Miracle-dz/UniFAR-FTUs huggingface dataset:
On the temporal analysis of scientific network evolution

In this paper we approach the definition of new methodologies for the visualization and the exploration of social networks and their dynamics. We present a recently introduced formalism called TVG (for time-varying graphs), which was initially developed to model and analyze highly-dynamic and infrastructure-less communication networks, and TVG derived metrics. As an application context, we chose the case of scientific communities by analyzing a portion of the arXiv repository (ten years of publications in physics). We discuss the dataset by means of both static and temporal analysis of citations and co-authorships networks. Afterward, as we consider that scientific communities are at the same time communities of practice (through co-authorship) and that a citation represents a deliberative selection of a work among others, we introduce a new transformation to capture the co-existence of citations' effects and collaboration behaviors.
Designing a Motivational Agent for Behavior Change in Physical Activity

People find behavior change particularly long-lasting change in habits regarding physical activity to be difficulty. While new technologies such as mobile applications that help individuals measure, log, and reflect on their activities hold great promise, they rely on individuals already having the motivation to change their behaviors. How can technology motivate people in increasing their level of physical activity? We explore this question through the design, development, and evaluation of an automated assistance system that draws on a counseling method called motivational interviewing (MI) and utilizes a humanlike robot as a motivational agent. Through dialogue and nonverbal engagement, the agent enables individuals to talk about and reflect on underlying reasons for their lack of motivation regarding physical activity in order to increase intrinsic motivation for behavior change. In this work-in-progress, we describe our system design and discuss findings from a preliminary study that evaluated the impact of the robot's use of MI on user motivation and behavior.
Configural processing enables discrimination and categorization of face-like stimuli in honeybees

SUMMARY We studied whether honeybees can distinguish face-like configurations by using standardized stimuli commonly employed in primate and human visual research. Furthermore, we studied whether, irrespective of their capacity to distinguish between face-like stimuli, bees learn to classify visual stimuli built up of the same elements in face-like versus non-face-like categories. We showed that bees succeeded in discriminating both face-like and non-face-like stimuli and categorized appropriately novel stimuli in these two classes. To this end, they used configural information and not just isolated features or low-level cues. Bees looked for a specific configuration in which each feature had to be located in an appropriate spatial relationship with respect to the others, thus showing sensitivity for first-order relationships between features. Although faces are biologically irrelevant stimuli for bees, the fact that they were able to integrate visual features into complex representations suggests that face-like stimulus categorization can occur even in the absence of brain regions specialized in face processing.
Vertical Moist Thermodynamic Structure and Spatial-Temporal Evolution of the MJO in AIRS Observations

Abstract The atmospheric moisture and temperature profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit on the NASA Aqua mission, in combination with the precipitation from the Tropical Rainfall Measuring Mission (TRMM), are employed to study the vertical moist thermodynamic structure and spatial–temporal evolution of the Madden–Julian oscillation (MJO). The AIRS data indicate that, in the Indian Ocean and western Pacific, the temperature anomaly exhibits a trimodal vertical structure: a warm (cold) anomaly in the free troposphere (800–250 hPa) and a cold (warm) anomaly near the tropopause (above 250 hPa) and in the lower troposphere (below 800 hPa) associated with enhanced (suppressed) convection. The AIRS moisture anomaly also shows markedly different vertical structures as a function of longitude and the strength of convection anomaly. Most significantly, the AIRS data demonstrate that, over the Indian Ocean and western Pacific, the enhanced (suppressed) convection is g.
A Measurement-Based Statistical Model for Industrial Ultra-Wideband Channels

The results of three ultra-wideband (UWB) measurement campaigns conducted in two different industrial environments are presented. A frequency range of 3.1-10.6 or 3.1-5.5 GHz was measured using a vector network analyzer and a virtual array technique enabling the investigation of small-scale statistics. The results show that the energy arrives in clusters, and that the abundance of metallic scatterers present in the factory hall causes dense multipath scattering. The latter produces a small-scale fading that is mostly Rayleigh distributed; the only exception being the delay bin containing the line-of- sight component. The power delay profile can be modeled by a generalized Saleh-Valenzuela model, where different clusters have different ray power decay constants. It is also noted that the number of multipath components required to capture a majority of the energy is quite large. More than a hundred components can be needed to capture 50% of the total available energy.
The Sunny Side of Fairness

