Reviewerly/exHarmony huggingface dataset:
Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing

Tremendous progress has been made in sequential processing with the recent advances in recurrent neural networks. However, recurrent architectures face the challenge of exploding/vanishing gradients during training, and require significant computational resources to execute back-propagation through time. Moreover, large models are typically needed for executing complex sequential tasks. To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing. The proposed neuron provides an efficient building block for projecting sequential inputs into spectral domain, which helps to retain long-term dependencies with minimal extra model parameters and computation. A new type of recurrent network architecture, named Oscillatory Fourier Neural Network, based on the proposed neuron is presented and applied to various types of sequential tasks. We demonstrate that recurrent neural network with the proposed neuron model is mathematically equivalent to a simplified form of discrete Fourier transform applied onto periodical activation. In particular, the computationally intensive back-propagation through time in training is eliminated, leading to faster training while achieving the state of the art inference accuracy in a diverse group of sequential tasks. For instance, applying the proposed model to sentiment analysis on IMDB review dataset reaches 89.4% test accuracy within 5 epochs, accompanied by over 35x reduction in the model size compared to LSTM. The proposed novel RNN architecture is well poised for intelligent sequential processing in resource constrained hardware.
FATNN: Fast and Accurate Ternary Neural Networks

Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts. However, 2 bits are required to encode the ternary representation with only 3 quantization levels leveraged. As a result, conventional TNNs have similar memory consumption and speed compared with the standard 2-bit models, but have worse representational capability. Moreover, there is still a significant gap in accuracy between TNNs and full-precision networks, hampering their deployment to real applications. To tackle these two challenges, in this work, we first show that, under some mild constraints, computational complexity of the ternary inner product can be reduced by a factor of 2. Second, to mitigate the performance gap, we elaborately design an implementation-dependent ternary quantization algorithm. The proposed framework is termed Fast and Accurate Ternary Neural Networks (FATNN). Experiments on image classification demonstrate that our FATNN surpasses the state-of-the-arts by a significant margin in accuracy. More importantly, speedup evaluation compared with various precisions is analyzed on several platforms, which serves as a strong benchmark for further research.
Learning with Noisy Labels via Sparse Regularization

Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer from severe overfitting to noisy labels. Robust loss functions that satisfy the symmetric condition were tailored to remedy this problem, which however encounter the underfitting effect. In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector. When the fixed vector is one-hot, we only need to constrain the output to be one-hot, which however produces zero gradients almost everywhere and thus makes gradient-based optimization difficult. In this work, we introduce the sparse regularization strategy to approximate the one-hot constraint, which is composed of network output sharpening operation that enforces the output distribution of a network to be sharp and the $\ell_p$-norm ($p\le 1$) regularization that promotes the network output to be sparse. This simple approach guarantees the robustness of arbitrary loss functions while not hindering the fitting ability. Experimental results demonstrate that our method can significantly improve the performance of commonly-used loss functions in the presence of noisy labels and class imbalance, and outperform the state-of-the-art methods. The code is available at this https URL.
Numerical Uncertainty in Density Estimation for Background Oriented Schlieren

Background Oriented Schlieren (BOS) is an image-based density measurement technique. BOS estimates the density gradient from the apparent distortion of a target pattern viewed through a medium with varying density using cross-correlation, tracking, or optical flow algorithms. The density gradient can then be numerically integrated to yield a spatially resolved estimate of the density [1]. A method was recently proposed to estimate the a-posteriori instantaneous and spatially resolved density uncertainty for BOS [2] and showed good agreement between the propagated uncertainties and the random error. However, the density uncertainty quantification method could not account for the systematic uncertainty in the density field due to the discretization errors introduced during the numerical integration, which could be much larger than the displacement random errors [2]. In this work, we propose a method to estimate the numerical uncertainty introduced by the density integration in BOS measurements, using a Richardson extrapolation framework. A procedure is also introduced to combine this systematic uncertainty with the random uncertainty from the previous work to provide an instantaneous, spatially-resolved total uncertainty on the density estimates. The method will be tested with synthetic fields and synthetic BOS images.
Detection of Wild Elephants Using Machine Learning Algorithms

