Reviewerly/exHarmony huggingface dataset:
An application improving the accuracy of image classification

There have been various research approaches to the problem of image classification so far. For image data containing kinds of objects in the wild, many machine learning algorithms give unreliable results. Meanwhile, deep learning networks are appropriate for big data, and they can deal with the problem effectively. Therefore, this paper aims to build an application combining a ResNet model and image manipulation to improve the accuracy of classification. The classifier performs the training phases on CIFAR-10 in a feasible time. In addition, it achieves around 93% accuracy of the test data. This result is better than that of some recently published studies.
Provenance Studies

The provenance of metal artefacts and the search for ore resources used in prehistoric and historic times for smelting processes is one of the basic questions of archaeology. The purpose of this research is the reconstruction of the obviously very complex trade relations, which, quite early on, reached near global geographic distances of more than a thousand kilometres. Supra-regionally, metal artefacts may occur in varying distributions. On the one hand, they are found in areas exceptionally rich in mineral deposits, such as the incredible masses of Bronze Age axes and other artefacts in the area of present-day Central Europe during the late third and second millennium BC (Tumulus culture, Urnfield culture; Parzinger 2016). On the other hand, they also occur in areas that do not have any ore deposits, as, e.g. in the case of the rich finds of gold, silver, copper and bronze in the Early Dynastic and Akkadic periods in Mesopotamia, also in the third millennium BC. In Mesopotamia, between the Euphrates and Tigris, there are only quaternary sediments, no ore deposits. Many of the metal objects found there were imported, sometimes from distant lands, e.g. from Makan (Oman), from northern Afghanistan or from Anatolia (Salzmann 2019; Jansen 2016, 2019).
Reform of Postgraduate Course Mode Based on MOOC Platform

With the advent of the "Internet +" era, the dividend of the rapid development of high-tech industry gradually radiates to the field of education, and new online education forms such as MOOC begin to emerge, bringing new opportunities and challenges to the development of graduate education. This study by analyzing the course of MOOC mode, from the perspective of three courses, students and universities points out the necessity of graduate student curriculum reform, and from preparation before class, the class teaching, after-school evaluation in three stages, course planning and selection, online learning and interaction between teachers and students, summative evaluation and process evaluation three aspects to discuss intelligent graduate course mode construction of ideas, in order to provide reference for the construction of postgraduate courses in the new era.
Forensic Extraction and Analysis of Signal Application in Android Phones

The popularity of smartphones has revolutionised the social media communication and messaging apps such as WhatsApp and Telegram have become the favourite chat apps for smartphone users. The instant messaging service is widely accepted, as it enables the users to easily get connected to the people at the other end. The chat service providers use advanced encryption techniques to ensure safe and secure end to end communication. The increased use of instant messaging apps resulted in rapid growth in the volume of digital evidence for forensic analysis. As there are a lot of privacy issues in WhatsApp and Telegram, people are now moving onto Signal app, which is known to be more privacy preserving and secure than other messaging apps. In this paper, we discuss the forensic acquisition and analysis of Signal messenger app. We propose a new methodology to acquire the Signal app data by the decryption of Signal backup file and recover the chat messages including attachments from Android devices.
Driving Action Recognition Based on 3D Convolution

With the number of motor vehicles rising year by year, traffic safety problems are becoming increasingly serious. In this paper, we study the application of convolutional networks to driving action recognition from the perspective of the driver's action in a car. Due to the lack of current driving action datasets, we construct a driving action video dataset DAD-10 containing the entire upper body of the driver. We compare and analyze the effectiveness of 2D convolution and 3D convolution in processing the driving action video data, and based on this, we design a network structure called M(2-1)D that mixes 2D convolution and 3D convolution. The M(2-1)D network uses residual learning and utilizes convolution splitting to reduce the number of network parameters. The network structure achieves a recognition accuracy of 98.5% on the DAD-10 dataset, which has a very high application value.
A traceable privacy‐aware data publishing platform on permissioned blockchain

Abstract The data publishing platform based on blockchain has advantages of decentralization, anti‐tampering function, and distributed storage. However, privacy leakage, untraceability, and the data tampering cannot be addressed. In order to solve these problems, this article proposes an aggregation signature scheme based on the bilinear mapping, and proposes a traceable privacy‐aware data publishing platform by applying the aggregation signature scheme to the data publishing platform on permissioned blockchain. Performance analysis shows that the traceable privacy‐aware data publishing platform reduces the communication cost, improves the efficiency of signature verification and transmission, provides the anonymity and traceability for the users in the platform.
Design Approach in Electronic Health Consultation Application: User Empowerment for Internet Addiction

