Miracle-dz/UniFAR-FTUs huggingface dataset:
Title: Neural Entrainment is Associated with Subjective Groove and Complexity for Performed but not Mechanical Musical Rhythms Authors, affiliations:

Authors, affiliations: Daniel J. Cameron Dept. of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada. 0000-0001-5543-9836 Ioanna Zioga School of Biological and Chemical Sciences, Queen Mary University of London, UK Job P. Lindsen Dept. of Psychology, Goldsmiths, University of London, UK Marcus T. Pearce School of Electronic Engineering and Computer Science, Queen Mary University of London, UK Geraint A. Wiggins AI Lab, Vrije Universiteit Brussel, Belgium & School of Electronic Engineering and Computer Science, Queen Mary University of London, UK, 0000-0002-1587-112X Keith Potter Dept. of Music, Goldsmiths, University of London, UK Joydeep Bhattacharya Dept. of Psychology, Goldsmiths, University of London, London, UK 0000-0003-3443-9049
Portable Parallel Simulator using MPI for 2 D and 3 D Domains : Design and Performance Testing

In this paper, we present the design and implementation deta ils of our highly modular parallel simulation code using MPI, overlap ping communication and computations. We outline the modularity of the implemen tation, potentially allowing the code to be adapted for other applications. For t he testbed Itanium2based 256-processor running Linux cluster at the Academic C omputer Centre TASK, Gdansk, Poland, we present the relation between the sp e d-ups and domain sizes as well as shapes of 3D domains with different rati os of nodes updated by each processor to the number of nodes exchanged with the processors holding neighboring domains. We also present the relation b etween the parallel efficiency and the idle time spent on finishing pending commun ication requests, for all tested domain configurations, from 1 to 32 processors . The latter could be beneficial for advance assessment of other applications run on the same cluster.
ENABLE INDUSTRIAL SERVICE INNOVATION – AN AFFORDANCE PERSPECTIVE

This paper explores the impact of digitized products on industrial service innovation. Digital technologies equip physical products with versatile material properties that create a multitude of opportunities for value co-creation. In particular, product-complementing service offerings are an obvious field for investigating service innovation that leverage digitized products. We contribute to research on digitally enabled service systems that progressively emerge in industrial settings. Anchored in a revelatory case study in the intra-logistics industry, we explore how digitized products are put to innovative uses. Specifically, we take an affordance perspective to identify goal-oriented action potentials that arise from material properties of the digitized product and organizational use contexts in service systems. Interpreting case data, we show how original equipment manufacturers create the potential to (1) monitor and control industrial products remotely; (2) empower technical customer service; (3) manage, optimize, and integrate product operations; and (4) offer performance-based contracting of industrial products. Besides identifying affordances and demonstrating how digitized products enable novel configurations in service systems, we contribute to theory by (1) proposing a framework to conceptualize affordances of digitized products for service innovation and (2) linking the service-dominant logic with affordance theory.
Displacing big data : How criminals cheat the system

Many technical approaches for detecting and preventing cybercrime utilise big data and machine learning, drawing upon knowledge about the behaviour of legitimate customers and indicators of cybercrime. These include fraud detection systems, behavioural analysis, spam detection, intrusion detection systems, anti-virus software, and denial of service attack protection. However, criminals have adapted their methods in response to big data systems. We present case studies for a number of different cybercrime types to highlight the methods used for cheating such systems. We argue that big data solutions are not a silver bullet approach to disrupting cybercrime, but rather represent a Red Queen’s race, requiring constant running to stay in one spot.
Soft Robotic Glove with Integrated Sensing for Intuitive Grasping Assistance Post Spinal Cord Injury

This paper presents a fully-integrated soft robotic glove with multi-articular textile actuators, custom soft sensors, and an intuitive state machine intent detection controller. We demonstrate that the pressurized actuators can generate motion and force comparable to natural human fingers through bench-top testing. We apply textile-elastomer capacitive sensors to the glove to track finger flexion via strain and detect contact with objects via force. Intuitive user control is achieved via a state machine controller based on signals from the integrated sensors to detect relative changes in hand-object interactions. Results from an initial evaluation with 3 participants with spinal cord injury (SCI), of varied injury levels and years since injury, wearing and controlling the glove show an average of 87% improvement in grasping force, and improvements in functional assessments for participants with recent injuries. A significant variation in response suggests further investigation is required to understand the adaptation needed across different injury levels and durations since injury. Additionally, we evaluate the controller and find an average of 3 seconds from user initiations to completed grasps, and 10% inadvertent grasp triggers and no false releases when objects are held.
Economic Impact of the New Mediterranean Rail Corridor in Andalusia : A Dynamic CGE Approach

