Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-functional requirements: from elicitation to modelling languages

Although Non-Functional Requirements (NFRs) have been present in many software development methods, they have been presented as a second or even third class type of requirement, frequently hidden inside notes and therefore, frequently neglected or forgotten. Surprisingly, despite the fact that non-functional requirements arc among the most expensive and difficult to deal with there are still few works that focus on NFRs as first class requirements. Although these Works have brought a contribution on how to represent and deal with NFRs, two aspects remain not sufficiently explored: how to elicit NFRs and how to merge these NFRs with conceptual models. Our work aims at filling this gap, proposing a strategy to elicit NFRs and to integrate them into conceptual models We focus our attention on conceptual models expressed using UML, and therefore, we propose extensions to UML such that NFRs can be expressed. More precisely, we will show how to integrate NFRs to the Class, Sequence and Collaboration Diagrams. We will also show how Use Cases and Scenarios can be adapted to deal with NFRs. This work was validated by three case studies and their results suggest that by using our proposal we can improve the quality of UML models.
Human Pose Estimation using Active Shape Models

Human pose estimation can be executed using Active Shape Models. The existing techniques for applying to human-body research using Active Shape Models, such as human detection, primarily take the form of silhouette of human body. This technique is not able to estimate accurately for human pose to concern two arms and legs, as the silhouette of human body represents the shape as out of round. To solve this problem, we applied the human body model as stick-figure, skeleton . The skeleton model of human body can give consideration to various shapes of human pose. To obtain effective estimation result, we applied background subtraction and deformed matching algorithm of primary Active Shape Models in the fitting process. The images which were used to make the model were 600 human bodies, and the model has 17 landmark points which indicate body junction and key features of human pose. The maximum iteration for the fitting process was 30 times and the execution time was less than . 03 sec.
Organizational Interoperability in Open Multiagent Systems-An Approach based on Metamodels and Ontologies

In open MultiAgent Systems (MAS), interoperability is a key issue. The idea of open MAS is extremely important since it forms an adequate conceptual basis to model inherently distributed and heterogeneous scenarios. In this paper we argue that in open MAS built around the concept of organization (OC-MAS), beyond the communication and domain models, the organization modelused by the agents can also be the source of interoperability problems. To deal with organizational interoperability issues we put forward an approach combiningmetamodelsand transformationsfrom Model Driven Engineering andontologiesfrom the Ontology Development.
Is quality of life affected by season and weather conditions in ankylosing spondylitis?

OBJECTIVE To examine the links between the quality of life (QOL) and season and weather conditions in ankylosing spondylitis (AS) patients. METHODS A cohort of 146 AS patients (67% males), with a mean age of 47.3 years and a mean disease duration of 12.8 years, answered a self-administered questionnaire, the AS-AIMS2 validated for AS, 4 times over a period of one year in a postal survey. Clinical measures were assessed in a subsample of the cohort. Meterological data including daily temperature, hygrometry, wind speed and atmospheric pressure were collected over the same period. RESULTS In this cohort, 106 patients completed the follow-up. A higher lumbar spine flexibility (Schober index) was associated with a higher climatic temperature and lower wind speed. Physical QOL improved in the summer, as did Social Interaction in the summer and fall, while Role QOL decreased in winter. A lower perceived QOL was significantly associated with a higher temperature and wind speed over the past 4 weeks, and with a higher hygrometry and atmospheric pressure over the past 2 days prior to completion of the questionnaire. CONCLUSION This study provides some support to the popular belief and frequent patient complaints of a link between health status and perceived quality of life, as assessed by a specific questionnaire, with season and weather conditions.
Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks

Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention, marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process. This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns. Our investigation shows the learning ability of both techniques to discriminate user
Hybrid SVM/HMM architectures for statistical model-based voice activity detection

