Reviewerly/exHarmony huggingface dataset:
What can Open Domain Model Tell Us about the Missing Software Requirements: A Preliminary Study

Completeness is one of the most important attributes of software requirement specification. Unfortunately, incompleteness is one of the most difficult violations to detect. Some approaches have been proposed to detect missing requirements based on the requirement-oriented domain model. However, these kinds of models are actually lack for lots of domains. Fortunately, the domain models constructed for different purposes can usually be found online. This raises a question: whether or not these domain models are useful for finding the missing functional information in requirement specification? To explore this question, we design and conduct a preliminary study by computing the overlapping rate between the entities in domain models and the concepts of natural language software requirements, and then digging into four regularities of the occurrence of these entities(concepts) based on two example domains. The usefulness of these regularities, especially the one based our proposed metric AHME (with 54% and 70% of F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> on the two domains), has been initially evaluated with an additional experiment.
Analysis of Unsatisfying User Experiences and Unmet Psychological Needs for Virtual Reality Exergames Using Deep Learning Approach

The number of consumers playing virtual reality games is booming. To speed up product iteration, the user experience team needs to collect and analyze unsatisfying experiences in time. In this paper, we aim to detect the unsatisfying experiences hidden in online reviews of virtual reality exergames using a deep learning method and find out the unmet psychological needs of users based on self-determination theory. Convolutional neural networks for sentence classification (textCNN) are used in this study to classify online reviews with unsatisfying experiences. For comparison, we set eXtreme gradient boosting (XGBoost) with lexical features as the baseline of machine learning. Term frequency-inverse document frequency (TF-IDF) is used to extract keywords from every set of classified reviews. The micro-F1 score of textCNN classifier is 90.00, which is better than 82.69 of XGBoost. The top 10 keywords of every set of reviews reflect relevant topics of unmet psychological needs. This paper explores the potential problems causing unsatisfying experiences and unmet psychological needs in virtual reality exergames through text mining and makes a supplement for experimental studies about virtual reality exergames.
NOS Personal Assistant to Engage Elderly People with Smart Home

The Internet of Things has increased among residential consumers. The main drivers for the growth of smart home market will be video doorbells, voice-assisted technologies, and surveillance systems due to the growing increase in consumer demand. Meanwhile, the world is aging rapidly. Elderly people want to live healthy and active in their homes; however, this population presents key challenges with smart homes. Most of the smart things targeting elderly population are focussed on improving the Quality of Life rather than providing a seamless and learning ecosystem for breaking their fears with technology. We described a personal assistant that allows the users to learn how to work with smart sensors, giving them different contexts to test, use, learn and at the same time to keep active and update regarding the next generation of homes. This personal assistant will promote voice interface and learning in the following contexts: to control television and router; to control lights; and to care their plants.
An Integrated Cybernetic Awareness Strategy to Assess Cybersecurity Attitudes and Behaviours in School Context

Digital exposure to the Internet among the younger generations, notwithstanding their digital abilities, has increased and raised the alarm regarding the need to intensify the education on cybersecurity in schools. Understanding of the human factor and its influence on children, namely their attitudes and behaviors online, is pivotal to reinforce their awareness towards cyberattacks, and to promote their digital citizenship. This paper aims to present an integrated cybersecurity and cyberawareness strategy composed of three major steps: (1) Cybersecurity attitude and behavior assessment, (2) self-diagnosis, and (3) teaching/learning activities. The following contributions are made: Two questionnaires to assess risky attitudes and behaviors regarding cybersecurity; a self-diagnosis to measure students’ skills on cybersecurity; a lesson plan addressing cyberawareness to be applied on Information and Communications Technology (ICT) and citizenship education curricular units. Cybersecurity risky attitudes and behaviors were evaluated in a junior high school population of 164 students attending the sixth and ninth grades. The assessment focused on two main subjects: To identify the attitudes and behaviors that raise the risk on cybersecurity among the participating students; to characterize the acquired students’ cybersecurity and cyberawareness skills. Global and individual scores and the histograms for attitudes and behaviors are presented. The items in which we have observed significant differences between sixth and ninth grades are depicted and quantified by their corresponding p-values obtained through the Mann–Whitney non-parametric test. Regarding the results obtained on the assessment of attitudes and behaviors, although positive, we observed that the attitudes and behaviors in ninth grade students are globally inferior compared to those attained by sixth grade students. The deployed strategy for cyberawareness was applied in a school context; however, the same approach is suitable to be applied in other types of organizations, namely enterprises, healthcare institutions and public sector.
Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers

