Reviewerly/exHarmony huggingface dataset:
Speech Enhancement using Convolution Neural Network-based Spectrogram Denoising

Regarding spectrogram as an image, this paper adopts a convolution neural network (CNN)-based image enhancement algorithm for spectrogram denoising. By doing so, speech denoising can be achieved when the spectrogram is enhanced by the proposed CNN-based image enhancement algorithm. The spectrogram clipping strategy was presented to obtain a large amount of training data, which gave rise to a smaller storage cost and avoided the limited depth development and problem of excessive complexity commonly presented in traditional speech features when training a recurrent neural network. Meanwhile, a deeper network was constructed to improve the capacity and flexibility to use the features of the spectrogram better, and it can also capture enough spatial information to make the noise reduction performance effectively. In addition, the proposed model utilized residual learning strategy in CNN training, with the combination of batch normalization, which greatly improved the performance of the model. The experimental results demonstrates that the proposed spectrogram denoising model has better learning ability and denoising performance, whether it is a known noise situation or a noise mismatch situation, so that the proposed system shows robust speech enhancement effect.
‘Immersed in Art’: Engaged learning in art and design history

Established approaches to art history pedagogy typically involve a primarily passive form of instruction incorporating the viewing of works projected on screens. While such approaches can convey valuable information, they can also contribute to student disengagement and do not necessarily support deep learning. This article examines three learning initiatives incorporating an immersive teaching space to determine how these forms of technology-enhanced active learning might enhance student comprehension and engagement. The article considers how learning design incorporating the affordances of such immersive environments can provide multimodal learning experiences that stimulate student imaginations and support learning and engagement in a manner that complements rather than replaces traditional modes of instruction.
Improved Multi-feature Fusion Approach Based on End-to-end Convolutional Neural Network for Dehazing

This paper presents an improved multifeature fusion approach based on end-to-end convolutional neural network (CNN) for day-time and night-time hazy scenes called IMFE-Net from the point of view of the atmospheric scattering model, which directly learns the haze-free image. The multifeatures of degraded image are composed of scene depth, hue, and atmospheric light weight, which are directly extracted from each layer of the IMFE-Net. We use a synthetic dataset and real-world hazy image training set to train the multifeature fusion network model to obtain an optimized network model. We have performed an extensive test on the real-world hazy dataset. Experimental results have proven that the model has satisfactory results. The result shows that day-time and night-time hazy scenes have a strong sense of reality processing with our method. The IMFE-Net is better than previous methods in maintaining color and detail information; it also ensures that the algorithm is real-time and can be further used for computer vision system.
Text hiding in an image using least significant bit and ant colony optimization

In today’s technological world, one of the most challenging problems is information security. In the past ten years, many methods were introduced for securing the secret data transmission over the public network (Internet). One of the best choices to solve such issue is to combine AI and steganography methods. In the presented work, the text will be hidden inside an image depending on the ant colony optimization algorithm for generation of secret key for hiding. First an image is chosen as a carrier media of the secret text, then a set of rows are chosen from carrier image and used as an entry point for the ant colony optimization algorithm where the matrix resulting from the algorithm is used as a key to determine the hiding pixels of secret text bits in an image. The secret text will convert to ASCII code then convert to binary bits and hiding these bits on pixels of image depending on generation key using least significant bits techniques. The experimental results were the high-quality stego images as well as its minimal histogram changeability, also validating the efficiency of the suggested method, and it’s hard to determine pixels of hiding and complexity to determine the sequence of hiding bits.
THREE MODEL-THEORETIC CONSTRUCTIONS FOR GENERALIZED EPSTEIN SEMANTICS

Abstract This paper introduces three model-theoretic constructions for generalized Epstein semantics: reducts, ultramodels and $\textsf {S}$ -sets. We apply these notions to obtain metatheoretical results. We prove connective inexpressibility by means of a reduct, compactness by an ultramodel and definability theorem which states that a set of generalized Epstein models is definable iff it is closed under ultramodels and $\textsf {S}$ -sets. Furthermore, a corollary concerning definability of a set of models by a single formula is given on the basis of the main theorem and the compactness theorem. We also provide an example of a natural set of generalized Epstein models which is undefinable. Its undefinability is proven by means of an $\textsf {S}$ -set.
PERBANDINGAN TINGKAT PENGEMBALIAN DAN RISIKO SEBELUM DAN KONDISI MULAI COVID-19 SERTA PADA POSISI REBOUND SAHAM

