Reviewerly/exHarmony huggingface dataset:
Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Abstract Real‐world datasets used for classification often face many challenges when they are imbalanced in nature which is unavoidable and need to be handled by analysts. Many researchers have proposed methods for handling imbalanced datasets and they mostly concentrated on handling binary classification with only two class labels. Only very few research works have been carried out for treating highly imbalanced datasets and fail to handle multiclass datasets. To address imbalance problem in multiclass datasets, this paper proposes a Variable Population sized Particle Swarm Optimization (VPPSO) which is a modified version of Particle Swarm Optimization (PSO) which works based on clustering. PSO usually has fixed population size and has high computational complexity. In order to reduce this, the population size is varied over generations and the particles are loaded into the population iteratively by retaining the balance nature of solutions. PSO optimizes the selection of training and testing samples from each class label in imbalanced datasets for improved classification results. From the implementation results, it is evident that using VPPSO, highly imbalanced datasets with multiclass attributes are classified more efficiently than state‐of‐the‐art algorithms. The statistical results also prove the superior performance of VPPSO.
HVH: A Lightweight Hash Function Based on Dual Pseudo-Random Transformation

Along with the popularization of RFID technology and wireless sensor network, it has been more and more difficult for common hash-functions to meet the application demands in the Internet of Things. Furthermore, the design and analysis of lightweight hash function have gradually become a research focus at current days. This paper presents the HVH family of lightweight hash functions based on the sponge iterative structure and the lightweight block cipher algorithm VH. HVH offers five different lengths of message digest for different constrained environments and security levels. Moreover, HVH can operate efficiently not only in hardware environments but also on software platforms, such as 8-bit microcontroller. Hardware implementation of HVH-88 is around 1129GE with a throughput of 44.44 Kbps at 100 kHz, which is comparable with the 1237 GE hardware implementation of SPONGENT-88. The software implementation of HVH-88 on 8-bit microcontroller is about 1.47 Mb/s, and its efficiency is 10 times as much as that of SPONGENT-88 in RFID environment. Security evaluation shows that HVH can achieve sufficient security margin against known attacks, such as linear cryptanalysis, differential cryptanalysis, impossible differential cryptanalysis, (second) pre-image and collision resistance.
Exploring Animation and Virtual Reality to Represent the Perceptual-Experiences of Art-Practitioners with Sight-Loss

OVERVIEWWhilst the sighted generally imagine people with blindness as living in darkness, only a small percentage of people have total vision loss and many visually impaired persons have some perception of light, form, movement, and shape. As mental images can be generated without sight, the ability to see is not necessary for the creation of visual art. This practice-based PhD project explores animation and virtual reality to represent the creative practice of artists with sight-loss. Using virtual worlds to enable sighted users to understand another person’s perceptual-experiences, this inductive research adopts an interpretive approach incorporating the strength of case-study to compare abstract concepts of blindness to actual lived experiences. The methodology will apply qualitative techniques, to understand the methods artists with sight-loss use and how visual impairment shapes their practice. Responses will be evaluated, by analysing the data to create an immersive animation, viewed, and experienced in virtual reality.
Traditional Logic and Computational Thinking

In this contribution, we try to show that traditional Aristotelian logic can be useful (in a non-trivial way) for computational thinking. To achieve this objective, we argue in favor of two statements: (i) that traditional logic is not classical and (ii) that logic programming emanating from traditional logic is not classical logic programming.
A face attribute based recommendation system via integrating denoising autoencoder and hash coding

Nowadays, with the rapid development of commerce, how to effectively improve the performance of an recommendation system has aroused great concern. However, traditional recommendation system requires users to log in their accounts, which brings poor user experience. This paper presents a novel recommendation system by using face recognition technologies to extract face attribute information as the input automatically. The system first obtains the user information of identity, gender, age, and then gets feedback by expression analysis. Based on the acquired face attributes, we propose to extract compact binary user features by integrating denoising autoencoder and hash coding, which can effectively improve the computing efficiency.The hash features from DAE-H-Face and DAE are further combined to enhance the representation ability. Finally, Hamming similarity-based collaborative filtering is used for recommendation. Experimental results on the MovieLens database show that the proposed recommendation method has better effectiveness and robustness. Moreover, the results also demonstrate its advantages to the cold start problem.
Sport action recognition by fusing multi‐source sensor information

