Miracle-dz/UniFAR-FTUs huggingface dataset:
HyVE: Hybrid Vibro-Electrotactile Stimulation for Sensory Feedback and Substitution in Rehabilitation

Electro- or vibro-tactile stimulations were used in the past to provide sensory information in many different applications ranging from human manual control to prosthetics. The two modalities were used separately in the past, and we hypothesized that a hybrid vibro-electrotactile (HyVE) stimulation could provide two afferent streams that are independently perceived by a subject, although delivered in parallel and through the same skin location. We conducted psychophysical experiments where healthy subjects were asked to recognize the intensities of electro- and vibro-tactile stimuli during hybrid and single modality stimulations. The results demonstrated that the subjects were able to discriminate the features of the two modalities within the hybrid stimulus, and that the cross-modality interaction was limited enough to allow better transmission of discrete information (messages) using hybrid versus single modality coding. The percentages of successful recognitions (mean ± standard deviation) for nine messages were 56 ±11% and 72 ±8% for two hybrid coding schemes, compared to 29 ±7% for vibrotactile and 44 ±4% for electrotactile coding. The HyVE can be therefore an attractive solution in numerous application for providing sensory feedback in prostheses and rehabilitation, and it could be used to increase the resolution of a single variable or to simultaneously feedback two different variables.
Authority Flow-based Ranking in Heterogeneous Networks: Prediction, Personalization, and Learning to Rank

Title of dissertation: AUTHORITY FLOW-BASED RANKING IN HETEROGENEOUS NETWORKS: PREDICTION, PERSONALIZATION, AND LEARNING TO RANK Hassan Sayyadi-Harikandehei, Doctor of Philosophy, 2014 Dissertation directed by: Professor Louiqa Raschid Smith School of Business Many real-world datasets, including biological networks, the Web, and social media, can be effectively modeled as networks or graphs, in which nodes represent entities of interest and links mimic the interactions or relationships among them. Such networks often contain multiple entity or relationship types, which are referred to as heterogeneous networks. Networks also evolve due to the existence of temporal features that characterize the entities or to the temporal relationships among them. Finding important/authoritative entities in real-world networks is a longstanding and well-defined challenge. In this dissertation, I focus on two variants of the problem. The first is the prediction of the ranking of scientific publications in a future state of a citation network. I introduce a new measure labeled the future PageRank score. I develop FutureRank, a prediction algorithm for predicting the future PageRank scores from the historical network structure, and evaluate the FutureRank algorithm on multiple bibliographic dataset. Next, I focus on personalized ranking in social media. I extend a social media dataset to include relationships (edge types) between authors, blog posts, categories (topics) of the posts, and events (collections of posts). I then apply personalized ranking algorithms over the historical posts and events that have been visited by a user and use the ranking to recommend additional posts. I evaluate the personalized recommendations through an experiment with real users, as well as an extensive study of synthetic users whose preferences are defined based on intuitive criteria. Finally, I present an approach for learning to rank (algorithms) applied to heterogeneous networks. Existing methods for learning to rank are typically limited to content-based features, while many real world problems correspond to relational features. I develop a framework for learning to rank, which targets authority flowbased ranking models on heterogeneous networks. I propose algorithms for both pointwise and pairwise learning. However, this framework can easily utilize any loss function from a non-relational learning domain. Experiments show that even with a small amount of training data, both pointwise and pairwise algorithms perform successfully and converge very fast. In addition, these solutions are shown to be robust against noise. Authority Flow-based Ranking in Heterogeneous Networks: Prediction, Personalization, and Learning to Rank
A Look at the Grouping Effect on Population-level Risk Assessment of Radon-Induced Lung Cancer

On the basis of considerable knowledge gained by studying health effects in uranium and other underground miners who worked in radon-rich environments, radon exposure has been identified as a cause of lung cancer. Recent pooled analyses of residential studies have shown that radon poses a similar risk of causing lung cancer in the general public when exposure occurs at generally lower levels found in homes. With the increasing accessibility of statistical data via the internet, people are performing their own analyses and asking why, in some cases, the lung cancer occurrence at the community level does not correlate to the radon levels. This study uses statistical data available to the general public from official websites and performs simple analyses. The results clearly show the difficulty in linking observed lung cancer incidence rates at the provincial/territorial level, with possible cause, such as smoking or radon exposure. Even the effect of smoking, a well-documented cause of lung cancer, can be overlooked or misinterpreted if the data being investigated is too general (i.e., summary data at population level) or is influenced by other factors. These difficulties with simple comparisons are one of the main reasons that epidemiological studies of lung cancer incidence and radon exposure requires the use of cohorts or case controls at the individual level as opposed to the more easily performed ecological studies at the population level.
Creating Customer-Centred Organisations: New Business Planning Tools for Australian Police Services

