Miracle-dz/UniFAR-FTUs huggingface dataset:
Dealing with Skewed Data in Structured Overlays Using Variable Hash Functions

Storing highly skewed data in a distributed system has become a very frequent issue, in particular with the emergence of semantic Web and Big Data. This often leads to biased data dissemination among nodes. Addressing load imbalance is necessary, especially to minimize response time and avoid workload being handled by only one or few nodes. Our contribution aims at dynamically managing load imbalance by allowing multiple hash functions on different peers, while maintaining consistency of the overlay. Our experiments, on highly skewed data sets from the semantic web, show we can distribute data on at least 300 times more peers than when not using any load balancing strategy.
Multi-semantic Scene Classification Based on Region of Interest

Automatic semantic scene classification is a challenging research topic in computer vision and it is also a promising solution to scene understanding and image semantic retrieval. In this paper, novel techniques are proposed to implement multi-semantic scene classification. We first extract some regions of interest (ROIs) from each image based on image-driven, bottom-up visual attention model, and then propose two multi-instance multi-label learning algorithms, EMDD-SVM and EMDD-KNN to cope with this problem, where images are viewed as bags, each of which contains a number of instances corresponding to regions of interest and belongs to multiple categories simultaneously. Experimental results show that our ROIs extraction algorithm could obtain different kinds of interested objects effectively under various complex clutters and is highly tolerant to the noise, and that EMDD-SVM and EMDD-KNN algorithms have achieved good performance on multi-semantic scene classification by integrating multi-instance learning and multi-label learning.
A fast algorithm for incremental distance calculation

A simple and efficient algorithm for finding the closest points between two convex polynomials is described. Data from numerous experiments tested on a broad set of convex polyhedra on R/sup 3/ show that the running time is roughly constant for finding closest points when nearest points are approximately known and is linear in total number of vertices if no special initialization is done. This algorithm can be used for collision detection, computation of the distance between two polyhedra in three-dimensional space, and other robotics problems. It forms the heart of the motion planning algorithm previously presented by the authors (Proc. IEEE ICRA, p.1554-9, 1990). <<ETX>>
EnergoWatcher – the platform for creating adaptable energy monitoring systems

Energy monitoring systems are widely spread among large productions and smart houses. It"s designed to collect, process, store data and provide users with aggregated and detailed information about the consumed resources. However, the developing price of these systems is large enough. It is required to have a technology for fast and cheap creation of power consumption monitoring system. These systems should be adaptable to varying of operating conditions and designed for small objects. EnergoWatcher is a development technology of program complexes of power consumption monitoring, which is described in this article. The systems created by means of this technology suppose dynamic adapting to varying of operating conditions and users requirements. The software provides possibility of connection to various sources of the information (to controllers of technical state of objects, to databases of the external applications, and so on).
Classification of Single-Food Images by Combining Local HSV-AKAZE Features and Global Features

— This paper presents a system for assisting nutrition management for solitary elderly persons. Since dealing with diseases is one of the important issues for solitary elderly, their health control in daily life has been in focus in recent years. As preprocessing to develop a nutrition management system for solitary elderly, systems for discriminating the category of a food image have been proposed. However, classification of food images is still a challenging task due to the variety of their shape and color. In order to improve the performance on the classification, we propose three regions of interests extracted by HSV-AKAZE . The three regions are used to extract various local features such as AKAZE , HSV-AKAZE
A Parametric Objective Quality Assessment Tool for Speech Signals Degraded by Acoustic Echo

This paper discusses the automatic quality assessment of echo-degraded speech in the context of teleconference systems. Subjective listening tests conducted over a carefully designed database of signals degraded by acoustic echo have been used to assess how this impairment is perceived and to determine which parameters have a significant impact on speech quality. The results have shown that, similarly to electric transmission line echo, acoustic echo is mainly influenced by echo delay and echo gain. Based on this observation, a mapping between these two parameters and the mean subjective score is devised. Moreover, a signal-based algorithm for the estimation of these parameters is described, and its performance is evaluated. The complete system comprising both the parameter estimators and the mapping function achieves a correlation of 94% between predicted and actual subjective scores, and can be employed as a non-intrusive monitoring tool for in-service quality evaluation of teleconference systems. Further validation indicates the operating range of the proposed quality assessment tool can be extended by proper retraining.
Using SVMS and discriminative models for speech recognition

In speech recognition, standard MAP decoders attribute speech data to the class with the highest posterior probability. This minimises the error rate under assumptions of model correctness. This assumption is invalid for speech recognition with HMMs. Hence, an interesting question is whether extra, useful information about the speech source can be extracted from the HMMs and used to lower error rates in practical systems, In this paper additional features are extracted from HMMs and incorporated into a multi-dimensional score-space. SVMs are then used to implement a decision rule. Preliminary experiments are performed on a small speaker-independent isolated letter task. Score-spaces based on discriminative models are used with previous results based on generative models. Both score-spaces outperform standard schemes.
Flipping the Graduate Qualitative Research Methods Classroom: Did It Lead to Flipped Learning?.

