Reviewerly/exHarmony huggingface dataset:
Psychological Data Breach Harms

Cybersecurity law, both in statutory and case law, is primarily based on the premise that data breaches result exclusively in financial harms. Intuitively, legal scholarship has largely been focused on financial harms to the exclusion of non-financial harms—emotional and mental—that also arise from data breaches. A critical mass of research in psychology, psychiatry, and internet studies shows that consumers whose information has been compromised suffer from serious emotional and mental conditions as a result. This Article seeks to evaluate cybersecurity law in light of this reality and proposes a framework to address these psychological data breach harms.Psychological data breach harms raise significant challenges for which the law does not adequately account. Consumers suffering these harms are unlikely to pursue litigation and, even if consumers do pursue litigation, are unlikely to prevail because of both standing and cause of action reasons. In a similar vein, different cybersecurity law frameworks, such as the Computer Fraud and Abuse Act, data security laws, data breach notification laws, and Federal Trade Commission enforcement, do not generally recognize any harms that are non-monetary in nature. Moreover, companies suffering data breaches are not legally required to offer any assistance or mitigation response for consumers who may suffer psychological harms. Contributing to these challenges is the fact that breached companies are often not even required to disclose breaches that are unlikely to cause future financial harm.Cybersecurity law currently overlooks a conceptual framework for psychological data breach harms; this Article offers that framework. First, this Article argues for the recognition of psychological data breach harms in the context of cybersecurity, from the very outset. Second, this Article makes concrete recommendations on how psychological data breach harms ought to be addressed, both by regulators and breached entities, as well as the appropriate remedies. Finally, this Article calls for a reconsideration of what we mean by “personal information” and for the expansion of information categories that cybersecurity law should protect.
Deep learning for recommendation systems

Faced with the ever-increasing complexity, volume and dynamism of online information, recommendation systems are among the solutions that anticipate the needs of users and offer them items (articles, products, web pages, etc.) that they are likely to appreciate. Unlike traditional recommendation models, deep learning offers a better understanding of user requests, characteristics of objects, historical interactions between them and it can process massive amounts of data. In this work we realize a recommendation system based on MLP deep learning adapted to data already defined by their characteristics. In addition to the use of deep learning, we offer a new hybrid recommendation system solution between the demographic approach and the content-based approach in order to eliminate the limits of each and to combine their strengths, through a deep neural network that harnesses the mass of data.” Experimentation with our approach has produced good results in terms of accuracy and speed, whether through the use of deep learning or the hybridization of content-based and demographic filtering, which is a particular case of collaborative filtering.
A Rating Scale Mixture Model to Account for the Tendency to Middle and Extreme Categories

A mixture of logit models is proposed that discriminates between responses to rating questions that are affected by a tendency to prefer middle or extremes of the scale regardless of the content of the item (response styles) and purely content-driven preferences. Explanatory variables are used to characterize the content-driven way of answering as well as the tendency to middle or extreme categories. The proposed model is extended to account for the presence of response styles in the case of several items, and the association among responses is described, both when they are content driven or dictated by response styles. In addition, stochastic orderings, related to the tendency to select middle or extreme categories, are introduced and investigated. A simulation study describes the effectiveness of the proposed model, and an application to a questionnaire on attitudes toward ethnic minorities illustrates the applicability of the modeling approach.
Mutual Human-Computer Tutoring Using Agents

Learning platforms provide educational tools and strategies to impart the necessary knowledge, skills and attitudes to learners. Effectiveness and the ability to deliver tangible benefits to learners largely depend on how they are designed, delivered and assessed. Smart tutoring systems are a solution to overcome the difficulties of learners by allowing them to have personalized learning and follow-up. This article presents a critique of the effectiveness of these platforms and proposes a generic architecture independent of the learning subject and which offers the learner Machine / Human monitoring.
SpoofCatch: A Client-Side Protection Tool Against Phishing Attacks

To protect against web spoofing attacks, most antiphishing solutions in the literature either escape certain attack patterns or are based on complex sets of parameters to identify phishing attacks or suffer from both. In this article, we propose that phishing attacks can be prevented by simply relying on an overall visual appearance of the web page that the user sees. To realize our claim, we propose a client-side protection mechanism based on visual similarity of web pages and implement our mechanism as a browser extension, dubbed SpoofCatch. For similarity comparison between genuine and phished web pages, four similarity algorithms have been implemented and integrated in the extension. To evaluate the solution, large scale and extensive experiments have been conducted that demonstrate SpoofCatch can capture all phishing attacks with acceptable overhead.
Day Ahead Solar Irradiance Forecasting using Different Statistical Techniques

