Reviewerly/exHarmony huggingface dataset:
Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis

In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational power while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis (PCA) subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit (CPU). This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm.
Low resource deep learning to detect waste intensity in the river flow

Waste has become a significant problem in Indonesia, especially in the capital city of Jakarta due to many disasters caused by it. The one cause of flooding is the blockage of river flow by waste. The monitoring of litter is essential to find out the waste intensity in the river. The research was formed which aims to produce an application that can detect, track, and calculate river waste using YOLO v3 algorithm. This research was done in order to simplify the process of monitoring waste in the river and can calculate waste using video. This research uses 340 images directly from photos and videos, captured by researchers-detection of waste processed frame by frame by changing video into several structures. From the acquired result from the experiment, it's proven that YOLO v3 can be used for detection and counting waste recorded on video. The result of this research is an application that can detect waste and it is able to detect said objects with 98.74% of confidence from case video.
A Vowel Harmony Testing Algorithm to Aid in Ancient Script Decipherment

Previous algorithms for deciphering lost languages assumed knowledge of cognate languages. Since that is not always possible a priori, this paper develops an algorithm that can test an important feature of the underlying language, namely the presence of vowel harmony in root words. The new algorithm detects this type of vowel harmony in the Minoan language, thereby greatly narrowing its possible set of cognate languages.
Developing Integrated Triplet Multi-Representation Virtual Laboratory in Analytic Chemical Materials

&lt;p&gt;Technological developments have triggered the emergence of various kinds of learning media innovations. Learning media is a very important part of the learning process. In this study, researchers designed a learning media in the form of a virtual laboratory on the practicum material of the basics of analytical chemistry which is integrated with multi-representation triplets to create and describe the results of learning media.&lt;/p&gt;The method used in this development was Research and Development (R&amp;amp;D) method using the 4-D (four-D) development model. Data collection used qualitative and quantitative data. The qualitative data were in the form of comments and suggestions from the validator, while the quantitative data were the validation questionnaire and legibility test. The results showed that the validation of the material obtained a percentage of 84% (very valid), media validation of 78% (valid), and the results of trials on correspondents obtained a percentage in the range 81-92% (very valid). It can be concluded that the virtual laboratory learning media on the analytical chemistry laboratory material with the integration of the developed multi-representation triplet explanations is feasible to use, especially in the practicum of the basics of analytical chemistry on the topic of group I cation analysis and argentometry. This shows that the virtual laboratory developed has the potential to support online practicum learning, and the macroscopic, submicroscopic, and symbolic depiction in the virtual laboratory can help the process of receiving information by students
A Framework Using Binary Cross Entropy - Gradient Boost Hybrid Ensemble Classifier for Imbalanced Data Classification

During the big data era, there is a continuous occurrence of developing the learning of imbalanced data gives a pathway for the research field along with data mining and machine learning concepts. In recent years, Big Data and Big Data Analytics having high eminence due to data exploration by many of the applications in real-time. Using machine learning will be a greater solution to solve the difficulties that occur when we learn the imbalanced data. Many real-world applications have to predict the solutions for highly imbalanced datasets with the imbalanced target variable. In most of the cases, the target variable assigns or having the least occurrences of the target values due to the sort of imbalances associated with things or events strongly applicable for the users who avail the solutions (for example, results of stock changes, fraud finding, network security, etc.). The expansion of the availability of data due to the rise of big data from the network systems such as security, internet transactions, finance manipulations, surveillance of CCTV or other devices makes the chance to the critical study of insufficient knowledge from the imbalance data when supporting the decision making processes. The data imbalance occurrence is a challenge to the research field. In recent trends, there is more data level and an algorithm level method is being upgraded constantly and leads to develop a new hybrid framework to solve this problem in classification. Classifying the imbalanced data is a challenging task in the field of big data analytics. This study mainly concentrates on the problem existing in most cases of real-world applications as an imbalance occurs in the data. This difficulty present due to the data distribution with skewed nature. We have analyses the data imbalance and find the solution. This paper concentrates mainly on finding a better solution to this nature of the problem to be solved with the proposed framework using a hybrid ensemble classifier based on the Binary Cross-Entropy method as loss function along with the Gradient Boost Algorithm.
A superconducting coplanar waveguide ring resonator as quantum bus for circuit quantum electrodynamics