To improve health levels, good health literacy is required, in which Indonesia still categorized as low in health awareness. Someone with low health literacy will experience more challenges when using or accessing health care system, especially to identify or being aware about Internet addiction. Thus, electronic consultation come into the picture to provide more engaging and interactive mechanism in discussing several and related issues in order to improve the quality of understanding regarding the topic. It also aims to allow users to browse and investigate the indicator for the purpose to stay healthy or identify the others through online without having to visit doctor at his workplace due to various reasons such as lack of free time. In this study, user experience (UX) perspective was used through RSM (Recognize, Scrutinize and Materialize) design approach by developing and evaluating the prototype with usability testing using concrete scenarios with alternative design. Also, this study implement alternative design in the design phases to expand the usability criteria and aspects to align with the user demand as the critical factors and trigger for success implementation.
HELIUS: A Blockchain Based Renewable Energy Trading System

Energy consumed during the peak demand period contributes the most towards the energy bill. There has been a substantial amount of research performed to solve the issue. This paper explores a peer-to-peer (P2P) sustainable energy exchange system using Blockchain and Deep learning algorithms. The main objective of this paper is to provide a general framework to design such a system with varying advanced components and their interaction. The proposed model is a novel mechanism for power system operations allowing users to trade energy during peak loads. The model also simulates sustainable energy production provided the system components and other respective variables for example location, time, and weather. The proposed system is integrated with a blind bidding mechanism and accompanying web application to demonstrate the feasibility in a real-world environment.
Trajectory optimization and closed-loop feedback control of multi-degree-of-freedom robot

Multi-degree-of-freedom robots are widely used in various industries. In order to improve the stability of multi-degree-of-freedom robots, this paper proposes a motion trajectory optimization method based on PSO optimization algorithm and a closed-loop feedback control method for multi-degree-of-freedom robots based on error feedback. The standard D-H parameter method is used for kinematics analysis, combined with the multi-factor constraint parameter fusion method to establish the closed-loop feedback adaptive control law of the robot, and the robot&#x0027;s pose is adaptively adjusted according to the parameter optimization results to realize the stability control of the robot. The analysis results show that the multi-degree-of-freedom robot control with the analysis method in this paper has less error, strong robustness and accuracy of the kinematics method.
SixFeet: An Interactive, Corona-Safe, Multiplayer Sports Platform

The global COVID-19 pandemic left few facets of our lives unchanged, including the possibility to play organised sports. Many gyms and sports clubs had to shut down due to social distancing measures, which resulted in reduced physical activity. Co-located group sports that promote social interaction and fun became almost impossible. This lack of physical interaction combined with social isolation and confinement have shown negative impacts on physical and mental well-being of people (for example, lack of motivation, anxiety, low immunity, low academic performance, negative mood and feelings) especially in children and young adults. Recent research therefore recommends exploring more creative approaches that allow physical activities during a pandemic, especially the ones that increase social interaction and engagement and allow users to share the same physical space. Towards this, we developed SixFeet, a novel digital-physical sports platform that allows its participants to play rigorous, collaborative sports – at a six feet distance. SixFeet ensures that the players are 1.5m away from each other at all times without them noticing it or without them having to worry about the social distancing measures. We developed a prototypical implementation of SixFeet and conducted a pilot study to evaluate its technical feasibility and practicality as well as obtain a first insight into user experience with the platform. Results show that participants never came within 1.5 meters of one another, felt connected nevertheless and were physically exerted. This paper presents the design and architecture of SixFeet, results of the preliminary user study, and a discussion on the versatility of SixFeet in accommodating training for different sports in times of a pandemic.
A Common Terminology for Software Risk Management

