Reviewerly/exHarmony huggingface dataset:
Variable-Length Genetic Algorithm and Multiple Entropic Functions-Based Satellite Image Segmentation

The genetic algorithm (GA) was invented to mimic the realistic biological evaluation for optimization. But, in literature, we can observe that the genetic operators are purely based on either mathematical or random operations. Our proposed genetic operators are designed to mimic realistic behavior. For this purpose, we have introduced the concept of gene casements in crossover and Levy flight function in mutation. We have also incorporated solution combination operation to give it an elitist nature. Otsu’s, Tsallis, and Reyni’s entropic functions were validated as efficient thresholding techniques. Researchers weren’t able to conclude which method is superior among them. Their performance is highly dependent on the dataset. So, we have considered all of these methods to frame our methodology. We have considered the dataset from the WorldView-2 satellite sensor for the experimental study. The performance of the proposed method is validated with three different state-of-the-art methods.
Explaining Bias in Deep Face Recognition via Image Characteristics

In this paper, we propose a novel explanatory framework aimed to provide a better understanding of how face recognition models perform as the underlying data characteristics (protected attributes: gender, ethnicity, age; non-protected attributes: facial hair, makeup, accessories, face orientation and occlusion, image distortion, emotions) on which they are tested change. With our framework, we evaluate ten state-of-the-art face recognition models, comparing their fairness in terms of security and usability on two data sets, involving six groups based on gender and ethnicity. We then analyze the impact of image characteristics on models performance. Our results show that trends appearing in a single-attribute analysis disappear or reverse when multi-attribute groups are considered, and that performance disparities are also related to non-protected attributes. Source code: https://cutt.ly/2XwRLiA.
Shaking Table Experiment of Human Comfort under Blasting Vibration and Engineering Application

Abstract To reasonably assess the impact of blasting disturbance on personnel in the adjacent area and to protect the safety and physical and mental health of personnel, it is the key to construct a human comfort evaluation system under the impact of blasting vibration. In view of the uncertainty of human subjective feelings in the existing evaluation system of human comfort under the influence of blasting vibration, the human vibration response experiment is carried out by simulating blasting vibration through a shaking table, and the quantitative relationship between blasting vibration parameters, ECG indicators, and human subjective feelings is analyzed by combining with human ECG indicators to construct the evaluation system of human comfort under the influence of blasting vibration. At the same time, the evaluation system is applied to the actual project with Wuhan Metro Line 5 as the engineering background by combining numerical calculation methods. The results show that: the vertical direction is the main vibration direction affecting human comfort, and the impact of vibration on the human body is related to the main vibration sensation parts of the human body, and the degree of impact gradually increases when the main vibration sensation parts moves up from the feet to the head; There is a quantitative relationship between the decreased ratio of human ECG index RMSSD and blasting vibration parameters as well as human subjective feelings, when the decrease ratio of human RMSSD is greater than 0.28, the extent to which the human feels the degree of annoyance cannot be ignored, and the vibration velocity and frequency (0.7cm/s, 80Hz) in the vertical direction of the ground surface at this time can be used as the control threshold of blasting works in the adjacent densely populated areas; When the distance from the blasting center is less than 62.57m, the physical and mental health of tourists in the Yellow Crane Tower scenic spot will be greatly affected, it is recommended that the maximum explosive charge in the single delay of the subway tunnel blasting excavation does not exceed 0.32kg.
Data Hiding—Steganography and Steganalysis

Confidentiality is an important aspect of information security. Confidentiality of data can be attained by using different data-hiding techniques. Steganography is one of the many data-hiding techniques that are used to maintain confidentiality of data by concealing information. The techniques involve hiding a secret message in the form of audio, text, or images, where only the receiver knows that there is hidden information in the received data. Cryptography and steganography are examples of such data-hiding techniques; in cryptography, the data are encrypted and any intruder who identifies the pattern can decrypt the data. However, in steganography, only the receiver will be able to identify the hidden message as the information is embedded either in text, image, or audio. With a prior discussion between the sender and the receiver, the stenography process cannot be identified by the intruder. To identify the embedded message in the image, text, or audio, we use techniques to reveal the hidden data and this process is called steganalysis. In this chapter, we will discuss the different steganographic techniques and the methods to perform steganalysis.
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information

In this paper, we consider to improve the stochastic variance reduce gradient (SVRG) method via incorporating the curvature information of the objective function. We propose to reduce the variance of stochastic gradients using the computationally efficient Barzilai-Borwein (BB) method by incorporating it into the SVRG. We also incorporate a BB-step size as its variant. We prove its linear convergence theorem that works not only for the proposed method but also for the other existing variants of SVRG with second-order information. We conduct the numerical experiments on the benchmark datasets and show that the proposed method with constant step size performs better than the existing variance reduced methods for some test problems.
Rotated Digit Recognition by Variational Autoencoders with Fixed Output Distributions

