Reviewerly/exHarmony huggingface dataset:
Anomaly Detection Technique Based on Sympathetic Nerve Activity for Detection of Cardiac Arrhythmia

In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-theart signal conditioning and machine learning methods is developed to perform robust detection of cardiac arrhythmia (CA). For this purpose, a signal conditioning technique which obtains SKNA and ECG from wideband recordings on IUC-SKNA database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for reliable detection of CA is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform robust detection of outliers that correspond to CA. The performance results of proposed technique over IUC-SKNA database showed that technique provides highly reliable detection of CA by performing robust detection of SKNA anomalies. Therefore, in cases where diagnostic information of ECG is not sufficient for reliable diagnosis of CA, proposed technique can provide early and accurate diagnosis of the disease, which can lead to a significant reduction in mortality rates of cardiovascular diseases.
Preventing SSRF (Server-Side Request Forgery) and CSRF (Cross-Site Request Forgery) Using Extended Visual Cryptography and QR Code

In this era of technology where technology advancement is at its peak, and new innovations are being introduced every day, Security of consumer plays an important role and can’t be sacrificed. The world is getting digital, everything’s going online and is advancing and so does the security needs to be advanced. This project focuses for the advancement of the current security trend in online transactions that will help to prevent the transactions from various attacks like CSRF, SSRF, Phishing, etc. using combined techniques of EVC, steganography, and QR code. The proposed project ensures that the online transaction system will get more secure as compared to the ongoing system and will be really complex for the hackers to get access to and steal the person’s information or money.
The Development Of Web 3d Application For Virtual Museum Of Lampung Culture

Lampung Museum is an institution that preserves a collection of artifacts and other artistic, cultural, and historical objects as well as providing the scientific and essential research about Lampung Province. As one of the public services, Lampung Museum aims to provide an essential information about the local culture and artifacts in Lampung. Unfortunately, most information can only be accessed manually, so the digital form of the museum content is expected, which leads to the research. The research is supported by the Web3D, hence it could help the Museum to provide more interactive and exciting cultural education to the public. The virtual museum was built by using WebGL, a 3D graphic Programming Interfaces  and it was supported by HTML5 and JavaScript. Virtual Museum acceptance testing is presented with descriptive statistics. The results of has shown that 88% of people agreed that the virtual museum is useful or beneficial, with up to 90% of respondents agreed that the virtual museum could accelerate their speed of work and 86% of the respondents agreed that virtual museum is user-friendly, with up to 89% of the respondents agreed that it is easy to comprehend and use as well.
Efficient Fully homomorphic encryption scheme using Ring-LWE

Abstract Data can be stored and processed in an encrypted format using fully homomorphic encryption, which makes cloud computing provider can process data well without even knowing data information. However, most existing homomorphic encryption schemes based on ring learning with errors, focus on the special classes of rings, such as power-of-two cyclotomic rings, which significantly limits its application efficiency in cloud computing. Therefore, in this letter, to solve this problem above, an efficient fully homomorphic encryption scheme is proposed based on ring learning with errors in arbitrary cyclotomic rings. Particularly, a “somewhat homomorphic encryption” scheme is firstly proposed based on ring learning with errors. A novel fully homomorphic encryption scheme is further presented using key-switching. With theoretical and experimental verification, the proposed scheme has advantage of high efficiency of encryption and decryption. The security of our scheme strictly reduces to hardness of decision ring learning with errors problem in the random oracle model.
Hybrid Solar Forecasting Method Based on Empirical Mode Decomposition and Back Propagation Neural Network

In order to improve the accuracy of solar radiation prediction and optimize the energy management system. This study proposes a forecasting model based on empirical mode decomposition (EMD) and Back Propagation Neural Network (BPNN). Empirical mode of decomposition (EMD)-based ensemble methods with powerful predictive abilities have become relatively common in forecasting study. First, the existing solar radiation datasets are decomposed into an intrinsic mode function (IMF) and one residue produces fairly stationary sub-series that can easily be modeled on BPNN. Next, both components of the IMF and residue are applied to create the respective BPNN models. Then, the corresponding BPNN is used to predict some sub-series. Finally, the predictive values of the original solar radiation datasets are determined by the sum of each predicted sub-series. Compared with traditional models such as conventional neural network or ARIMA time series, the hybrid EMD-BPNN model shows great results in term of RMSE with 28.13 (W/m2). On the other hand, the result of BPNN and ARIMA was 83.28 (W/m2) and 108.88 (W/m2), respectively. that the non-stationary and non-linear of solar radiation signal has less effect on the accuracy of the prediction.
KannadaRes-NeXt: A Deep Residual Network for Kannada Numeral Recognition

