Reviewerly/exHarmony huggingface dataset:
APPLICATION OF METHODS OF MATHEMATICAL ANALYSIS TO PROVE OLYMPIAD INEQUALITIES

The high requirements regarding the content of the knowledge, abilities and skills, which determines the capacity of the specialist to compete on the modern labour market are set to nowadays graduates. The tasks which require not only the knowledge of school curriculum, but also the creative application of this knowledge, in particular for inequalities solving are reviewed during Math’s course. This issue is quite relevant, because the tasks of this type are found in the tasks of school, district math Olympiads. Inequalities take a significant part of the school mathematics’ course. Applied tasks are written into Math’s language with the help of inequalities. In addition, inequalities are a tool that allows to repeat, fix, deepen the theoretical knowledge in each subject and to develop creative mathematical capacity. This topic contains many ways, methods of solving them and methods of proving them. Proof of inequalities must be given special attention because it plays an important role in shaping the logical thinking and mathematical culture. Tasks for proving inequalities make it possible to consolidate a wide range of theoretical issues studied in the school course of mathematics (theory of inequalities, properties of functions, questions of equivalent equations), they encourage the formation of critical thinking, the ability to ground actions logically. In addition, knowledge of classical inequalities and methods of proving them gives the opportunity to apply inequalities more widely in solving other problems, including applications. Since the tasks of proving inequalities are very diverse, there is no single general way to prove any inequality. Proving inequalities has a significant impact on the formation and development of creative thinking and creative personality of the student due to the availability of different ways to prove inequality. Different methods of inequalities solving are considered in this article. The peculiarities of pupils’ preparation by the method of proving contest and Olympiad inequalities, such as f12.jpg with the fixed sum of variables, are considered in this article. Let’s review the peculiarities of differential count set usage on the level of senior pupil. The ways of proving the inequalities with tangent or n-1 statement of equal meanings are analyzed, their advantages and disadvantages are reviewed. With the help of these notions it is possible to algorithmizate the process of proving several kinds of inequalities. Several ways of proving are introduced for some kinds of tasks, such methods of inequalities solving demand from pupils the basic knowledge in differential counting. Solving such problems contributes to intellectual development, the development of logical thinking and is a good material for the development of skills.
Effective Feature Extraction Method for SVM-Based Profiled Attacks

Nowadays, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine, are currently used for improving the effectiveness of the attack. One issue when using SVM-based profiled attack is extracting points of interest, or features from power traces. So far, studies in SCA domain have selected the points of interest (POIs) from the raw power trace for the classifiers. Our work proposes a novel method for finding POIs that based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). That is, VMD is used to decompose the power traces into sub-signals (modes) of different frequencies and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card run on the side channel evaluation board Sakura-G/W and the data set of DPA contest v4 to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces.
Improved Understanding of English Literacy with Small Group Discussion Model Assisted by Digital Comics

Literacy skills in reading English need to be mastered by junior high school students. However, the reality in Junior High School N 1 Wanadadi obtained many students who still can not read English. This indicates that his literacy ability is still low. Therefore, an innovation in English learning is needed by supporting technological developments. One of them is by learning Small Group Discussion assisted by digital comics. This study is a Class Action Research with two silus, and each cycle consists of three meetings. Classroom action research procedures in the form of planning, implementation, observation, and reflection. The data analysis technique used is descriptive analysis. The research subjects of students of class VIIID of SMP Negeri 1 Wanadadi semester 1 amounted to 30 students. The data collection techniques are observations, interviews, and tests. The results showed that the use of a Small Group Discussion model assisted by digital comics can improve English literacy skills. Students look actively studying in the classroom and can learn independently.
The Effectiveness of Social Media Utilization in Learning at Elementary School

The learning process of students in Indonesia has changed after the Covid-19 pandemic that originated in Wuhan, China and has almost spread to all regions of the world. The government has decided to limit social activities, call for social distancing, and avoid crowds. This is done in order to reduce the spread of the Covid-19 virus in Indonesia. The impact of such condition on education in Indonesia, learning that initially took place face-to-face now has to be done online. This paper describes the effectiveness of using social media platforms such as YouTube in online learning for elementary school students during online learning. The activity is in the form of providing Indonesian language materials through online media to elementary school students of SD 247 Sukapura, Bandung. The results of the activity show that the use of social media in learning can improve the digital literacy of elementary school students. Based on the questionnaires which distributed to the students showed an increase in understanding after being given learning materials through the Zoom application and using social media such as YouTube.
Damage Estimation of Reed Fibers During Recycling