Satellite-mediated quantum key distribution (QKD) is set to become a critical technology for quantum-secure communication over long distances. While satellite QKD cannot be effectively eavesdropped, we show it can be disrupted (or 'jammed') with relatively simple and readily available equipment. We developed an atmospheric attenuation and satellite optical scattering model to estimate the rate of excess noise photons that can be injected into a satellite QKD channel by an off-axis laser, and calculated the effect this added noise has on the quantum bit error rate. We show that a ground-based laser on the order of 1 kW can significantly disrupt modern satellite QKD systems due to photons scattering off the satellite being detected by the QKD receiver on the ground. This class of laser can be purchased commercially, meaning such a method of disruption could be a serious threat to effectively securing high-value communications via satellite QKD in the future. We also discuss these results in relation to likely future developments in satellite-mediated QKD systems, and countermeasures that can be taken against this, and related methods, of disruption.
LDSVM: Leukemia Cancer Classification Using Machine Learning

Leukemia is blood cancer, including bone marrow and lymphatic tissues, typically involving white blood cells. Leukemia produces an abnormal amount of white blood cells compared to normal blood. Deoxyribonucleic acid (DNA) microarrays provide reliable medical diagnostic services to help more patients find the proposed treatment for infections. DNA microarrays are also known as biochips that consist of microscopic DNA spots attached to a solid glass surface. Currently, it is difficult to classify cancers using microarray data. Nearly many data mining techniques have failed because of the small sample size, which has become more critical for organizations. However, they are not highly effective in improving results and are frequently employed by doctors for cancer diagnosis. This study proposes a novel method using machine learning algorithms based on microarrays of leukemia GSE9476 cells. The main aim was to predict the initial leukemia disease. Machine learning algorithms such as decision tree (DT), naive bayes (NB), random forest (RF), gradient boosting machine (GBM), linear regression (LinR), support vector machine (SVM), and novel approach based on the combination of Logistic Regression (LR), DT and SVM named as ensemble LDSVM model. The k-fold cross-validation and grid search optimization methods were used with the LDSVM model to classify leukemia in patients and comparatively analyze their impacts. The proposed approach evaluated better accuracy, precision, recall, and f1 scores than the other algorithms. Furthermore, the results were relatively assessed, which showed LDSVM performance. This study aims to successfully predict leukemia in patients and enhance prediction accuracy in minimum time. Moreover, a Synthetic minority oversampling technique (SMOTE) and Principal compenent analysis (PCA) approaches were implemented. This makes the records generalized and evaluates the outcomes well. PCA reduces the feature count without losing any information and deals with class imbalanced datasets, as well as faster model execution along with less computation cost. In this study, a novel process was used to reduce the column results to develop a faster and more rapid experiment execution.
Towards an image quality criterion to optimize Digital image correlation. Use of an analytical model to optimize acquisition conditions

Digital image correlation (DIC) is expanding in many fields. In particular, it requires that the acquisition conditions and speckle be adapted to each application. A new methodology allowing to define the quality of the images in a fast and precise way is proposed. An analytical model based on mean intensity gradient of speckle pattern criterion and the standard deviation of displacement on images has been proposed. The model can be used on the whole image or by creating subsets in the image. Standard deviation maps of displacement are obtained. The methodology makes it possible to save a considerable amount of time compared to the full DIC calculation to optimize the image acquisition conditions, the speckle and the DIC parameters.
Multi-ROI segmentation for effective texture features of mammogram images

Image segmentation is an emerging requirement in digital mammography for segmenting the mammogram images. It is performed by region of interest (ROI), which is useful for the extraction of suspected tumors or masses from the mammogram images. In the classification of mammogram images, the number of training images are required; in such cases, ROI demands more computational time. This paper is focused on this problem for resolving the time complexity issues with the proposed multi-ROI technique. Experiment is conducted on benchmarked datasets for demonstrating the efficiency of proposed multi-ROI with the comparison of the existing segmentation method.
SISE-PC: Semi-supervised Image Subsampling for Explainable Pathology Classification

