Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based approach for American Sign Language recognition using Edge Orientation Histogram

Hand Gesture Recognition System (HGRS) for detection of American Sign Language (ASL) alphabets has become essential tool for specific end users (i.e. hearing and speech impaired) to interact with general users via computer system. ASL has been proved to be a powerful and conventional augmentative communication tool especially for specific users. ASL consists of 26 primary letters, of which 5 are vowels and 21 are consonants. Proposed Real-time static Alphabet American Sign Language Recognizer- (A-ASLR) is designed for the recognition of ASL alphabets into their translated version in text (i.e. A to Z). The architecture of A-ASLR system is fragmented into six consequent phases namely; image capturing, image pre-processing, region extraction, feature extraction, feature matching and pattern recognition. We have used Edge Orientation Histogram (EOH) in A-ASLR system. The system is developed for detection of ASL alphabets based on Vision-based approach. It works without using colored gloves or expensive sensory gloves on hand. Our A-ASLR system achieves the recognition rate of 88.26% within recognition time of 0.5 second in complex background with mixed lightning condition.
Integration of the optical replica utrashort electron bunch diagnostics with the current-enhanced SASE in the LCLS

In this paper, we present a feasibility study of integrating the optical replica (OR) ultrashort electron bunch diagnostics with the current-enhanced SASE (ESASE) scheme in the LCLS. Both techniques use an external laser to energy- modulate the electron beam in a short wiggler and then convert the energy modulation to a density modulation in a dispersive section. While ESASE proposes to use the high- current spikes to enhance the FEL signal, the OR technique extracts the coherent optical radiation produced by a density modulated electron beam for frequency resolved optical gating (FROG) diagnostics. We discuss the optimization studies of combining the OR method with the ESASE after the second bunch compressor in the LCLS. Simulation results show that the OR method is capable of reproducing the expected double-horn current profile of a 200-fs bunch. The possibilities and limitations of reconstructing the longitudinal phase space profile are also explored.
Enhanced Biclustering for Gene Expression Data

-Microarray technology is a powerful method for monitoring the expression level of thousands of genes in parallel. Using this technology, the expression levels of genes are measured. Microarray data is represented in N × M matrix. Each row indicates genes and each column indicates condition. In Gene Expression data, standard clustering algorithms are called as global clustering. In global clustering, genes are analyzed under all experimental conditions based on their expression. Biclustering is a very popular method to identify hidden co-regulation patterns among genes and to identify the local structures of genes and conditions. In existing system, Cheng and Church biclustering algorithm is presented as an alternative approach to standard clustering techniques to identify local structures and also identify subsets of genes that shows similar expression patterns across specific subsets of experimental conditions and vice versa. Clustering the microarray data is based on user defined threshold value, this affects the quality of biclusters formed. In proposed scheme, threshold value ∂ is calculated rather than user defined threshold. Biclusters are formed based on the low mean squared residues and ∂ , which would improve the quality of the biclusters.
Twitris: Socially Influenced Browsing

In this paper, we present Twitris, a semantic Web application that facilitates browsing for news and information, using social perceptions as the fulcrum. In doing so we address challenges in large scale crawling, processing of real time information, and preserving spatiotemporal-thematic properties central to observations pertaining to realtime events. We extract metadata about events from Twitter and bring related news and Wikipedia articles to the user. In developing Twitris, we have used the DBPedia ontology.
γisoform‐selective changes in PKC immunoreactivity after trace eyeblink conditioning in the rabbit hippocampus