Abstract This paper demonstrates that a simple modification of the variational autoencoder (VAE) formalism enables the method to identify and classify rotated and distorted digits. In particular, the conventional objective (cost) function employed during the training process of a VAE both quantifies the agreement between the input and output data records and ensures that the latent space representation of the input data record is statistically generated with an appropriate mean and standard deviation. After training, simulated data realizations are generated by decoding appropriate latent space points. Since, however, standard VAE:s trained on randomly rotated MNIST digits cannot reliably distinguish between different digit classes since the rotated input data is effectively compared to a similarly rotated output data record. In contrast, an alternative implementation in which the objective function compares the output associated with each rotated digit to a corresponding fixed unreferenced reference digit is shown here to discriminate accurately among the rotated digits in latent space even when the dimension of the latent space is 2 or 3.
Convolutional Neural Network Based Modulation Classification over Multipath fading Channels

Abstract The authors proposed a method to classify the required modulations among the various kinds of distributed systems by using the Convolutional neural network (CNN). A supervised Machine learning (ML) algorithm, Support vector machine (SVM) is used to classify the required modulation among all the modulations due to its advantages majorly low complexity. In this paper, the CNN and SVM are explained in simple terms with relevant mathematical analysis. For better understanding of the classification of modulated techniques, some analog modulation techniques such as Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK), 8-ary phase shift keying (8-PSK), 16-ary Quadrature amplitude modulation (16-QAM), 64-ary Quadrature amplitude modulation (64-QAM), 4-ary pulse amplitude modulation (PAM4), Gaussian frequency shift keying (GFSK), Continuous phase frequency shift keying (CPFSK) and digital modulations such as Broadcast FM (B-FM), Double sideband amplitude modulation (DSB-AM), Single sideband amplitude modulation (SSB-AM) are considered. The time domain and the spectrogram representation of different modulations are presented by transmitting signals over Rayleigh and Rician channels. The impact of the noise over these signals is also studied.
Second Replication of Finn &amp;amp; Hudson Kam (2008) Experiment 3

We replicated Experiment 3 of Finn &amp;amp; Hudson Kam (2008) The curse of knowledge: First language knowledge impairs adult learners' use of novel statistics for word segmentation, Cognition, 108(2), 477-499. This is part of a larger project investigating the reproducibility and replicability of adult statistical word segmentation studies. We failed to replicate the key result: worse learning of statistically-defined words that violate phonotactic patterns of the speakers' native language.
Prompting as Probing: Using Language Models for Knowledge Base Construction

Language Models (LMs) have proven to be useful in various downstream applications, such as summarisation, translation, question answering and text classification. LMs are becoming increasingly important tools in Artificial Intelligence, because of the vast quantity of information they can store. In this work, we present ProP (Prompting as Probing), which utilizes GPT-3, a large Language Model originally proposed by OpenAI in 2020, to perform the task of Knowledge Base Construction (KBC). ProP implements a multi-step approach that combines a variety of prompting techniques to achieve this. Our results show that manual prompt curation is essential, that the LM must be encouraged to give answer sets of variable lengths, in particular including empty answer sets, that true/false questions are a useful device to increase precision on suggestions generated by the LM, that the size of the LM is a crucial factor, and that a dictionary of entity aliases improves the LM score. Our evaluation study indicates that these proposed techniques can substantially enhance the quality of the final predictions: ProP won track 2 of the LM-KBC competition, outperforming the baseline by 36.4 percentage points. Our implementation is available on https://github.com/HEmile/iswc-challenge.
A Study on the Optimum Design of Fuzzy Logic Control using Differential Evolution Algorithms for Omnidirectional Mobile Robot Navigation

Abstract Goal-oriented mobile robot navigation is significant for exploration, transport, and telerobotics ubiquitously. Among the existing mobile robot configurations, the omnidirectional platforms offer enhanced maneuverability and flexibility in navigation. Also, Fuzzy Logic (FL) is favorable regarding intelligible control rules and robustness to uncertainties among the existing control schemes. However, there exists a gap in the control of omnidirectional robots using FL, especially tackling stagnation and local-minima in goal-oriented navigation. This study presents a methodological framework, first of its kind to the best of our knowledge, combining the benefits of a behavioral FL and Differential Evolution for stagnation-free and goal-oriented omnidirectional robot navigation. Whereas FL considers a behavioral modulation between goal-seeking and obstacle avoidance, relevant classes of Differential Evolution (DE), which comprise exploration, exploitation, and self-adaptation modalities, tackle FL membership functions' search space. Through rigorous computational experiments, we demonstrate that Rank-Based Differential Evolution (RBDE)'s exploitative features show favorable performance in learning and tackling stagnation in the context of a small number of function evaluations (up to 1000) and unknown/partially known environments, both of which are relevant for quick adaptation to dynamic environments. After explaining the reason for learning efficiency and navigation performance in training-testing scenarios, we discuss the potential of rank-based learning algorithms.
Supporting Dynamic Update Scheme for Assured Deletion Based-Multi-Copy Association Tree