With the increase in local grid penetration, solar power technologies have experienced strong energy market growth in the last few years. This has put emphasis on the need for accurate forecasting. In this paper, various models are proposed for predicting solar irradiance for the location Bhadla, Jodhpur, India and a comparative analysis is done. The forecasting is proposed using different statistical models such as stochastic MARKOV model, Auto-regression Integrated Moving Average (ARIMA) model and Artificial Neural Network (ANN) model.
An Assessment of Water Sources for Heritage Listed Organic Mound Springs in NW Australia Using Airborne Geophysical (Electromagnetics and Magnetics) and Satellite Remote Sensing Methods

Discrete phreatophytic vegetation associated with organic mound springs is present in several places in the semi-arid Walyarta Conservation Park (Park) in northern Western Australia. The mound springs are heritage listed, having significant cultural and environmental significance. Increased industrial (mining and agriculture) development in the region, coupled with a growing demand for groundwater to support these developments, requires an enhanced understanding of how the springs operate and the source of water that sustains their presence. The springs are broadly believed to be situated on geological faults and receive groundwater from artesian sources. However, their association with deeper geological structures and aquifer systems, the focus of this study, is not well understood. This study employed regional- and finer-scale airborne geophysical data, including electromagnetics (AEM) and magnetics, to constrain the sub-basin-scale hydrogeology of the West Canning Basin in Western Australia and to detail tectonic deformation, sedimentological and hydrological processes. The AEM data were inverted using 1- and 2D methods to better define structural discontinuities in the Park, and the results identified the location of faults and other geological structures that were coincident with spring locations. A complementary analysis of spatiotemporal patterns of green vegetation was undertaken using remote sensing data. A model for the extent of green vegetation (in percent), calculated using a constrained linear spectral unmixing algorithm and applied to a select Landsat Thematic Mapper ™ image archive, showed the persistence of green vegetation aligned with interpreted fault systems through extended dry periods. These geophysical and remotely sensed datasets demonstrate that in the Park, the sedimentary aquifers and landscapes are highly compartmentalized and that this constrains aquifer distribution, groundwater quality and the location of wetlands and phreatophytic vegetation. Integrating key information from these datasets allows for the construction of a three-dimensional model that predicts the nature and extent of the critical zone which sustains perennial groundwater discharge within mound springs, drainages and wetlands and provides a framework to assess discharge rates, mixing and, ultimately, sensitivity to changed water availability.
Comparative Study of Conventional and Interval Type-2 Fuzzy Logic Controllers for Velocity Regulation in Lego Mindstorms Ev3 Humanoids

Recently, fuzzy logic has allowed complex systems to be designed and controlled more effectively than traditional approaches, since it provides a simple way to handle noisy or imprecise information. Nowadays, one of the great problems in the speed control cases is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system as a Proportional Integral (PI) and Integral-DerivativeProportional integral-derivative (PID). This chapter presents a comparative study using three types of controllers, FLC, PI and PIDProportional integral-derivative, applied to the speed control of a Robot built using the ev3 Lego MindstormsLego Mindstorms Ev3 Humanoids kit. Matlab and Simulink were used to validate the performance of the speed control obtained with the controller proposed in this paper. This particular type of robot has some similarities to Humanoid robotsHumanoid robots as it needs to move autonomously avoiding obstacles, so we plan to later implement the fuzzy controllers on larger and more complex Humanoid robotsHumanoid robots.
Winding Path: Characterizing the Malicious Redirection in Squatting Domain Names

An increasing number of adversaries tend to cover up their malicious sites by leveraging the elaborate redirection chains. Prior works mostly focused on the specific attacks that users suffered, and seldom considered how users were exposed to such attacks. In this paper, we conduct a comprehensive measurement study on the malicious redirections that leverage squatting domain names as the start point. To this end, we collected 101,186 resolved squatting domain names that targeted 2,302 top brands from the ISP-level DNS traffic. After dynamically crawling these squatting domain names, we pioneered the application of performance log to mine the redirection chains they involved. Afterward, we analyzed the nodes that acted as intermediaries in malicious redirections and found that adversaries preferred to conduct URL redirection via imported JavaScript codes and iframes. Our further investigation indicates that such intermediaries have obvious aggregation, both in the domain name and the Internet infrastructure supporting them.
Performance Study of Distance-Weighting Approach with Loopy Sum-Product Algorithm for Multi-Object Tracking in Clutter

