Reviewerly/exHarmony huggingface dataset:
Automating Multi-element Subspace Exploration via Reinforcement Learning

Machine learning enters many aspects of our lives and brings us great convenience. However, building an effective machine learning model for a specific task requires not only expertise but also a lot of time and resources. In order to solve this problem, more and more research projects focus on automated machine learning (AutoML). In this paper, we propose an algorithm that can simultaneously optimize the space of multiple datasets, multiple models, and multiple hyperparameters. We call this an automating multi-element subspace exploration algorithm. We first formalize this problem as a reinforcement learning problem and then we define the state, action and well-designed reward function in reinforcement learning system. In addition, we use some skills and experience to accelerate the entire optimization process. Finally, our experimental results on multiple tasks demonstrate that our method is effective.
Conversion of SLA document into Fuzzy Rule base approach and applied in MAPE-K

Abstract A Service Level Agreement (SLA) is the legal catalyst to monitor any contract violation between end users and ISPs and is embedded within a Quality of Service (QoS) framework. It strengthens and advances the quality of control over the user’s application and network resources and can be further stretched to fulfill the QoS terms through negotiation and re-negotiation. Moreover, the present literature does not focus on the combination of rule-based approaches and adaptation together to update the established learning repository. Therefore, this mainstream of this research in the context of SLAs is to fill in this gap by addressing the combination of rule-base uncertainties. The key to this exercise is the conversion of signed SLA document into set of rules in the rule base approach.
Blockchain-Based Social Network Infrastructure

More than ever, users’ security and confidential data is at the risk of exploitation due to the ever-burgeoning cybercrimes and newer vulnerabilities exposed or discovered every alternate day. With increased connectivity via social networks, there is also an increased risk to data confidentiality and user privacy, never known before. The networks store a huge amount of private user data mostly related to interactions online. This sensitive information is usually meant for a very few people to see and kept protected from the outside world or unauthorized access via various security techniques. This study investigates the online conduct of users and perceived benefits of using online social networks. This study features security issues and potential attacks on different parts of the user’s security, tends to provide a stable decentralized solution to control, manage and authenticate user’s personal data and thus ensures privacy eliminating the need of third party.
MODELING OF UNIVERSITY ENVIRONMENT: THEORETICAL ASPECT

The article is devoted to modelling as a method of studying the educational environment of university as a process of creating an imaginary system of educational environment, which makes it possible to study the psycho-pedagogical laws. The university’s educational environment is defined as a multi-faceted structure that purposefully and spontaneously influences the professional and personal development of the future specialist, ensuring his / her readiness for professional activity and / or continuation of training, successful self-realization in the process of life activity. The educational environment is a set of material factors of the educational process and interpersonal relationships that establish the subjects of education in pedagogical interaction. The educational environment is a multilevel multicultural entity that is individual to each student; the environment of constructing one’s self, which provides the creation of favorable conditions for actualization of the inner world of the individual, his personal growth, self-realization, the formation of self-consciousness. The essential component of the educational environment is defined by the set of conditions (opportunities) and resources (material, financial, personal, technological, organizational, reputational) for the education of the individual, which are formed in the institution of higher education. The author emphasizes on the main features of the modern university’s educational environment in an open society: flexibility; community and communication; eventfulness; configurability; cultural conformity; spherical; openness, capacity for development and self-development; systematic, organization and structure; sociality; display. Prospects for further research are to investigate the impact of the higher education institution’s educational environment on the personal and professional development of each participant in the educational process.
Key Technology and Equipment Development of Multi-mode Integrated Access Terminal for Regional Energy Interconnection

