Miracle-dz/UniFAR-FTUs huggingface dataset:
Auction-based serious game for bug tracking

Today, one of the challenges in software engineering is utilising application lifecycle management (ALM) tools effectively in software development. In particular, it is hard for software developers to engage with the work items that are appointed to themselves in these ALM tools. In this study, the authors have focused on bug tracking in ALM where one of the most important metrics is mean time to resolution that is the average time to fix a reported bug. To improve this metric, they developed a serious game application based on an auction-based reward mechanism. The ultimate aim of this approach is to create an incentive structure for software practitioners to find and resolved bugs that are auctioned where participants are encouraged to solve and test more bugs in less time and improve quality of software development in a competitive environment. They conduct hypothesis tests by performing a Monte Carlo simulation. The preliminary results of this research support the idea that using a gamification approach for an issue tracking system enhances the productivity and decreases mean time to resolution.
Enhancement of the Achievable throughput in Multi-Taper MIMO Spectrum Sensing using Cooperative Hard Decision Fusion Rules

— Cognitive radio is known as a smart technology due to its ability to adjust the operating parameters, according to the given conditions and environment. In this paper, a cooperative MT-MIMO (Multi-Taper Multiple Input Multiple Output) spectrum sensing system is proposed in order to enhance the sensing performance and the achievable throughput using different number of antennas. The obtained results of MT-MIMO are compared to the obtained results of Periodogram MIMO (PED-MIMO) under two scenarios. The first scenario is considered for single user detection while the second one is considered for multiple user detection. The proposed system should counteract the problem of hidden primary user nodes.
Design and Analysis of 1x2 Circular Patch Antenna Array for 2.4 GHz Applications

A 1x2 Circular micro strip patch antenna array operating at 2.4GHz using Ansys based HFSS simulator software is designed. It is useful for different WLAN (wireless local area network) applications, Bluetooth and IoT communications. The circular patch antenna is constructed on a FR4 substrate with dielectric constant = 4.4 and height of the substrate is 1.6mm and is fed using edge feeding technique. Several antenna characteristics such as return loss, radiation pattern, bandwidth, directivity, antenna gain, radiation efficiency etc. are studied.
Lexical Normalization of User-Generated Medical Text

In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature. The extraction of this knowledge is complicated by colloquial language use and misspellings. Yet, lexical normalization of such data has not been addressed properly. This paper presents an unsupervised, data-driven spelling correction module for medical social media. Our method outperforms state-of-the-art spelling correction and can detect mistakes with an F0.5 of 0.888. Additionally, we present a novel corpus for spelling mistake detection and correction on a medical patient forum.
Gravel Image Auto-Segmentation Based on an Improved Normalized Cuts Algorithm

The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Precise segmentation method plays a very important role in it. In this study, a digital image method using an improved normalized cuts algorithm is proposed for auto-segmentation of gravel image. It added grain-size estimation, and used the feature vector based on color. It has made great improvements in many respects, especially in accuracy of edge segmentation and automation. Compared with manual measurement methods and other image processing methods, the method studied in this paper is an efficient method for precisely segmenting gravel images.
Automatic Multi-Label Color Image Annotation for Natural Images

Digital images are available at ever growing rate. However, to find a required image for an ordinary user is a challenging task. In content based image retrieval , images are retrieved based on the image features. There exist a semantic gap between the image features and image semantics understood by the humans. To overcome semantic gap , automatic image annotation is a solution. Automatic Image annotation is to automatically learn semantic concept model from large number of image samples and use the model to label new images. Once the test images are annotated with the semantic labels, images can be retrieved by keywords which is similar to text document retrieval. In this paper , a system is proposed for annotating image of multiple regions. Images are initially segmented into multiple regions , image region color and texture features are extracted. A model is generated based on the image features using supervised classifiers. Model is also tested with sample images and the performance classifiers are analyzed. Keywords— Fast K-Means Clustering Segmentation, Color and Texture Features, Supervised Classifier, Multi-Label Image Annotation.
Encapsulation of independent transmissions on the internal interface of a distributed radio base station

