Miracle-dz/UniFAR-FTUs huggingface dataset:
A NOVEL K-VARIANT ALGORITHM FOR DOCUMENT CLUSTERING

Now a days most of the traditional clustering mecha nisms based on linear space. Relation exists betwee n the pair data objects either implicitly or explicitly. In the traditional mechan ism uses a single view point, In this paper we prop oses a novel mechanism for multiview point (i.e. n –dimensional space) with di fferent similarity measure. Using the multiple view points, more informative assessment of similarity can be achieved. Different mechanisms used for efficient clustering mechanism s.
Improved DYPSA Algorithm for Noise and Unvoiced Speech

The DYPSA algorithm detects glottal closure instants (GCI) in speech signals. We present an improvement in the algorithm in which a voiced/unvoiced/silence discrimination measure is applied in order to reduce the spurious GCIs detected incorrectly for noise and unvoiced speech. Speech classification is addressed by formulating a decision rule for the glottal closure instant candidates which classifies the candidates as voiced or non-voiced on the basis of feature measurements extracted from the speech signal alone. The technique of Dynamic Programming is then employed in order to select an optimum set of epochs from the GCI candidates. The algorithm has been tested on the APLAWD speech database with 87.23% improvement achieved in reduction of spurious GCIs.
Recognition of online handwritten Bangla characters using hierarchical system with Denoising Autoencoders

This work describes the recognition of online handwritten Bengali characters using Deep Denoising Autoencoder with Multilayer Perceptron (MLP) trained through backpropagation algorithm [1]. Initial pre-training has been done to the Denoising Autoencoder with MLP trained through backpropagation algorithm, to bring the weights of the Deep network to some good solution and then pre-trained Denoising Autoencoders are stacked to form a Deep Denoising Autoencoder (DDA). A final classification layer makes DDA to a Deep Classifier (DC) followed by a final fine-tune that gives the best classifier for the job of classification of Bengali characters. The overall system is hierarchical in nature and the system has been trained in two phase where the first phase has trained a broad classifier and in the second phase class specific recognizer has been trained. At the testing phase in this hierarchical approach, first a broad classifier has been used to recognize broad classes like Vowel, Consonant, Special Symbol and Numeral for a novel test sample. Once the broad class gets recognized then a class specific recognizer has been used to recognize the exact character the test sample belongs. Recognition performance of the hierarchical system is 93.12%.
ICP35 Is a TREX-Like Protein Identified in White Spot Syndrome Virus

ICP35 is a non-structural protein from White spot syndrome virus believed to be important in viral replication. Since ICP35 was found to localize in the host nucleus, it has been speculated that the function of ICP35 might be involved in the interaction of DNA. In this study, we overexpressed, purified and characterized ICP35. The thioredoxin-fused ICP35 (thio-ICP35) was strongly expressed in E. coli and be able to form itself into dimers. Investigation of the interaction between ICP35 and DNA revealed that ICP35 can perform DNase activity. Structural model of ICP35 was successfully built on TREX1, suggesting that ICP35 might adopt the folding similar to that of TREX1 protein. Several residues important for dimerization in TREX1 are also conserved in ICP35. Residue Asn126 and Asp132, which are seen to be in close proximity to metal ions in the ICP35 model, were shown through site-directed mutagenesis to be critical for DNase activity.
PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection

In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly-efficient parallelized graph-mining-based fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a "fraud" tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable sub-networks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods.
Fast parallel analysis of dynamic contrast-enhanced magnetic resonance imaging on GPGPU

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is adopted as an important diagnostic method to photograph organs in a body. However, its long analysis time prevents doctors from performing timely diagnosis and treatment. This paper proposes a parallel implementation of DCE-MRI analysis which exposes the parallelism of the fitting procedure. The implementation is mapped and executed on GPGPUs. When compared with the original sequential analysis procedure, we are able to speed up the analysis runtime by 125X.
Parabolic Flight Rat Head Direction Cell Responses in Zero-Gravity

