Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks

The rapid development of modern communication technologies—in particular, (mobile) phone communications—has largely facilitated human social interactions and information exchange. However, the emergence of telemarketing frauds can significantly dissipate individual fortune and social wealth, resulting in a potential slow down or damage to economics. In this work, we propose to spot telemarketing frauds, with an emphasis on unveiling the “<italic>precise fraud</italic>” phenomenon and the strategies that are used by fraudsters to precisely select targets. To study this problem, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. Through our study, we find that user's information might have been seriously leaked, and fraudsters have a preference over the target user's age and activity in mobile network. We further propose a novel semi-supervised learning framework to distinguish fraudsters from non-fraudsters. Experimental results on a real-world data show that our approach outperforms several state-of-the-art algorithms in accuracy of detecting fraudsters (e.g., <inline-formula><tex-math notation="LaTeX">$+0.278$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>+</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>278</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="xu-ieq1-2924431.gif"/></alternatives></inline-formula> in terms of F1 on average). We believe that our study can potentially inform policymaking for government and mobile service providers.
The potential for student performance prediction in small cohorts with minimal available attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student? s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those ? at risk? of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially ? at risk.
Naive Bayes vs Logistic Regression: Theory, Implementation and Experimental Validation

This article presents the theoretical derivation as well as practical steps for implementing Naive Bayes (NB) and Logistic Regression (LR) classifiers. A generative learning under Gaussian Naive Bayes assumption and two discriminative learning techniques based on gradient ascent and Newton-Raphson methods are described to estimate the parameters of LR. Some limitation of learning techniques and implementation issues are discussed as well. A set of experiments are performed for both the classifiers under different learning circumstances and their performances are compared. From the experiments, it is observed that LR learning with gradient ascent technique outperforms general NB classifier. However, under Gaussian Naive Bayes assumption, both classifiers NB and LR perform similar.
Moving target detection algorithm in 2D frequency domain

Usually,3D spatio-temporal filter bank is designed in 3D frequency domain to detect moving target,but the design method of the filter is difficult because of too more parameters and design difficulty.A novel moving target detection algorithm in 2D frequency domain is proposed.By decomposing the dynamic images into row and column sequences,the 3D frequency domain is translated into two groups of 2D frequency domain,and the filters design becomes easier.Then an adaptive filter is designed to extract the motion energy and pick out the frequency components of static background and noises,and the moving targets can be detected effectively.Simulation results show the robustness against image noises and matching errors.
Large Eddy Simulation on the Effects of Pressure on Syngas/Air Turbulent Nonpremixed Jet Flames

ABSTRACT The influence of increasing pressure on nonpremixed syngas/air turbulent jet flames is numerically investigated using large eddy simulations in conjunction with a steady laminar flamelet approach. The applicability of the steady flamelet approach is assessed through an extensive parametric study of laminar counterflow flames and tangential stretching rate analysis on target flame structures at different pressures. Two sets of large eddy simulations, exploring pressure values up to atm, are carried out. The first one (series A) is characterized by a constant jet Reynolds number, while the second one (series B) is characterized by a constant jet inlet velocity. Both campaigns show narrower flame brushes and reduced radical concentrations with increasing pressure. While for series A the flame length is not sensitive to pressure, a longer flame brush is noticed for series B, being mainly caused by the increased mass flow rate. The sensitivity of the local flame behavior to pressure, such as the OH layer thickness and position, is compared to the available experimental results, showing similar trends with a satisfactory agreement.
Electrocoagulation coupled adsorption for anaerobic wastewater post-treatment and reuse purposes

Water reuse is an indispensable reality in the search for an equilibrium between water resources and water scarcity. The recovery and production of reclaimed water with a guarantee of safety of use requires advanced treatment technologies. This study proposes a conjugated system, composed of electrocoagulation followed by adsorption, for the post-treatment of UASB effluent for reuse purposes. For this purpose, initially the electrocoagulation process (EC) was optimized using response surface methodology. The optimum conditions were for a current density of 29.44 A/m2 and pH 6.0, with 44.58% of COD removal and energy consumption of 31.1 kWh per kg of COD removed. The EC effluent was treated by adsorption on a fixed bed column of activated carbon. The conjugated system provided the complete removal of COD and BOD, and significant removals of turbidity (96.5%), phosphorus (97.5%) and total and thermotolerant coliforms. The quality of the final treated water reached parameters for reuse according to EPA.
What Static Analysis Can Utmost Offer for Android Malware Detection