With the development of energy Internet construction, there are more and more intelligent services such as distributed energy access control, “multi meter in one” information collection, electric vehicle charging and discharging orderly control, and the number of devices is increasing. The ubiquitous access of terminals makes the carrying capacity of local communication network difficult to meet the needs of diversified business scenarios. In addition, there are great differences in the underlying connection protocols of the communication network of regional energy internet terminal equipment, which limits the flexibility and reliability of communication, and restricts the realization of interconnection. This paper studies the multi-mode fusion access technology supporting the multi scenario application of intelligent energy, proposes the joint sensing method of regional energy internet terminal services, the mapping, exchange and adaptation method of multi-mode access in terminal layer, and the active networking and routing method of cooperation among terminal nodes, constructs the local intelligent data processing and business fusion model, and develops the multi-mode based multi-mode fusion technology The intelligent internet terminal equipment integrated with communication access technology has established a comprehensive access system for regional energy Internet, and carried out engineering and application pilot.
Information Theory-Based Feature Selection: Minimum Distribution Similarity with Removed Redundancy

Feature selection is an important preprocessing step in pattern recognition. In this paper, we presented a new feature selection approach in two-class classification problems based on information theory, named minimum Distribution Similarity with Removed Redundancy (mDSRR). Different from the previous methods which use mutual information and greedy iteration with a loss function to rank the features, we rank features according to their distribution similarities in two classes measured by relative entropy, and then remove the high redundant features from the sorted feature subsets. Experimental results on datasets in varieties of fields with different classifiers highlight the value of mDSRR on selecting feature subsets, especially so for choosing small size feature subset. mDSRR is also proved to outperform other state-of-the-art methods in most cases. Besides, we observed that the mutual information may not be a good practice to select the initial feature in the methods with subsequent iterations.
Text Message and Digital Image Secure for Discrete Shearlet Transform

“Steganography” is a strategy that defeats unapproved clients to approach the critical information, to imperceptibility and payload limit utilizing the diverse system like discrete cosine transform (DCT) and discrete shearlet transform (DST). The available methods till date result in good robustness but they are not independent of file format. The point of this exploration work is to build up an autonomous of record organize and secure concealing information conspire. The independent of file format and secure hiding data scheme is increased by combining DST and least significant bits (LSB) technique. In like manner a proficient plan is produced here that are having better MSE and PSNR against various characters.
Unique Indexing of Penrose Tiles and Its Application to Information-Bearing Markers

We propose a method of uniquely indexing tiles in a geometric pattern composed of kites and darts, known as Penrose tilings. This indexing is based on inflation, which is a property to generate larger tiles hierarchically, and independent of rotation and scaling of the pattern. By the indices of specific tiles in a pattern we can communicate information, as we do with Quick Response (QR) codes and Augmented Reality (AR) markers. We present a decoding method with these applications in mind, in which inflation is applied to identified tiles retrieving embedded code using pattern matching based on the geometric hashing. We also present a technique to identify specific tiles in an invisible way. We generated a pattern composed of 6251 tiles in which we can potentially embed 69813 bits of information using our method.
Catch Gesture: An Ultimate Action Recognition Technology Using IoT Device

Nowadays, the internet of things (IoT) which connects the objects with the network is one of the most important technology. Everyone needs to access the IoT devices from anywhere. A challenge in our world is how to provide an efficient and automatic approach to control the IoT devices. In this paper, a system is proposed which provide a smart IoT controlling system, for getting efficient communication between the IoT devices and the users. An IoT device controller named as catch gesture contains three modules 1) object recognition module, 2) eye tracking module, and 3) gesture recognition module. The IoT device is identified by using some deep learning-based methods for getting accurate output and performance. Subsequently, the IoT device control command is generated and transmitted to the IoT devices to control the IoT devices by using hand gesture. The performance evaluation gives the efficiency and usefulness of the proposed system in detecting the IoT devices.
Students’ Usage of Smartphones during the Lectures

The current study aims to investigate students’ usage of smartphones during the lectures. To achieve this aim, the survey model was used and a survey form was created by researchers to collect data in the study. The study group was selected among the undergraduate students at a state university. The study group determined by the snowball sampling method consists of 577 students in total. The descriptive analysis was used to analyze the collected data. At the end of the study, it was concluded that most of the students use their phones occasionally and to message, research on lecture subject, and connect to social networks. Causing distraction and the ban on use during the lectures also emerged as the reasons for not using their smartphones during the lectures. The most important reason for using the smartphones during the lectures is to research about the lecture subject and to use in emergencies. Also, students gave close answers to each other about the effects of using smartphone on academic performance. In the last part of the study, some suggestions were developed regarding the results obtained.
Covert Channels of Data Communication

