Reviewerly/exHarmony huggingface dataset:
A Comparative Study on Medical Image Watermarking using Hybrid Approach and RivaGAN

With the increased use of electronic medical records and computer networks, Medical Image Watermarking (MIW) now plays a very important role to preserve integrity and completeness of medical images. As of now, there are no perfect algorithms or solutions for invisible watermarking as there are trade-offs between visibility and robustness. In this study, we explored multiple implementations of image watermarking techniques using Hybrid Approach and Deep-Learning-Approach. The experiments to measure the limitations and robustness were done on a dataset of breast ultrasound images. 18 attacking methods were performed on the encoded images and performance were evaluated using PSNR and NCC. Encoded images were then being transmitted digitally using multiple transmission method to test its robustness against transmission platform. In conclusion, the Deep-Learning Approach of RivaGAN has shown best robustness despite many extreme attacks while the Hybrid Approach of DWT-DCT-SVD shown the best performance in terms of imperceptibility. We reject RivaGAN as the best solution for Medical Image Watermarking despite its robustness as it was created specifically for video invisible watermarking. Keywords: Invisible Watermarking, DCT, DWT, SVD, RivaGAN
Blockchain in Ecommerce

Blockchain offers in 2021 a backbone for a new type of internet.As the use of blockchain technology becomes more and more captivating in online retail and much more, it is not surprising that more and more companies are investing in this technology and incorporating it and relying more and more on e-commerce.This article aims to structure the notions involved in blockchain technology and to highlight what the blockchain opportunity in e-commerce really looks like.
Effects of Data Augmentations on Speech Emotion Recognition

Data augmentation techniques recently gained more adoption in speech processing, including speech emotion recognition. Although more data tends to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition. The experiments are conducted on the Japanese Twitter-based emotional speech corpus. The results show that for speaker-independent data, two data augmentations with glottal source extraction and silence removal exhibited the best performance among others, even with more data augmentation techniques. For the text-independent data (including speaker and text-independent), more data augmentations tend to improve speech emotion recognition performances. The results highlight the trade-off between the number of data augmentation and the performance of speech emotion recognition showing the necessity to choose a proper data augmentation technique for a specific application.
Security Control Strategy of Converged Media Platform UGC Based on Blockchain Technology

In the current society, the Internet of Things technology is gradually developing, among which there is blockchain technology. At present, it is also in a stage of steady development. After the information revolution, it is gradually entering the era of take-off, which is also the third project of the revolution. Blockchain technology is a general technology of digital currency. In this article, the comparison is made mainly from the perspectives of the safety factor of the fusion media platform, the efficiency of screening, the proportion of the quality of user-published content, and the credibility of user-generated content. This article uses the calculation method of the Merkle tree. The comparison found that the safety factor has increased by 22%, the screening efficiency has increased by about 10%, and the pros and cons of the creation of works have been significantly improved after the use of blockchain technology, at about 23%. The authenticity of the content on the converged media platform increased by 26%.
Analysis and Practice of Using Modern Information Technology for Classroom Teaching Mode Reform

