Reviewerly/exHarmony huggingface dataset:
Collaborative Filtering Approach for Movie Recommendations

This paper describes the development of a movies recommendation system by considering the history of the movie watched by the users through the collaborative filtering approach. This analysis are based on two datasets: the movies dataset and the ratings dataset with a total of 10,329 movie titles. Next, the matrix of the movie genre is built, and the search matrix is produced with 18 genres identified. The other matrices such as user and movie similarities matrices were created to ease the analysis of filtering. The visualization results showed that the movie Pulp Fiction has the greatest number of viewers, which is 325 views. The item-based collaborative filtering approach is also used to predict the movie to be recommended and the result revealed that two movies; Get Shorty (1995) and Bad Boys (1995) are highly recommended by the users. The item-based collaborative filtering helps the users to filter the movies according to their preference.
Massive Data Exploration using Estimated Cardinalities

Linguistic summaries are used in this work to provide personalized exploration functionalities on massive relational data. To ensure a fluid exploration of the data, cardinalities of the data properties described in the summaries are estimated from statistics about the data distribution. The proposed workflow also involves a vocabulary inference mechanism from these statistics and a sampling-based approach to consolidate the estimated cardinalities. The paper shows that soft computing techniques are particularly relevant to build concrete and functional business intelligence solutions.
Sign Language To Categorical Text using Convolutional Neural Network

Recognizing hand movement is very important in Sign Language detection. In Proposed paper Indian Sign Language (ISL) detection using Convolutional Neural Network (CNN) is used. RESNET 101 is used for representation of best features in image dataset. Indian Sign Languages has 0–9 digits and 26 alphabets. The working architecture model used in project contains input layer which is used for representing image in the form of pixels, activation layer for training the model to learn from different data, pooling layer for reducing the computational time by optimizing the dimensional space of each feature map, flatten layer to decrease the array dimension and dense layer is used in last layers where it gathers the data from multiple neurons. To prevent the overfitting dropout layer is used. Sign Language is used to express emotions, sentences, words, alphabets and numbers. The hearing and speech impaired people are struggling to express their emotions to normal people. The motivation behind developing such vision based system which can detect and recognize the hand movements and convert it into text, was to bring the hearing and speech impaired people to the normal community where they can express themselves easily using an economical and efficient application. In the paper gathering of data, design workflow and Convolutional Neural Network is discussed.
An Ontology-Based Dynamic Attack Graph Generation Approach for the Internet of Vehicles

With the development of automobile intelligence, the security of the Internet of Vehicles has become a key factor that affects the development of intelligent vehicles. However, existing security risk analysis methods for the IoV either focus only on certain levels, such as the component level, or perform only a static analysis. This paper proposes a dynamic attack graph generation method for the IoV to identify and visually display the security risks caused by the associated vulnerabilities in an IoV system. First, using the actual architecture of the IoV, this paper shows how to model the security elements and their relationships in the IoV system and proposes a network security ontology model for this system. Second, it shows how to construct a reasoning rule base according to the causal relationship between the vulnerabilities using the Semantic Web Rule Language Finally, in view of the rapid change in the network topology of the IoV, a dynamic attack graph generation algorithm based on an ontology reasoning engine is proposed, which can effectively reduce the overhead caused by the changes in the attack graph. The effectiveness of the algorithm is demonstrated through an actual security event scenario and a constructed scenario. The experimental results show that the algorithm can dynamically and accurately display the network attack graph of the IoV. The proposed method is helpful in globally analyzing the threat caused by the combined exploitation of the vulnerabilities in an IoV system and risk management.
Principal Component Analysis-based Customizable Feature Selection Algorithm

An algorithm based on principal component analysis that can help a user select important features in a data set is proposed. This algorithm combines the information obtained from the principal component analysis technique with adjustable criteria from users. These criteria are related to the importance value of each feature in the data set. The result of this algorithm is a data set with a smaller number of features. By decreasing the number of features to be used when training a classifier, the training time can be reduced. Also, if enough important information is maintained, there should be little or no effect on the quality of the obtained classifier. The proposed algorithm is implemented in Python and the reduced-feature data set obtained from this algorithm are used to train various classifiers to verify their quality. The results in terms of training time and accuracy of the classifier are compared and discussed.
Analysis of Efficient Network Security using Machine Learning in Convolutional Neural Network Methods

