Reviewerly/exHarmony huggingface dataset:
A Percentil Bat Algorithm an Application to the Set Covering Problem

The study and understanding of algorithms that solve combinatorial problems based on swarm intelligence continuous metaheuristics, is an area of interest at the level of basic and applied science. This is due to the fact that many of the problems addressed at industrial level are of a combinatorial type and a subset no less than these are of the NP-hard type. In this article, a mechanism of binarization of continuous metaheuristics that uses the concept of the percentile is proposed. This percentile concept is applied to the An Lion optimization algorithm, solving the set covering problem (SCP). Experiments were designed to demonstrate the importance of the percentile concept in the binarization process. Subsequently, the efficiency of the algorithm is verified through reference instances. The results indicate that the binary percentile bat Algorithm (BPBA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.
Micro-Expression Recognition Using Histogram of Image Gradient Orientation on Diagonal Planes

Micro-expression conveys a person's state-of-mind; therefore, it can be helpful in many cases. However, its micro-momentary and subtle characteristics make its recognition hard. This challenging task is too arduous in the conditions, where illumination changes. Thus, it is better to use the methods, which are insensitive to shadows and illumination change. Hence, in this paper, we propose the histogram of image gradient orientation on the diagonal planes for the feature extraction. Then, the obtained histogram is fed to the linear support vector machine for the micro-expression recognition. According to the results, the micro-expression recognition rate has been improved using our proposed method in comparison with the state-of-the-art methods.
A Time-Segmented Consortium Blockchain for Robotic Event Registration

A blockchain, during its lifetime, records large amounts of data. In a robotics environment, the old information is useful for human evaluation, or to perform analysis, but it is not useful for robots that require only current information to continue their work. This causes a storage problem in Blockchain nodes as in the case of nodes attached to robots that are usually built around embedded solutions. This paper presents a time-segmentation solution for devices with limited storage capacity, integrated into a particular robot-directed Blockchain called RobotChain. The experiments conducted show that the goal of restricting each node's capacity is reached without compromising all the benefits that arise from the use of Blockchains in these contexts and it allows for cheap nodes to use this Blockchain, reducing storage costs and allowing faster deployment of new nodes.
Conservation Through Conversation - A Provocation

The world is in the midst of an ecological crisis. Biodiversity is plummeting, the ice caps are melting, rainforest wildlife is being slaughtered wholesale for palm oil plantations, and we're consuming far too much of the planet's resources. The volume of carbon dioxide being pumped into the atmosphere, warming our planet, and destroying the environment, is unsustainable. CUI research won't matter if humanity itself can't provide its side of the conversation. This is everyone's problem, and everyone can be part of the solution, whether it's eating less meat or not binning our recyclables. Can we, as CUI researchers, do anything more specific? I think we can, and should, be making the conversation about conservation.
Collection and analysis of digital footprints in LMS

This article discusses the processes of collecting and analyzing digital footprints in LMS (Learning Management Systems). The concept of a digital footprint, the history of its appearance and examples are given. We consider the ethics of collecting these traces and the possibility of its use. A description of the tasks that need to be solved when collecting digital footprints is given, and a specific implementation in LMS "Vector" is described. This is followed by specific examples, analysis results and conclusions that have been made. At the end of the article, the main difficulties and possible ways of development and further application of this technology are described.
A One-Dimensional Probabilistic Convolutional Neural Network for Prediction of Breast Cancer Survivability

Abstract Today, machine learning plays a major role in different branches of the healthcare industry, from prognosis and diagnosis to drug development providing a significant perspective on the medical landscape for disease prevention or treatment and the improvement of human life. Recently, the use of deep neural networks in different machine learning applications has shown a great contribution to the improvement of the accuracy of predictions. In this paper, a novel application of convolutional neural networks on medical prognosis is presented. The proposed method employs a one-dimensional convolutional neural network (1D-CNN) to predict the survivability of breast cancer patients. After further examining the network architecture, a number of 8, 14 and 24 convolutional filters were considered within three layers, respectively, followed by a max-pooling layer after the second and third layers. In addition, regarding the probabilistic nature of the survivability prediction problem, an extra layer was added to the network in order to calculate the probability of the patient survivability. To train the developed 1D-CNN machine, the SEER database as the most reliable repository of cancer survivability was used to retrieve the required training set. After a pre-processing to remove unusable records, a set of 50 000 breast cancer cases including 35 features was prepared for training the machine. Based on the results obtained in this study, the developed machine could reach an accuracy of 85.84%. This accuracy is the highest level of accuracy compared to the previous prediction methods. Furthermore, the mean squared error of the calculated probability was 0.112, which is an acceptable value of error for a probability calculation machine. The output of the developed machine can be used reliably by physicians to make decision about the most appropriate treatment strategy.
Superpixel Combining Region Merging for Pancreas Segmentation

