Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling human status awareness in assistive environments based on advanced sound and motion data classification

The paper presents the concept and an initial implementation of a patient status awareness system that may be used for patient activity interpretation and emergency recognition in cases like elder falls and distress speech expressions. The awareness is performed through collecting, analyzing and classifying motion and sound data. The latter are collected through sensors equipped with accelerometers and microphones that are attached on the body of the patients and transmit patient movement and sound data wirelessly to the monitoring unit. Applying Short Time Fourier Transform (STFT) and spectrogram analysis on sounds detection of fall incidents is possible. The classification of the sound and movement data is performed using Support Vector Machines. Evaluation results indicate the high accuracy and the effectiveness of the proposed implementation. The system architecture is open and can be easily enhanced to include patient awareness based on additional context (e.g., physiological data).
An Italian Dataset of Textual Entailment Graphs for Text Exploration of Customer Interactions

English. This paper reports on the construction of a dataset of textual entailment graphs for Italian, derived from a corpus of real customer interactions. Textual entailment graphs capture relevant semantic relations among text fragments, including equivalence and entailment, and are proposed as an informative and compact representation for a variety of text exploration applications. Italiano. Questo lavoro riporta la costruzione di un dataset di grafi di implicazione testuale per la lingua italiana, derivati da un corpus di interazioni reali tra cliente e call centre. I grafi di implicazione testuale catturano relazioni semantiche significative tra porzioni di testi, incluse equivalenze e implicazioni, e sono proposti come un formato di rappresentazione informativo e compatto per applicazioni di esplorazione di contenuti testu-
Towards Explaining the Evolution of Domain Languages with Cognitive Simulation

We simulate the evolution of a domain language in small speaker communities. Data from experiments (Garrod et al., 2007; Fay et al., 2008) show that human communicators can evolve graphical languages quickly in a constrained task (Pictionary), and that communities converge towards a common language even in the absence of feedback about the success of each communication. We postulate that simulations of such horizontal evolution have to take into account properties of human memory (cue-based retrieval, learning, decay). We implement a model that can draw abstract concepts through sets of non-abstract, related concepts, and recognize such drawings. The knowledge base is a network with association strengths randomly sampled from a natural distribution found in a text corpus; it is a mixture of knowledge shared between agents and individual knowledge. In three experiments, we show that the agent communities converge, but that initial convergence is stronger when communities are structured so that the same pairs of agents interact throughout. Convergence is weaker in communities when agents do not swap roles (between recognizing and drawing), predicting the necessity of bi-directional communication in domain language evolution. Average and ultimate recognition performance depends on how much of the knowledge agents share initially.
2-D matrix multiplication on a 3-D systolic array

The introduction of systolic arrays in the late 1970s had an enormous impact on the area of special purpose computing. However, most of the work so far has been done with onedimensional and two-dimensional (2D) systolic arrays. Recent advances in three-<limensional VLSI (3D VLSI) and 3D packaging of2D VLSI components, has made the idea of 3D systolic arrays feasible in the near future. In this paper we introduce one algorithm for 2D matrix multiplication, using a 3D systolic array. We analyze advantages and disadvantages of 3D systolic arrays in the context of the analysis algorithm. The analytical work is combined with examples and discussions of relevant details.
Resource reservation for NEMO networks

Network mobility arises when a mobile router connecting an entire network to the Internet dynamically changes its point of attachment to the Internet. Such kind of network is referred to as a network that is mobile (NEMO). NEMO networks may lead to frequent handoffs between various access routers that are connected to the fixed network. Quality of service degradation or forced service termination may occur when there are insufficient resources required for handoff requests. This paper presents a new resource reservation protocol called NEMOR aiming at supporting QoS guarantee in a NEMO context. We focus on a reservation protocol adapted to NEMO specifications. For doing so, we use a generic signaling protocol called NSiS that may exploit advantages of both protocols; IntServ and DiffServ to provide a suitable QoS to NEMO.
Local correlation detection with linearity enhancement in streaming data

