Miracle-dz/UniFAR-FTUs huggingface dataset:
Prioritised Dynamic Communicating Processes: Part II

This paper reports continuing research on language design, compilation and kernel support for highly dynamic concurrent reactive systems. The work extends the occam multiprocessing language, which is both sufficiently small to allow for easy experimentation and sufficiently powerful to yield results that are directly applicable to a wide range of industrial and commercial practice. Classical occam was designed for embedded systems and enforced a number of constraints - such as statically pre-determined memory allocation and concurrency limits - that were relevant to that generation of application and hardware technology. Most of these constraints have been removed in this work and a number of new facilities introduced (channel structures, mobile channels, channel ends, dynamic process creation, extended rendezvous and process priorities) that significantly broaden occam? s field of application and raise the level of concurrent system design directly supported. Four principles were set for modifications/enhancements of the language. They must be useful and easy to use. They must be semantically sound and policed (ideally, at compile-time) to prevent mis-use. They must have very lightweight and fast implementation. Finally, they must be aligned with the concurrency model of the original core language, must not damage its security and must not add (significantly) to the ultra-low overheads. These principles have all been observed. All these enhancements are available in the latest release (1.3.3) of KRoC, freely available (GPL/open source) from: http://www.cs. ukc.ac. uk/projects/ofa/kroc/ .
Automatic and adaptable registration of live RGBD video streams sharing partially overlapping views

In this thesis, we introduce DeReEs-4v, an algorithm for unsupervised and automatic registration of two video frames captured depth-sensing cameras. DeReEs-4V receives two RGBD video streams from two depth-sensing cameras arbitrary located in an indoor space that share a minimum amount of 25% overlap between their captured scenes. The motivation of this research is to employ multiple depth-sensing cameras to enlarge the field of view and acquire a more complete and accurate 3D information of the environment. A typical way to combine multiple views from different cameras is through manual calibration. However, this process is time-consuming and may require some technical knowledge. Moreover, calibration has to be repeated when the location or position of the cameras change. In this research, we demonstrate how DeReEs-4V registration can be used to find the transformation of the view of one camera with respect to the other at interactive rates. Our algorithm automatically finds the 3D transformation to match the views from two cameras, requires no human interference, and is robust to camera movements while capturing. To validate this approach, a thorough examination of the system performance under different scenarios is presented. The system presented here supports any application that might benefit from the wider field-of-view provided by the combined scene from both cameras, including applications in 3D telepresence, gaming, people tracking, videoconferencing and computer vision.
Use Of Fuzzy Set In Textured Image Segmerttation

This paper presents an approach to texture segmentation by thresholding based on compactness measures of fuzzy sets to determine thresholds of an ill-defined image. The extension of fuzziness in the texture feature space provides more meaningful results than by considering fuzziness in gray scale domain. The effectiveness of the algorithm is demonstrated by comparison with other traditional non-fuzzy methods or the controversial fuzzy method in gray scale alone. In addition, the efficiency of our algorithm is further improved by parallel implementation using distributed shared memory workstations.
Evaluation of the 2012 Indian Ocean coseismic fault model in 3-D heterogeneous structure based on vertical and horizontal GNSS observation

Lack of observation network in the vicinity of oceanic intraplate earthquake lead the estimation of coseismic fault slip with high uncertainty. Satriano et al. [2] and Wei et al. [3] found NNE trending left-lateral slip as the primary features. In another hand, Yue et al. [4] and Hill et al. [5] proposed WNW trending right-lateral faults structure as the main characteristic. Here, we investigate the coseismic fault model that could explain the coseismic offset both vertical and horizontal in a 3-D heterogeneous earth structure. We constructed finite element model that include three-dimensional velocity structure, topography/bathymetry, spherical-earth and subducting slab. In this study, we employed scaling slip to adjust slip amount and total seismic moment. Instead of original slip amount, we preserve seismic moment as a basis comparison. Based on vertical and horizontal observation data, WNW trending right-lateral fault could fit better than NNE trending left-lateral fault. The present study demonstrates best-fit calculation using scaling slip optimized to the horizontal or vertical observation lead the both fault model worsen the misfit of vertical or horizontal component, respectively. This result analysis indicates a trade-off between vertical and horizontal component and reflects the importance of revisiting the fault slip modeling incorporating vertical and horizontal data equally.Lack of observation network in the vicinity of oceanic intraplate earthquake lead the estimation of coseismic fault slip with high uncertainty. Satriano et al. [2] and Wei et al. [3] found NNE trending left-lateral slip as the primary features. In another hand, Yue et al. [4] and Hill et al. [5] proposed WNW trending right-lateral faults structure as the main characteristic. Here, we investigate the coseismic fault model that could explain the coseismic offset both vertical and horizontal in a 3-D heterogeneous earth structure. We constructed finite element model that include three-dimensional velocity structure, topography/bathymetry, spherical-earth and subducting slab. In this study, we employed scaling slip to adjust slip amount and total seismic moment. Instead of original slip amount, we preserve seismic moment as a basis comparison. Based on vertical and horizontal observation data, WNW trending right-lateral fault could fit better than NNE trending left-lateral fault. The present study demonstrate.
Proposal for a postdoctoral position at INRIA, Bordeaux, France Title: Sparse predictive models for the analysis and classification of pathological speech

