Reviewerly/exHarmony huggingface dataset:
Absumption and subsumption based learning classifier systems

Learning Classifier Systems (LCSs) are a group of rule-based evolutionary computation techniques, which have been frequently applied to data-mining tasks. Evidence shows that LCSs can produce models containing human-discernible patterns. But, traditional LCSs cannot efficiently discover consistent, general rules - especially in domains that have unbalanced class distribution. The reason is that traditional search methods, e.g. crossover, mutation, and roulette wheel deletion, rely on stochasticity to find and keep optimum rules. Recently, absumption has been introduced to deterministically remove over-general rules, which is complementary to subsumption that deterministically removes over-specific rules. It is hypothesized that utilizing just assumption & subsumption transforms the search process from stochastic to deterministic, which benefits LCSs in evolving interpretable models and removing the need to tune search parameters to the problem. Interrogatable artificial Boolean domains with varying numbers of attributes are considered as benchmarks. The new LCS, termed Absumption Subsumption Classifier System (ASCS), successfully produces interpretable models for all the complex domains tested, whereas the non-optimal rules in existing techniques obscure the patterns. ACSC's ability to handle complex search spaces is observed, e.g. for the 14-bits Majority-On problem the required 6435 different cooperating rules were discovered enabling correct pattern visualization.
A General Framework to Understand Vulnerabilities in Information Systems

Firms and organizations are increasingly facing security issues related to vulnerabilities in their information systems.Firms, especially small and medium-sized enterprises, usually have very limited security resources and thus have difficulty understanding vulnerabilities and fixing them accordingly.This study aims to build a general framework that can help firms understand the characteristics of vulnerabilities in information systems: for instance, what category a specific vulnerability belongs to, what potential risks it poses, and what the key clues are to addressing it.To this end, we collect data on real vulnerabilities that have emerged in firms' information systems from a popular vulnerability report platform.Features are extracted at four different levels, namely, the word, phrase, topic, and record levels.The experimental results show that the general framework helps characterize the modes and patterns of various types of vulnerabilities.This study contributes to the security literature by providing a deeper understanding of the characteristics of vulnerabilities and their related suggested solutions.Firms can apply this framework to ensure information security.
Online Replanning With Human-in-the-Loop for Non-Prehensile Manipulation in Clutter — A Trajectory Optimization Based Approach

We are interested in the problem where a number of robots, in parallel, are trying to solve reaching through clutter problems in a simulated warehouse setting. In such a setting, we investigate the performance increase that can be achieved by using a human-in-the-loop providing guidance to robot planners. These manipulation problems are challenging for autonomous planners as they have to search for a solution in a high-dimensional space. In addition, physics simulators suffer from the uncertainty problem where a valid trajectory in simulation can be invalid when executing the trajectory in the real-world. To tackle these problems, we propose an online-replanning method with a human-in-the-loop. This system enables a robot to plan and execute a trajectory autonomously, but also to seek high-level suggestions from a human operator if required at any point during execution. This method aims to minimize the human effort required, thereby increasing the number of robots that can be guided in parallel by a single human operator. We performed experiments in simulation and on a real robot, using an experienced and a novice operator. Our results show a significant increase in performance when using our approach in a simulated warehouse scenario and six robots.
Virtual Reality Image Technology Assists Training Optimization of Motion Micro-Time Scale

Virtual reality image local features are highly repetitive and are not easily affected by target occlusion, so research on virtual reality image local feature extraction techniques has received increasing attention. For the purpose of global scale selection, this article considers the virtual reality image in the global scope from the theoretical perspectives of the continuity of virtual reality images, the repeatability of virtual reality image features, the scale of scene virtual reality images in the global scope, and the visual saliency. This paper gives a general framework of multi-scale geometric analysis and particle swarm optimization virtual reality image matching algorithm. For the problem of virtual reality image matching, the original particle swarm optimization algorithm is improved. A more reasonable inertia weight change formula, a method for selecting the optimal solution when two objectives are optimized at the same time, and a method for selecting a reasonable input are designed. In this paper, performance evaluation experiments are performed on several feature description algorithms. The experimental results show that the descriptors constructed by these feature description algorithms are significantly more robust than SIFT (Scale Invariant Feature Transform) descriptors. The improved multi-scale geometric analysis and particle swarm optimization are robust due to the introduction of multiple support domains. It has the best discriminative ability, but at the same time, the introduction of multiple support domains leads to the worst real-time algorithm; DGOH (Dual Gradient Orientation Histogram) algorithm is more robust than FRDOH (Fast Repre-sentation Based on a Double Orientation Histogram) and SIFT algorithm, and DGOH algorithm has the best real-time performance.
A data augmentation technique based on text for Vietnamese sentiment analysis

