Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Correlates of Probable Traumatic Brain Injury among Intimate Partner Violence Survivors

ABSTRACT Although intimate partner violence (IPV) related (TBI) is increasingly recognized as an important area of concern, there is no existing research that seeks to identify correlates of IPV related TBI. Given the profound consequences of TBI, it is important to identify individual and social-ecological conditions that are associated with probable TBI among persons with a history of physical violence in their intimate partnerships. In this paper, we are concerned specifically with physical abuse that occurs in violent intimate relationships. The sample included 130 participants who were formerly abused by an intimate partner. This study was part of a larger research project that utilized a web-based survey to learn about the socio-ecological conditions surrounding IPV-related TBI. The HELPS screening tool was employed to calculate risk for TBI. Approximately half of the survey respondents (n= 64) were determined to have likely experienced IPV-related TBI. This finding is consistent with existing literature showing that TBIs are highly prevalent among individuals who have experienced IPV. Logistic regression analysis was used to explore the correlates of IPV-related TBI. The results show that punishment for the abuser, time since the relationship ended, and seeking help for the health consequences of IPV were significantly associated with probable TBI. Results, limitations, future directions and implications are discussed.
Fast Dynamic Pointer Following via Link-Cut Trees

In this paper, we study the problem of fast dynamic pointer following: given a directed graph $G$ where each vertex has outdegree $1$, efficiently support the operations of i) changing the outgoing edge of any vertex, and ii) find the vertex $k$ vertices `after' a given vertex. We exhibit a solution to this problem based on link-cut trees that requires $O(\lg n)$ time per operation, and prove that this is optimal in the cell-probe complexity model.
SVM for sketch recognition: Which hyperparameter interval to try?

Hyperparameters are among the most crucial factors that effect the performance of machine learning algorithms. Since there is not a common ground on which hyperparameter combinations give the highest performance in terms of prediction accuracy, hyperparameter search needs to be conducted each time a model is to be trained. In this work, we analyzed how similar hyperparemeters perform on various datasets from sketch recognition domain. Results have shown that hyperparameter search space can be reduced to a subspace despite differences in dataset characteristics.
Large scale visual-based event matching

Organizing media according to real-life events is attracting interest in the multimedia community. Event-centric indexing approaches are very promising for discovering more complex relationships between data. In this paper we introduce a new visual-based method for retrieving events in photo collections, typically in the context of User Generated Contents. Given a query event record, represented by a set of photos, our method aims to retrieve other records of the same event, typically generated by distinct users. Similarly to what is done in state-of-the-art object retrieval systems, we propose a two-stage strategy combining an efficient visual indexing model with a spatiotemporal verification re-ranking stage to improve query performance. For efficiency and scalability concerns, we implemented the proposed method according to the MapReduce programming model using Multi-Probe Locality Sensitive Hashing. Experiments were conducted on LastFM-Flickr dataset for distinct scenarios, including event retrieval, automatic annotation and tags suggestion. As one result, our method is able to suggest the correct event tag over 5 suggestions with a 72% success rate.
On the challenges of business modeling in large-scale reengineering projects

Modeling existing and future business processes is crucial to the outcome of large-scale reengineering projects. The use of parameterized or standard components does not render business models redundant, but shifts the modeling focus even more from technical aspects to the real-world business processes. In current reengineering projects, however, there is often a lack of coordination of modeling activities and the consistency of conceptual models across project activities can be threatened. There are often several models of the same phenomenon that are used by different people, in different phases, or for different purposes. This paper discusses some of the challenges of business modeling and presents a three-tier model description that explains some of the model variants found in reengineering projects. The findings from a large-scale SAP project in Europe underline the importance of a balanced business model and show how individually tailored conceptual models may hamper the formation of a common understanding of the domain and badly affect the reengineering of the business processes.
A database and challenge for acoustic scene classification and event detection

An increasing number of researchers work in computational auditory scene analysis (CASA). However, a set of tasks, each with a well-defined evaluation framework and commonly used datasets do not yet exist. Thus, it is difficult for results and algorithms to be compared fairly, which hinders research on the field. In this paper we will introduce a newly-launched public evaluation challenge dealing with two closely related tasks of the field: acoustic scene classification and event detection. We give an overview of the tasks involved; describe the processes of creating the dataset; and define the evaluation metrics. Finally, illustrations on results for both tasks using baseline methods applied on this dataset are presented, accompanied by open-source code.
Rapid traffic information dissemination using named data