Recycled fibers retrieved from waste paper products have been widely utilized in the pulp and paper industry. However, the properties of fibers deteriorate after successive recycling processes, and the final products made of recycled fibers may not have the required quality. Thus, it is important to evaluate the properties of recycled fibers, especially tensile strength, during recycling. This paper aims to estimate and predict the tensile strength of reed fibers using machine learning methods. Reed fibers, as well as other non-wood fibers, have not attracted enough research interests. To evaluate properties of reed fibers during recycling, handsheets were formed, defibrated, and formed again to simulate recycling processes. After each recycling, density, water retention value, and tensile strength were measured. Gaussian process regression was utilized to estimate and predict the tensile strength values through supervised learning. It was chosen because of limited training data. The models with squared exponential kernel and matern 5/2 kernel were trained, and the validation root mean square errors were 1.7188 and 2.1616, respectively. However, the performance of the models with a testing dataset was not as expected, and the root mean square error was up to 4.67. One reason might be the testing data were obtained from filtered reed pulp.
Multi scale switchable atrous convolution for target detection based on feature pyramid

Repeated observation mechanism can effectively solve the problem of low efficiency of feature extraction. By extracting features for many times to strengthen target features, this paper proposed a multi-scale switchable atrous convolution based on feature pyramid, SPC. The head of the detector adopted pyramid convolution mode, constructs 3-D convolution in the feature pyramid, and detected the same target in different pyramid levels by using the shared convolution with different stride changes, which realized the repeated observation of target features on multi-scale. The module optimized the convolution layer, extracted the features of the same image by convolution check of different sizes, and then selected and integrated the extracted results by using switch function, which effectively expanded the field of view of convolution kernel. In this paper, we choosed retinanet as the baseline network, and improved the loss function of focal loss proposed by retinanet to further solved the problem of unbalanced number of samples and sample distribution in the network model. The proposed method performed well on MS coco data set, improved the average accuracy of 9.8% on the basis of retinanet to 48.9%, and achieved FPS of 5.1 in 1333 * 800 images.
Enhanced Class-Specific Spatial Normalization for Image Generation

We propose an enhanced class-specific spatial normalization, a simple yet effective layer to generate a photorealistic image given a spatial-class map. Under the assumption that pixels belonging to the same class share the same distribution in the feature space, we intuitively split an image into classes according to the map. By learning the class-specific distributions, our generator can distinguish one class from other classes. Further, our spatial normalization combines the spatial-class map and the class-specific distributions, by which our generator can produce instances in the desired locations. We apply the proposed normalization not only in semantic image generation but also in object transfiguration. The experimental results demonstrate that the spatial-class map can be efficiently utilized with our proposed method, which results in competing performances with much fewer parameters.
Tme: Temporal and Motion Enhancement for Action Recognition

The key to video understanding is to capture temporal information. 3D CNNs can achieve good performance by directly fusing spatial and temporal features but are computationally intensive. Conventional 2D CNNs perform well in image recognition, but the inability to extract temporal features leads to poor performance in video action recognition. In this work, we aim to catch temporal relationships for video action recognition. To this end, we propose a Temporal and Motion Enhancement (TME) module consisting of two submodules: Multi-path Temporal Enhancement(MTE) module and Long Short-range Motion Enhancement(LSME) module. The MTE module consists of four paths: Residual path, Temporal Aggregation(TA) path, Channel-wise Temporal Excitation(CTE) path and Spatial-attention Temporal Excitation(STE) path. The Residual path makes part of the information in the original activation accessible. The TA path employs convolution on temporal dimension to characterize temporal representation. The CTE path rebuilds channel-wise feature relationships in terms of the temporal aspect. The STE path introduces attention mechanisms and explores the temporal modeling. We aggregate features of different paths by splitting channels, which is efficient for model inference. The LSME module calculates feature-level temporal differences between long and short range frames, which enhances the motion-related features. TME module is plug-and-play and can be inserted into 2D CNNs to form an effective TMENet with limited extra computational cost. The extensive experiments on several action recognition datasets, such as Kinetics400, Something-Something V1\&V2, HMDB51, and UCF101, demonstrate that the TMENet outperforms the state-of-the-art methods with high efficiency. Code will be released soon.
Synthesis of quantum circuits based on incompletely specified functions and if-decision diagrams

