Reviewerly/exHarmony huggingface dataset:
ExypnoSteganos - A smarter approach to steganography

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.
Real-Time Covid-19 Risk Level Indicator for the Library Users

Covid-19 has highly affected the university library visit routine of students and faculty members. To investigate this further, university students were interviewed, and it was found out that they were quite concerned about their safety and health when visiting the library in Covid-19. To cater this, a smart interactive system is designed, which helps the students and faculty members avoid high-risk situations inside the library and can optimally manage their library visits. The system has two main components i.e. a public display and a mobile interface. Both the public display and the mobile interface were evaluated by university students in order to understand whether this system met the user needs and expectations as expressed during the informing phase. The results and their implications for the designs of similar systems are discussed.
Ensayo de aeroestructuras de material compuesto a altas frecuencias mediante la técnica de la correlación digital de imágenes proyectada

Complex composite structures have unexpected mechanical behaviour, specially under high dynamic loads or in bucking conditions. The complexity and the number of sensors of these types of structural testing require non-standard instrumentation in order to detect local phenomena before critical failure appears. The main problem introduced by accelerometers or strain sensors is the cabling, due to the weight and complexity introduced.Digital Image Correlation (DIC) is a strain and deformation measurement technique well know and extendedly applied in composite structural testing. In this paper it is proposed a new DIC methodology based on project the aleatory patter over the structure instead of paint it. Furthermore, an UAV wing full made of composite will be tested at high frequency and vibrations modes will be observed with a stroboscope and, simultaneously, the aleatory patter will be projected in order to measure the shape of the vibration mode applying the DIC technique. Additionally, strain and shape sensing at high frequencies will be validated with Distributed Fiber Optic at high frequency rates.In this paper, the different technologies will be tested and compared in order to validate their capabilities and limitations.
Prevention of Duplication of Encrypted Big Data in Cloud

Cloud computing means sharing, accessing, uploading, downloading, managing and storing the data on the internet. So, the cloud is the most important feature for any user to save the storage space. To provide the security and privacy for those data holders the data is stored in encrypted form. Because of encrypted data, many new challenges occur for cloud mainly data duplication. Hence, Deduplication is introduced. Deduplication is important for big data storage because it can save storage space. Traditional Deduplication plan does not work on encrypted data. And the existing deduplication scheme suffers from the security attacks such as brute force and dictionary attack. They do not flexibly support data accessing, controlling and revocation In this paper it is intended to do a scheme to deduplicate encrypted data stored in cloud which means to stored single copy of duplicated content. In the existing scheme, the duplication was detected based on the name generation of the file. But in the proposed scheme the duplication is checked on the content of the file. The file is encrypted independent of the format and then stored in the cloud.
Preliminary Exploration and Future Prospects of Literary Works Appreciation Under the Background of Artificial Intelligence

As a rapidly developing field of scientific and technological innovation, artificial intelligence has a profound impact on the development of literary works appreciation. This paper mainly studies the preliminary exploration and future prospect of literary works appreciation under the background of artificial intelligence. On the basis of extensive and in-depth analysis of relevant materials, this paper draws lessons from similar questionnaires, and designs the questionnaire scientifically and reasonably according to several years’ teaching experience. A total of 225 questionnaires were distributed and 202 valid questionnaires were collected. In this paper, a comprehensive and in-depth investigation of junior high school students’ Chinese learning, to understand their learning motivation and subjective understanding, from the standpoint of students to explore and study the feasibility and effectiveness of cultivating aesthetic appreciation ability. In this paper, anonymous survey is used to ensure the accuracy and objectivity of the survey results. Before the formal distribution of the questionnaire, a pre-test was carried out, and the consistency and reliability of the questionnaire were tested by SPSS software. The data shows that 14.8% of the students have good literary appreciation level, 68.4% of the students have a general level of literary appreciation, 14.8% of the students are in a poor state, and 2% of the students have poor literary appreciation level. The results show that artificial intelligence technology can promote the appreciation ability of literary works.
Stand-alone Lightweight Security System for Native Language Using SPI Interface