An immunocytochemical examination of the rabbit hippocampus was done to determine which of the Ca2+‐dependent protein kinase C (PKC) isoforms (PKCα, ‐βI, ‐βII, or ‐γ) are involved in associative learning. The hippocampally dependent trace eyeblink conditioning task was used for behavioral training, and pseudoconditioned and naive animals served as controls. Significant increases (P < 0.05) in staining intensity were found with antibodies reactive with the catalytic or the regulatory domain of PKCγ in conditioned animals compared with naive and pseudoconditioned subjects at a 24‐h post‐conditioning time point. The increase was found in CA1 and CA3 pyramidal cell bodies, in apical dendrites and the proximal part of the basilar dendrites, and in cell bodies of dentate granule cells. In contrast, no conditioning‐specific changes were found for PKCα, ‐βI, or ‐βII in hippocampal neurons. The increase in PKCγ immunoreactivity (ir) was significantly less (P < 0.05) in poor learners than in good learners. The correlation between the degree of PKCγ‐ir and the total number of conditioned responses across training sessions was both positive and significant. These results suggest that PKCγ is the major Ca2+‐dependent PKC isoform involved in hippocampal neurons during acquisition of associative memories. Immunoblots revealed no conditioning‐induced increase in the total amount or translocation of PKCγ at the 24‐h time point, and no proteolytic PKC fragments were observed. In agreement with the Western blot data, PKC activity did not differ among naive, pseudoconditioned, and trace conditioned animals. The conditioning‐induced increase in antibody binding to the γ‐isoform must therefore be due to an increased access to the antigenic site(s) as a result of alteration in the tertiary structure of PKCγ or in quaternary interactions of PKCγ in situ. Hippocampus 7:271–285, 1997. © 1997 Wiley‐Liss, Inc.
Hidden Conditional Random Fields for action recognition

In the field of action recognition, the design of features has been explored extensively, but the choice of action classification methods is limited. Commonly used classification methods like k-Nearest Neighbors and Support Vector Machines assume conditional independency between features. In contrast, Hidden Conditional Random Fields (HCRFs) include the spatial or temporal dependencies of features to be better suited for rich, overlapping features. In this paper, we investigate the performance of HCRF andMax-Margin HCRF and their baseline versions, the root model and Multi-class SVM, respectively, for action recognition on the Weizmann dataset. We introduce the Part Labels method, which uses explicitly the part labels learned by HCRF as a new set of local features. We show that only modelling spatial structures in 2D space is not sufficient to justify the additional complexity of HCRF, MMHCRF or the Part Labels method for action recognition.
Opinion Target Extraction in Chinese News Comments

News Comments on the web express readers' attitudes or opinions about an event or object in the corresponding news article. And opinion target extraction from news comments is very important for many useful Web applications. However, many sentences in the comments are irregular and informal, and sometimes the opinion targets are implicit. Thus the task is very challenging and it has not been investigated yet. In this paper, we propose a new approach to uniformly extracting explicit and implicit opinion targets from news comments by using Centering Theory. The approach uses global information in news articles as well as contextual information in adjacent sentences of comments. Our experimental results verify the effectiveness of the proposed approach.
Self-organized mechano-chemical dynamics in amoeboid locomotion of Physarum fragments

The aim of this work is to quantify the spatio-temporal dynamics of flow-driven amoeboid locomotion in small (∼100 μm) fragments of the true slime mold Physarum polycephalum. In this model organism, cellular contraction drives intracellular flows, and these flows transport the chemical signals that regulate contraction in the first place. As a consequence of these non-linear interactions, a diversity of migratory behaviors can be observed in migrating Physarum fragments. To study these dynamics, we measure the spatio-temporal distributions of the velocities of the endoplasm and ectoplasm of each migrating fragment, the traction stresses it generates on the substratum, and the concentration of free intracellular calcium. Using these unprecedented experimental data, we classify migrating Physarum fragments according to their dynamics, finding that they often exhibit spontaneously coordinated waves of flow, contractility and chemical signaling. We show that Physarum fragments exhibiting symmetric spatio-temporal patterns of endoplasmic flow migrate significantly slower than fragments with asymmetric patterns. In addition, our joint measurements of ectoplasm velocity and traction stress at the substratum suggest that forward motion of the ectoplasm is enabled by a succession of stick-slip transitions, which we conjecture are also organized in the form of waves. Combining our experiments with a simplified convection-diffusion model, we show that the convective transport of calcium ions may be key for establishing and maintaining the spatio-temporal patterns of calcium concentration that regulate the generation of contractile forces.
Computing as the 4th "R": a general education approach to computing education

