Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Fault-Prone Software Modules using Statistical and Machine Learning Methods

Demand for producing quality software has rapidly increased during the last few years. This is leading to increase in development of machine learning methods for exploring data sets, which can be used in constructing models for predicting quality attributes such as fault proneness, maintenance effort, testing effort, productivity and reliability. This paper examines and compares logistic regression and six machine learning methods (Artificial neural network, decision tree, support vector machine, cascade correlation network, group method of data handling polynomial method, gene expression programming). These methods are explored empirically to find the effect of static code metrics on the fault proneness of software modules. We use publicly available data set AR1 to analyze and compare the regression and machine learning methods in this study. The performance of the methods is compared by computing the area under the curve using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of model predicted using decision tree modeling is 0.865 and is a better model than the model predicted using regression and other machine learning methods. The study shows that the machine learning methods are useful in constructing software quality models. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org
Relevance weighted (2D)2LDA image projection technique for face recognition application

In this paper, a novel image projection technique for face recognition application is proposed based on the Linear Discriminant Analysis (LDA) combining with relevance weighted. The projection technique is performed through 2-directional and 2-dimensional LDA or (2D)2LDA approach which simultaneously work in row and column directions to overcome the “small sample size” problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and relevance weighted is also used in the within-class scatter matrix in order to weigh the information for solving confusable data in these classes. This technique is called the Relevance Weighted 2-Dimensional and 2-Directional LDA or RW(2D)2LDA which are used for a more accurate discriminant decision than the one that is produced by the conventional LDA, or 2DLDA. The proposed technique has been successfully tested on three face databases. Experimental results indicate that the proposed RW(2D)2LDA algorithm is more computationally efficient than the conventional algorithms in view of a little features and less times. It can also improve performance, and takes maximum recognition rate more over 97%.
A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach

ective and efficient strategies to acquire manage and analyze data leads to better decision making and competitive advantage. The development of cloud computing and the big data era, brings up challenges to traditional data mining algorithms. The processing capacity, architecture and algorithms of traditional database system are not coping with big data analysis. Big Data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this paper is to propose Parallel Weighted Decision Tree Classifier to handle complex spatial landslide big data using Map Reduce programming model. The Proposed Classifier performance is validated with massive dataset. The results indicate that our classifier exhibits both time efficiency and scalability. KeywordsData, Classifier, Spatial Data, Map Reduce, Landslide.
Efficient Approximation Algorithms for Tiling and Packing Problems with Rectangles

We provide improved approximation algorithms for several rectangle tiling and packing problems (RTILE, DRTILE, and d-RPACK) studied in the literature. Most of our algorithms are highly efficient since their running times are near-linear in the sparse input size rather than in the domain size. In addition, we improve the best known approximation ratios.
Evidence for gas accretion in galactic disks

Abstract: Studies of the HI in galaxies have clearly shown that subtle details of the HI distribution and kinematics often harbour key information for understanding the structure and evolution of galaxies. Evidence for the accretion of material has grown over the past many years and clear signatures can be found in HI observations of galaxies. We have obtained new detailed and sensitive HI synthesis observations of three nearby galaxies which are suspected of capturing small amounts of HI and show that indeed accretion of small amounts of gas is taking place in these galaxies. This could be the same kind of phenomenon of material infall as observed in the stellar streams in the halo and outer parts of our galaxy and M31
Controlled Syntax for Thai Software Requirements Specification

Ambiguity is the main problem for software requirements specifications written in natural language. This study describes the problems and ambiguities encountered in Thai software requirements specifications, and proposes a simple approach to writing software requirements specifications in Thai by using controlled syntax. Our approach uses Backus Naur Form to analyze the syntax. We evaluate our approach from the questionnaires received from clients and software engineers. The experiment reports that the average accuracy from clients and software engineers is 88.89% and 86.67% respectively.
A comparison of relay selection and repetition coding for free-space optical communication

A major problem in free-space optical (FSO) communication is the atmospheric turbulence-induced fading. In this paper, we consider two fading-mitigation techniques for FSO links. We study cooperative FSO links with relay selection and repetition coding with intensity modulation and direct detection (IM/DD) over lognormal fading channels. To compare these two scheme's performance, we derive Bit-Error-Rate (BER) expressions analytically. Furthermore, we confirmed our analytical results through Monte Carlo computer simulations. Our performance analysis results show that however in low SNR values repetition coding performs better than relay selection but in high SNR values relay selection outperforms repetition coding.
Two-Dimensional Differential Demodulation for 64-DAPSK Modulated OFDM Signals

