Reviewerly/exHarmony huggingface dataset:
Noncommutative graphs based on finite-infinite system couplings: Quantum error correction for a qubit coupled to a coherent field

Quantum error correction plays a key role for quantum information transmission and quantum computing. In this work, we develop and apply the theory of noncommutative operator graphs to study error correction in the case of a finite-dimensional quantum system coupled to an infinite-dimensional system. We consider as an explicit example a qubit coupled via the Jaynes-Cummings (JC) Hamiltonian with a bosonic coherent field. We extend the theory of noncommutative graphs to this situation and construct, using Gazeau-Klauder coherent states, the corresponding noncommutative graph. As the result, we find the quantum anticlique, which is the projector on the error-correcting subspace, and analyze it as a function of the frequencies of the qubit and the bosonic field. The general treatment is also applied to the analysis of the error-correcting subspace for certain experimental values of the parameters of the Jaynes-Cummings Hamiltonian. The proposed scheme can be applied to any system that possess the same decomposition of spectrum of the Hamiltonian into a direct sum as in JC model, where eigenenergies in the two direct summands form strictly increasing sequences.
Predicting the Anxiety of Patients with Alzheimer’s Dementia using Boosting Algorithm and Data-Level Approach

Since overfitting due to imbalanced data can cause prediction errors during the learning process of machine learning and degrades the prediction performance of the model (e.g., sensitivity), it is necessary to add an additional data sampling technique in the model development step to reduce overfitting to overcome this issue, in addition to selecting a machine learning algorithm suitable for the data.This study examined Alzheimer's patients living in South Korea to understand the predictors of anxiety using boosting algorithms (i.e., AdaBoost and XGBoost) and data-level approach (raw data, undersampling, oversampling, and SMOTE) and confirmed the machine learning algorithm with the best prediction performance.We analyzed 253 elderly people who were diagnosed with Alzheimer's disease (aged from 60 to 74 years old) who visited rehabilitation hospitals for early dementia screening.This study developed models for predicting the anxiety of Alzheimer's dementia patients using AdaBoost and XGBoost.Moreover, this study compared the prediction performance (i.e., accuracy, sensitivity, and specificity) of the models.The results of this study showed that XGBoost based on SMOTE (accuracy=0.84,sensitivity=0.85, and specificity=0.81) was identified as the model with the best prediction performance.Consequently, the results of this study presented that using a SMOTE-XGBoost model may provide higher accuracy than using a SMOTE-Adaboost model for developing a prediction model using outcome variable imbalanced data such as disease data in the future.
Small Target Detection Based on Squared Cross Entropy and Dense Feature Pyramid Networks

At present, the research hotspot of small target detection mainly focuses on the methods based on deep learning, and such algorithms still have some problems that need to be solved, such as the foreground-background class imbalance and the poor performance of multi-scale detection. For the foreground-background class imbalance, the Squared Cross Entropy (SCE) loss function is proposed here to help solve the problem. Meanwhile, as Feature Pyramid Networks (FPN) is a powerful means to deal with multi-scale detection problems, a new Dense FPN structure is designed based on FPN. The Dense FPN removes the up-sampling process in FPN, and after each feature extraction layer, a continuous convolutional layer with a decreasing number of layers is added. According to the experimental results, Dense FPN outperform the original FPN on various evaluation indicators like <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AP_{S}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AP_{M}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AP_{L}$ </tex-math></inline-formula> , showing the excellent performance of the Dense FPN in dealing with multi-scale detection problems.
Security, privacy and forensics in the enterprise information systems

Distributed information systems and their management have become a reality of today’s enterprises. Adoption of different computing and communication technologies has become an essential strategy fo...
Nreal: Ready-to-wear Mixed Reality Glasses

Nreal is unveiling a new mixed-reality headset for enterprise users, and it is also making its Nreal Light model available in additional countries after launching it in Korea and Japan last year. Hear the latest!
Sequence feature generation with temporal unrolling network for zero-shot action recognition