Online opinions are used as a data source that contains relevant information about customer sentiments toward a product or service. This can be used to make a specific decision for customers and management. In order to achieve the good models for sentiment analysis, we require a large human-labeled data which is costly to obtain. This paper proposes an approach based on text data augmentation based on product reviews in Vietnamese language. Several basic techniques are applied to generate more comments by random insertions, substitutions. The experimental results demonstrate the efficiency of the proposed approach.
Using Photoplethysmography for Simple Hand Gesture Recognition

A new wearable band is developed which uses three Photoplethysmography (PPG) sensors for the purpose of hand gesture recognition (HGR). These sensors are typically used for heart rate estimation and detection of cardiovascular diseases. Heart rate estimates obtained from these sensors are disregarded when the arm is in motion on account of artifacts. This research suggests and demonstrates that these artifacts are repeatable in nature based on the gestures performed. A comparative study is made between the developed band and the Myo Armband which uses surface-Electromyography (s-EMG) for gesture recognition. Based on the results of this paper which employs supervised machine learning techniques, it can be seen that PPG can be utilized as a viable alternative modality for gesture recognition applications.
'Pataphysical Software

These days, whether the problem is climate change or boredom, there is an app for that. The rhetoric of problem and solution, accelerated by commercial needs and salvific tech gurus, implies that software can save the world. This paper wants to start a movement/rebellion against the ubiquitous equation of P(problem) + S(software) = S(solution) as a rational approach to the ailments of this world. We question the technological effort to "playfully" afford order and control to humans through the provision of computational rules. Instead, we propose an alternative approach: designing 'pataphysical software to address familiar but ultimately imaginary problems. Defined by poet Alfred Jarry, 'pataphysics is the science of imaginary problems. Adopting the methods of 'pataphysics, we have developed mobile applications that explore invented problems and provide no solutions for them. We demonstrate how such an approach allows us to ask design questions through an aesthetic 'pataphysical practice of software development.
Communicate, Critique and Co-create (CCC) Future Technologies through Design Fictions in VR Environment

Design fiction enables HCI and design researchers to co-create, explore and speculate the future. It is growing in popularity given the growing complexities of emerging HCI systems and innovations. Diegetic props (like sound, videos, images) are sometimes used in design fiction to blur the lines between imagination and reality. These props enable the designers to be empathetic, feel present in the fiction as they investigate the complexity of technologies explored within the fiction, critique these technologies and think about their consequences. With a higher level of immersion and sense of embodiment, Virtual Reality (VR) can be a powerful tool for mediating and creating design fiction. However, there are few examples of VR as platform for design fiction. This workshop aims to investigate new opportunities for communicating, critiquing and co-creating design fiction narratives in immersive VR environments.
Analysis of the internet use and students' Web 2.0 digital competence in a Russian university

This study explores types of internet activities as a factor related to the students' perceived technological self-efficacy in Web 2.0 digital competence in the Russian higher education context. The "Web 2.0 basic digital competence of university students" (COBADI) questionnaire was translated and adapted from Spanish to collect the answers from 205 students. Students' profiles were identified through cluster analysis. Multiple regression analysis was used to examine the relationship between each digital competence, the type of internet use and the place of connection controlling for the participants' gender and age. The results of the analysis revealed a positive relationship between the students' use of the internet for socialising and their perceived Web 2.0 communication digital competence. There were no significant differences in gender in the students' perceived self-efficacy of their Web 2.0 digital competence.
Improving Object Tracking by Added Noise and Channel Attention

CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.
Classification of Red Blood Cell Morphology Using Image Processing and Support Vector Machine