The flipped, or inverted, classroom has gained popularity in a variety of fields and at a variety of educational levels, from K-12 through higher education. This paper describes the author’s positive experience flipping a graduate qualitative research methods classroom. After a review of the current literature on flipped classrooms in higher education, the author discusses his reasons for flipping, the steps he took to create the flipped classroom, and the outcomes of the flipped classroom experience. The author evaluates whether flipped learning occurred according to the four pillars of FLIP (FLN, 2014) and discusses suggestions for both future researchers and future classroom flippers.
An Efficient Region Based Intra Prediction Algorithm for H.264/Advanced Video Coding

H.264, MPEG-4 Part 10, is the latest digital video coding standard that is noted for achieving very high data compression by using several new coding features. One of the key features is the intra prediction based on the rate distortion optimization (RDO) procedure, which requires a true encoding of the intra block by trying all intra prediction modes. The computational burden of intra prediction with the RDO procedure is extremely high for real applications. In this paper, we propose an efficient fast mode decision method by using bi-region similarity detection (BSD) algorithm to reduce the encoding time. The proposed BSD algorithm effectively estimates the texture direction of the block to narrow down the predictive modes to reduce RDO computation. Experimental results show that the proposed algorithm achieves more than a 60% time reduction with negligible PSNR loss and bit-rate increment.
Quantifying the Importance of Vantage Point Distribution in Internet Topology Mapping (Extended Version)

The topology of the Internet has been extensively studied in recent years, driving a need for increasingly complex measurement infrastructures. These measurements have produced detailed topologies with steadily increasing temporal resolution, but concerns exist about the ability of active measurements to measure the true Internet topology. Difficulties in ensuring the accuracy of every individual measurement when millions of measurements are made daily, and concerns about the bias that might result from measurements along the tree of routes from each vantage point to the wider reaches of the Internet must be addressed. However, early discussions of these concerns were based mostly on synthetic data, oversimplified models or data with limited or biased observer distributions. In this paper, we show the importance that extensive sampling from a broad and well spread set of vantage points has on the resulting topology and bias. The majority of this paper is devoted to a first look at the importance of the distribution quality. We show that diversity in the locations and types of vantage points is required for obtaining an unbiased topology. We analyze the effect that broad distribution has over the convergence of various autonomous systems topology characteristics, and show that although diverse and broad distribution is not required for all inspected properties, it is required for some. Finally, claims against bias in active traceroute sampling are revisited, and we empirically show that diverse and broad distribution can question their conclusions.
A demonstration of the effect of permeant and impermeant solutes, and unstirred boundary layers on osmoti flow.

Professor Donald Barron's scientific interests and achievements extend over a remarkably broad range of subjects. As a scholar and investigator he, however, did not relegate teaching to a secondary position. We hope that this modest communication will serve in a small way to remind us of the simple and lucid laboratory demonstrations that Professor Barron used so effectively and memorably in his teaching of physiology.
Neural approach for personalised emotional model in human-robot interaction

Robotic technology is widely spreading into our everyday lives so the social interaction between robots and humans is becoming more important. We believe that the quality of human-robot interaction will determine the effectiveness of the collaboration and in general, the acceptance of robots in the society. With this motivation we propose a more intuitive way of interacting with robotic companions by recognizing human emotions and, on the other side, a methodology to construct a comprehensible way of expressing internal states of machines for human users. The novelty of our model consists in personalization of the emotional expressions for each of the users. The system consists of two parts: learning emotional expressions of humans using ARTMAP neural network and implementation of the personalised emotional model to a humanoid robot. This paper shows the first results based on experiments realized on the humanoid platform Nao. We achieved a personalised emotional behaviour especially useful in the area of social robotics.
P2PFAIR: Fair Exchange in P2P Sharing System without Dedicated TTP

