Miracle-dz/UniFAR-FTUs huggingface dataset:
WEDAR: Webcam-based Attention Analysis via Attention Regulator Behavior Recognition with a Novel E-reading Dataset

Human attention is critical yet challenging cognitive process to measure due to its diverse definitions and non-standardized evaluation. In this work, we focus on the attention self-regulation of learners, which commonly occurs as an effort to regain focus, contrary to attention loss. We focus on easy-to-observe behavioral signs in the real-world setting to grasp learners’ attention in e-reading. We collected a novel dataset of 30 learners, which provides clues of learners’ attentional states through various metrics, such as learner behaviors, distraction self-reports, and questionnaires for knowledge gain. To achieve automatic attention regulator behavior recognition, we annotated 931,440 frames into six behavior categories every second in the short clip form, using attention self-regulation from the literature study as our labels. The preliminary Pearson correlation coefficient analysis indicates certain correlations between distraction self-reports and unimodal attention regulator behaviors. Baseline model training has been conducted to recognize the attention regulator behaviors by implementing classical neural networks to our WEDAR dataset, with the highest prediction result of 75.18% and 68.15% in subject-dependent and subject-independent settings, respectively. Furthermore, we present the baseline of using attention regulator behaviors to recognize the attentional states, showing a promising performance of 89.41% (leave-five-subject-out). Our work inspires the detection & feedback loop design for attentive e-reading, connecting multimodal interaction, learning analytics, and affective computing.
Hardware Moving Target Defenses against Physical Attacks: Design Challenges and Opportunities

The concept of moving target defense (MTD) has entrenched itself as a viable strategy to reverse the typical asymmetries in cyber warfare. MTDs are technologies that seek to make target systems dynamically change in order to limit the time and information available to complete an attack, increase the likelihood of detection, and/or deter attackers from proceeding. The benefits of MTD have been shown for network-, operating system-, and application-level security. Hardware roots-of-trust, however, are static "sitting ducks", especially against physical attacks, and can therefore benefit from the dynamics brought about by MTDs. Although many MTD concepts seem transferable to hardware applications, there has hardly been any work to establish a functioning research pipeline for countermeasures to physical attacks. The aim of this paper is to introduce viable MTD concepts, describe the issues that they can address, and chart a path towards their realization for the community.
Improved accuracy of flexible systems by state estimation : applied to high-precision motion systems

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Multi-image feature matching using multi-scale oriented patches

The system and process is provided for identifying from a plurality of points corresponding to images of a scene. This is based on a new class of multi-invariant feature - is related to the view matching framework. Features are directed to and located at Harris corners in scale-space, using the local gray level of blurring. This defines a similarity invariant frame for sampling the feature descriptor. Descriptor actually formed is a bias / gain normalized patch of intensity values. Matching is achieved using a fast nearest neighbor procedure of using the index for the low frequency Haar wavelet coefficients. Jobs just six parameter model for matching patches are noise statistics are analyzed for correct and incorrect matches. This leads to a simple match verification procedure based on a per feature outlier distance. Scenes features, patches, matching, outliers, Haar wavelets
Learning robotic manipulation from user demonstrations

Personal robots that help disabled or elderly people in their activities of daily living need to be able to autonomously perform complex manipulation tasks. Traditional approaches to this problem employ task-specific controllers. However, these must to be designed by expert programmers, are focused on a single task, and will perform the task as programmed, not according to the preferences of the user. In this dissertation, we investigate methods that enable an assistive robot to learn to execute tasks as demonstrated by the user. First, we describe a learning from demonstration (LfD) method that learns assistive tasks that need to be adapted to the position and orientation of the user’s head. Then we discuss a recurrent neural network controller that learns to generate movement trajectories for the end-effector of the robot arm to accomplish a task. The input to this controller is the pose of related objects and the current pose of the end-effector itself. Next, we discuss how to extract user preferences from the demonstration using reinforcement learning. Finally, we extend this controller to one that learns to observe images of the environment and generate joint movements for the robot to accomplish a desired task. We discuss several techniques that improve the performance of the controller and reduce the number of required demonstrations. One of this is multi-task learning: learning multiple tasks simultaneously with the same neural network. Another technique is to make the controller output one joint at a time-step, therefore to condition the prediction of each joint on the previous joints. We evaluate these controllers on a set of manipulation tasks and show that they can learn complex tasks, overcome failure, and attempt a task several times until they succeed.
Toward (Greater) Consumer Surveillance in a ‘Cookie-less’ World: A Comparative Analysis of Current and Future Web Tracking Mechanisms