In order to improve and sustain their competitiveness over time, organisations nowadays need to undertake different initiatives to adopt frameworks, models and standards that will allow them to align and improve their business processes. In spite of these efforts, organisations may still encounter governance and management problems. This is where Risk Management (RM) can play a major role, since its purpose is to contribute to the creation and preservation of value in the context of the organisation's processes. RM is a complex and subjective activity that requires experience and a high level of knowledge about risks, and it is for this reason that standardisation institutions and researchers have made great efforts to define initiatives to overcome these challenges. However, the RM field nevertheless presents a lack of uniformity in its terms and concepts, due to the different contexts and scopes of application, a situation that can generate ambiguities and misunderstandings. To address these issues, this paper aims to present an ontology called SRMO (Software Risk Management Ontology) , which seeks to unify the terms and concepts associated with RM and provide an integrated and holistic view of risk. In doing so, the Pipeline framework has been applied in order to assure and verify the quality of the proposed ontology, and it has been implemented in Protégé and validated by means of competency questions. Three application scenarios of this ontology demonstrating their usefulness in the software engineering field are presented in this paper. We believe that this ontology can be useful for organisations that are interested in: (i) establishing an RM strategy from an integrated approach, (ii) defining the elements that help to identify risks and the criteria that support decision-making in risk assessment, and (iii) helping the involved stakeholders during the process of risk management.
Masking Scheme on AES Against Power Attack Based on Composite Field

The security of cryptographic algorithms is of great importance to information security. Power attack technology poses a serious threat to the security of cryptographic algorithm operations. In this paper a composite field-based AES algorithm mask protection design is proposed, realizing the inversion operation in the S-box through the decomposition of the composite field. And a random mask is added to each of the S-box operations based on this structure. Each intermediate value is randomized, and a cyclic shift is used to implement the mask transformation of each round of operation, thus improving the ability to resist power attacks. Theoretical analysis and experimental results show that, the scheme proposed can be implemented with higher security and lower resource overhead compared with similar design.
Ancient Sanskrit Line-level OCR using OpenNMT Architecture

There have been several Optical Character Recognition (OCR) related works happened for Indian languages like Hindi, Marathi, Bangla, etc. But there is very little OCR-related work done for the Sanskrit language of Devanagari script. Sanskrit is a very complex language. The large word length and old degraded documents add more challenges to Sanskrit OCR research. Due to these challenges, the word accuracy of available OCR systems is not very high for such documents. Most of the work happened to recognize Sanskrit character recognition only. There is only one attempt to recognize the whole Sanskrit line for 10 fonts.This paper shows the study of different hyperparameters of OpenNMT architecture for Sanskrit OCR of synthetically generated color line images. A neural encoder-decoder model with attention is presented to converting line images into editable text. An attention-based approach can tackle this problem in a better way in comparison to other neural techniques using CTCbased models. The main aim of this paper is to give a detailed analysis of data preparation and various hyperparameters (like the number of LSTM layers, LSTM direction, size of character embedding vector, batch size, number of iteration, and hidden unit size) of encoder-decoder in OpenNMT, and accuracy of various combinations. This paper also concludes the best accuracy model for Sanskrit OCR using OpenNMT. The text recognition performance of the proposed method on the test set is achieved 99.44%. Our major contribution is to show text recognization of degraded line images with a variety of fonts using OpenNMT architecture. Our contribution helps the researcher community in deciding hyperparameters of encoder-decoder architecture for Sanskrit language OCR.
A Real-Time Collision Detection System for Vehicles

A real-time collision detection system has become a crucial safety feature in vehicles today, mainly after the evolution of autonomous and self-driving vehicles. It is proved to be very effective in minimizing the number of road accidents. This paper presents an algorithm for a real-time detection system using the deep learning technology based on Mask-RCNN (Mask-Region based Convolutional Neural Network). We prepared a custom dataset from scratch to experiment with our algorithm and a detailed analysis of the results are provided. Experiments indicate that the developed algorithm gives highly accurate results. We achieved more than 95&#x0025; accuracy with overall prediction score of greater than 0.90.
Novel Approach to Phishing Detection Using ML and Visual Similarity

Sanghavi, Preet Kunchapu, Achyuth Kulkarni, Apeksha Solani, Devansh Anson, A.Phishing involves attempts to trick the user by extracting crucial important information that should otherwise not be leaked. Data like bank account numbers, social media accounts, company revenue reports, online transactions secrets are some of the examples that an attacker hopes to extract by exploiting the vulnerability of the user. In today’s day and age, it is very important to warn the populace against such attacks and provide relevant security awareness. Through our research, we aim to provide a brief study and novel insights into how machine learning and image visualization can be used to detect phishing webpages using a two-part implementation technique. The first part involves an analysis and overview of different machine learning classifiers across different datasets and features to find the most relevant technique to detect phishing websites efficiently. The second part introduces a novel approach of how discrete Fourier transform and image comparison can be used to compare suspicious webpages with real ones. This study aims at providing a new direction for researchers to understand how machine learning and automation can be inculcated in phishing detection to avoid any data breach.
Analysis on Single-Image Super-Resolution (SISR) Using Dictionary Learning and Sparse Representation Algorithm