P2P systems are currently the popular file-sharing platforms. The cooperation mechanism of P2P system can't offer fair exchange function. Because P2P system is a decentralized system, using a dedicated TTP server to support fair exchange is not suitable. A fair exchange protocol in P2P system without dedicated TTP is proposed, which is called P2PFair. P2PFair offers the fairness and secret guarantee. The fairness is provided by Dis_TTP which is composed of n random peers. P2PFair uses IBE to guarantee the secret of items exchanged. In order to eliminate collusion, Dis_TTP using threshold mechanism to manage the shadows of master key which is generated by distributed key generation algorithm. The threshold mechanism accommodates the fault-tolerant capacity at the same time. According to the probability analysis, the collusion probability is negligible. The protocol analysis shows that P2PFair is an effective solution of fair exchange in P2P sharing systems
Recent Advances in Superparamagnetic Iron Oxide Nanoparticles for Cellular Imaging and Targeted Therapy Research

Advances of nanotechnology have led to the development of nanomaterials with both potential diagnostic and therapeutic applications. Among them, superparamagnetic iron oxide (SPIO) nanoparticles have received particular attention. Over the past decade, various SPIOs with unique physicochemical and biological properties have been designed by modifying the particle structure, size and coating. This article reviews the recent advances in preparing SPIOs with novel properties, the way these physicochemical properties of SPIOs influence their interaction with cells, and the development of SPIOs in liver and lymph nodes magnetic resonance imaging (MRI) contrast. Cellular uptake of SPIO can be exploited in a variety of potential clinical applications, including stem cell and inflammation cell tracking and intra-cellular drug delivery to cancerous cells which offers higher intra-cellular concentration. When SPIOs are used as carrier vehicle, additional advantages can be achieved including magnetic targeting and hyperthermia options, as well as monitoring with MRI. Other potential applications of SPIO include magnetofection and gene delivery, targeted retention of labeled stem cells, sentinel lymph nodes mapping, and magnetic force targeting and cell orientation for tissue engineering.
The Effectiveness of Cognitive Therapy based on Mindfulness on Reducing Anxiety and Depression of Divorced Women

The aim of this research was to consider the effectiveness of cognitive therapy based on mindfulness on reducing anxiety and depression of divorced women in Rasht city, Iran. The research method was experimental which was used of pretest posttest control group design. The statistical society included of 100 divorced women that referred to the Behzisti organization of Rasht in 2014-2015. They were Headed households. The average of their age was between 35-50 years old. Statistical sample was 30 individuals of women that gained at least one score lower than the average, in the scale of anxiety and depression. They are randomly replaced in two available groups with 15 individuals (experiment and control). Pretest was performed in both of groups. Then, cognitive therapy based on mindfulness was trained to the experiment group that was 8 sessions, each session had 120 minutes and each week had one session. The control group was not trained during this time. At the end, the posttest was performed on both groups in the same time and same situation. Data was collected by use of Beck depression and anxiety inventory. Data analyzes was performed by use of independent t-test in SPSS 20 software. The results showed that there was significant difference (p<0.05) between two groups in terms of depression and anxiety. Therefore, it can be concluded that cognitive therapy based on mindfulness was effective on reducing anxiety and depression of divorced women.
Learning about *change: Information, motivation, and political perception

EVAN PARKER-STEPHEN: Learning about Change: Information, Motivation, and Political Perception. (Under the direction of James A. Stimson.) Does the American mass public, broadly speaking, meet its normatively prescribed duty to monitor and react to changes in the political-economic world? Do specific segments of the mass public pose an obstacle to this “monitorial model” of political competence, which is itself central to democratic accountability? This study explores these questions with three separate studies. It will be shown that citizen learning and inference rest on two crucial factors: real-world information and psychological motivation. Taken together, the three studies demonstrate how the relative importance of information and motivation changes as a function of both individual characteristics, including cognitive capability and partisanship, and the contextual characteristics of the political environment, especially political campaigns. Attention is given to differential learning across several socioeconomic and political partisan classes to assess the breadth of understanding about politically consequential real-world change. It will turn out that, on the issues that affect policy and election outcomes, politically relevant information is often distributed fairly evenly across the mass public. The prototypically ill-informed, which is to say, the people who have relatively low levels of education, income, and so forth, present a less serious obstacle to the monitorial model than do political partisans, whose perceptions are affected by their motives to preserve consistency with their partisan attitudes and beliefs. However, even though these partisans’ perceptions are affected by such “motivated reasoning,” their temporal-perception patterns reflect responsiveness to political-economic reality nonetheless. In broad perspective,
Supporting emotional well-being in schools: a pilot study into the efficacy of a mindfulness-based group intervention on anxious and depressive symptoms in children

