Reviewerly/exHarmony huggingface dataset:
Computing: The Turing way

Abstract This Chapter introduces the Turing paradigm of computation and computational complexity classes. It discusses the algorithmic way combinatorial optimization problems are traditionally solved and explains why some of them are particularly difficult to solve with algorithms. It anticipates the need for non-perturbative approaches to the solution of such problems.
Eye motion measured with retinal FreezEye tracker and with EyeLink 1000

Neurodegenerative diseases, such as multiple sclerosis (MS), negatively impact the aging global population. MS damage various parts of the central nervous system, leading to various eye-movement abnormalities. We have built a retinal FreezEye Tracker (FET) to measure a wide dynamic range of eye movements of up to 10 degrees with an ultrahigh temporal and spatial resolution during visual tasks, including fixations, experiments with saccades, and smooth pursuit. To compare, we performed the same experiments with pupil tracker EyeLink 1000. The amplitudes of detected saccades are similar in both devices, but FET provides high-resolution details on eye trajectory during fixation periods.
Knowledge Graphs in Text Information Retrieval

The article discusses the issues of texts ontological representations graph forms interactive use in tasks of information support by means of documentary type information retrieval systems in one of the most human activity complex types - scientific research - the new scientific knowledge output process, as result of which new facts are being established and generalized. Cognitive-like search tools on full texts based on knowledge graph is discussed. Examples of graph search using path search technologies and analysis of the neighborhood of an entity or property are given.
Cloud applications

Cloud application developers enjoy the advantages of a just-in-time infrastructure; they are free to design an application without being concerned where the application will run. Cloud elasticity allows applications to seamlessly absorb additional workload. Cloud users also benefit from the speedup due to parallelization. Sections 11.1, 11.2, and 11.3 cover cloud application developments and provide insights into workflow management. Coordination based on a state machine model is presented in Section 11.4, followed by an in-depth discussion of the MapReduce programming model and one of its applications in Sections 11.5 and 11.6. Hadoop, Yarn, and Tez are covered in Section 11.7, while systems such a Pig, Hive, and Impala are discussed in Section 11.8. An overview of current cloud applications and new application opportunities is presented in Section 11.9, followed by a discussion of cloud applications in science and engineering and in biology research in Sections 11.10 and 11.11, respectively. Social computing and software fault isolations are the subjects of Sections 11.12 and 11.13. Further readings and exercises and problems are contained in Sections 11.14 and 11.15.
Writing in Italian L2 A Study on the Referential Movement and the Mechanisms of Cohesion in Descriptive and Narrative Texts

This article presents a study on L2 writing in Italian and it aims to analyse the organisation of the information and the mechanisms of cohesion in descriptive and narrative texts written by Chinese speaking university students. For this purpose, the study, within the framework of the Quaestio model, examines, on the one hand, the global text structure and, on the other, the referential movement in the domains of time, space, persons and objects. Finally, it focuses on the cohesive devices employed by the informants. The results highlight differences between descriptive and narrative texts, concerning both the distribution of the information and cohesive relations.
Application to monitoring a USB control with Ruby in Windows and Linux Ubuntu

This work shows the development of an application in Ruby to detect the actions carried out on a USBNes control, with the purpose of achieving an application that can be used for teaching video game programming, connectivity and communication between devices, in the Ruby programming language. In the first stage, an analysis was made of the libraries available in Ruby such as Shoes and Gosu to create GUIs and video games and the detection of an USB device. In the second stage two prototypes were identified that were able to detect the actions carried out on the keyboard. In the last stage the final version was developed which detects the buttons pressed on the remote, the necessary tests were made, and the code was published in a repository in GITLAB for reference and future use. The application was developed and tested for Windows and Linux Ubuntu to prove Ruby's portability.
Non-deterministic Algorithms

This chapter covers a description of non-deterministic algorithms for ship safe trajectory planning. A non-deterministic algorithm can return a different solution for every run of calculations with the same input data. A stochastic algorithm is a type of a non-deterministic algorithm, which applies some probabilistic operations. Population-based stochastic algorithms are applying some probabilistic operations to a population of individuals, constituting candidate solutions in order to obtain the best solution to the considered problem. These methods are mostly inspired by the behavior of animals and other organisms, therefore are classified as nature-inspired algorithms, commonly categorized as heuristics. A heuristic is a technique composed of a rule or a set of rules, which seeks for a good solution to the considered problem in a reasonable amount of time. A heuristic does not guarantee the achievement of an optimal solution. It is aimed at the reception of an acceptable, near-optimal solution. In this chapter two of such approaches will be presented with the application to ship safe trajectory planning. These are: Ant Colony Optimization (Sect. 5.1) and Evolutionary Algorithms (Sect. 5.2).
An Accelerated Nonparametric Bayesian Approach for Anomaly Detection with Feature Selection