Abstract As one of the most important cloud computing services, cloud storage provides storage resources for resource-constrained users, which reduces their local overhead and computing cost. As an extension of cloud computing, fog computing introduces a fog layer between the cloud and users to deploy computing, storage, and other types of equipment, allowing users to operate outsourced data conveniently. Although cloud storage brings many conveniences to users, assured data deletion is still one of the crucial security challenges. In addition, since cloud servers usually store data in a decentralized manner, it is difficult for users to centrally and securely operate data blocks. This paper proposes an efficient and secure cloud data deletion scheme (SDUS-AD) that supports dynamic data updates in multi-copy scenarios. In this scheme, a new dynamic structure is designed, which improves the traditional Merkle hash tree, thereby realizing the dynamic update of outsourced data efficiently and safely. A cloud-fog-user layer structure is used to meet the needs of resource-constrained users (such as mobile users) to update data, and a secure and trusted fog cluster is constructed using the trusted cloud platform management model based on TPM alliance to ensure the confidentiality of data privacy. Security analysis shows that SDUS-AD meets real-world security requirements. Detailed performance analysis and simulation experiments show that SDUS-AD is efficient, safe, and feasible.
Online English Grammar Instruction Using Polysynchronous Approach During Pandemic Times

The purpose of this study is to report the best practices in online grammar learning through the polysynchronous learning model, as well as to explore students' perceptions of such learning model. This study is a descriptive case study covering the design of an online course carried out during the Covid-19 pandemic as the research object. The data processed were in the form of teacher reflections, documents of teaching materials and activities in the Learning Management System (LMS), and an open survey to explore students’ perceptions. The findings from this study indicate that the online grammar learning carried out had sufficient relevance to the concept of polysynchronous learning, which is considered appropriate to be implemented during and after the Covid-19 pandemic. For students' perceptions, in general, they expressed appreciation for the learning process, especially in terms of the personal touch and the teaching style carried out by the teacher, the learning activities, and the teaching materials. Furthermore, some constructive suggestions were also given by students, for example, in the area of overcoming technical problems and learning management. The practices presented in this paper are expected to be useful for other foreign language teachers and lecturers as a form of collective sharing to face the challenges of the online grammar learning process during and after the pandemic.
POLITENESS ANALYSIS IN THE USE OF MADURESE LANGUANGE TOWARDS PARENTS

Communication is a message or information which is given to communicant from communicator. It is imperative, and being fundamental, also universal among human beings to interact with others. People are as human being who lives perfectly than the other creatures, use language as a tool to communicate each other where it is ability in learning and acquiring knowledge. Indonesia is rich in regional languages and language is one of the regional identities. Each region must have its own regional language. Regional language or traditional language is a language that is used as a means of communication only in certain areas.
Audibly: Speech to American Sign Language converter

&lt;p&gt;Our proposed system, Audibly, is a desktop app which records the input using microphone. The system then converts the input audio into text and processed.After processing text, the text will be split to an individual word. Each word will be searched in the ASL dictionary and its corresponding videoclip will be merged to output video. If not found, the word will be stemmed to its root word and searched again else videoclip of each alphabet will be used. ASL dictionary contains videoclips of more than 800 words and alphabets. Output video will be formed by merging sub videos in mp4 format. This video will be displayed in output page and users can if needed. &lt;/p&gt;
Optimal strategy to certify quantum nonlocality

Certification of quantum nonlocality plays a central role in practical applications like device-independent quantum cryptography and random number generation protocols. These applications entail the challenging problem of certifying quantum nonlocality, something that is hard to achieve when the target quantum state is weakly entangled, or when the source of errors is high, e.g. when photons propagate through the atmosphere or a long optical fiber. Here, we introduce a technique to find a Bell inequality with the largest possible gap between the quantum prediction and the classical local hidden variable limit for a given set of measurement frequencies. Our method represents an efficient strategy to certify quantum nonlocal correlations from experimental data without requiring extra measurements, in the sense that there is no Bell inequality with a larger gap than the one provided. Furthermore, we also reduce the photodetector efficiency required to close the detection loophole. We illustrate our technique by improving the detection of quantum nonlocality from experimental data obtained with weakly entangled photons.
An Overview of New Education Policy 2020