publishes original articles on the function of the nervous system. It is published 12 times a year Journal of Neurophysiology Rat head direction cell responses in zero-gravity parabolic flight. Astronauts working in zero-gravity (0-G) often experience visual reorientation illusions (VRIs). For example, when floating upside down, they commonly misperceive the spacecraft floor as a ceiling and have a reversed sense of direction. Previous studies have identified a population of neurons in the rat's brain that discharge as a function of the rat's head direction (HD) in a gravitationally horizontal plane and is dependent on an intact vestibular system. Our goal was to characterize HD cell discharge under conditions of acute weightlessness. Seven HD cells in the anterior dorsal thalamus were monitored from rats aboard an aircraft in 0-G parabolic flight. Unrestrained rats locomoted in a clear plexi-glas rectangular chamber that had wire mesh covering the floor, ceiling, and one wall. The chamber and surrounding visual environment were relatively up-down symmetrical. Each HD cell was recorded across forty 20-s episodes of 0-G. All HD cells maintained a significant direction-specific discharge when the rat was on the chamber floor during the 0-G and also during the hypergravity pull-out periods. Three of five cells also showed direction-specific responses on the wall in 1-G. In contrast, direction-specific discharge was usually not maintained when the rat locomoted on the vertical wall or ceiling in 0-G. The loss of direction-specific firing was accompanied by an overall increase in background firing. However, while the rat was on the ceiling, some cells showed occasional bursts of firing when the rat's head was oriented in directions that were flipped relative to the long axis of symmetry of the chamber compared with the cell's preferred firing direction on the floor. This finding is consistent with what might be expected if the rat had experienced a VRI. These responses indicate that rats maintain a normal allocentric frame of reference in 0-G and 1-G when on the floor, but may lose their sense of directional heading when placed on a wall or ceiling during acute exposures to 0-G. are the most common. During space flight, an astronaut's sense of direction becomes quite labile and dependent on visual factors, since the " down " reference provided by gravity is absent. After a lifetime of upright living on earth, there is a natural tendency for astronauts to perceive that whatever interior surface of the …
FLIF: Free lossless image format based on MANIAC compression

We present a novel lossless image compression algorithm. It achieves better compression than popular lossless image formats like PNG and lossless JPEG 2000. Existing image formats have specific strengths and weaknesses: e.g. JPEG works well for photographs, PNG works well for line drawings or images with few distinct colors. For any type of image, our method performs as good or better (on average) than any of the existing image formats for lossless compression. Interlacing is improved compared to PNG, making the format suitable for progressive decoding and responsive web design.
Neurally-Guided Procedural Models: Learning to Guide Procedural Models with Deep Neural Networks

We present a deep learning approach for speeding up constrained procedural modeling. Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models, but they require many samples to produce desirable results. In this paper, we show how to create procedural models which learn how to satisfy constraints. We augment procedural models with neural networks: these networks control how the model makes random choices based on what output it has generated thus far. We call such a model a neurally-guided procedural model. As a pre-computation, we train these models on constraint-satisfying example outputs generated via SMC. They are then used as efficient importance samplers for SMC, generating high-quality results with very few samples. We evaluate our method on L-system-like models with image-based constraints. Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models.
Saturation throughput of IEEE 802.11 using carrier sense mechanism in backoff intervals

Most modifications on IEEE 802.11 DCF (distributed coordination function) so far have focused on updating of the CW (Contention Window) value and the carrier sense mechanism during a backoff interval is not considered. In this paper, we propose two novel schemes for DCF, which effectively utilize the carrier sense mechanism during backoff interval. The proposed schemes can be applied to any conventional ones, such as DCF and SD (slow CW decrease). Saturation throughput of the proposed schemes is analyzed by means of Bianchi's Markovian model. Computer simulation validates the accuracy of the analysis. Numerical results based on IEEE 802.11b show that up to 10-20% improvement of saturation throughput can be achieved by combining the proposed schemes with DCF or SD.
Texture adaptation for human tracking using statistical shape model

We propose a texture model construction method for human tracking based on a statistical texture-free shape model (distance map). In practical vision systems, a priori knowledge of the target object is often limited. In human tracking, since human figures involve a variety of images due to different clothes, the detected pixel values (color brightness) can vary significantly from person to person. Accordingly, we cannot assume textures for the human figures, which makes it difficult to directly utilize pixel values in human tracking. In this paper we describe a method to acquire texture models by detecting human position based on a texture-free shape model. Using our method, objects having. a variety of pixel values can be tracked properly with a simple appearance model, Experimental results show the effectiveness of the proposed approach.
Classification in the Presence of Class Noise

