Reviewerly/exHarmony huggingface dataset:
Application of Steganography for Secure Data Transmission Using Lossless Compression

Steganography is the art of hiding messages or files in a way that prevents the detection of the existence of these hidden messages. It encompasses several techniques, including physical methods like invisible ink on paper and digital techniques like hiding text on multimedia files like images and music files. In the modern digital era, steganography has become a useful tool to evade detection and perusal of secret messages. With the advent of social media, it is very easy to encode a message or file onto an image and upload it online for the intended recipients to access, decode, and read or use. In this case of digital steganography of messages or files onto images, an important factor to consider is the effect of image compression on the hidden message. Since most social media and other online image posting websites run some sort of compression, cropping, and other image transformations on the uploaded images, understanding these techniques and their effect on the hidden text can help one choose the most suitable steganography technique to use for a particular use case.
Introducing Blockchain, Smart Contract and Distributted Application concepts and practices from an extra-curricular university training

Despite all advantages proportionated by Blockchain, Smart Contracts and Distributed Applications technologies (trust, accuracy, security, savings, transparency, clear communication, storage & backup, etc.), we have experiencing in last years an increasing lack of qualified professionals having these technical skills, and this fact can be justified by high failure rates in STEM majors, and a reduced amount of technological practical activities in university classes. In such a way to mitigate such issues, we propose an extra-curricular training where undergraduate and graduate students can learn and practice such technologies, not usually presented in university programs. Such framework was experienced by students in three different universities in Bahia, Brazil, and the results found point out this training as an interesting and promising proposal.
KENet: Distilling Convolutional Networks via Knowledge Enhancement

For practical applications, deep neural networks need to be deployed with low memory and computing resources. To achieve this goal, we design a lightweight convolutional neural network namely KENet (Knowledge Enhance Network) and propose a knowledge distillation method to improve the performance of KENet. Our proposed KENet is a lightweight convolutional network derived from a wide residual network by replacing the normal convolutions with a hybrid of group convolutions and bottleneck blocks to reduce the number of parameters. However, the use of small kernels and group convolutions loses the information of both spatial and channel-wise dimensions. To solve this problem, we further propose a knowledge distillation method to enhance the information of these two dimensions. We extract both spatial and channel-wise knowledge from a ‘teacher’, and improve the attention transfer features for knowledge distillation. The experiment results on multiple datasets show that KENet is computationally cheap and memory saving with hardly any loss of precision. Moreover, we confirm that KENet can be effectively deployed in the advanced detectors with strong robustness and real-time performance.
Reduction of annotation efforts for multiclass object detection by using a domain awareness data combination strategy

Abstract To train convolutional neural networks (CNN) it is common practise to collect a huge amount of data. This is cost intensive and often not applicable. Up to date several studies have investigated the concept of few shoot learning, e.g. 1-3 samples per class. Suboptimal is still the over fitting resulting from the gap between training data and representative test data in the application. Since this is still a field of intensive research, an alternative and common approach is transfer learning with data- and image augmented pictures. However, collecting and labelling data for fine-tuning can still take an enormous amount of time, when it comes to multiclass pictures in industrial applications like assembly kit verification. The kits often contain stock lists with a small interclass and a high intraclass-distance. A specific characteristic of stock lists is that parts are easily adaptable and exchangeable. To bring object detection closer to the industry, we successfully show a dataset driven approach that combines a single class collection of pictures, which we call single class (SC) dataset and adapt with a few samples the specific multiclass use case. In result, we use a model trained on a huge SC dataset that can easily and fast be adapted to specific industrial use cases.
Evolution Process for SOA Systems as a Part of the MAD4SOA Methodology

In this paper we propose a process for evolving service-oriented systems. The evolution comprises a series of changes made to the system structure. Starting from the moment when a request for change is defined, and ending with the change review, we define a set of phases that take place during the course of a single evolution step. For each phase, we explain in detail the activities that are performed in its course. We also define a set of artefacts needed by each activity and produced as a result of it. The proposed evolution process is an integral part of the evolution and development methodology for service-oriented systems (MAD4SOA) developed by our team.
Consistent Wce Video Frame Interpolation Based On Endoscopy Image Motion Estimation