A distributed radio base station (20) comprises a radio equipment controller (REC) (22) situated at a main site (23) and a radio equipment (RE) (24) situated at a remote site (25). A remote unit (102, 104, 106, 108, 110, 124) configured to engage in direct communications with the radio equipment controller (REC) is also situated at the remote site (25). An internal interface (26) connects the radio equipment controller (REC) and the radio equipment (RE). Advantageously, the internal interface (26) also encapsulates the direct communications between the radio equipment controller (REC) (22) and the remote unit, thereby obviating a separate physical link between the radio equipment controller (REC) and the remote unit. A new physical link (130) transmits, between the radio equipment (RE) and the remote unit, the direct communications between the radio equipment controller (REC) and the remote unit which are encapsulated over the internal interface (26). The remote unit can take various differing forms, including that of an antenna (102) with remote electrical tilt control; a tower mounted amplifier (TMA) (104); a Transmission network unit (106); a separate radio base station (108) which is co-located at the remote site; a proprietary equipment unit (110); or even one or more cascaded radio equipments (RE) (124).
Two-Tier NOMA-Based Wireless Powered Communication Networks

In this article, we investigate two-tier nonorthogonal multiple access (NOMA)-based wireless powered communication networks. The considered network consists of a hybrid access point and two groups of users, namely near and far users. A near user and a far user are selected to form a NOMA pair, which harvest energy during the downlink phase and transmit information during the uplink phase. Specifically, we consider two cases of energy-transfer time and analyze the corresponding performance. We first analyze the performance of the case with fixed energy-transfer time and derive the exact expression for the ergodic rate. Due to the doubly near-far effect, the far user experiences poor performance. To address this issue, an adaptive energy-transfer time scheme is designed by maximizing the far user’s data rate, and the optimal solution is derived. To reduce the system complexity, a suboptimal solution is proposed, and the ergodic rate's exact expression is derived. Simulation results reveal that the suboptimal solution achieves near-optimal performance and has a lower computational complexity. Moreover, a one-tier NOMA network is set to be a benchmark, and numerical results show that the two-tier system outperforms the one-tier system significantly under the appropriate radius setting.
A signature of glucose metabolism and DNA methylation improves prognostic prediction for urinary bladder cancer

Background: Glucose metabolism and DNA methylation have been previously proved to play important roles in cancers. However, few studies have explored the potential connections between abnormal glucose metabolism and DNA methylation in tumor progression. In this study, we aimed to screen out glucose metabolism-related genes that were DNA methylation associated in order to establish a promising signature for the prognosis of bladder cancer (BLCA). Methods: With transcriptome data of BLCA samples from The Cancer Genome Atlas (TCGA) and methylation data downloaded from TCGA data base 450K microarray, glucose metabolism-related genes that were simultaneously methylation modulated and survival associated were obtained and a prognostic signature was established. Gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA) were performed for identification of gene and pathway enrichment. Two most methylation-related risky genes were selected to conduct functional validations. Results: 18 target genes were identified and the samples were survival-significantly divided into two groups by using the signature. Via multivariate Cox analysis, we considered the signature an independent prognostic factor. Several biological processes and signaling pathways were enriched in the high-risk group according to GSEA results and three modules of genes were identified by WGCNA. We demonstrated that UCHL1 and PYCR1 promoted proliferation, migration and invasion ability of bladder cancer cells. Conclusions: The signature based on the 18 target genes could serve as an independent prognostic factor for urinary bladder cancer patients.
Using protein-protein interactions data to improve predictions of the effect of aging-related genes on the longevity of model organisms

This work focuses on improving the predictive performance on the task of clas- sifying the e↵ect of aging-related genes on the longevity of model organisms. Usually, datasets for this problem are built using gene ontology features. We show that, by intro- ducing a new set of features based on protein-protein interactions, we can improve the classification in a statistically significant way. Although beneficial, the introduction of these features brings a new problem for this task: performing classification based on un- certain information. This happens because the values of the new features only represent how certain we can be that an interaction exists between two proteins. To cope with this problem, we propose a novel similarity coe cient, based on the Jaccard index, and show that, by using it, we can benefit from the introduced uncertain data without adding too much complexity to the classification solution. Finally, we demonstrate how to achieve even better results on this task by performing a feature selection procedure.
Higher order compact schemes and their applications to problems with complex geometries