During this century, there has been an ever increasing interest in the development of objective vocal biomarkers to assist in diagnosis and monitoring of neurodegenerative diseases and, recently, respiratory diseases because of the Covid-19 pandemic. The literature is now relatively rich in methods for objective analysis of dysarthria, a class of motor speech disorders [1], where most of the effort has been made on speech impaired by Parkinson’s disease. However, relatively few studies have addressed the challenging problem of discrimination between subgroups of Parkinsonian disorders which share similar clinical symptoms, particularly is early disease stages [2]. As for the analysis of speech impaired by respiratory diseases, the field is relatively new (with existing developments in very specialized areas) but is taking a great attention since the beginning of the pandemic.
the Realization of Spontaneous

Although high-recognition accuracy can be obtained for speech in the form of reading a written text or similar by using state-ofthe art speech recognition technology, the accuracy is quite poor for freely spoken spontaneous speech. From this perspective, a new national project for raising the technological level of speech recognition and understanding has recently commenced in Japan. This paper first briefly introduces the project and then reports some results of preliminary experiments which have been conducted at Tokyo Institute of Technology.
Average cost temporal-difference learning

We describe a variant of temporal-difference learning that approximates average and differential costs of an irreducible aperiodic Markov chain. Approximations are comprised of linear combinations of fixed basis functions whose weights are incrementally updated during a single endless trajectory of the Markov chain. We present results concerning convergence and the limit of convergence. We also provide a bound on the resulting approximation error that exhibits an interesting dependence on the "mixing time" of the Markov chain. The results parallel previous work by the authors (1997), involving approximations of discounted cost-to-go.
2 EURASIP Journal on Advances in Signal Processing

Complex approximately analytic wavelets provide a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. Similar to Gabor wavelets, they are insensitive to illumination variations and facial expression changes. The complex wavelet transform is, however, less redundant and computationally efficient. In this paper, we first construct complex approximately analytic wavelets in the single-tree context, which possess Gabor-like characteristics. We, then, investigate the recently developed dual-tree complex wavelet transform (DT-CWT) and the single-tree complex wavelet transform (ST-CWT) for the face recognition problem. Extensive experiments are carried out on standard databases. The resulting complex wavelet-based feature vectors are as discriminating as the Gabor wavelet-derived features and at the same time are of lower dimension when compared with that of Gabor wavelets. In all experiments, on two well-known databases, namely, FERET and ORL databases, complex wavelets equaled or surpassed the performance of Gabor wavelets in recognition rate when equal number of orientations and scales is used. These findings indicate that complex wavelets can provide a successful alternative to Gabor wavelets for face recognition.
A Benchmark of NonStop SQL on the Debit Credit Transaction

: NonStop SQL is an implementation of ANSI SQL on Tandem Computer Systems. Debit Credit is a widely-used industry-standard transaction. This paper summarizes a benchmark of NonStop SQL which demonstrated linear growth from 2 to 32 processors, and 14 to ·208 Debit Credit transactions per second. The benchmark also compared the perfonnance· of NonStop SQL to the perfonnance of a record-at-a-time file system interface
Renal masses: quantitative analysis of enhancement with signal intensity measurements versus qualitative analysis of enhancement with image subtraction for diagnosing malignancy at MR imaging.

