Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation of pose and gesture recognition accuracy using two kinect versions

The aim of this paper is to analyse the variation of the gesture recognition accuracy of several classifiers, based on input provided by two different sensors: Kinect for XBox 360 (Kinect 1) and its improved, newer version, Kinect for XBox One (Kinect 2). The accurate and meaningful recognition of body postures and gestures is a problem that depends on multiple factors, including the machine learning methods used, the characteristics of the database of poses and gestures (sample size, number of samples from each class, data noise, input sensor data) and the selected combination of gestures or poses we are interested to distinguish from each other (their duration/size and joints implied, complexity or similarity). The original contribution of this article consists of creating a database of meaningful poses using Kinect 2 and comparing the efficiency of several classifiers trained and tested on this database with previously obtained results for Kinect 1, considering sensor type, gesture complexity, as well as data size and the characteristics of the poses.
BPS orientifold planes from crosscap states in Calabi-Yau compactifications

We use the results of [1] on the simple current classification of open unoriented CFTs to construct half supersymmetry preserving crosscap states for rational Calabi-Yau compactifications. We show that the corresponding orientifold fixed planes obey the BPS-like relation M = eiQ. To prove this relation, it is essential that the worldsheet CFT properly includes the degrees of freedom from the uncompactified space-time component. The BPS-phase can be identified with the automorphism type of the crosscap states. To illustrate the method we compute crosscap states in Gepner models with each ki odd.
Thumbs-up: a game for playing to rank search results

Human computation is an effective way to channel human effort spent playing games to solving computational problems that are easy for humans but difficult for computers to automate. We propose Thumbs-Up, a new game for human computation with the purpose of playing to rank search result. Our experience from users shows that Thumbs-Up is not only fun to play, but produces more relevant rankings than both a major search engine and optimal rank aggregation using the Kemeny rule.
Monitoring Dynamic Integrity Constraints on Finite State Sequences and Existence Intervals

Correct database behaviour in the course of time can be spec- ied by requiring dynamic integrity constraints. These constraints can be expressed as temporal logic formulae which, however, usually are interpreted in innite state sequences. Since in practice only a partial, nite prex of an innite sequence is known, notions of partial admissibility of nite se- quences with respect to constraints have been introduced. Admissibility can be checked on each state transition by following corresponding paths in tran- sition graphs, which can be constructed for arbitrary temporal formulae. In this paper we modify temporal logic and transition graphs as introduced in our former work by giving them an interpretation in the empty state se- quence so that the theory can be extended to complete nite sequences. Since practical dynamic constraints are to refer to database objects only during their existence intervals, this extension is utilized to handle integrity checks on the deletion of objects.
Tasco®: A Product of Ascophyllum nodosum Enhances Immune Response of Caenorhabditis elegans Against Pseudomonas aeruginosa Infection

The effects of Tasco®, a product made from the brown seaweed (Ascophyllum nodosum) were tested for the ability to protect Caenorhabditis elegans against Pseudomonas aeruginosa infection. A water extract of Tasco® (TWE) reduced P. aeruginosa inflicted mortality in the nematode. The TWE, at a concentration of 300 µg/mL, offered the maximum protection and induced the expression of innate immune response genes viz.; zk6.7 (Lypases), lys-1 (Lysozyme), spp-1 (Saponin like protein), f28d1.3 (Thaumatin like protein), t20g5.7 (Matridin SK domain protein), abf-1 (Antibacterial protein) and f38a1.5 (Lectin family protein). Further, TWE treatment also affected a number of virulence components of the P. aeuroginosa and reduced its secreted virulence factors such as lipase, proteases and toxic metabolites; hydrogen cyanide and pyocyanin. Decreased virulence factors were associated with a significant reduction in expression of regulatory genes involved in quorum sensing, lasI, lasR, rhlI and rhlR. In conclusion, the TWE-treatment protected the C. elegans against P. aeruginosa infection by a combination of effects on the innate immunity of the worms and direct effects on the bacterial quorum sensing and virulence factors.
A localized cooperative wideband spectrum sensing for dynamic access of TV bands using RF sensor networks