The development of wireless capsule endoscopy (WCE) allows painless imaging of the internal digestive environment. Limited by the image acquisition and transmitting time, the frame rate of WCE device is not high enough to generate visually consistent video data. Therefore, in the present contribution, we propose a robust WCE video frame interpolation method, aiming at generating consistent and detail-rich gastrointestinal (GI) track video frames. Considering the special environment of digestive system, we firstly correct the WCE device image capturing position based on stereo geometry to align adjacent frames, and then estimate optical flow using PWC-Net flow estimation architecture. Using the well refined optical flow, intermediate frame between two adjacent frames is interpolated and projected back to the original WCE image form. Comparing with a state-of-the-art method DAIN, our work outperforms on both visual performance and frame prediction accuracy. This work significantly improves the smoothness of WCE video, and provides more completed image data for subsequent data processing.
A New Scheme of Mammographic Masses Classification Based on the BI-RADS Lexicon

Computer-aided diagnosis (CAD) systems are intended to assist the specialist in the interpretation of the medical images aiming to support the clinical conduct. In mammography breast tumor classification, the CAD systems commonly provide a benign and malignant classification disregarding provide more relevant information for the radiologist that explains the internal criteria that take the classifier to classify a tumor. Therefore, it is necessary to give more information compatible with the radiological language according to the BI-RADS standard. In this paper, we proposed a new scheme of mammographic tumors classification that includes the BI-RADS lexicon modeling based on malignancy probabilities to get a new feature vector to represent the tumor and explain the internal criterion considered by the classifier. For comparison purposes, a CAD system based on a typical pattern recognition scheme (called conventional CAD) is also evaluated. The results suggest that the proposed method obtained an accuracy of 90%, whereas the conventional CAD reached an accuracy of 88%. According to the results, the proposed method is suitable for the histopathological classification. Besides, it provides more information about the radiological language used by the radiologist in terms of the BI-RADS lexicon.
Face Mask Detection Using Feature Extraction

Facial recognition has been one of the most instigating technological advancements of the recent times. With its limitless and myriad scope of applications, the facial recognition technology is so flexible that it can be customized to fit its application requirements with ease. With the world, as we know it, acquiring a different definition in these uncertain times, owing to the COVID-19 pandemic, this technology can be put to use to serve a very significant purpose—facial mask detection. With increasing emphasis on the usage of facial masks as the only known means to offer the needed protection, we decided to put to use the facial detection technology to detect facial masks on a person’s face. In this project, we used the Viola–Jones detection framework with digital image processing to recognize a facial mask on a person’s face and the appropriate or inappropriate positioning of it. This project will help us identify whether a person has worn a mask or not and if yes whether it was positioned properly or improperly and an alert regarding the same is sent. This would enable appropriate screening of individuals, providing a secure work environment and/or public interactions.
When is the right time to outsource your security function?

The structure of IT security teams varies across different businesses. Of those that have a dedicated in-house security team, many roles are involved – CISO, risk and compliance groups, security ops centre, threat intelligence teams and malware analysis teams to name a few. This complex division of responsibilities may work harmoniously, but what happens when one element fails? In-house security teams involve many roles – CISO, risk and compliance groups, security ops centre, threat intelligence teams and malware analysis teams to name a few. This complex division of responsibilities may work harmoniously, but what happens when one element fails? Scott Dodds of Ultima MSP argues that outsourcing a business's security function to a managed service provider (MSP) could provide the ability to deliver greater compliance and greater efficiency of cyber security solutions. But how easy is it to effectively outsource the security function?
HDT Bitmap Triple Indices for Efficient RDF Data Exploration

The exploration of large, unknown RDF data sets is difficult even for users who are familiar with Semantic Web technologies as, e.g., the SPARQL query language. The concept of faceted navigation offers a user-friendly exploration method through filters that are chosen such that no empty result sets occur. Computing such filters is resource intensive, especially for large data sets, and may cause considerable delays in the user interaction. One possibility for improving the performance is the generation of indices for partial solutions. In this paper, we propose and evaluate indices in form of the Bitmap Triple (BT) data structure, generated over the Header-Dictionary-Triples (HDT) RDF compression format. We show that the resulting indices can be utilized to efficiently compute the required exploratory operations for data sets with up to 150 million triples. In the experiments, the BT indices exhibit a stable performance and outperform other deployed approaches in four out of five compared operations.
“An Optimistic Tragedy”: A. Kholminov’s Forgotten Opera

The purpose of the article is to provide an insight into a number of important issues concerning such general philosophical concepts as eventfulness, memory, truth in “An Optimistic Tragedy”, the first opera by A. Kholminov, the Russian composer of the second half of the XX century. Scientific novelty of the study lies in comparing the literary source, which is the play by Vs. Vishnevsky, productions of the play and the opera itself. As a result, it was found that the principles that are of fundamental importance in the artistic world of the first opera composed by Holminov are characteristic of his later works, which suggests a formation of the composer’s stylistic landmarks. In “An Optimistic Tragedy”, A. N. Kholminov articulated his creative experience and search for an individual dramatic solution, developed the vast world of images and, in doing so, revealed the most important themes of the era.
The nnlib2 library and nnlib2Rcpp R package for implementing neural networks

