Miracle-dz/UniFAR-FTUs huggingface dataset:
Gene Selection with Rough Sets for the Molecular Diagnosing of Tumor Based on Support Vector Machines

The development of microarray technology has motivated interest of its use in clinical diagnosis of tumor and drug discovery. However the accurate classification of tumor by selecting the tumor-related genes from thousands of genes is a difficulty task due to the large number of redundant genes. Therefore, we propose a novel hybrid approach which combines rough set theory with support vector machines to further improve the classification performance of gene expression data. Our approach is assessed on two well-known tumor datasets, and experiments indicate that gene selection based on the rough set theory is effective because most of the selected genes are relevant to tumor using rough set attribute reduction, and support vector machines classifier has a better performance on the selected informative genes.
Designing online video-making task to increase fourth graders' motivation for autonomous learning behaviour

This article argues that online video-making tasks motivate learners for an autonomous learning behavior. The article analyzes five behaviors of an autonomous learner, before and after applying a set of tasks based on motivational strategies for autonomy to a group of fourth graders (10 to 11 year olds). The tasks are based on three video-making web 2.0 tools: MashFace, Xtranormal, and Animoto. The article concludes that through a set of motivational strategies applied with the tasks mentioned, the students improve a set of autonomous learning behaviors.
A New SVM Algorithm and AMR Sensor Based Vehicle Classification

This paper proposes a new and efficient vehicle classification system base on support vector machine (SVM) algorithm and anisotropic magnetoresistive (AMR) sensor. The main point is that the AMR sensors detect the change of earth magnetic field which will be disturbed differently by different types of passing traffic vehicle. The characteristics of AMR sensor output model of the sample data, SVM learning classification algorithm, kernel function and model parameters are analyzed in detail. The results of our experiments show that this vehicle classification system base on AMR sensor and SVM algorithm is effective and efficient.
Geometric VLAD for Large Scale Image Search

We present a novel compact image descriptor for large scale image search. Our proposed descriptor - Geometric VLAD (gVLAD) is an extension of VLAD (Vector of Locally Aggregated Descriptors) that incorporates weak geometry information into the VLAD framework. The proposed geometry cues are derived as a membership function over keypoint angles which contain evident and informative information but yet often discarded. A principled technique for learning the membership function by clustering angles is also presented. Further, to address the overhead of iterative codebook training over real-time datasets, a novel codebook adaptation strategy is outlined. Finally, we demonstrate the efficacy of proposed gVLAD based retrieval framework where we achieve more than 15% improvement in mAP over existing benchmarks.
Sharing mutation genetic algorithm for solving multi-objective problems

Multi-objective optimization (MO) has been an active area of research in last two decade. In multi-objective genetic algorithm (MOGA), quality of new generated offspring of population will affect the performance of finding Pareto optimum directly. In this paper, an improved MOGA is proposed named SMGA to solving multi-objective optimization problem. For improving solution searching efficiency, an effective mutation named sharing mutation is adopted for generating potential offspring. Experiments were conducted on CEC-09 MOP test problems. The results showed that the proposed method exhibits better performance when solving these benchmark problems compared to related multi-objective evolutionary algorithm (MOEA).
Bactericidal activity of a superoxide anion-generating system. A model for the polymorphonuclear leukocyte

