Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning meets visualization for extracting insights from text data

Historically, text mining methods have been enriched substantially by both statistical learning and symbolic AI. Different approaches have been extensively applied over the last 30 years to extract "knowledge" from text. However, in scenarios where the path from data to decisions is unclear, or where different users may be interested in different solutions, the involvement of the user or analyst in the text mining process becomes crucial. Visual Text Analytics aims at addressing these problems by incorporating concepts from Visual Analytics to text mining and natural language processing (Keim et. al, 2010).
Assessing the Success of an Information System: the Case of Audits for O.P.E.K.E.P.E

Considering previous theoretical models and empirical studies, this study’s goal is to develop a tool for assessing the success of a web-based Information System and to evaluate it experimentally. This is Audits, a system used by a non-profitable public organization, O.P.E.K. E.P.E. Success is evaluated based on system’s effects on the employees and the organization, as well as the satisfaction of the end users. At the same time, this study explores the factors that facilitate or undermine the success of an IS according to data gathered from the technology acceptance related literature. The proposed model has been tested using primary data from a sample of 192 regular users of the specific system, who actually represent almost two fifths of its total active users. The empirical results only partly verify the relationships examined and contribute in the design of a valid and reliable conceptual model.
Unbounded Contention Resolution: k-Selection in Radio Networks

Using k-Selection in Radio Networks as an example of unique-resource dispute among k unknown contenders, the conflict-resolution protocol presented in this paper shows that, for any sensible probability of error ", all of them get access to such resource in asymptotically optimal time (e + 1 + )k +O(log 2 (1=")), where > 0 is any constant arbitrarily close to 0. This protocol works under a model where not even an upper bound onk is known and conflicts can not be detected by all the contenders.
On the Robust Modal Local Polynomial Regression

Modal local polynomial regression uses double kernel as the loss function to gain some robustness in the nonparametric regression. Current researches use the standard normal density function as the weight function to down-weigh the influences from the outliers. This paper extends the standard normal weight function to a general class weight functions. All the theoretical properties found by using normal weight function are preserved with general weight function without imposing additional conditions.
Technical Overview of the Common Language Runtime (or why the JVM is not my favorite execution environment)

In the last few years many language researchers have moved to the JVM as the delivery vehicle for their languages. Although the JVM is a great target for the Java TM programming language, it is not necessarily a good platform for other languages, especially languages that require semantic features that do not appear in Java TM . In this paper we compare the JVM with the new Microsoft . NET Common Language Infrastructure (CLI), which has been designed from the ground up to be a multi-language platform.
Methods and Approaches for Characterizing Learning Related Changes Observed in functional MRI Data — A Review

Brain imaging data have so far revealed a wealth of information about neuronal circuits involved in higher mental functions like memory, attention, emotion, language etc. Our efforts are toward understanding the learning related effects in brain activity during the acquisition of visuo-motor sequential skills. The aim of this paper is to survey various methods and approaches of analysis that allow the characterization of learning related changes in fMRI data. Traditional imaging analysis using the Statistical Parametric Map (SPM) approach averages out temporal changes and presents overall differences between different stages of learning. We outline other potential approaches for revealing learning effects such as statistical time series analysis, modelling of haemodynamic response function and independent component analysis. We present example case studies from our visuo-motor sequence learning experiments to describe application of SPM and statistical time series analyses. Our review highlights that the problem of characterizing learning induced changes in fMRI data remains an interesting and challenging open research problem.
Evaluating Wireless Network Effects for SLAM Robot Map Making

This work investigates how wireless network performance affects a rescue robot that is running a simultaneous localization and mapping (SLAM) algorithm. The SLAM algorithm is used to generate a map of an unknown disaster site. Odometry and laser range data collected by the robot is transferred via an ad-hoc wireless network to a central access point (AP) that is running the SLAM algorithm. Simulation is used to determine how losses, latency and the use of relays in the network affect the quality and completeness of the SLAM maps. This is illustrated using new metrics for SLAM map quality and completeness that are based on Hausdorff distance.
Effect of melatonin supplemented at the light or dark period on recovery of sciatic nerve injury in rats