The purpose of this research was to determine the difference in the rate of return on shares and the rate of risk in the stock sectors before, during the Covid-19 conditions and the rebound position. The sample used in this research is all sectors on the Indonesia Stock Exchange, and the period is September to December 2019, from January to April 2020, and from September to December 2020. The analysis method used in this research is descriptive which includes average return, standard deviation, volatility and coefficient of variance. The results of the study found that the rate of return of stock sectors on the IDX in the conditions of the outbreak of Covid-19 decreased compared to before the outbreak of Covid-19, and there was an increase in the rate of return in a rebound position. The risk level of the stock sectors on the IDX in the conditions of the outbreak of Covid-19 increased compared to before the outbreak of Covid-19, and there was a decrease in the level of risk in a rebound position.
Graph classification based on skeleton and component features

Most existing popular methods for learning graph embedding only consider fixed-order global structural features but lack hierarchical representation for structures. To address this weakness, we propose a novel graph embedding algorithm named GraphCSC that realizes classification leveraging skeleton information from anonymous random walks with fixed-order length, and component information derived from subgraphs with different sizes. Two graphs are similar if their skeletons and components are both similar. Thus in our model, we integrate both of them together into embeddings as graph homogeneity characterization. We demonstrate our model on different datasets in comparison with a comprehensive list of up-to-date state-of-the-art baselines, and experiments show that our work is superior in real-world graph classification tasks.
Color-Dust: A Data Visualization Application of Image Color Based on K-Means Algorithm

This application, Color Dust, extracts and filters the color of image pixels online through Canvas, then performs clustering analysis and statistics based on the K-Means algorithm. With the help of front-end libraries such as D3.js, Vue.js, and Vuetify, the data visualization of image color is realized. The color extracted by the K-Means algorithm is the average of several tones that frequently appear in the image, and it is a good reference for harmonizing colors. After the extraction is completed, various methods of data visualization are used to display the information about colors in the picture in real time with D3.js and Vue's responsiveness, and the color of the website can be changed according to different images in real time. In addition, this project encapsulated the algorithm steps of extraction process into npm packages, which can be quickly transplanted to Node.js or front-end of website.
Generalized Compass Operator for Edge Detection with the Required Angular Orientation

A generalized compass operator for detection on the image straight edges with an arbitrary angular orientation defined by the user is considered. It is shown, that for a mask with a size of 5×5 pixels the considered operator provides a selection of edges with an orientation in the range of ±10 degrees relative to the angle value defined by the user.
A benchmark generator of tree decomposition Mk landscapes

We introduce the CliqueTreeMk algorithm to construct tree decomposition (TD) Mk Landscapes and to compute their global optimum efficiently. TD Mk Landscapes are well suited to serve as benchmark functions for blackbox genetic algorithms that are not given a priori the structural problem information as specified by the tree structure and their associated codomain fitness values. Specifically, for certain types of codomains the use of linkage learning might prove to be necessary in order to be able to solve these type of fitness functions.
The Use of New Presentation Technologies in Electronic Sales Environments and Their Influence on Product Perception

New tools have appeared to improve interaction with products in online shopping: virtual reality (VR) applications, where customers are immersed in a wholly virtual environment; augmented reality (AR) applications, which allow virtual elements to be visualised in real environments; and 360° displays (360-D), which allow 3D representations to be rotated in any direction. The aim of this work is to analyse the influence of using these technologies for product presentation on the perception of different aspects of the products and also on the shopping experience, within the context of an online shopping, and compare them with traditional presentation of products through static 2D rendered images. A study has been conducted with a piece of furniture (a sideboard) and a household accessory (a gooseneck lamp). A questionnaire was developed simulating a website for selling products online, and showing the sideboard and the lamp by means of 2D images, 360-D, AR and VR techniques. The participants evaluated different aspects related to the perception of general, detailed, functional and aesthetic characteristics of the products, as well as aspects concerning the shopping experience and the use of the technologies. Results show a similar perception of product characteristics and shopping experience for the four techniques, except for the perception of details (worst for VR) and easiness of use (worst for AR and VR), with some differences depending on the type of product. In conclusion, 360-D and AR techniques are recommended for large appliances, while 360-D and 2D images are better for small ones.
Training Deep Generative Models in Highly Incomplete Data Scenarios with Prior Regularization