The acetaldehyde-xanthine oxidase system in the presence and absence of myeloperoxidase (MPO) and chloride has been employed as a model of the oxygen-dependent antimicrobial systems of the PMN. The unsupplemented xanthine oxidase system was bactericidal at relatively high acetaldehyde concentrations. The bactericidal activity was inhibited by superoxide dismutase (SOD), catalase, the hydroxyl radical (OH.) scavengers, mannitol and benzoate, the singlet oxygen (1O2) quenchers, azide, histidine, and 1,4-diazabicyclo[2,2,2]octane (DABCO) and by the purines, xanthine, hypoxanthine, and uric acid. The latter effect may account for the relatively weak bactericidal activity of the xanthine oxidase system when purines are employed as substrate. A white, carotenoid-negative mutant strain of Sarcina lutea was more susceptible to the acetaldehyde-xanthine oxidase system than was the yellow, carotenoid-positive parent strain. Carotenoid pigments are potent 1O2 quenchers. The xanthine oxidase system catalyzes the conversion of 2,5- diphenylfuran to cis-dibenzoylethylene, a reaction which can occur by a 1O2 mechanism. This conversion is inhibited by SOD, catalase, azide, histidine, DABCO, xanthine, hypoxanthine, and uric acid but is only slightly inhibited by mannitol and benzoate. The addition of MPO and chloride to the acetaldehyde-xanthine oxidase system greatly increases bactericidal activity; the minimal effective acetaldehyde concentration is decreased 100-fold and the rate and extent of bacterial killing is increased. The bactericidal activity of the MPO-supplemented system is inhibited by catalase, benzoate, azide, DABCO, and histidine but not by SOD or mannitol. Thus, the acetaldehyde-xanthine oxidase system which like phagocytosing PMNs generates superoxide (O.2-) and hydrogen peroxide, is bactericidal both in the presence and absence of MPO and chloride. The MPO-supplemented system is considerably more potent; however, when MPO is absent, bactericidal activity is observed which may be mediated by the interaction of H2O2 and O.2- to form OH. and 1O2.
2 Transmission Algorithms 2 . 1 Weighted Cumulative Expected Transmission Time ( WCETT )

To satisfy the quality of service (QoS) needs of video transmission in wireless mesh networks remains a significant research challenge, due primarily to link bandwidth limitations. This paper focuses on multi-channel multi-radio network technology and presents a comparative evaluation of two transmission algorithms that represent two alternative approaches to achieve QoS support. The Weighted Cumulative Expected Transmission Time (WCETT) algorithm aims to select an end-toend path that offers low delay. The Adaptive Split Transmission (AST) reduces delay by distributing a video flow across several sub-flows that are transmitted simultaneously using different radios. Our study is based on simulations using ns-2 and provides insight into the comparative performance of the two approaches. The main conclusion is that WCETT is better suited when offered traffic load is relatively low, whereas AST can work well even in highly congested situations.
Hybrid CDN structure with a P2P based streaming protocol

Over the last decade, internet has seen an exponential increase in its growth.With more and more people using it, efficient data delivery over the internet has become a key issue. Peer-to-peer (P2P)/seed sharing based networks have several desirable features for content distribution, such as low costs, scalability, and fault tolerance. While the invention of each of such specialized systems has improved the user experience, some fundamental shortcomings of these systems have often been neglected. These shortcomings of content distribution systems have become severe bottlenecks in scalability of the internet.In order to combine the desired features of classical Content Distribution Networks (CDNs) and P2P/seed sharing based networks, we propose a hybrid CDN structure with a P2P/seed sharing based streaming protocol in the access network . In this work, we focus on the problem of data redundancy (at each node) and show how severely it impacts the network economics and the experience of end-user and hence leads to low traffic load and redundancy
Portable and accurate collection of calling-context-sensitive bytecode metrics for the Java virtual machine

Calling-context profiles and dynamic metrics at the bytecode level are important for profiling, workload characterization, program comprehension, and reverse engineering. Prevailing tools for collecting calling-context profiles or dynamic bytecode metrics often provide only incomplete information or suffer from limited compatibility with standard JVMs. However, completeness and accuracy of the profiles is essential for tasks such as workload characterization, and compatibility with standard JVMs is important to ensure that complex workloads can be executed. In this paper, we present the design and implementation of JP2, a new tool that profiles both the inter- and intra-procedural control flow of workloads on standard JVMs. JP2 produces calling-context profiles preserving callsite information, as well as execution statistics at the level of individual basic blocks of code. JP2 is complemented with scripts that compute various dynamic bytecode metrics from the profiles. As a case-study and tutorial on the use of JP2, we use it for cross-profiling for an embedded Java processor.
Bioassay-Guided Isolation and Antioxidant Evaluation of Flavonoid Compound from Aerial Parts of Lippia nodiflora L.