Artificial Neural Networks (ANN or NN) are computing models used in various data-driven applications.Such systems typically consist of a large number of processing elements (or nodes), usually organized in layers, which exchange data via weighted connections.An everincreasing number of different neural network models have been proposed and used.Among the several factors differentiating each model are the network topology, the processing and training methods in nodes and connections, and the sequences utilized for transferring data to, within and from the model etc.The software presented here is a C++ library of classes and templates for implementing neural network components and models and an R package that allows users to instantiate and use such components from the R programming language.
Safe-System-on-Chip for Functional Safety

Various research institutions and semiconductor manufacturers have presented approaches for miniaturized safety systems based on redundant configurations in the last years. ICAS has been working on approaches for Safety Systems on Chip (SoC) since 2005 and has developed several certified variants of safety SoCs in this context together with well-known safety manufacturers. In parallel, the further development of the architecture of safety SoCs was also driven forward and manifested with the ReSCU-V1 in 2018. A logical consequence is the development of the ReSCU-V2, with which further architectural developments of the safe SoC design at ICAS in Kassel have succeeded with a practically completely redundant architecture. The resulting SoC was realized on a 180 nm process from UMC. It has a chip size of 5×5 mm and consumes less than 500 mW of power.
An Improved Particle Swarm Optimization Algorithm for Pigeon Brain Tissue MRI Image Segmentation

Pigeons have strong disease resistance and short reproductive cycle. It is an ideal sample for bird research. It has been widely used in scientific research and related experiments in the fields of biology, neurology, and medicine. Generally, after obtaining pigeon brain slices or images, a segmentation algorithm is used to extract brain tissue for further research. The segmentation accuracy of the algorithm has always been the focus and difficulty in the research. This paper used Otsu algorithm, fuzzy threshold segmentation algorithm, K-means algorithm and improved particle swarm algorithm to segment pigeon brain tissue. We calibrated the area of pigeon brain tissue by comparing the segmented image with the labeled pigeon brain tissue. According to certain evaluation criteria, the accuracy of brain tissue segmentation in different MRI images was calculated. By comparing the accuracy of each tissue segmentation, we can quantitatively judge the segmentation effect of the algorithm. Comprehensive analysis showed that the improved particle swarm optimization algorithm had a better segmentation effect.
Le politiche linguistiche nell’Unione africana

This paper illustrates the language policy implemented by the African Union (AU). It highlights the impact of language choices within AU institutions on local populations. Sixty years after the independence of most African countries and despite the fact that over two thousand languages are spoken in this continent, language policy is still highly Eurocentric. Moreover, the exclusive use of these few ex colonial languages, not known by the vast majority of the population, has social and linguistic repercussions on Africans who are unable to participate in the political life of their respective countries. AU has made some interesting proposals for the promotion and diffusion of local African languages, but much remains to be done.
Forecasting Earthquakes in Qinghai-Tibet Plateau Using Rescaled Range Analysis

Abstract Fractal analysis provides a powerful method for studying complicated natural phenomena. This paper employs fractal analysis to study seismology based on the statistical fractal concept and provides a simple overview for analysing the temporal distribution and fractal characteristics of seismic activity. We delve into the self-shot fractal characteristics of seismic activity by using an analysis of the Rescaled Range scale invariance of seismic time and time interval sequences.
Preventing Hidden Information Leaks Using Author Attribution Methods and Neural Networks

This paper addresses the problem of hidden information leakage detection through the use of text steganography. Presented comparative research results show how to perform this task by detecting changes in user's writing styles using neural networks and various types of text features. The framework for hidden leakages detection based on discovering changes in the author's writing style with deep neural networks (RNN, LSTM, GRU, CNN) is proposed. A series of experiments on text corpus containing Russian online texts were carried out to evaluate hidden leakages detection accuracy. The experiments showed that the LSTM and character 4-grams together allow achieving the accuracy of 87%. Text preprocessing significantly decreases accuracy which is also shown.
Grounding Dialogue History: Strengths and Weaknesses of Pre-trained Transformers

