Miracle-dz/UniFAR-FTUs huggingface dataset:
Session Control Protocol Exploiting Controlled Mobility in Multi-Robot Sensor Networks

Sensor networks using mobile robots have been proposed to provide more flexible and efficient sensing. One of the key challenges under this paradigm is a management of transferring a huge amount of data. In this paper, we propose a model and protocol for session control exploiting controlled mobility in multi-robot sensor networks (MRSNs) to transfer the huge amount of data efficiently. Since a communication in MRSNs is mainly provided by a physical movement of the mobile node, the performance of the session heavily depends on this movement. However, if a node initiating the session can utilize sufficient amount of nodes, the performance of the session significantly depends on the wireless communication. In this paper, we propose a suitable method of session control which is determined the balance between movement and wireless communications based on local information such as the location, number, maximum-velocity and buffer-size of surrounding nodes. WISER/s also takes a system policy of either delay minimization or energy efficiency into consideration. In this paper, we describe a method of how to initiate, terminate and manage the session including dynamic selection of optimal nodes' formation. We evaluate the performance of WISER/s using mobile robots which are equipped with MICA2 mote and comparing with non-optimized method. The experimental results demonstrate that WISER/s achieves better performance than non-optimized method.
Color Image Segmentation via Improved K-Means Algorithm

Data clustering techniques are often used to segment the real world images. Unsupervised image segmentation algorithms that are based on the clustering suffer from random initialization. There is a need for efficient and effective image segmentation algorithm, which can be used in the computer vision, object recognition, image recognition, or compression. To address these problems, the authors present a density-based initialization scheme to segment the color images. In the kernel density based clustering technique, the data sample is mapped to a high-dimensional space for the effective data classification. The Gaussian kernel is used for the density estimation and for the mapping of sample image into a high- dimensional color space. The proposed initialization scheme for the k-means clustering algorithm can homogenously segment an image into the regions of interest with the capability of avoiding the dead centre and the trapped centre by local minima phenomena. The performance of the experimental result indicates that the proposed approach is more effective, compared to the other existing clustering-based image segmentation algorithms. In the proposed approach, the Berkeley image database has been used for the comparison analysis with the recent clustering-based image segmentation algorithms like k-means++, k-medoids and k-mode.
A non-linear topic detection method for text summarization using Wordnet

This paper deals with the problem of automatic topic detection in text documents. The proposed method follows a non-linear approach. The method uses a simple clustering algorithm to group the semantically-related sentences. The distance between two sentences is calculated based on the distance between all nouns that appear in the sentences. The distance between two nouns is calculated using the Wordnet thesaurus. An automatic text summarization system using a topic strength method was used to compare the results achieved by the Text Tiling Algorithm and the proposed method. The obtained initial results shows that the proposed method is a promising approach.
Structured Output SVM Prediction of Apparent Age, Gender and Smile from Deep Features

We propose structured output SVM for predicting the apparent age as well as gender and smile from a single face image represented by deep features. We pose the problem of apparent age estimation as an instance of the multi-class structured output SVM classifier followed by a softmax expected value refinement. The gender and smile predictions are treated as binary classification problems. The proposed solution first detects the face in the image and then extracts deep features from the cropped image around the detected face. We use a convolutional neural network with VGG-16 architecture [25] for learning deep features. The network is pretrained on the ImageNet [24] database and then fine-tuned on IMDB-WIKI [21] and ChaLearn 2015 LAP datasets [8]. We validate our methods on the ChaLearn 2016 LAP dataset [9]. Our structured output SVMs are trained solely on ChaLearn 2016 LAP data. We achieve excellent results for both apparent age prediction and gender and smile classification.
Direct training of subspace distribution clustering hidden Markov model

It generally takes a long time and requires a large amount of speech data to train hidden Markov models for a speech recognition task of a reasonably large vocabulary. Previously, we proposed a compact acoustic model called "subspace distribution clustering hidden Markov model" (SDCHMM) with an aim to save some of the training effort. SDCHMMs are derived from tying continuous density hidden Markov models (CDHMMs) at a finer subphonetic level, namely the subspace distributions. Experiments on the Airline Travel Information System (ATIS) task show that SDCHMMs with significantly fewer model parameters-by one to two orders of magnitude-can be converted from CDHMMs with no loss in word accuracy. With such compact acoustic models, one should be able to train SDCHMM directly from significantly less speech data (without intermediate CDHMMs). We devise a direct SDCHMM training algorithm, assuming an a priori knowledge of the subspace distribution tying structure. On the ATIS task, it is found that both a context-independent and a context-dependent speaker-independent 20-stream SDCHMM system trained with 8 min of speech perform as well as their corresponding CDHMM system trained with 105 min and 36 h of speech, respectively.
Family history of diabetes as a new determinant of insulin sensitivity and secretion in patients who have undergone a simultaneous pancreas-kidney transplant.

