Reviewerly/exHarmony huggingface dataset:
Digital persona portrayal: Identifying pluridentity vulnerabilities in digital life

The increasing use of the Internet for social purposes enriches the data available online about all of us and promotes the concept of the Digital Persona. Actually, most of us are represented online by more than one identity, what we define here as a Pluridentity. This trend brings increased risks: it is well known that the security of a Digital Persona can be exploited if its data and security are not effectively managed. In this paper, we focus specifically on a new type of digital attack that can be perpetrated by combining pieces of data belonging to one same Pluridentity in order to profile their target. Some victims can be so accurately depicted when looking at their Pluridentity that by using the gathered information attackers can execute very personalized social engineering attacks, or even bypass otherwise safe security mechanisms. We characterize these Pluridentity attacks as a security issue of a virtual System of Systems, whose constituent systems are the individual identities and the humans themselves. We present a strategy to identify vulnerabilities caused by overexposure due to the combination of data from the constituent identities of a Pluridentity. To this end we introduce the Digital Persona Portrayal Metamodel, and the related Digital Pluridentity Persona Portrayal Analysis process that supports the architecting of data from different identities: such model and process can be used to identify the vulnerabilities of a Pluridentity due to its exploitation as a System of Systems. The approach has been validated on the Pluridentities of seventeen candidates selected from a data leak, by retrieving the data of their Digital Personae, and matching them against the security mechanisms of their Pluridentities. After analyzing the results for some of the analyzed subjects we could detect several vulnerabilities.
Early and late features fusion for kinship verification based on constraint selection

Kinship verification is an interesting topic of face analysis. In this paper, we propose to extract several local image descriptors including Local Binary Pattern, Local Ternary Pattern and Histogram of Gradient for extracting features from facial images. Next, we apply an extension of constraint score for features ranking and then select a subset of feature for training by Support Vector Machine. The fusion at an early stage (feature level) and fusion at a later stage (score level) are compared and evaluated on two benchmark kinship datasets KinFaceW-I and KinFaceW-II. Experimental results show that the proposed approach outperforms other works in the state-of-the-art.
A Novel Approach for Detecting Logic Similarity in Plagiarised Source Code

Source code plagiarism is a persistent problem in computer science education. Many tools have been developed to identify indications of source code plagiarism in large data sets. These tools are good at identifying simple cases of plagiarism (e.g. renaming identifiers or shuffling declarations), but they are vulnerable to semantics-preserving obfuscations. This is due to a reliance on analyzing the structure of source code, and not the implemented program logic. In this paper, a novel approach to source code plagiarism detection is proposed that compares two programs for logic similarity. This approach analyses the logic embedded in a program as a means of identifying similarity. The approach is evaluated on a data set of simulated plagiarism. The evaluation demonstrates that the approach is resilient to semantics-preserving transformations.
A Single Image Dehazing Method Based on Adaptive Gamma Correction

Image dehazing is an important task in control systems of autonomous vehicles. It is necessary to increase accuracy and performance of road obstacles recognition. In this paper, we propose a single image dehazing method based on adaptive gamma correction (DAGC). The DAGC method processes haze on the Value band of the HSV color space by using the Gamma-correction with estimating the adaptive parameter based on the weighting cumulative density function. Otherwise, it also improves the Saturation band. In the experiments, we tested the DAGC method on the dehazing dataset of TAU and compare with other similar dehazing methods. The results showed that, the proposed method outperformed other methods.
Decentralized Security Bounty Management on Blockchain and IPFS

The rise of decentralized applications (DApps) have received great attention over the recent years due to the surge of attention towards blockchain technologies. Motivated by these recent disruptions, this paper introduces a blockchain based bug bounty program named Bountychain. This DApp utilizes an Ethereum based smart contract system and an interplanetary file system (IPFS) storage paradigm intended to be used by companies, industries, and testers. The smart contract model provides a safe, secure, and transparent platform for a bug bounty program. Testers will submit bugs via the blockchain, and companies will accept or reject the defect via the blockchain. Thus, testers will automatically get paid via the in-built smart contract system on a web interface, which will allow the tester to gain recognition and status no matter which company's bugs they find. The transactions on the chain will serve as a persistent and transparent record of software bugs, and IPFS will serve as a long-term storage system for bug details.
Measuring method for the line spread function of specular reflection

