Reviewerly/exHarmony huggingface dataset:
Behaviour Anomaly Detection With Similarity-Based Sampling for Imbalanced Data

Mental health is a major issue in our society, and people treat this issue as a subject that should not be spoken about. So, many such individuals utilize social media as a platform to share their thoughts and fears. This emphasizes the researchers to identify sufferers who require treatment. Many approaches have been devised to detect early markers of mental health illness, some of which include learning algorithms based on the heuristic of equally distributed balanced data. However, they yield biased results towards the majority data (i.e., normal behaviour). Thus, new perception is needed to explore the available data. This research deals with the first identification of such users from weblog data, and the similarity-based sampled data is then given to the classifier. The experiment analysis shows the effectiveness of this work and will provide the user's mental state information early to take timely necessary steps.
The Role of Participatory Ergonomics Meetings on the Development of an Electronic Health Record for Support Collaborative Care of Children and Youth

Usability studies are focused on heuristics of efficiency, effectiveness, and completeness. However, they generally ignore the content of the work performed. Seeking to overcome this gap this paper aims to present and discuss the role of participatory ergonomics meetings on the development of an EHR for collaborative care of children and youth. As we will present, the construction of a common world will help integrate users to understand different work situations based on this understanding, they could propose solutions to support different phases of care and embracing different professional worlds. This process was developed based on the knowledge about work situations and the dynamics during the collaborative meeting, but also with the support of Intermediary Objects developed by the ergonomist for this purpose.
Algorithm for State-of-Polarization Generation in Polarization-Encoding Quantum Key Distribution

In this paper we propose and validate an algorithm for the automatic and deterministic state of polarization (SOP) generation, with direct application in polarization-encoded quantum key distribution (QKD) systems. The proposed SOP generation algorithm uses an electronic polarization controller (EPC), whose active elements entail the concatenation of several fiber-squeezing-based waveplates. Initially, we carry out a characterization of the waveplates. After that, we present in detail the working principle of the proposed algorithm. Finally, the effectiveness of the algorithm is experimentally assessed through the calculation of the quantum bit error rate (QBER), where an Arduino Due is used to control the different waveplates of the EPC. The QBER was measured for periods of 4 hours and 10 hours when generating two or four SOPs, respectively, with an overall average QBER value of 2%. Results show the potential of the proposed method regarding the generation of automatic and deterministic SOPs, thus demonstrating viability for its practical implementation in polarization-encoding QKD systems.
Low-illuminance color image enhancement method based on gradient domain guided filtering

The classic Retinex algorithm assumes that the image illumination changes uniformly, and uses a Gaussian filter as the center/surround function to estimate the illumination component. However, there may be light jumps at the edges of the image, and blurring of details and edge halo will occur. when the Retinex algorithm processes color images, there are obvious color distortions. In response to the above problems, this paper improves Retinex by using gradient domain guided filtering and multi-scale detail enhancement algorithms. First, the image to be processed is converted to HSI color space, and gradient domain guided filtering is used as an estimation function to decompose the I channel image into brightness and reflection components, It is fused after brightness enhancement and denoising respectively, and then multi-scale detail enhancement of the fused image, and finally the image is converted from HSI space back to RGB space. The experimental results show that the proposed method can effectively enhance the texture details in the dark regions of the image while improving the image brightness, and outperforms other algorithms in terms of objective evaluation metrics
Manfred Stede and Jodi Schneider: Argumentation mining. Synthesis lectures on human language technologies, edited by Graeme Hirst

Argumentation Mining is a complex task, requiring to identify argumentation structures in natural language text and to build representations of the supporting and opposing arguments. Schneider and Stede provide a systematic and rigorous review of the different subfields of the research in Argumentation Mining, with a book that represents an ideal introduction for students and academics who are new to the field.
Global Two-Stream Network for Temporal Action Proposal Generation

Abstract Temporal action detection is a practical but challenging task. The current temporal action detection task has major shortcomings in the accuracy of proposal generation. The extraction of long-range temporal information features and the fusion of two-stream features for the task of temporal action proposal generation are still a challenge that needs to be improved. In this paper, we propose the Global Two-Stream Network, which innovatively introduces the Non-Local operation to extract the global background information from the features of the generated candidate proposals. And the backbone network of two-streams is used for better utilization of two-stream features to generate temporal action proposal segments with precise bounds and high confidence.
A Smart e-Learning System for Data-Driven Grammar Learning