This paper proposes an accelerated variational approach for learning infinite generalized inverted Dirichlet mixture to deal concurrently with two challenging problems: clustering and feature weighting. The statistical framework considers a Dirichlet process (DP) prior for the generalized inverted Dirichlet (GID) that has proven to be efficient for the clustering of semi-bounded data. In high dimensional spaces, the selection of the most informative and relevant features has an important role in terms of clustering accuracy and thus we include feature weighting to emphasize relevant features. An application that concerns anomaly detection indicates the effectiveness of the proposed framework.
Kernel Error Path Algorithm

Tuning the values of kernel parameters plays a vital role in the performance of kernel methods. Kernel path algorithms have been proposed for several important learning algorithms, including support vector machine and kernelized Lasso, which can fit the piecewise nonlinear solutions of kernel methods with respect to the kernel parameter in a continuous space. Although the error path algorithms have been proposed to ensure that the model with the minimum cross validation (CV) error can be found, which is usually the ultimate goal of model selection, they are limited to piecewise linear solution paths. To address this problem, in this article, we extend the classic error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path algorithm (KEP) that can find the global optimal kernel parameter with the minimum CV error. Specifically, we first prove that error functions of binary classification and regression problems are piecewise constant or smooth w.r.t. the kernel parameter. Then, we propose KEP for support vector machine and kernelized Lasso and prove that it guarantees to find the model with the minimum CV error within the whole range of kernel parameter values. Experimental results on various datasets show that our KEP can find the model with minimum CV error with less time consumption. Finally, it would have better generalization error on the test set, compared with grid search and random search.
DynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Clustering

Understanding flow changes in network traffic has great importance in designing and building robust networking infrastructure. Recent efforts from industry and academia have led to the development of monitoring tools that are capable of collecting real-time flow data, predicting future traffic patterns, and mirroring packet headers. These monitoring tools, however, require offline analysis of the data to understand the big versus small flows and recognize congestion hot spots in the network, which is still an unfilled gap in research. In this study, we proposed an innovative unsupervised clustering approach, DynamicDeepFlow, for network traffic pattern clustering. The DynamicDeepFlow can recognize unseen network traffic patterns based on the analysis of the rapid flow changes from the historical data. The proposed method consists of a deep learning model, variational autoencoder, and a shallow learning model, k-means++. The variational autoencoder is used to compress and extract the most useful features from the flow inputs. The compressed and extracted features then serve as input-output pairs to k-means++. The k-means++ explores the structure hidden in these features and then uses them to cluster the network traffic patterns. To the best of our knowledge, this is one of the first attempts to apply a real-time network clustering approach to monitor network operations. The real-world network flow data from Energy Sciences Network (a network serving the U.S. Department of Energy to support U.S. scientific research) was utilized to verify the performance of the proposed approach in network traffic pattern clustering. The verification results show that the proposed method is able to distinguish anomalous network traffic patterns from normal patterns, and thereby trigger an anomaly flag.
Dynamics of photoinduced entanglement generation between remote electron-phonon systems

Abstract We numerically study the dynamics of quantum entanglement generation between remote electron-phonon systems by photoirradiation employing a model of multiple spins and boson modes. The Schmidt decomposition on the time-dependent wavefunction enables us to discuss the mechanism of the entanglement generation as dynamical behaviors of the singular vectors. The calculated results show that an effective entanglement source will be provided by an appropriate measurement on the phonon state.
MLCT: A multi‐level contact tracing scheme with strong privacy

Abstract With the outbreak of Covid‐19, both people's health and the world economy are facing great challenges. Contact tracing scheme based on Bluetooth of smartphones has been regarded as a viable way to mitigate the spread of Covid‐19. The existing schemes mainly belong to the centralized or the decentralized structure, both of which have their own limitations. It is infeasible for the existing schemes to balance the different demands of governments and users for user privacy and tracing efficiency at different periods of the epidemic. In this paper, we propose a hybrid contact tracing scheme named MLCT (multi‐level contact tracing scheme) which is mainly based on short group signature. MLCT provides multiple privacy levels by applying anonymous credential technology and secret sharing technology to desensitize user identity privacy and encounter privacy. Comparing to the previous schemes, MLCT fully considers the different demands of the government, patients, and close contacts for user privacy and tracing efficiency in the different stages of Covid‐19. The experimental results show viability in terms of the required resource from both server and mobile phone perspectives. And the security analysis demonstrates that MLCT can achieve the five targets security goals. It is expected that MLCT can contribute to the design and development of contact tracing schemes.
Practical algorithm substitution attack on extractable signatures