The decision function of support vector machine (SVM) using the likelihood ratios (LRs) is successfully used for statistical model-based voice activity detection (VAD). It is known to incorporate an optimised nonlinear decision over two different classes, instead of comparing the geometric mean of the LRs for the individual frequency bands with a given threshold for speech detection. However, the inter-frame correlation of the voice activity is not taken into consideration. In this paper, we explore a hybrid SVM/hidden Markov model (HMM) approach for the VAD, which retains discriminative and nonlinear properties of SVM, while modeling the interframe correlation powerfully through a first-order HMM. Experimental results show the significant improvement of the performance of the proposed VAD in comparison with the SVM-based VAD.
Predicting metabolic syndrome using decision tree and support vector machine methods

BACKGROUND Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. CONCLUSION The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According to this study, in cases where only the final result of the decision is regarded significant, SVM method can be used with acceptable accuracy in decision making medical issues. This method has not been implemented in the previous research.
Detecting candidate named entities in search queries

The information extraction task of Named Entities Recognition (NER) has been recently applied to search engine queries, in order to better understand their semantics. Here we concentrate on the task prior to the classification of the named entities (NEs) into a set of categories, which is the problem of detecting candidate NEs via the subtask of query segmentation.We present a novel method for detecting candidate NEs using grammar annotation and query segmentation with the aid of top-n snippets from search engine results and a web n-gram model, to accurately identify NE boundaries. The proposed method addresses the problem of accurately setting boundaries of NEs and the detection of multiple NEs in queries.
Norovirus Proteinase-Polymerase and Polymerase Are Both Active Forms of RNA-Dependent RNA Polymerase

ABSTRACT In vitro mapping studies of the MD145 norovirus (Caliciviridae) ORF1 polyprotein identified two stable cleavage products containing the viral RNA-dependent RNA polymerase (RdRp) domains: ProPol (a precursor comprised of both the proteinase and polymerase) and Pol (the mature polymerase). The goal of this study was to identify the active form (or forms) of the norovirus polymerase. The recombinant ProPol (expressed as Pro−Pol with an inactivated proteinase domain to prevent autocleavage) and recombinant Pol were purified after synthesis in bacteria and shown to be active RdRp enzymes. In addition, the mutant His-E1189A-ProPol protein (with active proteinase but with the natural ProPol cleavage site blocked) was active as an RdRp, confirming that the norovirus ProPol precursor could possess two enzymatic activities simultaneously. The effects of several UTP analogs on the RdRp activity of the norovirus and feline calicivirus Pro−Pol enzymes were compared and found to be similar. Our data suggest that the norovirus ProPol is a bifunctional enzyme during virus replication. The availability of this recombinant ProPol enzyme might prove useful in the development of antiviral drugs for control of the noroviruses associated with acute gastroenteritis.
An Online Automated Scoring System for Java Programming Assignments

 Abstract—This paper proposes a web-based system that automatically scores programming assignments for students. The system receives a Java application program submitted by a student and returns the test result immediately. The test consists of compiler check, JUnit test, and result test. The result test is very useful for students in elementary programming courses, because a typical program is composed of only a main method that reads/writes data from/to the standard input/output devices. The system is used experimentally in an actual course of our university. We confirmed that the accuracy of scoring is very high and the response time of the system is satisfactory.
The Greedy Procedure for Resource Allocation Problems: Necessary and Sufficient Conditions for Optimality

In many resource allocation problems, the objective is to allocate discrete resource units to a set of activities so as to maximize a concave objective function subject to upper bounds on the total amounts allotted to certain groups of activities. If the constraints determine a polymatroid and the objective is linear, it is well known that the greedy procedure results in an optimal solution. In this paper we extend this result to objectives that are "weakly concave," a property generalizing separable concavity. We exhibit large classes of models for which the set of feasible solutions is a polymatroid and for which efficient implementations of the greedy procedure can be given.
Identification of a Novel Dehydroergosterol Enhancing Microglial Anti-Inflammatory Activity in a Dairy Product Fermented with Penicillium candidum