In machine learning, class noise occurs frequently and deteriorates the classifier derived from the noisy dataset. This paper presents several possible solutions to this problem based on LSA, a probabilistic noise model proposed by Lawrence and Schölkopf (2001). These solutions include the Clustering-based Probabilistic Algorithm (CPA), the Probabilistic Fisher (PF), and the Probabilistic Kernel Fisher (PKF). The proposed algorithms enable standard classifiers to tolerate class noise, and extend the earlier work of Lawrence and Schölkopf in several ways. First, CPA applies LSA to non-Gaussian datasets. Second, PKF represents a novel incorporation of LSA in the Kernel Fisher Discriminant (KFD). It also relaxes the distribution assumption previously made. The methods were investigated on simulated noisy datasets and a real comparative genomic hybridization (CGH) dataset. The results show that the proposed approaches improve standard classifiers in noisy datasets. PKF achieves the largest performance gain in small size non-Gaussian datasets. For large sample size datasets, CPA tolerates the highest noise level in Gaussian datasets, and offers a nice alternative in non-Gaussian datasets in that it is computationally much simpler.
Beyond Text based sentiment analysis : Towards multi-modal systems

A huge number of videos are posted each day on social media platforms such as YouTube, Facebook etc. It makes the Internet an almost unlimited source of information. In the coming decades it will become increasingly challenging for researchers to cope with this information and mine useful knowledge from it. In this paper, we address the problem of multimodal sentiment analysis i.e. harvesting sentiment from Web videos by demonstrating a novel model which uses audio,visual and textual modalities as sources of information. Here, we first extract information from three modalities and then fuse these at both feature and decision level. We employ a kernel based fusion method to fuse the information extracted from multiple modalities. A thorough comparison with existing state-of-the-art works in this area is carried out. In preliminary experiments using the YouTube dataset, our proposed multimodal system is shown to achieve an accuracy of 78.20%,outperforming the best state-of-the-art systems by more than 22.90%.
Conceptual modeling of XML schemas

XML has become the standard format for representing structured and semi-structured data on the Web. To describe the structure and content of XML data, several XML schema languages have been proposed. Although being very useful for validating XML documents, an XML schema is not suitable for tasks requiring knowledge about the semantics of the represented data. For such tasks it is better to use a conceptual schema. This paper presents an extension of the Entity Relationship (ER) model, called X-Entity, for conceptual modeling of XML schemas. We also present the process of converting a schema, defined in the XML Schema language, to an X-Entity schema. The conversion process is based on a set of rules that consider element declarations and type definitions and generates the corresponding conceptual elements. Such representation provides a cleaner description for XML schemas by focusing only on semantically relevant concepts. The X Entity model has been used in the context of a Web data integration system with the goal of providing a concise and semantic description for local schemas defined in XML Schema.
Implementation of Single System Image under Virtualized Environment

A new single system image (SSI) system is implemented in order to simplify the complexity of management and programming of clusters. Resource in clusters is integrated by the combination of virtualization and distributed shared memory (DSM), and a single physical machine abstract is achieved for the guest operating system as a result of single system image. The prototype system proves the feasibility. This paper depicts the design and implementation of the prototype system and makes a preliminary evaluation
Altered proximal T cell receptor (TCR) signaling in human CD4+CD25+ regulatory T cells