The thesis is aimed to modify an already existing higher order compact (HOC) finite difference scheme to enhance its applicability and robustness. To examine the robustness of the proposed scheme, it is applied to lid-driven cavity problems, natural and mixed convection problems including some magnetohydrodynamic effects in some complex geometries. Numerical results computed through the proposed HOC schemes are compared with analytical or established numerical results available in the literature and excellent agreements are found in all the cases. A natural convection problem in a wavy enclosure in presence of magnetic field is studied numerically using the proposed scheme. The left and right walls of the enclosure are wavy whereas the top and bottom walls are straight. The bottom wall is much hotter compared to the side walls whereas the top wall is adiabatic. The effects of the use of porous media are studied. Streamlines and isotherms are plotted with different Rayleigh number, Hartmann number, Prandtl number and Darcy number. The effects of these parameters on the heat transfer rate are also discussed. In order to explore whether a geometry like trapezoidal enclosure enhances the heat transfer rate or not, a mixed convection problem is solved using the proposed HOC scheme. The bottom wall is heated uniformly, the top wall moving with a constant velocity is adiabatic and the inclined walls are at low temperature. The parameters used to describe the flow characteristics and the heat transfer rate are Grashof number, Richardson number and Prandtl number. The influences of height (of the trapezoidal cavity with fixed bottom width) and also the width (of the bottom wall with fixed height) on the fluid flow and the heat transfer rate are discussed. A mixed convection problem in a vented cavity with bottom wavy wall is also included in this thesis work. The inlet is considered at the bottom part of the left wall, the rest part of this left wall is heated with a constant heat flux, the other walls are adiabatic whereas the outlet is at the top portion of the right vertical wall. The effects of parameters such as Rayleigh number, Reynolds number on the fluid flow and the heat transfer rate are discussed. The effects of the number of waves in the bottom wall are also studied.
Nothing Blocks Me: Precise and Real-Time LOS/NLOS Path Recognition in RFID Systems

Radio frequency identification (RFID)-based localization and activity recognition have attracted much research attention recently. They rely on accurate measurements of signal features, e.g., phase and received signal strength (RSS), in line-of-sight (LOS) condition to estimate the location or activity status of the target objects. However, the LOS requirement might be frequently breached by obstacles between reader and tags in real deployed RFID systems. The resulting non-LOS (NLOS) signal will greatly reduce localization or activity recognition accuracy. How to filter out NLOS in the localization/activity recognition process is therefore practically important for guaranteeing accuracy. In this paper, we propose the first LOS/NLOS path recognition approach to differentiate the signals by LOS path from the ones by NLOS path. The proposed approach is both precise (with precision higher than 0.95) and real-time in nature (with recognition delay less than 400 ms) due to the following innovative designs. First, we design a new metric that can precisely distinguish LOS and NLOS paths by considering the joint variance of phase and RSS. Second, we propose an efficient method to mitigate the negative impacts of phase ambiguity on recognition precision. Third, we sample over a selected subset of channels and use only a handful of readings to perform LOS/NLOS path recognition, which greatly reduces the recognition delay without sacrificing precision. We conducted extensive experiments with commercial-off-the-shelf RFID devices. The results show that our approach achieves high precision and recall in all testing cases, with a precision of up to 0.969 and a recall of up to 0.991. Furthermore, our approach can also distinguish between different types of obstacles with an accuracy as high as 0.93.
Multiobjective evolutionary feature selection and fuzzy classification of contact centre data

In this work, a data set describing phone interactions arising in a multichannel and multiskill contact centre is considered with the aim of classifying inbound sessions into those that will be eventually managed by an agent and those that, instead, will be abandoned before. More precisely, the goal of the work is to extract interpretable pieces of information that allow us to predict whether a user will or will not abandon a call, which may turn out to be very useful for the purpose of contact centre managing. To this end, the performance of two well‐known, state‐of‐the‐art evolutionary algorithms for feature selection (evolutionary nondominated radial slots based algorithm and nondominated sorted genetic algorithm) is compared for the task of feature selection, under the criteria of accuracy and cardinality of the selection, as well as for the task of fuzzy rule extraction, under the criteria of interpretability, accuracy, and hypervolume test. The best obtained fuzzy classifier, chosen after a decision making process, is validated and interpreted by domain experts.
Detection of Suspicious Users Posting Claims about Cancer on Twitter

Due to the massive success of social media, online user-generated content has increased exponentially in the last years. Twitter, as a microblogging platform, allows users to share information about their opinions or activities by means of short posts called tweets. However, opinion spammers see social networks like Twitter as an opportunity to propagate their ideas, promoting or discrediting some target product or service, without showing their true intentions. In this study, we focused on detecting suspicious users who posted dubious claims about cancer treatment and prevention on Twitter. We addressed the task with a supervised learn- ing approach, a binary classi cation problem in which we had to predict whether users were suspicious or genuine. We collected a set of 60 thousand tweets related to cancer posted in October 2017, including more than 36 thousand users. Since manual labeling could be a very complicated process, we elaborated a set of features for each user, both related to the content of her posts and her behavior on Twitter, and combined them to compute a spam score. The basic idea was that suspicious users would have different feature distributions with respect to genuine users and that would help us to separate the two classes. Then, we generated a ranking using the spam score and exploited it to assign the labels. Finally, we ran a few classi ers on our labeled data, showing that suspicious users had different textual and behavioral patterns which could be used to distinguish them from genuine ones.
An Improvement Method for Semantic Mapping Database to Ontology