Malicious applications are widespread for Android despite the taken serious actions by the operating system. Static and dynamic analysis techniques are utilized to detect malware by identifying the signatures of malicious applications by inspecting both the resources and behaviors of malware, respectively. In this study, what static analysis can utmost offer to detect malware in Android ecosystem is discussed and experimented on commonly used datasets in the literature by proposing a novel Android malware detection approach based on static analysis techniques. Some novel static analysis features which are proved to be effective in terms of detecting malware in Android ecosystem and are underestimated by the related work in the literature are introduced by proving their effectiveness in this study. The experimental result shows that the proposed Android malware detection approach is very effective in terms of detecting Android malware. Each feature used by the proposed approach is evaluated by using different types of machine learning techniques in order to highlight its impact on detecting malware and inform the digital investigators. The accuracy of the proposed static analysis approach is calculated as high as 0.987 for 10,865 applications.
Evaluating environmental impacts of photovoltaic technologies using Data Envelopment Analysis

This study contributes to the literature by proposing a new method of complex evaluation of multiple life cycle environmental impacts of different PV technologies based on the Data Envelopment Analysis (DEA). The main advantage of DEA as a non-parametric technique is that it does not require prior knowledge of underlying production functions. An empirical production technology frontier is estimated based on best-practice boundary of the input-output relationship. DEA evaluates comparative or relative efficiency, which means the measurement with reference to some set of units we are comparing with each other. The proposed approach allows to aggregate disparate quantitative estimates of individual negative environmental effects from the literature and special databases in a transparent and easily understandable index or coefficient of ecology efficiency. The evaluation of environmental effects is performed on data from the EcoInvent Database. The results of this study clearly show that from an environmental point of view it is more practical to prefer technologies, which are less resource and energy intensive in manufacturing and upstream activities. As of right now, this requirement is met by thin-film technologies (amorphous silicon, cadmium telluride, and copper-indium-diselenide); however, their ecologic efficiency evaluation may change as we obtain more data on the final stages of the lifecycle for PV modules of various types. Based on results of this study a number of opportunities for improving existing government incentives and rationalizing the design of state programs under elaboration can be identified.
Symmetric Image Encryption Scheme Based on Chaotic System

This paper presents a new image encryption scheme based on composite chaotic system. An external key and two logistic systems are employed for encryption, which shuffle positions and substitute grey values of pixels of image. In the process of grey value substituting, it adopts sub key induced by external key to take samples. Through adding information of shuffle position matrix into the process of grey value substituting, processes of shuffle positions and substitute grey values are mixed in a simple and effective way, which improves coupling property of the encryption system.
Image Schemas and Design for Intuitive Use

‚Intuitive Benutzung’ wird in dieser Arbeit als die Benutzung eines Produktes definiert, die in unterschiedlichem Maße durch die unbewusste Anwendung von Vorwissen charakterisiert ist und zu einer effektiven und zufriedenstellenden Interaktion bei minimalem Verbrauch kognitiver Ressourcen führt. Als neues Gestaltungsmittel mit hohem Potential werden Image Schemata vorgeschlagen und die Nützlichkeit von Image Schema Theorie für die Gestaltung intuitiver Benutzung wird untersucht.
Application de la factorisation non-négative des matrices (NMF) pour le débruitage des signaux phonocardiographiques

Les signaux phonocardiographiques (PCG) sont d'interet pour l'analyse du fonctionnement mecanique du coeur. Cependant, ils sont difficilement exploitables directement, du aux interferences et aux bruits ambiants. Dans cet article, la factorisation non-negative de matrices (NMF) est consideree pour la decomposition du signal PCG bruite. Nous proposons alors d'identifier les composantes associees au signal utile grâce a la multimodalite en exploitant le signal ECG synchrone. Cette approche est appliquee a une base de donnees etablie a partir de signaux PCG reels, artificiellement bruites, utilisee pendant la campagne d'evaluation SiSEC 2016. Les performances sont superieures a ce qui a ete obtenu avec la decomposition modale empirique proposee par les participants et sont comparables pour la selection multimodale consideree et celle effectuee manuellement par un expert. Abstract-The phonocardiographic signals (PCG) are of interest for the analysis of the cardiac mechanical function. However, they are not always directly exploitable because of ambient interference. In this paper, non-negative matrix factorization (NMF) is considered for the decomposition of the noisy PCG signal. We also propose to identify the components associated to the signal thanks to multimodality by using the synchronous ECG signal. This approach is applied to a database consisting of real and artificially noisy PCG signals, used during the SiSEC 2016 evaluation campaign. Performance is superior to what was obtained with empirical modal decomposition proposed by the participants and is comparable for the multimodal selection considered and the manual selection by an expert.
Thylakoid membrane architecture in cyanobacteria