A novel two-dimensional differential demodulation (2-D DD) algorithm, which exploits both the time and frequency dimensions by using the orthogonal frequency division multiplexing (OFDM) frame structure, was investigated for 64-differential amplitude and phase shift keying (DAPSK) modulated OFDM signals. Without changing the transmitter, the proposed algorithm based on graph attempts to search for a more confident bypass, probably containing multiple DD operations, to demodulate one 64-DAPSK symbol rather than conventional direct DD. In order to carry out the optimal path searching process, the soft decision metrics of the amplitude ratio and phase difference were derived by making use of a Gaussian approximation. Simulation results show that the proposed algorithm can obtain substantial performance gains with reasonable computational complexity for uncoded AWGN and frequency selective fading channels. In addition, it is advantageous to withstand the Doppler effect, which is hence suitable for high mobile (e.g., in a car or train) broadband wireless access (BWA) systems.
Rule Pattern Parallelization of Packet Filters on Muti-core Environments

Packet filters are essential for most types of recent information network technologies. To achieve packet filters with high performance, flexibility, and cost-efficiency, the performance must be improved through multi-core processing and single instruction multiple data (SIMD) operations for software-based solutions on general-purpose CPUs. In this work, rule pattern parallelization for latency intensive filtering is investigated. Two types of rule pattern parallelization (range parallelization and modulo parallelization) are introduced and a performance model is analytically derived. Packet filter programs are implemented using range parallelization, modulo parallelization, and a hybrid of the two on two different hardware environments, i.e., the Cell and the Xeon cores. The experimental results validate the analytical model and show the baseline performance, which demonstrates the considerable potential of the rule pattern parallelization approach.
Digital compensation unit for impedance metrology working up to 2 MHz

This paper describes a design of two-channel digital compensation unit which has been developed for accuracy measurement of four-terminal-pair (4TP) impedance standards by the three-voltmeter method. The FPGA based compensation unit senses a residual voltage on the low potential port of the compared impedances continuously. The sensing process is made by means of a synchronous detection technique; a principle of the lock-in amplifiers is implemented on the FPGA for each channel. Simultaneously a voltage proportional to the residual voltage is injected into a main measuring circuit via an injection transformer. Thanks to this feedback, residual voltages at low potential port of measured impedances are reduced more than 1000 times. A typical level of residual voltages is about 5 μV. The compensation unit can be applied in the frequency range from 10 kHz up to 2 MHz. The boundary of the range are mainly limited by properties of digital phase-locked loop as well as parameters of CIC filters used as low-pass filters on the outputs of digital multipliers. The unit is controlled by a PC via optical insulated USB port.
Gene set analysis for self-contained tests: complex null and specific alternative hypotheses

MOTIVATION The analysis of differentially expressed gene sets became a routine in the analyses of gene expression data. There is a multitude of tests available, ranging from aggregation tests that summarize gene-level statistics for a gene set to true multivariate tests, accounting for intergene correlations. Most of them detect complex departures from the null hypothesis but when the null hypothesis is rejected, the specific alternative leading to the rejection is not easily identifiable. RESULTS In this article we compare the power and Type I error rates of minimum-spanning tree (MST)-based non-parametric multivariate tests with several multivariate and aggregation tests, which are frequently used for pathway analyses. In our simulation study, we demonstrate that MST-based tests have power that is for many settings comparable with the power of conventional approaches, but outperform them in specific regions of the parameter space corresponding to biologically relevant configurations. Further, we find for simulated and for gene expression data that MST-based tests discriminate well against shift and scale alternatives. As a general result, we suggest a two-step practical analysis strategy that may increase the interpretability of experimental data: first, apply the most powerful multivariate test to find the subset of pathways for which the null hypothesis is rejected and second, apply MST-based tests to these pathways to select those that support specific alternative hypotheses. CONTACT gvglazko@uams.edu or yrahmatallah@uams.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Peer assessment in a project-based engineering course: comparing between on-campus and online learning environments

Abstract As the number of participants in online distance learning courses increases, peer assessment is becoming a popular strategy for evaluating open assignments and for breaking the social isolation surrounding distance education. Yet, the quality and characteristics of peer assessment in massive online courses has received little attention. Hence, this study was set to examine peer feedback quality and grading accuracy in a project-based course. The study applied a sequential exploratory mixed methods design. It included 339 participants who studied the same engineering course, but in three different modes: on-campus (n = 77), small private online course (n = 110), and massive open online course (MOOC) (n = 152). Content analysis of feedback comments identified four categories: reinforcement, statement, verification and elaboration, arranged in an ascending scale of cognitive ability. The findings indicated that the MOOC participants provided more feedback comments and volunteered to assess more projects than their counterparts did. However, the on-campus students provided higher quality feedback and their peer grading was better correlated with the grades assigned by the teaching assistants.
Development of a coupled tendon-driven 3D multi-joint manipulator

