Reviewerly/exHarmony huggingface dataset:
Chaos-based Color Image Steganography Method Using 3 D Cat Map

Steganography is a technique to hide a secret message within a different multimedia carrier so that the secret message cannot be identified. The goals of steganography techniques include improvements in imperceptibility, information hiding, capacity, security, and robustness. In spite of numerous secure methodologies that have been introduced, there are ongoing attempts to develop these techniques to make them more secure and robust. This paper introduces a color image steganographic method based on a secret map, namely 3-D cat. The proposed method aims to embed data using a secure structure of chaotic steganography, ensuring better security. Rather than using the complete image for data hiding, the selection of the image band and pixel coordination is adopted, using the 3D map that produces irregular outputs for embedding a secret message randomly in the least significant bit (LSB) of the cover image. This increases the complexity encountered by the attackers. The performance of the proposed method was evaluated and the results reveal that the proposed method provides a high level of security through defeating various attacks, such as statistical attacks, with no detectable distortion in the stego-image. Comparison results ensure that the proposed method surpasses other existing steganographic methods regarding the Mean Square Error (MSE) and Peak Signal-to-Noise Ratio(PSNR).
Dual-Tree Complex Wavelet Transform-Based Direction Correlation for Face Forgery Detection

With the rapid development of face synthesis techniques, things are going from bad to worse as high-quality fake face images are unnoticeable by human eyes, which has brought serious public confidence and security problems. Thus, effective detection of face image forgeries is in urgent need. We observe that some subtle artificial artifacts in spatial domain can be easily recognized in transformation domain, and most facial features have an inherent directional correlation, and generative models would ruffle this kind of distribution pattern. Inspired by this, we propose a two-stream dual-tree complex wavelet-based face forgery network (DCWNet) to expose face image forgeries. Specifically, dual-tree complex wavelet transform is exploited to obtain six directional features (±75°, ±45°, ±15°) of different frequency components from original images, and a direction correlation extraction (DCE) block is presented to capture the direction correlation. Then, the direction pattern-aware clues and the original image are taken as two complementary network inputs. We also explore how specific frequency components work in face forgery detection and propose a new multiscale channel attention mechanism for features fusion. The experimental results prove that the proposed DCWNet outperforms the state-of-the-art methods in open datasets such as FaceForensics++ and achieves high robustness against lossy image compression.
THE PECULIARITIES OF TEACHING PERFORMING MUSIC COURSES TO STUDENTS OR CHOREOGRAPHY IN THE PROCESS OF THEIR PROFESSIONAL TRAINING

This article presents an analysis of the peculiarities of teaching performing music courses such as «orchestration tutorial» and «orchestral conducting» to students or choreography in the process of their professional training. The educational potential of these courses is shown to facilitate the development of professional qualities in a future choreography teacher. The research also focused on the psychological and pedagogical conditions of a successful pedagogical collaboration of the music teacher and a student of choreography. This study is aimed to contribute to the development of a new course of «Choreography» at the faculties specializing in art at state pedagogical universities of the country providing the modern teaching practice with an academic base in terms of the peculiarities of teaching performing music courses to students of choreography. teaching music and performance disciplines to students-choreographers has a problem-searching nature. This strengthens the subject-subject nature of the relationship between teacher and student, expands the possibilities of their creative interaction in classes on orchestral workshops and workshops on orchestral conducting. Successful interaction of a teacher-musician with a student-choreographer in the process of studying the laws of musical art and the basics of musical performance helps to stimulate interest in learning the latter, motivating the future of complex musical and choreographic activities in secondary school (performing, educational, creative or cognitive). on the formation of his personal qualities, professional skills, abilities, disclosure and realization of his creative potential. Psychological and pedagogical prerequisites for successful pedagogical interaction of a musician-teacher with a student-choreographer are provided by personality-oriented, competence and integration approaches of modern educational strategies.
ONLINE LEARNING IMPLEMENTATION