We introduce a superconducting coplanar waveguide, in the form of a ring resonator, as a quantum bus for circuit quantum electrodynamics (circuit QED). Due to its unique symmetry, the amplitude of the electromagnetic field inside is uniform along the resonator, which in principle leads to a location independent coupling between qubits and the resonator. For an experimental demonstration, we designed and fabricated a ring resonator capacitively coupled to two tunable Xmon superconducting qubits. We showed that nearly identical coupling was achieved between individual qubits and the resonator. In addition, the resonator was also used as a high quality quantum bus for generating Bell states between the qubits with a fidelity of F=98.6%, although an interference effect must be included for understanding the effective coupling between the two qubits. Our results suggest that ring resonators can be a useful addition to the toolbox of circuit QED.
Wearable magnetic induction-based approach toward 3D motion tracking

Abstract Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy. This work presents a motion tracking system based on magnetic induction (MI) to tackle the challenges and limitations inherent in designing a wireless monitoring system. We integrated a realistic prototype of an MI sensor with machine learning techniques and investigated one-sensor and two-sensor configuration setups for motion reconstruction. This approach is successfully evaluated using measured and synthesized datasets generated by the analytical model of the MI system. The system has an average distance root-mean-squared error (RMSE) error of 3 cm compared to the ground-truth real-world measured data with Kinect.
Infrared image impulse noise suppression using tensor robust principal component analysis and truncated total variation

Infrared image denoising is an essential inverse problem that has been widely applied in many fields. However, when suppressing impulse noise, existing methods lead to blurred object details and loss of image information. Moreover, computational efficiency is another challenge for existing methods when processing infrared images with large resolution. An infrared image impulse-noise-suppression method is introduced based on tensor robust principal component analysis. Specifically, we propose a randomized tensor singular-value thresholding algorithm to solve the tensor kernel norm based on the matrix stochastic singular-value decomposition and tensor singular-value threshold. Combined with the image blocking, it can not only ensure the denoising performance but also greatly improve the algorithm’s efficiency. Finally, truncated total variation is applied to improve the smoothness of the denoised image. Experimental results indicate that the proposed algorithm outperforms the state-of-the-art methods in computational efficiency, denoising effect, and detail feature preservation.
Adaptive Technique for Feature Selection in Modified Graph Clustering-Based Ant Colony Optimization

Modified graph clustering ant colony optimization (MGCACO) algorithm is an unsupervised feature selection (UFS) algorithm used in determining a subset of effective genes from microarray data.The feature subset construction is based on the ant colony optimization (ACO) algorithm, which guides the search process from clusters.However, the MGCACO algorithm is unable to choose all significant features from the clusters to form an optimal feature subset.This paper proposes an enhanced graph clustering ACO (EGCACO) to overcome the problem of feature selection in the MGCACO algorithm.A principal point of this algorithm is utilizing an adaptive selection technique that guides ACO for subset construction from the clusters of features.The adaptive technique for ant selection is based on the state of the search space.Experimental results indicated that the proposed EGCACO achieves the highest classification accuracy than five other common UFS algorithms on four classifiers, where it obtained 87.13%, 86 .19%, 87.38 % and 90.80 % for support vector machine, k-nearest neighbor, decision tree and random forest classifiers, respectively.In particular, the proposed algorithm can select the genes of the deoxyribonucleic acid microarray with consideration of relevance and redundancy among the genes.Therefore, the proposed EGCACO can be implemented to handle the high dimension feature space, such as image processing, text classification, and microarray data processing, which is critical for good and reliable results.
Critical Review on Effect of 5G Technology on Covid-19 and Human Health Issues