In the defense, the native language is highly preferred among the soldiers for data communication. The data contains sensitive information which is susceptible to attacks. Therefore, cryptography algorithms are used to overcome these attacks. In this paper, a stand-alone lightweight security system has been designed for the native language using the PRESENT algorithm. In this algorithm, plaintext and key are input in the native Hindi language. This algorithm is configured on Basys 3 FPGA, and Arduino Uno has been used to display Hindi characters. The communication protocol Serial Peripheral Interface (SPI) is used between FPGA and Arduino. The PRESENT algorithm with the SPI communication protocols is programmed on Vivado 2016.2 software using VHDL language, and Arduino controller coding is done on IDE using C/C++ language. The results show that the proposed methodology consumes almost the same resources and power as in PRESENT algorithm.
Assessment of methods online forensics in alleviation of the consequences of the attacks of extortioners

Abstract This article is devoted to disclosure of essence of a concept online forensics. On the basis of the analysis of practical activities and works of domestic and foreign experts in article the author’s concept characterizing estimated judgment of methods in an issue of alleviation of the consequences of the attacks of extortioners reveals.
Traditions of Expressionism in Modern Dramaturgy (by the Example of P. Turrini’s Plays)

“Finally, the End”) as works of expressionist art. The research findings are as follows: P. Turrini’s plays are typically expressionist both at the level of problems and at the level of the elements of drama and theatre poetics characteristic of this modernist movement: abstracted plot, oversimplified portrayal of characters (their deindividualisation), direct involvement of the audience in the action, genre originality of socio-philosophical dramas and dramas of “proclamation” and “transfiguration”, expressionist concept of the world and a man as a whole. The study is novel in that it is the first to consider P. Turrini’s works through the lens of expressionist aesthetics.
Wasserstein Autoencoders with Mixture of Gaussian Priors for Stylized Text Generation

Probabilistic autoencoders are effective for text generation. However, they are unable to control the style of generated text, despite the training samples explicitly labeled with different styles. We present a Wasserstein autoencoder with a Gaussian mixture prior for style-aware sentence generation. Our model is trained on a multi-class dataset and generates sentences in the style of the desired class. It is also capable of interpolating multiple classes. Moreover, we can train our model on relatively small datasets. While a regular WAE or VAE cannot generate diverse sentences with few training samples, our approach generates diverse sentences and preserves the style of the desired classes.
Some Game-Theoretic Remarks on Two-Player Generalized Cops and Robbers Games

In this paper, we study the two-player generalized cops and robbers (GCR) games introduced by Bonato and MacGillivray. Our main goals are to provide: (a) a game-theoretic formulation of GCR and (b) a self-contained game-theoretic proof that GCR has a value and an optimal strategy profile. To achieve our goals, we first formulate GCR (and CR as a special case) as a zero-sum stochastic game. Then we study a Vertex Labeling (VL) algorithm and prove it computes the value of the GCR game (for every starting “condition”) and that the vertex labels can be used to specify a positional deterministic optimal strategy for each player. We also compare our game-theoretic analysis to some of the usual graph theoretic/combinatorial approaches to CR/GCR.
Online system for staging hepatic fibrosis

Cirrhosis of liver may cause structural distortion of entire liver by fibrosis and parenchymal nodules, in which the image findings in MR/CT images can be interpreted by shape, texture, volume, elasticity analysis and so on. To ease the workload of radiologists from interpretation of the numerous medical images, an online Computer-aided diagnosis (CAD) system on liver has been developing for quantifying the diagnosis of fibrosis. Several technologies regarding image processing and pattern recognition are introduced in this paper, including Shape, Texture, Volume, Elasticity Analysis and Edge computing with FPGA acceleration. The results by the novel methods on the non-invasive assessment of liver fibrosis as well as the role of our CAD scheme to replace traditional liver biopsy will be presented and discussed.
Security Evaluation and Strategy Management Model Based on Fuzzy Comprehensive Evaluation and BP Neural Network

Based on the angle of risk management, this paper uses fuzzy comprehensive evaluation method and BP neural network method to carry out enterprise control activity design, operation risk status and safety level evaluation. In this paper, chemical fertilizer production enterprises are selected as evaluation samples. Combined with the business characteristics and development status of enterprises, and the scope of internal control activities, the empirical analysis of internal control activities of sample enterprises is carried out, and the evaluation results obtained by the two methods are analyzed, cross verified, and the evaluation results and the actual control activities of enterprises are obtained The results are basically consistent with each other, which verify the validity, reliability and practical guidance of the method.
An Automated Covid-19 Face Mask Detection and Warning System with Deep Learning