In foreign language teaching, grammar is an important subject that has benefited relatively little from recent advances in computer-assisted language learning. To address this bottleneck, we propose in this paper a data-driven approach to grammar teaching/learning in a smart learning environment. We describe the design and implementation of a smart e-learning system and propose a new algorithm for unsupervised extraction of grammar patterns. The system enables autonomous learning by allowing users to plug in customized corpora for personalized analysis. A preliminary evaluation on system-generated grammar patterns reveals that the quality and quantity achieved are comparable to previous supervised systems that rely on precompiled pattern lists and hand-coded rules. We discuss the potential application of the system in a smart learning environment and its integration into grammar teaching curriculums to assist teachers and students both in and outside the classroom.
Construction of Digital Dynamic Sports System Platform Based on VR Technology

With the development of social science and technology nowadays, various scientific and technological products are pouring into our lives. From 2D, 3D technology to VR )virtual reality( technology, the application fields are extremely wide. This paper, starting from the concept of VR technology, introduces the principle and characteristics of the technology, then summarizes the application of "VR+sports", and on this basis, analyzes the difficulties faced by "VR+sports" in China. Overall, VR will improve itself in the process of integrating with the sports field with the process of science and technology, and enjoy wider recognition and broader market.
A Novel Feature Correlation Approach for Brand Spam Detection

In this age of the internet, no person wants to make his decision on his own. Be it for purchasing a product, watching a movie, reading a book, a person looks out for reviews. People are unaware of the fact that these reviews may not always be true. It is the age of paid reviews, where the reviews are not just written to promote one's product but also to demote a competitor's product. But the ones which are turning out to be the most critical are given on brand of a certain product. This chapter proposed a novel approach for brand spam detection using feature correlation to improve state-of-the-art approaches. Correlation-based feature engineering is considered as one of the finest methods for determining the relations among the features. Several features attached with reviews are important, keeping in focus customer and company needs in making strong decisions, user for purchasing, and company for improving sales and services. Due to severe spamming these days, it has become nearly impossible to judge whether the given review is a trusted or a fake review.
New separations results for external information

We obtain new separation results for the two-party external information complexity of Boolean functions. The external information complexity of a function f(x,y) is the minimum amount of information a two-party protocol computing f must reveal to an outside observer about the input. We prove an exponential separation between external and internal information complexity, which is the best possible; previously no separation was known. We use this result in order to then prove a near-quadratic separation between amortized zero-error communication complexity and external information complexity for total functions, disproving a conjecture of the first author. Finally, we prove a matching upper bound showing that our separation result is tight.
The Conceptual Framework for VR/AR Application in Mobile Learning Environment

The concept of Mobile learning has paved a prospective innovation in modern education. Mobile learning environment creates new opportunity of rich technology enhanced presentation and interaction for learners. Virtual and Augmented reality (VR/AR) are new concept and tools that support them portability, accessibility, flexibility and timeless in various learning activities and resources, in the combination of real-world and computer-generated platform. Integrated VR/AR in learning environment considered for educators as being an effective approach to enhancing students’ creativity, competencies in practice. Furthermore, VR/AR integration in mobile learning requires concepts of the framework which can be related traditional learning theories, its potential applications, current challenges and future directions for designing and developing educational lesson-experiences. A conceptual framework has been presented to address the design and integration emerging technology VR/AR to support and facilitate immersive learning environment.
Improving the Quality of Reconstruction of Noisy Images in the Wavelet Region

The authors suggest an approach to improving the quality of reconstruction of noisy compressed images. To reduce the amount of transmitted data, they offer to use only a subband image after a low-frequency wavelet filter with the highest concentration of signal energy. To restore a noisy image in the wavelet region, which is a random Markov field, it is proposed to use a two-dimensional nonlinear filtering algorithm based on a mathematical model of a two-dimensional Markov chain. The considered method of recovering noisy compressed images is effective at signal-to-noise ratios up to -6 dB and compression ratio up to 64 times when computing resources on the transmitting side of the channel are reduced.
Global-to-Local Dynamic Feature Aggregation for Unsupervised Person Re-Identification

Most of existing unsupervised person re-identification algorithms use single-scale structure to extract global features. However, the features extracted from different scales and spatial locations are crucial for person re-identification task mainly using body parts to identify. In this paper, we propose a novel global-local architecture called dynamic feature aggregation network (DFANet) to learn discriminative patch features at various semantic levels on unlabelled datasets and aggregate them with a dynamic aggregation mechanism. Specifically, DFANet starts with a global feature learning stage to learn global features at various semantic levels. Then a local stage formed by attention patch generation network and dynamic aggregation module is deployed to extract distinct and notable local features on unlabelled datasets. Extensive experimental results on Market1501 and DukeMTMC show that our proposed method outperforms state-of-the-art works.
Selection of Edge Detection Techniques Based on Machine Learning Approach

