The experimental results exhibit the feasibility of the proposed scheme.
Real-Time Littering Activity Monitoring Based on Image Classification Method

This paper describes the implementation of real time human activity recognition systems in public areas. The objective of the study is to develop an alarm system to identify people who do not care for their surrounding environment. In this research, the actions recognized are limited to littering activity using two methods, i.e., CNN and CNN-LSTM. The proposed system captures, classifies, and recognizes the activity by using two main components, a namely camera and mini-PC. The proposed system was implemented in two locations, i.e., Sekanak River and the mini garden near the Sekanak market. It was able to recognize the littering activity successfully. Based on the proposed model, the validation results from the prediction of the testing data in simulation show a loss value of 70% and an accuracy value of 56% for CNN of model 8 that used 500 epochs and a loss value of 10.61%, and an accuracy value of 97% for CNN-LSTM that used 100 epochs. For real experiment of CNN model 8, it is obtained 66.7% and 75% success for detecting littering activity at mini garden and Sekanak River respectively, while using CNN-LSTM in real experiment sequentially gives 94.4% and 100% success for mini garden and Sekanak river.
Influence of Virtual Experience Immersion, Product Control, and Stimulation on Advertising Effects

With advances in communication technologies, virtual reality (VR) has become increasing popular. This study investigated the influence of virtual experiences on advertising effects. A 2 (virtual experience immersion: high vs. low) × 2 (virtual experience product control: high vs. low) × 2 (virtual experience product stimulation: high vs. low) between-subjects design was conducted. The results revealed that compared with the low-immersion virtual experience, the high-immersion virtual experience produced better advertisement attitudes. Furthermore, virtual experiences with high product control generated better advertisement attitudes when compared with virtual experiences with low product control. The two-way interactions between virtual experience product stimulation and immersion and between virtual experience product stimulation and virtual experience product control induced better brand attitude and purchase intention. In addition, telepresence and perceived enjoyment mediated the relationship between virtual experience and advertisement attitudes.
Python Programming

This is the first of two chapters in which you’ll review both Python and C programming languages. A basic understanding of computing and what programs are about is assumed although no prior knowledge of either Python or C is needed.
Two-stream network with phase map for few-shot classification

As an important research direction of deep learning, few-shot classification mainly solves problems of classification when training samples are scarce. Under this circumstance, how to effectively use the information of existing samples has become the key to solve few-shot classification problems. We propose a method to fully mine existing samples information, which can be implemented in the training and testing stages. Specifically, we introduce a two-stream neural network as an embedding model to extract features of the original image and its phase map, and perform fine-grained adaptive feature fusion to them. The algorithm can learn and extract more fully-informed fusion features under the network. Besides, we also introduce Circle Loss in loss function, which has a non-linear decision boundary. Under the joint supervision of Soft-max Loss and Circle Loss, the distinction between different classes of features in embedding space increases. We conduct experiments on miniImageNet, tieredImageNet, and CIFAR-FS, respectively. The accuracy rate is increased by 0.99%, 0.96%, and 0.91%, which proves the effectiveness of our algorithm.
2D Multi-view Discriminant Analysis

With the advent of technology, we have seen an explosion in high-definition images. However, due to the large size of images, 1D methods are less suitable to handle such data. This issue becomes even more pronounced when working with multi-view datasets. We propose a generalized method, 2D Multi-view Discriminant Analysis (2DMvDA), to tackle this issue. 2DMvDA is a 2D multi-view classification method based on discriminant analysis. It uses the 2D image matrices directly instead of extracting 1D features from them, leading to a considerable reduction in the size of the data to be processed. We compare the proposed method with 1D Multi-view Discriminant Analysis (MvDA) and single-view 2D Linear Discriminant Analysis (2DLDA). We experimentally show that the proposed method requires less than 0.1% training time and memory than MvDA. Also, though it needs the same memory, it requires 30–70% less training time than 2DLDA. Using less time and memory, 2DMvDA achieves a classification accuracy that is at par or better than both of these methods.
Enhanced-Memory Transformer for Coherent Paragraph Video Captioning