In traditional sport and training, the quality of training plan mainly relies on individual observation and experience of coaches, which inevitably depends on individual objective opinion. With the emerging of sensors and body area network (BAN), it becomes possible to automatically recognize athletes' posture. Thus, the coaches can make decision based on recognition results to enhance athletes' competitive ability, which can greatly improve the effectiveness and quality of the sport training. This paper proposes an automatically action recognition system for sport training. First, we collect body information from body sensors. Second, the information from multi‐source sensors is input into a Kalman filter to remove the interference noise. Third, the denoised signals are divided as several unit actions according to four limbs. Fourth, the unit actions are represented as a matrix consisting of acceleration, angular velocity, combined acceleration, and combined angular velocity. We extract frequency domain features and time domain features to represent the matrix. Lastly, the extracted features are used to train an intelligent classifier which is used to predict the future postures. The experimental results on a sport training dataset demonstrates the effectiveness of our framework for action recognition for sport training.
LP-Explain: Local Pictorial Explanation for Outliers

Outlier detection is of vital importance for various fields and applications. Existing works mainly focus on identifying outliers from underlying datasets, while how to provide sense-making explanations is largely ignored. In this paper, we propose to visualize data points in a set of scatter plots on two-dimensional (2-D) feature spaces that can provide meaningful explanations about the outlying behavior of outliers. Data are typically multidimensional and the number of 2-D combinations could be huge. Also, outliers may have diverse characteristics, and thus the global scatter plots containing all of outliers may degrade the explanation effectiveness for those outliers having idiosyncratic abnormal 2-D spaces. To address this problem, we propose a new outlier explanation approach, called LP-Explain, which tries to identify the set of best Local Pictorial explanations (defined as the scatter plots in the 2-D space of the feature pairs) that can Explain the behavior for cluster of outliers. We first define an effective measure to quantify the similarity between outliers, and then cluster outliers into different groups based on their abnormal feature pairs. We then propose to weigh the importance of feature pairs within each cluster through a multi-task learning framework to select the set of top feature pairs that best explain various outlier clusters. By adjusting a user-defined parameter indicating the “localization level”, the proposed method can attain both global and local results for the explanation of the outliers. 2-D visual explanations can be plotted for the top-weighted feature pairs of each cluster. We conduct experiments on various public datasets, which show that the proposed approach can provide more meaningful explanations about the outlying behavior in a dataset.
Gesture Recognizer and Communicator using Flex Sensors and Accelerometer with Logistic Regression

Muteness or mutism is an inability to speak, often caused by speech disorder or surgery. Regular people cannot understand sign language and therefore, gesture recognizer and communicator can be a solution to this problem. The idea is to attach sensors (flex sensors and accelerometers) to a glove and develop a machine learning model that predicts the gesture by reading the sensor values. The predicted result can be given as output through an audio module or a display.
Security-Aware Energy Management in Clouds

Cloud computing over the Internet reveals a remarkable potential to provide on-demand services to consumers with great flexibility in a cost-effective manner. Security issues coupled with resource allocations in cloud computing remain a challenging problem to be tackled by the industry and academia. While moving towards the concept of on-demand services and resource pooling in a distributed computing environment, security is a major obstacle for this new dreamed vision of computing capability. At the same time, the research on energy-efficient networking infrastructures is of great importance for service providers, network administrators, and equipment manufacturers. In this paper, we articulate novel energy-aware scheduling policies catered for virtual machines running on clouds, in which service-level agreements (SLAs) are fulfilled. After addressing security concerns in cloud computing, we advocate for a research roadmap towards future security-aware energy management in clouds. We propose a high-level design for a security- and frequency-ware DVFS model or SF-DVFS, which orchestrates security services, security overhead analysis, and DVFS control green cloud computing systems. We delve into the main technical challenges associated with the proposed SF-DVFS model. We expect that this paper will open exciting perspectives for future security research in energy-efficient cloud computing platforms.
ESSENCE Kernel in Overcoming Challenges of Agile Software Development