This paper applies the Named Data Networking (NDN) concept to vehicle-to-vehicle (V2V) communications. Specifically, we develop a simple traffic information dissemination application based on the data naming design from our previous work and evaluate its performance through simulations. Our simulation results show that data names can greatly facilitate the forwarding process for Interest and data packets. With adequate vehicle density, data can propagate over long distances robustly at tens of kilometers per second, and a requester can retrieve the desired traffic information 10km away in a matter of seconds.
A Practical Guide to Select Quality Indicators for Assessing Pareto-Based Search Algorithms in Search-Based Software Engineering

Many software engineering problems are multi-objective in nature, which has been largely recognized by the Search-based Software Engineering (SBSE) community. In this regard, Pareto- based search algorithms, e.g., Non-dominated Sorting Genetic Algorithm II, have already shown good performance for solving multi-objective optimization problems. These algorithms produce Pareto fronts, where each Pareto front consists of a set of non- dominated solutions. Eventually, a user selects one or more of the solutions from a Pareto front for their specific problems. A key challenge of applying Pareto-based search algorithms is to select appropriate quality indicators, e.g., hypervolume, to assess the quality of Pareto fronts. Based on the results of an extended literature review, we found that the current literature and practice in SBSE lacks a practical guide for selecting quality indicators despite a large number of published SBSE works. In this direction, the paper presents a practical guide for the SBSE community to select quality indicators for assessing Pareto-based search algorithms in different software engineering contexts. The practical guide is derived from the following complementary theoretical and empirical methods: 1) key theoretical foundations of quality indicators; 2) evidence from an extended literature review; and 3) evidence collected from an extensive experiment that was conducted to evaluate eight quality indicators from four different categories with six Pareto-based search algorithms using three real industrial problems from two diverse domains.
Analytical approach to evaluate fixed point accuracy for an iteration of decision operators

The use of fixed point arithmetic for digital signal processing applications has several benefits in terms of cost, energy consumption and time to market. The existing analytical approaches for evaluating fixed-point systems performance are not able to evaluate the error caused by quantization noise in the case of special configuration of un-smooth operators such as decision operators and especially when the quantization noise propagates through an iteration of decision operators. This problem is familiar in the case of equalization algorithms such as decision feedback equalization (DFE). In this paper, we propose an analytical model allowing the evaluation of decision error probability in the case of decision operator iteration. This model calculates the decision error probability taking into account the propagation of quantization noise from one decision operator to another based on the Newton-Raphson algorithm for resolution of non linear system. We have tested the approach for the DFE algorithm in the cases of BPSK, QPSK and 16-QAM constellations.
Examining indistinguishability-based security models for key exchange protocols: the case of CK, CK-HMQV, and eCK

Many recent key exchange (KE) protocols have been proven secure in the CK, CK-HMQV, or eCK security models. The exact relation between these security models, and hence the relation between the security guarantees provided by the protocols, is unclear. We show first that the CK, CK-HMQV, and eCK security models are formally incomparable. Second, we show that these models are also practically incomparable, by providing for each model attacks on protocols from the literature that are not considered by the other models. Third, our analysis enables us to find previously unreported flaws in protocol security proofs from the literature. We identify the causes of these flaws and show how they can be avoided.
StaRMAP---A Second Order Staggered Grid Method for Spherical Harmonics Moment Equations of Radiative Transfer

We present a simple method to solve spherical harmonics moment systems, such as the the time-dependent PN and SPN equations, of radiative transfer. The method, which works for arbitrary moment order N, makes use of the specific coupling between the moments in the PN equations. This coupling naturally induces staggered grids in space and time, which in turn give rise to a canonical, second-order accurate finite difference scheme. While the scheme does not possess TVD or realizability limiters, its simplicity allows for a very efficient implementation in Matlab. We present several test cases, some of which demonstrate that the code solves problems with ten million degrees of freedom in space, angle, and time within a few seconds. The code for the numerical scheme, called StaRMAP (Staggered grid Radiation Moment Approximation), along with files for all presented test cases, can be downloaded so that all results can be reproduced by the reader.
SmartGuide: Towards Single-image Building Localization with Smartphone

We introduce SmartGuide, a light-weighted and efficient approach to localize and recognize a distant unknown building. Our approach relies on shooting only a single photo of a target building via a smartphone and a local 2D Google map. SmartGuide first extracts a partial top view contour of a building from its side-view photo by applying vanishing point and the Manhattan World Assumption, and then fetches a candidate building set from a local 2D Google map based on smartphone's GPS readings. Partial top view shape, orientation and distance relative to the camera are used as input parameters in a probability model, which adversely recognizes the best candidate building in the local map. Our model is developed based on kernel density estimation that helps reduce noise in the smartphone sensors, such as GPS readings and camera ray direction reported by noisy accelerometer and compass. Experimental results demonstrate that our approach recognizes buildings ranging from 20m to 520m and achieves 92.7% accuracy in downtown areas where the Manhattan World Assumption is applicable. In addition, the processing time is no more than 6 seconds for 87% of cases. Compared with existing building localization schemes, SmartGuide offers numerous advantages. Our method avoids taking multiple photos, intricate 3D reconstruction or any initial deployment cost of database construction, making it faster and less labor-intensive than existing solutions.
Superlinear Speedup of Parallel Calculation of Finite Number Ising Spins Partition Function

