Miracle-dz/UniFAR-FTUs huggingface dataset:
Arabic Script Web Document Language Identifications Using Neural Network

This paper presents experiments in identifying language of Arabic script web documents using neural network. There are some difficulties when identifying those languages in Arabic script such as Persian, Turkish, Urdu, Jawi etc. Since there is a vast amount of information presented to the internet users, it is crucial to find an appropriate method in language identification for a variety of textual information. Several current approaches rely on collective dictionary and conventional statistical approach for language identification. We analyzed the feasibility of using windows algorithm in finding the best method of selecting best features for neural network. From the experiments, we have found that the non-sliding windows neural network has performed better in accuracy than the sliding windows neural network.
Dynamic framework for building highly-localized mobile web DTN applications

Proximity-based mobile applications are increasing in popularity. Such apps engage users while in proximity of places of interest (malls, bus stops, restaurants, theatres), but remain closed or unused after the user goes away. Since the number of `places of interest' is constantly growing and can be large, it is impractical to install a large number of corresponding native applications on the phone when each app engages the user for only a small period of time. In this paper, we propose a dynamic framework for deploying highly-localized mobile web applications. Such web applications are deployed locally to users in proximity, and can be opened in the browser. Communication in the web app is performed over the Delay-Tolerant Network of mobile users, removing the need of an Internet connection. DTN protocols can be dynamically added or removed at run-time, allowing each application to use a protocol best suited to its needs. After usage, the web application is closed either manually by the user, or automatically when the user goes away from the place of interest. We have implemented the framework on Android. Our analysis of the framework show that the memory and performance overhead incurred is small. Using this framework, we have written a simple DTN web application for bus stops to help the physically challenged.
Network-Based Information Brokers

The ability to obtain relevant information in a timely and cost efficient manner is central to the effective performance of most tasks in our society. The widespread availability of computer-based information brokers will provide that ability to large communities by significantly facilitating effective access to the broad range of information that is rapidly becoming available on the Internet. The general availability of the technology to build and maintain information brokers will enable an industry to be established whose primary products are computer-based network-accessible brokering services.
Parallel Approach for Diagnosis of Breast Cancer using Neural Network Technique

Classification is perhaps the most familiar and popular data mining technique. Inspired by biological neural networks, Artificial Neural Networks are developed to mimic the characteristics such as robustness and fault tolerance. To perform classification task of medical data, the neural network is trained. To speed up the training process parallel approach is adopted. In this paper a parallel approach by using neural network technique is proposed to help in the diagnosis of breast cancer. The neural network is trained with breast cancer data base by using feed forward neural network model and backpropagation learning algorithm with momentum and variable learning rate. The performance of the network is evaluated. The experimental result shows that by applying parallel approach in neural network model yields efficient result.
DAML-QoS ontology for Web services

As more and more Web services are deployed, Web service's discovery mechanisms become essential. Similar services can have quite different QoS levels. For service selection and management purpose, it is necessary to explicitly, precisely, and unambiguously specify various constraints and QoS metrics for Web services descriptions. This paper provides a novel DAML-QoS ontology as a complement for DAML-S ontology to provide a better QoS metrics model. Three layers are defined together with clear role descriptions for developments. Cardinality constraints are utilized to describe the QoS property constraints. Basic profile is presented for general Web service's description and the speed startup of ontology definition. Matchmaking algorithm for QoS property constraints is presented and different matching degrees are described. When incorporated with DAML-S, multiple service levels can be described through attaching multiple QoS profiles to one service profile. Well-defined Metrics can be further utilized by measurement organizations to guarantee the promised service level.
Trust management system for mobile cluster-based wireless sensor network

In this paper we consider a clustering wireless sensor network which needs to be protected. The main aim of this article is development of a secure clustering wireless sensor network model with built-in detection and counteraction mechanisms against malicious impacts (attacks). We develop trust management system (TMS), based on analyzing following parameters: residual energy level, send/received/forwarding data, control, routing packets, packets integrity, availability based on beacons, changing the address of the packet. System is efficient in preventing attacks and consumes less energy than analogs.
An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization

Location-based services (LBS) are services offered through a mobile device that take into account a device’s geographical location. To provide position information for these services, location is a key process. GNSS (Global Navigation Satellite System) can provide sub-meter accuracy in open-sky areas using satellite signals. However, for indoor and dense urban environments, the accuracy deteriorates significantly because of weak signals and dense multipaths. The situation becomes worse in indoor environments where the GNSS signals are unreliable or totally blocked. To improve the accuracy of indoor positioning for location-based services, an improved WiFi/Pedestrian Dead Reckoning (PDR) integrated positioning and navigation system using an adaptive and robust filter is presented. The adaptive filter is based on scenario and motion state recognition and the robust filter is based on the Mahalanobis distance. They are combined and used in the WiFi/PDR integrated system to weaken the effect of gross errors on the dynamic and observation models. To validate their performance in the WiFi/PDR integrated system, a real indoor localization experiment is conducted. The results indicate that the adaptive filter is better able to adapt to the circumstances of the dynamic model by adjusting the covariance of the process noise and the robust Kalman filter is able to mitigate the harmful effect of gross errors from the WiFi positioning.
Comprehensive software understanding with SEXTANT

Current tools for software understanding mostly concentrate on one comprehension technique, e.g., visualization, or bottom-up navigation through software elements via hyperlinks. In this paper, we argue that to effectively assist developers in understanding today's software systems, a combination of several comprehension techniques is needed including seamless integration of top-down querying and bottom-up navigation strategies that work across different kinds of software artifacts; furthermore, application-domain and/or technology specific relationships between software elements should be taken into consideration; last but not least, a tight integration of such tools into development environments is crucial. We present SEXTANT, a software exploration tool tightly integrated into the Eclipse IDE that satisfies these requirements. In two case studies, we demonstrate how SEXTANT's features are conducive in tracking down the source of erroneous behavior, respectively, in discovering 'bad smells' in the software structure which should lead to code refactorings.
Effect of Maternal Probiotic Intervention on HPA Axis, Immunity and Gut Microbiota in a Rat Model of Irritable Bowel Syndrome

Objective To examine whether maternal probiotic intervention influences the alterations in the brain-immune-gut axis induced by neonatal maternal separation (MS) and/or restraint stress in adulthood (AS) in Wistar rats. Design Dams had free access to drinking water supplemented with Bifidobacterium animalis subsp lactis BB-12® (3×109 CFU/mL) and Propionibacterium jensenii 702 (8.0×108 CFU/mL) from 10 days before conception until postnatal day (PND) 22 (weaning day), or to control ad lib water. Offspring were subjected to MS from PND 2 to 14 or left undisturbed. From PND 83 to 85, animals underwent 30 min/day AS, or were left undisturbed as controls. On PND 24 and 86, blood samples were collected for corticosterone, ACTH and IgA measurement. Colonic contents were analysed for the composition of microflora and luminal IgA levels. Results Exposure to MS significantly increased ACTH levels and neonatal fecal counts of aerobic and anaerobic bacteria, E. coli, enterococci and clostridia, but reduced plasma IgA levels compared with non-MS animals. Animals exposed to AS exhibited significantly increased ACTH and corticosterone levels, decreased aerobic bacteria and bifidobacteria, and increased Bacteroides and E. coli counts compared to non-AS animals. MS coupled with AS induced significantly decreased anaerobes and clostridia compared with the non-stress adult controls. Maternal probiotic intervention significantly increased neonatal corticosterone levels which persisted until at least week 12 in females only, and also resulted in elevated adult ACTH levels and altered neonatal microflora comparable to that of MS. However, it improved plasma IgA responses, increased enterococci and clostridia in MS adults, increased luminal IgA levels, and restored anaerobes, bifidobacteria and E. coli to normal in adults. Conclusion Maternal probiotic intervention induced activation of neonatal stress pathways and an imbalance in gut microflora. Importantly however, it improved the immune environment of stressed animals and protected, in part, against stress-induced disturbances in adult gut microflora.
The UN local communities and Indigenous peoples' platform: A traditional ecological knowledge‐based evaluation

This review evaluates the potential of the proposed local communities and Indigenous peoples’ platform to effectively engage traditional ecological knowledge (TEK) for climate policy. Specifically, we assess the platform's potential to enable greater representation and participation of Indigenous peoples (IPs) within the United Nations Framework Convention on Climate Change (UNFCCC). An analytical framework based on the extensive TEK and environmental management literature is developed, with a set of criteria identified against which to evaluate the platform. We find that although the process of designing the platform appears to be inclusive of Indigenous views, the structure itself does not recognize the roles that unequal power relations and colonialism play in marginalizing IPs. Limited attention is paid to the institutional barriers within the UNFCCC and the drawbacks of pursuing knowledge “integration” as an end in itself. Based on this, recommendations for improving the platform structure are put forward including using a rights‐based framing, giving greater decision‐making power to IPs, and developing mechanisms to ensure the holistic integrity of TEK and build the overall resilience of climate mitigation and adaptation systems.
Performance-optimal clustering with retiming for sequential circuits