The anticipated shift of the advertising industry away from third-party cookies has been marketed as ‘privacy friendly. ’ New cookie-less tracking technologies are being proposed, but the consumer privacy implications of those technologies are far from clear. To what extent ad-networks are going to change their practices, and no longer rely on cross-site consumer surveillance or historically rich consumer profiles for advertising purposes? Can the new tracking technologies become GDPR-compliant, given the significant compliance pushback against cookie-based advertising mechanisms? Our study seeks to evaluate the potential privacy harms of cookie-less advertising ID solutions by (1) building novel typology of tracking specifications to assess the privacy impacts of tracking technologies; (2) deductively analyzing cookie-based tracking mechanisms by collecting novel data on persistent user identification across 50 popular websites that work with all four main supply side platforms (SSPs) - Pubmatic, OpenX, AppNexus, and Rubicon; and (3) contrasting those findings with deductive analysis of data collected from technical industry documentation on the three main cookie-less ID architectures - The Trade Desk Unified ID 2.0, LiveRamp ID, and Secure Web Addressability Network (SWAN). We find how the new tracking architectures can make consumer surveillance in the Web dynamically wider, more persistent over time, and extra vulnerable to integration of first-and third-party data by advertisers. This could lead to the circumvention of consumer targeting restrictions posed by the main advertising platforms, in case advertisers choose to bid on ads based on sensitive profile categories. In contrast to existing criticism on cookie-less tracking solutions that mostly focuses on deficiencies in consent mechanisms or the lack of a governing body for the new solutions, our study underscores the structural impact of ad networks on the potential privacy harms caused by the proposed tracking mechanisms. Structures and processes of ad networks might lead to potentially longer, wider, and richer consumer surveillance, compared to cookie-based tracking mechanisms. Our findings also question the ability of suggested tracking architectures to become GDPR compliant. Despite civil society pushback against the current cookie-based tracking ecosystem
EFFECTS OF DIFFERENT IRRIGATION DEPTHS ON THE CONTENT OF TSH1188 COCOA SEEDLINGS PHOTOSYNTHETIC PIGMENTS

Cacao (Theobroma cacao L.) is a plant whose use is distributed in many different ways, being used for human consumption and as a raw material in the production of chocolate, cocoa powder, jellies, juice pulp, honey and butter. In animal handling, it is associated with the manufacture of feed and can also be used for agricultural production such as the use of fertilizers. Given the expressive importance of the crop, this study aimed to evaluate the effect of different irrigation depths on the levels of photosynthetic pigments in seedlings of TSH 1188 genotype cocoa. For this purpose, five irrigation depths were applied, corresponding to 4, 6, 8, 10 and 12 mm day-1. The experimental design was completely randomized with 20 seedlings per treatment. At 55 days after sowing, photosynthetic pigments were extracted and quantified, and the content of chlorophyll a, chlorophyll b, total chlorophyll and carotenoids was determined. The results were submitted to analysis of variance by the F test at 5% probability. Regression models that best applied to the effects of the irrigation depth on the evaluated characteristics were adjusted. The irrigation depth of 7.74 mm d-1 had the highest total chlorophyll content, being the most recommended for the production of TSH 1188 cocoa seedlings.
Recent Advances in Pedestrian Inertial Navigation Based on Smartphone: A Review

Indoor location-based service is a hot research topic whose application market size is expected to grow from $\$ $ 41 billion by 2022 and $\$ $ 58 billion by 2023. As a portable communication device, smartphones have inherent advantages (such as being an essential gadget in daily life, embedding various sensors, and having powerful computing and storage ability), which provides a great opportunity for human activity recognition and pedestrian navigation. Smartphone-based pedestrian inertial navigation systems (PINSs) utilize measurements from inertial sensors embedded in smartphones to reckon the pedestrian’s location. Compared with other indoor position technologies, PINS has the advantage of autonomy and continuity, and can achieve high-precision positioning in a short period. Though various PINS methods based on the smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods, and solutions of PINS are relatively less so far in the literature. This article aims to provide an elaborated, timely, and valuable survey of different infrastructure-free pedestrian positioning and navigation techniques, including pedestrian inertial navigation methods, performance evaluation, and applications. Finally, current challenges and future research trends are discussed. This work propels a better understanding of existing pedestrian inertial positioning methods. It is helpful for researchers to further design and develop more accurate and robust pedestrian positioning and navigation systems based on inertial sensors embedded in the smartphone.
Analyse de grappe des données de catégories et de séquences étude et application à la prédiction de la faillite personnelle

