Reviewerly/exHarmony huggingface dataset:
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising

Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution. Prevalent discriminative networks trained on synthetic noise models have been shown to generalize poorly to real-world noisy images. While curating real-world noisy images and improving ground truth estimation procedures remain key points of interest, a potential research direction is to explore extensions to the widely used convolutional neuron model to enable better generalization with fewer data and lower network complexity, as opposed to simply using deeper Convolutional Neural Networks (CNNs). Operational Neural Networks (ONNs) and their recent variant, Self-organized ONNs (Self-ONNs), propose to embed enhanced non-linearity into the neuron model and have been shown to outperform CNNs across a variety of regression tasks. However, all such comparisons have been made for compact networks and the efficacy of deploying operational layers as a drop-in replacement for convolutional layers in contemporary deep architectures remains to be seen. In this work, we tackle the real-world blind image denoising problem by employing, for the first time, a deep Self-ONN. Extensive quantitative and qualitative evaluations spanning multiple metrics and four high-resolution real-world noisy image datasets against the state-of-the-art deep CNN network, DnCNN, reveal that deep Self-ONNs consistently achieve superior results with performance gains of up to 1.76dB in PSNR. Furthermore, Self-ONNs with half and even quarter the number of layers that require only a fraction of computational resources as that of DnCNN can still achieve similar or better results compared to the state-of-the-art.
LSB steganography strengthen footprint biometric template

Steganography is the science of hiding secret data inside another data type as image and text. This data is known as carrier data; it lets people interconnect secretly. This suggested paper aims to design a Steganography Biometric Imaging System (SBIS). The system is constructed in a hybridization manner between image processing, steganography, and artificial intelligence techniques. During image processing techniques the system receives RGB foot-tip images and preprocesses the images to get foot-template images. Then a chain code is illustrated for personal information within the foot-template image by Least Significant Bit (LSB). Accurate recognition operation is performed by artificial bee colony optimization (ABC). The automated system was tested on a live-took about ninety RGB foot-tip images known as the cover image and clustered to nine clusters that authorized visual database. The Least Significant Bit method transforms the foot template to a stego image and is stored on a stego visual database for further use. Features database was constructed for each stego footprint template. This step converts the image to quantities data and stored in an Excel feature database file. The quantities data was used at the recognition stage to produce either a notification of rejection or acceptance. At the acceptance choice, the corresponding stego foot-tip template occurrence was retrieved, it is corresponding individual data were extracted and cluster position on the stego template visual database. Indeed, the foot-tip template is displayed. The suggested work consequence is affected by the optimum feature selection via the artificial bee colony optimization usage and clustering, which declined the complication and subsequently raised the recognition rate to 93.65 %. This rate competes out the technique over others’ techniques in the field of biometric recognition
Factors Influencing Learning Behavior and Its Implications for Guidance and Counseling Services

Learning behavior is influenced by internal factors and external factors. This research is motivated by the phenomenon of students who have poor learning behavior. This study aims to describe internal and external factors that influence student learning behavior. This type of descriptive research using quantitative methods. The research population of students of SMA Negeri 5 Padang was 1,025 students, and the research sample of 273 students were selected by proportional stratified random sampling technique. Data collection uses a questionnaire about the factors that influence the learning behavior of the Likert Scale model. Data were analyzed using descriptive statistical techniques. The results of this study describe (1) internal factors that influence learning behavior namely physiological aspects are in the high category (50%), and psychological aspects are in the high category (49%), (2) external factors that influence learning behavior namely the social environment in the high category (68%), and the nonsocial environment is in the high category (63%). Based on the research findings, it is suggested to BK teachers to be able to provide Guidance and Counseling services in the form of information services, content mastery services, individual counseling services, group guidance services, and group counseling services to students, so that students can overcome the problems or obstacles they experience.
Coarse-Fine Networks for Temporal Activity Detection in Videos.