Machine Learning (ML) plays an important role in Image Processing where we can apply different algorithms of ML for better analysis of an image. In this communication, we present that the application of ML may help in selecting a particular edge detection technique for image analysis. We consider various components of confusion matrix and other parameters to assess different edge detection techniques.
BSO feature selection based machine learning solar radiation prediction

Abstract Benefits of solar power production is constantly increasing to the electrical power grid. Renewable energy sources are becoming alternatives for energy resource around the world. In order to reduce environmental pollution and CO2 emissions, an ideal solution is provided to overcome the energy crisis. Renewable energy forecasting improves the accuracy and significantly improved by developing more solar forecasting models using numerical weather predictions. The solar radiation value reaching the system is very important in determining the energy production potential of the solar energy system. In this, we discuss the development of the project with machine learning combined with multiple metrological models to improve the accuracy of solar radiation forecasting. To implement combination of two models, Bird Swarm Optimization algorithm for select features and for classification Convolutional Neural Network is used. CNN is a system prediction which are including numerous atmospheric based on satellite images or several other weather prediction products.
Diversity Control in the Hybridization GA-PSO with Fuzzy Adaptive Inertial Weight

In this paper, a new stochastic optimization methodology based on hybridization involving Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is proposed, with the goal of obtaining an optimization method with fast and non-premature convergence. The slow and premature convergence occurs due to inefficient control of position diversity in the search space. Diversity control can be achieved by parametric adaptation of the inertial weight. However, obtaining formulations that describe the dynamic behavior of the inertial weight in order to control diversity is not trivial. For this, in the proposed methodology, a Mamdami fuzzy system is used with the goal of adapting the inertial weight and thus to obtain a search process with a fast and non-premature convergence.
An Improved Firework Algorithm Based on Local Search Optimization

Aiming at the problems of the firework algorithm (FWA) that is easy to fall into the local optimum and the search efficiency and accuracy are low, a firework algorithm with local search optimization (LSFWA) is proposed. In this paper, we present two performance enhancing strategies. The first strategy is that the iterative local search is performed to find the current optimal location. The second strategy is the perturbation strategy, which increases the diversity of sparks to a certain extent by introducing a probability perturbation, then, search for sparks with better fitness near the local extremum to improve the ability to search for the global optimal location. The simulation experiment on 9 test functions shows that LSFWA displays better optimization speed and optimization accuracy compared to the typical algorithm FWA, FAGSO, PSO and GSA.
Design of Low-Artifact Interpolation Kernels by Means of Computer Algebra

We present a number of new piecewise-polynomial kernels for image interpolation. The kernels are constructed by optimizing a measure of interpolation quality based on the magnitude of anisotropic artifacts. The kernel design process is performed symbolically using the Mathematica computer algebra system. An experimental evaluation involving 14 image quality assessment methods demonstrates that our results compare favorably with the existing linear interpolators.
The Impact of Working Environment on Job Satisfaction of Software Professionals in Software Industry: A Case Study of Wipro Technologies, Greater Noida, India

A long working hours is a common issue in a software industry. This research study was carried out to investigate the impact of working environment on job satisfaction of software professionals in software industry. Work environment, results that are affected by job satisfaction on employeeâ€™s performance are investigated in this paper. It was indicated that how software professionals were getting experience and satisfaction from the work environment. The well-designed pretested questionnaire was used for data collection. The total sample size engaged for research study was taken to be 100. Among 100 employees, 65 were male and 35 were females. The convenience sampling method was employed for the study and analysis of data were completed by means of the statistical tools correlation and regression. The regression analysis confirmed that the work environment dimension influences only 21.8% to job satisfaction of software professionals at Wipro Technologies, Greater Noida, India. So based on the findings of this study, the study concluded that work environment has got substantial influence over job satisfaction of software professional at Wipro Technologies, Greater Noida, India.
Cheat Proof Escrow System for Blockchain