Elephant menace in the area adjacent to forests is a perennial problem. To recede this problem people near to the forest should be alerted. This paper provides a solution by identifying the elephants through images. Identifying an elephant in an image was executed by using machine learning algorithms. However, for the classification, two datasets of elephants and black rhinos respectively were used. The image analysis is done for both datasets using feature extractions. For feature extractions, template matching, and RGB to GRAY was used for the image analysis and training both the datasets into two categories by using the machine learning algorithms. Python coding language was used for training the datasets with machine learning algorithms. Three algorithms namely the support vector machine, K-nearest neighbor, and the random forest were implemented to get accurate results. Accuracies of three algorithms were observed and compared. Suggestions were made depending on the comparison outcomes, the random forest classifier is the best choice out of the given options with 0.78% accuracy and computational time 17sec. The work if found accurate can be ported to an embedded platform to operate as an individual stand-alone system.
A Two-Phase Mitosis Detection Approach Based on U-Shaped Network

This paper proposes a deep learning-based method for mitosis detection in breast histopathology images. A main problem in mitosis detection is that most of the datasets only have weak labels, i.e., only the coordinates indicating the center of the mitosis region. This makes most of the existing powerful object detection methods hardly be used in mitosis detection. Aiming at solving this problem, this paper firstly applies a CNN-based algorithm to pixelwisely segment the mitosis regions, based on which bounding boxes of mitosis are generated as strong labels. Based on the generated bounding boxes, an object detection network is trained to accomplish mitosis detection. Experimental results show that the proposed method is effective in detecting mitosis, and the accuracies outperform state-of-the-art literatures.
Implementation of a Constrained Quantum Optimization Method in Resource Distribution Management with Considering Queueing Scenarios

Cloud resource demands are rapidly increasing exponentially with the development of cloud computing. For this sake, new modern methods and techniques are needed to improve its quality of service. In this work, the problem of resource distribution management in cloud computing is formulated as an optimisation problem. To this end, we exploit the constrained quantum optimisation algorithm (CQOA) in resource distribution management by considering queueing aspect in order to optimise and improve its performance in terms of computational complexity, accuracy, and energy consumption. Next, we study the implementation and the configuration of the CQOA. After, we investigate the computational complexity of selecting the optimal distribution scenario that corresponds to minimum energy consumption. We also express the lower and upper bounds of the overall possible assignment scenarios within the database. Finally, we validate the efficiency of the proposed implemented CQOA by constructing a simulation environment.
A Study on Knowledge Management on Working Productivity

Knowledge management nowadays had been considered as a main element for achieving organizational success and survival in today’s knowledge age and competitive environments. The performance of the Lebanese Banking Sector nowadays is fluctuating, and the main reason behind that is the absence of knowledge management in the workplace and by that absence of communication among employees and managers in their workplace. The primary motivation which lies behind the research is to identify the importance of knowledge sharing, and how it affects employees’ involvement and engagement in the workplace. The research had implemented both quantitative and qualitative methods, and the data were analyzed using both descriptive and inferential statistics to validate the research hypotheses scientifically. The findings of the research proved that there is a significant positive relationship between Knowledge enhancement, E-Learning, Knowledge management and organizational commitment.
FcaNet: Frequency Channel Attention Networks

Attention mechanism, especially channel attention, has gained great success in the computer vision field. Many works focus on how to design efficient channel attention mechanisms while ignoring a fundamental problem, i.e., using global average pooling (GAP) as the unquestionable pre-processing method. In this work, we start from a different view and rethink channel attention using frequency analysis. Based on the frequency analysis, we mathematically prove that the conventional GAP is a special case of the feature decomposition in the frequency domain. With the proof, we naturally generalize the pre-processing of channel attention mechanism in the frequency domain and propose FcaNet with novel multi-spectral channel attention. The proposed method is simple but effective. We can change only one line of code in the calculation to implement our method within existing channel attention methods. Moreover, the proposed method achieves state-of-the-art results compared with other channel attention methods on image classification, object detection, and instance segmentation tasks. Our method could improve by 1.8% in terms of Top-1 accuracy on ImageNet compared with the baseline SENet-50, with the same number of parameters and the same computational cost. Our code and models are publicly available at this https URL
VERBAL MODELLING OF INNOVATIVE EDUCATIONAL COMMUNICATION IN THE DIGITAL DISCOURSE