The problem of synthesis and optimisation of logical reversible and quantum circuits from functional descriptions represented as decision diagrams is considered. It is one of the key problems being solved with the aim of creating quantum computing technology and quantum computers. A new method of stepwise transformation of the initial functional specification to a quantum circuit is proposed, which provides for the following project states: reduced ordered binary decision diagram, if-decision diagram, functional if-decision diagram, reversible circuit and quantum circuit. The novelty of the method consists in extending the Shannon and Davio expansions of a Boolean function on a single variable to the expansions of the same Boolean function on another function with obtaining decomposition products that are represented by incompletely defined Boolean functions. Uncertainty in the decomposition products gives remarkable opportunities for minimising the graph representation of the specified function. Instead of two outgoing branches of the binary diagram vertex, three outgoing branches of the if-diagram vertex are generated, which increase the level of parallelism in reversible and quantum circuits. For each transformation step, appropriate mapping rules are proposed that reduce the number of lines, gates and the depth of the reversible and quantum circuit. The comparison of new results with the results given by the known method of mapping the vertices of binary decision diagram into cascades of reversible and quantum gates shows a significant improvement in the quality of quantum circuits that are synthesised by the proposed method.
Prediction Analysis of Noise Component Using Median-Based Filters Cascaded with Evolutionary Algorithms

Denoising a digital image is the most investigated field in an image processing. The process of image denoising is to mutate the noise population in digital image and retain its original feature. Mainly the medical images such as mammogram and ultrasound images will get degraded because the system of these devices is composed of low-dose X-ray radiation. These interferences caused by the screening devices gradually reduce the interpretation of the constructive features. The noises that occur in these medical images are salt and pepper, Poisson, white Gaussian and speckle noise. Even a high radiation screening devices like magnetic resonance imaging (MRI) have shortcomings with the noise occurrence from the background surrounding, human interference-like breathing and movement of the body parts. While processing the medical image degraded with the noises, it should be carefully handled, because it contains many minute features. Usually, medical images undergo the processes like denoising, segmentation, restoration, reconstruction and classification, and recognition. Whatever the process it deals with, initially it should process with denoising stage, which is mandatory for medical images. Advance medical imaging devices may not require preprocessing, because of their superior technology and features. But mammogram and ultrasound-like systems need the preprocessing step. Efficient filters are needed for eliminating these noises from the medical image. These filters should not degrade the image features and fine details. There exist many conventional denoising filters applied to various applications, but working on with medical images is infrequent at research level. The novel optimized denoising framework for high-density impulse noise medical image is studied in this work. The denoising filter used in this work is median based and consists of two different stages of processing such as detection and filtering the noise candidate pixels. The performance of the median-based filter is increased by cascading the evolutionary algorithms such as artificial bee colony (ABC) and bacterial foraging optimization (BFO) techniques. The proposed optimized filters are compared with the existing filters to observe its performance in both subjective and objective views. The introduced new optimized framework gives less complexity in terms of computation and preserves the edges and fine information of medical image.
Experimental demonstration of adaptive model selection based on reinforcement learning in photonic reservoir computing

Reservoir computing provides superior information processing ability for a time series prediction based on appropriate learning prior to task execution. The performance of reservoir computing, however, may degrade if the characteristics of the input signal drastically change over time because the internal model of reservoir computing deviates from the subjected input signal trains. We propose a method for adaptive model selection using reinforcement learning in electro-optic delay-based reservoir computing. We experimentally show that an adaptive model selection is effective when different dynamical models for the input signals change dynamically over time.
Spreading of quantum information through a disordered quantum walk

We propose a quantum probing protocol, a Mach-Zehnder-like interferomet-ric setup, employing quantum walks to explore quantum information spreading patterns, such as anomalous and classical transport, as well as Anderson localization.
Mental Health Status Detection through Handwriting Analysis