Deep generative frameworks including GANs and normalizing flow models have proven successful at filling in missing values in partially observed data samples by effectively learning –either explicitly or implicitly– complex, high-dimensional statistical distributions. In tasks where the data available for learning is only partially observed, however, their performance decays monotonically as a function of the data missingness rate. In high missing data rate regimes (e.g., 60% and above), it has been observed that state-of-the-art models tend to break down and produce unrealistic and/or semantically inaccurate data. We propose a novel framework to facilitate the learning of data distributions in high paucity scenarios that is inspired by traditional formulations of solutions to ill-posed problems. The proposed framework naturally stems from posing the process of learning from incomplete data as a joint optimization task of the parameters of the model being learned and the missing data values. The method involves enforcing a prior regularization term that seamlessly integrates with objectives used to train explicit and tractable deep generative frameworks such as deep normalizing flow models. We demonstrate via extensive experimental validation that the proposed framework outperforms competing techniques, particularly as the rate of data paucity approaches unity.
An Intelligent Recommendation System Based on Collaborative Filtering and Grid Structure

The rapid growth of location-based networks during the twenty- first century has greatly increased, so there is a need of providing suggestions to personals about their interest activities. Nowadays, location-based social networks (LBSN) become a common platform for users to share interests. In this paper, our main concern is to design a recommendation system that will provide suggestions to the user according to their interests. We have developed a framework based on Collaborative Filtering (CF) that analyses user activities to find the similar user. CF helps us to enrich each user profile by rating unvisited places which we can include in their interest hierarchy. Then we calculate the similarity of the user profile with the Point of Interest (POI) extracted from the user’s current location and make recommendations. Here Grid Structure is used to analyse the POIs extracted from Google.
Providing a framework for the reuse of research data based on the development dynamic framework of United Nations Development Program (UNDP)

Purpose The present research is aimed at presenting a framework for the reuse of research data in Iran through applying the United Nations Development Program (UNDP). Design/methodology/approach The research at hand has a mixed methods design. In the qualitative section, the authors first carried out meta-synthesis and then an interview was conducted. Likewise, in the quantitative section, the reliability of the recommended framework was measured through carrying out a survey. Finally, the framework for data reuse was presented in five dimensions, namely human, organizational, policies and laws, technical, implementation and analysis. Findings Through structural equation modeling, the fitness of the framework was confirmed, and it was found out that the dimensions of policies, human and organizational played more prominent roles in the explanation of the framework in comparison with the other two dimensions. Originality/value Research studies in the area of data reuse have been conducted either quantitatively or qualitatively and in most of them interviews or questionnaires were used as tools for collecting data; however, due to the nature of this area and its relatively new literature in Iran, it is necessary to use mixed methods in order to be able to arrive at a proper understanding of this concept using both quantitative and qualitative approaches.
NausiChain: a Mobile Decentralized App Ensuring Service Continuity to University Life in Covid-19 Emergency Times

In an effort to contain the Covid-19 pandemic emergency, nations have sponsored smartphones apps tracing contacts. The ultimate aim is to alert people when a Covid-19-positive has been found and it occurs that they have been in close contact with the positive. In the context of contact tracing, transparency of data gathered is of utmost importance to show beyond doubt that the people privacy is preserved as much as possible. The proposed approach aims at making gathered data publicly available in such a way that anonymity is preserved and stored data are immutable and not owned by a single organization. For this, we equip users in a university environment with an app that reveals their presence as a proximity to a WiFi access point. While the server-side provides location ids to apps, the location history is kept within the app and checks for a possible contact are performed by the app. The server aggregates presence data related to each WiFi access point according to time-slots, and stores these periodically in a blockchain for maximum transparency and immutability. The tests on the developed prototype have shown the accuracy in determining presence data, the feasibility for apps to interact with smart contracts and to have daily presence data stored in a blockchain. The ability to exhibit official and immutable statistics on people gathering at university should be useful to give evidence of the effort to have controlled accesses and gathering avoidance.
Virtual Physical Task Training: Comparing Shared Body, Shared View and Verbal Task Explanation

While video lectures are very successful in explaining theoretical aspects, Virtual Reality (VR) also offers the possibility to teach physical skills. While a large body of research focuses on virtual teaching explaining tasks during the task completion, it remains unclear what type of task instructions work best before task execution.
A Survey on Single Image Deblurring

Since image devices are developing at a rapid speed but the images we obtained easily get blurred due to the camera shake or bad weather, single image dehazing method becomes an advanced aspect in the task of vision. This paper summarizes the recently published image dehazing methods, which concentrates on dehazing models, dataset for training the models, index for assessing the models as well as application of these models. Details of each model is explained in the article. All models in this paper are published in recent years, which concludes the latest situation in this area.
Proof Searching in PVS Theorem Prover Using Simulated Annealing