An algorithm substitution attack (ASA) can undermine the security of cryptographic primitives by subverting the original implementation. An ASA succeeds when it extracts secrets without being detected. To launch an ASA on signature schemes, existing studies often needed to collect signatures with successive indices to extract the signing key. However, collection with successive indices requires uninterrupted surveillance of the communication channel and a low transmission loss rate in practice. This hinders the practical implementation of current ASAs, thus causing users to misbelieve that the threat incurred by ASA is only theoretical and far from reality. In this study, we first classify a group of schemes called extractable signatures that achieve traditional security (unforgeability) by reductions ending with key extraction, thus demonstrating that there is a generic and practical approach for ASA with this class of signatures. Further, we present the implementation of ASAs in which only two signatures and no further requirements are needed for the extraction of widely used discrete log-based signatures such as DSA, Schnorr, and modified ElGamal signature schemes. Our attack presents a realistic threat to current signature applications, which can also be implemented in open and unstable environments such as vehicular ad hoc networks. Finally, we prove that the proposed ASA is undetectable against polynomial time detectors and physical timing analysis.
Topological features of DMMs

Abstract This Chapter shows that DMMs employ critical points and trajectories between them (instantons) to efficiently solve combinatorial optimization problems. It does so by introducing supersymmetric Topological Field Theory. It then shows that temporal and spatial dynamical long-range order emerges during the dynamics of DMMs. This is key for their efficient exploration of the phase space.
Validation of solid mechanics models using modern computation techniques of Zernike moments

Numerical simulation plays an vital role in mechanical engineering, as it allows the prediction of the mechanical behavior of a component, contributing to the improvement of its performance and the optimization of the design. However, before using the numerical model’s valuable information, its reliability must be verified in a process called validation. Validation is usually carried out experimentally, measuring the mechanical behavior of a mechanical part or component. If there is a good agreement between the model’s prediction and the experimental measurements, the numerical simulation and its predictions are validated. Traditionally, the validation technique consists of identifying the critical areas of the component and checking the agreement between the simulation and the measurements by means of strain gauges located in these areas. More recently, optical techniques for the deformations measurement have gained much interest due to their ability to measure the entire extension of a component captured by the image. This property, called full-field measurement, allows the validation of the whole numerically simulated sample, improving the reliability and precision of the validation. In this procedure, the comparison between the simulated and experimental data requires an image compression process, typically using descriptors based on Zernike moments or any polynomial decomposition. This article presents an improved validation process based on the efficient and accurate calculation of Zernike moments. Concerning the currently used procedures, this new procedure makes it possible to significantly increase the order of image decomposition, the size (i.e., the resolution) of the analyzed region of interest (ROI), and reduce the processing time. All this results in an improvement in validation reliability and the possibility of validating strains/displacements with arbitrary shapes, even with discontinuities.
COMPUTER-AIDED THERAPY USING AUTOMATIC SPEECH RECOGNITION TECHNIQUE FOR DELAYED LANGUAGE DEVELOPMENT CHILDREN

Objectives: This study aims to develop a computer-aided therapy (CAT) application to help children who suffer from delayed language development (DLD) improve their language, especially during the COVID-19 pandemic. Methods: The implemented system teaches the children their body parts using the Egyptian dialect. Two datasets were collected from healthy children (2800 words) and unhealthy children (236 words) who have DLD at the clinic. The model is implemented using a speaker-independent isolated word recognizer based on a discrete-observation hidden Markov model (DHMM) classifier. After the speech signal preprocessing step, K-means algorithm generated a codebook to cluster the speech segments. This task was completed under the MATLAB environment. The graphical user interface was implemented successfully under the C# umbrella to complete the CAT application task. The system was tested on healthy and DLD children. Also, in a small clinical trial, five children who have DLD tested the program in an actual trial to monitor their pronunciation progress during therapeutic sessions. Results: The max recognition rate was 95.25% for the healthy children dataset, while 93.82% for the DLD dataset. Conclusion: DHMM was implemented successfully using nine and five states based on different codebook sizes (160, 200). The implemented system achieved a high recognition rate using both datasets. The children enjoyed using the application because it was interactive. Children who have DLD can use speech recognition applications.
A VR-based CAD 3D Modeling System for Smart Home Products