CD4+CD25+ regulatory T cells play an important role in peripheral tolerance. Upon T cell receptor (TCR)‐mediated activation, the cells fail to proliferate but are induced to have a suppressor function. The intracellular signaling events that lead to their responses have not been elucidated. In this study, we have examined the proximal TCR signaling events in freshly isolated human CD4+CD25+ regulatory T cells after TCR ligation. In contrast to CD4+CD25– T cells, TCR ligation of CD4+CD25+ regulatory T cells by anti‐CD3 cross‐linking resulted in a lower calcium influx and extracellular signal‐regulated kinase 1/2 phosphorylation. Examination of the CD3ζ chain phosphorylation status indicated that CD4+CD25+ regulatory T cells have poor phosphorylation of the protein and consequently, reduced recruitment of ζ‐associated protein‐70 to the TCR immunoreceptor tyrosine motif. The adaptor protein, Src homology 2 domain‐containing leukocyte phosphoprotein of 76 kDa, which relays signals to downstream signaling components, also showed reduced phosphorylation, which correlated with reduced VAV guanine nucleotide exchange factors association. Consistent with other findings, the defect is accompanied with impaired actin cap formation, implicating a failure of actin remodeling of the cells. Together, our results demonstrate that CD4+CD25+ regulatory T cells have altered TCR proximal signaling pathways, which could be critical for inducing the distinct behavior of these cells.
Reliable object detection and segmentation using inpainting

This paper presents a novel object detection and segmentation method utilizing an inpainting algorithm. Inpainting is a concept of recovering missing image regions based on their surroundings, which were originally used for restoration of damaged paintings. In this paper, we newly utilize inpainting to judge whether an object candidate region includes the foreground object or not. The key idea is that if we erase a certain region from an image, the inpainting algorithm is expected to recover the erased image only when it belongs a background area (i.e. only when there is no object in it). By measuring the similarity between the inpainted region and the original image region, our approach filters out false detections while maintaining true object detections. Furthermore, we take advantage of the inpainting for object segmentation, since our approach is designed to explicitly distinguish foreground areas from its background. Experimental results confirm that our approach applied to baseline detectors enables better recognition of objects, obtaining higher accuracies. We illustrate how our inpainting-based detection/segmentation approach benefits the object detection using two different pedestrian datasets.
Enabling real-time charging for smart grid infrastructures using in-memory databases

The emerging trend towards smart grids defines new requirements for designing enterprise applications for the energy market. Current solutions were built to process single billing runs as time-consuming batch jobs. Rather than processing some readings per year and household, a constant stream of meter readings has to be processed in context of a smart power grid. Additionally, consumers demand for convenient ways to monitor power consumption while getting real-time charging information on a daily basis. In this paper, we share our experiences of integrating meter readings in an industry-specific enterprise application for utilities to support real-time billing. Our prototype enables customers to track consumptions and to view billing details online. Besides, we discuss new business scenarios enabled by this real-timeliness.
Semidefinite and Spectral Relaxations for Multi-Label Classification

In this paper, we address the problem of multi-label classification. We consider linear classifiers and propose to learn a prior over the space of labels to directly leverage the performance of such methods. This prior takes the form of a quadratic function of the labels and permits to encode both attractive and repulsive relations between labels. We cast this problem as a structured prediction one aiming at optimizing either the accuracies of the predictors or the F 1-score. This leads to an optimization problem closely related to the max-cut problem, which naturally leads to semidefinite and spectral relaxations. We show on standard datasets how such a general prior can improve the performances of multi-label techniques.
Ethical Thoughts in Accounting And Their Effects On Accounting Practice

This study examined the effect of ethical thoughts in accounting on accounting practice in Nigeria. To achieve this objective, data were collected from primary sources through a well-structured questionnaire of four sections of twenty questions administered to a sample of one hundred and sixty one (161) out of which one hundred and forty eight (148) were completed and returned. The questionnaires were administered to the registered members of the ICAN District Society in Calabar, Nigeria and data generated from the questionnaires were analysed using IBM SPSS V.20. The results revealed a significantly negative relationship between ethical thoughts and the accounting practices of corporate organizations in Nigeria. On the basis of the findings, the study recommended the following among others: Professional accountants as custodians and producers of accounting information should adhere to the codes of professional best practices issued by relevant professional bodies. Each organization in Nigeria should establish ethics departments to ensure that activities are in line with the codes of ethics, including the financial reporting process. The composition of the Board of Directors and Audit Committees should be made up of people with corporate experience, proven integrity and financial expertise. Greater efforts should be made by the relevant professional bodies to review their various ethical codes guiding members’ conduct or behaviour in practice and widespread enlightenment coupled with stiffer penalties for professional misconduct could also be a way out. Keywords: Integrity, objectivity, professional behaviour, professional competence and due care and accounting practice. DOI : 10.7176/RJFA/10-6-16 Publication date :March 31 st 2019