Despite the ever-increasing number of dementia patients worldwide, fundamental therapeutic approaches to treat this disease remain to be established. Preventive approaches such as diet, exercise and learning attract attention. Several epidemiological studies suggest that ingestion of fermented dairy products prevents cognitive decline in the elderly. These reports indicate that specific ingredients in the fermented dairy products elicit an anti-inflammatory or anti-oxidative activity that facilitates neuroprotection. The responsible components remain to be investigated. A number of studies have shown that inflammation caused by microglia is closely related to exaggeration of the pathology and cognitive decline seen in the elderly. Many researchers have proposed that controlling microglial activities could be effective in preventing and possibly curing dementia. In the present study, to elucidate specific compounds that regulate microglial activity from dairy products, repeated purification by HPLC, combined with evaluation using primary microglia, facilitated the identification of dehydroergosterol (DHE) as a novel component of the extract that enhances microglial anti-inflammatory activity. DHE contains three conjugated double bonds in a steroid ring system and is an analogue of ergosterol. Despite their related chemical structures, the anti-inflammatory activity of DHE is markedly stronger than that of ergosterol. P. candidum for camembert cheese produces DHE, but P. Roqueforti for blue cheese and Aspergillus do not. DHE also induces CD11b-positive microglia cells into CD206-positive M2 type microglia. Neurotoxicity and neuronal cell death induced by excessively activated microglia is suppressed by treatment with DHE. Thus, this is the first report to demonstrate that DHE, identified as a responsible compound in dairy products, can induce microglia into a preferable phenotype for our brain environment and can be safely introduced into the body by consumption of dairy products. We believe the uptake of DHE might help to prevent the onset of dementia.
The micro-structure of Shangjuxu acupoint (ST37) by X-ray phase-contrast CT imaging based on synchrotron radiation

In this paper, the micro-structure of the Shangjuxu acupuncture point or tissue where there were no acupoints in the fractional rabbit hind limb were studied by in-line phase contrast CT imaging (IL-XPCI-CT) methods based on synchrotron radiation. Then the imaging results are reconstructed in three-dimensional limit, and the 3D images for different tissues are compared. The experiments were performed at X-ray imaging and biomedical application beam-line (BL13W1) of Shanghai Synchrotron Radiation Facility (SSRF). The organization of microvascular alignment can be visualized easily in the projection. The microvascular become rich and have a tendency to be concentrated on the point area. In the slices, there is the phenomenon of more micro reuniting. Using three-dimensional reconstruction, the distribution of microvascular can be seen throughout acupuncture area, there are both the trend along strike meridians and the intermittent aggregation. The density of micro-vessels was calculated for tissues with acupoints or without acupoints. Our results showed that there was one significantly higher density of the micro-vessels, where Shangjuxu acupoint was located. In the acupoint, there was a large number of involutedly microvascular structures, which was segmental. The width of each segment is not equivalent, there is a tendency that the intermediate segment is relatively wide, both sides of the acupuncture are relatively narrow which width is a Gaussian distribution. The FWHM values of Gaussian distribution curve can be determined from the scope of the Shangjuxu acupoints, which are basically consistent with the acupuncture point size in the traditional Chinese medicine theory. Nevertheless, in non-acupoint area, the microvascular structure was relatively simple and flat.
A Conservative Approach to the Management of a Dental Trauma for Immediate Natural Esthetics

Introduction: The fracture of front teeth is one of the routine presentations of traumatic injuries. The treatment of a fractured tooth involving the pulp includes root canal therapy and post placement followed by core build-up or by the extraction of the fractured tooth if it is not restorable. Case Presentation: We report a case of an adult male who had traumatized both his maxillary central incisors following a blow experienced during domestic violence. He had lost a fractured fragment of the right central incisor, while the left incisor had complicated fractures with fragments retained attached to the soft tissue. Following radiovisiography (RVG), both incisors were conservatively treated in a single visit by reattachment and post and core techniques. Conclusions: The treatment reported for reattachment of the tooth fractures and post and core techniques are reasonably easy while providing immediate and lasting results in patients’ regaining of social confidence and functionality.
A Revised Protocol for the Adaptive Harvest Management of Eastern Mallards June 14 , 2002