The high-performance parallel algorithm for rigorous calculation of partition function of lattice system with finite number Ising spins was developed. The parallel calculations run by C++ code with using of Message Passing Interface. The superlinear speedup was obtained for given executing code. The reasons of experimentally observed Amdahl's law violation are considered.
The role of mdr2 P‐glycoprotein in hepatobiliary lipid transport

The small apical (canalicular) domains of hepatocytes form a luminal meshwork of tubules between adjacent hepatocytes and are the sites of primary bile formation. Organic compounds are transported across this membrane domain against high concentration gradients. It has been recognized in recent years that the hepatocyte is harnessed with a set of canalicular ATP‐dependent transport proteins, specialized in this uphill transport. Bile salts, organic anions, cations, and neutral amphipaths are all pumped into the bile via such primary active transporters. Functionally, these transporters resemble ABC transporters overex‐pressed in cells with the multidrug resistance phe‐notype. Indeed, those transporters that have been characterized at the molecular level turn out to be new, or already recognized, members of this family. Phospholipid secretion across the canalicular membrane of the mouse is also mediated by a mem‐ber of this family, mdr2 P‐glycoprotein. This was demonstrated by the absence of phospholipid secre‐tion into bile of mice with a disrupted mdr2 gene and by subsequent demonstration of phospholipid translocation in cells that overexpress this protein. The recognition of mdr2 P‐glycoprotein as a phospholipid flippase sheds new light on the function of P‐glycoproteins and is an important step in understanding the mechanism of biliary lipid secretion. — Oude Elferink, R. P. J., Tytgat, G. N. J., and Groen, A. K. The role of mdr2 P‐glycoprotein in hepatobiliary lipid transport. FASEB J. 11, 19‐28 (1997)
A reference architecture for providing tools as a service to support global software development

Global Software Development (GSD) teams encounter challenges that are associated with distribution of software development activities across multiple geographic regions. The limited support for performing collaborative development and engineering activities and lack of sufficient support for maintaining and resolving dependencies and traceability across heterogeneous tools are major challenges for GSD teams. The lack of insufficient support for cross platform tools integration also makes it hard to address the stated challenges using existing paradigms that are based upon desktop and web-based solutions. The restricted ability of the organizations to have desired alignment of tools with software engineering and development processes results in administrative and managerial overhead that incur increased development cost and poor product quality. Moreover, stakeholders involved in the projects have specific constraints regarding availability and deployments of the tools. The artifacts and data produced or consumed by the tools need to be governed according to the constraints and corresponding quality of service (QoS) parameters. In this paper, we present the research agenda to leverage cloud-computing paradigm for addressing above-mentioned issues by providing a framework to select appropriate tools as well as associated services and reference architecture of the cloud-enabled middleware platform that allows on demand provisioning of software engineering Tools as a Service (TaaS) with focus on integration of tools.
Genome Sequence of Lactobacillus curieae CCTCC M 2011381T, a Novel Producer of Gamma-aminobutyric Acid

ABSTRACT Lactobacillus curieae CCTCC M 2011381T is a novel species of the genus Lactobacillus and a gamma-aminobutyric acid producer that was isolated from stinky tofu brine. Here, we present a 2.19-Mb assembly of its genome, which may provide further insights into the molecular mechanisms underlying its beneficial properties.
Spatial Data Clustering based on Self Organizing Map

Recently, research area in neural network based spatial analysis have been receiving increasing attention in the last few years. There are number of reasons and the strongest appeal of Artificial Neural Network (ANN) is the suitability for machine learning in computational adaptivity. Machine learning in computational neural network consist of adjusting connection weights to improve the performance of a network[1]. This paper presents the introduction of SOM using the spatial data clustering. The aim of this paper is to improve Artificial Neural Network(ANN) in spatial analysis and stimulate SOM in spatial analysis which are methodologically and technically sound, rather than ad hoc.
Critical Appraisal of Reporting Quality of Case-Control Articles Indexed in SID Website Based on STROBE Statement