In this paper, we discuss the benefits and challenges of agile programming when used in large-scale software development. We enumerate the myths and ground realities of prevalent agile practice. Agile programming has promises and potentials with small delivery cycles. But at the same time, in practice, individual excellence or infrastructural building blocks as essential components are often prioritized less. Thus, the entire quality may suffer with staggered timelines and compromises. In this context, ESSENCE, a SEMAT kernel is proposed to be used in conjunction with suitably adapted and customized Agile process in order to help mitigating the risks and challenges. We propose to use ESSENCE Alpha cards and competency for health-check of process, tools, procedures and resources in a timely manner. OMG has adopted SEMAT and its kernel, ESSENCE, as an official OMG standard. Essential ESSENCE use with agile practice is a definite way forward for timely saving of catastrophes.
Securing the digital witness identity using blockchain and zero-knowledge proofs

The digital witness concept is underpinned by the Internet of Things (IoT) which brings serious privacy challenges and concerns. Digital witnesses deliver digital evidence to authorised entities by means of a digital chain of custody. This chapter provides an alternative approach by giving users behind the digital witness, the ability to disclose or anonymise their identities by means of a decentralised identifier addressing privacy concerns. The model is scoped within a city environment to demonstrate the attacks and offences. A novel approach is used within a blockchain environment to authenticate and verify members by using zero-knowledge proofs within the digital witness ecosystem. The model is based on a self-sovereign approach which validates transactions in an unbiased manner, and the threat model prepares the groundwork for a mitigation strategy to counter measure the models’ threats. Further research suggestions are provided to encourage areas that need to be researched to expand on the concept.
The Legal Regulation of Facial Recognition

Over the past decade, facial recognition has emerged as one of the most powerful biometric technologies capable of identifying and verifying individuals based on a digital image or a video frame. Facial recognition technology has many useful and social benefit applications. Improvements in facial recognition systems are fast but still unproven as an emerging technology. Facial recognition has inevitably risks. The threat is high as facial recognition can indeed very seriously and irremediably interfere with our fundamental human rights and civil liberties. However, it is the opinion of the author that liberal democracies cannot simply ban this technology because of its potential abuses. The extent to which the benefits are maximised and the risks mitigated depends on a well-balanced regulation, to come after a public debate, experimentations and a complete understanding of the challenges at stake in both public and private spheres. Safeguards may be introduced without necessarily hindering innovation, state's competitiveness and sovereignty in artificial intelligence along with the irremediable technological evolution of our society. The need for allying business and ethics takes here on its full dimension. This article takes a careful and analytical methodology on a comparative legal approach. It has no aim to be exhaustive on the subject matter especially on its technology aspect. It designs preliminary guidelines for a regulation on facial recognition in liberal democracies.
Structure Improved Conditional Spectral Normalization Generative Adversarial Networks for Image Recognition

Generative adversarial networks (GANs) have become popular generative models. However, the problem of generating instability of network training is hard to eliminate. A new method (SICS-GANs) is proposed by integrating the advantages of previous models. In the improved conditional deep convolutional generative adversarial networks model, spectrally normalized global weights are exploited. Using group normalization to process the input of the hidden layer can speed up training and improve the quality of generated samples. The model employs the global average pooling layer to replace the full connection layer to avoid overfitting. In addition, the improved method is applied to image recognition on two datasets, CIFAR-10 and SVHN. Compared with other stable methods, the experiments consequence show that this approach not only generates better images, but also effectively increases the accuracy of image recognition.
User Experience Evaluation of Egrang Traditional Game Application