The proof development process in PVS theorem prover is interactive in nature, that is not only laborious but consumes lots of time. For proof searching and optimization in PVS, a heuristic proof searching approach is provided where simulated annealing (SA) is used to search and optimize the proofs for formalized theorems/lemmas in PVS theories. In the proposed approach, random proof sequence is first generated from a population of frequently occurring PVS proof steps that are discovered with sequential pattern mining. Generated proof sequence then goes through the annealing process till its fitness matches with the fitness of the target proof sequence. Moreover, the performance of SA with a genetic algorithm (GA) is compared. Obtained results suggest that evolutionary/heuristic techniques can be combined with proof assistants to efficiently support proofs finding and optimization.
New Lightweight Hybrid Encryption Algorithm for Cloud Computing (LMGHA-128bit) by using new 5-D hyperchaos system

The “cloud” is considered as a collection of hardware, storage data, multiple applications, interfaces, services, and networks, that provide the way through which users and companies can access the infrastructures remotely via the Internet on-demand from anywhere, at any time, which are independent of locations. Also can be increasing capacity or dynamically incorporating without investment in new facilities, training or licensing new programs to new workers. It extends the IT capabilities currently. Cloud computing is a common option for users and businesses for several benefits including increased productivity, high execution speed, low computer costs, efficiency, improved performance, and data reliability. Because of these benefits, each business holder and organization wish to store their data in the cloud [1]. But as more data and files are being placed in the cloud by people and organizations, worries are beginning about how safe the environment is starting to rise. The ideal safety mechanism for user’s data in rest is to encrypt the data and store only the encrypted files into cloud storage, where providers of cloud or any unauthorized party unable to access the encryption key. So we need to use one or more cryptographic algorithms and protocols to encrypt the user's data before transfer to the clouds [2]. The main aim of this particular research is to protect the transmitted data with the help of encryption and decryption techniques. This research paper presents a new model for encrypting the data transmitted through cloud. The algorithms used in this model are: new lightweight hybrid encryption algorithm. We proposed to apply a hybrid of one part from PRESENT algorithm (PA) and another from TWINE algorithm (TA) by using different chaos keys in both encryption and decryption. For making the cloud more secure and to Confidence the data. The algorithm Chaos Keys Generation was used to produce random numbers by using a new chaos system with different initials and parameter values in order to generate 5-D chaos keys.
Social bots detection in Brazilian presidential elections using natural language processing

In recent years, we have seen an expressive increase in the number of users participating in social networks. Social networks, in general, have proven to be quite effective in spreading opinions and influencing people as messages can be shared with thousands of people in a few minutes. However, this ability has been exploited in a negative way, to manipulate opinions and spread misinformation and/or fake news. A common way of doing this is through the use of bots, computer algorithms that mimic human behavior, disseminating topics and news, demonstrating support or rejection to personalities, and interacting with other users, which can impact even democratic discussions. For this reason, the present work aims to show and compare approaches for detecting social bots using Twitter user’s posts data extracted during the Brazilian presidential election period of 2018. Using a dataset of Twitter users labeled as bots or humans, this research applies five natural language processing (NLP) techniques to extract characteristics from the content of the user’s messages on the social network. In order to analyze the impact of features extracted through NLP in the task of detecting bots, five different classifiers were tested including pre-processing techniques and feature selection. The best results were achieved through a union of all the extracted features using the Random Forest classifier, achieving an accuracy of 0.91 for the bot class and AUC of 0.83.
Digging Deeper: An Analysis of Domain Impersonation in the Lower DNS Hierarchy

Attackers use various techniques to lure victims to malicious domains. A typical approach is to generate domains which look similar to well-known ones so that a confused victim is tricked into visiting the domain. An important attack technique in practice is the impersonation of domains in the lower DNS hierarchy as subdomains of otherwise unsuspiciously looking domains, such as paypal.com.foo.example.com. In this paper, we present an in-depth, empirical measurement study of low-level domain impersonations to understand their prevalence and provide a basis for the development of corresponding countermeasures. We introduce a generic measurement approach to find and analyze such domains in phishing feeds from three large anti-phishing vendors (PhishLabs, Phishtank, and OpenPhish) covering multiple years and a data set consisting of one and a half years of certificate transparency logs (CTL). In our measurement study, we discovered more than 122,000 cases of domain impersonations detected during the last seven years in PhishLabs, almost 3,000 in Phishtank, and a couple of hundred instances in OpenPhish. Additionally, we compared the usage of low-level domain impersonation with other well-known domain squatting techniques and find that low-level domain impersonation is among the most popular squatting techniques in the wild.