In this paper, we introduce Coarse-Fine Networks, a two-stream architecture which benefits from different abstractions of temporal resolution to learn better video representations for long-term motion. Traditional Video models process inputs at one (or few) fixed temporal resolution without any dynamic frame selection. However, we argue that, processing multiple temporal resolutions of the input and doing so dynamically by learning to estimate the importance of each frame can largely improve video representations, specially in the domain of temporal activity localization. To this end, we propose (1) Grid Pool, a learned temporal downsampling layer to extract coarse features, and, (2) Multi-stage Fusion, a spatio-temporal attention mechanism to fuse a fine-grained context with the coarse features. We show that our method outperforms the state-of-the-arts for action detection in public datasets including Charades with a significantly reduced compute and memory footprint. The code is available at this https URL
Keyphrase Extraction And Source Code Similarity Detection-A Survey

Abstract Keyphrase extraction is the starting phase of feature extraction and many other NLP tasks. Hence, keyphrase extraction ensures the separation of essential and relevant information from the rest of the document or corpus. A similarity score is used for detecting similarities between two or more documents. In this paper, a summary of existing research conducted in the field of keyphrase extraction and source code similarity detection is presented. The literature focuses on more concept-based research instead of going towards the domain level. There exist a promising potential for the creation of recommendation systems by employing similarly situated research techniques.
A machine learning based approach for user privacy preservation in social networks

With the development of Internet technology, service providers can provide users with personalized services to enrich user experience, however, this often requires a large number of users’ private data. Meanwhile, the protection of their private data and the evaluation of the risk of leaked datasets become a matter of great concern to many people. To resolve these issues, in this paper, we develop a machine learning-based approach in online social networks (OSNs) to efficiently correlate the leaked datasets and accurately learn millions of users’ confidential information. Moreover, a trust evaluation model is developed in OSNs to identify malicious service providers and secure users’ social activities via direct trust computing and indirect trust computing. Extensive experiments are conducted by using real-world leaked datasets, and the results show that the efficiency and effectiveness of the proposed approach in terms of user privacy protection and accuracy of privacy leakage evaluation.
Bolzano, (the Appropriate) Relevant Logic, and Grounding Rules for Implication

Abstract The Bohemian philosopher Bernard Bolzano introduced the notion of logical deducibility, anticipating by almost one hundred years the much more famous Tarskian result. Besides the notion of logical deducibility, he also introduced the notion of grounding, which, on the one hand, he distinguished from logical deducibility, but he also regarded as importantly connected to it. In particular, he saw a close connection between grounding and exact deducibility (a special case of deducibility). This paper examines this latter notion and compares it to notions of relevant entailment. Although the literature on this subject compares the notion of exact deducibility with that of relevant entailment as defined by Anderson and Belnap, here it is argued that Neil Tennant’s system CR is the best model for Bolzano’s ideas: it is shown that the two approaches share many important features, the most notable of which is the lack of full transitivity. It is also argued that Tennant’s system CR can serve as a framework for developing grounding rules for conditionals.
An Interactive Adaptable Learning Interfacefor E-Learning Sessions

Introduction of augmented reality into the E-learning systems brings a new era in personal remote education. While E-learning brings a lot of benefits for both instructor and student it also has various difficulties, such as absence of vital personal interaction that can influence on student motivation and inspiration. To decrease this gap we have started the development of an Interactive Adaptable Learning Interface (IALI) that with the help of speech synthesizers, emotions and voice recognition systems tries to adapt the ongoing E-learning session to the student behavior, simulating the way instructor adapt the lesson to the student reactions. As the IALI is initially planned for Ukrainian language students, we have made a deep analysis of nowadays applicable solutions for Ukrainian speech synthesis and recognition. In this paper we present the conducted analysis and application of the most widely applied speech synthesizers, voice and emotions recognition systems that were used to develop first version of IALI.
A Multimedia Instructional Package on for Enhancing Achievement in basic Science

This study ascertained the effectiveness of Multimedia instructional package in improving primary school pupils' achievement in basic science at both post and follow-up measures.The design of the study was a randomized control trial experimental design.This design was apt since randomization of subjects was not done and intact classes were used.The sample size was 85 pupils from the two intact classes formed the participant for the study.Basic Science Achievement Test (BSAT) was used to collect data for the study.The internal consistency reliability index of BSAT was determined to be 0.87 using Kuder-Richardson formula 20 while the estimate of the temporal stability of the BSAT was 0.91 using person correlation.Analyses of covariance (ANCOVA) was used in testing the hypotheses at the 5% probability level.The use of multimedia projection in the form of Computer Assisted Learning (CAL) significantly (p < .050)improved pupils' achievement in basic science.The efficacy of the CAL in improving the pupils' achievement in Basic science was retained at the follow-up measure.Multimedia projection package proved very effective in enhancing the achievement in basic science.
Learning Disentangled Representation of Web Address via Convolutional-Recurrent Triplet Network for Classifying Phishing URLs