In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. DA plays an important role when tracking multiple targets using measurements of uncertain origin. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with false alarms and missed detections. We are interested in two performance metrics: tracking accuracy and computation time. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time.
Parallel, True Random Number Generator (P-TRNG): Using Parallelism for Fast True Random Number Generation in Hardware

Currently, True Random Number Generation (TRNG) is relatively slow, utilizing natural, physical processes as its entropy source. Other types of random numbers, such as Pseudo-Random Numbers, lack true randomness as eventually they repeat, but can be generated with great speed. The main idea of the work presented in this paper is to introduce parallel random number generation as a scalable means of increasing the rate of true random number generation in hardware to match and surpass that of Pseudo-Random Number Generators (PRNGs). Using a Raspberry Pi platform, this work will combine two (2) standard TRNGs to form one (1) unified, Parallel - True Random Number Generator (P-TRNG), with more than twice the output performance rate of its component TRNGs, and with demonstrated scalability.
New approach to determine the optimal number of clusters K in unsupervised classification

One of the most common problems in the field of information processing is the definition of the optimal number of clusters when using unsupervised classification. The wrong choice of the number of clusters leads to insignificant and inconsistent results. In this paper, we present a simple automatic technique for estimating the optimal number of clusters when using a classification algorithm based on k-means. The proposed algorithm is based on detecting the immutable clusters during the different experiments and takes them as a reference in order to specify the best classification.
User Profile-Driven Large-Scale Multi-agent Learning from Demonstration in Federated Human-Robot Collaborative Environments

Learning from Demonstration (LfD) has been established as the dominant paradigm for efficiently transferring skills from human teachers to robots. In this context, the Federated Learning (FL) conceptualization has very recently been introduced for developing large-scale human-robot collaborative environments, targeting to robustly address, among others, the critical challenges of multi-agent learning and long-term autonomy. In the current work, the latter scheme is further extended and enhanced, by designing and integrating a novel user profile formulation for providing a fine-grained representation of the exhibited human behavior, adopting a Deep Learning (DL)-based formalism. In particular, a hierarchically organized set of key information sources is considered, including: a) User attributes (e.g. demographic, anthropomorphic, educational, etc.), b) User state (e.g. fatigue detection, stress detection, emotion recognition, etc.) and c) Psychophysiological measurements (e.g. gaze, electrodermal activity, heart rate, etc.) related data. Then, a combination of Long Short-Term Memory (LSTM) and stacked autoencoders, with appropriately defined neural network architectures, is employed for the modelling step. The overall designed scheme enables both short- and long-term analysis/interpretation of the human behavior (as observed during the feedback capturing sessions), so as to adaptively adjust the importance of the collected feedback samples when aggregating information originating from the same and different human teachers, respectively.
Street Cleanliness Monitoring System using Deep Learning

Street cleanliness is a major concern for any country. It gives an idea of the country's well-being to its tourists and individuals in general. One of the major causes of illnesses in a city is typically the lack of cleanliness in the streets. In many ways, artificial intelligence today shapes society. The current artificial intelligence technologies investigation can regulate this street cleanliness problem. The street-clean dataset developed containing photos from the web. The images in the dataset are divided into three classes that can classify a model (good, average, and poor). The model produced more than 90% accuracy. Everyone in our society is concerned about the cleanliness of their buildings, whether it is their home or their workplace, but no one cares about working outside of the clean environment. The dirty surroundings are home may cause many virus diseases.
Trajectory planning of tractor semitrailers

In this chapter, a decision-making algorithm for a double lane change maneuver of an articulated vehicle in real dynamic circumstances is studied. A novel method for determining the decision conditions is used based on the articulated vehicle kinematics and dynamics. Through this method, several points of the articulated vehicle are considered in various situations when conducting a double lane change maneuver, and the critical points are determined. The sliding mode control method is utilized to increase the method efficiency. Therefore, the least safe time to perform the double lane change maneuver is extracted based on the sliding mode control method as tracking control. A new articulated vehicle least safe time formulation is determined for dynamic circumstances. Based on the results of the simulated test, the acceptable time range is also established for conducting the lane change maneuver. The lane change maneuver is generalized to the double lane change maneuver. A decision-making algorithm is introduced based on real traffic situations.
Point-line incidence on Grassmannians and majority logic decoding of Grassmann codes