Gloss is evaluated based on not only the intensity of reflected light but also the sharpness of the light source image. In this paper, we discuss the line spread function of specular reflection (SR-LSF). We developed a measurement apparatus for the SR-LSF based on the visual inspection of gloss. The measurement results of six types of samples with different glossiness values are shown. These samples were measured using the point spread function of specular reflection reported previously by the authors to verify the SR-LSF. It is shown that the SR-LSF can be measured easily and efficiently from an image recorded for visual inspection.
Short Term Price Prediction for Preemptible VM Instances in Cloud Computing

Amazon utilizes its free capacity by launching a special type of VMs known as spot instances in Amazon Elastic Compute Cloud (EC2). Users can get these instances by placing bids. These instances are low-cost resources with the risk of reliability. There is a trade-off between cost and reliability in the use of spot instances. So, there is a scope of research in the efficient use of spot instances. The prices of spot instances vary with time, so, one can efficiently utilize spot instances by accurate prediction of prices before bidding. In this article, an algorithm has been proposed for short term prediction of spot prices. The algorithm uses probability between price transitions from the observed history and it can predict the price for next epoch. One can get the short-term price prediction for a particular instance type and product type in a specific region. We have compared the proposed algorithm with other existing prediction algorithms.
Towards Smart Classroom: Affordable and Simple Approach to Dynamic Projection Mapping for Education

In order to transform the traditional classroom into an interactive space, we developed a projection mapping toolbox: an affordable, simple alternative to complex systems. Our system uses Python and Microsoft Kinect V2 for dynamic projection mapping (PM). We demonstrated three ways PM can be used to make Augmented Reality (AR) applications for classroom use. We projected free body diagrams onto boxes, made AR quiz tacking for teachers, and made an AR game. The goal of the projection mapping toolbox is to help students learn more efficiently and raise academic achievement.
Disfluency Detection Based on Speech-Aware Token-by-Token Sequence Labeling with BLSTM-CRFs and Attention Mechanisms

This paper presents a new method for token-by-token sequence labeling that can leverage not only lexical information but also speech information without any alignments. Our motivation is to detect disfluencies such as fillers and word fragments robustly from spontaneous speech. Disfluency detection is often modeled as a token-by-token sequence labeling using a transcribed text via automatic speech recognition. However, utilizing the lexical information alone is not sufficient because the disfluencies cause changes to speech information. One problem is that the speech and the transcribed text need to be aligned when we handle speech and lexical information simultaneously. This prevents introducing speech information to the disfluency detection. To solve this problem, we propose a method for token-by-token sequence labeling, one that can simultaneously use lexical and speech information without requiring any alignments. To this end, we introduce attention mechanisms into a method for neural sequence labeling based on bi-directional long short-term memory recurrent neural network conditional random fields. The attention mechanisms enable us to find the term of disfluencies from speech automatically. Our experimental results show that the proposed method using acoustic and prosodic features improves the labeling accuracy compared with that using lexical features alone.
User-oriented Generation of Contextual Visualization Sequences

A visualization sequence is an effective representation of meaningful data stories.Existing visualization sequencing approaches use heuristics to arrange charts in a meaningful order.While they perform well in specific scenarios, they do not customize the generated sequences to individual users' preferences. In this work, we present VisGuide, an assistive data exploration system that helps a user create contextual visualization sequence trees by sequentially recommending meaningful charts tailoring to the user's preference on data exploration. Our results show that VisGuide can recommend chart sequences that interest users and are also considered meaningful by domain experts.
Migration-based near real-time detection and location of microearthquakes with parallel computing