Zero-Shot Action Recognition (ZSAR) aims to recognize unseen action classes not included in the training dataset. Existing generative methods for ZSAR synthesize a feature of unseen action from a class embedding to overcome the absence of training data. Specifically, previous methods synthesize a feature which is averaged along a time axis, even though a video is extracted as a sequence of feature vectors. They suffer from the ambiguity of temporal information, which leads to confusion among actions sharing similar subactions. To tackle the problem, we first propose to synthesize not an averaged feature but a sequence consisting of feature vectors along the time axis. Hence, we design Sequence Feature Generative Adversarial Network (SFGAN) with Temporal Unrolling NEtwork (TUNE), which unrolls a class embedding into a set of condition vectors for generating sequences of features. Also, we employ a sequence discriminator as the second teacher. Through extensive experiments on the three benchmarks, HMDB51, UCF101, and Olympic, we validate the efficacy of sequence generation for ZSAR, and our method achieves the state-of-the-art generalized zero-shot learning performances.
Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

Abstract Existing software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.
Protect Against Unintentional Insider Threats: The Risk of an Employee’s Cyber Misconduct on a Social Media Site

Social media is a cybersecurity risk for every business. What do people share on the Internet? Almost everything about oneself is shared: friendship, demographics, family, activities and work-related information. This could become a potential risk in every business if the organisation’s policies, training and technology fail to properly address these issues. In many cases, it is the employees’ behaviour that can put key company information at danger. Social media has turned into a reconnaissance tool for malicious actors and users accounts are now seen as a goldmine for cyber criminals. Investigation of social media is in the embryonic stage and thus is not yet well understood. This research project aims to collect and analyse open-source data from LinkedIn, discover data leakage and analyse personality types through software as a service (SAAS). The final aim of the study is to understand if there are behavioural factors that can predict one’s attitude towards disclosing sensitive data.
Claims Solutions Using a Blockchain System in International Logistics

A claim can be understood as a negative result of the performance of a contract, as it is necessary to fulfill the obligation and/or indemnify (freight damage claim, for example). Today’s claims management faces a variety of problems, such as inefficient claims postings, possible data forgery, and a very lengthy claims resolution process. All these problems can be solved by using a synthesis of innovative tools, e.g., a blockchain system, game theory, and statistical methods. Theoretical possibilities and practical examples regarding solving claims management problems were identified by analyzing various scientific sources. However, it remained unclear whether these methods could interact with each other, complicate the existing claims settlement process or not, or whether a synthesis of such measures would be viable. Therefore, a qualitative study was conducted and answers to the above questions were found. The research revealed that 75% of international Lithuanian logistics companies can apply the synthesis of these methods in claims management and solutions. Using the concordance coefficient formula, the value of W was calculated. This coefficient indicated that the opinions of the experts who participated in the study were somewhat similar. The value of the actual χ2 distribution was also calculated. The result obtained shows that the experts’ opinions were somewhat harmonized and that their compatibility was not accidental. The use of blockchain system synthesis in combination with game theory and statistical methods could help international logistics companies to reduce their costs, speed up the management process, and increase data security and reliability while ensuring structured one-stop claims management.
The distance learning during the pandemic: university teachers’ and students’ opinion

This article studies the problem of distance teaching in the pandemic of spring and autumn 2020. The topic of the necessary transition to the computer-mediated interrelation between university teachers and students gains important nowadays, as the worldwide events lead to an inevitable transformation of the educational structure. The changes in the higher educational institutions make students and teaсhers review their attitude to the system and methods of education. We can find the analysis of distance learning in many sources, but the remote learning in state of emergency has not been fully examined. The purpose of this article is to analyze the situation, connected with the distance learning in higher educational institutions, its impact on the level of students’ knowledge and skills. This article gives the perspectives of education taking into consideration information and communication technologies. The authors have conducted the theoretical and comparative analysis of literature, observed the learning process at university, and offered a questionnaire to university teachers and students. The researchers have revealed that, in spite of the technologies being a part of our lives, the quality of education depends on teachers who can use these technologies to their best. A technology without a person does not work. According to the majority of the respondents, the principles of teaching dominate over technologies. However, some elements of teaching should be definitely automated. The pandemic revealed the specific mistakes and weaknesses of high education in Russia, and the authors suggest some ways for its improvement in the period of sanitary and epidemiological restrictions.
Intelligent Approach for Enhancing Prediction Issues in Scalable Data Mining