A coherent description is an ultimate goal concerning video captioning through multiple sentences since it may directly impact consistency and intelligibility. A paragraph describing a video is affected by different events extracted from it. When generating a new event, it should produce a detailed narrative of the video content. But it also might provide some clues that may help reduce the textual repetition occurring in the final description. Recently, transformers have emerged as an appealing solution to several tasks, including video captioning. An augmented transformer with a memory module can somehow cope with text repetition. Thus, to further increase the coherence among the generated sentences, we propose the adoption of attention mechanisms to enhance memory data in a memory-augmented transformer. This new approach, called Enhanced-Memory Transformer (EMT), assesses the data importance (about the video segments) contained in the memory module and uses that to improve readability by reducing repetition. Experimental evaluation of EMT using the test split of the ActivityNet Captions dataset achieved 22.84 in CIDEr-D score, and 4.55 in Reduction-4 score (R@4), representing improvements of 1.03% and 16.36%, respectively (compared to the literature). The obtained results show the great potential of this new approach as it provides increased coherence among the various video segments, decreasing the repetition in the generated sentences and improving the description diversity.
Research on Privacy Data Protection Based on Trusted Computing and Blockchain

Aiming at the problems of low protection accuracy and long time consumption in traditional privacy data protection methods, a privacy data protection method based on trusted computing and blockchain is proposed. Set up the Internet node secure transmission protocol through the trusted node uplink transmission protocol and the downlink transmission protocol, and according to the transmission protocol, combined with the blockchain technology, the ECC elliptic curve encryption algorithm is used to encrypt the amount of data existing in the blockchain, and the AES symmetric encryption algorithm is used to encrypt the private data that exists in the nonblockchain, thereby completing the protection of network private data. The simulation experiment results show that the privacy data protection accuracy of the proposed method is higher and the work efficiency is faster.
ML Classifier Comparative Performance Analysis of Prediction on Cervical Cancer

Identification of cancer at an early stage in treating patients has been worthwhile. Cervical cancer occurs by alteration of cells connecting the uterus and vulva to anomalous changed state which can culminate in infecting deeper tissue. By making an early prediction on the result of the biopsy test, it may come to the verge of a cure. For making prognostication on biopsy test we used a dataset provided by the hospital Universitario de Caracas. Analytical and statistical survey on the dataset has led us to a conjecture of making predictions on biopsy using the following six classifiers which are LR, random forest, NB, SVM, SGD, and kNN, and finally done by comparing performance analysis. Among them, multinomial NB has stood out as the best performer with an accuracy of 97.76 percent.
An Algorithmic Approach for Generating Behavioral UML Models Using Natural Language Processing

The process of transformation from informal requirements stated in natural language into a formal specification such as Unified Modeling Language (UML) is an important challenge. User requirements that are expressed in natural language can be very problematic, which makes the requirements analysis a difficult task. In this paper, we propose a method to analyze the natural language requirements and generate sequence and collaboration diagrams from these requirements, which are commonly used to describe the behavior of software systems. A case study was accomplished to compare the diagrams generated by the proposed approach to the diagrams produced by other approaches. The results showed that the elements of the sequence and collaboration diagrams extracted through our approach are very satisfactory and they would be acceptable as initial analysis models.
The Presence of the Body in Digital Education: A Phenomenological Approach to Embodied Experience

In a context of pervasive digitalization of the social world, both before and during the COVID-19 pandemic, the field of education has undergone major changes with the development of digital practices and settings. However, the physical presence of the subjects and the body remain something primordial and irreplaceable in traditional educational processes. Thus, it is often assumed that virtuality is opposed to the corporeal reality of the subjects involved in teaching, learning and studying. In this paper we aim to critically challenge this assumption by addressing the phenomenon of virtuality in a more original sense: as a fundamental dimension of corporeality itself. Drawing on Merleau-Ponty's phenomenological insights, we argue that the experience of the virtual is inherently embodied and fully real. We understand corporeality and virtuality as entangled and multidimensional phenomena. At the same time, this leads us to rethink the digital as only one possible medium in which virtuality can appear as inherent to embodied experience. We develop our phenomenological approach in three instances. First, we question and bracket two common assumptions about corporeality. From our critical perspective, assumptions about a disembodied digitality and a devirtualized corporeality represent extremes within which educational discourses currently move. Second, we address the inherent virtuality of embodied experience. We outline the heuristic concept of the virtual body (Merleau-Ponty) to describe corporeality and virtuality as entangled and multidimensional phenomena that encompass extension, intercorporeality and intermediality. Finally, we make some remarks on the relationships of corporeality and virtuality considering the challenges of digital education.
DARe: DropLayer-Aware Manycore ReRAM architecture for Training Graph Neural Networks