Pancreas segmentation is an essential predetermination for effective treatment of pancreac organs. To boost the capability of pancreas segmentation, this paper proposes a pancreas segmentation method combining superpixel and region merging. Firstly, a medical superpixel segmentation method is proposed to preprocess the CT image to ameliorate the verge features of the pancreas in the images. Then, this paper uses the New Maximal Similarity-Based Region Merging (NMSRM) way to obtain the pancreas segmentation results on the preprocessed images. We evaluated it on the NIH dataset and compared it with the latest methods, where the Dice-Sørensen coefficient (DSC) of 94%.
RedBird: Rendering Entropy Data and ST-Based Information into a Rich Discourse on Translation

This study investigates the relationship between machine translation (MT) and human translation (HT) through the lens of word translation entropy, also known as HTra (i.e., a metric that measures how many different translations a given source text word has). We aligned different translations from multiple MT systems (three different target languages: Japanese, Arabic, and Spanish) with the same English source texts (STs) to calculate HTra for each language, and we then compared these values to additional HT data sets of the same STs and languages. We found that MT HTra correlates strongly with HT HTra within and across the languages. We also annotated the ST in terms of word class, figurative expressions, voice, and anaphora in order to examine the relationships these ST features have with HTra. For this same purpose, we normalized all HTra values (nHTra) in order to compare HTra values across all six data sets. We found that these source text features are, in general, associated with HTra in the same manner regardless of target language or the distinction between MT and HT.
Image-based many-language programming language identification

Programming language identification (PLI) is a common need in automatic program comprehension as well as a prerequisite for deeper forms of code understanding. Image-based approaches to PLI have recently emerged and are appealing due to their applicability to code screenshots and programming video tutorials. However, they remain limited to the recognition of a small amount of programming languages (up to 10 languages in the literature). We show that it is possible to perform image-based PLI on a large number of programming languages (up to 149 in our experiments) with high (92%) precision and recall, using convolutional neural networks (CNNs) and transfer learning, starting from readily-available pretrained CNNs. Results were obtained on a large real-world dataset of 300,000 code snippets extracted from popular GitHub repositories. By scrambling specific character classes and comparing identification performances we also show that the characters that contribute the most to the visual recognizability of programming languages are symbols (e.g., punctuation, mathematical operators and parentheses), followed by alphabetic characters, with digits and indentation having a negligible impact.
Progressive Knowledge-Embedded Unified Perceptual Parsing for Scene Understanding

Human can naturally understand scenes in depth with the help of various knowledge accumulated and by a comprehensive visual concept organization including category labels and different-level attributes. This inspires us to unify professional knowledge at different levels with deep neural network architectures progressively for scene understanding. Different from the general embedding approaches, we construct different knowledge graphs for different levels of vision tasks by organizing the rich visual concepts accordingly. We employ a gated graph neural network and relational graph convolutional networks to propagate node messages for different levels of tasks and generate progressively different levels of knowledge representation through the graph. Compared with existing methods, our framework has a main appealing property leading to a novel progressive knowledge-embedded representation learning framework that incorporates different level knowledge graphs into the learning of networks at corresponding level. Extensive experiments on the widely used Broden+ dataset demonstrate the superiority of the proposed framework over other existing state-of-the-art methods.
YakReID-103: A Benchmark for Yak Re-Identification

Precision livestock management requires animal traceability and disease trajectory, for which discriminating between or re-identifying individual animals is of significant importance. Existing re-identification (re-ID) methods are mostly proposed for persons and vehicles, compared with which animals are extraordinarily more challenging to be re-identified because of subtle visual differences between individuals. In this paper, we focus on image-based re-ID of yaks (Bos grunniens), which are indispensable livestock in local animal husbandry economy in Qinghai-Tibet Plateau. We establish the first yak re-ID dataset (called YakReID-103) which contains 2, 247 images of 103 different yaks with bounding box, direction-based pose, and identity annotations. Moreover, according to the characteristics of yaks, we modifiy several person re-ID and animal re-ID methods as baselines for yak re-ID. Experimental results of the baselines on YakReID-103 demonstrate the challenges in yak re-ID. We expect that the proposed benchmark will promote the research of animal biometrics and extend the application scope of re-ID techniques.
QA4GIS: A novel approach learning to answer GIS developer questions with API documentation

Abstract Community‐based question answering websites have attracted more and more scholars and developers to discuss domain knowledge and software development. In this article, we focus on the GIS section of the Stack Exchange website and develop a novel approach, QA4GIS, a deep learning‐based system for question answering tasks with a deep neural network (DNN) model to extract the representation of the query–API document pair. We use the LambdaMART model to rerank the candidate API documents. We begin with an empirical analysis of the questions and answers, demonstrating that API documents could answer 52.93% of the questions. Then we evaluate QA4GIS by comparing it with 10 other baselines. The experiment results show that QA4GIS can improve 21.39% on the MAP score and 22.34% on the MRR score compared with the best baseline SIF.
IoT in Effective Distance Learning Process

IOT in distance learning and self education allow to increase the efficiency of studying up to 20% more. During the video lesson the IOT devices are monitoring the level of the brain activity and sending the feedback to the software program. Digital image and video stream are modulated with the signals which switch the brain into the active state. At the periods of tiredness or/and the important moments of the lecture course these signals are keeping the brain in active state and allow to increase the efficiency of memorisation and boost up the overall learning process.
Validating Formal Semantics by Property-Based Cross-Testing