In the context of continuous technological progress, the application of information technology in the field of education has gradually opened the door to innovation. The integration of information technology and curriculum is the use of information technology to present the curriculum in a new way in the teaching process. Therefore, compared with the traditional teaching mode, the integration of information technology and curriculum is more flexible and can show the boring classroom vividly and graphically in front of students, thus motivating students to learn actively and improving classroom efficiency. In recent years, with the continuous development of computer technology, the rise of information technology such as data analysis and the Internet of Things has caused a wave of “Internet+” education. With the promotion of education informatization, the smart classroom has been developed based on modern education technology. As a result, the classroom teaching mode based on modern information technology has become one of the hot spots for research. As a product of the integration of new technologies and teaching, the smart classroom creates a better learning environment for students with features such as intelligence, diversity, and personalization. At the same time, this teaching model gradually cultivates the information literacy of teachers and students while improving learning effectiveness. Also, with the rapid development of modern information technology, integrated teaching has become a popular and common teaching method. In this context, integrated teaching has a great impact on traditional teaching methods. This requires teachers to make corresponding changes in classroom teaching methods, approaches, and strategies in line with the trend of the times. However, although there are many changes, the corresponding teaching theory has not been updated. While modern information technologies such as artificial intelligence and big data have been widely used to track, collect, and analyze human behavior, it has been difficult for teachers to integrate these technologies into their teaching to help them teach effectively. In recent years, a growing number of experts and scholars have joined the discussion on instructional design that incorporates modern information technologies. There is a need to explore how to teach students in depth through big data and other tools, but there is no complete theoretical chain. Therefore, it is necessary to explore the reform and practice of modern Internet-based classroom teaching models. This study begins with an understanding of the current utilization of smart learning platforms in some schools and the teaching needs of IT subjects. After identifying the value of the IT smart classroom teaching model, the current research status of the smart classroom is outlined, and related concepts and theories are outlined and understood. After that, the characteristics of smart classrooms are summarized, and the traditional classroom, flipped classroom, and smart classroom are compared and analyzed.
Digital Competencies Needed by Teachers for Utilizing Mobile Learning Applications for Effective Lesson Delivery in Technical Colleges in Aba Education Zone of Abia State

This study was set to determine the digital competencies needed by teachers for utilizing mobile learning applications for effective lesson delivery in Technical Colleges in Aba Education Zone of Abia State. Two research questions guided the study in line with two hypotheses. The study adopted descriptive survey research design and was carried out in Aba, Abia State. The population for the study constitutes 43 Technical College Teachers (sampled purposively) and 55 IT Practitioners (obtained through preliminary survey). Data pertinent to the study were collected using a structured questionnaire developed by the researchers titled “Digital Competencies Needed for Utilizing Mobile Learning Applications Questionnaire (DCNUMLAQ)”. The questionnaire was validated by three research experts and the reliability of the instrument was established using Cronbach Alpha which gave a reliability co-efficient result of 0.77. Mean and standard deviation were used to answer the research questions while the null hypotheses were tested using t- test at .05 level of significance and appropriate degree of freedom. Some of the findings include that the ability to; provide students with task management tools to organize their work, exploit computer games for pedagogical purposes, create screen capture tutorials, being able to detect plagiarized works in students assignments are part of the digital skills needed by teachers for utilizing mobile learning applications for effective lesson delivery in technical colleges in Aba Education Zone. Based on the findings of the study, the recommendations made among others include that technical college teachers should be retrained for capacity development and adoption of trending technological skills into classroom teaching for enhanced learning outcome, and education administrators/policy makers should enact policies especially for on-time assessment of teachers’ capacity in the use of ICT for teaching in technical colleges for effective lesson delivery.
ANALISIS KEBIJAKAN PENDIDIKAN TERKAIT IMPLEMENTASI PEMBELAJARAN PADA MASA DARURAT COVID 19

ANALYSIS OF EDUCATIONAL POLICY RELATED TO THE IMPLEMENTATION OF LEARNING IN THE EMERGENCY OF COVID 19This study that was utilising a descriptive research approach was intended to factually and accurately describing the facts, the nature and also the relationship among the phenomena of the research object. The purpose of this descriptive research was to generate a systematic, factual and accurate description of the available facts, characteristics and relationships among the phenomena. The type of descriptive method used was library research. Library research is an activity of observing various literatures related to the issues arose in the form of books, papers and writings that may help researchers so they can be used as guidelines in the research process. This study was intended to determine (1) the distance learning system that might be applied, (2) the impact of the distance learning system on student achievement, (3) the obstacles in the implementation of the distance learning system. The research method used in this study was a qualitative method with a qualitative descriptive approach. The results of this study indicated that the implementation of distance learning was still fasing many obstacles such as inadequate tools and poor internet connection. Beside that the gaining of learning success was still small and ineffective.
MAGPIE: Machine Automated General Performance Improvement via Evolution of Software