The set of population models used for the adaptive harvest management (AHM) of eastern mallards has been reviewed and revised. The revised set of six models: (1) rely solely on federal and state waterfowl surveys rather than the Breeding Bird Survey to index breeding-population size; (2) allow for the possibility of a positive bias in estimates of survival and reproductive rates; and (3) incorporate competing hypotheses of strongly and weakly density-dependent reproduction. A retrospective updating of model weights since 1995 suggests that the bias-corrected models were better predictors than the uncorrected models by a ratio of 3:1, and that the models assuming strongly density-dependent reproduction outperformed those assuming weakly density-dependent reproduction by a ratio of 2:1. Based on the revised models and most recently available model weights (i.e., those from 2001), eastern mallards appear to have considerable potential to tolerate sport harvest without adverse impact. However, we believe caution is warranted in promulgating hunting regulations in the Atlantic Flyway based solely on the status of eastern mallards because of potential adverse impacts to duck species with lower harvest potential.
The Case for High Level Programming Models for Reconfigurable Computers

In this paper we first outline and discuss the issues of currently accepted computational models for hybrid CPU/FPGA systems. Then, we discuss the need for researchers to develop new high-level programming models, and not just focus on extensions to programming languages, for enabling accessibility and portability of standard high level applications across the CPU/FPGA boundary. We then present hthreads, a unifying programming model for specifying application threads running within a hybrid CPU/FPGA system. Threads are specified from a single pthreads (POSIX threads) multithreaded application program and compiled to run on the CPU or synthesized to run on the FPGA. The hthreads system, in general, is unique within the reconfigurable computing community as it abstracts the CPU/FPGA components into a unified custom threaded multiprocessor architecture platform. A hardware thread interface (HWTI) component has been developed that provides an abstract, platform-independent compilation target. Thus, the HWTI enables the use of standard thread communication and synchronization operations across the software/hardware boundary.
On the Development of Discrete Calculus Methods

A general methodology for the solution of Partial Differential Equations (PDEs) is described in which the discretization of the calculus is exact and all approximation occurs as an interpolation problem on the material constitutive equations. The fact that the calculus is exact gives these methods the ability to capture the physics of PDE systems well. For simplicity, the construction of a cell based method applied to the unsteady heat equation on an unstructured mesh is used to illustrate the Discrete Calculus approach, although the approach can be used to construct node-, faceor edge-based methods applied to any PDE system. Numerical experiments are included to reveal the accuracy and time performance of the method.
Loopy Belief Propagation as a Basis for Communication in Sensor Networks

Sensor networks are an exciting new kind of computer system. Consisting of a large number of tiny, cheap computational devices physically distributed in an environment, they gather and process data about the environment in real time. One of the central questions in sensor networks is what to do with the data, i.e. how to reason with it and how to communicate it. This paper argues that the lessons of the UAI community, in particular that one should produce and communicate beliefs rather than raw sensor values, are highly relevant to sensor networks. We contend that loopy belief propagation is particularly well suited to communicating beliefs in sensor networks, due to its compact implementation and distributed nature. We investigate the ability of loopy belief propagation to function under the stressful conditions likely to prevail in sensor networks. Our experiments show that it performs well and degrades gracefully. It converges to appropriate beliefs even in highly asynchronous settings where some nodes communicate far less frequently than others; it continues to function if some nodes fail to participate in the propagation process; and it can track changes in the environment that occur while beliefs are propagating. As a result, we believe that sensor networks present an important application opportunity for UAI.