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods are wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing face masks while at work clearly reduces the risk of transmission. As the result, to create an efficient and economic approach of using Artificial Intelligence (AI)for safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, by using OpenCV to do real-time face detection from a live stream via our webcam. The use of dataset is to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. The goal is to identify whether the person on video stream is wearing a face mask or not with the help of computer vision and (RCNN) deep learning.
Gesture Recognition System Based on Neural Networks by Using COTS RFID Tag Array

Nowadays, gesture recognition plays a more and more important role in human-computer interaction. In this regard, contact sensors or computer vision have made some progress, but they also have shortcomings in portability or privacy. In this work, we propose a gesture recognition system which uses RFID tag array and neural networks to recognize gestures. By using an RFID tag array, we can obtain gesture information in a non-contact, non-infringing manner. By combining CNN and LSTM as CNN-LSTM, we can focus on both spatial and temporal features and get better performance. Experiments show that the accuracy of the system on the test set is 92.17%, and it performs well in recognizing different gestures of different users at different speeds.
Classification of Equipment Operation Phases Based on Mobile Device Data

The purpose of this article is to describe the classification of phases of equipment operation based on data from a mobile device. This article contains an example of the architecture of a software package for tracking the state of activity of special equipment based on vibrations. The data source is the accelerometer and gyroscope. These sensors are integrated into the mobile devices from which data is collected and do not require an additional installation. Data is collected from a mobile device using a developed application and then transmitted via a software package to a neural network. The neural network is responsible for analyzing the data received from a mobile device.
User-Centered Design for Website and MobileApplication to Monitor Recovery Process of Post-Treatment Mental Disorders

Mental health is still considered as a significant health problem inthe world, including in Indonesia. Most patient's family are lackingthe knowledge on how to assess the patient in the post-treatmentperiod is one main factor of this problem, causing many posttreatmentpsychiatric patients left ignored. A proper implementationof information technology can resolve this problem by developingan application that can help the patient's family indirectly connectedwith the hospital or health center, making the communication in thepost-treatment period easier. However, several aspects need to becarefully prepared when building a software, including determiningthe interface and functions available within the application. Thedeveloper must understand the user requirements in using theapplication, as well as how to make it user friendly. Therefore thedesign process is an important measurement tool in terms ofsoftware development. One method that can determine the userrequirement is the User-Centered Design method. This methoddirectly involves the user at an early stage of software developmentso that the users can tell what they need in the application, providesuggestions for the interface design, and fill in the usability scalesystem questionnaire to help the developer assess whether theCode : INF_07Book of Abstracts - Informatics | 100interface design is acceptable or not. This study successfullydeveloped the interface design of JiwaMuKu application and hasbeen able to meet all the user requirements by using the User-Centered Design method. The interface design test was carried outusing the system usability scale and scored 75.33 which isconsidered good and acceptable for further development.keyword : user-centered design, mental disorder, system usabilityscale, user interface, user experience,
Identifying Positive Socioeconomic Factors of Worker Roles

Workers incorporating new technologies into their workflow is crucial for organizations. Treating workers as an asset that can be trained to harness the potential of new artificial intelligence (AI) and automation technologies is a middle ground not afforded by most organizations. This study is to aid stakeholders in understanding the positive socioeconomic factors to create solutions to workforce displacement that can incorporate AI technologies. To ensure the creation of effective training solutions, this study is focused on stakeholders immediately affected, workers, and their role. I ask, “what socioeconomic factors should automation and AI technology developers consider for training humans in the workplace?” In answering the research question, the technology developers will be sensitive to socioeconomic factors as potential data points in a training solution. Designing roles incorporating the productivity metrics of job attributes, agency in decisions, and job satisfaction were socioeconomic factors found to have a positive effect on workers.
WITHDRAWN: Cybercrime legislation applicable and enforceable