OBJECTIVES We used homeostasis model assessment to investigate insulin sensitivity and secretion after a simultaneous pancreas-kidney transplant or kidney transplant alone. In that model, fasting plasma glucose and C-peptide levels are used to evaluate insulin sensitivity and beta-cell function. MATERIALS AND METHODS Factors (eg, age, sex, race, delayed kidney allograft function) were correlated with homeostasis model assessment of beta-cell function and homeostasis model assessment of insulin sensitivity values after simultaneous pancreas-kidney transplant (n=89) or kidney transplant alone (n=68), and the results were compared with those in healthy subjects (n=49). RESULTS Homeostasis model assessment of beta-cell function values were similar in patients who underwent kidney transplant alone or a simultaneous pancreas-kidney transplant, and were higher than homeostasis model assessment of beta cell function values in healthy subjects. The homeostasis model assessment of insulin sensitivity showed intermediate values for patients who underwent a simultaneous pancreas-kidney transplant and correlated with prednisone dosages (in those who underwent kidney transplant alone) and tacrolimus levels (in patients who underwent a simultaneous pancreas-kidney transplant). Homeostasis model assessment of beta-cell function values correlated with prednisone dosages in both groups and with tacrolimus levels in only those who underwent a simultaneous pancreas-kidney transplant. The body mass index of subjects who underwent kidney transplant alone correlated with both homeostasis model assessment of beta-cell function results and homeostasis model assessment of insulin sensitivity results. A family history of diabetes in subjects who underwent a simultaneous pancreas-kidney transplant correlated with homeostasis model assessment of beta-cell function results and homeostasis model assessment of insulin sensitivity results. CONCLUSIONS Immunosuppressive regimen and body mass index were linked with reduced insulin sensitivity after kidney transplant. A family history of diabetes was linked with higher values of insulin secretion and lower insulin sensitivity in patients who underwent a simultaneous pancreas-kidney transplant.
Evidence for extensive locus heterogeneity in Naxos disease.

Karima Djabali, Amalia Martinez-Mir, Liran Horev, Laurent Klapholz, Benjamin Glaser, Angela M Christiano and Abraham Zlotogorski Department of Dermatology, Hadassah Medical Center, Jerusalem, Israel Departments of Dermatology and Genetics & Development, Columbia University, New York, New York, U.S.A. Departments of Endocrinology, Hadassah Medical Center, Jerusalem, Israel Correspondence: Dr Abraham Zlotogorski, Department of Dermatology, Hadassah Medical Center, P.O. B. 12000, Jerusalem, Israel, 91120. Email: zloto@cc.huji. ac.il
Typing Massive JSON Datasets

Cloud-specific languages are usually untyped, and no guarantees about the correctness of complex jobs can be statically obtained. Datasets too are usually untyped and no schema information is needed for their manipulation. In this paper we sketch a typing algorithm for JSON datasets. Our approach can be used to infer a succinct type from scratch for a collection of JSON objects, as well as to validate a dataset against a human-designed type and, if necessary, to adapt and improve this type.
Rhythmic Oscillations of Visual Contrast Sensitivity Synchronized with Action

It is well known that the motor and the sensory systems structure sensory data collection and cooperate to achieve an efficient integration and exchange of information. Increasing evidence suggests that both motor and sensory functions are regulated by rhythmic processes reflecting alternating states of neuronal excitability, and these may be involved in mediating sensory-motor interactions. Here we show an oscillatory fluctuation in early visual processing time locked with the execution of voluntary action, and, crucially, even for visual stimuli irrelevant to the motor task. Human participants were asked to perform a reaching movement toward a display and judge the orientation of a Gabor patch, near contrast threshold, briefly presented at random times before and during the reaching movement. When the data are temporally aligned to the onset of movement, visual contrast sensitivity oscillates with periodicity within the theta band. Importantly, the oscillations emerge during the motor planning stage, ∼500 ms before movement onset. We suggest that brain oscillatory dynamics may mediate an automatic coupling between early motor planning and early visual processing, possibly instrumental in linking and closing up the visual-motor control loop.
Sexual behaviour change in countries with generalised HIV epidemics? Evidence from population-based cohort studies in sub-Saharan Africa