SUMMARY Prompt detection and accurate location of microseismic events are of great importance in seismic monitoring at local scale and become essential steps in monitoring underground activities, such as oil and gas production, geothermal exploitation and underground gas storage, for implementing effective control procedures to limit the induced seismicity hazard. In this study, we describe an automatic and robust earthquake detection and location procedure that exploits high-performance computing and allows the analysis of microseismic events in near real-time using the full waveforms recorded by a local seismic network. The implemented technique, called MigraLoc, is based on the space–time migration of continuous waveform data and consists of the following steps: (1) enhancement of P and S arrivals in noisy signals through a characteristic function, by means of the time–frequency analysis of the seismic records; (2) blind event location based on delay-and-sum approach systematically scanning the volume of potential hypocentres; (3) detection notification according to the information content of the hypocentre probability distribution obtained in the previous step. The technique implies that theoretical arrival times are pre-calculated for each station and all potential hypocentres as a solution of the seismic-ray equation in a given 3-D medium. As a test case, we apply MigraLoc to two, low-magnitude, earthquake swarms recorded by the Collalto Seismic Network in the area of the Veneto Alpine foothills (Italy) in 2014 and 2017, respectively. Thanks to MigraLoc, we can increase the number of events reported in the network catalogue by more than 25 per cent. The automatically determined locations prove to be consistent with, and overall more accurate than, those obtained by classical methods using manual time-arrival picks. The proposed method works preferably with dense networks that provide signals with some degree of coherency. It shows the following advantages compared to other classical location methods: it works on the continuous stream of data as well as on selected intervals of waveforms; it detects more microevents owing to the increased signal-to-noise ratio of the stacked signal that feeds the characteristic function; it works with any complex 3-D model with no additional effort; it is completely automatic, once calibrated, and it does not need any manual picking.
Error-tolerant approximate graph matching utilizing node centrality information

Graph matching is the task of finding the similarity between the two graphs. Error-tolerant graph matching is the process of computing the similarity between the two graphs, where some flexibility to noise or error is allowed to approximate the value of graph matching. In this paper, we present a framework for graph matching by utilizing the centrality measures to ignore the least central nodes of the graphs. Experimental evaluation shows that this approach can be useful to reduce the overall matching time and it can provide time versus accuracy trade-off.
A Survey of Privacy Protection and Network Security in User On-Demand Anonymous Communication

In an untrusted Internet environment, there is a large amount of user privacy information being vulnerable to various attacks, and it is an important security concern for users on how to protect their privacy, and to further provide the anonymity guarantee.Recently, most privacy-sensitive users prefer anonymous communication to protect their private information from eavesdropping by attackers.Each user has different privacy protection demands for anonymous communication.However, anonymous communication methods cannot meet the various anonymity needs of users.Hence, the user on-demand anonymous communication is proposed, which can dynamically adjust the anonymity level according to a user's anonymity needs.However, the defense capabilities against attacks are insufficient in the existing user on-demand anonymous communication.Some malicious users in the network employ anonymous communication to hide their identities and attack the Internet.Moreover, the existing user on-demand anonymous communication is weak against traffic classification attacks based on machine learning.Therefore, this paper investigates its progresses and defects in privacy protection and network security.User on-demand anonymous communication is mainly composed of the user side and the transmission side.We separately investigate privacy protection and network security of user on-demand anonymous communication from the user side and the transmission side.By surveying various identity anonymity of users on the user side and the hierarchical anonymous transmission on the transmission side, we find two kinds of serious attacks in user on-demand anonymous communication, i.e., abuse of anonymous communication and traffic classification attacks based on machine learning.To solve the above security issues, we suggest the balancing mechanism of anonymous communication and behavior tracking which is used to prevent anonymous abuse, and the secure defense mechanism which is used to resist traffic classification attacks.To inspire follow-up research, we identify some open problems and emphasize future trends concerning user on-demand anonymous communication.
Theory of waveguide QED with moving emitters

We theoretically study a system composed by a waveguide and a moving quantum emitter in the single excitation subspace, treating the emitter motional degree of freedom quantum mechanically. We first characterize single-photon scattering off a single moving quantum emitter, showing both nonreciprocal transmission and recoil-induced reduction of the quantum emitter motional energy. We then characterize the bound states within the bandgap, which display a motion-induced asymmetric phase in real space. We also demonstrate how these bound states form a continuous band with exotic dispersion relations. Finally, we study the spontaneous emission of an initially excited quantum emitter with various initial momentum distributions, finding strong deviations with respect to the static emitter counterpart both in the occupation dynamics and in the spatial distribution of the emitted photons. Our work extends the waveguide-QED toolbox by including the quantum motional degree of freedom of emitters, whose impact in the few-photon dynamics could be harnessed for quantum technologies.
Digital Random Number Generator Hardware Accelerator IP-Core for Security Applications