This paper addresses the challenges in detecting the potential correlation between numerical data streams, which facilitates the research of data stream mining and pattern discovery. We focus on local correlation with delay, which may occur in burst at different time in different streams, and last for a limited period. The uncertainty on the correlation occurrence and the time delay make it difficult to monitor the correlation online. Furthermore, the conventional correlation measure lacks the ability of reflecting visual linearity, which is more desirable in reality. This paper proposes effective methods to continuously detect the correlation between data streams. Our approach is based on the Discrete Fourier Transform to make rapid cross-correlation calculation with time delay allowed. In addition, we introduce a shape-based similarity measure into the framework, which refines the results by representative trend patterns to enhance the significance of linearity. The similarity of proposed linear representations can quickly estimate the correlation, and the window sliding strategy in segment level improves the efficiency for online detection. The empirical study demonstrates the accuracy of our detection approach, as well as more than $30\%$ improvement of efficiency.
Beyond one Bayesian brain: Modeling intra- and inter-personal processes during social interaction: Commentary on “Mentalizing homeostasis: The social origins of interoceptive inference” by Fotopoulou & Tsakiris

In their thought-provoking and integrative target article, Fotopoulou and Tsakiris cut across different bodies of literature to argue for a second-person account of interoception and social cognition. More specifically, they argue for the constitutive role of embodied social interactions in the development of interoceptive abilities and the ability for self-other differentiation. Furthermore, they review evidence to suggest that social interactions have a specific role in binding together subjective feeling states with the perception of the body and the (social) world. Along this line of reasoning, they refer to predictive coding and active inference frameworks of the “Bayesian brain” function to suggest that basic inferential processes of embodied perception and action may be turned into more advanced forms of social understanding. We applaud Fotopoulou and Tsakiris for advocating a second-person account of social cognition and for connecting the predictive coding account of interoception to the dynamics of social interaction conceptually. We discuss these contributions in light of the existing literature and encourage the authors to be more precise about the computational processes, which they suggest may connect social interaction and interoception at the individual level. Furthermore, we describe a multilevel Bayesian framework that could be used to formally test a proposal, such as the one discussed by the authors, but also allows for going beyond one Bayesian brain, by modeling interpersonal processes during social interaction.
Object Segmentation from Long Video Sequences

Most existing video segmentation methods are focused on extracting the primary objects in test video sequences. They assumed that only one object appeared through the whole video sequences, which is impractical in many applications. In this paper, we focus on the object segmentation from the long video sequences which consist of many different scenes, shot cuts and various motion patterns, etc. In order to solve this problem, we propose a framework to segment the objects in relative video shots, while discarding the irrelative video shots. A graph is constructed to model the video object detection and final segmentation is obtained by getting the superpixels in the detection boxes. We also introduce a new long video segmentation dataset which corresponds to the pixel-wise ground truth. The experiments demonstrate that our proposed method can deal with the object segmentation in long video sequence.
Internet gambling in relation to Internet addiction, substance use, online sexual engagement and suicidality in a Greek sample

Abstract Research has found gambling to be related to a variety of other addictive behaviours. The study objective was to evaluate the association of Internet gambling with Internet addiction, online sexual engagement, suicidality and substance use, in a sample of Greek adults. The study sample consisted of 789 military personnel. During their annual medical examination the study participants anonymously completed a series of self-reported questionnaires in relation to socio-demographic data, Internet gambling practices, online sexual engagement, Internet addiction, suicidality and psychoactive substance use. We found that Internet addiction significantly predicted engagement with online gambling, followed by substance use in general, and, in particular, the use of cocaine or heroin. Finally, two other predictive indicators were self-reported suicide attempts and engagement with online sex. Online gambling is associated with various behaviours related to impulsivity such as Internet addiction, online sexual engagement, suicidality and substance use. Future research will increase our knowledge on the contribution of new technologies and the Internet in Internet gambling dimensions, as well as on the associations with other high-risk behaviours such as substance use, pornography and suicidality.
Lying, Uptake, Assertion, and Intent

A standard view in social science and philosophy is that a lie is a dishonest assertion: to lie is to assert something that you think is false in order to deceive your audience. We report four behavioral experiments designed to evaluate some aspects of this view. Participants read short scenarios and judged several features of interest, including whether an agent lied. We found evidence that ordinary lie attributions can be influenced by aspects of audience uptake, are based on judging that the agent made an assertion (assertion attributions), and, at least in some contexts, are not based on attributions of deceptive intent. The finding on assertion attributions is predicted by the standard view, but the finding on intent attributions is not. These results help to further clarify the ordinary concept of lying and shed light on the psychological processes involved in ordinary lie attributions and related judgments.
The Relationship between Social Media Bullying and the Adolescent's Self Report of Emotional Health: A Study of Urban Youth on an Acute Inpatient Psychiatric Unit