Oxygenic photosynthesis takes place at a specialized membrane system called the thylakoids. The evolutionary origin of thylakoid membranes present in algae and plants are the ancestors of present-day cyanobacteria. In the cyanobacterium Synechocystis sp. PCC 6803, the thylakoid membranes are sheets oriented parallel to the plasma membrane, toward which the thylakoids occasionally converge. These regions are therefore termed convergence zones. The biogenesis of the thylakoid-embedded photosystem II (PSII) complex starts at the convergence zones, which are hence also named biogenesis centers. The PSII biogenesis is a complex stepwise mechanism, which is highly ordered and relies on several assembly factors. These are required to place the 20 protein subunits, many cofactors such as pigments or iron-sulfur clusters, and metal ions in the correct manner. Further research is needed for a comprehensive understanding of the architecture and function of the biogenesis centers, which continue to be controversially debated. This study focusses on two assembly factors, which have been investigated and characterized: the tetratricopeptide repeat protein Slr0151, and CurT, the cyanobacterial homologue of the grana-shaping CURVATURE THYLAKOID1 protein family from Arabidopsis thaliana. The Slr0151 protein plays a role during the attachment of the inner core antenna proteins to the PSII reaction center assembly intermediate. Furthermore, in the slr0151- mutant, the thylakoid lumina are increased, which implies an additional structural function. The CurT protein possesses a thylakoid shaping function as well. In the curT- knockout, the biogenesis centers are completely absent, which results in a less efficient PSII assembly and thus a reduction in PSII accumulation, activity, and repair. CFP-tagged CurT forms a network-like pattern at the cell periphery in regions of reduced chlorophyll autofluorescence, which resemble the biogenesis centers. Moreover, the architecture of the convergence zones is analyzed with high resolution cryo-electron tomography. It was found that converging thylakoids are interconnected in those regions and form a convergence membrane, which is oriented perpendicularly to the thylakoid sheets. Occasionally, in some areas the convergence membrane is in very close proximity to the plasma membrane, separated only by a ~2.5 nm gap. This contact area was termed the thylapse as it is reminiscent of a synapse. The resolution of the recorded tomograms enabled structural studies of large macromolecular complexes, i.e. the phycobilisomes and ribosomes. Noticeably, the convergence membrane and the thylapse were devoid of phycobilisomes but ribosomes were present, which substantiates the hypothesis that convergence membranes are indeed biogenic regions. The data presented in this thesis characterize proteins involved in defining the thylakoid architecture and show comprehensively, for the first time, the ultrastructure of the convergence zones.
Normal‐Based Bas‐Relief Modelling via Near‐Lighting Photometric Stereo

We present a near‐lighting photometric stereo (NL‐PS) system to produce digital bas‐reliefs from a physical object (set) directly. Unlike both the 2D image and 3D model‐based modelling methods that require complicated interactions and transformations, the technique using NL‐PS is easy to use with cost‐effective hardware, providing users with a trade‐off between abstract and representation when creating bas‐reliefs. Our algorithm consists of two steps: normal map acquisition and constrained 3D reconstruction. First, we introduce a lighting model, named the quasi‐point lighting model (QPLM), and provide a two‐step calibration solution in our NL‐PS system to generate a dense normal map. Second, we filter the normal map into a detail layer and a structure layer, and formulate detail‐ or structure‐preserving bas‐relief modelling as a constrained surface reconstruction problem of solving a sparse linear system. The main contribution is a WYSIWYG (i.e. what you see is what you get) way of building new solvers that produces multi‐style bas‐reliefs with their geometric structures and/or details preserved. The performance of our approach is experimentally validated via comparisons with the state‐of‐the‐art methods.
Automatic Personality Identification Using Students’ Online Learning Behavior

Adaptive e-learning can be used to personalize learning environment for students to meet their individual demands. Individual differences depend on the students’ personality traits. Numerous studies have indicated that understanding the role of personality in the learning process can facilitate learning. Hence, personality identification in e-learning is a critical issue in education. In this study, we propose the enhanced extended nearest neighbor (EENN) algorithm to automatically identify two of the Big Five personality traits from students’ behavior in online learning: openness to experience and extraversion. The performance of the proposed method is evaluated using a fivefold cross-validation approach on data from 662 senior high school students. The experimental results indicate that the EENN method can automatically recognize personality at an average accuracy of 0.758. The optimized method that combines EENN with particle swarm optimization significantly improves the identification, resulting in an average accuracy of 0.976. The results can benefit students by increasing the accuracy of personalization based on their personality traits, while simultaneously allowing them to be better understood and possibly allowing their instructors to provide more appropriate learning interventions.
Study on Variation of Polar Metabolites in Control and Water Stressed Gossypium hirsutum L. Using NMR Spectroscopy