This analytic research paper discusses the effects of emerging futuristic 5th Generation (5G) cellular technology over human health. The technology is growing with three major advantages of high speed, lower latency and to connect more devices at the same time. In the recent pandemic event of Covid-19, many believe that for the achievement of high speed in 5G, it will be required to have high power and this high power will decrease the immune system in short term. But actually, the reality doesn’t support the above condition. This paper defines how the speed will increase with the help of low bit error rate and also through different useful techniques thereby not just by increasing the power. In addition of high-frequency 5G radiation to an already complex mix of lower frequencies, the implementation will contribute to a negative public health outcome both from physical and mental health perspectives to a certain extent.&#x0D;
Survey on Securing IoT Data using Homomorphic Encryption Scheme

In today’s world everyone is using cloud services. Every user uploads his/her sensitive data on cloud in encrypted form. If user wants to perform any type of computation on cloud data, user has to share credentials with cloud administrator. Which puts data privacy on risk. If user does not share his/her credentials with cloud provider, user has to download all data and only then decryption process and computation can be performed. This research, focuses on ECC based homomorphic encryption scheme is good by considering communication and computational cost. Many ECC based schemes are presented to provide data privacy. Analysis of different approaches has been done by selecting different common parameters. Based on the analysis minimum computation time is 0.25 Second required for ECC based homomorphic encryption (HE).
Elastic-Degenerate String Matching via Fast Matrix Multiplication

An elastic-degenerate (ED) string is a sequence of n sets of strings of total length N which was recently proposed to model a set of similar sequences.The ED string matching (EDSM) problem is to find all occurrences of a pattern of length m in an ED text.The EDSM problem has recently received some attention in the combinatorial pattern matching community, and an \scrO (nm 1.5 \surd log m+N )-time algorithm is known [Aoyama et al., CPM 2018].The standard assumption in the prior work on this question is that N is substantially larger than both n and m, and thus we would like to have a linear dependency on the former.Under this assumption, the natural open problem is whether we can decrease the 1.5 exponent in the time complexity, similarly as in the related (but, to the best of our knowledge, not equivalent) word break problem [Backurs and Indyk, FOCS 2016].Our starting point is a conditional lower bound for the EDSM problem.We use the popular combinatorial Boolean matrix multiplication (BMM) conjecture stating that there is no truly subcubic combinatorial algorithm for BMM [Abboud and Williams, FOCS 2014].By designing an appropriate reduction, we show that a combinatorial algorithm solving the EDSM problem in \scrO (nm 1.5 - \epsilon + N ) time, for any \epsilon > 0, refutes this conjecture.Our reduction should be understood as an indication that decreasing the exponent requires fast matrix multiplication.String periodicity and fast Fourier transform are two standard tools in string algorithms.Our main technical contribution is that we successfully combine these tools with fast matrix multiplication to design a noncombinatorial \ \scrO (nm \omega - 1 + N )-time algorithm for EDSM, where \omega denotes the matrix multiplication exponent and the \\scrO (\cdot ) notation suppresses polylog factors.To the best of our knowledge, we are the first to combine these tools.In particular, using the fact that \omega < 2.373 [Alman and Williams, SODA 2021; Le Gall, ISSAC 2014; Williams, STOC 2012], we obtain an \scrO (nm 1.373 + N )-time algorithm for EDSM.An important building block in our solution that might find applications in other problems is a method of selecting a small set of length-\ell substrings of the pattern, called anchors, so that any occurrence of a string from an ED text set contains at least one but not too many (on average) such anchors inside.
Productive fitness in diversity-aware evolutionary algorithms

Abstract In evolutionary algorithms, the notion of diversity has been adopted from biology and is used to describe the distribution of a population of solution candidates. While it has been known that maintaining a reasonable amount of diversity often benefits the overall result of the evolutionary optimization process by adjusting the exploration/exploitation trade-off, little has been known about what diversity is optimal. We introduce the notion of productive fitness based on the effect that a specific solution candidate has some generations down the evolutionary path. We derive the notion of final productive fitness, which is the ideal target fitness for any evolutionary process. Although it is inefficient to compute, we show empirically that it allows for an a posteriori analysis of how well a given evolutionary optimization process hit the ideal exploration/exploitation trade-off, providing insight into why diversity-aware evolutionary optimization often performs better.
The relationships between the digital game addiction, alexithymia and metacognitive problems in adolescents