Blood serves as an indicator of health, a complete blood count (CBC) provides the clinician a view of the blood components. Diagnosis of the shape of RBC contributes information about relevant pathological diseases and condition. Red blood cells vary from the size of the cell, variations of shape, and presence of central pallor. With these observations several diseases and conditions showed correlations with the characteristic morphologic variations of red blood cells. The conventional use of peripheral blood smears remains laborious, time-consuming procedure and the lack of expertise of the microscopist is a factor to inaccurate results. The advancement of technology provided the medical field the benefits of an automated recognition and numerous studies are applying different methods in classifying RBCs. Every study differs, in what algorithm to use and what RBC is to be identified. Most of all studies were able to provide a satisfiable output and so continuous studies and development are being applied. This paper aims to develop a system that will correlate associated anemia conditions once the red blood cell was identified having an abnormality with its variations in shape or size. The proposed system was able to develop a reliable system to identify 7 different type of red blood cells normal, echinocytes, elliptocytes, dacrocytes, spherocytes, target cells, stomatocytes, and an unknown each cell achieved an accuracy of 98.33%,100%, 98.33%, 97.5%, 100%, 100%, 99.17%, and 95% respectively.
Late orogenic gold mineralization in the western domain of the Karagwe-Ankole Belt (Central Africa): Auriferous quartz veins from the Byumba deposit (Rwanda)

Gold mineralization in the KAB (Karagwe-Ankole Belt) of Central Africa dominantly occurs as quartz lode and ferruginous breccia systems. Primary gold mineralization in the KAB is possibly linked to a deformational event in the early-Neoproterozoic which is accompanied by shear movements and post compressional magmatism. The Byumba deposit is chosen due to its position in the KAB Mesoproterozoic metasedimentary stratigraphy (Akanyaru Supergroup), interesting structural trend (NW trending shear zone) and its closeness to magmatic intrusions. Explorative drillings from the Byumba deposit in Rwanda were logged and sampled for further petrographic investigation, in situ Rb-Sr geochronology (Laser Ablation Inductively-Coupled-Plasma Triple Quadrupole Mass Spectrometry) and Micro X-ray fluorescence (µXRF) major and trace element mapping. Distinct phases of folding and shear deformation are present at Byumba and three main phases of quartz veining are identified (pre-, syn-, post-folding). The first quartz vein generation is layer-parallel and folded along the bedding, indicative of a pre-folding origin (V1 generation). A second generation of quartz veins (V2a) is concentrated in fold hinges and related to the primary folding of the layers. Cleavage parallel and often boudinaged V2b quartz veins are emplaced during late-stage folding. Post-folding (V3) massive or sigmoidal quartz veins, crosscut the folds and cleavage. Element mapping of the gold by using µXRF shows that Chlorite-rich quartz veins (V3) host the primary gold mineralization in the form of sub-micron gold and of small disseminated blebs. Cores additionally recorded a supergene enrichment phase of gold at reduction zone boundaries. In situ Rb-Sr geochronology on white mica of the crosscutting post-folding quartz veins (V3) point towards an Early-Neoproterozoic age of ∼900 Ma. The gold mineralization at Byumba is interpreted to be related to the regional early-Neoproterozoic compressional deformation event based on 1) the post-folding timing of the auriferous veins, 2) the observation of shear-related textures both in the host-rock and the veins, and 3) the location of the Byumba deposit within a regional shear structure. This association with compressional shearing points towards an orogenic origin for gold mineralization. However, a magmatic-hydrothermal influence from contemporaneous granitic magmatism cannot be excluded.
Blockchain for Internet of Things: Architecture, Consensus Advancements, Challenges and Application Areas

The main purpose behind Internet of Things (IoT) is to extract extensive economic as well as other benefits and to alter day to day life of human being. but along with so many benefits IoT has a major disadvantage that it is considered to be an unreliable platform. However, in recent times block has received a enormous attention. One of the key features of blockchain is that it is a distributed and tamper- resistant ledger which holds a congruous record of data at distinct levels as a result of which it helps in overcoming the security issues in IoT networks. Applications based on blockchain are emerging, covering a number of platforms. However, blockchain is still facing a number of obstructions in its way which need to be solved. In this paper a review based on blockchain technology has been presented focusing on the characteristics, architecture and application areas of blockchain technology. The paper also highlights the advancements in various consensus algorithms and the challenges faced in implementing blockchain in the IoT domain. Finally the paper addresses several open research directions in the related field.
Autoscaled RabbitMQ Kubernetes Cluster on single-board computers