The transport sector is the life blood of the economy and serve as engine that fuels economic growth. From this perspective, the European Union is favoring the improvement of communications all around Europe. The goal is to create a European net of transports (TEN-T), and in this net the port of Algeciras in the strait of Gibraltar is an important node, as a primary rail hub for both the Mediterranean and Atlantic TEN-T rail corridors of the European Core Network. This paper assesses the impact of this new infrastructure in the economy of Andalusia through the use of a Computational General Equilibrium (CGE) model. The CGE will also add a dynamic component to evaluate the effect along several periods of time, based on the growth model of Ramsey with a representative consumer with infinite lifetime. The calibration of the model is done with the Social Accounting Matrix (SAM) of Andalusia of 2010 where the different modes of transport have been previously disaggregated. This work evaluates the long-term impact of the new rail infrastructure in the port of Algeciras, in terms of an increase of the traffic and also the shift from the road transport to the train. This work, however, is not taking into account other effects such as the impact of the construction of the infrastructures, neither the effect of the attractiveness of the area to the installation of new industries. Although impact analysis has been previously applied to assess the impact of transport infrastructures in Spain, these have made use of linear models, which underlay assumptions that are very restrictive. CGE models shifts these limitations, providing more realistic values; therefore, this work fill in this gap by introducing a Dynamic CGE model that overcome some of the limitations of linear models.
Effect of Compiler Optimizations on the Error Resilience of Soft Computing Applications

While hardware errors are on the rise as chip sizes reduce, users of commodity systems expect a near faultless experience with low degradation in performance. Developers tune for higher performance by enabling compiler optimizations on code, but these optimizations affect the resilience of applications, making it difficult to maintain an error resilience guarantee when multiple optimizations are applied together (e.g., -O3 in gcc). We focus on soft computing applications, (e.g., multimedia applications) that can tolerate most hardware errors as long as the erroneous outputs do not deviate significantly from error-free outcomes. We term outcomes that deviate significantly from the errorfree outcomes as Egregious Data Corruptions (EDCs). We study how four specific compiler optimizations affect the resilience of soft computing applications. Further, we investigate how the optimizations affect the detector placement locations for detecting EDC causing faults. This helps us identify safe compiler optimizations that maintain a certain guarantee on the error resilience of the application. Our work is a first step towards identifying the performance-resilience tradeoff space.
Water vapor characterisation over Oklahoma using DIAL

The knowledge of the distribution and variability of the water vapor with time and height yields important hints to atmospheric processes like the development of an atmospheric boundary layer (ABL). Measurements with high temporal and spatial resolution are necessary to characterise the ABL appropriately. With a time resolution of 10 seconds and a height resolution of 60m within the ABL for the absolute humidity the differential absorption lidar (DIAL) is well suited for the observation and analysis of turbulent processes in the atmosphere which are coupled with the development of the ABL. The determination of mean values of the ABL top height is necessary for the scaling of atmospheric parameters like water vapor distribution functions, which can be used for a more realistic parameterisation in numerical forecast or dispersion models. In addition the DIAL technique yields the instantaneous ABL height with a high temporal resolution, which is important for e.g the examination of entrainment processes. The use of DIAL offers the advantage of providing 2 different parameters for the retrieval of ABL heights, water vapor and aerosol distributions. While the results of both methods generally show good agreement, there are more complex situations where only the combined information from both parameters yields satisfactory results.
Face Recognition on E-Voting Using Fisher Face Algorithm

E-voting is most of researchers focusing the e-voting it is the main concept of our democratic country. Voting system, people votes is denoted by whose are governed by the government. So, voting is more secure and perfect. we propose the face recognition technique it is the face recognition, voters face are captured by the webcam then it is match by the previously stored the voter database, voter face are matched by the voter database, which Fisher face algorithm to be used that the face compared by the data variance algorithm, that the people can eligible to vote otherwise that the face is doesn’t be matched that the people cannot be eligible to vote, whether that person is right to vote or not it compared by the face recognition using fisher face algorithm. That the eligible persons are to be voted and then votes are calculated and viewed it. The illegal votes are avoided. So, face recognition using Fisher face algorithm to be more secured by voting.
BIROn-Birkbeck Institutional Research Online Nguyen, C. and Ronald, Angelica (2014) Are autism spectrum disorders comparable in boys and girls? An examination of sex differences in behaviour, cognition and psychopathology in a predominantly low functioning