Today's modern game that many children play is mobile-based. The rapid development of smartphones makes mobile gaming technology develop too. Traditional games have been developed in Indonesia. The purpose of elevating traditional games into mobile game applications is so that children now know the traditional game of the nation's culture even though it is now in the digital age. One of the traditional games raised in mobile games is Stilts. This study discusses the evaluation of traditional Egrang game application users. Respondents from this study are children aged 7 to 13 years. The tool used for processing and analyzing respondent data is the User Experience Questionnaire (UEQ). UEQ will measure 6 User Experience scales, including Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. The results of this study obtained Perspicuity and Novelty Scale data in the Above Average range. Attractiveness, Efficiency, and Dependability scales are in the Below Average range.In contrast, the Stimulation scale is in the Bad range. From these data, it can be seen that the stimulation scale most needs to get more attention in the evaluation or development of subsequent applications. In contrast, the one who gets the highest score is Perspicuity. So it can be concluded that respondents said the game was easy to understand when playing for the first time but was still not motivated to play it repeatedly and tended to get bored quickly.
Enhancing Mixup-Based Semi-Supervised Learningwith Explicit Lipschitz Regularization

The success of deep learning relies on the availability of large-scale annotated data sets, the acquisition of which can be costly requiring expert domain knowledge. Semi-supervised learning (SSL) mitigates this challenge by exploiting the behavior of the neural function on large unlabeled data. The smoothness of the neural function is a commonly used assumption exploited in SSL. A successful example is the adoption of mixup strategy in SSL that enforces the global smoothness of the neural function by encouraging it to behave linearly when interpolating between training examples. Despite its empirical success, however, the theoretical underpinning of how mixup regularizes the neural function has not been fully understood. In this paper, we offer a theoretically substantiated proposition that mixup improves the smoothness of the neural function by bounding the Lipschitz constant of the gradient function of the neural networks. We then propose that this can be strengthened by simultaneously constraining the Lipschitz constant of the neural function itself through adversarial Lipschitz regularization, encouraging the neural function to behave linearly while also constraining the slope of this linear function. On three benchmark data sets and one real-world biomedical data set, we demonstrate that this combined regularization results in improved generalization performance of SSL when learning from a small amount of labeled data. Our code is available at https://github.com/Prasanna1991/Mixup-LR.
Research on emitter individual identification technology based on Automatic Dependent Surveillance–Broadcast signal

Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.
AI-based Rock-Paper-Scissors plug and play system

In this paper, we present a plug and play Rock-Paper-Scissors game set that utilizes CNN-based static hand gesture recognition and a Markov chain-based bot. Each game round, the camera output is fed to the neural network and the hand gesture result is compared to the current Markov chain state. We utilize a CNN trained with a dataset containing rock-paperscissors hand gestures and unwanted inputs, and a multi-state Markov chain. Automated tests confirm the bot prediction capabilities and practical tests verify the precision of static hand gesture recognition. Tests against a verification dataset reveal a recognition accuracy of 97.4%.
A Comparative Study of The Students’ Critical Reading Competency on High Order Thinking Items in A Junior High School

This study aims to analyze and describe the students’ reading competencies and comparing simultaneously the students reading competencies on high order thinking items across classes, text genres, and reading indicators. The design implemented was descriptive in order to answer the first research question and comparative research in order to answer the second research questions. The instrument used for data collection was reading competency test. The data were then analyzed descriptively and inferentially by implementing a parametric statistic, namely: central tendency measures; measures of dispersion; and simultaneous comparison. The present study was applied one-way ANOVA as the inferential analysis. The result showed, the significant value of one-way ANOVA on the students Reading Competency was 0.75. The significant figure was higher than 0.05. As a result, the students reading competency in high order thinking items was not significant. Therefore, the null hypothesis was accepted. It can be drawn a conclusion that the students reading competencies on high-order-thinking items across classes, text genres, and reading indicators are in below average.
Bibliometric Analysis Related to Mathematical Research through Database Dimensions

Abstract A bibliometric study of the publication of research results and mathematical thinking at this time is needed to see the development of progress and trends that are happening. The purpose of this study is to conduct bibliometric research in online scientific publications. The research method uses quantitative studies from electronic information sources through the Dimension database. Data obtained from 2015-2019 from research results published in leading international journals. Data is processed using the Vosviewer application to create predetermined formulas and produce visualizations. The results of this study have described the number of publications that have been made through international journals, the distribution of titles in international journals, the types of publications produced, and the level of productivity of authors from the name of the institution. The results of the research are then carried out an analysis related to the trends that occur related to education policy in Indonesia in the obligation and culture of writing in scientific publications.