Miracle-dz/UniFAR-FTUs huggingface dataset:
Reprogramming of Strawberry (Fragaria vesca) Root Transcriptome in Response to Phytophthora cactorum

Crown rot (Phytophthora cactorum) causes significant economic losses in strawberry production. The best control strategy would be to use resistant cultivars, but polygenically inherited resistance makes the breeding of the garden strawberry (Fragaria × ananassa) challenging. The diploid wild strawberry Fragaria vesca Hawaii 4 genotype was shown previously to have resistance against crown rot. To explore the resistance mechanisms, we inoculated the roots of Hawaii 4 with P. cactorum in a novel in vitro hydroponic system to minimize interference caused by other microbes. Major reprogramming of the root transcriptome occurred, involving 30% of the genes. The surveillance system of the plant shifted from the development mode to the defense mode. Furthermore, the immune responses as well as many genes involved in the biosynthesis of the defense hormones jasmonic acid, ethylene and salicylic acid were up-regulated. Several major allergen-like genes encoding PR-10 proteins were highly expressed in the inoculated plants, suggesting that they also have a crucial role in the defense responses against P. cactorum. Additionally, flavonoids and terpenoids may be of vital importance, as several genes involved in their biosynthesis were up-regulated. The cell wall biosynthesis and developmental processes were down-regulated, possibly as a result of the down-regulation of the key genes involved in the biosynthesis of growth-promoting hormones brassinosteroids and auxin. Of particular interest was the expression of potential resistance genes in the recently identified P. cactorum resistance locus RPc-1. These new findings help to target the breeding efforts aiming at more resistant strawberry cultivars.
Categorization for Grouping Associative Items Mining in Item-Based Collaborative Filtering

Recommendation systems have been investigated and implemented in many aspects. Particularly, in case of collaborative filtering system, more important issue is how to manipulate the personalized recommendation results for better user understandability and satisfaction. Collaborative filtering system predicts items of interest for users based on predictive relationship discovered between the item and others. In this paper, the categorization for grouping associative items mining, for the purpose of improving accuracy and performance in the item-based collaborative filtering, is proposed. It is possible that, if the associative item is required to be simultaneously regrouped in all other groups in which they occur, the proposed method regrouped the associative items into the relevant group. In addition, the proposed method can result in improved predictive performance under the sparse data and cold-start circumstance that starts from small items in the collaborative filtering. And this method can increase the prediction accuracy and the scalability because of removing the noise generated by ratings on items of dissimilar content or interest. The approach is empirically evaluated for comparison with k-means, average link, and robust, using the MovieLens dataset. This method was found to significantly outperform the previous method.
Inferring Visualization Task Properties, User Performance, and User Cognitive Abilities from Eye Gaze Data

Information visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities, and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to the individual user. To this end, this article presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict properties of the user's visualization task; the user's performance (in terms of predicted task completion time); and the user's individual cognitive abilities, such as perceptual speed, visual working memory, and verbal working memory. We provide a detailed analysis of different eye gaze feature sets, as well as over-time accuracies. We show that these predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are then discussed with a view to designing visualization systems that can adapt to the individual user in real time.
Eclectic rule extraction from Neural Networks using aggregated Decision Trees

Neural Network is a powerful pattern recognition algorithm capable of learning complex non-linear patterns. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible or transferable thus making it unsuitable tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensible rules from a trained Network. In this paper, we present an algorithm called HERETIC that uses a symbolic learning algorithm (Decision Tree) on each unit of the Neural Network. Experiments and theoretical analysis show HERETIC generates highly accurate rules that closely approximates the Neural Network.
Psychological Needs Fulfillment and Engagement Within After School Programming: A Thematic Analysis of Program Rentention

The current qualitative study draws on Larson’s theory of positive youth development and on concepts from Self-Determination Theory to postulate that program participants will derive a sense of enjoyment and challenge in program activities when they perceive that the program supports their psychological needs for competence, autonomy, and relatedness. Selfdetermination theory suggests that when programs support participants’ need for autonomy, competence, and relatedness, the participants will in turn experience a higher sense of motivation and engagement within the program. The emergent themes found from the thematic analysis helped to support this theory by suggesting that Cool Girls participants experience a sense of engagement and motivation through program activities that promote autonomy/independence and peer/adult connections. Further, possible variations in perceived needs fulfillment across age groups were explored. INDEX WORDS: After School Program, Adolescent, Engagement, Self-Determination, Retention PSYCHOLOGICAL NEEDS FULFILLMENT AND ENGAGEMENT WITHIN AFTER SCHOOL PROGRAMMING: A THEMATIC ANALYSIS OF PROGRAM RETENTION
Quantized nonnegative matrix factorization

Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of interest (with in 1-4dB), 2) admits to practical encoding techniques by learning a factorization which is simpler than NMF's (by a factor of 20-70) and 3) exhibits a complexity which is comparable with state-of-the-art NMF methods. These properties are achieved by considering the quantization residual via an outer quantization optimization step, in an extended NMF iteration, namely QNMF. This approach comes in two forms: QNMF with 1) quasi-fixed and 2) adaptive quantization levels. Quantized NMF considers element-wise quantization constraints in the learning algorithm to eliminate defects due to post factorization quantization. We demonstrate significant reduction in the cardinality of the factor signal values set for comparable Signal-to-Noise-Ratios in a matrix decomposition task.
Regio’s en grenzen van mobiele telefonie in België en in het Brussels stadsgewest

In dit artikel onderzoeken we het bestaan van regio’s en van grenzen in Belgie op basis van een originele databank die gegevens bevat van meer dan 200 miljoen mobiele telefoongesprekken en van een wiskundige methode die toelaat op automatische en natuurlijke wijze netwerken in coherente groepen te verdelen. Op die manier kan een geografie van mobiel telefoonverkeer voorgesteld worden op basis van de relatieve frequenties van de telefoongesprekken en hun gemiddelde duur. Als we de relatieve frequentie van de gesprekken als criterium gebruiken, krijgen we een kaart van Belgie die bestaat uit 17 groepen of “telefoonbassins” die ruimtelijk verankerd zijn in gemeenten die allemaal verbazend aangrenzend zijn. Op deze eerste kaart is het enkel het bassin van Brussel dat de taalgrens nadrukkelijk doorbreekt en de drie institutionele gewesten van het land bedekt. Als we de gemiddelde duur van de gesprekken als criterium hanteren, krijgen we een groep in het noorden en een groep in het zuiden van het land. Binnen deze twee groepen vindt ongeveer 98% van de gesprekken plaats; slechts 2% van het telefoonverkeer gaat van de ene groep naar de andere. De zuidelijke groep bevat de 19 Brusselse gemeenten, alle gemeenten van het Waals Gewest en acht faciliteitengemeenten die in het Vlaams Gewest liggen. Het is opmerkelijk dat alle faciliteitengemeenten van de Brusselse rand (met uitzondering van Wemmel) deel uitmaken van de groep van het zuiden van het land.
Constructing Narrative Visualizations as a Means of Increasing Learner Engagement

Increasingly visualization systems are using storytelling to present complex data. However, many approaches neglect enabling users to independently explore details within the story. The research presented in this paper provides an overview of the implementation and discusses the evaluation of a novel framework (VisEN), which aims to allow users to construct narratives containing multiple exploration paths. The narratives are told through dynamically generated visualization techniques, which are personalized for individual end users, and where every visualization technique in the narrative can be further explored. The evaluation described assesses the role personalized visual narratives had in increasing engagement of weaker students with an online database SQL course. It was found that weaker students who regularly interacted with their personalized visual narratives showed an improvement in engagement.
On the Security Enhancements of Wireless Sensor Network

This paper studies the fabricated report with false votes on real reports attack in wireless sensor networks. To deal with secondary attack, we propose a probabilistic voting-based scheme (PVS). On the basis of the en-route filtering scheme, PVS combines cluster-based organization, probabilistic key assignment, and voting methods. Through analysis and simulation, we demonstrate that PVS could achieve strong protection against both attacks while maintaining a sufficiently high filtering power.
Levels of Automation for Human Influence of Robot Swarms