Several excellent devices can communicate without the need for human intervention. It is one of the fastest-growing sectors in the history of computing, with an estimated 50 billion devices sold by the end of 2020. On the one hand, IoT developments play a crucial role in upgrading a few simple, intelligent applications that can increase living quality. On the other hand, the security concerns have been noted to the cross-cutting idea of frameworks and the multidisciplinary components connected with their organization. As a result, encryption, validation, access control, network security, and application security initiatives for gadgets and their inherent flaws cannot be implemented. It should upgrade existing security measures to ensure that the ML environment is sufficiently protected. Machine learning (ML) has advanced tremendously in the last few years. Machine insight has evolved from a research center curiosity to a sensible instrument in a few critical applications.
Encoder-Decoder Structure with Multiscale Receptive Field Block for Unsupervised Depth Estimation from Monocular Video

Monocular depth estimation is a fundamental yet challenging task in computer vision as depth information will be lost when 3D scenes are mapped to 2D images. Although deep learning-based methods have led to considerable improvements for this task in a single image, most existing approaches still fail to overcome this limitation. Supervised learning methods model depth estimation as a regression problem and, as a result, require large amounts of ground truth depth data for training in actual scenarios. Unsupervised learning methods treat depth estimation as the synthesis of a new disparity map, which means that rectified stereo image pairs need to be used as the training dataset. Aiming to solve such problem, we present an encoder-decoder based framework, which infers depth maps from monocular video snippets in an unsupervised manner. First, we design an unsupervised learning scheme for the monocular depth estimation task based on the basic principles of structure from motion (SfM) and it only uses adjacent video clips rather than paired training data as supervision. Second, our method predicts two confidence masks to improve the robustness of the depth estimation model to avoid the occlusion problem. Finally, we leverage the largest scale and minimum depth loss instead of the multiscale and average loss to improve the accuracy of depth estimation. The experimental results on the benchmark KITTI dataset for depth estimation show that our method outperforms competing unsupervised methods.
MagSnoop

Samsung Pay, one of the most representative mobile payment services, allows mobile users to make payment transactions almost anywhere using only their smartphone. This is thanks to MST (Magnetic Secure Transmission) that supports communication between smartphones and payment terminals for magnetic cards by transferring payment tokens via magnetic waves. Several attack methods have targeted this new technology by eavesdropping on magnetic fields to intercept the tokens, but with the use of dedicated hardware. This paper raises new security concerns for mobile payment users in a different, yet more effective way; by introducing MagSnoop, a novel framework that infers payment tokens from listening to MST sounds generated during the activation of MST payment transactions. More specifically, we first explore the principle, causing the generation of MST sounds, and the fundamental characteristics of these sounds. We then use these observations to infer payment tokens with a high degree of accuracy, robustness, applicability, and data efficiency. Our experiments with a prototype of MagSnoop demonstrate that it can support high accuracy in token inference (more than 77.8%). In addition, MagSnoop can maintain a reasonable level of accuracy regardless of the payment environments (e.g., 69.2% with a noise level of 50 dBA) and even in the real world (an inference success rate of 68.0% with 15 real-world users).
Keywords Generator From Paragraph Text Using Text Mining in Bahasa Indonesia

When reading a document text such as news or article, people tend to read related information topic. However, when they try to use search engine for looking into it, they don't have any idea what the keywords are. Copying the whole text to the search engine's bar is not the best solution, since it will increase the searching process time but make the search engine nonoptimal. It also makes people get unrelated topic. Therefore, this research has a purpose to make an application that generates keyword from inputted text of paragraph. Despite of input the whole paragraph or text in the search bar, user can input the text in the developed application and get the best keyword based on the text. The main method that has been used in this research is a text mining. First, we perform a pre-processing which are turn the inputted text to be in lower case. The second, we remove the stop words and some characters from text. The third, we perform a tokenizing method to separate each word and store it in an array. The main step is calculating the score of each word that has been stored in array. The result of this research is three words that generated by the system from inputted paragraph text. The keywords that generated by system are the same with the method's calculation. However, this application system needs to improve more to get a better performance.
Prevention of Man in the Middle Attacks on Electronic Health Records over Internet, using Block Chain