The Kannada Numeral system is unique to Kannada language. In our knowledge very little work has been previously attempted towards the recognition of Kannada handwritten numerals. Therefore, this chapter proposes a framework to correctly recognise Kannada handwritten numerals. The proposed KannadaRes-NeXt framework is based on deep residual network ResNeXt to correctly classify the images of handwritten Kannada numerals. The Kannada-MNIST dataset comprises of 60,000 images for training with an equal number of images for each of the ten glyphs in the Kannada numeral system drawn by native speakers of the Kannada language. This dataset also comprises of main test set with 10,000 images for testing purpose drawn by native speakers. Dig-MNIST dataset serves as an out-of-domain test dataset and comprises of 10,240 images of glyphs drawn by people unfamiliar with the Kannada language. In order to further evaluate the robustness of our Kannada Numeral Recognition system we have introduced two other datasets of 10,000 AugKannada-MNIST test set images and 10,240 AugDig-MNIST dataset images to their respective test sets. KannadaRes-NeXt is observed to have achieved an accuracy of 97.36% on the Kannada-MNIST test set, an accuracy of 79.06% on Dig-MNIST dataset, accuracies of 96.32 and 77.31% on augmented main test set and augmented Dig-MNIST test set respectively. These results also indicate efficiency of our proposed framework as KannadaRes-NeXt performs better than other deep learning techniques for Kannada Numeral Recognition.
Research on fire inspection robot based on computer vision

Abstract With the rapid development of robots in the world, robots in various application scenarios have come into people's sight, especially various inspection robots appear in people's lives. At present, the application of fire patrol robots is more and more. Now, the fire patrol robots used in the warehouse are all land patrol robots. When they work on the designated route, they have high requirements for the environment. When there is a complex environment with uneven ground, it will have a great impact on the robot. This rail mounted fire inspection robot is running on the track, and the platform can be inspected without dead angle. It has low environmental requirements, high inspection efficiency, real-time alarm, and uses computer vision platform to track, which improves the accuracy of fire source identification. In this design, STM32F4 single chip microcomputer is used as the main control of the whole system, which is completed with a number of servo motors, alarms and other hardware, and computer vision and robot main control data interaction.
WaNet - Imperceptible Warping-based Backdoor Attack

With the thriving of deep learning and the widespread practice of using pre-trained networks, backdoor attacks have become an increasing security threat drawing many research interests in recent years. A third-party model can be poisoned in training to work well in normal conditions but behave maliciously when a trigger pattern appears. However, the existing backdoor attacks are all built on noise perturbation triggers, making them noticeable to humans. In this paper, we instead propose using warping-based triggers. The proposed backdoor outperforms the previous methods in a human inspection test by a wide margin, proving its stealthiness. To make such models undetectable by machine defenders, we propose a novel training mode, called the ``noise mode. The trained networks successfully attack and bypass the state of the art defense methods on standard classification datasets, including MNIST, CIFAR-10, GTSRB, and CelebA. Behavior analyses show that our backdoors are transparent to network inspection, further proving this novel attack mechanism's efficiency.
Accounting Research Opportunities for Cryptocurrencies

ABSTRACT During recent years, cryptocurrency has gained the attention of many large companies such as Facebook, Expedia, Apple, and Overstock to name a few. The acceptance and use of cryptocurrency in business transactions can have a major impact on a company's accounting system. However, accounting research exploring issues related to cryptocurrencies is sparse. Therefore, we identify research questions that the accounting profession should address concerning cryptocurrencies. We categorize these research questions using Rogers' theory of diffusion of innovation in an attempt to encourage and extend accounting research in cryptocurrencies to influence the design, development, adoption, implementation, and operation of the technology. We recommend that researchers take a more prescriptive approach to research, rather than a descriptive approach, given that cryptocurrency is in the infancy stage of development and adoption.
Current situation of Bitcoin management and use: perspectives from the world and recommendations for vietnam

The research aimed to evaluate suitability of Bitcoin and its platform in emerging markets such as Vietnam. We used qualitative analysis combined with data collection method published, statistics, analysis, synthesis, comparison, to generate qualitative comments and discussion; evaluate results, the article analyzed and evaluated the impacts of Bitcoin and virtual currency on society of Vietnam, both positive and negative sides. It was found that virtual currency not accepted in Vietnam as means of payment yet, while many nations in the world accept it. We need to complete the legal framework for virtual currencies in general, Bitcoin in particular. The State should continue to have policies to improve information technology infrastructure, build and improve the capacity of the contingent of financial experts, encryption, and security experts and give warning risks in virtual currency transactions. The scientific value of paper is using experiences from previous studies in other countries to generate recommendations for conditions of Bitcoin development in merging markets. Last but not least, the research was limited to the case of Vietnam; hence, we can expand research to other Asian countries or other emerging markets.
A Machine Learning Approach to Approximate the Age of a Digital Image