The Internet has become the central nervous system in society, cybersecurity is to protect the privacy, integrity, and availability of information as it need legal measures by developing a model of cybercrime legislation that is universally applicable and enforceable. It’s necessary to activate procedural and technical measures and Organizational structures to help prevent attacks against, detect and respond to vital information infrastructures. In addition to develop strategies to enhance knowledge and expertise to raise the profile of cyber security on the national policy agenda. The threaten of malware could be divided into spam, worms, Trojans, spyware, Ransomware so it's important to use types of network security for example access control, antivirus, Firewalls, application security, Email security and etc.
Quantum Semi Non-negative Matrix Factorization

Semi Non-negative Matrix Factorization (SNMF) is a machine learning algorithm that is used to decompose large data matrices where the data matrix is unconstrained (i.e., it may have mixed signs). We develop the quantum version of SNMF using quantum gradient descent, and we show that the quantum version of SNMF provides an exponential speedup compared to its classical version. Our approach is well adapted for high-dimensional problems since in the context of using a quantum approach only a few number of iterations is enough to obtain a close approximation of the factorization. From the algorithmic analysis point of view, the proposed algorithm for Quantum Semi Non-negative Matrix Factorization (QSNMF) yields a performance \(T\mathcal {O}( polylog K (M+N)),\) while the classical version of Semi Non-negative Matrix Factorization (SNMF) yields a performance \(T\mathcal {O}(poly (K M N))\) where M and N are the dimensions of the matrix, K is the number of clusters and T is the maximum number of iterations to prepare the quantum states.
Research on Multi-channel Pulse Amplitude Analyzer Based on FPGA

Multi-channel pulse amplitude analyzer is a key component of energy spectrometer, of which performance determines the measurement accuracy. Therefore, analysis technology is an important research topic in nuclear radiation measurement technology. This paper presents a design method of multi-channel pulse amplitude analyzer based on FPGA technology. In the design, 100 MHz crystal oscillator is used, and the frequency is increased to 200 MHz by digital phase-locked frequency multiplier (PLL). Compared with the traditional 12 MHZ analyzer, this analyzer not only greatly improves the accuracy of the system, but also improves the differential nonlinearity of the system. Through the USB controller composed of FPGA, the design realizes the function of communication with USB2.0 interface. The two-stage constant current source linear discharge method is adopted to improve the channel width equilibrium of ADC and reduce the dead time of the system. Finally, Modelsim and other testing software are used to simulate the designed system and test the real object. The experiment shows that the performance of the system is well.
Research and Design of Mobile Learning Platform Based on Collaborative Filtering Algorithm

Advent of the era of Internet plus and big data, how to enrich educational resources and optimize teaching process has become ducational development. The new teaching mode electronic information technology, especially network technology, so that the teaching can develop towards the direction of Therefore, the informatization, digitization and intellectualization of traditional education mode become the inevitable development trend on the road of education reform in the future.
Regression Analysis of Dependency Between Related Courses on 1<sup>st</sup> Year of Study on Faculty of Information Technologies

This research analyzes prediction of student's success in related courses on Universities. Syllabus of the Faculty of Information Technologies of University "Dzemal Bijedic" in Mostar contains linked courses which are conditioned by each-other. These courses which are pre-requisite to others are in some cases on the same academic years and some of them are in following year. In this research authors proposed regression analysis of student's success dependency on two courses on first year of study. Correlational analysis indicated existence of moderate correlation. Regression analysis showed that the proposed model indicates weak determination of correlation between subjects. One variant of regression equation is rejected since independent variable was not significant. Regression equation y = ax is accepted.
Research on the Application of Jigsaw Generative Adversarial Network to Face Generation

In recent years, the development and research of GAN[1] is nothing more than a change in the number or structure of the generated network. The method of discriminate network is only to map different regional features to different discriminators and integrate them. As we all know, the quality of generator can affect the effectiveness of the entire network directly, but I think the function of the discriminator is the important pillar to support the entire network. So the author proposed a face-generation network named Jigsaw Generative Adversarial Network, which uses a single generator network with a discriminator as a layer. Each layer creates different parts to enhance the authenticity of each part, and then the face is composed in a jigsaw way, and is matched with the style module of Style GAN[2] to achieve more highly build quality. The proposed system can select the desired style for data set generation. For example, you can select the adult male style to generate a data set of all adult men and use this data set for research, or use the extracted style modules to apply to other generation networks and control the direction of the generated graph of the generated network.