Unlike the normal transactions in blockchain, transactions made for property exchange are different because buyer or seller do not want to reveal any information to anyone and also, they are not ready to transfer either the property or money before other person transfers. This results in a dilemma who should transfer first. In order to eliminate this dilemma, escrow systems were introduced. One of the major problems with the escrow system is that the mediator can act maliciously and transfer whole funds to his/her account. This paper focuses on introducing a cheat proof escrow system. An algorithm is proposed which provides security and privacy and also guarantees unbiased service to buyer as well as seller along with a cheat proof escrow.
FEATURES OF FORMATION OF READINESS OF FUTURE TEACHERS TO USE DIGITAL EDUCATIONAL RESOURCES

The Article is devoted to the current topic of today's digital society -the problem of forming the readiness of future teachers to use digital educational resources. the authors analyze the concepts of digital educational resources, their types, ways of using them in the educational process, determine their psychological and pedagogical basis. also, the concept of "future teacher's readiness to use digital educational resources" is specified and an attempt is made to reveal the features of forming the readiness of future teachers to use digital educational resources. the Authors cover and analyze a large amount of theoretical and methodological materials related to the use of digital educational resources in the educational process and summarize their thoughts.
Detection of Asynchronous Concatenation Emergent Behaviour in Multi-Agent Systems

Multi-Agent Systems (MASs) use collective agents to attain a common goal. However, the implementation of MASs may present a potential for failure caused by the system’s unexpected behaviours, known as Emergent Behaviours (EBs) or Implied Scenarios (ISs). The early assessment of these behaviours in the development life cycle can reduce the cost, effort and maintenance time and contribute to the sustainability of the process. To detect EBs implied by the Asynchronous Concatenation (AC) of scenarios, we verify that the agents’ interactions are compliant with the system specifications. The proposed methodology allows automatic detection of EBs by the direct analysis of scenario-based specifications. Moreover, this methodology allows the anticipated correction of EBs and can potentially support the development of MASs. We studied the validity of this methodology by adopting a formerly published model and a pattern mining technique to prove the existence of the EBs detected by our approach in the runtime. The results show that our methodology outperforms those in previous related works in the detection of AC EBs.
Changes in Learning Consciousness of Engineering Students Brought by Dialogue with Business Persons

In this research we analyzed the change of students’ consciousness after participating in annual interactive meeting on enterprise technology with business persons of domestic companies held at Niigata Institute of Technology. After the meeting we asked students to think about the technology, knowledge and human resourcefulness that should be learned in college days and had them write it down in a questionnaire on Moodle. We analyzed their sentences by text mining and clarified that higher grade students tend to have more intrinsic motivation by embodying the image after work, and to notice importance of more utilization of learning mechanisms in college.
A New Altitude Estimation Algorithm for 3D Surveillance Radar

This paper proposes an adaptive altitude estimation (AE) algorithm to improve the 3D surveillance radar’s accuracy. Firstly, the altitude measurement error is derived by the radar’s measurement error matrix theoretically. Then, we design multiple models (MM) for altitude estimation, which both contain maneuvering and constant velocity (CV) models working parallel. The proposed AE algorithm adaptive chooses the optimal result by comprehensively using Kalman filter’s residual and altitude velocity with limited sliding window data. The performance of the proposed AE algorithm is evaluated via simulations of two tracking scenarios. Experiment results show that the proposed AE algorithm may greatly improve accuracy performance of altitude estimation under different scenarios.
Rain Streaks Removal Using Discrete Cosine Transform and Diffusion based Technique

Abstract Images acquired by the camera are sometimes affected by many environmental conditions such as fog, rain, snow, and clouds, etc., which influence the visible quality of the images and several computer vision applications. In this manuscript, we developed a new rain removal technique. Firstly, an exiting haze removal technique is used so that rain streaks could be visible and after that apply Discrete cosine transform (DCT) and diffusion-based technique to remove rain streaks from the rainy images. The experimental section shows the supremacy of the proposed method over the existing methods.
HTA: A Scalable High-Throughput Accelerator for Irregular HPC Workloads

We propose a new architecture called HTA for high throughput irregular HPC applications with little data reuse. HTA reduces the contention within the memory system with the help of a partitioned memory controller that is amenable for 2.5D implementation using Silicon Photonics. In terms of scalability, HTA supports 4 $$\times $$ higher number of compute units compared to the state-of-the-art GPU systems. Our simulation-based evaluation on a representative set of HPC benchmarks shows that the proposed design reduces the queuing latency by 10% to 30%, and improves the variability in memory access latency by 10% to 60%. Our results show that the HTA improves the L1 miss penalty by 2.3 $$\times $$ to 5 $$\times $$ over GPUs. When compared to a multi-GPU system with the same number of compute units, our simulation results show that the HTA can provide up to 2 $$\times $$ speedup.