The present study was designed to identify the DPPH (2, 2-diphenyl-1-picrylhydrazyl) free-radical scavenging constituents from methanol extract of L. nodiflora using bioassay-guided fractionation. The ethyl acetate fraction (EAF) revealed a strong antioxidant activity, compared to other fractions through in vitro DPPH radical-scavenging assay. The repeated fractionation of active EAF by silica gel column chromatography yielded a compound with strong antioxidant potential. The isolated bioactive compound was determined as 2-(3, 4-dimethoxyphenyl)-5-hydroxy-7-methoxy-4H-chromen-4-one (5-hydroxy-3′, 4′, 7-trimethoxyflavone), by comparing spectral data (UV, IR, 1H NMR, 13C NMR, and MS) with literature reports. The isolated compound demonstrated an excellent antioxidant activity through all antioxidant assays and also significantly inhibited lipid peroxidation at a concentration of 50 μg/mL. The results obtained suggested that extracts from L. nodiflora or its derived phytocompound can be used potentially as a bioactive source of natural antioxidants by contributing beneficial health effects.
Realtime file processing based on Map-Reduce framework

Every day we find hundreds of thousands of new malicious samples. Among them there are a lot of clean files. Deciding which file is infected and which is clean requires intensive processing. Handling such volumes of files and extracted metadata demanded a distributed system. Based on MapReduce, a concept proposed by Google and used by many others companies like Yahoo! and Facebook, we developed a file processing system which will try to fulfill our need of sample processing. The system is able to use hardware computing systems with different hardware configuration to run a series of different tasks and will automatically adjust them on every hardware system. We use a cascade of map and reduce tasks for extracting and processing the data and a key-value RAM database as data link between them. In order to be able to prioritize some task over the others, we created an algorithm which will try to favor a task with higher priority in disfavor of a one with lower priority when system runs at full capacity, trying to balance the cost of moving the same data over the network multiple times. Reliability and horizontal scalability are also things that we took into consideration when designing the system. Having one or multiple hardware failures will not affect the system and adding more hardware systems will have a linear impact.
Hadoop MapReduce Framework to Implement Molecular Docking of Large-Scale Virtual Screening

Traditional virtual screening in the grid needs chemists to upload small molecule files and collect the results manually, which cannot implement docking and collection of results automatically. This caused heavy workload to chemists. In this paper, we took advantage of Hadoop platform in the massive data storage. We stored and managed small molecule files and docking results files using HDFS. In addition, MapReduce programming framework is used for parallel molecular docking to preliminarily process results files, in order to achieve the automation of the virtual screening molecular docking. The research of this thesis will be helpful to drug researcher by offering a massive data storage management system for large-scale virtual screening, and will also provide a reference for drug discovery in the cloud environment to promote the development of computational chemistry e-science.
Polluted Air Increases Perceived Corruption

Drawing upon the moral cleanness metaphor and the power height metaphor, we proposed the clear sky effect: polluted air increases perceived corruption. To test the effect, we established a correlation (Studies 1 and 2) and causal link (Studies 3, 4, and 5) between haze pollution and corruption perception. This correlation is unique, in that, of various air pollutants, only the major haze indicator was positively correlated with corruption perception at city (Study 1) and country (Study 2) levels. In addition, recalling feelings concerning haze (Studies 3 and 5) or experiencing hazy days (Study 4) increased corruption perception. Furthermore, in support of embodiment, this effect was moderated by body awareness (Study 4) and mediated by bodily stress responses (Study 5). Taken together, these findings suggest that environmental pollution could be one of the factors that influence our trust in government.
On SPIM detection in LTE networks

The telecommunication enterprise networks are continuously facing increasing amount of messages and multi-media traffic due to growing deployment of messaging applications. The detection of SPAM over Instant Messaging (SPIM) is essential for the mobile service providers to achieve user satisfaction and efficient network load balancing. However, accurate SPIM detection is challenging at the server side due to large volume of data packets and near-real-time communication of messages. In this setting, we propose a message classification approach to collect valuable insight on SPIM inside LTE cellular telecommunication framework. The contribution of this paper is threefold: First, it identifies the location and scope of the SPIM detection component inside LTE architecture. Second, we compare the detection accuracy, training and testing time of different classification algorithms. Finally, we evaluate the additional delay in messaging for adding such a module inside LTE architecture using an OPNET simulation environment.
Synthesis of Mannosylated Polyethylenimine and Its Potential Application as Cell-Targeting Non-Viral Vector for Gene Therapy