Peripheral nerve injuries can cause disabilities, social or economic problems. Melatonin, the secretory product of the pineal gland has antioxidant and anti-inflammatory actions. The aim of the present study was to investigate the effect of melatonin on the recovery of sciatic nerve after injury, comparing its effect when given in the light or the dark periods. Forty adult male Albino rats were allocated into four groups: control, nerve injury, nerve injury + melatonin given at light and nerve injury + melatonin given at dark. Nerve injury was initiated by clamping the sciatic nerve. Sciatic functional index (SFI) was measured preoperatively and postoperatively. Melatonin was given daily for six weeks. Recovery of the function was analyzed by functional analysis, electrophysiological analysis and biochemical measurement of Superoxide dismutase (SOD), Interleukin 1-beta (IL-1 β), Nerve growth factor (NGF), and bcl-2. Melatonin improved SFI, nerve conduction velocity (NCV) and the force of gastrocnemius muscle contraction as compared to the untreated rats. SOD activity, NGF, and bcl-2 were significantly increased, while IL-1β was significantly decreased after melatonin treatment as compared to the untreated injury group. SFI reached the control level; muscle contraction and IL-1B were significantly improved in the group treated with melatonin in the dark. Melatonin fastened the neural recovery and may be used in the treatment of nerve injury and it induced better nerve regeneration when the rats were treated during the dark period.
CNET: an algorithm for Reverse Engineering of Causal Gene Networks

: We present a novel Reverse Engineering algorithm, CNET, to reconstruct Gene Regulatory Networks from microarray time series data. CNET can be considered an improvement of the Mutual Information approach, present in the REVEAL [5] algorithm, with an innovative scoring function, to cope with noise, quantization errors and gene characteristic transcription delays. We tested the algorithm on simulated data and on real microarray data, from an experiment on human genes involved in cell cycle. CNET definitely outperforms REVEAL and its performance is not significantly different from that of widely used continuous methods, such as Dynamic Bayesian Networks.
Merging of systems biology models with semanticSBML

Model repositories like BioModels.net or JWS online provide a large number of biochemical pathway models in the data format SBML (Systems Biology Markup Language). We have developed semanticSBML, an interactive model merging tool that helps users build, annotate, check, and combine SBML models. It supports the current standard for biological annotations within SBML and uses a large collection of synonyms and database identifiers for the matching and comparison of model elements. During model merging, semanticSBML automatically detects the various kinds of syntactic and semantic conflicts and guides the user in resolving them. Here we demonstrate its use with a simple example: following a work by Snoep et al., we merge three models describing the glycolytic pathway, the production of glycerol, and the degradation of the toxic byproduct methylglyoxal. The example shows typical issues that arise in automatic model merging and emphasises the importance of extensive and detailed biological annotations. SemanticSBML can be freely downloaded at http://semanticsbml.sourceforge. net.
Putting Tangible User Interfaces in Context : A Unifying Framework for Next Generation HCI

Here, we propose the notion of Reality-Based Interaction (RBI) as a basis for understanding the role of tangible user interfaces (TUIs) within the broader context of emerging human-computer interaction styles. We are developing a Reality-Based Interaction framework to understand, compare, and relate current paths of HCI research. Viewing tangible interaction through the lens of RBI may provide insights for designers and may allow us to find gaps or opportunities for future development. Furthermore, we are using RBI to develop new evaluation techniques for features of emerging interfaces that are currently unquantifiable.
Toward personalized query expansion

Social networking and tagging have taken off at an unexpected scale and speed, opening huge opportunities to enhance the user search experience. We present Gossple, a new, user-centric, approach to improve the exploration of the Internet. Underlying Gossple lies the intuition that while social networks provides news from your old buddies, you can learn a lot more from people you don't know, but with whom you share many (tagging) interests. More specifically, considering a collaborative tagging system with active taggers annotating content, Gossple expands the search query, of any user u, with tags that are considered "close" enough with respect to users that are "close" to u. Gossple users create their own network of social acquaintances in a gossip-based manner, by dynamically computing the estimation of a distance between taggers, based on cosine similarity between tags and items. These connections are used to feed a TagMap: our central abstraction that captures the personalised relationships between tags. The TagMap is then used by Gossple to meaningfully expand queries leveraging the personalised network. This is achieved through the TagRank algorithm, an adaptation of the celebrated pagerank algorithm, which automatically determines which tags best expand a list of tags in a given query. Gossple has no central authority: every user stores its own items and its tagging behaviour is stored only by its neighbours. The resulting networks are live, dynamic and do not require any underlying structure. We report on our evaluation of Gossple with CiteUlike traces, involving 33,834 users. In short, we show that, with little information stored at every peer, Gossple enables to retrieve items that cannot be retrieved with state of the art search systems (completeness).
The Norm of the Product of a Large Matrix and a Random Vector

Given a real or complex $n \times n$ matrix $A_n$, we compute the expected value and the variance of the random variable $\| A_n x\|^2/\| A_n \|^2$, where $x$ is uniformly distributed on the unit sphere of $R^n$ or $C^n$. The result is applied to several classes of structured matrices. It is in particular shown that if $A_n$ is a Toeplitz matrix $T_n(b)$, then for large $n$ the values of $\| A_n x\|/\| A_n \|$ cluster fairly sharply around $\| b \|_2/\| b \|_\infty$ if $b$ is bounded and around zero in case $b$ is unbounded.
POTENTIAL OF FUSION OF FUZZY BASED AND NEURAL NETWORK CLASSIFIERS FOR UNSUPERVISED CLASSIFICATION