This paper calls for Australian police services to articulate a strategic declaration or vision of how they would operate (and be viewed by customers) in a genuinely customer-focused environment. Despite claims to be ‘customer focused’, Australian police services have conceptualised customers as a unity rather than an amalgam of discrete groups. Furthermore, police services have not specified the desirable elements of a customer-focused organisation beyond modest corporate statements, nor formulated rigorous indicators of success along the road to achieving customer service excellence. Against the backdrop of a philosophical discussion about the reinvention of communities as customers in corporate planning strategies, some of the broad-level business planning techniques that Australian police services should apply, it is argued, before claiming to be truly ‘customer focused’ are identified. These new planning tools — defining customer segments, competitive positioning and ‘blue-sky’ planning — are introduced as useful techniques to move police organisations from their current position to a desired future position as customer-centred organisations.
Efficient Removal of Impulse Noise in Digital Images

Image Noise Suppression is a highly demanded approach in digital imaging systems. Impulsive noise is one such noise, which is frequently encountered problem in acquisition, transmission and processing of images. In the area of image restoration, many state-of-the art filters consist of two main processes, classification (detection) and reconstruction (filtering). Classification is used to separate uncorrupted pixels from corrupted pixels. Reconstruction involves replacing the corrupted pixels by certain approximation technique. In this paper such schemes of impulsive noise detection and filtering thereof are proposed. Here we presents a comparative study on six methods such as median filter, Progressive switching median filter, Fuzzy switching median filter, Adaptive median filter, Simple adaptive median filter and its modified version i.e. Modified Simple Adaptive median filter. Objective evaluation parameters i.e. mean square error; peak signal-to- noise ratio is calculated to quantify the performance of these filters.
Designing TEL products for poor comprehenders: evidences from the evaluation of TERENCE

Developing the capabilities to read and comprehend texts is fundamental for the development of children and for their full participation in society. The FP7 European project TERENCE faced the problem of poor text comprehenders and created the first adaptive learning system for text comprehension for primary school children. The paper, after a brief introduction to the research problem behind TERENCE and an overview of the system, reports on the findings of four round of evaluations aimed at assessing both the usability and the psycho-pedagogical effectiveness of the system, and report them as hints useful for researchers and designers.
A spectral feature process for speech recognition using HMM with MFCC approach

Speech recognition using technology has always been a uphill task. Researchers have been trying since decades to replicate the acoustic system to improve speech recognition by machines. In this paper we tried to improve the feature extraction by modeling it in accordance with the non-linearity of the human audio perception. The human ear is not equally sensitive to all the frequencies of sound within the entire spectrum. From the acoustic information, the speech recognition system tries to extract linguistic information, i.e. to recognize the speaker's utterance. The speech feature vectors are called observations. The Simulation results show an improvement in the quality metrics of word recognition with respect to computational time, learning accuracy for a speech recognition system.
GLD-4-Mediated Translational Activation Regulates the Size of the Proliferative Germ Cell Pool in the Adult C. elegans Germ Line

To avoid organ dysfunction as a consequence of tissue diminution or tumorous growth, a tight balance between cell proliferation and differentiation is maintained in metazoans. However, cell-intrinsic gene expression mechanisms controlling adult tissue homeostasis remain poorly understood. By focusing on the adult Caenorhabditis elegans reproductive tissue, we show that translational activation of mRNAs is a fundamental mechanism to maintain tissue homeostasis. Our genetic experiments identified the Trf4/5-type cytoplasmic poly(A) polymerase (cytoPAP) GLD-4 and its enzymatic activator GLS-1 to perform a dual role in regulating the size of the proliferative zone. Consistent with a ubiquitous expression of GLD-4 cytoPAP in proliferative germ cells, its genetic activity is required to maintain a robust proliferative adult germ cell pool, presumably by regulating many mRNA targets encoding proliferation-promoting factors. Based on translational reporters and endogenous protein expression analyses, we found that gld-4 activity promotes GLP-1/Notch receptor expression, an essential factor of continued germ cell proliferation. RNA-protein interaction assays documented also a physical association of the GLD-4/GLS-1 cytoPAP complex with glp-1 mRNA, and ribosomal fractionation studies established that GLD-4 cytoPAP activity facilitates translational efficiency of glp-1 mRNA. Moreover, we found that in proliferative cells the differentiation-promoting factor, GLD-2 cytoPAP, is translationally repressed by the stem cell factor and PUF-type RNA-binding protein, FBF. This suggests that cytoPAP-mediated translational activation of proliferation-promoting factors, paired with PUF-mediated translational repression of differentiation factors, forms a translational control circuit that expands the proliferative germ cell pool. Our additional genetic experiments uncovered that the GLD-4/GLS-1 cytoPAP complex promotes also differentiation, forming a redundant translational circuit with GLD-2 cytoPAP and the translational repressor GLD-1 to restrict proliferation. Together with previous findings, our combined data reveals two interconnected translational activation/repression circuitries of broadly conserved RNA regulators that maintain the balance between adult germ cell proliferation and differentiation.
Speaker age estimation and gender detection based on supervised Non-Negative Matrix Factorization