Cluster analysis is one of the most important and useful data mining techniques, and there are many applications of cluster analysis in pattern extraction, information retrieval, summarization, compression and other areas. The focus of this thesis is on clustering categorical and sequence data. Clustering categorical and sequence data is much more challenging than clustering numeric data because there is no inherently meaningful measure of similarity between the categorical objects and sequences. In this thesis, we design novel efficient and effective clustering algorithms for clustering categorical data and sequence respectively, and we perform extensive experiments to demonstrate the superior performance of our proposed algorithm. We also explore the extent to which the use of the proposed clustering algorithms can help to solve the personal bankruptcy prediction problem. Clustering categorical data poses two challenges: defining an inherently meaningful similarity measure, and effectively dealing with clusters which are often embedded in different subspaces. In this thesis, we view the task of clustering categorical data from an optimization perspective and propose a novel objective function. Based on the new formulation, we design a divisive hierarchical clustering algorithm for categorical data, named DHCC. In the bisection procedure of DHCC, the initialization of the splitting is based on multiple correspondence analysis (MCA). We devise a strategy for dealing with the key issue in the divisive approach, namely, when to terminate the splitting process. The proposed algorithm is parameter-free, independent of the order in which the data is processed, scalable to large data sets and capable of seamlessly discovering clusters embedded in subspaces.
TRAJECTORIES OF CHANGE IN PSYCHOLOGICAL TREATMENT: ANALYSIS OF CLIENT-RESPONSE TO TREATMENT IN A UNIVERSITY CLINIC

This study explored treatment response trajectories in a naturalistic population of clients served at a university psychotherapy training clinic. Using data collected through repeated administrations of a treatment outcome, and methods new to the field of psychotherapy research (growth mixture models), this study attempted to model the shape of change of clients through the course of therapy. Specifically, this study explored the possibility that treatment response is better captured when groups or clusters of responders are modeled. In addition, this study examined the way in which pre-treatment symptom severity relates to trajectories of change. Three separate groups of treatment responders were found. Pre-treatment symptom severity was found to differentiate one low-symptom group from two higher-symptom groups but failed to differentiate between the two higher-symptom groups. These results indicate that Growth Mixture Models may be a useful tool for examining treatment outcome data and that pretreatment symptom severity has limited usefulness in predicting treatment outcome.
Correspondence Analysis to Know Factors Related to the Use of Reducant Herbicide on Pagaralam Coffee Farmers

Weed control is an attempt to care for agricultural land that can affect coffee production. This study aims to analyze the factors that have a relationship with the use of reductant herbicide in Pagaralam coffee farmers by using simple correspondence analysis. The research data included 19 variables and 3 categories of respondents based on the use of reductant herbicide, namely non-users, new users, and users. At the initial stage, each variable was carried out a mean difference test between 2 categories of respondents. Furthermore, each variable is divided into several categories. Then, by using the independence test, the categories of each variable are associated with the category of reductant use. There are 7 factors that have a relationship with the use of reductants, namely education of respondents, age of trees, length of harvest, frequency of herbicide use, frequency of chemical fertilizers used, frequency of organic fertilizers used, and number of labour outside the family (TL). The results of the correspondence analysis plot can show differences in the characteristics of the respondent's categories according to the use of reductant herbicide. The user category is dominantly characterized by having junior high school education, tree age more than 25 years, tend not to use organic fertilizer, and the harvest period can reach 3 months.
Discovery of a magnetic/nonmagnetic double-degenerate binary system

We report the discovery of an unresolved binary system consisting of two degenerate stars: one a polarized object with an inferred magnetic field among the largest yet found on a white dwarf; the other a normal DA with no detectable field. We have deconvolved the composite 1250 A-2.2 μm energy distribution into individual spectra and find that, with the exception of the graphic difference in magnetic field strength, the two stars are remarkably similar. For the normal DA, T eff ≃14,500 K, log g ≃8.5, and M=0.91±0.07 M ○. , while the magnetic component has T eff ≃16,000 K, log g ≃8.5, and 0.76≤M/M ○. < 1.00
Response to recharge variation of thin rainwater lenses and their mixing zone with underlying saline groundwater