In-field image sensor defects develop almost continually over a camera’s lifetime. Since these defects accumulate over time, a forensic analyst can approximate the age of an image under investigation based on the defects present. In this context, the temporal accuracy of the approximation is bounded by the different defect onset times. Thus, the approximation of the image age based on in-field sensor defects can be regarded as a multi-class classification problem. In this paper, we propose to utilize two well-known machine learning techniques (i.e. a Naive Bayes Classifier and a Support Vector Machine) to solve this problem. The accuracy of each technique is empirically evaluated by conducting several experiments, and the results are compared to the current state-of-the art in this field. In addition, the prediction results are assessed individually for each class.
A Virtual Reality Etruscan Museum Exhibition – Preliminary Results Of The Participants’ Experience

The current global health emergency has posed the need to reflect upon how to guarantee high standard of quality in 100% virtual exhibition. In this case study, we present one of the possible solutions to design a VR museum exhibition for educational purposes. The Centre for Museum Studies designed “The E-Trouria App”, a VR exhibition which is aimed at providing participants with personalised learning path based on an Etruscan museum collection in Rome. The App was designed by combining different pedagogical methods such as Digital Storytelling and Reflective Questioning. The goals of the research were to understand visitors' evaluation of their experience. 20 postgraduate students (F = 17; M = 3; Average age = 36 years) in Museum Education took part in the pre-pilot experimentation. Participants expressed very positive evaluation on the visit and their features (narratives, soundtrack and multimodality). Participants were emotionally engaged during the visit and the most reported emotions were “pleasure” and “wondering”. Future research steps are illustrated.
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic

Safe and reliable electricity transmission in power grids is crucial for modern society. It is thus quite natural that there has been a growing interest in the automatic management of power grids, exempliﬁed by the Learning to Run a Power Network Challenge (L2RPN), modeling the problem as a reinforcement learning (RL) task. However, it is highly challenging to manage a real-world scale power grid, mostly due to the massive scale of its state and action space. In this paper, we present an off-policy actor-critic approach that effectively tackles the unique challenges in power grid management by RL, adopting the hierarchical policy together with the afterstate representation. Our agent ranked ﬁrst in the latest challenge (L2RPN WCCI 2020), being able to avoid disastrous situations while maintaining the highest level of operational efﬁciency in every test scenarios. This paper provides a formal description of the algorithmic aspect of our approach, as well as further experimental studies on diverse power grids.
Real-Time Multiobject Tracking Based on Multiway Concurrency

This paper explored a pragmatic approach to research the real-time performance of a multiway concurrent multiobject tracking (MOT) system. At present, most research has focused on the tracking of single-image sequences, but in practical applications, multiway video streams need to be processed in parallel by MOT systems. There have been few studies on the real-time performance of multiway concurrent MOT systems. In this paper, we proposed a new MOT framework to solve multiway concurrency scenario based on a tracking-by-detection (TBD) model. The new framework mainly focuses on concurrency and real-time based on limited computing and storage resources, while considering the algorithm performance. For the former, three aspects were studied: (1) Expanded width and depth of tracking-by-detection model. In terms of width, the MOT system can support the process of multiway video sequence at the same time; in terms of depth, image collectors and bounding box collectors were introduced to support batch processing. (2) Considering the real-time performance and multiway concurrency ability, we proposed one kind of real-time MOT algorithm based on directly driven detection. (3) Optimization of system level—we also utilized the inference optimization features of NVIDIA TensorRT to accelerate the deep neural network (DNN) in the tracking algorithm. To trade off the performance of the algorithm, a negative sample (false detection sample) filter was designed to ensure tracking accuracy. Meanwhile, the factors that affect the system real-time performance and concurrency were studied. The experiment results showed that our method has a good performance in processing multiple concurrent real-time video streams.
Prediction of Bending Force in the Hot Strip Rolling Process Using Multilayer Extreme Learning Machine