In many criminal cases, evidence might be in the form of telephone conversations or tape recordings. Therefore, law enforcement agencies have been concerned about accurate methods to profile different characteristics of a speaker from recorded voice patterns, which facilitate the identification of a criminal. This paper proposes a new approach for speaker gender detection and age estimation, based on a hybrid architecture of Weighted Supervised Non-Negative Matrix Factorization (WSNMF) and General Regression Neural Network (GRNN). Evaluation results on a corpus of read and spontaneous speech in Dutch confirms the effectiveness of the proposed scheme.
Post-stroke Mood and Emotional Disturbances: Pharmacological Therapy Based on Mechanisms

Post-stroke mood and emotional disturbances are frequent and diverse in their manifestations. Out of the many post-stroke disturbances, post-stroke depression, post-stroke anxiety, post-stroke emotional incontinence, post-stroke anger proneness, and post-stroke fatigue are frequent and important symptoms. These symptoms are distressing for both the patients and their caregivers, and negatively influence the patient’s quality of life. Unfortunately, these emotional disturbances are not apparent and are therefore often unnoticed by busy clinicians. Their phenomenology, predicting factors, and pathophysiology have been under-studied, and are under-recognized. In addition, well-designed clinical trials regarding these symptoms are rare. Fortunately, these mood and emotional disturbances may be treated or prevented by various methods, including pharmacological therapy. To administer the appropriate therapy, we have to understand the phenomenology and the similarities and differences in the pathophysiological mechanisms associated with these emotional symptoms. This narrative review will describe some of the most common or relevant post-stroke mood and emotional disturbances. The phenomenology, factors or predictors, and relevant lesion locations will be described, and pharmacological treatment of these emotional disturbances will be discussed based on presumable pathophysiological mechanisms.
Educational Data Mining using Improved Apriori Algorithm

Educational Data Mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses different computational approaches to analyze educational data in order to study educational questions. Different data mining techniques has been applied in this area. One of the most widely used techniques in EDM is association rules mining. Apriori algorithm is the first and best-known algorithm for association rules mining. This paper surveys the most relevant studies carried out in EDM using Apriori algorithm. Based on the Apriori algorithm analysis and research, this paper points out the main problems on the application Apriori algorithm in EDM and presents an improved support-matrix based Apriori algorithm. The improved Apriori algorithm proposed in this research uses bottom up approach along with standard deviation functional model to mine frequent educational data pattern.
A Qualitative Analysis of South African Health Professionals’ Discussion on Distrust and Unwillingness to Refer Organ Donors

Introduction: South Africa is faced with very low deceased organ donor numbers. Often, sociocultural practices, which are thought to be fundamentally opposed to deceased organ donation, are hailed as the cause. However, other factors such as context, social perceptions, and clinical environment may play a role. Aim: The aim of this article is to present research that explored communication in organ transplant and identified barriers to organ donation decisions in a province of South Africa. Methods: Qualitative methods were used. Thirty semistructured interviews with transplant professionals and 2 focus groups with transplant coordinators took place across 6 health institutions in Gauteng Province. Results: Barriers that may prevent transplant professionals from referring potential donors were identified: The wider public and transplant professionals may be suspicious of biomedicine and have a perception that people could be killed for their organs. Organ donation was sometimes framed as “murder,” “killing,” or a “bunch of vultures. ” Doctors may be unwilling to refer brain-dead patients, as this was seen as failing in one’s professional duty to cure the patient. The role of sociocultural practices was inconclusive, with the sample divided based on the extent of their influence. Conclusion: Low donor numbers may be a manifestation of barriers to referral in the clinical setting. These barriers interplay in a context of suspicion and are framed by a clinical transplant discourse that is sometimes loaded with negative connotation. Sociocultural practices are influential, but they may not be the overriding cause of low donor numbers.
A High Capacity Data Hiding Method for JPEG 2000 Compression System

Data hiding is an important branch of information security. Imperceptibility and Hiding Capacity are very important aspects for efficient secret communication. It is necessary to increase hiding capacity for JPEG2000 baseline system because available redundancy is very limited. In this paper Redundancy Evaluation method is used for increasing hiding capacity. This method determines embedding depth adaptively for increasing hiding capacity. Large quantity of data is embedded into bitplanes, but at the cost of slightly change in Peak Signal to Noise Ratio (PSNR). This method easily implemented in JPEG2000 compression encoder and produced stego stream decoded normally at decoder. Simulation result shows that this method is secure and increases hiding capacity.
Applying Swarm Ensemble Clustering Technique for Fault Prediction Using Software Metrics