Autonomous swarm algorithms and human-robot interaction (HRI) have both attracted increasing attention from researchers in recent years. However, HRI has rarely extended beyond single robots or small multi-robot teams. While one of the benefits of robot swarms is their robust capabilities and the ability of their distributed algorithms to deal autonomously with the complex interactions amongst swarm members, there is undoubtedly a need for humans to influence such swarms in some circumstances—especially when these swarms are operating in unknown or hostile environments. In this paper, we approach the problem of human-swarm interaction (HSI) using previous research in levels of automation (LOAs) in HRI. We create a target searching task whereby the swarm can operate at two different levels of autonomy: an autonomous dispersion algorithm, or user-defined goto points. We investigate what environmental conditions are conducive to different amounts of human influence, and at what point further human intervention has a detrimental effect on the swarm’s performance. The results show that for complex environments containing numerous obstacles and small passageways, there is indeed a need for some human influence; however, after a certain point, further influence causes performance degradation.
Vehicle License Plate Recognition System

The vehicle license plate recognition system has greater efficiency for vehicle monitoring in automatic zone access control. This Plate recognition system will avoid special tags, since all vehicles possess a unique registration number plate. A number of techniques have been used for car plate characters recognition. This system uses neural network character recognition and pattern matching of characters as two character recognition techniques. In this approach multilayer feed-forward back-propagation algorithm is used. The performance of the proposed algorithm has been tested on several car plates and provides very satisfactory results
Reduced Right Frontal Fractional Anisotropy Correlated with Early Elevated Plasma LDL Levels in Obese Young Adults

Objective To investigate the underlying physiological mechanisms of the structural differences in gray matter (GM) and white matter (WM) associated with obesity in young Chinese adults. Materials and Methods A total of 49 right-handed obese or overweight (n = 22, mean age 31.72±8.04 years) and normal weight (n = 27, mean age 29.04±7.32 years) Han Chinese individuals were recruited. All participants underwent voxel-based morphometry analysis of T1-weighted MRI and tract-based spatial statistics analysis of diffusion tensor imaging. Partial correlation analysis was performed between the physiological data obtained and the abnormal structural alterations. Results In the OO group, GM atrophy occurred in the left prefrontal cortex, bilateral cingulate gyrus, and the right temporal lobe, while enlargement was observed in the bilateral putamen. WM atrophy was observed predominantly in the regions that regulate food intake, such as the bilateral basal ganglia, the right amygdala, and the left insula. The OO group exhibited lower fractional anisotropy (FA) in bilateral frontal corticospinal tracts and the right brainstem. Significant negative correlations were observed between FA values of those three clusters and BMI, and waist circumference, while the volume of bilateral putamen positively correlated with both BMI and waist circumference. High plasma LDL levels were correlated with low FA values in the right frontal corticospinal tract. Interestingly, the negative correlation was limited to male participants. Conclusions Obesity-related alterations of GM and WM volumes were observed predominantly in food reward circuit, which may motivate abnormal dietary intake. Further, early elevated plasma LDL might contribute to low right frontal FA values of male adults, which requires further demonstration by larger-scale and longitudinal studies.
Context-Aware Multilayer Hierarchical Protocol for Wireless Sensor Network

Evolution in Wireless Sensor Network has broadened its pervasive and ubiquitous applications in numerous fields. These applications often require accurate information collecting as well as uninterrupted, prolonged active service. Among the constituting elements of WSN, an energy efficient routing protocol plays a significant role in fulfilling such requirements. In this paper, an energy efficient routing protocol, Context Aware Multilayer Hierarchical Protocol (CAMHP) is proposed where the cluster formation is based on the context of the environment. This three layer hierarchical protocol implements cluster head and super cluster head role rotation techniques for uniform energy consumption across the network. Moreover, an efficient procedure is utilized to avoid similar data traffic from nodes to base station. Simulation results show that the proposed hierarchical protocol achieves significant performance improvement through progressive energy efficiency and longer active service.
Interactive viewpoint control and three-dimensional operations

Techniques are discussed for creating a rendered view into a 3D scene, interactively based on the locations and orientations of the observer’s head and the display surface. Stereoscopic headmounted displays (HMDs) demonstrate a simplified, special case of these techniques, because the eyes and monitors move in unison. A largely overlooked class of interactive displays uses the relative positions between the eyes and monitor as input. These displays can be stereo or monoscopic, fixed or mobile, and the rendering process should incorporate the correct perspective distortion, which depends on the locations of the viewpoint(s) and the display monitor. Three real-time graphics display systems were prototyped and examined: a high-resolution display which corrects the perspective projection based on the location of the observer’s eye; the same display, extended to modify the view as the monitor is tilted and swiveled; and a handheid LCD display which can be freely moved and rotated as it displays a view based on the eye and monitor positions. A simple experiment indicates that tracking the head and providing the appropriate view improves the ability to pick specific 3D locations in space using a 2D display, when compared to a fixed view and a mouse-controlled view.
The Development of a Listening Scale