In the present study, the variation in polar metabolites in Gossypium hirsutum L. during water stress was observed by nuclear magnetic resonance spectroscopy (NMR) techniques. Discriminating metabolites were elucidated by 1H NMR measurements and 2D-NMR (COSY and HSQC) techniques. Total 46 polar metabolites were detected in control and water stressed G. hirsutum leaf and stem. Nine metabolites in the leaf while thirteen metabolites in the stem were quantified. Highly significant quantitative variations were noticed, among the metabolites, i.e., glucose, glycinebetaine (GB), alanine in leaf and glycinebetaine (GB), scyloinositol in stem. This change in metabolite profile during water stress in G. hirsutum represents the change in metabolomic flux in different pathways.
The influence of tin ions on growth and enzymatic activity of entomopathogenic fungi

In this in vitro study, the influence of tin ions at concentrations of 1–1,000 ppm on the development and enzymatic activity of four entomopathogenic fungi ( Beauveria bassiana , B. brongniartii , Isaria fumosorosea , and Metarhizium robertsii ), that are commonly used in biological plant protection, are examined. Each of the fungal species tested reacted differently to contact with the Sn 2+ ions at the tested concentrations. Exposure to Sn 2+ ions affected the rate of development, morphology, and enzymatic activity of fungi. Of the four fungal species studied, M. robertsii was the most resistant and showed complete growth inhibition at the highest Sn 2+ concentration tested (1,000 ppm). For the other entomopathogenic fungi, the fungicidal effect of Sn 2+ ions was noted at the concentration of 750 ppm. Exposure to Sn 2+ ions (up to 500 ppm) resulted in enhanced biochemical activity; and all entomopathogens that were tested showed increased production of N -acetyl-β-glucosaminidase (NAG) as well as several proteases. Moreover, B. brongniartii and M. roberstii showed increased lipases synthesis. These changes may increase the pathogenicity of the fungi, thereby making them more effective in limiting the population of pest insects. The exposure of the entomopathogenic fungi to a medium containing Sn 2+ ions, at concentrations that were appropriate for each species, induced hyperproduction of hydrolases, which might be involved in aiding the survival of entomopathogenic fungi in the presence of heavy metals. This study shows that the fungistatic effect of Sn 2+ on entomopathogenic fungi did not restrict their pathogenicity, as evidenced by the stimulation of the production of enzymes that are involved in the infection of insects.
A polymer scaffold with drug-sustained release and antibacterial activity

Abstract Total alkaloids from Semen Strychnine (TASS), as an antibacterial drug, was loaded into polyetheretherketone (PEEK)/polyglycolide acid (PGA) scaffolds fabricated via 3 D printing technology. It focused on studying microstructure, drug-release behavior, antibacterial activity and biocompatibility of scaffolds. Results indicated that TASS had a good physiochemical compatibility with polymer matrix, and a sustained release for longtime in TASS-PEEK/PGA scaffolds. Moreover, the scaffolds manifested antimicrobial activity against E. coli and S. aureus in antibacterial tests and superb biocompatibility in vitro cell culture. These suggested that TASS-PEEK/PGA scaffolds were able to efficiently sustain TASS delivery and were a promising implant for treating bacterial infection. Graphical Abstract
An Optimization for Differential Power Analysis Based on Time Series Verification

As an effective method of side-channel analysis, DPA (short for differential power analysis) uses statistical analysis to obtain the key by analyzing the correlation between power consumption data and encryption key. If the power consumption data collected from the target contains a large amount of interference noise, it will affect the result of DPA. Even if preprocessing methods are used to process the noise, the result of DPA still has error. This paper proposes a differential power analysis method based on time series verification for the AES algorithm in smart card devices, which extracts intermediate values from the four steps of the single round operation in the AES algorithm, and uses the power model for differential power analysis to obtain the corresponding key, and then respectively calculates the correlation coefficients of the other three steps under each key in the order of time series, and finally obtains the optimal solution key by comprehensive judgment. Experiments show that the result of this method is significantly better than traditional DPA. Keywords—DPA; time series; AES algorithm; round transformation