There is still ample room for further optimization of the application effect of virtual reality (VR) in smart products, and further expansion of the reconstruction research of smart home products. Hence, the related algorithms of spatial reconstruction and three-dimensional (3D) Computer-Aided Design (CAD) item reconstruction is proposed based on the relevant concepts of CAD and convolutional neural network. The algorithms aim to construct the virtual application scene and the simulation state of product usage by VR technology, and stimulate customers' purchase decision. Through the experiments, it is found that the Fully Convolutional Network optimized by Visual Geometry Group Network can effectively realize scene reconstruction and item reconstruction. The average overall recognition accuracy of scene segmentation is 91.3%, and the mean Intersection over Union is 63.4%, showing a better recognition effect than other similar algorithms. Moreover, in the detection of indoor items, the maximum detection accuracy of the MaskR-CNN algorithm is 0.89, and the detection effect is also better than that of other algorithms. The experiments achieve a good spatial reconstruction structure, with the average voxel Prec rates in unsheltered space and sheltered space of 94.4% and 55.6%, and the average voxel Recall of 92.2% and 36.5%, respectively. The experimental results indicate an excellent spatial reconstruction effect. This study can provide a more scientific reference for subsequent VR system research.
A quick-test method for the two-photon spectral correlation quantification

We propose a quick-test method for the two-photon spectral correlation quantification based on a dual-channel filter and a dispersive element, where the spectral correlation coefficient can be readily evaluated from directly measurable parameters in a coincidence measurement. Verified experiments have been conducted on two biphoton sources with different types of spectral correlation, presenting nice agreement with the joint spectrum measurement. Because of its simple implementation and fast measurement within seconds, this scheme is promising to be a plug-and-play tool for spectral correlation quantification, which would facilitate various quantum information applications with on-demand spectral correlation.
Small Wins and Feeling Good: An Innovative Book Distribution Program

Abstract Many of our children continue to live in book deserts, neighborhoods bereft of reading materials. It is an educational problem that, at times, may seem unsolvable. In this article, my colleagues and I approach the problem from a perspective of a small win, a term first coined by organizational theorist Karl Weik. Small wins act like micro‐innovations, tackling big problems by breaking them down into smaller, more manageable projects which can demonstrate promise. The article describes an innovative book distribution program through vending machines in a neighborhood, and provides examples of the ways in which a small win can lead to progress in our understanding of reading and its importance in a community.
Threat Modeling Analysis on FireStormcx’s Webcam System

Since the COVID 19 pandemic creates enormous casualties in the world, it rapidly changes the working environment. It significantly increased the virtual meeting among the team members at work. These require many pieces of equipment. Among the components, a webcam is becoming the most important piece of equipment in virtual meetings. Since the webcam becomes the important factor in the meeting, the attacks on the webcam are enormously increased. In many cases, when a group of hackers attacks a webcam, it creates critical privacy breaches. Therefore, to prevent these types of critical breaches, this paper closely conducts the Threat Modeling analysis based on DFD and STRIDE techniques on FireStormcx’s webcam. Additionally, these types of Threat Modeling analysis result to create a recommended threat remediation plan.
Application to Machine Learning and Quantum Mechanics

Abstract This Chapter shows how to employ MemComputing in the field of Machine Learning and Quantum Mechanics. It demonstrates that both the supervised and unsupervised training of neural networks can be improved substantially. It also shows a possible way to use MemComputing to find the ground state of quantum Hamiltonians and the reconstruction of quantum states (quantum tomography).
Unfair Review Detection on Amazon Reviews Using Sentiment Analysis

Reviews provided by the user have significant value of product within the world of e-market. Individuals and organizations depend heavily on social media now a days for customer reviews in their decision-making on purchases. However, for private gains like profit or fame, people post fake reviews to market or demote certain target products also in order to deceive the reader. To get genuine user experiences and opinions, there's a requirement to detect such spam or fake reviews. Sentiment Analysis (SA) has been used which is now the topic generating the major attentiveness in the text analysis field. Sentiment classification methods are used on a dataset of user reviews for unfair reviews detection and trend patterns. Various supervised and deep learning approaches like Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) are used to determine the overall semantic of customer reviews by classifying them into positive and negative sentiment. The performance of sentiment classification is also evaluated by using accuracy, precision, and recall as performance measures and select the best approach for classification.
Efficiency of a small power photovoltaic installation connected to the low voltage network

Abstract Deploying photovoltaic installations to cover the electrical energy requirements of home and industrial consumer is both a national and an international trend. The aim of the research is to analyse the efficiency of the photovoltaic installation based on recordings and measurements. In this work, we present a case study for a 40 kW photovoltaic installation deployed for an industrial consumer. For a period of eleven days, we measured the energy levels that the consumer absorbed both from the national energy network and from the photovoltaic installation. We conclude that the photovoltaic installation covers approximately half of the energy required by the industrial consumer.