Mannose polyethylenimine with a molecular weight of 25 k (Man-PEI25k) was synthesized via a phenylisothiocyanate bridge using mannopyranosylphenyl isothiocyanate as a coupling reagent, and characterized by 1H NMR (nuclear magnetic resonance) and FT-IR (Fourier transform infrared spectroscopy) analysis. Spherical nanoparticles were formed with diameters of 80–250 nm when the copolymer was mixed with DNA at various charge ratios of copolymer/DNA (N/P). Gel electrophoresis demonstrated that the DNA had been condensed and retained by the PEI derivates at low N/P ratios. The Man-PEI25k/DNA complexes were less cytotoxic than the PEI complexes with a molecular weight of 25 k (PEI25k) at the same N/P ratio. Laser scan confocal microscopy and flow cytometry confirmed that the Man-PEI25k/DNA complexes gave higher cell uptake efficiency in (Dendritic cells) DC2.4 cells than HeLa cells. The transfection efficiency of Man-PEI25k was higher than that of PEI25k towards DC2.4 cells. These results indicated that Man-PEI25k could be used as a potential DC-targeting non-viral vector for gene therapy.
Journal of Internet Banking and Commerce the Impact of Electronic Commerce on Organizational Structure: a Case Study of E- Commerce Decentralization

He has a BS and an MS in Business Administration from the University of São Paulo. He has worked for 18 years in the IT industry managing projects in ERP, CRM and e-business implementation. His current research interests are innovation management, technology strategy and e-business. Abstract This paper focuses on the problem of centralizing vs. decentralizing an organizational structure for e-commerce. First, a conceptual framework is designed based on the literature. Then a case study of the Brazilian subsidiary of a major chemical multinational is explained and analyzed. A decision-making method is applied to (a) identify the alternatives for organizational structures and evaluation criteria, and (b) determine which criteria enable one to identify an alternative as better than the others. In the context of the case, a centralized e-commerce structure was recommended. This paper makes two main contributions to theory. First, it shows the usefulness of the literature on R&D and innovation management as theoretical support for studies in other fields, in this case e-commerce organization. Second, it provides a methodology, which can be adapted for use by companies facing the same decision problem. Thoughts on possible future studies close the article.
Durable Pharmacological Responses from the Peptide ShK-186, a Specific Kv1.3 Channel Inhibitor That Suppresses T Cell Mediators of Autoimmune Disease

The Kv1.3 channel is a recognized target for pharmaceutical development to treat autoimmune diseases and organ rejection. ShK-186, a specific peptide inhibitor of Kv1.3, has shown promise in animal models of multiple sclerosis and rheumatoid arthritis. Here, we describe the pharmacokinetic-pharmacodynamic relationship for ShK-186 in rats and monkeys. The pharmacokinetic profile of ShK-186 was evaluated with a validated high-performance liquid chromatography-tandem mass spectrometry method to measure the peptide's concentration in plasma. These results were compared with single-photon emission computed tomography/computed tomography data collected with an 111In-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid-conjugate of ShK-186 to assess whole-blood pharmacokinetic parameters as well as the peptide's absorption, distribution, and excretion. Analysis of these data support a model wherein ShK-186 is absorbed slowly from the injection site, resulting in blood concentrations above the Kv1.3 channel-blocking IC50 value for up to 7 days in monkeys. Pharmacodynamic studies on human peripheral blood mononuclear cells showed that brief exposure to ShK-186 resulted in sustained suppression of cytokine responses and may contribute to prolonged drug effects. In delayed-type hypersensitivity, chronic relapsing-remitting experimental autoimmune encephalomyelitis, and pristane-induced arthritis rat models, a single dose of ShK-186 every 2 to 5 days was as effective as daily administration. ShK-186's slow distribution from the injection site and its long residence time on the Kv1.3 channel contribute to the prolonged therapeutic effect of ShK-186 in animal models of autoimmune disease.
Remote pipes and procedures for efficient distributed communication