In the hot strip rolling process, accurate prediction of bending force can improve the control accuracy of the strip flatness and further improve the quality of the strip. In this paper, based on the production data of 1300 pieces of strip collected from a hot rolling factory, a series of bending force prediction models based on an extreme learning machine (ELM) are proposed. To acquire the optimal model, the parameter settings of the models were investigated, including hidden layer nodes, activation function, population size, crossover probability, and hidden layer structure. Four models are established, one hidden layer ELM model, an optimized ELM model (GAELM) by genetic algorithm (GA), an optimized ELM model (SGELM) by hybrid simulated annealing (SA) and GA, and two-hidden layer optimized ELM model (SGITELM) optimized by SA and GA. The prediction performance is evaluated from the mean absolute error (MAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The results show that the SGITELM has the highest prediction accuracy in the four models. The RMSE of the proposed SGITELM is 11.2678 kN, and 98.72% of the prediction data have an absolute error of less than 25 kN. This indicates that the proposed SGITELM with strong learning ability and generalization performance can be well applied to hot rolling production.
Civil Liability Applicable to Artificial Intelligence: A Preliminary Critique of the European Parliament Resolution of 2020

On 20 October 2020, the European Parliament approved a Resolution with recommendations to the Commission on a civil liability regime for artificial intelligence (2020/2014(INL)) (the 2020 Resolution). The Resolution highlighted the importance of defining a clear and harmonised civil liability regime in Europe for the development of artificial intelligence technologies and the products and services that benefit from them, so as to provide due legal certainty for producers, operators, affected persons and other third parties.The same motivation had previously led the European Parliament to put forward a series of proposals on the subject of liability in its 2017 Resolution (European Parliament Resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)). There are some important differences between the two documents. These differences reflect the deeper analysis that the European Commission, in particular, has been engaged in with regard to civil liability for harm attributable to artificial intelligence systems. On this subject, we may note, lastly, the “Report on the safety and liability implications of Artificial Intelligence, the Internet of Things and robotics” (COM(2020) 64 final - Report), which accompanies the European Commission White Paper on Artificial Intelligence – COM(2020) 65 final, of 19 February 2020 (White Paper).One important element underpinning the 2020 Resolution is the distinction made between two different regimes for establishing civil liability for harm attributed to artificial intelligence-systems: strict liability for high-risk situations, and subjective liability with a presumption of fault, in other situations. The aim of these brief notes is to, on one hand, provide a critical assessment of how far this option corresponds to the assumptions on which the Resolution is based, also taking a brief look at the concept of wrongdoing proposed and the scope of compensable harm, and, on the other hand, to assess the adequacy of the distinction indicated in the light of the choices made in the 2017 Resolution and, above all, in the light of the challenges that the development of artificial intelligence raises for the traditional foundations on which compensation regimes have been based up to the present.
Low-Light Image Enhancement Using Deep Convolutional Network

Low-light image enhancement is generally regarded as a challenging task in image processing, especially for the images captured at nighttime or images taken in low-light conditions. It is because various factors of the image such as contrast, sharpness and color coordination should be handled simultaneously and effectively. To reduce the blurs or noises on the low-light images, many papers have contributed by proposing different techniques. One such technique addresses this problem using a pipeline neural network and a convolutional neural network. Due to the irregularity of intensity calculation in the working of the pipeline neural networks model, a hidden layer is added to the model which results in a decrease in irregularity. The CNN algorithm uses more number of hidden layers in identifying features of the image in the pixel level, and the process of repairing in the pixel level will increase the intensity of the image and thus increases the clarity of the image. The proposed model using DCNN increases the intensity accuracy percentage by 10–15%.
Graph-Based Indexing and Retrieval of Lifelog Data

Understanding the relationship between objects in an image is an important challenge because it can help to describe actions in the image. In this paper, a graphical data structure, named “Scene Graph”, is utilized to represent an encoded informative visual relationship graph for an image, which we suggest has a wide range of potential applications. This scene graph is applied and tested in the popular domain of lifelogs, and specifically in the challenge of known-item retrieval from lifelogs. In this work, every lifelog image is represented by a scene graph, and at retrieval time, this scene graph is compared with the semantic graph, parsed from a textual query. The result is combined with location or date information to determine the matching items. The experiment shows that this technique can outperform a conventional method.
Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning

Credit card sharp practice detection is one of the most important issues which must be motivated to save the financial institution from huge losses. Several machine larning models such as random forest, logistic regression, Naive Bayes, and XGBoost have been used to verify whether the transaction is fraudulent or genuine one. The data sets which is used in the research has been taken out from European Cardholder consisting of 284,807 transactions. As the data sets are highly imbalanced, so, SMOTE oversampling technique has been used. This experiment is carried out in three phases. First with individual standard model next with soft voting and finally with AdaBoost to know which model gives better results. F1 and MCC have been used for evaluation of the model as accuracy might leads to misclassification problem.