ABSTRACT&#x0D; This research aimed to find out how online learning implementation affected learning practices in a senior high school in an undeveloped regency in West Kalimantan Province. Descriptive quantitative method was applied in this research. To collect the data, students’ and teachers’ questionnaire were used. The questionnaires adopted Likert scale with five options. The populations of this research were 150 students and 40 teachers, while the sample involved were 90 students and 40 teachers all together. The student sample were selected using simple random sampling. Overall, this research found that both teachers and students had similar views regarding the application of the online learning policy during covid 19 pandemic. In this regard they agreed to claim that the online learning implementation tended to show more negative influence than positive to almost all classroom learning processes. The average percentage of teachers and student who found the negative impacts of the online learning policy implementation were, respectively, 50.8% and 73.86%. Nonetheless, the percentage proportion of students who did not claim the implementation of the online learning policy brought more negative effect than positive was also big. These numbers brought down strikingly interesting findings, as discussed in this study.&#x0D; &#x0D; Keywords: Covid-19, Online Learning, high school students, English, learning impact
Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks

We make two related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC-Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of partially-aggregated estimators, proving that these lead to unbiased lower-variance output and gradient estimators; (2) we reformulate a PAC-Bayesian bound for signed-output networks to derive in combination with the above a directly optimisable, differentiable objective and a generalisation guarantee, without using a surrogate loss or loosening the bound. We show empirically that this leads to competitive generalisation guarantees and compares favourably to other methods for training such networks. Finally, we note that the above leads to a simpler PAC-Bayesian training scheme for sign-activation networks than previous work.
Emergency Responses to the COVID-19 Crisis in Education: A Shift from Chaos to Complexity

This paper presents a pilot research of approaches taken by European Union (EU) Member States (MSs) in the emergency response to the COVID-19 crisis in pre-tertiary education. It includes a multi-case study of four MSs, examining their education systems’ digital readiness and “fitness for change”, as well as decision-making practices. We use the Cynefin framework of decision contexts to explore different paths in shifting from chaos to complexity. The preliminary findings of this research indicate that a factor positively influencing education systems’ response was not exclusively their digital readiness, but also “fitness” in terms of ongoing reforms and preparedness for change. The findings also suggest that MSs generally continued with their usual decision-making practices, with tendencies towards centralisation of crucial decisions, which is in line with the Cynefin framework, arguing for stronger leadership in chaotic contexts.
Query Specific Focused Summarization of Biomedical Journal Articles

During COVID-19, a large repository of relevant literature, termed as "CORD-19", was released by Allen Institute of AI.The repository being very large, and growing exponentially, concerned users are struggling to retrieve only required information from the documents.In this paper, we present a framework for generating focused summaries of journal articles.The summary is generated using a novel optimization mechanism to ensure that it definitely contains all essential scientific content.The parameters for summarization are drawn from the variables that are used for reporting scientific studies.We have evaluated our results on the CORD-19 dataset.The approach however is generic.
Quantum Photonic Processor based on Programmable Integrated Silicon Nitride Circuits

Recently, quantum advantage over classical computation has been claimed using photons [1] . To control photonic quantum computations such as Boson sampling, a non-universal approach on quantum computation, large-scale quantum processors are needed [2] , [3] . To realize scalable and robust photonic quantum processors integrated photonics is a key technology. In our approach we use the integrated waveguide platform based on silicon nitride [4] , due to its low intrinsic losses and technological maturity. The low losses result in a high fidelity of the quantum photonics processor which is mandatory for reliable quantum computation while maturity of the platform allows for scaling up of the dimensions of the processor. Here, we present a universal 12-mode quantum photonics processor which is the largest of its kind to date.
Leadership Skills Training for Teachers as Leaders: The Research Report on the Erasmus+ CBHE Project. Development of Skills and Teachers Training for Leadership/DeSTT

