Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Product Review Spammer Detection Based on Users' Behavior

(Abstract )In order to find the review spammers, this paper proposes a user review spammer detecting method that is based on users' behavior. Starting from the purpose of review spammers, it makes spammer's five behavior patterns as the index of spammer detection. Based on the 1 470 reviewers, it gets from the JOYO Amazon website, according to a single indicator selection and 5 indicators integration selection, finally gets the 25 of most likely spammers and 25 of the most unlikely spammers, and markes artificially the 50 of suspicious reviewers, according to the artificial results trained a supervised linear regression model which is based on the 5 indicators. Experimental results show that the model of the spammer detection finds 88 review spammers in the 1 470 reviewers, and is more effective than those based on user voting usefulness of the baseline method. (Key words )user behavior; linear regression model; review spammer detection; short text; product review; review spam
mhirano at the FinSBD Task: Pointwise Prediction Based on Multi-layer Perceptron for Sentence Boundary Detection

This paper proposes a pointwise prediction for a sentence boundary detection task. The proposed pointwise prediction is combined with our original word embedding method and three-layered perceptron. It predicts whether the targeted words have the role of the beginning/end of a sentence or not by using word features around the targeted words. We tested our model by changing some parameters in our model and then ensembled these models with various parameters. Consequently, the ensembled model achieved 0.88 and 0.84 averaged f1-score by testing the data both in English and French, and it also obtained 0.84 in English and 0.86 in French as the ﬁnal results of this shared task. In addition, we developed a baseline model, that is, a rule-based prediction model, for comparison. The result shows that the proposed pointwise prediction model out-performed the rule-based prediction model in any index.
Refinement of Cartesian Abstraction Heuristics

In classical planning as heuristic search, the guiding heuristic function is typically treated as a black box. While many heuristics support refinement operations, they are typically only used for its initialization before search, but further refinement during search could make use of additional information not available in the initial state. We explore online refinement for additive Cartesian abstraction heuristics. These abstractions are computed through counter-example guided abstraction refinement, which can be applied online as well to further improve the abstractions. We introduce three operations, refinement, merging, and reordering, which are combined to a converging online-refinement algorithm. We describe how online refinement can effectively be used in A∗ and evaluate our approach on the IPC benchmarks, where it outperforms offline-generated abstractions in many domains.
VERIFICATION OF STRENGTH DURABILITY MODELS FOR GLASS FIBRE TEXTILE REINFORCED CEMENT

The brittleness of cementitious materials can be overcome by the introduction of fibres. Often glass fibres are used because of their stiffness, availability and low cost. Despite significant material optimisation over recent decades, as well for matrix as for fibres, durability is still an issue. Four strength durability models available in the literature are discussed: a linear or kinetic model, a square-root or diffusion model, a nonlinear diffusion model, and a combined kinetic-diffusion model. The parameters of these models are calibrated with a large dataset of strength results from accelerated ageing tests obtained by immersion of specimens in water at a range of temperatures for various durations. A model selection method is proposed which allows determining the best approximating model for a dataset at hand by applying a statistical method, Akaike’s Information Criterion. This criterion is weighing up the complexity of adding more model parameters against the enhanced fit to the experimental data. The effect of scatter of the experimental results on the uncertainty of the model prediction is also addressed.
Predicting Market Prices Using Deep Learning Techniques

This paper discusses the relevance of dynamically changing algorithms to predict the volatile stock market. When an investor decides to buy or sell stock, his decision is highly dependent on the rise or fall of share market price. This study uses a model-independent approach to reveal the hidden dynamics of stock market data using various deep learning techniques such as RNN, LSTM, and GRU. The data from automobile and banking sectors which are listed in NSE is used for the study. Sliding window approach is used for the data analysis and the performance is evaluated using percentage error
The Impact of Improved Maize Germplasm on the Lives of the Extreme Poor : The Case of Tuxpeño-Derived Material in Mexico 1 February 28 , 2003

This study documents how poor small-scale farmers in lowland tropical Mexico use improved maize germplasm and how their use contributes to their well being. It does this by assessing both the direct adoption of improved varieties as well as by examining the process of their “creolization. ” By exposing improved varieties to their conditions and management, continually selecting seed of these varieties for replanting, and in some cases promoting their hybridization with landraces, either by design or by accident, farmers produce what they recognize as “creolized” varieties. Our key hypothesis is that poor farmers benefit from improved germplasm through creolization. Creolization provides farmers with new options, as they deliberately modify an improved technology generated by the formal research system to suit their own circumstances and needs. Different methodologies such as participatory methods, ethnography, case studies, a household sample survey, and a collection and agronomic evaluation of maize samples were used. This study was carried out in two regions: the coast of Oaxaca and the Frailesca in the states of Oaxaca and Chiapas respectively, two of the poorest states in Mexico. These regions are contrasting—one subsistence-oriented and the other commercial, in both extreme poverty is pervasive. Maize continues to play a key role in the livelihoods of the poor in both regions. Modern varieties and particularly creolized varieties are widely planted in both regions. The results support the hypothesis. The implications of the findings are discussed.
Microarray Data Analysis