Although automated pathology classification using deep learning (DL) has proved to be predictively efficient, DL methods are found to be data and compute cost intensive. In this work, we aim to reduce DL training costs by pre-training a ResNet feature extractor using SimCLR contrastive loss for latent encoding of OCT images. We propose a novel active learning framework that identifies a minimal sub-sampled dataset containing the most uncertain OCT image samples using label propagation on the SimCLR latent encodings. The pre-trained ResNet model is then fine-tuned with the labelled minimal sub-sampled data and the underlying pathological sites are visually explained. Our framework identifies upto 2% of OCT images to be most uncertain that need prioritized specialist attention and that can fine-tune a ResNet model to achieve upto 97% classification accuracy. The proposed method can be extended to other medical images to minimize prediction costs.
A Survey on Graph Neural Networks for Recommendation

The rapid development of the Internet has caused information overload, and recommendation algorithms have emerged. In recent years, graph neural network (GNN) has been increasingly used in recommendation algorithms due to its strong performance in learning the node and relationship representation of networks. Based on the perspective of graph structure, this paper provides a new taxonomy of graph neural networks for recommendation models, and introduces the latest typical models in each category. We compare the differences and commonalities of different models from the step of information aggregation and information combination of graph neural networks, and summarize the challenges and difficulties still faced by GNN recommendation, hoping to provide reference for future research.
Teacher’s Obstacles in English Online Learning during Covid-19 Pandemic

&lt;p&gt;The study investigated some difficulties on the implementation of learning English through online learning due to Covid-19 pandemic on students from Islamic Family Law of IAIN Surakarta in the academic year of 2020/2021. The type of this research is qualitative descriptive. The data in this study are learning problems that occur in English language online learning activities. The data collection technique was carried out by documentation techniques and observations. Data analysis used in this research is data collection, data reduction, data presentation and conclusion. The study found that there are three main difficulties which hinder English language teaching and learning process namely: (1) student’s limited skills of English, (2) lack of teacher supervision and interactions during the learning process, and (3) inadequate learning facilities and internet networks for online learning. The right implementation of learning strategy, communication, patience, and consideration on students’ condition become the key factors to overcome all the problems during online learning in pandemic era.&lt;/p&gt;
Sharing Cyber Threat Intelligence and Collaboration

With the developing technology, cyber threats are developing rapidly, and the motivations and targets of cyber attackers are changing. In order to combat these threats, cyber threat information that provides information about the threats and the characteristics of the attackers is needed. In addition, it is of great importance to cooperate with other stakeholders and share experiences so that more information about threat information can be obtained and necessary measures can be taken quickly. In this context, in this study, it is stated that the establishment of a cooperation mechanism in which cyber threat information is shared will contribute to the cyber security capacity of organizations. And using the Zack Information Gap analysis, the deficiency of organizations in sharing threat information were determined and suggestions were presented. In addition, there are cooperation mechanisms in the USA and the EU where cyber threat information is shared, and it has been evaluated that it would be beneficial to establish a similar mechanism in our country. Thus, it is evaluated that advanced or unpredictable cyber threats can be detected, the cyber security capacities of all stakeholders will increase and a safer cyber ecosystem will be created. In addition, it is possible to collect, store, distribute and share information about the analysis of cyber incidents and malware analysis, to improve existing cyber security products or to encourage new product development, by carrying out joint R&D studies among the stakeholders to ensure that domestic and national cyber security products can be developed. It is predicted that new analysis methods can be developed by using technologies such as artificial intelligence and machine learning.
Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification

Most real-time systems are nonlinear in nature, and their optimization is very difficult due to inherit stiffness and complex system representation. The computational intelligent algorithms of evolutionary computing paradigm (ECP) effectively solve various complex, nonlinear optimization problems. The differential evolution algorithm (DEA) is one of the most important approaches in ECP, which outperforms other standard approaches in terms of accuracy and convergence performance. In this study, a novel application of a recently proposed variant of DEA, the so-called, maximum-likelihood-based, adaptive, differential evolution algorithm (ADEA), is investigated for the identification of nonlinear Hammerstein output error (HOE) systems that are widely used to model different nonlinear processes of engineering and applied sciences. The performance of the ADEA is evaluated by taking polynomial- and sigmoidal-type nonlinearities in two case studies of HOE systems. Moreover, the robustness of the proposed scheme is examined for different noise levels. Reliability and consistent accuracy are assessed through multiple independent trials of the scheme. The convergence, accuracy, robustness and reliability of the ADEA are carefully examined for HOE identification in comparison with the standard counterpart of the DEA. The ADEA achieves the fitness values of 1.43 × 10−8 and 3.46 × 10−9 for a population size of 80 and 100, respectively, in the HOE system identification problem of case study 1 for a 0.01 nose level, while the respective fitness values in the case of DEA are 1.43 × 10−6 and 3.46 × 10−7. The ADEA is more statistically consistent but less complex when compared to the DEA due to the extra operations involved in introducing the adaptiveness during the mutation and crossover. The current study may consider the approach of effective nonlinear system identification as a step further in developing ECP-based computational intelligence.
Integration of language, psychology, and technology and the concept of independence learning in reading characters in indonesian children's films as media and learning materials in character building for elementary school students-indonesia