Very long-reach snake-like robotic arms in the range of 14 meters are expected to be used in decommissioning work inside nuclear reactor containers. We developed a tendon-driven system which has the advantage of placing electronic devices protected in the arm's base part which stays out of the reactor container, and only few expensive highly radiation hardened sensors and tools are mounted in the arm tip. Generation of large joint torque is necessary to realize such a very long arm. We applied the concept of coupled tendon-driven mechanism formerly used to generate restricted two dimensional motions, and extended its concept by proposing a new joint arrangement which makes possible three dimensional motions in a large workspace. Mechanical design, compact storage method and derivation of the arm motion control are detailed. Moreover, we built a preliminary mechanical prototype called ”Mini 3D CT-Arm”, and the experimental results demonstrated the validity of the newly proposed concept.
“ye word kis lang ka hai bhai?” Testing the Limits of Word level Language Identification

Language identification is a necessary prerequisite for processing any user generated text, where the language is unknown. It becomes even more challenging when the text is code-mixed, i.e., two or more languages are used within the same text. Such data is commonly seen in social media, where further challenges might arise due to contractions and transliterations. The existing language identification systems are not designed to deal with codemixed text, and as our experiments show, perform poorly on a synthetically created code-mixed dataset for 28 languages.We propose extensions to an existing approach for word level language identification. Our technique not only outperforms the existing methods, but also makes no assumption about the language pairs mixed in the text a common requirement of the existing word level language identification systems.This study shows that word level language identification is most likely to confuse between languages which are linguistically related (e.g., Hindi and Gujarati, Czech and Slovak), for which special disambiguation techniques might be required.
An application-level framework for UAV/rover communication and coordination

The paper proposes AirGround, a flexible and expandable framework to support the collaboration and the coordination between aerial and terrestrial drones in order to accomplish a common mission in a more effective and fast way. In particular the designed application-level framework allows the dynamic tasks assignment to the different devices involved into the communication process and the distributed leader election according to specific executive parameters and system conditions (i.e., residual energy, computational power, abilities offered by specific on board sensors). The AirGround effectiveness has been evaluated throughout a real testbed to measure the overall system performance in terms of both neighbour discovery and leader election speed by increasing the number of drones in different channel and environmental conditions.
On detection of edges and interest points for omnidirectional images in spherial domain

In this study, the principle that any single-viewpoint catadioptric projection is equivalent to a spherical projection followed by a point projection is explained. Applicability of Gaussian smoothing, line detection and interest point detection using this principle and stereographic projection is investigated. Particularly, Scale Invariant Feature Transform (SIFT) is emphasized.
Mass Nouns and Plurals

Determinerless (or “bare”) mass and plural noun phrases also show a parallel alternation in interpretation, depending on the predicate with which they combine. If the predicate is stagelevel (in the terminology of Carlson 1977a,b), the noun phrase is understood as existentially quantified, as in (4)a. and b. , which are roughly equivalent to Some water leaked into the floor and Some raccoons were stealing my corn, respectively:
Consensus clustering by graph based approach

In this paper, we propose G-Cons, an extension of a graph minimal coloring paradigm for consensus clustering. Based on the co- association values between data, our approach is a graph partitioning one which yields a combined partition by maximizing an objective function given by the average mutual information between the consensus partition and all initial combined clusterings. It exhibits more important consensus clustering features (quality and computational complexity) and enables to build a combined partition by improving the stability and accuracy of clus- tering solutions. The proposed approach is evaluated against benchmark databases and promising results are obtained compared to other consensus clustering techniques.
Live Salmonella Recruits N-Ethylmaleimide–Sensitive Fusion Protein on Phagosomal Membrane and Promotes Fusion with Early Endosome