Little is known about the positive emotional impact of fairness or the process of resolving conflict between fairness and financial interests. In past research, fairness has covaried with monetary payoff, such that the mental processes underlying preference for fairness and those underlying preference for greater monetary outcome could not be distinguished. We examined self-reported happiness and neural responses to fair and unfair offers while controlling for monetary payoff. Compared with unfair offers of equal monetary value, fair offers led to higher happiness ratings and activation in several reward regions of the brain. Furthermore, the tendency to accept unfair proposals was associated with increased activity in right ventrolateral prefrontal cortex, a region involved in emotion regulation, and with decreased activity in the anterior insula, which has been implicated in negative affect. This work provides evidence that fairness is hedonically valued and that tolerating unfair treatment for material gain involves a pattern of activation resembling suppression of negative affect.
Algorithms for Molecular Biology Fall Semester , 1998 Lecture 5 : January 3 , 1999

5.1. 1 Problem Definition Given a family S = (S1, . . . , Sk) of k sequences, such that the sequences are “similar” to each other, we would like to find out the common characteristics of this family. Aligning each pair of sequences from S separately, often does not reveal this common information. A multiple alignment of S is a new set of sequences S ′ = (S ′ 1, . . . , S ′ k) such that: • All the strings in S ′ are of equal length. We denote this length by l. • Each S ′ i was generated from Si by inserting spaces.
Efficient 2-Body Statistics Computation on GPUs: Parallelization &amp;amp; Beyond

Various types of two-body statistics (2-BS) are regarded as essential components of data analysis in many scientific and computing domains. Due to the quadratic time complexity, use of modern parallel hardware has become an obvious direction for research and practice in 2-BS computation. This paper presents our recent work in designing and optimizing parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). First, we classify 2-body applications into three groups based on their data output pattern. Then, we introduce a straightforward parallel algorithm under the CUDA framework. To that end, we split the algorithm into two stages: pairwise distance function computation and writing output. Then, we present modifications to the basic algorithm by integrating various techniques at each stage. Our algorithms design focuses on effective use of hardware/software features that are unique in GPU platforms. Experiments run on modern GPU hardware show that our GPU algorithms outperform the best known CPU program by at least an order of magnitude in various applications. Furthermore, our implementation achieves very high level of GPU resource utilization, indicating near-optimal performance. This work builds a solid foundation towards realizing our vision of a framework that can automatically generate optimized code for any new 2-BS problems.
Adaptive force control of position/velocity controlled robots: theory and experiment

This paper addresses the problem of achieving exact dynamic force control with manipulators possessing the low level position and/or velocity controllers typically employed in industrial robot arms. Previously reported approaches and experimental results are reviewed. A new adaptive force control algorithm for velocity/position controlled robot arms in contact with surfaces of unknown linear compliance is reported. The controller provably guarantees global asymptotic convergence of force trajectory tracking errors to zero when the robot is under exact or asymptotically exact inner loop velocity control. An additional result which guarantees arbitrarily small force errors for bounded inner loop velocity tracking errors is presented. Comparative experiments show the new adaptive velocity (position) based controller and its non-adaptive counterpart to provide performance superior to that of previously reported position based force controllers.
Minimizing power supply noise through harmonic mappings in networks-on-chip