Deep learning models for phishing URL classification, based on the convolutional-recurrent neural network for modeling the character-level and word-level features, have achieved good performance in terms of accuracy. However, there have been issues in the sampling stage due to the class imbalance of the URL phishing data and problems in constructing feature spaces. Therefore, this study aimed to address the class imbalance issues in the URL domain, in terms of the deep learning-based URL feature space generation, and to propose a modified triplet network structure that learns the similarity between URLs. The proposed method was verified using 60,000 URL datasets collected from real-world web addresses, and it achieved a performance improvement compared to the latest deep learning based methods. The modified triplet network was evaluated by a 10-fold cross-validation per time resolution, and it demonstrated a 45 percent improvement in recall to confirm the validity of the metric learning approach in the field of phishing URL classification.
A Study on the Function of Definition Statement in Secondary Science Textbooks - To explore the linguistic educational contents of Korean explain achievement standards -

In the field of Korean Language Education, the linguistic repertoires of educational contents for the language of learning are a given for language across the curriculum. However, in reality, the educational contents of the Korean Language Education explanation achievement standards are far too abstract. The educational contents do not reflect the authenticity of the language material. As a result, it could be difficult for the aforementioned contents to be a part of the language for learning in other subjects, not to mention that of Korean Language Education. As such, this thesis is focused on analyzing the definition of sentences within Science Textbooks based on the premise that detailed language experiences should be provided as language for learning in Korean Language Education. After all of the definition sentences are extracted from the secondary school Science Textbooks, the core explanatory concepts are then analyzed. As a result, the definitional explanation of the core concept is identified with a formulaic expression like ‘-ㄴ/는 NP1을/를 NP2(이)라고 한다.’. At this point, the explanation of the definition sentences is realized while the embedded clauses, which are equivalent to ‘-ㄴ/는 NP’ and paraphrase the preceding contents of the Science Textbooks, provide an additional explanation for the core concept ‘NP2’. During the paraphrasing stage, differences in how the explanations are identified influence the function of ‘-ㄴ/는 NP’ as a formulaic expression which paraphrase the preceding contents. In this thesis, the variety of explanatory functions are analyzed, and the paraphrasing forms are realized by classifying the paraphrasing type based on three functions: ‘condensing contents’, ‘generalizing examples’, and ‘specifying concepts’.
DEVELOPMENT OF AN EVOLUTIONARY OPTIMIZATION METHOD FOR FINANCIAL INDICATORS OF PHARMACIES

The work is devoted to the problem of optimizing the financial performance of pharmacies. To solve this problem, a genetic method of multicriteria optimization was developed with a mutation operator modification to study the degree of influence of factors on the financial performance of pharmacies and the choice of optimization model. The fundamental difference between the developed genetic algorithm and its existing counterparts is the ability to control the mathematical distribution of the values of the solution, which prevents premature convergence of the genetic algorithm and uses all proposed genes in fractions according to the chosen distribution model. A comparative analysis of the work of classical GA and modified versions shows that the best results are achieved in the cognitive-style determination. Three modifications of the mutation operator were developed. The application of the developed methods will lead to a more effective use of the pharmacy area, to reduce unmet demand and, ultimately, to reduce the retail cost of drugs by reducing the costs of storing and servicing the suboptimal loading of the pharmacy.
Gravity in the Microworld

A brief review article gives examples of using the physical model based on the mechanistic interpretation of J. Wheeler's geometrodynamics. The examples show the need to consider gravity in the microworld. The latter is based on the balance of magnetic and gravitational forces.The gravitational constant was used in calculating the masses of quarks, neutrinos, proton size, coupling constants, etc. A new deviation of 28 GeV in the physical experiments of CMS Collaboration was confirmed by calculations. The unusual value of s- quark and b- quark masses is explained.
Grouping news events using semantic representations of hierarchical elements of articles and named entities

Enormous amount of news articles are being generated through different news agencies. The variation in journalistic content and online availability of news content, makes it difficult to monitor and interpret in real time. Organizing news articles would play a crucial role in its consumption and interpretation. Our work assists end user by grouping news articles based on the story. We present here a novel approach of grouping news articles based on a multi-level embedding representation of articles, coupled with a standard TF-IDF score based on named entities. Our results shows that combining the syntactic(TF-IDF) as well as the semantic (Bert) representations can boost the performance of the news grouping task.
Research on emotional analysis of netizens and topic distribution under public health emergencies : ——A Case Study of COVID-19