Background: The female autism spectrum disorder (ASD) phenotype is currently underresearched. Girls with ASD may differ from boys with ASD, yet few studies have tested this hypothesis, particularly among low functioning individuals. This study compared girls and boys with predominantly low functioning ASD and typically developing girls during middle and late childhood across autism symptoms, cognition, sensory overresponsivity, and co-occurring psychopathology. Methods: Three mental-age–matched groups were compared: girls with ASD (N = 27), boys with ASD (N = 27), and typically developing girls (N = 17). Their ages ranged from 7 to 19 years old. The majority of individuals in the ASD sample had an intelligence quotient of less than 70. Participants were assessed on standard social cognition and attention to detail tasks. Parents completed behavior questionnaires. Results: Mean levels of autism symptoms were not significantly different for boys and girls with ASD, and they were significantly higher than those of typically developing girls. There were some weak trends for boys with ASD to show more compulsive behavior, inattention/hyperactivity and taste sensory overresponsivity than girls with ASD, but differences were not significant after controlling for multiple comparisons. The Block Design task, assessing attention to detail, showed a significant sex difference, with boys with ASD outperforming both girls with ASD and typically developing girls. Conclusions: Predominantly low functioning girls with ASD differed from typically developing girls but did not differ from boys with ASD with regard to their levels of autism symptoms, sensory overresponsivity, or co-occurring psychopathology. These data feed into debates about whether ASD assessment tools require sex-specific criteria and to what degree treatment should be tailored to the sex of the individual.
Random sampling for indoor flight

A challenging problem in the research field of Micro Air Vehicles is to achieve vision-based autonomous indoor flight. Approaches to this problem currently hardly make use of image appearance features, because these features generally are computationally expensive. In this article, we demonstrate that the broadly applicable strategy of random sampling can render the extraction of appearance features computationally efficient enough for use in autonomous flight. Random sampling is applied to a height control algorithm that estimates the height at which an image is taken by processing small image patches. The patches are extracted at random locations in the image. We vary the specific number of image patches to directly influence the trade-off between processing time and the accuracy of the height estimation. The algorithm is first tested on image sets and then on videos taken from a real platform. Subsequently, the algorithm is tested on a 15-gram ornithopter in an office room. The experiments show that very few image patches ( 0.56% of all possible patches) are already sufficient for the task of height control.
Perception of Nonverbal Cues for Human-Robot Interaction

Socially interactive robots need the same behaviors and capabilities of people to interact naturally with humans. One of these capabilities is the perception of nonverbal cues, especially those play a significant role in conveying human’s feedback. Usually, people use feedback to exchange information about some communicative functions. This thesis contributes to the development of a biologically inspired nonverbal feedback perception system for human-robot interaction. Focusing on the vision modality, the system recognizes nonverbal feedback of interaction partner during the conversation. Guided by psychological concepts and ideas, the system provides two major cooperative phases: bottom-up and top-down information processing. In bottom-up information processing, the perception system starts with the stimuli which are primitive data received from the sensory system, processes them, and propagates the perceived cues to the higher levels. Each step in this phase carries out more complex analysis of the input data. The low-level perception receives signals from different sensors, at a slice of time, and treats them independently. It represents the extraction of low-level information, referred to as percepts, for all gestural articulators. Head movements, facial expressions, and hand movements are examples of low-level percepts. High-level perception accumulates the low-level percepts over time and interprets them within short periods. In addition to the interpretation of these percepts, this step is responsible for the detection of some dynamic properties of these percepts such as movement shape and speed. This step uses a behavior-based network to build up higher level information. Feedback perception and interpretation is the highest level in this hierarchy. This step interprets the feedback of the interaction partner according to the current situation. Contrary to the bottom-up, top-down uses the contextual information to guide the perception process. Starting from the received nonverbal cues and the expected feedback, this stage focuses on a specific kind of information that is relevant to the ongoing event. Propagation of the required information from the highest level the lower level enables seeking particular information. Omitting irrelevant data in each step reduces the information space tremendously and leads to less processing time. This process is based on the previous experience and the current perception of the available information. Experiments with two humanoid robots roman and robin have shown promising results. Interactive 20 Questions1 game scenarios have been realized. The experiments demonstrated correct interpretations of feedback cues of the interaction partner.
Incremental Specification and Design of a Pipelined Protocol Converter

The incremental design process we propose aim at helping the hardware designer during the design of a component. A complex component is built by addition of new behaviours to a simpler previously verified component. These new behaviours model the reactions to new events that were ignored in the simplest component. We investigate the way CTL properties that are verified on a simple model are preserved or may be transformed to be verified into the complex model, in the particular context of pipeline structures. This paper defines the increments modeling treatment delay or treatment abortion and their composition; then it derives a set of transformations of CTL formulae corresponding to these increments; finally it shows how these increments are composed to model the control of a protocol converter and how CTL properties are built.
A Robust Exact Differentiator Toolbox for Matlab Simulink: New Discrete-Time Realization