Abstract: A person's handwriting is thought to reflect his/her mental and physical coordination. Handwriting analysis is used to make inferences about a person's mental health. Emotions are evoked subconsciously and describe a person's mental condition. They inspire, organise, and direct people's perceptions, thoughts, and actions. They may be positive or negative. Depression, anxiety, and stress are all expressions of negative emotions. It is vital to recognise negative emotion in a person who may require counselling or psychiatric treatment. The suggested technique aims to create a system that employs machine learning to forecast a person's mental health without the need for human intervention. The major goal of this study is to extract characteristics from handwritten text, which will subsequently be normalised, scaled, and utilised to make mental health judgments.
A Critical Discourse Analysis of the Language Used in the Mobile Phone by the Students of Social Sciences in Higher Institutions of the Punjab

The objectives of our study are to find out how teachers and students deal with this issue in Higher Education Institutions of Punjab, Pakistan.We are trying to justify our assumption that classroom teachers, especially teachers who do not have smartphones, have questions about mobile phones.We address ibid problem, a survey methodology was adopted, data were collected through online interviews with 250 college students and 30 teachers in nine Punjab divisions of Pakistan.Results shows that 70% students used smartphone for entertainment and 30% teachers respectively while 45% teachers used mobile phone for education.Teachers and students used mobile phones during class for information gathering, file sharing, searching educational material and online lectures.Research recommended that Higher Educational Institutions are required to use handheld devices to enhance academic and curriculum activities to cope new challenges.
The harmonic and melodic connection numbers involving the mutual inclusions among the generic groups of notes arbitrarily emitted

The present paper is aimed to provide specific details and new results related to the music network described in the previous study by the author.While the first paper served as an introduction and extensive analysis of the model in all its potential applications, the present study is aimed at providing supplementary data to quantitatively support the previous one.The music concepts described in the present paper concern the evaluation of all the potential mutual inclusions and connections among chords, scales, and more in general any generic group of notes arbitrarily played by each musician.These concepts characterize the specific algorithm purposely developed and implemented in an interactive software application tool that can be employed for performing the melodic and harmonic analyses of any kind of tune.Geometric music graphs that take into account these groups are automatically designed by the software in order to trace the harmonic and melodic zones touched by each music composition.These geometric graphs are constituted by the connections (that can also represent mutual inclusions) among the generic groups of notes.The primary focus of the present paper is on the quantitative results and data characterizing the inner structure related to these graphs.
Interactive learning in the context of students independent activities at higher educational institutions

The article presents such aspects of the organization of interactive learning as technologies combination and creative approach while useing them in the process of students self-education. The possibilities of informatization are analyzed and the main directions of its use in the educational sphere are determined new opportunities for the development of students ' thinking; the selection of individual methods of obtaining knowledge through independent work with the help of information and computer technologies as a factor of convergence of educational institutions. Interactive studying made it possible to take advantage of group and individual forms, thanks to the group form of training, opportunities were provided to cooperate, establish interpersonal relationships, and learn about the world around us together. This form was implemented in compact groups of students who are united by a common educational goal, and it consists in direct guidance of the teacher through the tasks offered to the group by him. A methodology has been developed, the main tasks of which are to reveal the significance of information technologies in general and professional education, to clarify the psychological and pedagogical aspects of mastering disciplines, to reveal the practical significance of methods and means of the latest information technologies, the prospects for their application. The ways of developing a creative approach to perform laboratory and practical works, the formation of knowledge and skills necessary for independent study of the educational process, analysis of various methodological problems and psychological and pedagogical situations are shown; increasing the effectiveness of using new information technologies in learning, activating their cognitive activity, creativity, finding new knowledge with an independent research character. The criteria of students independent work (according to the manner of management and control of the teacher in quality of knowledge; by the degree of mandatory as mandatory, established by educational plans and working programs; recommended and vaulted: according to the degree of demonstration of creativity: reproductive, reconstructive, heuristic and research. The formation of students independent activity with information technologies has been developed. The method of skills forming of students independent activity in the "Moodle" is specified. Key words: interactive learning, independent activity, students of higher education institutions, informatization, methodology, criteria, creative approach.