In this article, we consider the decoding problem of Grassmann codes using majority logic. We show that for two points of the Grassmannian, there exists a canonical geodesic between these points once a complete flag is fixed. These geodesics are used to construct a large set of parity checks orthogonal on a coordinate of the code, resulting in a majority decoding algorithm.
Integrated Harris Hawks optimization based classifier: An approach to cardiovascular disorder prediction

Assessment of contractile function of heart plays a major role in characterization of cardiac abnormality. Worldwide, cardiovascular diseases (CVD) are of primary concern in healthcare. Hence, aim of this study is to evaluate the diagnostic accuracy of myocardial geometric features extracted from cardiovascular magnetic resonance (CMR) images and optimized classifier in monitoring of the CVD progression. This work investigates CMR slices of 100 subjects (Normal - 24, Mild - 23, Moderate - 21, Severe - 14, Hypertrophic - 18) from Kaggle challenge database. The segmentation of myocardium is carried out in all the slices at end-diastole (ED) and end-systole (ES) using LabVIEW. Further, geometric features have been extracted to capture the anatomical variations among the different classes. The appropriate choice of kernel parameters and feature set has significant impact on the performance of the classifier. Harris Hawks optimization (HHO) algorithm is used in integration with the geometric feature set to improve the prediction accuracy. The intensity based segmentation is able to effectively capture the myocardial boundaries at ED and ES. The entire feature set has provided an accuracy of 70 % prior to optimization. As the geometric features are able to capture the structural changes in myocardium under various cardiac abnormalities, the HHOSVM yields improved classification accuracy of 76.7 % than without optimizer. Thus, this framework would aid disease prognosis and planning of effective treatment strategies.
The multivariate functional de Jong CLT

We prove a multivariate functional version of de Jong’s CLT (J Multivar Anal 34(2):275–289, 1990) yielding that, given a sequence of vectors of Hoeffding-degenerate U-statistics, the corresponding empirical processes on [0, 1] weakly converge in the Skorohod space as soon as their fourth cumulants in $$t=1$$ vanish asymptotically and a certain strengthening of the Lindeberg-type condition is verified. As an application, we lift to the functional level the ‘universality of Wiener chaos’ phenomenon first observed in Nourdin et al. (Ann Probab 38(5):1947–1985, 2010).
Sparse and smooth functional data clustering

Abstract A new model-based procedure is developed for sparse clustering of functional data that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of the domain. The proposed method is referred to as sparse and smooth functional clustering (SaS-Funclust) and relies on a general functional Gaussian mixture model whose parameters are estimated by maximizing a log-likelihood function penalized with a functional adaptive pairwise fusion penalty and a roughness penalty. The former allows identifying the noninformative portion of the domain by shrinking the means of separated clusters to some common values, whereas the latter improves the interpretability by imposing some degree of smoothing to the estimated cluster means. The model is estimated via an expectation-conditional maximization algorithm paired with a cross-validation procedure. Through a Monte Carlo simulation study, the SaS-Funclust method is shown to outperform other methods that already appeared in the literature, both in terms of clustering performance and interpretability. Finally, three real-data examples are presented to demonstrate the favourable performance of the proposed method. The SaS-Funclust method is implemented in the package , available on CRAN.
Geoethics of Bulk Groundwater Abstraction in an Ecologically Sensitive Area: Steenbras Wellfield (Cape Town)

The City of Cape Town initiated its "New Water Programme" in 2017 to diversify its bulk water supply, thereby improving long-term water security and resilience against future droughts—this includes bulk groundwater abstraction of potentially ~140–400 Ml/day from the major fractured Peninsula and Nardouw Aquifers of the Table Mountain Group (TMG; in the mountain catchments to the east of the city). Current TMG groundwater exploration and wellfield development are taking place in the vicinity of Steenbras Dam, in the form of the ~15–20 Ml/day Steenbras Wellfield scheme. The TMG aquifers are also essential in sustaining groundwater-dependent ecosystems associated with the Cape Floral Kingdom—a global biodiversity hotspot with exceptional endemic diversity, but also a global extinction hotspot. A strong geoethical, "no-regrets" approach is, therefore, required to develop TMG wellfield schemes for the City of Cape Town (and other towns/cities in the Western/Eastern Cape), in order to reduce the risk of any negative ecological and environmental impacts, while still enhancing the drought resilience of the city, providing water for future urban growth, and meeting Sustainable Development Goals 6 and 11.