Miracle-dz/UniFAR-FTUs huggingface dataset:
An intelligent tutoring system for deaf learners of written English

This paper describes progress toward a prototype implementation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) signers of American Sign Language (ASL). We envision a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that student in a tutorial dialogue, enabling the student to generate appropriate corrections to the text. A strong focus of this work is to develop a system which adapts this process to the knowledge level and learning strengths of the user and which has the flexibility to engage in multi-modal, multi-lingual tutorial instruction utilizing both English and the native language of the user.
Do Parents Matter? Examination of White College Students’ Intergroup Experiences and Attitudes

The purpose of the study was to examine the association between parents’ attitudes towards diversity and their young adult children’s intergroup experiences and attitudes. We surveyed a sample of non-Latino White, first-year university students (n = 154) and one of their parents (n = 154) at the start of the academic year; a subsample of these students (n = 87) and one of their parents (n = 87) was also surveyed again at the end of their first year. We found that, among parents who expressed greater openness to diversity, young adult children were more likely to appreciate diversity and less likely to endorse racial colorblindness. We found similar effects regarding parents’ openness to diversity on students’ likelihood to engage in campus diversity experiences, which subsequently increased students’ diversity appreciation and decreased students’ endorsement of racial colorblindness. Implications and future directions are discussed.
Why are long-term large-scale wireless sensor networks difficult: early experience with GreenOrbs

Wireless sensor networks in the past decade have achieved remarkable progress, while the real applications are still far from being long-term or large-scale. This paper presents GreenOrbs [1], the latest effort to explore the fundamental challenges of long-term large-scale wireless sensor networks. GreenOrbs supports a series of forestry applications. Based on the early experience with GreenOrbs, this paper further discusses the future research directions.
Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

Background The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease. Methods Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression. Results The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways. Discussion This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets.
Context and relationships: Using the systemic approach with music therapy in work with children, adolescents and their families

This article considers the theoretical and practical use of the systemic approach in music therapy. An overview of the basic elements of the systemic approach is given. The potential benefits the approach can bring to music therapy practice are outlined with reference to the author’s work with young people with social, emotional and behavioural difficulties and their families. The potential for the systemic approach to contribute to the ongoing development of music therapy theory and practice is also explored, with particular reference to community music therapy. Two case studies are used to illustrate the use of the approach in practice.
Prognostic Modeling of Individual Patient Risk and Mortality Impact of Ischemic and Hemorrhagic Complications: Assessment From the Acute Catheterization and Urgent Intervention Triage Strategy Trial

Background— Both ischemic and hemorrhagic complications increase mortality rate in acute coronary syndromes. Their frequency and relative importance vary according to individual patient risk profiles. We sought to develop prognostic models for the risk of myocardial infarction (MI) and major bleeding to assess their impact on risk of death and to examine the manner in which alternative antithrombotic regimens affect these risks in individual patients. Methods and Results— The Acute Catheterization and Urgent Intervention Triage Strategy (ACUITY) trial randomized 13 819 patients with acute coronary syndrome to heparin plus a glycoprotein IIb/IIIa inhibitor, bivalirudin plus a glycoprotein IIb/IIIa inhibitor, or bivalirudin alone. By logistic regression, there were 5 independent predictors of MI within 30 days (n=705; 5.1%) and 8 independent predictors of major bleeding (n=645; 4.7%), only 2 of which were common to both event types. In a covariate-adjusted, time-updated Cox regression model, both MI and major bleeding significantly affected subsequent mortality rate (hazard ratios, 2.7 and 2.9, respectively; both P<0.001). Treatment with bivalirudin versus heparin plus a glycoprotein IIb/IIIa inhibitor was associated with a nonsignificant 8% increase in MI and a highly significant 50% decrease in major bleeding. Given the individual patient risk profiles and the fact that bivalirudin prevented ≈6 major bleeds for each MI that might occur from its use, the estimated reduction in bleeding was greater than the estimated increase in MI by bivalirudin alone rather than heparin plus a glycoprotein IIb/IIIa inhibitor for nearly all patients. Conclusions— Consideration of the individual patient risk profile for MI and major bleeding and the relative treatment effects of alternative pharmacotherapies permits personalized decision making to optimize therapy of patients with acute coronary syndrome. Clinical Trial Registration— clinicaltrials.gov Identifier: NCT00093158.
Conversational content in the context of safety of social networks