A database for recording patient medical information received from many sources such as intelligent devices, smart sensors, and diagnostic imaging equipment is known as an Electronic Health Record (EHR). An electronic health record (EHR) contains sensitive personal information on individuals and the management of their illnesses. It's also frequently shared across other members, including healthcare providers, insurance firms, medical researchers, and others. The most difficult aspect of EHR data management is obtaining, storing, and distributing patient data without jeopardizing privacy and security. A new method of managing EHR data has been proposed using blockchain technology. This article proposes a hybrid architecture for EHR information management that employs both the on Hyperledger public blockchain and an off-chain perimeter node. This design is used to verify users without compromising sensitive patient data, as well as to authenticate encrypted EHR data in edge nodes. EHR activities and patient identification activities are saved in a database for accountability and tracking in the on-chain method. Edge nodes used an off-chain approach to store encrypted EHR data. As a result, the combining of on-chain and off-chain techniques only allows the authorized data user to cipher the Electronic health records and satisfy the EHR attracting increasing attention. If an unauthorized user modifies EHR data, a new hash code is created, which differs from the previous hash code recorded in the blockchain. As a consequence, the user can quickly determine whether or not their EHR data has been compromised.
Real Time Object Detection using Tensor Flow

Using today's technology, an item can be detected, located, and tracked using computer vision techniques like Object Detection with Tensor Flow. In the realm of computer imagining and foresight, object detection is closely linked. Photographs are intelligently interpreted in the same way as human imagination and foresight are. It distinguishes between images and objects rather than just detecting objects. Object detection frameworks including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and faster RNN are revived and briefly introduced in this study (YOLO). After that, the recommended object detection architectures are reworked on. A little object is picked up by the traditional version when a picture is taken. A few tweaks are made to the current iteration. The ultimate outcome is nice and accurate, due to this strategy.
COVID-19 contact tracing in the hospitals located in the North Denmark region: A retrospective review

The Department of Infection Control, at our University Hospital conducted contact tracing of COVID-19 positive patients and staff members at all hospitals in the North Denmark Region.To describe the contact tracing performed during the COVID-19 pandemic in the Region and its outcomes.Data from each contact tracing were collected prospectively during 14 May 2020-26 May 2021. Data included information about the index case (patient or hospital staff member), presentation (asymptomatic vs symptomatic), probable source of transmission (community-acquired or hospital-acquired), number of close contacts and if any of these were SARS-CoV-2 PCR-test positive.362 contact tracing were performed. A total of 573 COVID-19 positive cases were identified among 171 (30%) patients and 402 (70%) staff members. 192 (34%) of all cases were tested due to symptoms of COVID-19, whereas two-third were tested for other reasons including outbreak and systematic screening tests. A total of 1575 close contacts were identified, including 225 (14%) patients and 1350 (86%) staff members. 100 (6%) close contacts, including 24 patients and 76 staff members, were infected with SARS-CoV-2, of which 33 (43%) staff members was positive at day 0 i.e. the same day as being identified as close contacts.We found a three to one of close contacts to each index case, but only 6% became SARS-CoV-2 positive, with a surprisingly high number of those identified at day 0. Our data confirm that regular testing of patients and staff will identify asymptomatic carriers and thereby prevent new cases.
Dynamic Attack Trees Methodology

Cyber-Physical Systems (CPS) are special kinds of systems with seamless integration of different entities. They are deployed mostly in safety-critical sectors, which make them a target for a wide range of attacks. Identifying potential threats to these kinds of systems is crucial and difficult as they consist of many modules, different producers, different technologies like programming, machine learning, and sometimes different manufactures. In this paper, we proposed a way of model potential ways in which attackers may compromise such systems using attack trees. We define a set of threat environments (i.e., changes in the vulnerability landscape of the system overtime) with each consisting of different sets of vulnerabilities, and use an attack tree generation algorithm to produce a tree at each threat environment. We further apply an information flow property - the opacity, to check a special kind of attacker who can observe the systems&#x0027; non-secret states.
Investigate GPS Signal Loss Handling Strategies for a Low Cost Multi-GPS system based Kalman Filter

Navigation was easier nowadays due to the advanced GPS system. Human activity relied on the GPS system in everyday life especially for traveling by car. One issue of using GPS system in the urban area was the GPS signal loss caused by skyscrapers that block a GPS module from satellites. The Kalman filter could help improving location tracking for a car. In this research, the multi-GPS system was developed for experiments. Kalman filter was defined and used for tracking the car location. Collected latitude and longitude data from GPS module was converted into UTM coordinate to suit Kalman filter algorithm. There were two proposed handling strategies in this research which were the skip and frozen handling strategies. Multi-GPS system was mounted on top of the car. Then, a car was driven, and the GPS data was collected for experiments. Signal loss points was created by removing or freezing points in GPS data. Then, Kalman filter was experimented for both cases. The result showed that Kalman filter could track the location with some error in both cases but if the signal loss points were increased, then, the skip strategy showed less fluctuations in the results. Therefore, the skip strategy has more promising result than the frozen strategy.
Processing and Recognition of Characters Image in Complex Environment