The paper main focus is the inquiry into the verbal premises and patterns of comprehensive structuring of the innovative educational communication in the digital discourse (innovative educational communication in the digital ambient as construed by the transformative neological strata of modern European and Oriental languages of international communication -English, Spanish, French, Chinese, Japanese, accordingly).The problem of theoretical and methodological substantiation of cross-cutting principles, directions, mechanisms, and results of qualitative dynamics of linguistic macro-and microstructures of thesaurus in the realm of digital educational communication, as a consolidated linguistic object, is investigated in depth.Mosaic, simulation, and multidimensional approaches to understanding complex dynamic linguistic phenomena and entities, prioritized by this methodological context, allowed us to identify the ontological nature of lexical innovations of the globalized languages in the sphere of innovative educational communication.The ICT, e-learning, hybrid learning, digital competences thesaurus, that facilitates innovative educational communication, is structured to define and categorize the key components of innovative cyber terminology, instrumental to e-learning environment construction and functioning.Suggested is the ICT Thesaurus macro-, micro-and mega-structures modelling based on three groundwork principles: 1) Intranet educational anthroposphere (the anthropic environment within World Wide Web); 2) Outernet educational anthroposphere (e-reality components, functions outside the realm of World Wide Web); 3) Technogenic educational anthroposphere (transoriented anthropic environment components with an anthropic-for-congenous substituted onthological parameter).All units, listed and clusterised in the Thesaurus are supplied with a dominant or recessive conceptual marker or a combination of markers, indicative ofthe unit allegiance to the corresponding ontological categories of cyber-anthropospheric e-learning environment.The dynamic interaction of the structural tiers of the ICTs content plane within the strata of innovative educational communication is characterized by the anthropogenic parameters of the content plane, mediated by the subjective and collective cognitive experience of digital education stakeholders, mediated by the use of globalized languages.
Impact of “The Online League of Legends (LoL) GAME School” Program on the COVID-19 Education Crisis: Focusing on Korean Students’ Affective Domain

Purpose: This study aims to discover the changes made to the affective domain of the students who participat-ed in “the Online League of Legends(LoL) Game School” program during the COVID-19 education crisis. The pro-gram was conducted by the Seoul Metropolitan Office of Education’s Student Education Institute from August 2020 to July 2021 for elementary school students(fifth grade onward) to juniors in high schools in Seoul. The pro-gram consisted of game English, game science, game humanities, game writing, game-related songs, game music composition, game analysis and game strategies about the online game League of Legends, and adventure-based counseling(ABC) activities. Method: Twenty-nine students participated in the survey to determine positive and negative affections, intrinsic motivation, and the most interesting and challenging activities. The PANAS scale was used to find out whether participants had positive or negative affections about the program. This survey was highly reliable, and the ques-tionnaires were scored on a 7-point Likert scale each. To examine the students’ intrinsic motivation, four intrinsic motivation factors were selected: pleasure, flow, challenge, and growth potential. The participants described the most interesting and challenging activities of the Online LoL Game School program with the help of open-ended questions. Results: The participants had strong positive affections for the program(PA: 5.3928) instead of negative affec-tions(NA: 1.9598), and experienced “flow”(5.26) the most among the four factors. Also, the most interesting and challenging activity of the program was game English. Conclusion: These study findings revealed that 1)the students had positive affections about the Online LoL Game School program, 2)among the four intrinsic motivation factors, pleasure, flow, challenge, and growth po-tential, the students experienced flow the most, 3)the most interesting and challenging activity was game English. This study uncovered that the Online Game School program influenced the students’ positive affections and in-trinsic motivation. Online education programs related to games, in which many students showed interest, can be triggers for students to engage in active learning and reconsider their growth and future careers. The study find-ings and attempts can help create a positive school climate and bring about social change to provide Generation Z students with a pleasurable and meaningful learning experience.
SVD based Audio Watermarking Algorithm Using Dual Watermark for Copyright Protection