Graph Neural Networks (GNNs) are a variant of Deep Neural Networks (DNNs) operating on graphs. GNNs have attributes of both DNNs and graph computation. However, training GNNs on manycore architectures is a challenging task because it involves heavy communication that bottlenecks performance. DropEdge and Dropout, which we collectively refer to as DropLayer, are regularization techniques that can improve the predictive accuracy of GNNs. Moreover, when implemented on a manycore architecture, DropEdge and Dropout are capable of reducing the on-chip traffic. In this paper, we present a ReRAM-based 3D manycore architecture called DARe, tailored for accelerating on-chip training of GNNs. The key component of the DARe architecture is a Network-on-Chip (NoC) that reduces the amount of communication using DropLayer. The reduced traffic prevents communication hotspots and leads to better performance. We demonstrate that DARe outperforms conventional GPUs by up to 6.7X (5.6X on average) in terms of execution time, while being up to 30X (23X on average) more energy efficient for GNN training.
A CSCM-SA method for Multi-UAV Task Assignment

The unmanned aerial vehicle (UAV) has huge application potential in disaster rescue, natural detection, and military reconnaissance. In these scenarios, task assignment is essential and facing the problem of NP-hard, and as the scale of the Multi-UAV system increase, it is harder to solve the problem. In this paper, a CSCM-SA (Cosine Similarity Clustering Method- Simulated Annealing) is proposed. Firstly, the cosine similarity clustering method is used for pre-clustering of large-scale targets; Secondly, simulated annealing is employed to optimize routers for each UAV; Finally, to cope with the instability of CSCM results, two strategies of employing genetic algorithm are studied. Simulation results show that the CSCM-SA algorithm is effective to conduct task assignments for the large-scale multi-UAV systems.
Prediction of the effect of temperature on electric power in photovoltaic thermal systems based on natural zeolite plates

The present study aims to predict the effect of the panel temperature on the electrical power obtained by the photovoltaic thermal system (PVT) based on natural zeolite plates. It was carried out using the long short-term memory (LSTM) and multilayer feed forward (MLF) algorithms, which are popular regression-based deep learning methods. Models have been developed that can predict the effect of temperature on electrical power by using the regression data obtained from experimental measurements on the PVT system integrated with natural zeolite plates. In the study, a polycrystalline PVT panel was chosen, in the designed models, temperature, solar radiation, and photovoltaic panel temperature values were evaluated as input, and the amount of power to be produced as output. Mean absolute error, root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 functions were used for the prediction results of LSTM and MLF models designed with determined optimum hyperparameters. MAPE and R2 values for long short-term memory (LSTM) and MLF algorithms were determined as 0.100 and 0.996, as well as 0.131 and 0.923, respectively. As can be understood from these values, the effect of the designed temperature on the PVT panel can be calculated with a very small margin of error with the LSTM algorithm.
PDSTD - The Portsmouth Dynamic Spontaneous Tears Database

Abstract The vast majority of research on human emotional tears has relied on posed and static stimulus materials. In this paper, we introduce the Portsmouth Dynamic Spontaneous Tears Database (PDSTD), a free resource comprising video recordings of 24 female encoders depicting a balanced representation of sadness stimuli with and without tears. Encoders watched a neutral film and a self-selected sad film and reported their emotional experience for 9 emotions. Extending this initial validation, we obtained norming data from an independent sample of naïve observers ( N = 91, 45 females) who watched videos of the encoders during three time phases (neutral, pre-sadness, sadness), yielding a total of 72 validated recordings. Observers rated the expressions during each phase on 7 discrete emotions, negative and positive valence, arousal, and genuineness. All data were analyzed by means of general linear mixed modelling (GLMM) to account for sources of random variance. Our results confirm the successful elicitation of sadness, and demonstrate the presence of a tear effect , i.e., a substantial increase in perceived sadness for spontaneous dynamic weeping. To our knowledge, the PDSTD is the first database of spontaneously elicited dynamic tears and sadness that is openly available to researchers. The stimuli can be accessed free of charge via OSF from https://osf.io/uyjeg/?view_only=24474ec8d75949ccb9a8243651db0abf .