LandSat ETM +7 is still in use because of its spec tral variety of bands moreover; it is used in the production and updating small scale map s. The research reported in this paper was targeted to advance the work on unsupervised cl assification of satellite images. It is a trial to improve some existing classifiers by combi ning two of them with each other. Comparative software was developed to achieve the r es arch work objectives. The new software is called DIPRS (Digital Image Processor f or Remote Sensing) was developed using VB6 programming language. Artificial data was used to test DIPRS before using it for real remote sensing data. This research has foc used on three fuzzy set theory-based classification algorithms and four neural networks based classification algorithms. The first fuzzy set theory based clustering algorithm i s Hard C-Means Alternating Optimization (HCM-AO), which has widely been utiliz ed to perform hard clustering of remote sensing data. The other two fuzzy set theory based clustering algorithms are Fuzzy C-Means Alternating Optimization (FCM-AO) and Possibilistic C-Means Alternating Optimization (PCM-AO). The four neural network based clustering algorithms are Competitive Learning Neural Network (CLNN), Generalized CLNN (GCLNN), CLNN followed by Learning Vector Quantizat ion (CLNN-LVQ) and CLNN followed by Back Propagation Neural Network (CLBPNN ). In addition, three suggested fused algorithms, CLNN-HCM-AO, CLNN-FCM-AO and CLNN -PCM-AO were tested. These fused algorithms were merged to study the pot ntial of the fusion process in order to improve the quality of clustering process. © 2010 Faculty of Engineering, Al-Azhar University, Cairo, Egypt. All rights reserved.
A Pyramidal Layered HMM for Multiview Human Behavior Recognition in Asynchronous Video Streams

Extracted features which are obtained from a multiview video stream form a special case of a multi-sensor observation sequence. If the sensors are not synchronous, the observed features of views are not aligned together and this makes some difficulties in classification applications. A new architecture for hidden Markov model, namely pyramidal layered hidden Markov model, is proposed in this paper to handle this situation. This is accomplished by means of separate decoding in each view stream in bottom layer and then fusion of the aligned decoded symbols in top layer. Structure and algorithms of the new structure are introduced and are then used for human behaviour recognition in multiview video sequences. Considering collected information from all views of a multiview human action recognition system, one expects the recognition rate to increase and some problems like occlusion to be rectified. Several experiments have been performed in this paper. The experimental results show high performance, about 93.8% in average, in multiview human behavior recognition, as well as accuracy improvement compared to similar methods. The results are also compared with other contributions on three different multiview behavior datasets. General Terms Video Processing, Classification, Recognition, Hidden Markov Models
Reduced Serum Butyrylcholinesterase Activity Indicates Severe Systemic Inflammation in Critically Ill Patients

Systemic inflammation is an immune response to a nonspecific insult of either infectious or noninfectious origin and remains a challenge in the intensive care units with high mortality rate. Cholinergic neurotransmission plays an important role in the regulation of the immune response during inflammation. We hypothesized that the activity of butyrylcholinesterase (BChE) might serve as a marker to identify and prognose systemic inflammation. By using a point-of-care-testing (POCT) approach we measured BChE activity in patients with severe systemic inflammation and healthy volunteers. We observed a decreased BChE activity in patients with systemic inflammation, as compared to that of healthy individuals. Furthermore, BChE activity showed an inverse correlation with the severity of the disease. Although hepatic function has previously been found essential for BChE production, we show here that the reduced BChE activity associated with systemic inflammation occurs independently of and is thus not caused by any deficit in liver function in these patients. A POCT approach, used to assess butyrylcholinesterase activity, might further improve the therapy of the critically ill patients by minimizing time delays between the clinical assessment and treatment of the inflammatory process. Hence, assessing butyrylcholinesterase activity might help in early detection of inflammation.
Elevated levels of salivary lactoferrin, a marker for chronic periodontitis?