Computing and computation are increasingly pervading our lives, careers, and societies - a change driving interest in computing education at the secondary level. But what should define a "general education" computing course at this level? That is, what would you want every person to know, assuming they never take another computing course? We identify possible outcomes for such a course through the experience of designing and implementing a general education university course utilizing best-practice pedagogies. Though we nominally taught programming, the design of the course led students to report gaining core, transferable skills and the confidence to employ them in their future. We discuss how various aspects of the course likely contributed to these gains. Finally, we encourage the community to embrace the challenge of teaching general education computing in contrast to and in conjunction with existing curricula designed primarily to interest students in the field.
Impact of California Reformulated Gasoline On Motor Vehicle Emissions. 1. Mass Emission Rates

This paper addresses the impact of California phase 2 reformulated gasoline (RFG) on motor vehicle emissions. Phase 2 RFG was introduced in the San Francisco Bay Area in the first half of 1996, resulting in large changes to gasoline composition. Oxygen content increased from 0.2 to 2.0 wt%; and alkene, aromatic, benzene, and sulfur contents decreased. Gasoline density and T50 and T90 distillation temperatures also decreased. Light-duty vehicle emissions rates were measured in a Bay Area roadway tunnel in summers 1994-1997. Vehicle speeds and driving conditions inside the tunnel were similar each year. The average model year of the vehicle fleet was about one year newer each successive summer. Large reductions in pollutant emissions were measured in the tunnel over the course of this study, due to a combination of RFG and fleet turnover effects. Between summers 1994 and 1997, emissions of carbon monoxide decreased by 31 ± 5%, non-methane volatile organic compounds (VOC) decreased by 43 ± 8%, and nitrogen oxides (NOx) decreased by 18 ± 4%. It was difficult to separate clearly the fleet turnover and RFG contributions to these changes. Nevertheless, it was clear that the effect of RFG was greater for VOC than for NOx. The RFG effect on vehicle emissions of benzene was estimated to be a 30-40% reduction. Use of RFG increased formaldehyde emissions by about 10%, while acetaldehyde emissions did not change significantly. RFG effects reported here may not be the same for other driving conditions or for other vehicle fleets. RFG effects on evaporative emissions are also important. The combined effect of phases 1 and 2 of California’s RFG program was a 20% reduction in gasoline vapor pressure, about one-fifth of which occurred following the introduction of phase 2 RFG.
Systematically reviewing remote e-workers’ well-being at work: a multidimensional approach

ABSTRACT The practice of remote e-working, which involves work conducted at anyplace, anytime, using technology, is on the increase. The aim of this systematic literature review is to gain a deeper understanding of the association between remote e-working, within knowledge workers, and the five dimensions of well-being at work: affective, cognitive, social, professional, and psychosomatic. Sixty-three studies employing quantitative, qualitative and mixed-method designs have been included in the review. Findings indicate that we know more about remote e-workers’ affective state and their social and professional life than we know about their cognitive functioning and psychosomatic conditions. Whilst the research indicates a positive focus there are some negative aspects of this way of working which are highlighted within this review; such as social and professional isolation, and perceived threats in professional advancement. This review may be of great importance for academics, to continue the theoretical advancement of research into remote e-working, and practitioners, to implement and manage remote e-working attitudes and policies more effectively.
Identifying the Motor Neuron Disease in EMG Signal Using Time and Frequency Domain Features with Comparison