We describe a new communication model for distributed systems that combines the advantages of remote procedure call with the efficient transfer of bulk data. Three ideas form the basis of this model. First, remote procedures are first-class values which can be freely exchanged among nodes, thus enabling a greater variety of protocols to be directly implemented in a remote procedure call framework. Second, a new type of abstract object, called a <italic>pipe</italic>, allows bulk data and incremental results to be efficiently transported in a type-safe manner. Unlike procedure calls, pipe calls do not return values and do not block a caller. Data sent down a pipe is received by the pipe's sink node in the order sent. Third, the relative sequencing of pipes and procedures can be controlled by combining them into <italic>channel groups</italic>. Calls on the members of a channel group are guaranteed to be processed in order. Application experience with this model, which we call the <italic>Channel Model</italic>, is reported. Derived performance bounds and experimental measures demonstrate <italic>k</italic> pipe calls can perform <italic>min</italic>( 1 + (<italic>r</italic>/<italic>p</italic>), <italic>k</italic>) times faster than <italic>k</italic> procedure calls, where <italic>r</italic> is the total roundtrip remote communication time and <italic>p</italic> is the procedure execution time.
On the Robustness of Filter Bank Frame to Quantization and Erasures

This paper studies the robustness of filter bank (FB) frame in l2 (Zopf) to quantization and erasures from the perspective of both frame and FB theory. It is shown that the equal-norm property, imposed frequently in previous papers, is not necessary for optimization of frames under certain conditions and not desirable from the viewpoint of designing FB frames. We introduce a novel notion, named frame energy, which actually dictates the quantization performance of FB frames. Then, the erasure effects on the structures and quantization performance of FB frames are investigated. The design freedom obtained from removing the equal-norm property is explained and illustrated with examples.
Fuzzy-based CMOS circuit partitioning in built-in current testing

We propose a fuzzy-based approach which provides a soft threshold to determine the module size for CMOS circuit partitioning in built-in current testing (BICT). Experimental results show that our design approach indeed provides a feasible way to exploit the design space of BICT partitioning in comparison with other approaches with a fixed threshold, and a better module size can thus be determined to reflect a change of circuit properties.
Using 2DGabor values and kernel fisher discriminant analysis for face recognition

A novelty method of 2DGabor-KDA(kernel Fisher discriminant analysis) for face recognition is proposed. First of all, every facial image is segmented into several sub-areas according to the five particular face parts and then the features of five key parts are extracted through 2DGabor wavelet, average values are calculated from feature vectors gained from the corresponding pixel of each test sample and then the eigenvectors are gained, in the next place, KDA is applied to kernel-process the gained eigenvectors, and then SVM(Support Vector Machine) is adopted to recognize the face images. The numerical experiments on face database of ORL demonstrate that this method achieves better results of face recognition than other methods and shows stronger robustness to changes of illumination, expressions, poses and so on.
Recent Applications of Parametric Modeling to EEG Signals Analysis

In this paper, recent applications of autoregressive (AR) and adaptive autoregressive (TVAR) models to EEG signals for detection of epileptic seizures are addressed. First of all, AR/TVAR models based the complexity measure with the order of AR model and the spectrum estimation with online AR model are introduced and employed to analyze the EEG signals with epileptic seizures. Then, three new applications of AR and TVAR models to EEG signals are addressed in detail. The first method is to combine a TVAR model and a dynamic linear model to decompose an EEG signal for revealing the latent structure of the EEG signal. The second method is to combine the information retrieve and AR model to discover the dynamic characteristic, damping time, of an EEG signal. The synchronization measure of multi-channel EEG signals based on AR model is the third method. The test results of real EEG signals showed that the decomposition, the damping time and the synchronization measure of the EEG signals could discover the characteristics of epileptic seizures and help to diagnose the epilepsy patents.