Overview This section describes a minimum setup requirement and step-by-step procedure to setup an environment for Affymetrix oligo microarray analysis. A sample experiment with input and output files is also described for basic steps in " microarray data analysis ". You can modify the procedure to fit your own analysis; however, it may require additional software packages, techniques, and programming to analyze more complex experiment which will not be described herein. We selected Bioconductor freeware and R-statistical package, AffylmGUI (Wettenhall, 2006) for several reasons: (1) Free software that has excellent support from community; (2) Ease of installation and Graphic User Interface to guide non-programmer; (3) Utilization of comprehensive and standardized microarray analysis methods.
D Visualization of Medical Image

Two techniques have been chosen to produce the 3D models. The first technique is known as Volume Rendering and the second technique is known as the Marching Cubes Algorithm. Both techniques use voxels (3D square pixels) to determine the 3D area to be constructed. The Marching Cubes (MC) algorithm by Lorensen and Cline is most popular algorithm for extraction of isosurface out of volume data. Several drawbacks of MC algorithm are solved by using new improved version of MC algorithm. We proposed an improved version of the Marching Cubes algorithm which gives a topologically correct triangular approximation of the isosurface for any cube configuration. Unlike the past work on Marching Cube algorithm, a robust triangulation strategy, complementary and rotation operations is presented. Our algorithm is adaptive to the small the changes of data or the small changes of the threshold, and obtains more reasonable result of triangulation of isosurface than those produced by standard MC algorithm. Marching Cubes Algorithm is considered a thresholding technique because it uses only the pixel information at the eight corners of the voxel
Bank-Firm Relationships and Accounting Conservatism : Evidence from Japan

Banks play a central role in corporate governance in many economies around the world. We compare the extent of conditional and unconditional conservatism between firms with and without close working relationships with their bani ' in order to gain insights into how bank-fimi relationships affiect the conservatism of financial reports. When bank-firm relationships are strong, we posit that investors will be less concemed about the timely recognition of economic losses (i.e., conditional conservatism should be weaker) because these investors can rely on the banks to monitor management However, Japanese bar、c have incentives to direct managers to report lower earnings (i.e., to be unconditionally conservative) so that managers can benefit when negotiating payouts to the other takeholders. As predicted, empirical analyses reveal that i ini' s with close bank-firm relationships recognize economic losses in a less timely manner, consistent with less conditional conservatism, and that these firms' accruals are more income-decreasin consistent with greater unconditional conservatism.
Isolation and Identification of anaerobic cultivable iron reducing bacteria from crude oil and detection of biofilms formation on carbon steel surfaces

Microbiologically influenced corrosion (MIC) that r esponsible for corrosion problems, especially in oi l and gas industry. The microorganism capable of iron reduction (IRB) that involving in MIC mechanisms, was contributing as a m in problem for locali corrosion. Shewanella species are considered to be major iron whether any of the Shewanella sp.are important in formation of biofilm on carbon ste el surfaces. Over 158 colony associated with anaerobic cultivable and high produ cing H bacterial strains were identified as Shewanella most commonly encountered, among the genera of bact eri observed. followed by Shewanella algae.34 isolates were selected as and change VMNI color to black during 1 crystal violate method, we observed that the 34 str ains were able to produce biofilm. The 34 strains strains from S. chilikensis and S. algae, respectively) strong biofilm producers, 15 (8 and 7 strains from respectively) moderate biofilm producers, and only 1 from gene sequence analysis confirmed the identification of these two major groups. This finding is importa nt to increase the info isolate, identify and perform initial characterizat on on these associated bacte
Flap Noise Prediction Method for a Powered Lift System

A method is presented for estimating the noise generated by deflec-tion of the engine exhaust for under-the-wing and over-the-wing versions of an externally blown flap configuration for powered lift. Correlation equations and curves are given for the OASPL and directivity and for spectra scaled to a high bypass 25 000-pound thrust size engine. Data are r--taken from TF34 engine tests and from large cold flow model tests. The f» correlations are empirical, and thus application of this prediction proce-i dure is limited to geometrically similar configurations. Application of the method is illustrated by calculated sample footprints.
A process tolerant analog ASK baseband for UHF RFID reader and implantable applications