Performance is one of the most important qualities of software. Several techniques have thus been proposed to improve it, such as program transformations, optimisation of software parameters, or compiler flags. Many automated software improvement approaches use similar search strategies to explore the space of possible improvements, yet available tooling only focuses on one approach at a time. This makes comparisons and exploration of interactions of the various types of improvement impractical. We propose MAGPIE, a unified software improvement framework. It provides a common edit sequence based representation that isolates the search process from the specific improvement technique, enabling a much simplified synergistic workflow. We provide a case study using a basic local search to compare compiler optimisation, algorithm configuration, and genetic improvement. We chose running time as our efficiency measure and evaluated our approach on four real-world software, written in C, C++, and Java. Our results show that, used independently, all techniques find significant running time improvements: up to 25% for compiler optimisation, 97% for algorithm configuration, and 61% for evolving source code using genetic improvement. We also show that up to 10% further increase in performance can be obtained with partial combinations of the variants found by the different techniques. Furthermore, the common representation also enables simultaneous exploration of all techniques, providing a competitive alternative to using each technique individually.
Litho-Bio Facies Analysis and Systematic Paleontology of Sediments in Amansiodo-1 Well, Anambra Basin, Southeastern Nigeria

ditch cutting samples were obtained from Amansiodo-1 well within the Anambra Basin, Nigeria, at depth intervals between 16 -2191 m and analyzed for lithofacies, Foraminiferal, and systematically described.A total of thirty (30) benthonic foraminiferal species were retrieved: twenty-six (26) arenaceous and four (4) calcareous.The age of the well is determined to be Maastrichtian base on the appearance of benthonic arenaceous Haplophragmoides saheliense (Petters) species.Facies analysis of the samples indicated ten (10) lithofacies and two (2) facies associations.The facies comprise a base to top, the shale facies, shale/sandstone facies, shale/mudstone facies, mudstone/coal facies, sandstone/clay facies, mudstone/clay facies, very coarse sandstone facies, coarse sandstone facies, sandstone/clay facies and siltstone/clay facies, association FA-1 (subtidal) and FA-2 (Intertidal) corresponding to the Mamu Formation in the Anambra Basin.The studied sediments are assigned a shallow marine depositional environment based on the triangular plot of foraminiferal suborder test type (arenaceous, porcelaneous, and hyaline) and lithofacies distribution.The paleosalinity studies suggest a transition from brackish to an open marine depositional environment.Also, paleo-oxygenation of the sediments is anoxic based on foraminiferal distribution and the ratio of epifaunal to infaunal benthonic foraminifera respectively.
Rainfall Prediction in Blora Regency Using Mamdani's Fuzzy Inference System

In the case study of weather prediction, there are several tests that have been carried out by several figures using the fuzzy method, such as the Tsukamoto fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Time Series, and Sugeno. And each method has its own advantages and disadvantages. For example, the Tsukamoto fuzzy has a weakness, this method does not follow the rules strictly, the composition of the rules where the output is always crisp even though the input is fuzzy, ANFIS has the disadvantage of requiring a large amount of data. which is used as a reference for calculating data patterns and the number of intervals when calculating data patterns and Sugeno has the disadvantage of having less stable accuracy results even though some tests have been able to get fairly accurate results. In research on the implementation of the Mamdani fuzzy inference system method using the climatological dataset of Blora Regency to predict rainfall, it can be concluded as follows: (1) The fuzzy logic of the Mamdani method can be used to predict the level of rainfall in the city of Blora by taking into account the factors that affect the weather, including temperature, wind speed, humidity, duration of irradiation and rainfall. (2) Fuzzy logic for prediction with uncertain input values is able to produce crisp output because fuzzy logic has tolerance for inaccurate data. (3) The results of the accuracy of calculations using the Mamdani fuzzy inference system method to predict rainfall in Blora Regency are 66%.
SRP: A Microscopic Look at the Composition Mechanism of Website Fingerprinting