Background: Bullying through social media, known as cyberbullying, is increasingly prevalent amongst today's youth. There is little research on a wide range of internalizing symptoms in conjunction with a measure of selfconcept in relationship to cyberbullying. Methods: This prospective research study examined the cross-sectional relationship between cyberbullying victims in children and adolescents 10-17 years of age within an acute inpatient psychiatric unit. Participants completed four separate measurements of anxiety and depression (the Modified Cyberbullying Questionnaire, the Children's Depression Inventory, the Screen for Child Anxiety Related Emotional Disorders (SCARED), and the Piers-Harris Children's Self Concept Scale). Results: 51 subjects completed the study. Twenty-four of the 51 participants reported some form of bullying (47.1%.) Of the 24 bullied participants, bullying via Facebook was most commonly reported (63% of bullied participants), followed by text messaging (50% of bullied participants). The mean total score on the Children's Depressive Inventory for the group in which social media bullying was reported was significantly higher than those in which no bullying was reported (14.00 (±3.56) versus 9.07 (±2.34), (p=0.020)). The mean total score on the SCARED for the group in which social media bullying was reported was statistically significant (26.83 (±6.52) versus 14.33 (±3.98), (p=0.0015)). The mean total T-score on the Piers-Harris Children's Self Concept Scale (2nd ed) for the group in which social media bullying was reported was significant (42.75 (±5.20) versus 49.26 (±3.97), (p=0.043)). The Behavioral Adjustment domain was the only individual domain in which statistical significance was not achieved (bullied versus nonbullied (43.18 ± 4.21 vs. 44.41 ± 3.49), p=0.61)). Discussion: Results indicated significant differences between mean total depression scores, mean total anxiety scores, and mean total T-score for the self-concept scale. These findings indicate an association between depression, anxiety, and self-esteem/self-concept and the presence of cyberbullying.
NLP-based feature extraction for automated tweet classification

Twitter conveys the opinions and interests of people in various topics and domains. In this paper we focus on political data with the purpose of automatically classifying opinion polarities and topic categories. Our experiments show that natural language processing techniques alone do not capture the information wanted. As a result, we use a hybrid method and we propose adding language features in a machine learning process that improved our results.
Telemanipulation Assistance Based on Motion Intention Recognition

In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user’s motion intention. In this paper, user’s motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the end-effector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user’s motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.
A spectroscopic study of the open cluster NGC 6475 (M 7) - Chemical abundances from stars in the range Teff = 4500–10 000 K

Aims. Clusters of stars are key objects for studying the dynamical and chemical evolution of the Galaxy and its neighbors, and are the most important laboratories to test the theory of stellar evolution. In particular, chemical composition is obtained from different kinds of stars (hot main-sequence stars, cool main-sequence stars, horizontal-branch stars, RGB stars) using different methodologies. Our first aim is to apply these methodologies to the stars of the open cluster NGC 6475 and, by obtaining a census of the most important elements, we will be able to test their consistency. Our second aim is to study the evolution of the surface chemical abundances as a function of the evolutionary phase of a star. We finally want to establish more robust fundamental parameters for this cluster. Methods. We selected high S/N high resolution spectra of 7 stars of the open cluster NGC 6475 from the ESO database covering the T eff range 4500-10 000 K and of luminosity class V (dwarf) and III (giants). We determined the chemical abundances of several elements. For hot stars (T eff > 9000 K), we applied the Balmer line fitting method to obtain atmospheric parameters, while for cool stars (T eff < 6500 K), the abundance equilibrium of FeI/II lines. For the two groups of stars, the use of different line-lists was mandatory. LTE approximation together with NLTE correction for some elements (C, N, O, Na, Mg) were applied. The abundances of many elements were obtained by measuring of the equivalent width of spectral lines. For those ones having only blended lines (O, He), real spectra were compared to synthetic ones. Hyperfine structure was taken into account for V and Ba. Results. First of all, we showed that the two methodologies we used give abundances that agree within the errors. This implies that no appreciable relative systematic effects are present for the derived chemical content of cool and hot stars. On the other hand, giants stars show clear chemical peculiarities with respect to the dwarfs affecting light elements (up to Si) and maybe Ba. This can be explained as an evolutionary effect. Then, once we had a new estimation of the metallicity for the cluster ([Fe/H] = +0.03 ± 0.02, [α/Fe] = -0.06 ± 0.02), we fitted suitable isochrones to the CMD of the cluster to obtain the basic parameters (E(B - V) = 0.08 ± 0.02, (m - M) o = 7.65 ± 0.05, Age = 200 ± 50).
UI Ripping in Android: Reverse Engineering of Graphical User Interfaces and its Application