Flexibility of Internet technology gives rise to concern about the protection of digital data. Digital audio watermarking is the art of hiding important data in Digital Audio. This research paper deals with a new methodology which helps to enhance the robustness of Digital Audio Watermarking technique based on Discrete Wavelet Transform (DWT) and singular value decomposition (SVD). Objectives: To develop an audio watermarking algorithm in transform domain and improve the performance of proposed algorithm against common signal processing attacks such as filtering, adding noise, resampling, re-quantization, &amp; volume scaling, etc. using subjective &amp; objective measures. Findings: In this approach, multiple watermarks (Image -64 X 64 pixels and text) are embedded into the approximate coefficients of Discrete Wavelet Transform of an audio signal. Haar wavelet is used for decomposition while SVD is applied to get the Eigen values of approximate coefficients of audio signal. Experimental work demonstrates that watermarked audio has same perceptual quality as that of original one. The performance of the proposed work is evaluated with the help of objective measures such as Signal to Noise ratio, Peak signal to Noise ratio, Bit error rate, Normalized Cross correlation, etc. Additionally, simulation results on audio signals prove that the capacity of the proposed scheme is considerably better in comparison with the existing watermarking methods. Furthermore, the proposed work is applied for different audio signals such as classical, instrumental, flute, pop &amp; rock to check the robustness against various attacks.
Usability and Users’ Satisfaction on Online Electronic Voting System

This paper identifies the respondents’ views towards e-Voting (electronic technology voting) such as the process, users’ satisfaction, and relevancy, and to determine the usability criteria of e- voting’s interface, in which it employs a quantitative method, using self-administered adapted questionnaires. The survey involves 250 respondents (voters), who immediately completed five Likert scales survey after they had voted for the committee members in two general elections: a social club for the university’s staff (using Internet) and an organization for the female staff of the university (using the Intranet). The respondents were of different demographic backgrounds and they casted their online votes at two different periods (two years difference). The data were analysed using the SPSS software. The study found positive responses from most of the voters in which they expressed their satisfaction that e-Voting was convenient and easy to be used in the voting process, where the system illustrated relevant content and generated adequate procedures. Notably, the usability criteria of the interface of e-Voting as indicated by most respondents are between good to very good scales (navigation, graphic, user-friendliness andconsistency, content, and transparency).
EFL Students’ Coping Strategies for Speaking Anxiety During Online Learning

The anxiety of learning a foreign language can be an obstacle in achieving the learning goals. Concerning the learning anxiety, the students of the English Education Study Program of FKIP UHAMKA were also dealing with the anxious experience when learning speaking conducted online. It seems unavoidable for students of English Foreign Language (EFL) to face anxiety when online learning. For this reason, the purpose of this study is to investigate the coping strategies that most students use to overcome speaking anxiety during online learning. 62 students in the fourth semester were respondents to this study. The data were then qualitatively collected using two types of questionnaires: closed-ended and open-ended questionnaires. The collected data were analyzed using descriptive analysis, which described and tabulated the students’ responses.  After analyzing the closed-ended and open-ended responses, it is reasonable to conclude that the respondents mostly used the strategies of preparation, positive thinking, and peer seeking to cope with speaking anxiety in the context of online learning. In addition, based on open-ended responses, preparation was the most commonly used strategy by them.
Cognitive Analysis Of Civilizational Clashes And Dialogues

During analysis of diverse aspects of civilization clash concept there has been noticed the demand for the model approach. We show that in this case it is reasonable to use the methodology of cognitive analysis as scientific tools. Its analytical advantages are connected with possibility in principle to present the structure and dynamics of the operation of the process under study visually. The conceptual model of the process of civilization clashes by S. Huntington is presented. The model is a contour-nodal structure of an active cyclically generated conflict. The nuclear of the model integrates five basic factors into the closed contour: Cultural identity of civilizations, Cross-cultural tolerance, Rift of civilizations, Conflicts and Escalation of conflicts. Change of value of any of these factors (increase or decrease) will be supported (strengthened or weakened) in the cycle of positive feedback. Hence it appears that the described model is efficiently capable to lead to both growth of cross-cultural tolerance, smoothing of civilizational rifts and conflict decrease due to positive feedback. As a result there has been developed a conceptual model of counteraction to civilizations clash based on the dialogue. The clear advantage of the proposed model is the possibility in principle of «freeze» (prevention of conflict escalation) reached due to additional highly dynamic contour of counteraction (with negative feedback) and presence of external (from international organizations) resource base of counteraction.
Constructing an effective ontology for web page recommendation

Web page recommendation is an approach for suggesting web pages to users based on their past navigational behaviour discovered from their web usage data. The knowledge of the user's behaviour is ex...