Power supply integrity has become a critical concern with the rapid shrinking of device dimensions and the ever increasing power consumption in nano-scale integration. Particularly, power supply noise is strongly correlated to the spatial distribution of activity densities and this can be attributed to the on-chip communication, which dictates the power dissipation and overall system performance in networks-on-chip. In this paper, we propose a new mapping strategy aiming to create a balanced activity distribution across the whole chip. We formulate the problem of application mapping as a minimization of the activity density by employing a repulsive force-based objective function. Metrics of regional activity density and characteristics of its impact on power supply noise are considered. The proposed method has been rigorously evaluated based on a large set of real-application benchmarks. Significant reduction in power supply noise can be achieved with negligible energy overhead. This new approach would provide a more scalable solution for future large-scale system integration.
Soft skills, hard skills, individual innovativeness, and migration: evidence from Indonesia

Der Mensch hat unterschiedliche Rollen im sozialen und wirtschaftlichen Kontext. Im sozialen Kontext existieren Individuen in einer Familie, in der Gesellschaft und in einer Nation. Sie haben hier Sozialkapital. Aus okonomischer Sicht haben Individuen auch als Humankapital in einer Familie, in einer Firma und in einer breiteren Organisation oder Institution auf lokaler, regionaler oder internationaler Ebene eine Bedeutung. Daruber hinaus Spielt Humankapital auf makrookonomischer Ebene eine Rolle fur den komparativen Vorteil unter den Nationen der Welt (Benhabib und Spiegel 1994). Besseres Humankapital kann in angemessenem Mase im sozialen und wirtschaftlichen Umfeld und ihrer Entwicklung beitragen. Mitarbeiter konnen Innovation und feste Leistung vorantreiben. Sie nutzen ihr Wissen, ihre Fahigkeiten, ihre Erfahrung und ihr Netzwerk fur ihre taglichen Aufgaben. Ihr Wissen und ihre Fahigkeiten konnen durch Erziehung, Ausbildung und Interaktionen entwickelt werden (Blundell et al., 1999). Individuen lernen voneinander durch Wissenstransfer (knowledge transfer), allerdings immer in Abhangigkeit ihrer individuellen Absorptionsfahigkeit (absorptive capacity). Innovation ist der Prozess zur Schaffung eines neuen Produkts oder einer neuen Dienstleistung, entweder mit neuen Methoden oder neuen Technologien, die am Ende von einigen Kunden auf dem Markt akzeptiert werden. Die Neuheit der Produkte oder Dienstleistungen kann dabei inkrementell oder radikal sein. Insgesamt betreffen Innovationen die gesamte Managementfunktion in einer Organisation. Es ist mit den Forschungs- und Entwicklungsaktivitaten, dem Produktions- und Betriebsprozess, dem Finanzmanagement, dem Humankapital und dem Management sowie dem Marketingmanagement verbunden. Jede Funktion sollte neue Methoden zur Unterstutzung des Innovationsprozesses liefern und vorschlagen.
Video quality in multiple HEVC encoding-decoding cycles

Paper concerns accumulation of video-quality losses in multiple HEVC encoding and decoding cycles. The results of extensive experiments with standard video test sequences are reported. General conclusions has been drawn about relative quality losses after each cycle, as well as about total quality loss with respect to first cycle. In general, quality losses after each cycle are decreasing and total quality loss is saturating after several cycles. Total quality losses after several cycles are hardly dependent on sequence content.
Absence of Arcuate Fasciculus in a Child with Bilateral Perisylvian Polymicrogyria

• Mild mental retardation (IQ 43 and SQ 58.25) was documented on Korean-Wechsler Intelligence Scale for Children-third edition and Social Maturation Scale. • However, receptive language scale (71.4) was better than expressive one (35.71) documented by Preschool Receptive-Expressive Language scale test. Brain Neurorehabil. 2018 Sep;11(2):e14 https://doi.org/10.12786/bn.2018. 11.e14 pISSN 1976-8753·eISSN 2383-9910
PREDICTION OF HEART DISEASE USING GENETIC ALGORITHM FOR SELECTION OF OPTIMAL REDUCED SET OF ATTRIBUTES