The anticipated article concentrates on the conversational content of communication in social network. It defines the conversational content, its elaboration and analysis. It describes the present status of text-mining in communication content. It deals with the use of communication content for the identification of an author. As part of this contribution the issue of suitable applications and mining techniques of communication content is being elaborated as well. It describes in more detail the principle of Naïve Bayes Classification functioning within the given problem issue. It puts the chosen applications into coherence with theme modeling. In this context the interconnection of term and style based approach is being emphasized which by their mutual connection create an opportunity of theme identification and author identification of the conversational content for the sake of solution of the given problem.
Scalable video streaming over overlay/underlay cognitive radio network

Cognitive radio (CR) with dynamic spectrum allocation (DSA) allows new or secondary devices to opportunistically access a portion of spectrum, which belongs to primary (licensed) users (PU). This allows the secondary users (SU) to transmit data services when the PUs are not using the spectrum. However, problem arises when SU experiences interrupted services if the PU needs to use the spectrum. The objective of this paper is to provide uninterrupted multimedia data services (video transmission) to SU by allowing the SU to receive data in both the overlay and underlay mode of CR. Since the underlay mode CR is characterized by low power and low data rate, base layer video streaming of a scalable video coding (SVC) is an attractive solution. In overlay mode, the enhancement layers and the base layer of the SVC are streamed without interruptions. The base layer video is protected from video packet loss by using I-frames insertion. Simulation results show that it is a great challenge to transmit video in the underlay CR. Nevertheless, with high protection of the base layer video of the SVC, acceptable video quality can be achieved.
Distributed under Creative Commons Cc-by 4.0 the Crouching of the Shrew: Mechanical Consequences of Limb Posture in Small Mammals

An important trend in the early evolution of mammals was the shift from a sprawling stance, whereby the legs are held in a more abducted position, to a parasagittal one, in which the legs extend more downward. After that transition, many mammals shifted from a crouching stance to a more upright one. It is hypothesized that one consequence of these transitions was a decrease in the total mechanical power required for locomotion, because side-to-side accelerations of the body have become smaller, and thus less costly with changes in limb orientation. To test this hypothesis we compared the kinetics of locomotion in two mammals of body size close to those of early mammals (< 40 g), both with parasagittally oriented limbs: a crouching shrew (Blarina brevicauda; 5 animals, 17 trials) and a more upright vole (Microtus pennsylvanicus; 4 animals, 22 trials). As predicted, voles used less mechanical power per unit body mass to perform steady locomotion than shrews did (P = 0.03). However, while lateral forces were indeed smaller in voles (15.6 ± 2.0% body weight) than in shrews (26.4 ± 10.9%; P = 0.046), the power used to move the body from side-to-side was negligible, making up less than 5% of total power in both shrews and voles. The most power consumed for both species was that used to accelerate the body in the direction of travel, and this was much larger for shrews than for voles (P = 0.01). We conclude that side-to-side accelerations are negligible for small mammals–whether crouching or more upright–compared to their sprawling ancestors, and that a more upright posture further decreases the cost of locomotion compared to crouching by helping to maintain the body's momentum in the direction of travel.
Agaric — A hybrid cloud based testing platform

Cloud computing technology enables developers spend much more time on application quality without considering computing resource constraint, load balancing and performance tuning, etc. It raises challenges along with the benefits it offers to software testing. This paper is motivated with the concerns on how to test the online web applications in a scalable and diverse way. The resources occupied in the proposed cloud testing are diverse in computing ability, network performance, and software configuration (including operating system and browser, etc.). This paper at first identifies the eight unique features to define cloud testing. Then, this paper introduces the design of Agaric — hybrid cloud based testing platform. In Agaric we use both the diverse internet distributed and user owned computing resources, and the centered computing resources. The key to organize the test network is the proposed Test Flow Control Protocol (TFCP). TFCP is verified formally with Colored Petri Net and the platform is evaluated with both controlled and uncontrolled experiments.
A camera calibration technique based on OpenCV