The STROBE (strengthening the reporting of observational studies in epidemiology) statement presents recommendations to improve the reporting of observational studies. This study aimed to evaluate the quality of reporting the case-control articles indexed in the Scientific Information Database (SID). This descriptive study was conducted in 2016 in the all case-control articles indexed in the SID website over a 10year period (2006 to 2015). The researchers visited the SID website and used the keywords of "case" and "control" in titles or keywords of the articles. Then, the STROBE checklist was completed for each article. Data were analyzed using descriptive statistics. The first item of the checklist was observed in 100% of the studied articles. None of these articles had described "duration of exposure" and "how missing data were addressed. " None of these articles had used the flowchart. The quality of reporting in studied articles was acceptable, but some of the items in the STROBE checklist need more attention. © 2018 Tehran University of Medical Sciences. All rights reserved. Acta Med Iran 2018;56(12):777-783.
A putative 6‐transmembrane nitrate transporter OsNRT1.1b plays a key role in rice under low nitrogen

Abstract OsNRT1.1a is a low‐affinity nitrate (NO3 −) transporter gene. In this study, another mRNA splicing product, OsNRT1.1b, putatively encoding a protein with six transmembrane domains, was identified based on the rice genomic database and bioinformatics analysis. OsNRT1.1a/OsNRT1.1b expression in Xenopus oocytes showed OsNRT1.1a‐expressing oocytes accumulated 15N levels to about half as compared to OsNRT1.1b‐expressing oocytes. The electrophysiological recording of OsNRT1.1b‐expressing oocytes treated with 0.25 mM NO3 − confirmed 15N accumulation data. More functional assays were performed to examine the function of OsNRT1.1b in rice. The expression of both OsNRT1.1a and OsNRT1.1b was abundant in roots and downregulated by nitrogen (N) deficiency. The shoot biomass of transgenic rice plants with OsNRT1.1a or OsNRT1.1b overexpression increased under various N supplies under hydroponic conditions compared to wild‐type (WT). The OsNRT1.1a overexpression lines showed increased plant N accumulation compared to the WT in 1.25 mM NH4NO3 and 2.5 mM NO3 – or NH4 + treatments, but not in 0.125 mM NH4NO3. However, OsNRT1.1b overexpression lines increased total N accumulation in all N treatments, including 0.125 mM NH4NO3, suggesting that under low N condition, OsNRT1.1b would accumulate more N in plants and improve rice growth, but also that OsNRT1.1a had no such function in rice plants.
Using a Trust Model in Decision Making for Supply Chain Management

One of the critical factors for a successful cooperative relationship in a supply chain partnership is trust. Many real-world applications, such as Supply Chain Management (SCM), can be modeled using multi-agent systems. One shortcoming of current SCM models is that their trust models are ad hoc and do not have a strong theoretical basis. As a result, they are unable to model subtleties in agent behavior that can be used to build a more accurate trust model. We propose a trust model for SCM that is grounded in probabilistic game theory. In this model, trust can be gained through direct interactions and/or by asking for information from other trustworthy agents. We will use this model to simulate and study supply chain market behavior.
Prioritization of Functional Test Suites Using Closed Dependency Structures

2,3 Abstract: Test cases organize the whole testing process. If the test cases are prepared with the requirements of a particular system then it helps in testing whether the requirements are fulfilled or not. A defect is an error in coding or logic that causes a program to malfunction or to produce incorrect/unexpected results. Increasing the rate of fault detection can provide earlier feedback to system developers, improving fault fixing activity and ultimately software delivery. The system uses the knowledge based and model based prioritization to prioritize the test case. So the efficiency of the test case is increased and the running time for the test cases is decreased. When using coarse grained technique the fault is identified easily. Due to the earlier feedback to system developers which makes the software delivery earlier.
Multiclass Approaches for Support Vector Machine Based Land Cover Classification

SVMs were initially developed to perform binary classification; though, applications of binary classification are very limited. Most of the practical applications involve multiclass classification, especially in remote sensing land cover classification. A number of methods have been proposed to implement SVMs to produce multiclass classification. A number of methods to generate multiclass SVMs from binary SVMs have been proposed by researchers and is still a continuing research topic. This paper compares the performance of six multi-class approaches to solve classification problem with remote sensing data in term of classification accuracy and computational cost. One vs. one, one vs. rest, Directed Acyclic Graph (DAG), and Error Corrected Output Coding (ECOC) based multiclass approaches creates many binary classifiers and combines their results to determine the class label of a test pixel. Another catogery of multi class approach modify the binary class objective function and allows simultaneous computation of multiclass classification by solving a single optimisation problem. Results from this study conclude the usefulness of One vs. One multi class approach in term of accuracy and computational cost over other multi class approaches.