To describe the behaviour of programs in a programming language we can define a formal semantics for the language, and formalise it in a proof assistant. From this semantics we can derive the behaviour of each particular program in the language. But there remains the question of validating the formal semantics: have we got the formalisation right?
OENS: An Octomap Based Exploration and Navigation System

Unknown area mapping requires the ability to explore and navigate in an unmapped environment. Current state of the art systems mainly uses frontier detection-based approaches based on occupancy grids. In this paper we present a conceptually similar octomap based exploration system that is capable of identifying unexplored regions and an occupancy grid-based motion planning system that uses occupancy grids derived from the octomap for path planning. Exploration system evaluates the octomap utilizing the state of voxels and calculates target locations for navigation using a distance cost function. We have also proposed a performance evaluation criterion for exploration systems that considers the time, power consumption, and stability of the robot. Results of experiments show that the system proposed in this paper is about 38% faster than Explore-Lite system while mapping the same area of exploration.
Scheduling of Jobs on Computational Grids by Fuzzy Particle Swarm Optimization Algorithm Using Trapezoidal and Pentagonal Fuzzy Numbers

Grid computing is a computational technique used to meet growing computational demands. Scheduling of independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. This paper introduces fuzzy particle swarm optimization (FPSO) using trapezoidal and pentagonal fuzzy numbers for job scheduling problems. It is to be noted that the Job scheduling on computational grid is an N-P Complete problem, this requires a lot of attention because of its practical importance and complexity. The position and velocity of particles in conventional particle swarm optimization (PSO) is extended from real vectors to fuzzy matrices where trapezoidal and pentagonal fuzzy numbers play a key role. We evaluated the performances of FPSO with trapezoidal fuzzy numbers and FPSO with pentagonal fuzzy numbers. Results are compared for scheduling.
A Deeper Look at the Energy Consumption of Lightweight Block Ciphers

In the last few years, the field of lightweight cryptography has seen an influx in the number of block ciphers and hash functions being proposed. In the past there have been numerous papers that have looked at circuit level implementation of block ciphers with respect to lightweight metrics like area power and energy. In the paper by Banik et al. (SAC‘15), for example, by studying the energy consumption model of a CMOS gate, it was shown that the energy consumed per cycle during the encryption operation of an r-round unrolled architecture of any block cipher is a quadratic function in r. However, most of these explorative works were at a gate level, in which a circuit synthesizer would construct a circuit using gates from a standard cell library, and the area power and energy would be estimated by estimating the switching statistics of the nodes in the circuit. Since only a part of the EDA design flow was done, it did not account for issues that might arise when the circuit is finally mapped into silicon post route. Metrics like area, power and energy would need to be re-estimated due to the effect of the parasitics introduced in the circuit by the connecting wires, nodes and interconnects. In this paper, we look to plug this very gap in literature by re-examining the designs of lightweight block ciphers with respect to their performances after completing the placement and routing process. This is a timely exercise to do since three of the block ciphers we analyze in the paper are used in around 13 of the 32 candidates in the second round of the NIST lightweight competition being conducted currently.
Data set entity recognition based on distant supervision

Purpose This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain. Design/methodology/approach Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities. Findings In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition. Originality/value This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.
Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology

Because the mine is damp and dark, it is not easy to detect the rigid tank channel’s structural failure directly. Therefore, we judged the tank channel’s surface condition by detecting the magnitude of the vibration displacement of the lifting container. In our study, we used a laser vision system to measure the structural vibration displacement. In order to accurately segment the laser spot information from the vibration image, we proposed an approach that links the relationship between the gray value of the area adjacent to the threshold point and the background’s gray value to the target in the image. We used MCE to evaluate the segmentation effect of threshold segmentation and verified the improved algorithm’s accuracy by detecting the pixel centroid of laser spots. Results show that the improved algorithm in our study has the best threshold segmentation effect, the error classification can be close to 0.0003, and the minimum deviation of the obtained vibration displacement is close to 0.1 pixels, which can realize the accurate extraction of the vibration signal of the vertical shaft tank. The novelty of this method lies in the accurate threshold segmentation and noise reduction processing of the laser speck vibration image under various interference environments in the operation of the mine hoisting system and the accurate acquisition of vibration signals. The research work provides a basis for the accurate evaluation of mechanical faults of automation technology.
Interest of pseudo-focused images for key-points detection in plenoptic imaging

Light-Field (LF) cameras allow the extraction not only of the intensity of light but also of the direction of light rays in the scene, hence it records much more information of the scene than a conventional camera. In this paper, we present a novel method to detect key-points in raw LF images by applying key-points detectors on Pseudo-Focused images (PFIs). The main advantage of this method is that we don’t need to use complex key-points detectors dedicated to light-field images. We illustrate the method in two use cases: the extraction of corners in a checkerboard and the key-points matching in two view raw light-field images. These key-points can be used for different applications e.g. calibration, depth estimation or visual odometry. Our experiments showed that our method preserves the accuracy of detection by re-projecting the pixels in the original raw images.