Support vector regression (SVR) is one of the supervised machine learning algorithms that can be exploited for prediction issues. The main enhancement issue of SVR is attempting to select a reliable parameter to assure the high performance of SVR. In this paper, the intelligent approach is based on integrating the enhanced particle swarm optimization PSO with the SVR to achieve the proper SVR parameters that are used to improve SVR performance. The enhanced PSO is performed by implementing parallelized linear time-variant acceleration coefficients (TVAC) and inertia weight (IW) of PSO, called PLTVACIW-PSO. The proposed approach is evaluated by performing the experimental comparisons of the proposed algorithm with eleven different algorithms. These comparisons are performed by applying the proposed algorithm and these algorithms to 21 different datasets varying in their scales.
A String Diagrammatic Axiomatisation of Finite-State Automata

Abstract We develop a fully diagrammatic approach to finite-state automata, based on reinterpreting their usual state-transition graphical representation as a two-dimensional syntax of string diagrams. In this setting, we are able to provide a complete equational theory for language equivalence, with two notable features. First, the proposed axiomatisation is finite— a result which is provably impossible for the one-dimensional syntax of regular expressions. Second, the Kleene star is a derived concept, as it can be decomposed into more primitive algebraic blocks.
Salp Swarm Optimization Search Based Feature Selection for Enhanced Phishing Websites Detection

Internet-connected devices are increasing rapidly. This facilitates transferring most of the real-world transactions to the cyber world. It follows that eCrime is growing continuously. Phishing is a cyber-attack carried out by intruders. They aim to deceive the users of the Internet to achieve their malicious goals. Therefore, experts have developed different approaches to protect financial transactions and personal login information of the users. Their primary concern is to detect the security breaches for online use of the Internet channels (e.g. emails, SMS, webpages, and social platforms). In this paper, we propose a new phishing detection system based on the Salp Swarm Algorithm (SSA). The main objective is to maximize the classification performance and minimize the number of features of the phishing system. Different transfer function (TF) families: S-TFs, V-TFs, X-TFs, U-TFs, and Z-TFs are used to convert the continuous SSA into binary. Sixteen different binary versions of the SSA algorithm are produced based on different TFs. A comparison analysis is performed to pick up the best binarization method. The phishing system is validated by comparing it with three state-of-the-art algorithms. The results show that BSSA with X-TFs achieved the best results in terms of the used evaluation measures.
Transfer Learning and Augmentation for Word Sense Disambiguation

Many downstream NLP tasks have shown significant improvement through continual pre-training, transfer learning and multi-task learning. State-of-the-art approaches in Word Sense Disambiguation today benefit from some of these approaches in conjunction with information sources such as semantic relationships and gloss definitions contained within WordNet. Our work builds upon these systems and uses data augmentation along with extensive pre-training on various different NLP tasks and datasets. Our transfer learning and augmentation pipeline achieves state-of-the-art single model performance in WSD and is at par with the best ensemble results.
Analysis of whether news on the Internet is real or fake by using deep learning methods and the TF-IDF algorithm

Internet use has become increasingly widespread nowadays. In addition, there is a significant increase in the amount of text content produced in digital media. However, the accuracy and inaccuracy of the news we read and the content produced in a large number are also unknown. In this study, classification and analysis of whether the news is real or not were done by using Deep Learning methods. For the English news, the data set created by Katharine Jarmul was used. The data set contained a total of 6336 news items. The distribution of this data set, which consisted of political and political news, was 50% fake and 50% real. The method used in text classification was Term Frequency - Inverse Document Frequency (TF-IDF). The classification was made with the data set used and 93.88% success and 6.12% error were obtained as a result of the analysis.
Aspect-Based Sentiment Analysis in Hindi: Comparison of Machine/Deep Learning Algorithms