Motor neuron diseases are the most common neurological disorders found in the age ranges between 35-70 years, which selectively affect the motor neurons. Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease that assails the nerve cells in the brain. This disease progressively degenerates the motor cells in the brain and spinal cord, which are responsible for controlling the muscles that enable human to move around, breathe, speak, and swallow. The electromyography (EMG) signals are the biomedical signals that are used to study the muscle function based on the electrical signal originated from the muscles. As the nervous system controls the muscle activity, the EMG signals can be viewed and analyzed in order to detect the indispensable features of the ALS disease in individuals. In this paper, analyzing the time and frequency domain behaviour of the EMG signals obtained from several normal persons and the ALS patients, some characteristic features, such as autocorrelation, zero crossing rate and Fourier transform are proposed to identify the ALS disease. For the pupose of classification, K-nearest neighbothood classifier is employed in a leave-one out cross validation technique. In order to show the classification performance, an EMG database consisted of 7 normal subjects aged 21-37 years and 6 ALS patients aged 35-67 years is considered and it is found that the proposed method is capable of distinctly separating the ALS patients from the normal persons.
Comparing Alignment Factors in SMEs and Large Organizations: A Planning Integration Perspective

Measurement of the alignment between business strategies and information systems (IS) has demonstrated positive impact for the organizational performance. The factors that have proved relevant when assessing the maturity level of alignment are: communication, competency/value measurement, governance, partnership, architecture & scope, and skills. Existing research, however, has focused on the assessment of these factors on large organizations and has barely explored their impact on Small and Medium Enterprises (SME). This paper uses the data provided by 127 participants from large and SMEs in order to identify whether the aforementioned factors are also relevant for assessing the level of alignment maturity in SMEs. The results from this research suggest that there are not significant differences between large organizations and SMEs when assessing those factors. In addition, this research also explored the relation between different planning integration of alignment (independent, sequential and simultaneous) in order to measure the perceived relevance of the factors. The results suggest that the planning integration identified on SMEs and large organizations has a positive correlation on how these factors are ranked. For both SMEs and large organizations where the formulation is simultaneous, the relevance of the factors is higher perceived than it is for those where the formulation is independent or sequential.
Multimodal Analysis of Car Advertisements

Critical discourse analysis (CDA) has an interest in concepts like communication patterns in public institutions, media discourse (reports, advertisements discourse, television broadcasts etc.), the constitution of individual and group identity that convey ideological attitudes, power and status. One of the media discourse used in this study is advertisement discourse. The researcher took three popular and most brand values; they are Toyota, Mercedes-Benz and BMW produced in 2017. The aim of this study is to analyze the data using CDA and semiotic analysis. The researcher used qualitative method in the methodology approach. The result showed that all the advertisements have many aspects based on the good advertisement.
A STOCHASTIC APPROXIMATION ALGORITHM FOR STOCHASTIC SEMIDEFINITE PROGRAMMING

Motivated by applications to multi-antenna wireless networks, we propose a distributed and asynchronous algorithm for stochastic semidefinite programming. This algorithm is a stochastic approximation of a continuous-time matrix exponential scheme which is further regularized by the addition of an entropy-like term to the problem's objective function. We show that the resulting algorithm converges almost surely to an ɛ-approximation of the optimal solution requiring only an unbiased estimate of the gradient of the problem's stochastic objective. When applied to throughput maximization in wireless systems, the proposed algorithm retains its convergence properties under a wide array of mobility impediments such as user update asynchronicities, random delays and/or ergodically changing channels. Our theoretical analysis is complemented by extensive numerical simulations, which illustrate the robustness and scalability of the proposed method in realistic network conditions.
Self-Similarity of AQM Filtered Traffic