Image Super-Resolution (SR) is a technique in order to produce High-Resolution (HR) image from the corresponding Low-Resolution (LR) image by removing the degradation caused by imaging process of LR camera. In this work, a Single-Image Super-Resolution (SISR) image reconstruction scheme based on dictionary learning process with sparse representation method is proposed. As a result, the image quality of the obtained HR image decreased significantly with increasing of the upscale factor. Then, the analysis showed that the HR image obtained by applying the proposed work was able to produce a better performance in terms of Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Matric (SSIM) values as compared to the bicubic interpolation operation. Therefore, the work done in this paper is able to solve the LR problem in images by proposing a SISR image reconstruction scheme based on dictionary learning process with sparse representation algorithm. Lastly, this work can be improved by testing on different types of images such as biometric images.
An Adaptive ResNet Based Speaker Recognition in Radio Communication

In this paper, a speaker recognition strategy in military radio communication is applied. In military operations, the most commonly used method of information transmission is radio communication. Speaker recognition technology can confirm the sender&#x0027;s identity, and effectively prevent the enemy from pretending to be our military commander to issue false orders. However, the datasets of the military commander from the radio are confidential, and there are no large open-source datasets. Consequently, speaker recognition accuracy is not ideal if we only train a small sample of speaker datasets. Therefore, we propose a transfer learning method for training. We pre-train a Deep Residual neural network (ResNet) with large sample datasets and re-train a novel adaptive model with a simple sample dataset in radio communication. Experiments are carried out using the aishell-2 dataset and the self-collected radio military command datasets. Experimental results demonstrate that the adaptive network with transfer learning method improves the performance by 23.55&#x0025; relatively compared to the baseline system in radio communication.
ENT Randomness Test on DM-PRESENT-80 and DM-PRESENT-128-based Pseudorandom Number Generator

In cryptography, random numbers hold a special importance in which they can be utilized as keys, to generate challenges, or merely as a value. The Pseudorandom Number Generator (PRNG), as the name suggests, makes the generation of random numbers possible. It is widely accepted that there are two different categories of PRNG algorithms, namely the specifically built and those based on existing cryptography algorithms. The block cipher-based hash function scheme is one of the mostly used algorithms to generate outputs of a PRNG. In this study, we performed the ENT randomness test on PRNG which is based on hash function based on block cipher. Since the hash function produces a fixed output, the data set will be used to match the required sample. The hash function scheme used is Davies-Meyer with block cipher construction, namely the PRESENT-80 and PRESENT-128 algorithms. The results showed that the output sequences produced by one iteration and two iterations of DM-PRESENT-80 and DM-PRESENT-128 as a whole passed the ENT test, which means that the PRNG has a random output.
An Unsupervised Cross-lingual Word Alignment Approach based on Cycle-GAN and Hybrid Training

Cross-lingual word alignment is an important task which would produce an effect on many downstream tasks. Most work on cross-lingual word alignment learn a mapping between the word vector features of the two languages on large labeled parallel corpora. Without any labeled parallel corpora, most unsupervised methods learning an orthogonal mapping between the two monolingual spaces. However, the performance of these methods are limited by the degree of isomorphism of the two monolingual spaces. while the embedding spaces for the two languages are less isomorphic, these methods perform not well. To address this problem, this paper proposes a novel unsupervised cross-lingual word alignment approach based on cycle-GAN and hybrid training (FMGAN). It maps two monolingual spaces through correcting the consistency of bidirectional mapping, which performs better in low isomorphism language pairs. Experimental results demonstrate that our approach is better than several state-of-the-art approaches in cross-lingual word alignment task.
Improving Short-term Output Power Forecasting Using Topological Data Analysis and Machine Learning

Forecasting, also referred to as now-casting, has been deployed in various areas such as weather, economics, power generation, and load forecasting. Different techniques are used for short-term forecasting. These techniques include traditional methods such as auto-regressive integrated moving average (ARIMA), and artificial intelligence (machine learning) techniques such as artificial neural networks and support vector machines. In this paper, we report the improvement in the power utility&#x0027;s short-term output power generation using a combination of machine learning techniques and topological data analysis (TDA). The coefficient of determination is used as a metric to assess the performance of various machine learning schemes used. From the results obtained, it can be observed that this coefficient of determination improves in cases where TDA is used as a pipeline to machine learning schemes. These results underline the significance of the approach reported in this paper.