PURPOSE To retrospectively compare quantitative and qualitative methods of assessing magnetic resonance (MR) imaging contrast enhancement as the basis for diagnosing renal malignancy. MATERIALS AND METHODS MR imaging was performed by using a gadolinium-enhanced breath-hold fat-suppressed three-dimensional T1-weighted gradient-echo sequence in 71 patients (48 men and 23 women; mean age, 62 years; age range, 26-87 years) with 93 renal lesions for which pathologic correlation was available. For quantitative measurements of enhancement, the relative increase in signal intensity values was measured by one investigator with manually defined regions of interest, and the threshold of an increase of 15% or greater was used to distinguish malignant from benign masses. For qualitative assessment, two investigators independently reviewed the subtracted images of all lesions and subjectively determined whether enhancement was present or absent. The sensitivity, specificity, and positive and negative predictive values for each method were calculated and compared. Mean (+/- standard deviation) and median values of relative enhancement were also calculated for benign and malignant lesions. RESULTS At pathologic analysis, 74 (80%) of the 93 lesions were malignant, and 19 (20%)-including seven oncocytomas-were benign. For diagnosing malignancy based on enhancement alone, sensitivity and specificity, respectively, were 95% (70 of 74 lesions) and 53% (10 of 19 lesions) at quantitative analysis and 99% (73 of 74 lesions) and 58% (11 of 19 lesions) at qualitative analysis. All seven oncocytomas were considered to be malignant with both methods. When the oncocytomas were excluded, specificities increased to 83% (10 of 12 lesions) and 92% (11 of 12 lesions) for the quantitative and qualitative evaluations, respectively. Three of the four malignant lesions incorrectly characterized as benign at quantitative assessment were hyperintense on unenhanced MR images; all were diagnosed correctly at qualitative evaluation. CONCLUSION Image subtraction enables accurate assessment of renal tumor enhancement, particularly in the setting of masses that are hyperintense on unenhanced MR images.
Open Source Software Defined Radio Using GNU Radio AND USRP

Cognitive radio is a approach, which automatically find outs the available channels in wireless spectrum, on top of that it will changes its transmission or reception parameters to basically allows concurrent wireless communication in a given spectrum band at one location, which helps us to provide dynamic spectrum utilization with the next generation network and it will leads to proper management of fixed assigned spectrum. In today’s world of wireless communication for small changes we need to replace costly hardware for such aspect cognitive radio concept comes into picture. According to this concept of SDR (Software Defined Radio) Replacing rigid Hardware with flexible software based solutions. So that configuring hardware by making little changes in software is much efficient. Along with this performance of the upper layer protocols likely routing and transport are introduced.
A Critical Evaluation on the Role of Aerodynamic and Canopy–Surface Conductance Parameterization in SEB and SVAT Models for Simulating Evapotranspiration: A Case Study in the Upper Biebrza National Park Wetland in Poland

Evapotranspiration (ET) estimation through the surface energy balance (SEB) and soil-vegetation-atmosphere-transfer (SVAT) models are uncertain due to the empirical parameterizations of the aerodynamic and canopy-substrate conductances (gA and gS) for heat and water vapor transfers. This study critically assessed the impact of conductance parameterizations on ET simulation using three structurally different SEB and SVAT models for an ecologically important North-Eastern European wetland, Upper Biebrza National Park (UBNP) in two consecutive years 2015 and 2016. A pronounced ET underestimation (mean bias −0.48 to −0.68 mm day−1) in SEBS (Surface Energy Balance System) was associated with an overestimation of gA due to uncertain parameterization of momentum roughness length and bare soil’s excess resistance to heat transfer (kB−1) under low vegetation cover. The systematic ET overestimation (0.65–0.80 mm day−1) in SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) was attributed to the overestimation of both the conductances. Conductance parameterizations in SEBS and SCOPE appeared to be very sensitive to the general ecohydrological conditions, with a tendency of overestimating gA (gS) under humid (arid) conditions. Low ET bias in the analytical STIC (Surface Temperature Initiated Closure) model as compared to SEBS/SCOPE indicated the critical need for calibration-free conductance parameterizations for improved ET estimation.
EXAMINING FACTORS INFLUENCING CUSTOMERSATISFACTION AND TRUST TOWARDS VENDORSON THE MOBILE INTERNET

This study aims to examine factors influencing customer satisfaction and trust towards vendors on the mobile Internet. Data were analysed among 200 respondents who completed the questionnaire by employing multiple regression analysis. Results revealed that customer satisfaction towards the vendor was significantly influenced by ease-of-use, responsiveness, and brand image. Meanwhile, customer trust towards the vendor in m-commerce is affected by responsiveness, brand image and satisfaction towards the vendor in m-commerce. The findings imply there is a need for a vendor in m-commerce to put greater focus on the factors that can generate more satisfaction and trust from the customers. The paper rounds off with conclusions and an agenda for future research in this area.
International Journal of Advanced Research in Computer Science and Software Engineering

— The growth of technology such as WWW, Social networking, Internet of things (IoT), electronic media etc. are responsible for the generation of vast amount of data in our daily routine by interacting with Internet world, Social networking media etc. across the world. It is very necessary to analyse and understand this unstructured datasets. So, Sentimental [14] analysis is one of the technologies which is used for determining whether the given piece of writing is positive, negative or neutral. It is also known as opinion mining. It means that it is deriving the attitude of a speaker. The main aim of this technology is to check or discover how different people think or feel about particular topic, celebrity or politicians by using text mining, NLP, linguistic computation [6] etc. This paper shows the brief review of different sentimental analysis techniques that are apply on the different type of datasets like product review data collected from Amazon.com, twitter datasets, Online Movies Review data etc. The most popular approach is knowledge based feature extraction which uses bag of words technique to explore the sentimental analysis of twitter using R performed on different data set domain .
Performance Study of Mutation Operator in Genetic Algorithms on Anticipatory Scheduling