It has been 27 years since the beginning of the HIV epidemic in Africa and, although we understand more about the transmission and treatment of the disease, our knowledge of how new infections can be prevented remains limited.1 The risk of HIV acquisition is known to be closely associated with unprotected sexual intercourse,2 3 and adoption of safer sexual behaviour is still the main message of most national HIV prevention programmes. However, while declines in HIV prevalence have been associated with changes in sexual behaviour,4 ecological studies have failed to find associations between risk behaviour and the prevalence of HIV5 or other sexually transmitted infections (STI),6 and scientific trials show no evidence for a population-level effect of behavioural interventions.7 8 Measurement and comparison of sexual behaviour is challenging because, for example, variations in social desirability and other reporting bias can distort data on levels and trends in behaviour.9 Thus, limitations in the reliability and consistency of the methods used to collect and analyse data on sexual behaviour, particularly when made across time or space, could contribute to these apparently contradictory findings. This supplement presents 10 recent analyses of sexual behaviour data from longitudinal studies in five countries—Uganda, Tanzania, Malawi, Zimbabwe and South Africa—experiencing different sizes and stages of the HIV epidemic. The results provide valuable information for use in evaluating trends in HIV epidemics and the impact of HIV prevention programmes. An underlying purpose of this is to highlight appropriate methods and to encourage better analysis and presentation of sexual behaviour data, especially as they relate to HIV and HIV prevention. The Demographic and Health Surveys (DHS) conducted in many African countries have provided a wealth of data on age at first sex. …
Herding Generalizes Diverse M -Best Solutions

We show that the algorithm to extract diverse M -solutions from a Conditional Random Field (called divMbest [1]) takes exactly the form of a Herding procedure [2], i.e. a deterministic dynamical system that produces a sequence of hypotheses that respect a set of observed moment constraints. This generalization enables us to invoke properties of Herding that show that divMbest enforces implausible constraints which may yield wrong assumptions for some problem settings. Our experiments in semantic segmentation demonstrate that seeing divMbest as an instance of Herding leads to better alternatives for the implausible constraints of divMbest.
Leadership and Organizational Culture: An Investigation of Big Ten and Mid-American Conference Campus Recreation Administrations

This descriptive research study was conducted to investigate the concepts of transformational leadership and organizational culture within the administrative levels of campus recreation programs of Big Ten and Mid-American Conference universities. While transformational leadership was quantitatively measured by the Leadership Behavior Questionnaire (LBQ), the Culture Strength Assessment (CSA) and Culture Building Activities (CBA) instruments provided two quantitative measures of organizational culture. Qualitative data were also collected and analyzed to enrich and cross validate the findings. The researcher concluded that high transformational leaders direct programs that (a) possess stronger organizational cultures and (b) carry out culture-building activities, specifically the “customer orientation” function, to a greater extent than other leaders do. An interaction effect between leadership and conference was uncovered for this variable. No significant difference was uncovered between the high and low.
Freshwater discharges drive high levels of methylmercury in Arctic marine biota

Significance Estuaries are the predominant hunting and fishing territory for northern indigenous populations whose way of life is threatened by both climate change and industrial development. Direct measurements and modeling conducted as part of this study show enhanced production of methylmercury, a potent neurotoxin, and uptake by plankton in stratified oxic seawater. Enhanced climate-driven stratification of ocean margin areas with sea-ice melt will likely elevate biological methylmercury concentrations in the Arctic. Elevated biological methylmercury levels will be exacerbated by hydroelectric development planned throughout many northern regions. Our experimental measurements indicate that, over the next decade, regional increases in methylmercury concentrations resulting from flooding associated with hydroelectric development will be greater than those expected from climate change. Elevated levels of neurotoxic methylmercury in Arctic food-webs pose health risks for indigenous populations that consume large quantities of marine mammals and fish. Estuaries provide critical hunting and fishing territory for these populations, and, until recently, benthic sediment was thought to be the main methylmercury source for coastal fish. New hydroelectric developments are being proposed in many northern ecosystems, and the ecological impacts of this industry relative to accelerating climate changes are poorly characterized. Here we evaluate the competing impacts of climate-driven changes in northern ecosystems and reservoir flooding on methylmercury production and bioaccumulation through a case study of a stratified sub-Arctic estuarine fjord in Labrador, Canada. Methylmercury bioaccumulation in zooplankton is higher than in midlatitude ecosystems. Direct measurements and modeling show that currently the largest methylmercury source is production in oxic surface seawater. Water-column methylation is highest in stratified surface waters near the river mouth because of the stimulating effects of terrestrial organic matter on methylating microbes. We attribute enhanced biomagnification in plankton to a thin layer of marine snow widely observed in stratified systems that concentrates microbial methylation and multiple trophic levels of zooplankton in a vertically restricted zone. Large freshwater inputs and the extensive Arctic Ocean continental shelf mean these processes are likely widespread and will be enhanced by future increases in water-column stratification, exacerbating high biological methylmercury concentrations. Soil flooding experiments indicate that near-term changes expected from reservoir creation will increase methylmercury inputs to the estuary by 25–200%, overwhelming climate-driven changes over the next decade.
Detecting Anomalous Sensor Events in Smart Home Data for Enhancing the Living Experience