In this paper we address and simulate a Radio Frequency (RF) sensor network for a cooperative spectrum sensing and localization scheme. The proposed method integrates a Wavelet based Multi-Resolution Spectrum Sensing (MRSS), an N-bit hard combination technique for cooperative decision making and a Received Signal Strength (RSS) based localization algorithm to detect the availability of frequency bands and the location of the usable base station. We develop an N-bit hard combination technique and compare its performance to a traditionally used 2-bit hard combination for cooperative sensing. The key idea is to design a novel RF sensor network based cooperative wideband spectrum sensing and localization scheme by using a wavelet based Multi-Resolution Spectrum Sensing (MRSS) and Received Signal Strength (RSS) Localization techniques which were originally proposed for cognitive radio applications. The performance evaluations are also done to show the different detection accuracies for varying parameters such as number of sensor nodes, Signal to Noise Ratios (SNR) and number of averaged Power Spectral Densities (PSD). The proposed scheme improves the problems of shadowing, fading and noise. In addition, the RSS based localization technique was shown to be an acceptable means of estimating the position of the available transmitter.
An Impovement for Adaptiong the Alignment Template Based Statistical Machine Translation Model to Pervasive Computing Environments

Machine translation (MT) has a variety of applications in pervasive computing environments. Thin clients such as personal digital assistants (PDA) are always deployed with small memory. In this paper, a novel method to improve the performance of alignment template based translation model (ATTM), which is the state-of-art MT models, was proposed. The key element, alignment template of ATTM, was extended to be context sensitive alignment templates (CSAT) based on the context information. After applying CSAT, the ATTM would use less time and memory during the decoding stage, and thus might be more suitable for small memory environments. In the end of this paper, an experiment was presented and the results showed that this proposed method could improve the performance greatly
Modeling and Predicting Popularity Dynamics via an Influence-based Self-Excited Hawkes Process

Modeling and predicting the popularity dynamics of individual user generated items on online social networks has important implications in a wide range of areas. The challenge of this problem comes from the inequality of the popularity of content and the numerous complex factors. Existing works mainly focus on exploring relevant factors for prediction and fitting the time series of popularity dynamics into certain class of functions, while ignoring the underlying arrival process of attentions. Also, the exogenous effect of user activity variation on the platform has been neglected. In this paper, we propose a probabilistic model using an influence-based self-excited Hawkes process (ISEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures three ingredients: the intrinsic attractiveness of a microblog with exponential time decay, the user-specific triggering effect of each forwardings based on the endogenous influence among users, and the exogenous effect from the platform. We validate the ISEHP model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that our proposed model consistently outperforms existing prediction models.
Integrating Development and Environment to Model Reproductive Performance in Natural Populations of an Intertidal Gastropod1

Abstract Functional challenges can differ among life-history stages, yet performance at one stage may be linked to the outcome of performance at others. For example, adult performance, in terms of the location or timing of reproduction in response to environmental signals, can set conditions that affect the performance of developmental stages. In marine invertebrates, however, early performance has been studied primarily in the laboratory. I outline an integrative approach to the study of field reproductive performance in a marine gastropod that undergoes development in intertidal habitats. Embryos within gelatinous masses experience high variability in development temperature and frequent exposure to thermal stress. In laboratory experiments, developmental performance was measured as a function of maximum temperature (Tmax) experienced during fluctuations that mimicked field tidal profiles. Performance curves showed declines that coincided with temperature thresholds for heat shock protein (Hsp) expression, a signal of cellular stress. Application of laboratory results to field records of Tmax predicted large variation in the survival of embryos deposited on different days. Timing of field reproduction was non-random with respect to Tmax, suggesting that adults could help to buffer embryos from environmental stress. Embryo survival, however, was not predicted to benefit from the non-random pattern of adult reproduction. Adults may be constrained to respond to information that only weakly predicts conditions that embryos will experience. Studies that incorporate linkages between life cycle stages in the field may better reveal how performance capacities and constraints at one stage can influence performance and selection at others.
Analysis of High-Efficiency Power Amplifier Using Second Harmonic Manipulation: Inverse Class-F/J Amplifiers

In this paper, an analysis of a power amplifier manipulated using a second harmonic (PA-2HM) is described using a remarkable correlation between the fundamental and second harmonic impedances. The output loading condition, made up of an optimum fundamental impedance mapped to the conditional second harmonic reactance, allows us to achieve a high-efficiency power amplifier (PA) with a simple output matching circuit. For the analysis, the output voltage and current waveforms are modeled in terms of their output loading in order to extend the output power and efficiency. Specific PA-2HM cases, inverse Class-F and Class-J modes, are analyzed using different second harmonic reactance conditions. The allowable second harmonic reactance for maintaining maximum efficiency has been found to be spread throughout a wide range. To justify the analysis, a harmonic load-pull simulation and measurement are conducted and compared with analysis results. For verification, a commercially available 60-W gallium-nitride (GaN) device was used for different types of PA (inverse Class-F and Class-J) with appropriate second harmonic impedance. In terms of the output power and drain efficiency, the measured results are in good agreement with not only computer-aided design simulations, but also with our analysis and load-pull results.
Evaluating the relevance of the “Real Access” criteria as a framework for rural HCI research