The healthcare industry collects huge amounts of healthcare data which is not feasible to handle manually. Due to advancements in technology, today, many hospitals use information systems to store and manage data. These large amounts of data are very important in the field of data mining to extract useful information and generate relationships amongst the attributes. Thus, data mining is used to develop a mechanism to predict risk of heart disease. With the tremendously growing population, the doctors and experts available are not in proportion with the population. Also, symptoms of heart disease may not be significant and thus, may often be neglected. So we propose an Intelligent Heart Disease Decision Support System to help the doctors reach out to those people who are deprived of these medical services. In general, it can serve as a training tool to train nurses and medical students to diagnose patients having risk of heart disease. In this paper we have discussed two modeling techniques: Naive Bayes' Rule and Genetic Algorithm which predict the risk level of heart disease. In Genetic Algorithm, optimal reduced set of attributes are found using Genetic Search method. The 13 attributes in the original list have been reduced to 6. In Naive Bayes' technique, a historical heart disease database is used to generate relationships amongst the attributes using the concepts of conditional probability.
Pronunciation Adaptation For Disordered Speech Recognition Using State-Specific Vectors of Phone-Cluster Adaptive Training

Pronunciation variation is a major problem in disordered speech recognition. This paper focus on handling the pronunciation variations in dysarthric speech by forming speaker-specific lexicons. A novel approach is proposed for identifying mispronunciations made by each dysarthric speaker, using state-specific vector (SSV) of phone-cluster adaptive training (Phone-CAT) acoustic model. SSV is low-dimensional vector estimated for each tied-state where each element in a vector denotes the weight of a particular monophone. The SSV indicates the pronounced phone using its dominant weight. This property of SSV is exploited in adapting the pronunciation of a particular dysarthric speaker using speaker-specific lexicons. Experimental validation on Nemours database showed an average relative improvement of 9% across all the speakers compared to the system built with canonical lexicon. Index Terms: Dysarthric speech recognition, phone-CAT, lexical modeling, pronunciations, phone confusion matrix
Assessment of Constructs Related to Childhood Aggression

This study provides empirical support for the comprehensive theoretical model of aggression proposed by Meloy (2001) by examining characteristics of children who have a longstanding history of aggression across home and educational settings. The theoretical model first proposed by Meloy (2001) found attachment, anxiety, and attention difficulties in adults who evidenced severe aggression problems. The current study clarified how the adult model applies to children. That is, this study examined how attachment, anxiety, and attention are related to the overall functioning of children requiring treatment for aggression. Results are discussed in terms of child development issues, treatment selection, and intervention considerations.
Characterization of Non-Anthocyanic Flavonoids in Some Hybrid Red Grape Extracts Potentially Interesting for Industrial Uses

Previous studies showed that hybrid grapes often have qualitatively and quantitatively higher polyphenolic contents than the common V. vinifera grape varieties. In general, these compounds are studied for grape chemotaxonomy and for nutraceutical purposes due to their relevant antioxidant activity. Non-anthocyanic flavonoid composition of five red hybrid grape varieties produced by crossing of V. vinifera, V. aestivalis, V. cinerea, V. berlandieri, V. labrusca, V. lincecumii, and V. rupestris were studied by liquid chromatography/high-resolution mass spectrometry. Thirty-one compounds were identified, including methylnaringenin, a tetrahydroxy-dimethoxyflavanone-hexoside, two flavonols (quercetin and a pentahydroxyflavone isomer), 20 glycoside flavonols (four quercetin, two myricetin, two kaempferol, three isorhamnetin, one laricitrin, two syringetin, one kaempferide and two dihydroflavonol derivatives; myricetin-glucoside-glucuronide; myricetin-diglucoside; syringetin-dihexoside), three flavan-3-ols (−)-epicatechin, (+)-catechin, (−)-epicatechin gallate) and four proantocyanidins (procyanidin B1, procyanidin B2, procyanidin B3 or B4/B5, procyanidin T2 or T3/T4/C1). Seibel 19881, Seyve Villard 12-347 and Seyve Villard 29-399 were particularly rich in polyphenols. These findings emphasize that these grapes are especially interesting for the production of antioxidant extracts for nutraceutical and pharmaceutical uses.
Enhanced hyperspectral image segmentation using wavelets transform