Disk schedulers in current operating systems schedule a request as soon as the previous request has been completed. If the disk read requests are issued synchronously with short period between the scheduler switches to a request from another process with the assumption that the last process has no more requests. This condition is called deceptive idleness. It can be overcome by anticipatory disk scheduling framework, which introduces a short, controlled delay period, during which the disk scheduler waits for additional requests to arrive from the process that issued the last serviced request. Genetic algorithms, the most widely known types of evolutionary computation methods can be used for optimizing the anticipatory scheduling. In this paper, we propose to use the mutation operators while tuning the anticipatory scheduling with genetic algorithms. In general, a mutation operator is regarded as a secondary genetic operator but it plays a decisive role in the global functioning of the algorithm. We make the study on Linux operating systems kernel 2.6. 16. We use various mutation operators and make a comparison on the performance of anticipatory scheduling.
Iso-Map: Energy-Efficient Contour Mapping in Wireless Sensor Networks

Contour mapping is a crucial part of many wireless sensor network applications. Many efforts have been made to avoid collecting data from all the sensors in the network and producing maps at the sink, which is proven to be inefficient. The existing approaches (often aggregation based), however, suffer from heavy transmission traffic and incur large computational overheads on each sensor node. We propose Iso-Map, an energy-efficient protocol for contour mapping, which builds contour maps based solely on the reports collected from intelligently selected ¿isoline nodes¿ in wireless sensor networks. Iso-Map achieves high-quality contour mapping while significantly reducing the generated traffic from O(n) to O(¿n), where n is the total number of sensor nodes in the field. The pernode computation overhead is also restrained as a constant. We conduct comprehensive trace-driven simulations to verify this protocol, and demonstrate that Iso-Map outperforms the previous approaches in the sense that it produces contour maps of high fidelity with significantly reduced energy cost.
Which are the Best Features for Automatic Verb Classification : a Corpus Study of Levin Verb Classification

Much research in lexical acquisition has concentrated on the hypothesis that the behavior of a verb, particularly with respect to the expression of arguments and the assignment of semantic roles is to a large extent driven by deep semantic regularities. We focus on the empirical basis for a central part of this research, Levin (1993), (which we abbreviate as Levin from now on). The core of Levin’s work is a set of claims about the connections between meaning regularities and syntactic alternation patterns. These connections form the basis for a detailed classification of a large number of English verbs. In computational linguistics, Levin’s classification has been shown to reduce redundancy in verb descriptions and to enable potentially useful generalizations across similar verbs. This has motivated a wealth of work on automatic acquisition of verb lexicons from labeled and unlabeled corpora. This is a clear demonstration of the transfer of useful ideas from theoretical linguistics to computational linguistics. Until now, there has been little transfer in the opposite direction. In the present case study, we investigate the relation between Levin’s approach, the theoretical motivations for her work and the corpus data. Concretely, we wish to discover maximally informative and effective features for automatic verb classification. We develop and evaluate a wide range of different feature sets, evaluating each against Levin-style verb classification. We perform automatic classification experiments using Bayesian Multinomial Regression (a state-ofthe-art log-linear modeling framework which we found to outperform SVMs for this task) with the proposed feature spaces. Our experiments suggest that subcategorization frames, despite their theoretical credibility, are not the most effective basis for automatic classifiers. Rather, we find that the best-performing classifier use a combination of dependency information and simple co-occurrence features. This observation is consistent with the hypothesis that robust and practically useful extensions of Levin’s classes will be based not only on alternation patterns but also on semantic similarities.
T ransactions on H uman - C omputer I nteraction THCI

Dennis Galletta was the accepting Senior Editor. This article was submitted on 2/3/2010 and accepted on 9/23/2010. It was with the authors 13 weeks and 6 days for 1 revision. Reisen, N. and U. Hoffrage (2010) “The Interactive Choice Aid: A New Approach to Supporting Online Consumer Decision Making,” AIS Transactions on Human-Computer Interaction (2) 4, pp. 112-126
Subjective evaluation of visual quality and simulator sickness of short 360° videos: ITU-T Rec. P.919

—Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360° video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360° video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360° video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360° sequences. The following factors’ inﬂuences were also analyzed: assessment methodology, sequence