Random numbers are widely employed in cryptography and security applications, and they represent one of the main aspects to take care of along a security chain. They are employed for creation of encryption keys, and if generation process is weak, the whole chain can be compromised: weaknesses could be exploited to retrieve the key, thus breaking even the strongest cipher. This paper presents the architecture of a digital Random Number Generator (RNG) IP-core to be employed as hardware accelerator for cryptographically secure applications. Such design has been developed starting from specifications based on literature and standards, and in order to assess the randomness degree of generated output, it has been successfully validated through the official NIST Statistical Test Suite. Finally the RNG IP-core has been characterized on Field Programmable Gate Array (FPGA) and ASIC standard-cell technologies: on Intel Stratix IV FPGA it offers a throughput of 720 Mbps requiring up to 6000 Adaptive Logic Modules, while on 45 nm it reaches a throughput of 4 Gbps with a complexity of 119 kGE.
Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem. Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced. Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance.
Computer Vision-Based System for Classification and Sorting Color Objects

Abstract Interest in computer vision has grown considerably in industrial applications of robotic arms such as packaging, sorting, and other applications. The real challenge is how to improve the current sorting system based on image processing in the four-points to identify, manipulate, select and sort objects depending on color and geometry. This paper presents a mechatronic sorting system with the application of image processing using a web camera by sensing existing parts and capturing them in real-time, then defining colors and analyzing geometric data. This information is processed for the handling mechanism. A robotic arm has 5 DOF is used in sorting the objects have different geometries (circle, rectangular, and triangle) and a different color (red, green, blue, brown, orange, and cyan). The project deals with an automated material handling system. It aims to classify color objects by color, size, and geometry, which comes on a conveyor belt by selecting and placing the parts in their pre-programmed location. Image processing is applied in EmguCV and visual studio software. An image is a matrix of pixels, image processing is the processes applied on image to convert it into digital form and implement some process on it to improve an image. The Canny edge detection algorithm is used for shape identification in image processing. A microcontroller with computer vision is used to automate the sorting process. Experimental results reveal the effectiveness, high accuracy and low cost of computer vision with a robotic arm in the sorting process according to color and shape.
Improvement Parameters for Design Brushless DC Motor by Moth Flame Optimization

Abstract This contribution deals with an improved design of a brushless DC motor, using optimization algorithms, based on collective intelligence. For this purpose, the case study motor is perfectly explained and its significant specifications are obtained as functions of the motor geometric parameters. In fact, the geometric parameters of the motor are considered as optimization variables. Then, the objective function has been defined. This function consists of three terms; losses, construction cost and the volume of the motor which should be minimized simultaneously. The three algorithms are Moth Flame, Genetic and Particle Swarm have been studied in this paper. It is noteworthy that Moth flame optimization (MFO) algorithm has been used for the first time for brushless DC motor design optimization. A comparative study between the mentioned optimization approaches shows that moth flame optimization algorithm has been converged to optimal response in less than 250 iterations and its standard deviation is ± 0.03, while the convergence rate of the genetic and particle swarm algorithms are about 400 and 450 iterations with standard deviations of ± 0.07 and ± 0.06, respectively for the case study motor. The obtained results show the best performance for the Moth Flame Optimization algorithm among all mentioned algorithms in brushless DC motor design optimization.
Zeno dynamics of quantum chirps

We investigate the Zeno dynamics of quantum chirps. More specifically, we analyze the Zeno dynamics of a chirped solitary wave solution of the non-dimensional nonlinear Schr\"odinger equation (NLSE) with a gain/loss term. We show that the motion of the quantum chirp can be inhibited by frequent measurements in the observation domain. In order to assess the effect of Zeno dynamics on the quantum chirp imaging resolution, we analyze and compare its point spread functions (PSF) under free evolution and under Zeno dynamics. Additionally, we investigate the effect of observation frequency on the quantum chirp profile as well as on its probability of lingering in the observation domain. Our results can be immediately used to investigate the signal properties of quantum chirps and their possible jamming properties by Zeno dynamics in quantum radar technology. Additionally, many potential applications in atomic physics and optics including nonlinear phenomena and chirps can be investigated using the approach presented in our paper.