Remote sensing technologies are very useful due to their wide practical applications. The latest new technology, hyperspectral imaging, could be a valuable tool to study and evaluate many economic and environmental issues. It offers an advanced monitoring technique in more detailed spectral information than previous remote sensing systems. The present work is devoted to the study of segmentation of hyperspectral imaging. The proposed approach depends on the principal component analysis to select the best features of the HS image. Then applying the wavelet transform to get different scales of the scene. Finally using the mean shift clustering technique we get the segmented image. The developed method is evaluated and proved result improvement over related work; it proved also that the segmented regions of the proposed approach are very smooth.
A Novel 3D Mesh Blind Watermarking Algorithm Based on Nielson Affine Invariant Norm

In this paper, a novel blind watermarking method for 3D model is proposed based on Nielson affine invariant norm. In our method, the affine invariance is computed for each vertex firstly. At next, we get grouping of them by using clustering algorithm. Then, one bit of watermark is embedded to each group using QIM method. Experimental results indicate the ability of the proposed method to deal with the 3D affine transformation, noising, vertices recomposition and their combined attacks.
Future care: rethinking technology enhanced aged care environments

Purpose Cutting-edge hospital and residential care architecture and interior design aim to address the emotional and practical needs of patients, staff and visitors. Yet, whilst improving on past practice, current approaches to design still rarely recognise or respond to individuals. The purpose of this paper is to provide a review of design-led research into digital technology across disciplines for the personalisation of healthcare environments and is informed by the authors’ ongoing hospital-based research. Design/methodology/approach This review is based on a design anthropology framework providing insight into designing for changing the experience for older patients in current healthcare contexts and future focused strategies, integrating digital technologies and human-centred design across scale and disciplines. It is informed by ongoing hospital studies based on design-led research methodology, drawing on design anthropology and ethnographical methods. Findings Technology enhanced, human-centred, assistive devices and environments implemented into healthcare across scale are developing but integration is needed for meaningful experiences. Research limitations/implications This review is a positioning paper for design-led research into digital technology across scale and medium. Practical implications This paper provides the basis for practical research including the ongoing hospital-based research of the authors. Social implications This approach potentially enhances emotional experiences of connected healthcare. Originality/value Future care scenarios are proposed, with technology and human experience as key drivers. Individualised and personalised solutions better cater for diversity. Within this context, it is strategic to question and test new ways of crafting the older persons care experience. This paper brings new direction to this discussion.
Scaling Web-based Acquisition of Entailment Relations

Paraphrase recognition is a critical step for natural language interpretation. Accordingly, many NLP applications would benefit from high coverage knowledge bases of paraphrases. However, the scalability of state-of-the-art paraphrase acquisition approaches is still limited. We present a fully unsupervised learning algorithm for Web-based extraction of entailment relations, an extended model of paraphrases. We focus on increased scalability and generality with respect to prior work, eventually aiming at a full scale knowledge base. Our current implementation of the algorithm takes as its input a verb lexicon and for each verb searches the Web for related syntactic entailment templates. Experiments show promising results with respect to the ultimate goal, achieving much better scalability than prior Web-based methods.
The Integration of Creative Writing into Academic Writing Skills in EFL Classes

Writing is regarded as the most challenging task and the most difficult language skill among EFL students. Students need to employ different cognitive strategies in order to convey the meaning to the reader. Due to the complexity of the process, students display negative attitudes towards writing and the level of success is low. This study investigates B2 level creative writing and its impact on the success level of students in academic writing. The study included 15 students, 10 of them are females (%66,7) and 5 of them are males (%33,3). The t-test results obtained from midterm and final exams indicate that students’ success level improves in academic writing skills. Participants’ pretest and post test results revealed that creative writing help them develop critical thinking skills and when SCAMPER method is used, students’ problem solving skills are promoted