Lately, Kubernetes has begun to become an essential and ubiquitous component of cloud infrastructure. This is due to the benefits that Kubernetes brings on the market, such as an application-focused interface, plug-and-play processes of horizontal scaling and self-healing mechanisms. In this paper, we present the design and implementation of a model-based self-scaling solution for the RabbitMQ Kubernetes Cluster. We first develop the Ansible formulas for the automatic provision of the infrastructure of the entire cluster. Then, we use these models to dynamically determine the resources needed to complete RabbitMQ work, and we automatically perform a horizontal bridge self-scaling to successfully complete these jobs. Our experimental results are on a single RabbitMQ Kubernetes cluster that is ready to be introduced in the IoT project.
Canonical forms of two-qubit states under local operations

Canonical forms of two-qubits under the action of stochastic local operations and classical communications (SLOCC) offer great insight for understanding nonlocality and entanglement shared by them. They also enable geometric picture of two-qubit states within the Bloch ball. It has been shown [Phys. Rev. A 64, 010101(R) (2001)] that an arbitrary two-qubit state gets transformed under SLOCC into one of the two different canonical forms. One of these happens to be the Bell diagonal form of two-qubit states and the other a nondiagonal canonical form is obtained for a family of rank deficient two-qubit states. The method employed by Verstraete et al. [Phys. Rev. A 64, 010101(R) (2001)] required highly nontrivial results on matrix decompositions in $n$-dimensional spaces with an indefinite metric. Here we employ an entirely different approach---inspired by the methods developed by Rao et al. [J. Mod. Opt. 45, 955 (1998)] in classical polarization optics---which leads naturally towards the identification of two inequivalent SLOCC invariant canonical forms for two-qubit states. In addition, our approach results in a simple geometric visualization of two-qubit states in terms of their SLOCC canonical forms.
Fighting Cyberbullying: An Analysis of Algorithms Used to Detect Harassing Text Found on YouTube

Cyberbullying is a form of harassment that occurs through online communication with the intention of causing emotional distress to the intended target(s). Given the increase in cyberbullying, our goal is to develop a machine learning classification schema to minimize incidents specifically involving text extracted from image memes. To provide a current corpus for classification of the text that can be found in image memes, we collected a corpus containing approximately 19,000 text comments extracted from YouTube. We report on the efficacy of three machine learning classifiers, naive Bayes, Support Vector Machine, and a convolutional neural network applied to a YouTube dataset, and compare the results to an existing Formspring dataset. Additionally, we investigate algorithms for detecting cyberbullying in topic-based subgroups within the YouTube corpus.
Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting

In this paper, quantized residual preference is proposed to represent the hypotheses and the points for model selection and inlier segmentation in multi-structure geometric model fitting. First, a quantized residual preference is proposed to represent the hypotheses. Through a weighted similarity measurement and linkage clustering, similar hypotheses are put into one cluster, and hypotheses with good quality are selected from the clusters as the model selection results. After this, the quantized residual preference is also used to present the data points, and through the linkage clustering, the inliers belonging to the same model can be separated from the outliers. To exclude outliers as many as possible, an iterative sampling and clustering process is performed within the clustering process until the clusters are stable. The experiments undertake indicate that the proposed method performs even better on real data than the some state-of-the-art methods.
Application of Naïve Bayes classifiers for refactoring Prediction at the method level

Software refactoring is a technique of redesigning the existing code without changing its functionality in order to improve on code readability, code adherence, maintainability and portability. Recent years have witnessed the advancement of research in the field of improvement in code quality. The challenges involved in the field has attracted many software practitioners to identify methods or classes that need refactoring. We propose a model to predict refactoring candidates by Naïve Bayes classifiers (Gaussian, Multinomial and Bernoulli (GNB, MNB, BNB)) at method level refactoring in terms of AUC and Accuracy. Method level refactoring is carried out on data set from the Tera-Promise repository and then validated. Min-max normalization and Imbalancing techniques are then applied. Then using the Wilcoxon rank test, 8 sets of significant features are drawn out of 103 sets of input features.The experimental results on the performance of 3 Naïve Bayes classifiers shows that the Bernoulli Naïve Bayes classifier gives more accuracy as compared to the other two classifiers. Statistical tests applied on all features (AF) and significant features (SF), shows that significant features gives more accurate prediction than all features.