We propose an exact clustering with retiming algorithm to minimize the clock period for sequential circuits. Without moving flip-flops (FFs) by retiming, conventional clustering algorithms can only handle combinational parts and therefore cannot achieve the best cycle time. Pan et al. [1998] have proposed an optimal algorithm under the unit gate delay model. We propose a more powerful and faster algorithm that produces optimal results even under the more realistic general gate delay model. Experimental results show that our algorithm is twice as fast as Pan's.
Involvement of Hepcidin in the Anemia of Multiple Myeloma

Purpose: Hepcidin is a liver-produced peptide implicated in the anemia of inflammation. Because interleukin (IL)-6 is a potent inducer of hepcidin expression and its levels are elevated in multiple myeloma, we studied the role of hepcidin in the anemia of multiple myeloma. Experimental Design: Urinary hepcidin and serum levels of IL-6, ferritin, C-reactive protein, tumor necrosis factor-α, and IL-1β were studied in newly diagnosed myeloma patients. In vitro hepcidin induction assay was assessed by real-time PCR assay. Results: Pretreatment urinary hepcidin levels in 44 patients with stage III multiple myeloma were 3-fold greater than normal controls. In the subset of multiple myeloma patients without renal insufficiency (n = 27), a marked inverse correlation was seen between hemoglobin at diagnosis and urinary hepcidin level (P = 0.014) strongly supporting a causal relationship between up-regulated hepcidin expression and anemia. The urinary hepcidin also significantly (P < 0.05) correlated with serum ferritin and C-reactive protein, whereas its correlation with serum IL-6 levels was of borderline significance (P = 0.06). Sera from 14 multiple myeloma patients, with known elevated urinary hepcidin, significantly induced hepcidin mRNA in the Hep3B cells, whereas normal sera had no effect. For 10 patients, the ability of anti-IL-6 and anti-IL-6 receptor antibodies to prevent the serum-induced hepcidin RNA was tested. In 6 of these patients, hepcidin induction was abrogated by the anti-IL-6 antibodies, but in the other 4 patients, the neutralizing antibodies had no effect. Conclusions: These results indicate hepcidin is up-regulated in multiple myeloma patients by both IL-6-dependent and IL-6-independent mechanisms and may play a role in the anemia of multiple myeloma.
Constructing and evaluating sensor-based statistical models of human interruptibility

While people can typically make a rapid assessment of another person's interruptibility, current systems generally have no way to consider whether an interruption is appropriate. Systems therefore tend to interrupt at inappropriate times or unduly demand attention. Sensor-based statistical models of human interruptibility are one approach to addressing this problem. In a series of studies, we examine the feasibility and robustness of sensor-based statistical models of human interruptibility, creating models that perform better than human observers. We then present a tool to enable non-expert development of applications that use sensor-based statistical models of human situations. Our first study collects audio and video recordings in the normal work environments of several office workers. We measure their interruptibility by collecting interruptibility self-reports via experience sampling. We then use a Wizard of Oz method to examine the recordings and simulate many potential sensors. Building statistical models from these simulated sensors, we are able to evaluate potential sensors without actually building them. In our second study, human observers view the recordings and estimate the interruptibility of the office workers. Statistical models based on our simulated sensors perform better than these human observers. Our third study examines the robustness of this result by implementing and deploying real sensors with a more diverse set of office workers. While different sensors are more predictive for different types of office workers, even a general model performs better than the human observers. Because these first three studies are dominated by social engagement, our fourth study explicitly examines task engagement. We show that low-level programming environment events can be used to model when a programmer will choose to defer an interruption. We then develop Subtle, a tool to enable further research into how human computer interaction can best benefit from sensor-based statistical models of human situations. With an extensible sensing library, fully-automated iterative feature generation, and support for model deployment, Subtle enables non-expert development of applications that use sensor-based statistical models of human situations. Subtle allows human computer interaction researchers to focus on compelling applications and datasets, rather than the difficulties of collecting appropriate sensor data and learning statistical models. Finally, we present a summary of contributions and plans for future work.
Physical employment standards for U.K. fire and rescue service personnel.