An envelope generation circuit which generates the top and bottom envelope for PR-ASK, SSB-ASK and DSB-ASK with modulation depths (m) from 10% to 100% with steps of 10% for use in EPC and ISO compliant RFID reader is described. It employs a VTH independent level shifter biased with a VTH tracking circuit so that the change in VTH due to process variation does not distort m. The highest level shifter error is 7% under extreme PVT when the VTH varies by 63%. Measured results include a modulation depth accuracy of 2%, modulation steps of 10% under supply and temperature extremes.
The Swelling Responsiveness of pH-Sensitive Hydrogels in 3 D Arbitrary Shaped Geometry

The pH-sensitive hydrogels are physically responsive to a change in the pH of surrounding solution, which often resemble to biomaterials. These hydrogels can expand up to 4 times to its original dimensions under certain pH of surrounding buffer. We present the simulation of swelling characteristic of 3Darbitrary-geometry, pH-sensitive hydrogel in steady state conditions. Three nonlinear partialdifferential equations that are representing responsible physical phenomena namelychemical, electrical, and mechanical can describe the swelling responses to a chemical change in surrounding solution. Finite element analysis used for present study was carried out by full coupling of above three partial-differential equations with variable material properties in COMSOL Multiphysics®. Employing a moving mesh method for 3D geometry, the FEM simulation was performed to account for largeswelling of the pH-sensitive hydrogel.
EXTRACTION AND CLASSIFICATION OF HUMAN GAIT FEATURES FROM ACCELERATION DATA

This paper discusses the methodology for extracting and classifying a style and characteristic component from a walking motion. The walking motion is measured using four wearable motion sensors for acquiring segmented body motion. To extract the style and characteristic component, we use the singular value decomposition of the measured data and evaluate the contribution of each sensor module for gait identification by using the degree of class separation. From these results, the characteristic component of human gait features can be extracted by using singular vectors of whole data of walking motion. In addition, the singular vectors of higher order modes can be used for identifying individuals by proper choice of the modes. Furthermore, using the degree of class separation, the important body segments for gait identification can be indicated by combinations of sensors with the high degree of separation.
RUNNING HEAD: Content-dependent “liking” on social media

A number of studies have examined the motives behind the use of one-click communication features such as the “like” button. This study considered how personality might shape how users employ the “liking” button in order to establish different online impressions, particularly when online posts include both everyday (normal) or risky (controversial) content. Using an online survey, we tested whether self-monitoring, agreeableness, affinity seeking and need for gratification would influence the likelihood with which participants would “like” posts given the transparent nature of social networks. Results based on an online sample (N=217) revealed that “liking” of normal posts was positively predicted by agreeableness and need for gratification. However, only need for gratification positively predicted “liking” of controversial posts. This indicates that the content of the post as well as the personality characteristics play a role in online one-click interaction, reflecting different means to manage impressions online.
EasyChair Preprint No 445 The Role of Coherence in Facial Expression Recognition

Recognizing facial expressions from static images or video sequences is a widely studied but still challenging problem. The recent progresses obtained by deep neural architectures, or by ensembles of heterogeneous models, have shown that integrating multiple input representations leads to state-of-the-art results. In particular, the appearance and the shape of the input face, or the representations of some face parts, are commonly used to boost the quality of the recognizer. This paper investigates the application of Convolutional Neural Networks (CNNs) with the aim of building a versatile recognizer of expressions in static images that can be further applied to video sequences. We first study the importance of different face parts in the recognition task, focussing on appearance and shape-related features. Then we cast the learning problem in the Semi-Supervised setting, exploiting video data, where only a few frames are supervised. The unsupervised portion of the training data is used to enforce two types of coherence, namely temporal coherence and coherence among the predictions on the face parts. Our experimental analysis shows that coherence constraints can improve the quality of the expression recognizer, thus offering a suitable basis to profitably exploit unsupervised video sequences.
LG ] 12 S ep 2 01 6 LEARNING SPARSE GRAPHS UNDER SMOOTHNESS PRIOR

In this paper, we are interested in learning the underlying g raph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine lear ning task. To realize this, we assume that the data is smooth with r espect to the graph topology, and we parameterize the graph topolog y using an edge sampling function. That is, the graph Laplacian is ex pressed in terms of a sparse edge selection vector, which provides an xplicit handle to control the sparsity level of the graph. We solve th e sparse graph learning problem given some training data in both the n oiseless and noisy settings. Given the true smooth data, the pose d sparse graph learning problem can be solved optimally and is based o n simple rank ordering. Given the noisy data, we show that the join t sparse graph learning and denoising problem can be simplified to des igning only the sparse edge selection vector, which can be solve d using convex optimization.
DEEP NEURAL NETWORK FOR PHONOCARDIOGRAM SIGNAL CLASSIFICATION