The DeSTT concept of teacher training for leadership examines the importance of non-formal education, i.e., training for teachers. The monitoring study revealed the urgent needs of Kazakhstani teachers in training their leadership skills. The paper analyzes two sets of research questions (each consists of 6 sub-questions), which we defined as follows: 1) What are the challenges of teacher training/upskilling for leadership and their involvement in the local community? This group of questions was studied at the stage of preparing the project proposal. 2) What are the impacts of DeSTT training on teachers' leadership skills and experiences? This group of questions was studied during the 2nd year of the project lifetime. The purpose of this paper is to indicate the findings and implementation of the concept of preparing teachers for leadership from the project proposal launch to the execution of pilot trainings. Research methods employed in the study are interviews of universities specialists and analyzes of the State data/reports for the project proposal. Observations of training participants and post-training interviews were used to study the 2nd group of research questions. The findings of the study confirm the data obtained in both groups of interviews and observations. Participants were enthusiastic and interested in the pieces of training, aware and confident of the need to continue learning, share experiences, and develop leadership skills achieved in DeSTT training. The reflection on the central terms of leadership and training has proved to be crucial for teachers. Further research is to survey the implications of the DeSTT project for all its consumers. The dissemination and sustainability perspective of the project is to collaborate with the National Center ORLEU for training leadership skills to the instructors from 17 regional branches who, in turn, will train teachers for leadership.&#x0D; The authors acknowledge the Erasmus Plus CBHE for funding the DeSTT project.
A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index

Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments.
About ensuring security in the field of the internet of things

The rapid development of the Internet of Things (IoT) and its capabilities in terms of services have made it one of the fastest-growing technologies that have a huge impact on both social life and the business environment of a person. The widespread adoption of connected devices in the IoT has created a huge demand for reliable security in response to the growing demand of billions of connected devices and services around the world. But at the same time, the number of threats continues to grow every day, and attacks are increasing both in number and complexity. The number of attackers is also growing, and the tools they use are constantly being improved and becoming more effective. Therefore, it is necessary to constantly protect against threats and vulnerabilities for IoT. In this article, we will analyze the development of IoT, consider existing threats, attacks on IoT, as well as methods of protecting devices from threats and vulnerabilities for IoT.
Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

Twitter is considered a significant source of exchanging information and opinion in today's business. Analysis of this data is critical and complex due to the size of the dataset. Sentiment Analysis is adopted to understand and analyze the sentiment of such data. In this paper, a Machine learning approach is employed for analyzing the data into positive or negative sentiment (opinion). Different arrangements of preprocessing techniques are applied to clean the tweets, and various feature extraction methods are used to extract and reduce the dimension of the tweets' feature vector. Sentiment140 dataset is used, and it consists of sentiment labels and tweets, so supervised machine learning models are used, specifically Logistic Regression, Naive Bayes, and Support Vector Machine. According to the experimental results, Logistic Regression was the best amongst other models with all feature extraction techniques.
Hardware–Software Co-Design Framework for Data Encryption in Image Processing Systems for the Internet of Things Environment

Data protection is a severe constraint in the heterogeneous IoT era. This article presents a Hardware-Software Co-Simulation of AES-128 bit encryption and decryption for IoT Edge devices using the Xilinx System Generator (XSG). VHDL implementation of AES-128 bit algorithm is done with ECB and CTR mode using loop unrolled and FSM-based architecture. It is found that AES-CTR and FSM architecture performance is better than loop unrolled architecture with lesser power consumption and area. For performing the Hardware-Software Co-Simulation on Zedboard and Kintex-Ultra scale KCU105 Evaluation Platform, Xilinx Vivado 2016.2 and MATLAB 2015b is used. Hardware emulation is done for grey images successfully. To give a practical example of the usage of proposed framework, we have applied it for Biomedical Images (CTScan Image) as a case study. Security analysis in terms of the histogram, correlation, information entropy analysis, and keyspace analysis using exhaustive search and key sensitivity tests is also done to encrypt and decrypt images successfully.
Novel Approach for Steganography to Camouflage Digital Information Using Least Significant Bit

Steganography is the knack of hiding data for many purposes such as to secure confidential information and to maintain private information and so on. Securely transformation of the information over the public network is a very important issue. So, in order to transfer the information securely to the terminus, there are various methods like steganography and cryptography. In precise, steganography is the exercise of hiding data, image, video or file in another message, image, video or file. In this paper, we propose a least significant bit (LSB)-based steganographic approach for camouflaging the information by applying the least significant bit algorithm for embedding the information into the images, which is implemented through Python and also provided integrity via message-digest algorithm for further security. In our proposed steganography approach, the pixel value will be depending on the data bit, if the data bit in binary is 0 then the pixel value should be even, if it is odd then that value should be decreased by 1 to make it even. If the data bit is binary 1 then the image pixel value should be odd, if it is even then a decrement by 1 will make it odd. In the end, we will have an image, which has undergone the steganography process and this will be sent through any regular medium to the destination and then it is decrypted back to obtain the information hidden.
Global Transformation of Higher Education: From Traditional to Entrepreneurial University