The need to have a secure lifestyle at home is in demand more than ever. Today's home is more than just four walls and a roof. Technology at home is on the rise and the place for smart home solutions is growing. One of the major concerns for smart home systems is the capability of adapting to the user. Personalizing the behavior of the home may provide improved comfort, control, and safety. One of the challenges of this goal is tackling anomalous events or actions. This work proposes using machine learning techniques to address this issue of detecting anomalous events or actions in smart environment datasets. The approaches are validated using real-world sensor data captured from a smart home testbed.
Evolutionarily conserved human targets of adenosine to inosine RNA editing

A-to-I RNA editing by ADARs is a post-transcriptional mechanism for expanding the proteomic repertoire. Genetic recoding by editing was so far observed for only a few mammalian RNAs that are predominantly expressed in nervous tissues. However, as these editing targets fail to explain the broad and severe phenotypes of ADAR1 knockout mice, additional targets for editing by ADARs were always expected. Using comparative genomics and expressed sequence analysis, we identified and experimentally verified four additional candidate human substrates for ADAR-mediated editing: FLNA, BLCAP, CYFIP2 and IGFBP7. Additionally, editing of three of these substrates was verified in the mouse while two of them were validated in chicken. Interestingly, none of these substrates encodes a receptor protein but two of them are strongly expressed in the CNS and seem important for proper nervous system function. The editing pattern observed suggests that some of the affected proteins might have altered physiological properties leaving the possibility that they can be related to the phenotypes of ADAR1 knockout mice.
Empathy in medical students as related to academic performance, clinical competence and gender

Context Empathy is a major component of a satisfactory doctor–patient relationship and the cultivation of empathy is a learning objective proposed by the Association of American Medical Colleges (AAMC) for all American medical schools. Therefore, it is important to address the measurement of empathy, its development and its correlates in medical schools.
Distributed situation assessment for traffic emergent events

The concept of the Internet of Things provides a new model to access the information of the objective world. A new approach based on the Internet of Things for traffic emergent events is proposed. The network architecture entails a hierarchy of capability, information and control, where nodes in the network expected to possess resources for networking and computing and presume autonomy through multi-functional modules for sensory processing and situation assessment. For the data fusion of multi-sensor device, the paper puts forward three different methods of date fusion according to different integrality levels of raw information. Finally, Bayesian network method is used to get situational assessment of the fused data. Comprehensive experiments on urban traffic emergent events of Dalian and comparisons with several other methods show that the Bayesian network combined with the Internet of Things is a very promising and effective approach for traffic emergent events' situation assessment modeling and forecasting, both for complete data and incomplete data.
Is it too late for PAKE ?

The most common web authentication technique in use today is password authentication via an HTML form, where a user types her password directly into a web page from the site to which she wishes to authenticate herself. The problem with this approach is that it relies on the user to determine when it is safe to enter her password. To resist phishing and other social engineering attacks, a user must rely on the browser’s security indicators and warning messages, e.g., the URL bar and the site’s SSL certificate, to authenticate the website and determine when it is safe to enter her password. Unfortunately, studies suggest that many users habitually click through SSL certificate warnings [7, 18] and misunderstand or ignore browser security indicators [5, 9, 17]. We revisit the idea of applying Password Authenticated Key Exchange (PAKE) [1, 2, 3, 4, 10, 12, 13] protocols to web authentication. A PAKE protocol is a cryptographic protocol that allows two parties who share knowledge of a password to mutually authenticate each other and establish a shared key, without explicitly revealing the password in the process. One hope of using PAKE protocols for web authentication is to help make it easier for users to authenticate websites and reduce the attack surface of social engineering attacks against their accounts. With a PAKE protocol, if a user mistakenly attempts to authenticate herself to a phisher, the protocol will fail, but the user’s password will remain safe. Since the phisher does not know the user’s password, the phisher will not be able to successfully complete the protocol, and the browser can alert the user of the failure. Goals and contributions. In this paper, we perform a systematic investigation of various practical issues and challenges in deploying PAKE for web authentication. Although many PAKE protocols have been proposed, there is little momentum for integrating PAKE protocols into web authentication. We investigate two categories of issues: 1) security issues related to user interface (UI) design, and 2) deployment and integration hurdles. The primary threat model we consider is an attacker who uses phishing or other