To understand intracellular trafficking modulations by live Salmonella, we investigated the characteristics of in vitro fusion between endosomes and phagosomes containing live (LSP) or dead Salmonella (DSP). We observed that fusion of both DSP and LSP were time, temperature and cytosol dependent. GTPγS and treatment of the phagosomes with Rab-GDI inhibited fusion, indicating involvement of Rab-GTPases. LSP were rich in rab5, α-SNAP, and NSF, while DSP mainly contained rab7. Fusion of endosomes with DSP was inhibited by ATP depletion, N-ethylmaleimide (NEM) treatment, and in NEM-sensitive factor (NSF)–depleted cytosol. In contrast, fusion of endosomes with LSP was not inhibited by ATP depletion or NEM treatment, and occurred in NSF-depleted cytosol. However, ATPγS inhibited both fusion events. Fusion of NEM-treated LSP with endosomes was abrogated in NSF- depleted cytosol and was restored by adding purified NSF, whereas no fusion occurred with NEM-treated DSP, indicating that NSF recruitment is dependent on continuous signals from live Salmonella. Binding of NSF with LSP required prior presence of rab5 on the phagosome. We have also shown that rab5 specifically binds with Sop E, a protein from Salmonella. Our results indicate that live Salmonella help binding of rab5 on the phagosomes, possibly activate the SNARE which leads to further recruitment of α-SNAP for subsequent binding with NSF to promote fusion of the LSP with early endosomes and inhibition of their transport to lysosomes.
Genome-Wide Association Study Identifies That the ABO Blood Group System Influences Interleukin-10 Levels and the Risk of Clinical Events in Patients with Acute Coronary Syndrome

Introduction Acute coronary syndrome (ACS) is a major cause of mortality worldwide. We have previously shown that increased interleukin-10 (IL-10) levels are associated with poor outcome in ACS patients. Method We performed a genome-wide association study in 2864 ACS patients and 408 healthy controls, to identify genetic variants associated with IL-10 levels. Then haplotype analyses of the identified loci were done and comparisons to levels of IL-10 and other known ACS related biomarkers. Results Genetic variants at the ABO blood group locus associated with IL-10 levels (top SNP: rs676457, P = 4.4 × 10−10) were identified in the ACS patients. Haplotype analysis, using SNPs tagging the four main ABO antigens (A1, A2, B and O), showed that O and A2 homozygous individuals, or O/A2 heterozygotes have much higher levels of IL-10 compared to individuals with other antigen combinations. In the ACS patients, associations between ABO antigens and von Willebrand factor (VWF, P = 9.2 × 10−13), and soluble tissue factor (sTF, P = 8.6 × 10−4) were also found. In the healthy control cohort, the associations with VWF and sTF were similar to those in ACS patients (P = 1.2 × 10−15 and P = 1.0 × 10−5 respectively), but the healthy cohort showed no association with IL-10 levels (P>0.05). In the ACS patients, the O antigen was also associated with an increased risk of cardiovascular death, all causes of death, and recurrent myocardial infarction (odds ratio [OR] = 1.24–1.29, P = 0.029–0.00067). Conclusion Our results suggest that the ABO antigens play important roles, not only for the immunological response in ACS patients, but also for the outcome of the disease.
Top-down modulation of visual processing and knowledge after 250 ms supports object constancy of category decisions

People categorize objects more slowly when visual input is highly impoverished instead of optimal. While bottom-up models may explain a decision with optimal input, perceptual hypothesis testing (PHT) theories implicate top-down processes with impoverished input. Brain mechanisms and the time course of PHT are largely unknown. This event-related potential study used a neuroimaging paradigm that implicated prefrontal cortex in top-down modulation of occipitotemporal cortex. Subjects categorized more impoverished and less impoverished real and pseudo objects. PHT theories predict larger impoverishment effects for real than pseudo objects because top-down processes modulate knowledge only for real objects, but different PHT variants predict different timing. Consistent with parietal-prefrontal PHT variants, around 250 ms, the earliest impoverished real object interaction started on an N3 complex, which reflects interactive cortical activity for object cognition. N3 impoverishment effects localized to both prefrontal and occipitotemporal cortex for real objects only. The N3 also showed knowledge effects by 230 ms that localized to occipitotemporal cortex. Later effects reflected (a) word meaning in temporal cortex during the N400, (b) internal evaluation of prior decision and memory processes and secondary higher-order memory involving anterotemporal parts of a default mode network during posterior positivity (P600), and (c) response related activity in posterior cingulate during an anterior slow wave (SW) after 700 ms. Finally, response activity in supplementary motor area during a posterior SW after 900 ms showed impoverishment effects that correlated with RTs. Convergent evidence from studies of vision, memory, and mental imagery which reflects purely top-down inputs, indicates that the N3 reflects the critical top-down processes of PHT. A hybrid multiple-state interactive, PHT and decision theory best explains the visual constancy of object cognition.