Education is essential for realizing human potential, developing a just and equitable society and promoting national development. Universal access to quality education is critical to India's continued rise and global leadership in economic growth, social justice and equality, scientific advancement, national integration and cultural protection. Different countries have adopted different education systems based on traditions and cultures, and different life cycle stages in school and university education to make them effective.
Improving the quality of video processing based on the use of an artificial YOLO neural network

The paper considers the processing of video images of metal structures that potentially contain defects. Such processing is proposed to be performed with the use of convolutional neural networks like Yolo. To improve the quality of processing, options are offered in using the results of processing previous frames of a video sequence. In this case, it is supposed to make a prediction of the placement of previously selected objects on subsequent frames using pseudo-gradient procedures to estimate deformation parameters in a video sequence. For the areas of possible location of defects predicted in this way, it is proposed to perform an adaptive adjustment of the neural network hyperparameters that determine the probability of a false positive. The conclusion has been drawn about the viability and effectiveness of such approaches.
Sound Feedback for Social Distance: The Case for Public Interventions During a Pandemic

Within the field of movement sensing and sound interaction research, multi-user systems have gradually gained interest as a means to facilitate an expressive non-verbal dialogue. When tied with studies grounded in psychology and choreographic theory, we consider the qualities of interaction that foster an elevated sense of social connectedness, non-contingent to occupying one’s personal space. In reflection of the newly adopted social distancing concept, we orchestrate a technological intervention, starting with interpersonal distance and sound at the core of interaction. Materialised as a set of sensory face-masks, a novel wearable system was developed and tested in the context of a live public performance from which we obtain the user’s individual perspectives and correlate this with patterns identified in the recorded data. We identify and discuss traits of the user’s behaviour that were accredited to the system’s influence and construct 4 fundamental design considerations for physically distanced sound interaction. The study concludes with essential technical reflections, accompanied by an adaptation for a pervasive sensory intervention that’s finally deployed in an open public space.
Provision for Energy: Federated Learning in Edge Systems

Abstract An energy-efficient transmission and computation resource allocation problem for federated learning (FL) on wireless communication networks is investigated. Based on the considered model, each user exploits limited local computing resources to train a local FL model with its collected data. The local FL model is then transmitted to a base station (BS), which aggregates the local FL model and broadcasts it back to all users. Based on the learning accuracy level, computation and communication latency are determined by the exchange of learning models between users and BS. During the FL process, both the local computation energy and the transmission energy must be considered. Due to wireless users’ limited energy consumption, the communication problem is formulated as an optimization problem whose objective is to minimize the overall energy consumption of the system with a latency limitation. To solve this problem, we resort to an iterative algorithm with a solution of bandwidth, power, computational and other factors. Numerical results show that the proposed algorithms can reduce energy consumption compared to the conventional FL method.
PROFESSIONAL COMPETENCE OF THE FUTURE FOREIGN LANGUAGE TEACHERS AS AN INDICATOR HIS PROFESSIONALISM

This article considers the problem of the professional competence of a teacher in the context of the training of future teachers for teaching and upbringing activities.
INNOVATIVE METHODS OF VOCAL FORMATION CULTURES OF THE FUTURE MUSIC TEACHER

The article touches on the problem of professional training of music teachers, the main aspect of which is the usage of effective methods for formation of vocal culture offuture music teachers. It's suggested some ways and methods of further perfection in the vocal performing skill of students during the process of specially organized work.
DEVELOPMENT OF SPIRITUAL PERSONALITY IN CONDITIONS HUMANIZATION OF EDUCATION

In the article the ways of the inner personality's development in the conditions of humanizing education, thehistorical aspect of this problem and the modern approaches to its solving are discovered. The comparative analysis of the concepts “moral upbringing” and “inner development is realized\ the interrelationship o f a personality’s inner and social coming-to-be is emphasized.
QUESTIONS OF PERFORMANCE ANALYSIS IN FORMATION OF CREATIVE PERSONALITY IN LESSONS MUSIC IN SCHOOL

In the article are considered problems of tasking the approach in conditions of realization of strategy individually focused training. The given approach promotes the formation of such qualities of the person, as possession of original receptions of interpretation of a piece of music, reflections, and receptions of universal thinking.
FORMATION OF ENVIRONMENTAL CONSCIOUSNESS FUTURE TEACHER AS A LEADING TASK OF ENVIRONMENTAL EDUCATION AND EDUCATION: MODERN APPROACHES TO THE DEFINITION OF THE CONCEPT ECOLOGICAL CULTURE

This article is devoted to the modern problems of ecological consciousness forming of teachers and the lijferent apporoaches for definition of the essence, of the notion ecological consciousness are analysed.