Camera calibration is very improtant in 3D computer vision systems. In this paper, the camera model in OpenCV (open source computer vision library) is discussed, the non-linear distortion of the tangential and radial distortion aberration are considered. Based on these, we introduce the corner extraction of the Camera Calibration Toolbox for Matlab, and an arithmetic of camera calibration based on OpenCV is given. The proposed algorithm can achieve accurate results. It has features such as computational efficiency and good cross-platform portability. It is able to meet the needs of 3D computer vision systems. Experimental results show the feasibility of the proposed method.
Classification model analysis for the prediction of leptospirosis cases

Leptospirosis is a disease that affects mainly low-income populations, with an incidence of 500,000 cases per year worldwide[1]. The disease has symptoms often confused with other febrile syndromes, such as dengue, influenza and viral hepatitis. Improved diagnosis of patients with leptospirosis is very important for health professionals, epidemiological surveillance and primarily for rapid evaluation and appropriate treatment of patients. In this work, an analysis of the data mining techniques classification was performed, evaluating algorithms of the methods of Decision Tree, Classification Rules and Bayesian Classification. Of these, JRip was the model with the best performance, yielding 85% sensitivity and 81% specificity. The algorithms successfully predicted the disease and may represent a new tool to assist health professionals in the daily hospital routine, especially in endemic areas for leptospirosis, accelerating targeted treatment, and minimizing disease exacerbation and mortality.
A Survey on Factors Affecting Iran's Fuel Rationing Smart Card User Acceptance and Security

Smart card technology has resulted in vast developments in many aspects of modern human life. User acceptance of fuel rationing smart cards based on adoption model involves many factors such as: satisfaction, security, external variables, attitude toward using, etc. In this study, user acceptance and security factors for fuel rationing smart cards in Iran have been evaluated based on an adoption model by distributing a questionnaire among UTM (University Technology Malaysia) Iranian students, MMU (Multimedia University) Iranian students, either asking by e-mail from people who are not available.
Fully-Automatic Synapse Prediction and Validation on a Large Data Set

Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of connections (synapses) between neurons. As manual extraction of this information is very time-consuming, there has been extensive research efforts to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively little research on automatic detection of the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite for the first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections. However, recent research has demonstrated that contact area alone is not a sufficient predictor of a synaptic connection. Moreover, as segmentation improved, we observed that synapse annotation consumes a more significant fraction of overall reconstruction time (upwards of 50% of total effort). This ratio will only get worse as segmentation improves, gating the overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect presynaptic neurons and their postsynaptic partners. In particular, presynaptic structures are detected using a U-Net convolutional neural network (CNN), and postsynaptic partners are detected using a multilayer perceptron (MLP) with features conditioned on the local segmentation. This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. When applied to the output of our method on EM data from Drosphila, these metrics demonstrate that a completely automatic prediction can be used to effectively characterize most of the connectivity correctly.
Direct signal cancellation in passive bistatic DVB-T based radar

English summary Passive radars receive target reflections from transmitters of opportunity. They do not emit any radiation, which makes them harder to find. A challenge with the passive radar is to suppress the strong direct signal incoming at the surveillance antenna, emitted from the transmitter, in order to be able to detect weaker targets. A direct signal cancellation method called " Extensive Cancellation Algorithm " is the focus of this report. The algorithm minimizes the contribution of the direct signal to the surveillance signal, by means of a least mean squares method. We demonstrate the use of this cancellation algorithm on real data, and give an estimate on how much the signal to noise ratio of a target can be increased. Two different antenna configurations are used. Antenna configuration A has an angle of about 90 • between the direction to the transmitter and the surveillance antenna main lobe. In configuration B, the surveillance antenna is pointing towards the transmitter, which should maximize the direct signal interference. The target signal to noise ratio of ten targets observed with antenna configuration A experience an average increase of about 10dB after direct signal cancellation. The average increase is about 20dB for configuration B. Some targets from this configuration had initially a signal level lower than the estimated noise floor. After cancellation, the SNR had increased such that they were able to be detected as a target. The corresponding reduction in noise floor level are about 10dB and 14dB for configuration A and B, respectively. The result show that the effect of direct signal cancellation depends on where the direct signal interferes in the surveillance antennas antenna pattern.
3D reconstruction and visualization of alternatives for restoration of historic buildings: A new approach