The article is dedicated to covert channels of data communication in the protected operating system based on the Linux kernel with mandatory access control. The channel which is not intended by developers violates security policy and can lead to disclosure of confidential information. In this paper the covert storage channels are considered. Authors show opportunities to violate the secrecy policy in the protected operating system based on the Linux kernel experimentally. The first scenario uses time stamps of the last access to the files (“atime” stamp), the second scenario uses unreliable mechanism of the automatic login to the user session with another level of secrecy. Then, there are some recommendations to prevent these violations. The goal of this work is to analyze the methods of using covert channels, both previously known and new. The result of the article is recommendations allowing to eliminate security threats which can be embodied through covert channels.
An Analytical Study of CNN-based Video Frame Interpolation Techniques

Videos are made up of a series of continuous image frames. Given these consecutive two frames, video frame interpolation techniques aim for synthesizing video frame or frames that lie temporally in between the given frames, and that is/are spatially adjusted. Video frame interpolation finds various use cases in computer vision activities including but not limited to video restoration i.e., to generate clear video frames in sections where a video is blurred, generating video animations (software editing tools), view synthesis and so on. Relying upon standard optical flow-based video frame interpolation methods is difficult as they often produce blurry results in the cases of occlusion and extensive motion handling between the objects in the frames. Remarkable achievements have been made in the recent past to capture varied large scale motion using deep convolution networks. In this paper, we will discuss how some deep convolution networks based methods have evolved over the years to improve the quality of the synthesized frames both qualitatively and quantitatively for video frame interpolation task.
Understanding and Analyzing Methods of Character Identification in Movies

Film and television industry is one of the fastest- growing sectors across the globe. Character identification and its method classification have attracted a lot of attention from researchers because of its applications in video summarization, personalized video retrieval, and semantic video analysis. In this paper, we study and analyze 14 algorithms categorizing them into three categories and compare them based on various parameters and present our findings in a tabular form.
Algorithms for Automated Sentiment Analysis of Posts in Social Networks

Currently, data analysis from social networks is referred to as big data analysis (BigData). The analysis of big data is difficult primarily due to the fact that all the data is fragmented, have different structure and purpose. Nowadays there are no any universal algorithms that would allow a full analysis of a social network user's profile. For the most part, these tools evaluate content quantitatively (how many photos, videos, audio, posts on a user's page), by the time of user activity, and by the most frequently used words.This article discusses approaches to automating the sentiment analysis of the text content in the social network vk.com, located in the public access. Provided an overview of similar studies and a description of different software. This paper examines libraries developed on the basis of machine learning algorithms, as well as algorithms that use tonality dictionaries. The paper compares the probability with which the described algorithms determine the tone of texts. Made the conclusions about the mechanisms of their operation, and given assumptions about the causes of errors are.
The Application of Image Style Transformation Based on GAN in the Intelligent Mobile Terminal

Generative Adversarial Network (GAN) is a kind of deep learning model and one of the most promising methods for unsupervised learning in complex distribution in recent years. Due to the need for higher hardware support, it has certain limitations in practical application. Therefore, how to use the fast-developing Web technology and Cloud technology to promote and share it is a difficult problem. To address this issue, the theme is based on the hot model of GAN, combined with the thinkPHP framework, using python and php language, then, by means of the Cloud computing technology and bootstrap technology, the interactive response system for image style transformation between the mobile terminal and the server terminal is subsequently realized, which conforms to the development trend and expansion characteristics of the mobile terminal. This scheme can be promoted to art creators or the general public to meet their needs for artistic inspiration or image style transformation and ultimately to achieve a complete chain of resource collection, information processing and service feedback.
SomethingFinder: Localizing undefined regions using referring expressions

Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or identifiable objects near a target location. Instead, references may need to rely on basic visual attributes, such as color or geometric clues. An expression like "a red something beside a blue vertical line" could still pinpoint a target location. As such, we begin to explore the open challenge of computational object-agnostic reference by constructing a novel dataset and by devising a new set of algorithms that can identify a target region in an image when given a referring expression containing only basic conceptual features.
Distributed Video Analysis for Mobile Live Broadcasting Services

While webcast platforms on mobile devices are becoming more and more prevalent, inspection for irregularities is getting harder and harder. To solve this problem, the convolution neural network(CNN) has been applied to recognize or detect specified objections in pictures and videos. However, when supervising large platforms, it isn’t very easy to collect mountain piles of video data and send them to the computation center. Other problems like long time delay and the high computational burden will reduce system performance, especially when dealing with data from live streams. This paper presents a method to coordinate mobile devices with remote servers(computers or embedded systems) to achieve real-time monitoring of live streams. The system can make use of computational capacity on mobile devices and reduce the cost of sending data while guaranteeing accuracy for supervision.
Automatic Stereo Disparity Search Range Detection on Parallel Computing Architectures

From the earliest to the state-of-the-art algorithms, stereo depth estimation techniques often require a disparity search range (DSR) value to be chosen manually. However, the optimal DSR varies from one scene to another making the results depend on the operator input and operator having to optimize the configuration by using trial-and-error. In this paper we present a novel technique suitable for parallel computing architectures which detects the optimum DSR for a given scene without requiring operator input or prior knowledge of the scene. Experiments on stereo images from Middlebury, KITTI and Sceneflow bench-mark datasets indicate that our technique can automatically extract a suitable DSR value from different scenes, which leads to better consistency in matching. The technique presented here can be used with existing stereo algorithms to limit the size of the cost volume as it is being built (without requiring pre-processing or operator input). A CUDA based implementation of our method can deliver real-time performance on consumer grade GPUs at high frame rates.
Comparative study of relational and graph databases

We live in a time of incredible technological development. In a fraction of a second, millions of information are generated and processed all over the world. The need to develop new models of databases is related to the dynamic and the amount of data stored. Using database schemes which are rigid and do not quickly adapt to changes occurring in the real world is ineffective and sometimes simply impossible. The solution of all these problems could be, among others, graph databases.In this talk, we present a detailed comparison of relational and graph databases, compiling the stages of design, implementation and testing. At each stage, we keep the same data in both databases, checking how easily they can be modelled in each logical model, then imported, and finally made queries and modifications on them.
Competencias digitales en estudiantes de nivel medio y universitario ¿Homogéneas o heterogéneas?

Today's work world demands a set of competencies that are key and highly valued, among them, digital skills. Within this framework, the paper presents data about the digital skills of current students. A total of 454 students from different university degrees (engineering, economics and humanities) and from a secondary school participated in the research. They responded to a questionnaire on ICT uses and self-perceptions of their digital competencies for the performance of activities that require the use of technology. The results show different levels of mastery (high, medium and low) with respect to 18 activities consulted. In the same way, 10 of the 18 activities showed differences when groups where compared. Engineering students generally reported greater mastery. Discussion revolves around highlighting the importance of generating, from academic contexts, opportunities for the development of different digital skills.
A Variable Threshold Image Secret Sharing Method

Abstract Image secret sharing has been widely discussed in recent years because of its high security. The threshold variable image secret sharing method is a hot research topic recently. The Lagrange interpolation method is easy to establish. Therefore, most of the existing secret sharing schemes are based on Lagrange interpolation polynomials. However, when the secret is updated, the existing secret sharing scheme needs to re-establish the polynomial, and the original computing resources cannot be utilized. When the cheater is found, it is not convenient to add or delete participants flexibly, resulting in a large waste of computer resources. Therefore, an image secret sharing method with variable threshold based on Newton interpolation method is proposed, which can reduce the computational complexity of the system and the amount of repeated computation. The experimental results show that the method is superior when the threshold is changed.
IoT Confidentiality: Steganalysis breaking point for J-UNIWARD using CNN

The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%.