Developing ICT software that is useful and usable in a rural context poses many problems. One of the major difficulties is understanding the real needs of the end users and the constraints imposed by the rural environment. Many techniques exist in the field of Human Computer Interaction (HCI) that attempt to understand the needs of the end users but many are not useful in a rural context, or at least not when applied in a standard way. This paper presents some existing HCI research techniques that are applicable in a rural context and shows how they fit into the bridges.org ‘Real Access’ framework.
A Novel Diagnostic Approach Based on Support Vector Machine with Linear Kernel for Classifying the Erythemato-Squamous Disease

The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being a large margin classifier is a powerful pattern recognition and machine learning methodology that is widely used for both linear and non-linear classification problems. Comparing testing on different kernel methods, we have noticed that our method gives the better accuracy. Choosing the optimal value of the parameters is a crucial criterion and this was achieved by performing 3 fold cross-validations.
Hybrid Spot Size Converter on Semi-Insulating InP Substrate at 1 . 55 μ m Wavelength

— This article describes the design of a hybrid spot size converter (SSC) on Semi Insulating (SI)-InP substrate, which utilizes two lateral and vertical tapers. It can be monolithically integrated with the active and the passive components such as photodetector, modulator, laser, Arrayed Waveguide Grating (AWG), and Mach-Zehnder Interferometer (MZI). The taper loss and mode-mismatch loss are about 1.8 and 0.8 dB, respectively, for a 1.5 mm long vertical and 0.15 mm long lateral tapers. The shallow input waveguide is with a width of 2 µ µ µ µm and the output waveguide is with a width and a thickness of 10 and 5 µ µ µ µm, respectively, which is suitable for coupling of the light between a standard single mode fiber and an optical chip.
Automatic feature selection for named entity recognition using genetic algorithm

This paper presents a feature selection approach for named entity recognition using genetic algorithm. Different aspects of genetic algorithm including computational time and criteria for evaluating an individual (i.e., size of the feature subset and the classifier's accuracy) are analyzed in order to optimize its learning process. Two machine learning algorithms, k-Nearest Neighbor and Conditional Random Fields, are used to calculate the accuracy of the named entity recognition system. To evaluate the effectiveness of our genetic algorithm, feature subsets returning by our proposed genetic algorithm are compared to feature subsets returning by a hill climbing algorithm and a backward one. Experimental results show that feature subsets obtained by our genetic algorithm is much smaller than the original feature set without losing of predictive accuracy. Furthermore, these feature subsets result in higher classifier's accuracies than that of the hill climbing algorithm and the backward one.
Murvis: enhancing the visualization of multiple response survey

In a survey, when participants are allowed to give multiple response answers, the results will be presented in patterns of clustering based on similarity factors. Multidimensional scaling (MDS) is often used to reduce the dimension of data for presenting information in clusters that allow results to be interpreted according to the survey subjects and attributes. However, too many subjects and attributes will produce massive output points (coordinates) in the results that provide difficulties in the presentation. In this paper, we propose a tool called Murvis (Multiple Response Visualization) to provide users (researchers) to visualize the MDS output coordinates in 2D and 3D space with flexible views manipulation, and results reclassification based on colored attributes. We use Java programming language to read all the MDS output coordinates and apply a distance ratio algorithm to visualize the output points in height. At the same time, the Java program also reads all coordinates for MDS output attributes to assign colours to any particular attribute analysis. As a case study, we work on 50 data coordinates and perform the testing on two more other datasets. The first dataset consists of 200 cases to look into the ease of visualization technique. The second dataset has different subjects and cases to test on the flexibility of the tool to reclassify the attribute results with colours. Our study benefits the researchers or statisticians for analyzing their findings for multiple response answers.
The impact of neighbourhood deprivation on adolescent violent criminality and substance misuse: a longitudinal, quasi-experimental study of the total Swedish population.