In the last few decades, there has been a radical change in the paradigm of universities – from organizations engaged in teaching and conducting basic research to institutions that actively interact with the real sector and commercialize the results of intellectual activity. In the scientific literature, this new model was called an "entrepreneurial university". The main hypothesis is the assumption that there is a global trend of turning traditional universities into entrepreneurial ones, as well as integral external drivers of this process. The purpose of this study is to describe the mechanisms of the university sector transformation in different countries, both at the national level and on the example of individual universities. The methodology of the work combines several research areas: a review of theoretical concepts of the university's activity format evolution; the study of key state tools for the transformation of the university sector; a comparative analysis of statistical data illustrating the cooperation of university science with the business sector; consideration of illustrative examples or cases of university transformation into world-class entrepreneurial universities. The main results and conclusions include the following provisions. First, the deployment of multi-stage mechanisms for creating centers of technological growth of regional and sometimes national economies based on individual universities or their consortia has become a global practice. Secondly, statistical indicators show the existence of a noticeable trend of strengthening cooperation between private companies and universities in conducting R&amp;D, which indicates a gradual reorientation of the latter towards solving applied research tasks that are in demand in the market. Thirdly, the process of universities transforming into entrepreneurial organizations can occur in various ways, but the vital factor is the presence of a real demand for the results of university research and development. The described cases can have practical application when drawing up a strategy for the transformation of Russian universities into entrepreneurial universities.
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer

Expert-layman text style transfer technologies have the potential to improve communication between members of scientific communities and the general public. High-quality information produced by experts is often filled with difficult jargon laypeople struggle to understand. This is a particularly notable issue in the medical domain, where layman are often confused by medical text online. At present, two bottlenecks interfere with the goal of building high-quality medical expert-layman style transfer systems: a dearth of pretrained medical-domain language models spanning both expert and layman terminologies and a lack of parallel corpora for training the transfer task itself. To mitigate the first issue, we propose a novel language model (LM) pretraining task, Knowledge Base Assimilation, to synthesize pretraining data from the edges of a graph of expert- and layman-style medical terminology terms into an LM during self-supervised learning. To mitigate the second issue, we build a large-scale parallel corpus in the medical expert-layman domain using a margin-based criterion. Our experiments show that transformer-based models pretrained on knowledge base assimilation and other well-established pretraining tasks fine-tuning on our new parallel corpus leads to considerable improvement against expert-layman transfer benchmarks, gaining an average relative improvement of our human evaluation, the Overall Success Rate (OSR), by 106%.
SOUTH AFRICAN POLICE SERVICE CAPACITY TO RESPOND TO CYBERCRIME: CHALLENGES AND POTENTIALS

The growing threat of cybercrime poses significant challenges for police organizations. Due to the sophisticated methods used to commit cybercrime, the implementation and investigation of cybercrime have become more demanding and time-consuming. For the police to successfully investigate and punish acts of crime, it is necessary to know and understand the operations of the perpetrators. A qualitative research approach utilizing purposive sampling was adopted to explore the capacity of policing cybercrime in the study area. The findings collected through semi-structured interviews with eight key informants comprising of South African Police Service (SAPS) Crime Intelligence Unit officials suggest that there is a limited cybercrime investigative expertise within the specialized investigation units, coupled with the cyber-elements that are part of almost every traditional crime investigation today, has caused a significant increase in the workload of cybercrime investigative units. The study also found a lack of corporation amongst relevant role-players to boost internal cybercrime investigative resources, resulting in capacity challenges in keeping up with the workload. In addition, as the study showed, the lack of a clear legal framework makes it difficult to investigate and prosecute crimes committed with the Internet, as the authorities are forced to rely on the Criminal Procedure Law. The recommendations are presented as a potential step to developing educational packages and awareness programs to provide at-risk groups with effective mechanisms to protect themselves from cybercrime.