This paper puts forward a 3D reconstruction methodology applied to the restoration of historic buildings taking advantage of the combined speed, range and accuracy of a total geodetic station. The measurements of geo-referenced points produced a fully interactive and photorealistic geometric mesh of an historic monument named `Neoria'. `Neoria' is a Venetian building located by the old harbour at Chania, Crete, Greece. The integration of tacheometry acquisition and computer graphics puts forward a novel integrated software framework for the accurate 3D reconstruction of a historical building. The main technical challenge of this work was the production of an accurate 3D mesh based on a sufficient number of tacheometry measurements acquired fast and at low-cost. Interpolation methods ensured that a detailed geometric mesh was constructed based on a few points. Advanced interactive functionalities are offered to the user in relation to identifying restoration areas and visualizing the outcome of such works in a fully interactive application based on game engine technologies. Moreover, the user could photorealistically visualize the actual or restored monument and calculate distances between points.
Appearance Feature Extraction versus Image Transform-Based Approach for Visual Speech Recognition

In this paper we propose a new appearance based system which consists of two stages: visual speech feature extraction and classification, followed by recognition of the extracted feature, thereby the result is a complete lip-reading system. This lip-reading system employs our Hyper Column Model (HCM) approach to extract and classify the visual features and uses the Hidden Markov Model (HMM) for recognition. This paper addresses mainly the first stage; i.e. feature extraction and classification. We investigate the HCM performance to achieve feature extraction and classification and then compare the performance when replacing HCM with Fast Discrete Cosine Transform (FDCT). Unlike FDCT, HCM could extract the entire features without any loss. Also the experiments have shown that HCM is generally better than FDCT and provides a good distribution of the phonemes in the feature space for recognition purposes. For fair comparison, two databases are exploited with three different sets of resolution for each database. One of these two databases is designed to include shifted and scaled objects. Experiments reveal that HCM is capable of recovering and dealing with such image restrictions whereas the effectiveness of FDCT drops drastically especially for new subjects.
Protective Effects of Tualang Honey against Oxidative Stress and Anxiety-Like Behaviour in Stressed Ovariectomized Rats

The present study aims to evaluate the antioxidant and anxiolytic-like effect of Tualang honey in stressed ovariectomized (OVX) rats. The animals were divided into; (i) nonstressed sham-operated control rats, (ii) sham-operated control rats exposed to stress, (iii) nonstressed OVX rats, (iv) OVX rats exposed to stress, (v) OVX rats exposed to stress and treated with 17 β-oestradiol (E2) (20 μg daily, sc), and (vi) OVX rats exposed to stress and treated with Tualang honey (0.2 g/kg body weight, orally). The open field test was used to evaluate the anxiety-like behaviour and ELISA kits were used to measure oxidant/antioxidant status of the brain homogenates. The result showed that anxiety-like behavior was significantly increased in stressed OVX compared to other groups, and administering either E2 or Tualang honey significantly decreased anxiety-like behaviour in stressed OVX rats. The levels of malondialdehyde (MDA) and protein carbonyl (PCO) were significantly decreased while the levels/activities of superoxide dismutase (SOD), glutathione S-transferases (GST), glutathione peroxidase (GPx), and glutathione reductase (GR) were significantly increased in the brain homogenates of treated stressed OVX groups compared to untreated stressed OVX. In conclusion, Tualang honey has protective effects against brain oxidative stress and may be useful alternative anxiolytic agent especially for postmenopausal women.
JPE 11-4-20 Design and Implementation of a Single Input Fuzzy Logic Controller for Boost Converters

This paper describes the design and hardware implementation of a Single Input Fuzzy Logic Controller (SIFLC) to regulate the output voltage of a boost power converter. The proposed controller is derived from the signed distance method, which reduces a multi-input conventional Fuzzy Logic Controller (CFLC) to a single input FLC. This allows the rule table to be approximated to a one-dimensional piecewise linear control surface. A MATLAB simulation demonstrated that the performance of a boost converter is identical when subjected to the SIFLC or a CFLC. However, the SIFLC requires nearly an order of magnitude less time to execute its algorithm. Therefore the former can replace the latter with no significant degradation in performance. To validate the feasibility of the SIFLC, a 50W boost converter prototype is built. The SIFLC algorithm is implemented using an Altera FPGA. It was found that the SIFLC with asymmetrical membership functions exhibits an excellent response to load and input reference changes.