We focus on visually grounded dialogue history encoding. We show that GuessWhat?! can be used as a “diagnostic” dataset to understand whether State-of-the-Art encoders manage to capture salient information in the dialogue history. We compare models across several dimensions: the architecture (Recurrent Neural Networks vs. Transformers), the input modalities (only language vs. language and vision), and the model background knowledge (trained from scratch vs. pre-trained and then fine-tuned on the downstream task). We show that pre-trained Transformers, RoBERTa and LXMERT, are able to identify the most salient information independently of the order in which the dialogue history is processed. Moreover, we find that RoBERTa handles the dialogue structure to some extent; instead LXMERT can effectively ground short dialogues, but it fails in processing longer dialogues having a more complex structure.
Atom Aligned Sparse Representation Approach for Indonesian Emotional Speaker Recognition System

Automatic Speaker Recognition system is a system that determines speaker identity through sound waves. This system can facilitate various daily services such as bank transaction via telephone. Nowadays, i-vector based Automatic Speaker Recognition system for Indonesian has not been able to handle the problem of emotional difference. However, in reality, speaker enrollment and recognition is often done in different emotional condition. This emotional difference frequently degrades the performance of existing systems. Therefore, this research focuses on constructing Automatic Speaker Recognition system for Indonesian that could handle emotional difference problem by applying i-vector modelling technique and Atom Aligned Sparse Representation (AASR) transformation technique. The emotion classes used in this study are angry, happiness, sadness, and contentment. Compared to the baseline system that was built using i-vector method only, the AASR system shows an increase in performance, namely a decrease in Equal Error Rate (EER) of 3.79% in non-neutral emotion test data. In neutral emotion test data, the AASR system also experiences a decrease in EER of 2.24%. Overall, the AASR system improves speaker recognition performance by reducing the EER by 3.46%.
Perancangan Sistem Informasi Online Self service Perpustakaan Universitas Muhammadiyah Magelang

Library systems of Muhammadiyah University of Magelang still uses manual way for the borrowing, renewals and returns processes, so it takes a long time to get information needed. Therefore, it needs adequate library information system for the automatic borrowing, repayment, and borrowing requests processes which produces information quickly and accurately. This research aimed to design an online Self service information system for procces in the library of Muhammadiyah University of Magelang. This system uses the online Self service for the book reservation and book extension process. Self service on this study is in the transaction book service, because the system is able to record borrowing transaction data automatically after the lending process is completed. Thus it is not necessary to input the data manually by officers transaction. This research is able to make a reservation and extension system of books online. In addition the borrowing transactions will be automatically recorded if the user uses the system. The conclusions obtained from this study is the borrower will be easier to borrow and renew online. In addition, the system can also allow officers to keep the records of transactions and to monitor the books on library Muhammadiyah University of  Magelang
Dynamic Dirichlet process mixture model for identifying voting coalitions in the United Nations General Assembly human rights roll call votes

Scholars have been interested in the politicization of humans rights within the United Nations for some time. However, previous research typically looks at simple associations between voting coalitions and observable variables, such as geographic location or membership in international organizations. Our study is the first attempt at estimating the latent coalition structure based on the voting data. We propose a Bayesian Dynamic Dirichlet Process Mixture (DDPM) model to identify voting coalitions based on roll call vote data across multiple time periods. We also propose post-processing methods for analyzing the outputs of the DDPM model. We apply these methods to the United Nations General Assembly (UNGA) human rights roll call vote data from 1992 to 2017. We identify human rights voting coalitions in the UNGA after the Cold War, and polarizing resolutions that divide countries into different coalitions.
Problems and Prospects of Customer Relationship Management Digital Tools Development in the Global Economy

Existing digital tools of customer relationship management, although rather advanced when compared to the methods used before their development, are still often lacking in terms of both functionality and integration with other digital solutions. The main goal of the present study is to define their current state, major capabilities and traits, outline the essential problems and, based on the possible solutions and demand on the market, specify the prospects of their development.
Parallelizing optimal ate pairing on BLS-48 curve

A pairing is a bilinear map from two rational points on an elliptic curve to an element on a finite field. Pairing is necessary to construct the next generation cryptography including ID-based encryption, functional encryption etc. Many researchers have studied efficiency improvement for pairing computations to make it practical. Several pairing computation methods have been proposed and an optimal computation method is called optimal ate pairing. Also, the accelerating methods for pairing computations using parallel computation and precomputation are studied. Recently, Kim and Barbulescu proposed a new attack for pairings and so the security levels of pairings were reduced. Consequently, Kiyomura et al. and other researchers proposed new parameters against the attack. The speeding up of pairing computation for these new parameters are required. However, studies about the parallel computation are not enough. In this paper, we propose the parallel computation method for the optimal ate pairing with the new parameter proposed by Kiyomura et al. We also provide the theoretical estimation of the computational cost. Furthermore, we provide implementation results on Raspberry Pi 4.