—This paper presents a new release of A Robust Exact Differentiator Toolbox for Matlab/Simulink proposed in [1]. This release features a new discrete-time realization of the continuous-time robust exact differentiator. The implemented discretization scheme is less sensitive to gain overestimation and does not suffer from the discretization chattering effect. Hence, the single tuning parameter of the new version of the implemented differentiator is more intuitive to tune. Furthermore, it shows superior estimation performance in the case of large sampling times in comparison to the previous release. This is conﬁrmed by the presented results obtained by numerical simulations and a real world application.
The Role of HCV E 2 Protein Glycosylation in Functioning of Virus Envelope Proteins in Insect and Mammalian Cells

The hepatitis C virus (HCV) envelope proteins E1 and E2, being virion components, are involved in the formation of infectious particles in infected cells. The detailed structure of the infectious particle of HCV remains poorly understood. Moreover, the virion assembly and release of virions by the cell are the least understood processes. It is believed that virion properties depend on glycosylation of the virus envelope proteins in a cell, while glycansat several glycosylation sites of these proteins play a pivotal role in protein functioning and the HCV life cycle. N-glycans of glycoproteins can influence viral particle formation, virus binding to cell surface, and HCV pathogenesis. We studied the effect of glycans on the folding ofthe E2 glycoprotein, formation of functional glycoprotein complexes and virus particles in insect and mammalian cells. In order to investigate these processes, point mutations of the N-glycosylation sites of HCV protein E2 (genotype 1b strain 274933RU) were generated and the mutant proteins were further analyzed in the baculovirus expression system. Elimination of the single glycosylation sites of the E2 glycoprotein, except for the N6 site, did not affect its synthesis efficiency in Sf9 insect cells, while the electrophoretic mobility of mutant proteins increased in proportion to the decrease in the number of glycosylation sites. The level of synthesis of HCV glycoprotein E2 in human HEK293T cells depended on the presence of glycans at the N1 and N8 glycosylation sites in contrast to Sf9 cells. At the same time, elimination of glycans at the N1, N2, and N10 sites led to the accumulation of unproductive E1E2 dimers as aggregates and productive assembly suppression of virus-like particles both in insect and mammalian cells. In addition, elimination of single glycosylation sites of HCV E2 had no impact on the RNA synthesis of structural proteins and formation of virus-like particles in insect and mammalian cells.
Deep Image Classification of a Wild Data Set for Olympic Sports

Automatic retrieval of information is better achieved when the data is previously classified in categories. However, given the amount of currently available information, and being constantly produced, it is very hard to manually classify all of them. Thus, it is essential to establish a methodology that would be able to not only retrieving data, but also to present them in its correct class. One way to accomplish this later goal is to use Machine Learning algorithms for classification. In this work we tackle this problem using a Deep Learning technique, given the increasingly success of this area in classification tasks. Particularly, we address the automatic classification of images related to the Olympic Games, focusing on predicting which sport a image is associated with. We present an extensive empirical study encompassing the training the Deep Learning Network known as GoogLeNet with 26 of the 42 Olympic sports disciplines and 5362 images. From the results, we show that GoogLeNet is indeed able to correctly classify most of the images that were not presented to the network in the training phase.
User information modelling in social communities and networks

User modelling is the basis for social network analysis, such as community detection, expert finding, etc. The aim of this research is to model user information including user-generated content and social ties. There have been many algorithms for community detection. However, the existing algorithms consider little about the rich hidden knowledge within communities of social networks. In this research, we propose to simultaneously discover communities and the hidden/latent knowledge within them. We focus on jointly modelling communities, user sentiment topics, and the social links. We also learn to recommend experts to the askers based on the newly posted questions in online question answering communities. Specifically, we first propose a new probabilistic model to depict users' expertise based on answers and their descriptive ability based on questions. To exploit social information in community question answering (CQA), the link analysis is also considered. We also propose a user expertise model under tags rather than the general topics. In CQA sites, it is very common that some users share the same user names. Once an ambiguous user name is recommended, it is difficult for the asker to find out the target user directly from the large scale CQA site. We propose a simple but effective method to disambiguate user names by ranking their tag-based relevance to a query question. We evaluate the proposed models and methods on real world datasets. For community discovery, our models can not only identify communities with different topic-sentiment distributions, but also achieve comparable performance. With respect to the expert recommendation in CQA, the unified modelling of user topics/tags and abilities are capable of improving the recommendation performance. Moreover, as for the user name disambiguation in CQA, the proposed method can help question askers match the ambiguous user names with the right people with high accuracy.