With the evolving digital era, the amount of online data generated such as product reviews in different languages via various social media platforms. Information analysis is very beneficial for many companies such as online service providers. This task of interpreting and classifying the emotions behind the text (review) using text analysing techniques is known as sentiment analysis (SA). Sometimes, the sentence might have positive as well as negative polarity at the same time, giving rise to conflict situations where the SA models might not be able to predict the polarity precisely. This problem can be solved using aspect-based sentiment analysis (ABSA) that identifies fine-grained opinion polarity towards a specific aspect associated with a given target. The aspect category helps us to understand the sentiment analysis problem better. ABSA on the Hindi benchmark dataset, having reviews from multiple web sources, is performed in this work. The proposed model has used two different word embedding algorithms, namely Word2Vec and fastText for feature generation and various machine learning (ML) and deep learning (DL) models for classification. For the ABSA task, the LSTM model outperformed other ML and DL models with 57.93 and 52.32% accuracy, using features from Word2Vec and fastText, respectively. Mostly, the performance of classification models with Word2Vec embedding was better than the models with fastText embedding.
RSA Algorithm Using Performance Analysis of Steganography Techniques in Network Security

The stable network has now become a prerequisite for every relationship. The security hazards are expanding and rendering wired/remote organizations and Internet providers fast, shaky and problematic. Today, safety efforts are all the more important in meeting the cutting edge demands of current emerging businesses. The need is also discussed in areas such as guards, where the key points of concern are safe and checked admission of properties identified with data security. In this paper, the author described the important measures and limits for the establishment of a secure organization with regard to enormous industry / authoritative needs. In providing remote organization access to different assets and interfacing various gadgets remotely, Wi-Fi networks are extremely basic. To deal with Wi-Fi hazards and organizational hacking attempts, different prerequisites are required. This article discusses important protection efforts identified with different organizational conditions, so that an association may be set up with a fully assured organizational environment. Besides, a contextual investigation was examined to delineate the insignificant arrangement of measures necessary in any association to construct network security.
Traffic Monitoring System in Smart Cities Using Image Processing

The problem of traffic control is rapidly increasing as the number of vehicles is increasing day to day. There are several ways already implemented to control the traffic. These methods include timer-based traffic control and others that uses sensors in order to detect the number of vehicles passing and hence produce signal that cycles. These systems are effective but still there are areas in which the traditional system does not produce effective control over the traffic. We introduce a system for traffic monitoring by implementing image-based control in traffic lights instead of the timer or sensor-based systems. This paper discusses a novel approach toward traffic monitoring that can be applied in smart cities and prevent traffic congestions that may happen due to ineffective traffic signal lights. The image of real-time traffic is captured and edge detection technique is used to find the number of vehicles present at a time at traffic light stops. The edge detected image is compared with a reference image for matching purpose. Calculating the number of vehicles effectively can be used to decide the amount of time given to each side of traffic.
A goodness-of-fit test based on neural network sieve estimators

Neural networks have become increasingly popular in the field of machine learning and have been successfully used in many applied fields (e.g., imaging recognition). With more and more research has been conducted on neural networks, we have a better understanding of the statistical proprieties of neural networks. While many studies focus on bounding the prediction error of neural network estimators, limited research has been done on the statistical inference of neural networks. From a statistical point of view, it is of great interest to investigate the statistical inference of neural networks as it could facilitate hypothesis testing in many fields (e.g., genetics, epidemiology, and medical science). In this paper, we propose a goodness-of-fit test statistic based on neural network sieve estimators. The test statistic follows an asymptotic distribution, which makes it easy to use in practice. We have also verified the theoretical asymptotic results via simulation studies and a real data application.
Coupled Relational Symbolic Execution for Differential Privacy

Abstract Differential privacy is a de facto standard in data privacy with applications in the private and public sectors. Most of the techniques that achieve differential privacy are based on a judicious use of randomness. However, reasoning about randomized programs is difficult and error prone. For this reason, several techniques have been recently proposed to support designer in proving programs differentially private or in finding violations to it. In this work we propose a technique based on symbolic execution for reasoning about differential privacy. Symbolic execution is a classic technique used for testing, counterexample generation and to prove absence of bugs. Here we use symbolic execution to support these tasks specifically for differential privacy. To achieve this goal, we design a relational symbolic execution technique which supports reasoning about probabilistic coupling, a formal notion that has been shown useful to structure proofs of differential privacy. We show how our technique can be used to both verify and find violations to differential privacy.