Purpose: This study presents the development and initial validation of a Listening Scale (LS) that was used to assess the listening skills of a sample of Hong Kong counseling professionals and the potential penetration of psychoanalysis among this sample. Method: A total of 217 Hong Kong social workers, psychologists, and counselors were surveyed to develop and assess the scale’s internal psychometric properties. Results: Factor analysis confirmed a single factor structure for the LS, with 8 items. Internal reliability was good and discriminative validity was supported by comparisons with background information. Discussion: The LS shows promise as a meaningful, reliable, and valid measure. The LS could be useful to assess important dimensions of the practice of counseling professionals and to better assist their client-centered practice, and as a formative instrument for use by counseling professionals. Finally, the LS can assess the penetration of psychoanalysis where the psychoanalytic theory is not strong.
Pattern-Dependent Noise Predictive Soft Detection in the Post-Processor With Error Filters

This paper investigates a pattern-dependent noise predictive soft detection method for channel architectures that are based on a long target response and post-processing rather than a short target response and base-line wander compensation. We utilize properties of the autoregressive pattern-dependent noise model to compare tentative Viterbi sequence with alternative sequences in the post-processor and efficiently compute soft information. Even though the post-processor cannot consider all possible sequences like trellis-based detectors can, we demonstrate for a short target response that our post-processing solution does not experience any loss in performance compared to the optimal maximum a posteriori trellis-based soft detector. The complexity of the computations in the post-processor grows only linearly with the target length, as opposed to the exponential growth in complexity in trellis-based detectors. In this way we can efficiently perform nearly optimal pattern-dependent soft detection in the post-processor for a very long target response without base-line wander compensation.
Multimodality across Communicative Settings, Discourse Domains and Genres

The volume edited by Veronica Bonsignori and Belinda Crawford Camiciottoli has its origin in the workshop Multimodal Perspectives on Language Teaching and Research (University of Pisa, 2015), where the contributions collected in the volume were first presented. The publication stresses the pivotal role multimodality plays in “live” communicative events. As van Leeuwen states in the Foreword, “[m]ultimodality matters. It is not just an add on, a matter of style. It partakes meaningfully in the representational as well as in the interpersonal and textual functions of communication” (page ix). In general terms, the edited volume deals with multimodality in diverse communicative settings, and pays particular attention to the interplay of different semiotic modes and to the way the meaning of verbal expressions is reinforced by nonverbal elements. More specifically, it highlights the great importance of multimodality in language teaching and learning. The book is composed of ten chapters divided in two sections: the five chapters in the first part revolve around multimodal resources employed in teaching contexts and activities, considering also learners with disabilities. In the second part, composed of five chapters as well, multimodality is discussed as a fundamental element of communication in different genres (e.g. TED talks, political interviews, film genres) and domains (e.g. legal discourse, political discourse).
THEORETICAL AND EXPERIMENTAL ADSORPTION STUDIES OF POLYELECTROLYTES ON AN OPPOSITELY CHARGED SURFACE

Using self-assembly techniques, x-ray reflectivity measurements, and computer simulations, we study the effective interaction between charged polymer rods and surfaces. Long-time Brownian dynamics simulations are used to measure the effective adhesion force acting on the rods in a model consisting of a planar array of uniformly positively charged, stiff rods and a negatively charged planar substrate in the presence of explicit monovalent counterions and added monovalent salt ions in a continuous, isotropic dielectric medium. This electrostatic model predicts an attractive polymer-surface adhesion force that is weakly dependent on the bulk salt concentration and that shows fair agreement with a Debye–Huckel approximation for the macroion interaction at salt concentrations near 0.1 M. Complementary x-ray reflectivity experiments on poly(diallyldimethyl ammonium) chloride monolayer films on the native oxide of silicon show that monolayer structure, electron density, and surface roughness are likewise indepen.