ABSTRACT Objective: This study examines the efficacy of a six week child mindfulness program (TRIPLE R) in teaching mindfulness skills and reducing negative emotional symptoms in a school setting. Method: Using a correlational within-subjects repeated measures design, the relationships between child self-reported mindfulness skills and negative emotional symptoms were explored. Mindfulness skills were measured using the Child and Adolescent Mindfulness Measure, and emotional symptoms were measured using the Revised Children’s Anxiety and Depression Scale. A sample of 57 Australian grade 5 children in three primary schools completed the measures pre and post intervention. Results: There was a small to moderate increase in mindfulness skills post intervention (Cohen’s d = 0.32), however negative emotional symptoms did not significantly improve. Increased mindfulness skills were significantly associated with decreased emotional symptoms, most notably for symptoms related to social phobia (R = −.61), separation anxiety (R = −.42) and generalised anxiety (R = −.32). Discussion: This study provides preliminary support for the TRIPLE R program and the potential benefits of school-based mindfulness interventions in improving children’s well-being. The limited improvement in negative emotional symptoms is likely related to the non-clinical sampling. The relationship between increased mindfulness skills and decreased emotional symptoms is discussed, and recommendations for further research are presented.
Partisan journalism and the issue framing of the Euro crisis: Comparing political parallelism of German and Spanish online news

Empirical studies investigating political parallelism in news coverage mostly look at the frequency and evaluation of party actors, though political parallelism is not only tied to parties but also to broader political ideologies. By contrast, this study examines country differences in political parallelism focusing on the alignment of media’s issue interpretations with political ideologies. Based on a content analysis of 7256 statements in 961 articles about the Euro crisis in German and Spanish online newspapers, issue frames were identified in a data-driven approach before examining how the argumentative structure of these frames referred to ideologies. Results show that ideology-guided framing is present in nearly half of all articles. But Spanish media more often used frames corresponding to their political alignment than their German counterparts indicating stronger political parallelism in the Mediterranean Model. Thus, the results support the revision of the Hallin/Mancini Model of media systems proposed by Brüggemann et al.
Standards of practice to standards of evidence: developing assessment capable teachers

Abstract Teacher education is a hotly debated policy area in higher education and schooling portfolios, with increasing emphasis on standards and accountability. It is in this environment that The Standards Project (2013–2015) presented in this article began. It has at its core a three-part commitment: first, to undertake a comprehensive audit and analysis of all teacher education programmes in the state of Queensland, Australia, to establish the approaches and practices Universities relied on to preparing beginning teachers as assessment capable; second, to take account of multiple perspectives and approaches in initial teacher education to integrating data into how beginning teachers are prepared to source and use evidence for improving learning and teaching; and further, to develop new principles, policy and practices for reviewing and moderating teacher education programmes against professional standards. The paper proposes a move beyond the discourse of professional standards of practice towards a complementary discourse of standards of evidence. In our collaboration we drew on two fields, namely the writing on teacher education including reviews, and the field of assessment, both considered within broader sociocultural theory applied to assessment.
Iterative Joint Channel Estimation andDecoding in Flat Correlated

Performance evaluation of trellis and turbo-codes in at correlated Rayleigh fading traditionally assumes ideal channel interleaving and availability of perfect Channel State Information (CSI) at the receiver. This paper presents a joint iterative decoding and channel estimation scheme, which performs very well in the at Rayleigh fading channel with moderate to high Doppler rates, without channel interleaving or external CSI. The decoding algorithm models the phase trajectory of the channel as a Markov process, the dynamics of which depend on the Doppler rate. At each iteration it considers all possible phase trajectories to determine the maximum a posteriori probability (MAP) data sequence. This decoding procedure is a natural extension of standard turbo decoding, and permits decoding without pilot symbols, at the expense of introducing a signi cant complexity increase.
Osmotic pressure can regulate matrix gene expression in Bacillus subtilis

Many bacteria organize themselves into structurally complex communities known as biofilms in which the cells are held together by an extracellular matrix. In general, the amount of extracellular matrix is related to the robustness of the biofilm. Yet, the specific signals that regulate the synthesis of matrix remain poorly understood. Here we show that the matrix itself can be a cue that regulates the expression of the genes involved in matrix synthesis in Bacillus subtilis. The presence of the exopolysaccharide component of the matrix causes an increase in osmotic pressure that leads to an inhibition of matrix gene expression. We further show that non‐specific changes in osmotic pressure also inhibit matrix gene expression and do so by activating the histidine kinase KinD. KinD, in turn, directs the phosphorylation of the master regulatory protein Spo0A, which at high levels represses matrix gene expression. Sensing a physical cue such as osmotic pressure, in addition to chemical cues, could be a strategy to non‐specifically co‐ordinate the behaviour of cells in communities composed of many different species.