BACKGROUND AND OBJECTIVE Whole saliva is a complex mixture of fluids essential for the well-being of the oral hard and soft tissues. Saliva contains numerous antimicrobial proteins that help protect the oral ecosystem from infectious agents. Chronic periodontitis is an infectious chronic inflammatory condition that affects the tooth-supporting structures and leads to their destruction. The aim of the present study was to investigate differences in concentrations of salivary lactoferrin in subjects with and without periodontal disease and correlate these values with clinical variables associated with periodontal disease. MATERIAL AND METHODS Stimulated whole saliva was collected from 17 subjects with chronic periodontitis and 17 periodontally healthy control subjects. Data relating to bleeding on probing, probing pocket depth and horizontal bone loss were registered. Concentrations of lactoferrin, lysozyme and IgA in stimulated whole saliva were quantified using ELISA. RESULTS Subjects with chronic periodontits showed higher concentrations of lactoferrin in stimulated whole saliva compared with periodontally healthy control subjects (p < 0.05). Salivary concentrations of lactoferrin were positively correlated with bleeding on probing (p < 0.001) and the number of sites with probing pocket depth ≥ 6 mm (p < 0.001). CONCLUSION Lactoferrin is raised in stimulated whole saliva in subjects with chronic periodontitis and is correlated with probing pocket depth ≥ 6 mm.
The Relationship between Personality Traits, Perfectionism and Job Burnout: The Case of Iranian High-School Teachers

The present study aimed to investigate the relationship between personality traits and job burnout among high school teachers. To this end, 205 high school teachers were randomly selected through multi-stage sampling in Kashan, Iran. The data collection instruments included NEO Five-Factor Inventory – Form S (NEO-FFI-S), Maslach Burnout Inventory (MBI), and Hill Perfectionism Questionnaire. Pearson Correlation and Stepwise Multiple Regression were run in SPSS-22 to analyze the data. The results indicated that job burnout has a direct relationship with neuroticism and an inverse relationship with extroversion, agreeableness, openness, and conscientiousness. In addition, there is a statistically significant relationship between negative perfectionism and job burnout. Also, neuroticism, as the best predictor, as well as conscientiousness, negative perfectionism, and agreeableness could explain 27% of job burnout variance among teachers. Therefore, these traits could predict job burnout and, in turn, be good indices for job satisfaction. However, those with higher negative perfectionism are prone to experience more job burnout because of self-skepticism and self-criticism. Hence, more attention should be paid to the roles of these trai ts in teachers’ job burnout.
Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types

New methods for species distribution models (SDMs) utilise presence–absence (PA) data to correct the sampling bias of presence‐only (PO) data in a spatial point process setting. These have been shown to improve species estimates when both datasets are large and dense. However, is a PA dataset that is smaller and patchier than hitherto examined able to do the same? Furthermore, when both datasets are relatively small, is there enough information contained within them to produce a useful estimate of species’ distributions? These attributes are common in many applications. A stochastic simulation was conducted to assess the ability of a pooled data SDM to estimate the distribution of species from increasingly sparser and patchier datasets. The simulated datasets were varied by changing the number of presence–absence sample locations, the degree of patchiness of these locations, the number of PO observations, and the level of sampling bias within the PO observations. The performance of the pooled data SDM was compared to a PA SDM and a PO SDM to assess the strengths and limitations of each SDM. The pooled data SDM successfully removed the sampling bias from the PO observations even when the presence–absence data were sparse and patchy, and the PO observations formed the majority of the data. The pooled data SDM was, in general, more accurate and more precise than either the PA SDM or the PO SDM. All SDMs were more precise for the species responses than they were for the covariate coefficients. The emerging SDM methodology that pools PO and PA data will facilitate more certainty around species’ distribution estimates, which in turn will allow more relevant and concise management and policy decisions to be enacted. This work shows that it is possible to achieve this result even in relatively data‐poor regions.
Influence of Mindfulness Practices on Feelings of Place Illusion in Virtual Reality

This study investigates how mindfulness influences feelings of place illusion in Virtual Reality (VR) experiences, i.e. the feeling of being inside the mediated world that is displayed through the VR technology. To research the effects, a design called Mindsition was proposed that consists of two Virtual Environments (VEs). The VEs transfer the user from the physical world to a task environment in VR, and alters the user’s state of mind. In the first VE, a guided meditation exercise was introduced to bring the user to a more mindful state, changing how the mediated world was perceived. The user was then brought to the task environment to complete a task. The design was evaluated using a betweensubjects experimental design in which half of the participants were exposed to the entire experience, while the other half only experienced the task environment. Results are inconclusive, but revealed tentative evidence that Mindsition does increase feelings of place illusion, as participants felt more captivated by the environment and had a stronger overall feeling of ’being inside’ the VE. However, the results also show that Mindsition compromises reality judgement, i.e. how veritable the environment felt, as participants were more aware that the virtual world co-existed with the physical world. Overall, the study suggests that mindfulness has the potential to make users more observant about various aspects of the VE, and place less attention on the fact that the environment is perceived through a screen, making memories about the VR experience more vivid. Implications of these findings are discussed in relation to place illusion as well as mindfulness, and directions are given for future research.