Mohammed Harun Babu R , Sai Bhanuja B , Vinayakumar R,Sowmya V Mtech Student, Mtech Student, Reaearch Associate ,Professor Centre for Computational Engineering and Networking(CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidhyapeetham, India. ______________________________________________________________________________________________________ Abstract: The methods for diagnosing the patients of cardiovascular diseases have advanced from period to period. Based on the PhysioNet challenge 2016, this work concentrates on the Phonocardiogram (PCG) data classification. The PCG data of heartbeat can be analyzed to get the condition of heart. For this, the data has to be pre-processed and need to be fed to a classifier. In the present work, deep neural network (DNN) architecture classifies the PCG data into normal and abnormal classes. To improve the accuracy of the classifier, we have proposed the DNN architecture with seven layers. The proposed DNN architecture for PCG data classification provides high accuracy, when compared to all the classical machine learning algorithms.
An overview on Advanced Research Works on Brain-Computer Interface

A brain–computer interface (BCI) is a proficient result in the research field of humancomputer synergy, where direct articulation between brain and an external device occurs resulting in augmenting, assisting and repairing human cognitive. In our overview study past and recent research works on brain computer interface are sincerely explored as well as their productivity in the field of BCI and critical discussions about the proposed research methods for future developments are presented in this paper. Generation of brain-computer interface switch technologies for intermittent (or asynchronous) control in natural environments , developing braincomputer interface by Fuzzy logic Systems , implementation of wavelet theory to drive the efficacies of BCI are few of the advanced and challenging researches on BCI that are still going on and few outcomes are accomplished till now. This paper emphasizes on discussing such and more advanced and recent research trends in BCI modeling and design as the requirements to develop the brain machine interface is also growing day by day for its efficient and faster used in varied technological and scientific uses i.e. like neuropsychological rehabilitation, emotion control, etc. Brief discussions of control theory as well biomedical signal processing technologies has been carried out in this paper for developing brain machine interface system.
TITLE : Mapping research in assisted reproduction worldwide RUNNING TITLE : Topic Modelling on ART research

1 Clínica EUGIN, Barcelona 08029, Spain 2 SIRIS Lab, Research Division of SIRIS Academic, Barcelona, Spain 3 Ingenio (CSIC-UPV), Universitat Politècnica de València, València, Spain & Centre for Science and Technology Studies (CWTS), University of Leiden, Netherlands *Correspondence: Clínica EUGIN, Travessera de les Corts 322, 08029 Barcelona, Spain. Email: rvassena@eugin.es Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 15 April 2019 doi:10.20944/preprints201904.0170. v1
Synchronous Reference Frame method for Mitigation of Current Harmonics with PI and FLC based Shunt Active Filter under Load Variation

Abstract: Recent advancements in power electronics has encouraged large scale use of non-linear loads such as Adjustable Speed Drives (ASD), traction drives etc. Power electronic equipment like frequency drives, computers, SMPS etc. introduce great amount of harmonic content into the power system. Harmonics overheat the power system equipment, increase transmission losses, cause nuisance tripping of circuit breakers and errors in measurements and medical diagnosis. They also interfere in the nearby communication systems. Conventionally, passive filters were used for mitigation of harmonics in the power system. However these L-C filters introduce tuning, aging and resonance (with network impedance) problems. They are suited for fixed harmonics compensation and are large in size. Active filters have been proven to be an efficient, flexible and alternative solution for the problem of power quality. The advantage of active filtering is that it automatically adapts to network and load variations, provides dynamic compensation and eliminates the risk of resonance. Active filters take up very little space compared to passive filters. As a result, active filters have gained much more attention in the recent times. However, active filters seem to have contradictions with different control strategies. Therefore this paper aims at providing a comparative study of Shunt Active Filter with PI and Fuzzy Logic based Controllers. Extensive simulations were carried out; Simulations were performed with PI and FLC with Triangular, Trapezium and Gaussian Membership Functions.
A Semantic Web Services Classification approach based on IG-C4.5

Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. The increasing number of available Web services has raised the need for their automated and accurate classification. Services Classification would be useful to enact powerful capabilities such as automatic selection, invocation, composition, location or discovery of web services. In this paper, We proposes a approach of Semantic Web Services classification taking into account the feature of textual description and the semantic annotations of OWL-S advertisements. An experimental test of the proposed techniques is reported, showing positive results, the efficiency and accuracy of the automatic service portfolio has been improved.