Enormous amount of available data in relational database (RDB) format creates a demand for automatic transforming them into OWL ontology to reuse in the Semantic Web. Many approaches have been proposed, however, most of them simply generate output ontology as the same flat structure with the original database and result in redundancy of ontology data. As an attempt to resolve the redundant problem, we propose a novel approach to generate OWL ontology from relational database while focusing on the similarity measure of duplicate attributes in relational tables. Experimental results show that the proposed method reliably predicts semantic similarity of duplicate columns and produces a better-quality OWL ontology.
Osmium Containing Double Perovskite Ba2XOsO6 (X = Mg, Zn, Cd): Important Candidates for Half-Metallic Ferromagnetic and Spintronic Applications

In this chapter, Osmium-based double perovskites Ba 2 XOsO 6 (X = Mg, Zn, Cd) have been investigated for their magnetic structure, electronic, elastic, mechanical and thermodynamic belongings. These materials have been recently reported experimentally for their magnetic structure. Here, we report the first successful ab initio calculations on the physical properties of these materials. The structural optimization for these Ba 2 XOsO 6 (X = Mg, Zn, Cd) double perovskite compounds has been finalized within density functional theory via full potential linearized augmented plane wave (FP-LAPW) method. The structural investigation exposes the ferromagnetic phase stability of these compounds. The spin-polarized electronic and magnetic properties were calculated within generalized gradient approximation (GGA), Hubbard approximation (GGA+U) and modified Becke-Johnson approximation (mBJ). The electronic profile establishes the half-metallic nature for all the three compounds. The total spin magnetic moment was found to be an integer value of 2 μ b . The elastic constants have been calculated and used to predict mechanical stuffs like Shear modulus (G), Poisson ratio ( v ) and anisotropic factor. The calculated B/G and Cauchy pressure (C 12 -C 44 ) both characterize these materials as brittle. The thermodynamic parameters like heat capacity and Debye temperature have been predicted in the temperature range of 0 – 1000 K.
Film thickness measurement by double-wavelength infrared transmittance method

Thickness measurement for the optical thin films is very important for the industries of mechanics, printing, battery and so on. Infrared transmittance method is a very useful method to achieve the physical response, which is related to the film thickness according to the Lambert-Beer law. A detail measurement system is designed with two light paths of reflectance and transmittance, consisting of the light source, filter motor, beam splitter, paraboloid mirror, PbSe detector, and so on. An experimental setup is also established with two different infrared wavelengths by two different bandpass optical filters. The stability of the measurement system is tested to be only about 0.3% in 10 min. The experiments show the feasibility of the proposed double-wavelength infrared transmittance method for film thickness measurement.
Klasifikasi Gender Berdasarkan Suara Menggunakan Support Vector Machine

Klasifikasi gender berdasarkan suara mempengaruhi tingkat performansi dari identifikasi suara. oleh karena itu penelitian tugas akhir ini penulis membuat sistem klasifikasi gender berdasarkan suara. penelitian ini menggunakan dataset berupa rekaman suara sebanyak 2260 suara yang terbagi menjadi dua kelas yaitu "laki-laki" dan "perempuan". Untuk proses ekstrasi ciri digunakan Mel-Frequency Cepstral (MFCC) untuk menghasilkan vektor ciri dari sinyal suara. dalam proses klasifikasi digunakan metode Support Vector Machine (SVM). Hasil akurasi SVM tertinggi untuk klasifikasi gender berdasarkan suara dapat mencapai 100\% menggunakan kernel Polynomial dengan degree = 1.
Mobile anchor assisted localization and path-planning techniques in wireless sensor networks: Challenges and Solutions

Localization is one of the most significant technique in wireless sensor networks (WSNs). The location information of nodes can be provided by Global Positioning System (GPS), but the cost is huge if every node equips GPS. This problem can be solved by deploying a set of mobile anchors which can exchange information with other nodes that estimating their locations. Planning an adaptive path can reduce the cost by using smaller number of mobile anchors and estimate the location of other nodes precisely in WSNs. In recent years, many path planning algorithms have been proposed. In this paper, a comprehensive survey is presented to point out key problems and challenges of mobile anchors. We compare the differences among the algorithms. Also, we make a discussion for future research and show new challenges in this field.