Number of defects remaining in a system provides an insight into the quality of the system. Defect detection systems predict defects by using software metrics and data mining techniques. Clustering analysis is adopted to build the software defect prediction models. Cluster ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. In this paper, the clustering ensemble using Particle Swarm Optimization algorithm (PSO) solution is proposed to improve the prediction quality. An empirical study shows that the PSO can be a good choice to build defect prediction software models.
TraceWeb : Traçabilité Automatisée et Contrôle pour les Projets de Développement Coopératif à Grande Échelle

Face a la globalisation actuelle marches, des chenteles et des travaux de developpement de logiciel ainsi qu'a l'existance de nombreux formes et niveaux de colaboration, les entreprises investissent dans des environnement de developpement de logiciel qui facilitent la collaboration entre equipes et ameliorent efficacite et la qualite des produits tout en present la propriete intellectuelle. Dans le present article, les autres decrivent un environnement d'outils integres qui automatise la tracabilite d'un a l'autre des artefacts produits. Cet environnement engendre et maintient a jour une matrice de tracabilite dynamique et, entre autres services, produit de nombreux rapports configurables a la demande sur les dependances le courent, la couverture et les manques quant aux artefacts. Il automatise le recueil des mesures, fournit des aides au suivi dynamique des projets et assiste la collaboration entre equipes.
An offline optimization method for efficiency optimized operation of interior permanent magnet synchronous machines

Interior permanent magnet machines have several advantages compared to permanent magnet synchronous machines having the magnets on the rotor surface. The drawback is the quite more complex control structure for energy efficient and stable operation of this machines. In a prior work, some current control structures have been discussed assuming the energy optimized current setpoints to be given. In this work, the calculation of the setpoints is explained to achieve energy optimized operation of the IPMSM. The proposed method for calculating the setpoints uses an offline optimization method to derive matrices which easily could be implemented in a microcontroller as look-up tables. Comparing the proposed optimized current control to a non optimized current control for salient pole machines on a test bench, yields an energy efficiency improvement of up to 23% in the lower field weakening area.
Premorbid Antisocial Personality and Substance Use Disorder in Traumatic Brain Injury: A Case Report and Review of Literature

We report on a 29 year-old male with a history of antisocial personality and polysusbtance use disorder, who sustained a traumatic brain injury five months before presenting to the psychiatric emergency room in an agitated state and with suicidal and homicidal ideations. We discussed medication management and psychotherapeutic interventions in this case, and we performed a literature review on brain traumatic injury.
Solving Substitution Ciphers

We present QUIPSTER, an experimental system for the automatic solution of short substitution ciphers (Cryptoquotes). The sys tem operates using ann-gram model of English characters and stochastic local sear ch over the space of 26! 4 1026 possible keys. Experimental results show a median of 94% cipher letters correctly decoded, which is typically good enough for an unskilled human to finish decoding th e cipher with minimal additional effort. Extensions incorporating a dictionary with word frequencies and a database of word patterns are als o discussed.
Increased Avian Diversity Is Associated with Lower Incidence of Human West Nile Infection: Observation of the Dilution Effect

Recent infectious disease models illustrate a suite of mechanisms that can result in lower incidence of disease in areas of higher disease host diversity–the ‘dilution effect’. These models are particularly applicable to human zoonoses, which are infectious diseases of wildlife that spill over into human populations. As many recent emerging infectious diseases are zoonoses, the mechanisms that underlie the ‘dilution effect’ are potentially widely applicable and could contribute greatly to our understanding of a suite of diseases. The dilution effect has largely been observed in the context of Lyme disease and the predictions of the underlying models have rarely been examined for other infectious diseases on a broad geographic scale. Here, we explored whether the dilution effect can be observed in the relationship between the incidence of human West Nile virus (WNV) infection and bird (host) diversity in the eastern US. We constructed a novel geospatial contrasts analysis that compares the small differences in avian diversity of neighboring US counties (where one county reported human cases of WNV and the other reported no cases) with associated between-county differences in human disease. We also controlled for confounding factors of climate, regional variation in mosquito vector type, urbanization, and human socioeconomic factors that are all likely to affect human disease incidence. We found there is lower incidence of human WNV in eastern US counties that have greater avian (viral host) diversity. This pattern exists when examining diversity-disease relationships both before WNV reached the US (in 1998) and once the epidemic was underway (in 2002). The robust disease-diversity relationships confirm that the dilution effect can be observed in another emerging infectious disease and illustrate an important ecosystem service provided by biodiversity, further supporting the growing view that protecting biodiversity should be considered in public health and safety plans.