Android applications are user interaction intensive programs, that makes UI become an indispensable part of mobile applications. UI also reflects information, such as the functions of applications, that makes the study of Android UI very significant. We design a UI modeling method based on attribute graph by the technology of reverse engineering and program analysis for applications on Android. The proposed method is also applied to malware repackage detection and evaluation of the application family resemblance. This method depicts Widgets consisted in UI and relationships between UIs, based of the assumption that 1) repackaged applications are similar on UI and 2) functions and appearances are highly similar between the members of families. Our method achieves 94.74% success rate at UI model and detects 2231 (26.13%) repackaged applications discovering around 50.0% applications have the exact same UI. The result shows that the UI modeling method helps develop more comprehensive application to detect repackaged applications include malicious ones.
Mining the graph structures of the web

Graph structures is a general way of modeling entities and their relationships and they are used to describe a wide variety of data including the Internet, the Web, social networks, metabolic networks, protein-interaction networks, food webs, networks of citations among papers, and many more. In the recent years there has been an increasing amount of literature on studying properties, models
The metabolic syndrome, inflammation, and risk of cognitive decline.

CONTEXT Several studies have reported an association between the metabolic syndrome and cardiovascular disease. Despite an increasing awareness that cardiovascular risk factors increase risk of cognitive decline and dementia, there are few data on the metabolic syndrome and cognition. OBJECTIVE To determine if the metabolic syndrome is a risk factor for cognitive decline and if this association is modified by inflammation. DESIGN AND SETTING A 5-year prospective observational study conducted from 1997 to 2002 at community clinics at 2 sites. PARTICIPANTS A total of 2632 black and white elders (mean age, 74 years). MAIN OUTCOME MEASURES Association of the metabolic syndrome (measured using National Cholesterol Education Program guidelines) and high inflammation (defined as above median serum level of interleukin 6 and C-reactive protein) with change in cognition (Modified Mini-Mental State Examination [3MS]) at 3 and 5 years. Cognitive impairment was defined as at least a 5-point decline. RESULTS Compared with those without the metabolic syndrome (n = 1616), elders with the metabolic syndrome (n = 1016) were more likely to have cognitive impairment (26% vs 21%, multivariate adjusted relative risk [RR], 1.20; 95% confidence interval [CI], 1.02-1.41). There was a statistically significant interaction with inflammation and the metabolic syndrome (P = .03) on cognitive impairment. After stratifying for inflammation, those with the metabolic syndrome and high inflammation (n = 348) had an increased likelihood of cognitive impairment compared with those without the metabolic syndrome (multivariate adjusted RR, 1.66; 95% CI, 1.19-2.32). Those with the metabolic syndrome and low inflammation (n = 668) did not exhibit an increased likelihood of impairment (multivariate adjusted RR, 1.08; 95% CI, 0.89-1.30). Stratified multivariate random-effects models demonstrated that participants with the metabolic syndrome and high inflammation had greater 4-year decline on 3MS (P = .04) compared with those without the metabolic syndrome, whereas those with the metabolic syndrome and low inflammation did not (P = .44). CONCLUSION These findings support the hypothesis that the metabolic syndrome contributes to cognitive impairment in elders, but primarily in those with high level of inflammation.
Regulation of MicroRNAs, and the Correlations of MicroRNAs and Their Targeted Genes by Zinc Oxide Nanoparticles in Ovarian Granulosa Cells

Zinc oxide (ZnO) nanoparticles (NPs) have been applied in numerous industrial products and personal care products like sunscreens and cosmetics. The released ZnO NPs from consumer and household products into the environment might pose potential health issues for animals and humans. In this study the expression of microRNAs and the correlations of microRNAs and their targeted genes in ZnO NPs treated chicken ovarian granulosa cells were investigated. ZnSO4 was used as the sole Zn2+ provider to differentiate the effects of NPs from Zn2+. It was found that ZnO-NP-5 μg/ml specifically regulated the expression of microRNAs involved in embryonic development although ZnO-NP-5 μg/ml and ZnSO4-10 μg/ml treatments produced the same intracellular Zn concentrations and resulted in similar cell growth inhibition. And ZnO-NP-5 μg/ml also specifically regulated the correlations of microRNAs and their targeted genes. This is the first investigation that intact NPs in ZnO-NP-5 μg/ml treatment specifically regulated the expression of microRNAs, and the correlations of microRNAs and their targeted genes compared to that by Zn2+. This expands our knowledge for biological effects of ZnO NPs and at the same time it raises the health concerns that ZnO NPs might adversely affect our biological systems, even the reproductive systems through regulation of specific signaling pathways.