BACKGROUND A number of studies suggest associations between neighbourhood characteristics and criminality during adolescence and young adulthood. However, the causality of such neighbourhood effects remains uncertain. METHODS We followed all children born in Sweden from 1975-1989 who lived in its three largest cities by the age of 15 years and for whom complete information was available about individual and contextual factors (N = 303 465). All biological siblings were identified in the sample (N = 179 099). Generalized linear mixed-effects models were used to assess the effect of neighbourhood deprivation on violent criminality and substance misuse between the ages of 15 and 20 years, while taking into account the cross-classified data structure (i.e. siblings in the same families attending different schools and living in different neighbourhoods at age 15). RESULTS In the crude model, an increase of 1 SD in neighbourhood deprivation was associated with a 57% increase in the odds of being convicted of a violent crime (95% CI 52%-63%). The effect was greatly attenuated when adjustment was made for a number of observed confounders (OR 1.09, 95% CI 1.06-1.11). When we additionally adjusted for unobserved familial confounders, the effect was no longer present (OR 0.96, 95% CI 0.84-1.10). Similar results were observed for substance misuse. The results were not due to poor variability either between neighbourhoods or within families. CONCLUSIONS We found that the adverse effect of neighbourhood deprivation on adolescent violent criminality and substance misuse in Sweden was not consistent with a causal inference. Instead, our findings highlight the need to control for familial confounding in multilevel studies of criminality and substance misuse.
Radix-r Non-Adjacent Form and Its Application to Pairing-Based Cryptosystem ∗

Recently, the radix-3 representation of integers is used for the efficient implementation of pairing based cryptosystems. In this paper, we propose non-adjacent form of radix-r representation (rNAF) and efficient algorithms for generating rNAF. The number of non-trivial digits is (r − 2)(r + 1)/2 and its average density of non-zero digit is asymptotically (r − 1)/(2r − 1). For r = 3, the non-trivial digits are {±2,±4} and the nonzero density is 0.4. We then investigate the width-w version of rNAF for the general radix-r representation, which is a natural extension of the width-w NAF. Finally we compare the proposed algorithms with the generalized NAF (gNAF) discussed by Joye and Yen. The proposed scheme requires a larger table but its non-zero density is smaller even for large radix. We explain that gNAF is a simple degeneration of rNAF—we can consider that rNAF is a canonical form for the radix-r representation. Therefore, rNAF is a good alternative to gNAF. key words: non-adjacent form, radix-r representation, signed window method, elliptic curve cryptosystem, pairing based cryptosystem
A Globally Optimal Algorithm for Scheduling Both Hard Periodic and Soft Aperiodic Tasks 1

Tasks that form the base load of a real-time system usually come with hard deadlines and need to be executed periodically. In addition to these periodic tasks, aperiodic tasks may be invoked randomly in response to environmental stimuli. These randomly-arriving aperiodic tasks usually come with soft deadlines, but their response times may still be important to the overall system performance. It is therefore important to schedule tasks so as to provide responsive services for soft aperiodic tasks without missing any periodic task deadline. In this paper, we propose an algorithm, called the last chance (LC) algorithm, that meets this objective. The LC algorithm is globally optimal in the sense that it provides the minimal aperiodic task response time while meeting all periodic task deadlines among all possible periodic and aperiodic task scheduling methods. Since the LC algorithm may require a signiicant amount of on-line computation, we also propose a suboptimal algorithm whose on-line computation requirement is low. Any opinions, ndings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reeect the views of the funding agencies.
Current Understanding of Acute Bovine Liver Disease in Australia

Acute bovine liver disease (ABLD) is a hepatotoxicity principally of cattle which occurs in southern regions of Australia. Severely affected animals undergo rapid clinical progression with mortalities often occurring prior to the recognition of clinical signs. Less severely affected animals develop photosensitization and a proportion can develop liver failure. The characteristic histopathological lesion in acute fatal cases is severe, with acute necrosis of periportal hepatocytes with hemorrhage into the necrotic areas. Currently there are a small number of toxins that are known to cause periportal necrosis in cattle, although none of these have so far been linked to ABLD. Furthermore, ABLD has frequently been associated with the presence of rough dog’s tail grass (Cynosurus echinatus) and Drechslera spp. fungi in the pasture system, but it is currently unknown if these are etiological factors. Much of the knowledge about ABLD is contained within case reports, with very little experimental research investigating the specific cause(s). This review provides an overview of the current and most recently published knowledge of ABLD. It also draws on wider research and unpublished reports to suggest possible fungi and mycotoxins that may give rise to ABLD.
Declarative Data Exchange: Spreadsheets to RDF/OWL

Integrating and accessing data from the many existing formats is a key challenge for the Semantic Web. In particular, representing and understanding non-RDF/OWL data in terms of ontologies is important for reasoning about this data and its relationships to other data, and for extracting or accessing the data itself. This paper presents a declarative method for mapping and translating spreadsheet data to RDF/OWL. First, a declarative mapping language is presented. Then, for this syntax, the paper presents a declarative semantics in terms of matrix operations as abstractions over spreadsheets and the relations therein. The language supports arbitrary search and indexing functions over spreadsheet data and allows the generation of complex associations and aggregations in the RDF/OWL output. Imperative and functional implementations of the mapping and extraction technology are presented.