Self-similarity of network traffic is a well-known paradigm for more than a decade, now. Following the first realization of this property, it has been a popular issue to question if the self-similarity persisted with the changing network architectures and conditions. However, the effects of Active Queue Management (AQM) mechanisms over the self-similarity of network traffic have almost been left uninvestigated. AQM mechanisms are developed to aid congestion control over the network. Recently, they are applied to commercial routers and they have become notable alternatives to droptail queues. It is easy to foretell that their priority aware variants, e.g. RIO, will find application in heterogeneous networks in a nearby future. The main principle of all AQM mechanisms relies on the adaptive congestion control mechanism of Transport Control Protocol (TCP). TCP sources adapt their sending rates to network conditions by decreasing packet transmission rates during congestion. AQM mechanisms notify congestion to TCP sources more effectively than droptail queues where they start dropping packets before the queue actually overflows, in a way taking a precaution before “the disaster happens”. Obviously their different loss pattern will change the statistical properties of the traffic and the degree of selfsimilarity. For this reason we find the joint behavior of TCP and AQM mechanisms worth investigating. In this work, we run simulations in NS-2 to analyze the impacts of queue management mechanisms; droptail, RED and BLUE on the self-similarity of network traffic. The degree of self-similarity is measured by the Hurst parameter (H). We estimate H with three well known methods: Aggregated Variances, Higuchi and R/S methods. We additionally analyze the throughput, goodput, queuing delay and loss rate measurements for these queue management techniques. It is observed that the H estimates are correlated with throughput, goodput, queuing delay and loss rate. We show that droptail RED and BLUE yield different degrees of self-similarity. Furthermore, we examine RED and BLUE under different parameter settings and show that the parameter selection of AQM mechanisms affects the H estimates.
Learning naïve bayes transfer classifier throughclass-wise test distribution estimation

Text classification is a well-known problem for various applications. For last decades, it is beleived that a large corpus is one of the most important aspects for better classification. However, even though a great number of documents is available for training a classifier, it is practically impossible to achieve an ideal performance, since the distributions of labeled and unlabeled documents are often different. To overcome this problem, this paper describes a novel Naïve Bayes classifier for text classification under distribution difference between training and test data. The proposed method approximates test distribution by weighting labeled documents to cope with the distribution difference. Unlike other transfer learning which estimates the weights of labeled documents, the proposed method considerd both the documents and their estimated class labels. Therefore, the proposed method naturally combines the advantage of semi-supervised learning with those of transfer learning.
Conductance and Eigenvalue

We show the following. \begin{theorem} Let $M$ be an finite-state ergodic time-reversible Markov chain with transition matrix $P$ and conductance $\phi$. Let $\lambda \in (0,1)$ be an eigenvalue of $P$. Then, $$\phi^2 + \lambda^2 \leq 1$$ \end{theorem} This strengthens the well-known~\cite{HLW,Dod84, AM85, Alo86, JS89} inequality $\lambda \leq 1- \phi^2/2$. We obtain our result by a slight variation in the proof method in \cite{JS89, HLW}; the same method was used earlier in \cite{RS06} to obtain the same inequality for random walks on regular undirected graphs.
A Drosophila ABC Transporter Regulates Lifespan

MRP4 (multidrug resistance-associated protein 4) is a member of the MRP/ABCC subfamily of ATP-binding cassette (ABC) transporters that are essential for many cellular processes requiring the transport of substrates across cell membranes. Although MRP4 has been implicated as a detoxification protein by transport of structurally diverse endogenous and xenobiotic compounds, including antivirus and anticancer drugs, that usually induce oxidative stress in cells, its in vivo biological function remains unknown. In this study, we investigate the biological functions of a Drosophila homolog of human MRP4, dMRP4. We show that dMRP4 expression is elevated in response to oxidative stress (paraquat, hydrogen peroxide and hyperoxia) in Drosophila. Flies lacking dMRP4 have a shortened lifespan under both oxidative and normal conditions. Overexpression of dMRP4, on the other hand, is sufficient to increase oxidative stress resistance and extend lifespan. By genetic manipulations, we demonstrate that dMRP4 is required for JNK (c-Jun NH2-terminal kinase) activation during paraquat challenge and for basal transcription of some JNK target genes under normal condition. We show that impaired JNK signaling is an important cause for major defects associated with dMRP4 mutations, suggesting that dMRP4 regulates lifespan by modulating the expression of a set of genes related to both oxidative resistance and aging, at least in part, through JNK signaling.