The purpose of this research is to integrate psychology, technology, and language in reading the characters in Indonesian children's films as media and learning materials for character building for elementary school students in Indonesia. This research design uses mixed methods. The source of research data is sourced from elementary school students' learning activities in Indonesian subjects using Indonesian children's films as media and learning materials. Data were collected using questionnaires, interviews, and focus group discussions. Respondents of this study were elementary school teachers as many as 98 teachers. Qualitative data were analyzed by interpreting techniques and presented in the form of descriptions, while quantitative data were analyzed by calculating the average student learning outcomes presented in the form of percentages. The findings of this study are twofold, namely, the findings of this study are twofold, namely by using Indonesian children's films as a medium as well as a source of learning and learning materials can shape students' character and improve student achievement.
Efficient Tensor Slicing for Multicore NPUs using Memory Burst Modeling

Although code generation for Convolution Neural Network (CNN) models has been extensively studied, performing efficient data slicing and parallelization for highly-constrained Multicore Neural Processor Units (NPUs) is still a challenging problem. Given the size of convolutions&#x0027; in-put/output tensors and the small footprint of NPU on-chip memories, minimizing memory transactions while maximizing parallelism and MAC utilization are central to any effective solution. This paper proposes a TensorFlow XLA/LLVM compiler optimization pass for Multicore NPUs, called Tensor Slicing Optimization (TSO), which: (a) maximizes convolution parallelism and memory usage across NPU cores; and (b) reduces data transfers between host and NPU on-chip memories by using DRAM memory burst time estimates to guide tensor slicing. To evaluate the proposed approach, a set of experiments was performed using the NeuroMorphic Processor (NMP), a multicore NPU containing 32 RISC-V cores extended with novel CNN instructions. Experimental results show that TSO is capable of identifying the best tensor slicing that minimizes execution time for a set of CNN models. Speed-ups of up to 21.7&#x0025; result when comparing the TSO burst-based technique to a no-burst data slicing approach.
Dual Prior Learning for Blind and Blended Image Restoration

Unsupervised single image restoration approach, Deep Image Prior (DIP), aims to restore images by learning enough raw image statistic priors from the corrupted observation. However, it is not uncommon that an image is contaminated by the multiple unknown distortions. Thus it is hard to disentangle the clean and the hybrid distortion signals by solely relying on image prior learning to restore the images. To overcome this problem, we propose the Dual Prior Learning (DPL) method by taking both image and distortion priors into account. DPL goes beyond DIP by considering an additional step to explicitly learn the blended distortion prior. Furthermore, to coordinate the learning of two priors and avoid them learning the same knowledge, we exploit unpaired training data to enforce a weakly supervision in an adversarial manner to encourage disentangling two priors. Extensive experiments show the effectiveness and appealing performance of the proposed DPL on restoring images with challenging unknown blended distortions.
Design of Facial Recognition System Based on Visual Communication Effect

At present, facial recognition technology is a very cutting-edge science and technology, and it has now become a very hot research branch. In this research, first, the thesis first summarized the research status of facial recognition technology and related technologies based on visual communication and then used the OpenCV open source vision library based on the design of the system architecture and the installed system hardware conditions. The face detection program and the image matching program are realized, and the complete face recognition system based on OpenCV is realized. The experimental results show that the hardware system built by the software can realize the image capture and online recognition. The applied objects are testers. In general, the OpenCV-based face recognition system for testers can reliably, stably, and quickly realize face detection and recognition in this situation. Facial recognition works well.
An Analytical Study of Algorithmic and Expert Summaries of Legal Cases

Automatic summarization of legal case documents is an important and challenging problem, where algorithms attempt to generate summaries that match well with expert-generated summaries. This work takes the first step in analyzing expert-generated summaries and algorithmic summaries of legal case documents. We try to uncover how law experts write summaries for a legal document, how various generic as well as domain-specific extractive algorithms generate summaries, and how the expert summaries vary from the algorithmic summaries. We also analyze which important sentences of a legal case document are missed by most algorithms while generating summaries, in terms of the rhetorical roles of the sentences and the positions of the sentences in the legal document.