In coastal zones with saline groundwater, fresh groundwater lenses may form due to infiltration of rain water. The thickness of both the lens and the mixing zone, determines fresh water availability for plant growth. Due to recharge variation, the thickness of the lens and the mixing zone are not constant, which may adversely affect agricultural and natural vegetation if saline water reaches the root zone during the growing season. In this paper, we study the response of thin lenses and their mixing zone to variation of recharge. The recharge is varied using sinusoids with a range of amplitudes and frequencies. We vary lens characteristics by varying the Rayleigh number and Mass flux ratio of saline and fresh water, as these dominantly influence the thickness of thin lenses and their mixing zone. Numerical results show a linear relation between the normalised lens volume and the main lens and recharge characteristics, enabling an empirical approximation of the variation of lens thickness. Increase of the recharge amplitude causes increase and the increase of recharge frequency causes a decrease in the variation of lens thickness. The average lens thickness is not significantly influenced by these variations in recharge, contrary to the mixing zone thickness. The mixing zone thickness is compared to that of a Fickian mixing regime. A simple relation between the travelled distance of the centre of the mixing zone position due to variations in recharge and the mixing zone thickness is shown to be valid for both a sinusoidal recharge variation and actual records of daily recharge data. Starting from a step response function, convolution can be used to determine the effect of variable recharge in time. For a sinusoidal curve, we can determine delay of lens movement compared to the recharge curve as well as the lens amplitude, derived from the convolution integral. Together the proposed equations provide us with a first order approximation of lens characteristics using basic lens and recharge parameters without the use of numerical models. This enables the assessment of the vulnerability of any thin fresh water lens on saline, upward seeping groundwater to salinity stress in the root zone.
Toeplitz Algebras on Strongly Pseudoconvex Domains

Abstract In the present paper, it is proved that the K 0-group of a Toeplitz algebra on any strongly pseudoconvex domain is always isomorphic to the K 0-group of the relative continuous function algebra, and is thus isomorphic to the topological K 0-group of the boundary of the relative domain. Further there exists a ring isomorphism between the K 0-groups of Toeplitz algebras and the Chern classes of the relative boundaries of strongly pseudoconvex domains. As applications of our main result, K-groups of Toeplitz algebras on some special strongly pseudoconvex domains are computed. Our results show that the Toeplitz algebras on strongly pseudoconvex domains have rich structures, which deeply depend on the topological structures of relative domains. In addition, the first cohomology groups of Toeplitz algebras are also computed.
Elastic Wave Propagation in Transversely Isotropic Media

In this monograph I record those parts of the theory of transverse isotropic elastic wave propagation which lend themselves to an exact treatment, within the framework of linear theory. Emphasis is placed on transient wave motion problems in two- and three-dimensional unbounded and semibounded solids for which explicit results can be obtained, without resort to approximate methods of integration. The mathematical techniques used, many of which appear here in book form for the first time, will be of interest to applied mathematicians, engeneers and scientists whose specialty includes crystal acoustics, crystal optics, magnetogasdynamics, dislocation theory, seismology and fibre wound composites. My interest in the subject of anisotropic wave motion had its origin in the study of small deformations superposed on large deformations of elastic solids. By varying the initial stretch in a homogeneously deformed solid, it is possible to synthesize aniso tropic materials whose elastic parameters vary continuously. The range of the parameter variation is limited by stability considerations in the case of small deformations super posed on large deformation problems and (what is essentially the same thing) by the of hyperbolicity (solids whose parameters allow wave motion) for anisotropic notion solids. The full implication of hyperbolicity for anisotropic elastic solids has never been previously examined, and even now the constraints which it imposes on the elasticity constants have only been examined for the class of transversely isotropic (hexagonal crystals) materials. "
Experimental verification of the 3-step model of photoemission for energy spread and emittance measurements of copper and CsBr-coated copper photocathodes suitable for free electron laser applications

This paper presents measurements and analysis of the quantum efficiency (QE) and intrinsic emittance of Cu and CsBr coated Cu photocathodes. The data analysis uses expressions for the quantum efficiency and the intrinsic emittance for metal cathodes previously derived from Spicer's three-step model of photoemission. Data taken with a 257 nm CW laser on (100) Cu crystals indicate an emittance of 0.77 (μm/mm-rms) for CsBr coated and 0.42 (μm/mm-rms) for uncoated cathodes. The high quantum efficiency and low emittance observed for CsBr coated cathodes have applications in free electron laser and other devices requiring high brightness electron beams.
Micromagnetic analysis of injection locking in spin-transfer nano-oscillators

The injection-locking mechanism of a spin-transfer nano-oscillator is studied for a system which is traversed by a spin-polarized electric current with both dc and ac components. The uniform mode theory is used to predict the control parameters for the synchronization between the magnetization self-oscillation and the external microwave current. It is found that micromagnetic simulations at zero temperature are in very good agreement with the theory, showing that magnetization dynamics is practically uniform in space. Then, the effect of temperature on synchronized regimes is analyzed. The locking band as well as the hysteretic character of the oscillation response are determined as function of the control parameters. Computations of the power spectral densities (PSD) of injection-locked and unlocked oscillations reveal that synchronized oscillations are quite stable with respect to thermal fluctuations. Secondary peaks in the PSD of injection-locked oscillations are reported and theoretically explained.