The relationships between the digital game addiction, alexithymia and metacognitive problems in adolescents Ergen yaş grubunda dijital oyun bağımlılığı, aleksitimi ve üst biliş problemleri arasındaki ilişkinin incelenmesi SUMMARY Objective: Digital game addiction has become a diffuse problem among adolescents.The aim of this study is to investigate the relationships between digital game addiction, alexithymia personality traits and metacognitive problems in adolescents.Method: 664 adolescents (51% male, n=339, 49% female, n=325) from three secondary school in Istanbul were included in this study.The mean age of male and female participants was 12.89±1.29,and 12.58±1.53respectively.Digital game addiction scale for children (DGASFC), 20 item Toronto alexithymia scale (TAS-20), and the metacognition questionnaire for children and adolescents (MCQ-C) were applied to participants.The correlation coefficients between the scales were analyzed with Spearmen's rank order correlation test.The predictability of TAS-20 and MCQ-C subscale scores, gender and age on the status of digital game addiction was tested with binary logistic regression analysis.Results: There were positive correlations between DGASFC and TAS-20 total (r=.275),factor 1 (r=.250),factor 2 (r=.159), factor 3 (r=.175)scores, and MCQ-C total (r=.180) and factor 1 (r=.109)scores.Results of the binary regression analysis revealed that TAS-20 factor 1 and factor 3, and MCQ-C factor 1 scores, and the gender predict the status of digital game addiction, significantly.Discussion: It is suggested that addressing the problems of identifying and expressing the emotions, and metacognitive problems may increase the treatment success of the adolescents presenting with digital game addiction.
Ontology Extraction and Usage in the Scholarly Knowledge Domain1

Ontologies of research areas have been proven to be useful resources for analysing and making sense of scholarly data. In this chapter, we present the Computer Science Ontology (CSO), which is the largest ontology of research areas in the field, and discuss a number of applications that build on CSO to support high-level tasks, such as topic classification, metadata extraction, and recommendation of books.
NEW GENERALIZATIONS OF HESITANT AND INTERVAL-VALUED FUZZY IDEALS OF SEMIGROUPS

In this paper, we introduce the notion of $\sup$-hesitant fuzzy ideals of semigroups, which is the general notion of hesitant fuzzy ideals and interval-valued fuzzy ideals. In addition, $\sup$-hesitant fuzzy ideals are characterized in terms of sets, fuzzy sets, hesitant fuzzy sets and interval-valued fuzzy sets. Finally, $\sup$-hesitant fuzzy translations and $\sup$-hesitant fuzzy extensions of $\sup$-hesitant fuzzy ideals of semigoups are discussed, and their relations are investigated.
Computer Science Technology Trends in The Internet of Things (IOT) with A Proposal of IOT Device for Vehicle and Human Safety

In this paper we are going to deal with the types of Internet of Things (IOT) devices used in a Smart City, role of computer science in IOT in various streams and some Case Studies from different sources. We are presenting a cost-efficient and effective way of proposing a IOT device that is used to detect Drunk and Driven cases, Over speed cases, Health issues during driving, Sleepiness during driving and also to detect Theft of the vehicles. In this paper we discuss about numerous types of Internet of Things (IOT) devices, some main causes of road accidents, how to detect them, and a proposal. We are still in the basic stage of proposal and future work to be done.
Review: Multilingual Acoustic modeling of Automatic Speech Recognition(ASR) for low resource languages

Multilingual Automatic Speech Recognition Systems survey. Multilingual speech recognition is a new era of speech and Natural language tool research to facilitate the integrates of many Monolingual system techniques that have been developed using recent methods to address the unique challenge of the multilingual field. What are techniques hidden Markov model (HMM), Gaussian Mixture Model (GMM), Deep Learning (DL) have been used so far and increasing database usage for Multilingual speech processing has become a topic of ongoing interest to a researcher for several years, and the field is now attracting increased attention due to strong drivers of change? Multilingual Speech Recognition. Speech technology is an emerging technology and monolingual automatic speech recognition has made progress in recent years, but many researchers are drawn to multilingual speech recognition. Ideally, speech from different languages should be transcribed by a multilingual speech identifier, which determines both the language used and the series of words spoken by the speaker.