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Derivative Graphs in Global Structures of Bayesian Algebraic Networks

The article is aimed at summarizing the concepts of a derivative graph and a primitive graph for graphs with backbone connectivity. Theorems are formulated and proved on the main connectedness of the graph of the derivative and on the primitive graph of the main connected graphs. The theoretical and practical significance of the result is to simplify the search for successful visualization of algebraic Bayesian networks, which would help to identify the features of their structure, as well as the definition of new types of global structures of these networks. Such structures would allow us to store the same information, but use other output algorithms, which would simplify the software implementation of this model. Note that maintaining the property of trunk connectivity when finding the graph of the derivative is considered in this article for the first time.
AdaSpeech: Adaptive Text to Speech for Custom Voice

Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few speech data. Custom voice presents two unique challenges for TTS adaptation: 1) to support diverse customers, the adaptation model needs to handle diverse acoustic conditions that could be very different from source speech data, and 2) to support a large number of customers, the adaptation parameters need to be small enough for each target speaker to reduce memory usage while maintaining high voice quality. In this work, we propose AdaSpeech, an adaptive TTS system for high-quality and efficient customization of new voices. We design several techniques in AdaSpeech to address the two challenges in custom voice: 1) To handle different acoustic conditions, we use two acoustic encoders to extract an utterance-level vector and a sequence of phoneme-level vectors from the target speech during training; in inference, we extract the utterance-level vector from a reference speech and use an acoustic predictor to predict the phoneme-level vectors. 2) To better trade off the adaptation parameters and voice quality, we introduce conditional layer normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this part in addition to speaker embedding for adaptation. We pre-train the source TTS model on LibriTTS datasets and fine-tune it on VCTK and LJSpeech datasets (with different acoustic conditions from LibriTTS) with few adaptation data, e.g., 20 sentences, about 1 minute speech. Experiment results show that AdaSpeech achieves much better adaptation quality than baseline methods, with only about 5K specific parameters for each speaker, which demonstrates its effectiveness for custom voice. Audio samples are available at https://speechresearch.github.io/adaspeech/.
A Supervised Learning Framework for Physics-based Controllers Using Stochastic Model Predictive Control

본 논문에서는 확률적 모델예측제어(model predictive control)
MICROSTRUCTURAL OBSERVATIONS OF AM DEPOSITED 15-5PH – STELLITE SAMPLE

AM (Additive Manufacturing) is an increasingly widespread technology in the world.This technique was used in the present work to deposit material on the baseplate in order to find out its ability to repair damaged parts (baseplate material).The material of the baseplate was AKT1TD martensitic steel used in the aviation and energy industry.The steel could suffer from corrosion and erosion, and AM could be the potential method for damaged parts repairing.As a first step, a material similar to the baseplate was deposited.After that, a deposition of a stellite protective layer took place to increase the corrosionerosion resistance.The whole build was observed by OM (optical microscopy) and SEM (scanning electron microscopy) to evaluate the quality of deposition.
Application Names, Android Package Name, and ID

Android uses multiple naming conventions to uniquely identify an Android application; these have been summarized and broken down into more detail in this chapter.
Logistic Regression Analysis

This chapter covers the logistic regression concept and implementation in a structured way. Preceding chapters introduced supervised learning and concentrated on the parametric method. In supervised learning, we present a model with a set of correct answers, and we then allow a model to predict unseen data. We use the parametric method to solve regression problems (when a dependent variable is a continuous variable).
Classification of Mammography Images by Transfer Learning

Breast cancer is the most common cancer type in women worldwide. Diagnosis and early detection of cancer by mammography images are of great importance in cancer treatment. The use of deep learning in Computer Assisted Diagnostic systems has gained a great momentum especially since 2012. In this study, benign and malignant mass images were reproduced with data augmentation and the data sets obtained were classified with deep learning networks. In this study, a scratch Convolutional Neural Network (CNN) architecture was created and transfer learning was realized with different network models which trained on IMAGENET images. In the transfer learning section, separate training results were obtained by performing feature extraction and fine tuning of network parameters. As a result of the study, the best results were obtained with MobileNet, NASNetLarge and InceptionResNetV2 models which are used in transfer learning models.