Aiming at the recognition in complex backgrounds using the Optical Character Recognition (OCR) technology, a model with high detection and recognition accuracy for small texts and codes in the images is proposed in this paper. Before recognition, the Retinex image enhancement and median filtering are performed to weaken the influence of ambient lighting and to enhance the image features. The Otsu threshold segmentation is adopted to segment the small text images from the background. Finally, the Differentiable Binarization (DB) algorithm is applied to detect the characters and the Convolutional Recurrent Neural Network (CRNN) algorithm is employed to recognize the detected characters. The experimental results show that in the life scenes, the workpiece components scenes, and the Chinese characters scenes, the recognition accuracies of the proposed model are 95.6%, 98.4%, and 90.9%, respectively; and the recognition times are 0.78s, 0.59s, and 0.63s, respectively. Overall, the average recognition accuracy of the model proposed in this paper reaches up to 94.9%, and the average recognition time is only 0.67s, which verifies the effectiveness and the advancement of the model.
ATLE2FC: Design of an Augmented Transfer Learning Model for Explainable IoT Forensics using Ensemble Classification

Existing forensic models are majorly focussed towards improving efficiency of data analysis, but a very few models are proposed for intelligent evidence extraction & explain-ability. Inspired by these limitations, this text proposes design of ATLE2FC, a model for explainable IoT forensics using ensemble classification. Performance of the model was compared with various state-of-the-art methods, and it was observed that the proposed model outperforms existing methods in terms of accuracy of evidence-based event detection, classification precision, and evidence classification delay.
Traveling-wave model of coherent Ising machine based on fiber loop with pulse-pumped phase-sensitive amplifier

A coherent Ising machine (CIM) is an optical system that physically solves combinatorial optimization problems based on the Ising model. This paper presents an analytical model describing the operation of a CIM using a long fiber loop with a phase-sensitive amplifier. Instead of the conventional analytical model based on the mean-field approximation in a cavity, a traveling-wave model, which traces the state evolution of each pulse circulating along the loop, was applied. A difference equation for the state of a pulse moving around the loop was derived, and simulations were performed. These results reflect the CIM operation reasonably well.
Greedy algorithm based circuit optimization for near-term quantum simulation

Abstract Simulating quantum systems is believed to be one of the most important applications of quantum computers. On noisy intermediate-scale quantum (NISQ) devices, the high-level circuit designed by quantum algorithms for Hamiltonian simulation needs to consider hardware limitations such as gate errors and circuit depth before it can be efficiently executed. In this work, we develop a hardware-agnostic circuit optimization algorithm to reduce the overall circuit cost for Hamiltonian simulation problems. Our method employ a novel sub-circuit synthesis in intermediate representation and propose a greedy ordering scheme for gate cancellation to minimize the gate count and circuit depth. To quantify the benefits of this approach, we benchmark proposed algorithm on different Hamiltonian models. Compared with state-of-the-art generic quantum compilers and specific quantum simulation compiler, the benchmarking results of our algorithm show an average reduction in circuit depth by 16.5× (up to 64.1×) and in gate count by 7.8× (up to 23.7×). This significant improvement helps enhance the performance of Hamiltonian simulation in the NISQ era.
Forecasting of production and scrap amounts using artificial neural networks

The increase in consumer needs and the scarcity of production resources cause the concept of ‘productivity’ to be essential for companies. Reducing costs is an essential factor for increasing competitiveness, and therefore, businesses are taking action to reduce scrap costs and increase efficiency. Since the increase in scrap will reduce productivity, it may cause production delays and thus customer dissatisfaction. In this study, the slitting line of one of the essential Japanese supplier companies operating in the automotive sector in Turkey is discussed. The proposed model aims to predict the amount of production and scrap that may occur to increase productivity in the slitting line by using an artificial neural network (ANN) and increasing the efficiency of the slitting line with the measures to be taken. In this context, different ANN designs were made for production and scrap. During the execution of the ANN models, the production and scrap amounts were forecasted at 99 and 85%, respectively. While measuring the successful performance of the ANN models, root mean squared error, mean absolute percent error and R 2 indicators were used; the forecasted values produced by the ANNs that were successful in terms of performance indicators were compared with the actual values, and the reliability of the study was increased.