Tor serves better at protecting users’ privacy than other anonymous communication tools. Even though it is resistant to deep packet inspection, Tor can be de-anonymized by the website fingerprinting (WF) attack, which aims to monitor the website users are browsing. WF attacks based on deep learning perform better than those using manually designed features and traditional machine learning. However, a deep learning model is data-hungry when simulating the mapping relations of traffic and the website it belongs to, which may not be practical in reality. In this paper, we focus on investigating the composition mechanism of website fingerprinting and try to solve data shortage with bionic traffic traces. More precisely, we propose a new concept called the send-and-receive pair (SRP) to deconstruct traffic traces and design SRP-based cumulative features. We further reconstruct and generate bionic traces (BionicT) based on the rearranged SRPs. The results show that our bionic traces can improve the performance of the state-of-the-artdeep-learning-based Var-CNN. The increment in accuracy reaches up to 50% in the five-shot setting, much more effective than the data augmentation method HDA. In the 15/20-shot setting, our method even defeated TF with more than 95% accuracy in closed-world scenarios and an F1-score of over 90% in open-world scenarios. Moreover, expensive experiments show that our method can enhance the deep learning model’s ability to combat concept drift. Overall, the SRP can serve as an effective tool for analyzing and describing website traffic traces.
Efficient Modulation Classification Based on Complementary Folding Algorithm in UVLC System

Modulation classification (MC) has become a widely used technology, which is of great value in both commercial and civil applications. It actually completes the classification task of modulation signal through various means. In recent years, modulation format recognition based on deep learning (DL) has achieved great success. However, in practical application, the computational cost and model complexity have become the biggest obstacles of the traditional MC based on DL. To solve this problem, we propose complementary folding algorithm (CFA). This is an algorithm based on classical modulation classification (CMC), which folds and splices the features of the input neural network (NN), so that these features have both large-scale and small-scale dual branch receptive fields. The research results prove that under the same network structure and data quantity, both the correctness rate and the convergence speed of CFA are significantly improved in the communication experiment based on underwater visible light communication (UVLC). It is also worth mentioning that because of the particularity and complexity of the channel, UVLC system can be divided into different regions. In any region, CFA performs better than CMC, so we can prove that this algorithm also has excellent robustness.
Upper Bounded Minimal Solution of the Max-Min Fuzzy Relation Inequality System

Resolution of the minimal solutions plays an important role in the research on fuzzy relation equations or inequality systems.Most of the existing works focused on the general minimal solutions or some specific minimal solutions that optimize particular objective functions.In a recently published work [43], the restricted minimal solution of fuzzy relation inequalities with addition-min composition was studied by M. Li et al.Motivated by the idea presented in [43], we investigate the so-called upper bounded minimal solution of fuzzy relation inequalities with max-min composition in this work.The upper bounded minimal solution is defined as the minimal solution that is no more than a given vector.Here, the given vector can be viewed as the upper bound.The major content in this work consists of two components: the existence and the resolution of the upper bounded minimal solution.First, we provide some necessary and sufficient conditions to determine whether the upper bounded minimal solution exists with respect to a given vector.Second, when it exists, we further develop two algorithms to search for the upper bounded minimal solution in a step-by-step approach.The validity of our proposed Algorithms I and II is formally proved in theory.The computational complexities of Algorithms I and II are O (mn) and O (mn 2 ), respectively.Moreover, our proposed algorithms are illustrated by some numerical examples.
HabitEst3D: A User-Friendly Software for Estimating Mixed Crystal Habits from Two-Dimensional Sections in Igneous Rocks

Crystal habit in igneous rocks provides a window to understand magmatic processes or reveal crystallization environments. Generally, we can obtain the two-dimensional (2D) crystal habits directly from the thin section, which is easy to access. However, the three-dimensional (3D) habit cannot be directly observed in thin sections and needs the stereological conversion from 2D habits. Statistical methods have been developed for stereological conversion, but they cannot identify mixed habits. Our study uses the distributions of the cut-sections of pre-set habits to match the unknown sample and enumerates habit combinations to find the best-match results for mixed habits. The specialized program, HabitEst3D, is developed according to our model in this study. The program is written in Python and is a cross-platform with a user-friendly graphical interface. The input data are the aspect ratio of 2D sections. After setting the parameters, the program finds the best-match estimations fitting the sample, visualizes the results, and saves them in multiple file formats. The program is robust and is not sensitive to outliers to obtain more accurate results. It traverses all possible combinations and needs memory and time but effectively explores the mixed crystal habits in the sample, contributing to investigating magmatic processes in more detail.
Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach

Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.