BACKGROUND Evidence-based physical employment standards are vital for recruiting, training and maintaining the operational effectiveness of personnel in physically demanding occupations. AIMS (i) Develop criterion tests for in-service physical assessment, which simulate the role-related physical demands of UK fire and rescue service (UK FRS) personnel. (ii) Develop practical physical selection tests for FRS applicants. (iii) Evaluate the validity of the selection tests to predict criterion test performance. METHODS Stage 1: we conducted a physical demands analysis involving seven workshops and an expert panel to document the key physical tasks required of UK FRS personnel and to develop 'criterion' and 'selection' tests. Stage 2: we measured the performance of 137 trainee and 50 trained UK FRS personnel on selection, criterion and 'field' measures of aerobic power, strength and body size. Statistical models were developed to predict criterion test performance. Stage 3: matter experts derived minimum performance standards. RESULTS We developed single person simulations of the key physical tasks required of UK FRS personnel as criterion and selection tests (rural fire, domestic fire, ladder lift, ladder extension, ladder climb, pump assembly, enclosed space search). Selection tests were marginally stronger predictors of criterion test performance (r = 0.88-0.94, 95% Limits of Agreement [LoA] 7.6-14.0%) than field test scores (r = 0.84-0.94, 95% LoA 8.0-19.8%) and offered greater face and content validity and more practical implementation. CONCLUSIONS This study outlines the development of role-related, gender-free physical employment tests for the UK FRS, which conform to equal opportunities law.
Historical abiotic events or human-aided dispersal: inferring the evolutionary history of a newly discovered galaxiid fish

Range expansion of obligate freshwater fishes in the Cape Floristic Region (CFR) of South Africa has mostly been attributed to river capture events and confluence of rivers following sea-level regression. The role of low drainage divides and interbasin water transfers has received less attention. This study analyzed mitochondrial and nuclear DNA sequences to assess the processes that could have influenced the phylogeographic patterns of a newly discovered lineage of Galaxias zebratus (hereafter Galaxias zebratus “Joubertina”) that occurs across two currently isolated river systems close to the Joubertina area in the eastern CFR. Results from both analyses revealed that observed genetic differentiation cannot be explained by isolation between the two river systems. No genetic differentiation was found between the Krom River system and a population from one of the Gamtoos tributaries. Shallow genetic differentiation was found between the Krom and the other Gamtoos populations. Historical river capture events and sea-level changes do not explain the present distribution of Galaxias zebratus “Joubertina” across the Krom and Gamtoos River systems. Interbasin dispersal during pluvial periods, recent river capture, or recent human-mediated translocation seems to be the most plausible explanations.
Expansion of the Homeostasis Model Assessment of β-Cell Function and Insulin Resistance to Enable Clinical Trial Outcome Modeling Through the Interactive Adjustment of Physiology and Treatment Effects: iHOMA2

OBJECTIVE To describe and make available an interactive, 24-variable homeostasis model assessment (iHOMA2) that extends the HOMA2 model, enabling the modeling of physiology and treatment effects, to present equations of the HOMA2 and iHOMA2 models, and to exemplify iHOMA2 in two widely differing scenarios: changes in insulin sensitivity with thiazolidinediones and changes in renal threshold with sodium glucose transporter 2 (SGLT2) inhibition. RESEARCH DESIGN AND METHODS iHOMA2 enables a user of the available software to examine and modify the mathematical functions describing the organs and tissues involved in the glucose and hormonal compartments. We exemplify this with SGLT2 inhibition modeling (by changing the renal threshold parameters) using published data of renal effect, showing that the modeled effect is concordant with the effects on fasting glucose from independent data. RESULTS iHOMA2 modeling of thiazolidinediones effect suggested that changes in insulin sensitivity in the fasting state are predominantly hepatic. SGLT2 inhibition modeled by iHOMA2 resulted in a decrease in mean glucose of 1.1 mmol/L. Observed data showed a decrease in glucose of 0.9 mmol/L. There was no significant difference between the model and the independent data. Manipulation of iHOMA2's renal excretion threshold variable suggested that a decrease of 17% was required to obtain a 0.9 mmol/L decrease in mean glucose. CONCLUSIONS iHOMA2 is an extended mathematical model for the assessment of insulin resistance and β-cell function. The model can be used to evaluate therapeutic agents and predict effects on fasting glucose and insulin and on β-cell function and insulin sensitivity.
Multisensor track-to-track association for tracks with dependent errors

The problem of track-to-track association has been considered until recently in the literature only for pairwise associations. In view of the extensive recent interest in multisensor data fusion, the need to associate simultaneously multiple tracks has arisen. This is due primarily to bandwidth constraints in real systems, where it is not feasible to transmit detailed measurement information to a fusion center but, in many cases, only local tracks. As it has been known in the literature, tracks of the same target obtained from independent sensors are still dependent due to the common process noise. This paper derives the likelihood function for the track-to-track association problem from multiple sources, which forms the basis for the cost function used in a multidimensional assignment algorithm that can solve such a large scale problem where many sensors track many targets. While a recent work derived the likelihood function under the assumption that the track errors are independent, the present paper incorporates the (unavoidable) dependence of these errors.