Miracle-dz/UniFAR-FTUs huggingface dataset:
Contributions of different modalities to the attribution of affective-epistemic states

The focus of this study is the relation between multimodal and unimodal perception of emotions and attitudes. A point of departure for the study is the claim that multimodal presentation increases redundancy and often thereby also the correctness of interpretation. A study was carried out in order to investigate this claim by examining the relative role of unimodal versus multimodal visual and auditory perception for interpreting affective-epistemic states (AES). The abbreviation AES will be used both for the singular form “affective-epistemic state” and the plural form “affective-epistemic states”. Clips from video-recorded dyadic interactions were presented to 12 subjects using three types of presentation, Audio only, Video only and Audio+Video. The task was to interpret the affective-epistemic states of one of the two persons in the clip. The results indicated differences concerning the role of different sensory modalities for different affectiveepistemic states. In some cases there was a “filtering” effect, rendering fewer interpretations in a multimodal presentation than in a unimodal one for a specific AES. This occurred for happiness, disinterest and understanding, whereas “mutual reinforcement”, rendering more interpretations for multimodal presentation than for unimodal video or audio presentation, occurred for nervousness, interest and thoughtfulness. Finally, for one AES, confidence, audio and video seem to have mutually restrictive roles.
Real-time depth map generation using hybrid multi-view cameras

In this paper, we present a hybrid multi-view camera system for real-time depth generation. We set up eight color cameras and three depth cameras. For simple test scenarios, we capture a single object at a blue-screen studio. The objective is depth map generation at eight color viewpoints. Due to hardware limitations, depth cameras produce low resolution images, i.e., 176×144. Thus, we warp the depth data to the color cameras views (1280×720) and then execute filtering. Joint bilateral filtering (JBF) is used to exploit range and spatial weights, considering color data as well. Simulation results exhibit depth generation of 13 frames per second (fps) when treating eight images as a single frame. When the proposed method is executed on a computer per depth camera basis, the speed can become three times faster. Thus, we have successfully achieved real-time depth generation using a hybrid multi-view camera system.
A meta-grammatical framework for dependency grammar

We propose a meta-grammatical framework for dependency grammar, accommodating any number of dimensions and restricting their licensed models through the application of parametric principles. We first instantiate this framework to obtain the version of Topological Dependency Grammar (TDG) proposed by Duchier and Debusmann 2001. We then describe an extended instantiation which adds support for semantic dependencies (thus also providing an account of control and raising constructions) and meaning assembly.
Distributed joint optimization of radio and computational resources for mobile cloud computing

Mobile computation offloading of computational intensive tasks from mobile devices to surrogate cloud servers has been recently envisaged as a promising technique to enhance the computational capacity of the mobile devices. Within this framework we consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio and computational resources in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications. To solve this non-convex problem we hinge on successive convex approximation techniques by showing that the original problem can be decomposed in parallel convex subproblems. Hence we devise an iterative algorithm which can be implemented in a distributed manner across the access points through dual/primal decomposition techniques requiring limited coordination/signaling with the cloud.
Modeling and simulation of the mixing process of fluids in microchannels promoted by acoustic streaming

Work supported by FEDER funds through the "Programa Operacional Factores de Competitividade – COMPETE" and by national funds by FCT- Fundacao para a Ciencia e a Tecnologia project reference PTDC/BIO/70017/2006. S. O. Catarino thanks the FCT for the SFRH/BD/61767/2009 grant
Know Thy Neighbor: Crypto Library Detection in Cloud

Abstract Software updates and security patches have become a standard method to fix known and recently discovered security vulnerabilities in deployed software. In server applications, outdated cryptographic libraries allow adversaries to exploit weaknesses and launch attacks with significant security results. The proposed technique exploits leakages at the hardware level to first, determine if a specific cryptographic library is running inside (or not) a co-located virtual machine (VM) and second to discover the IP of the co-located target. To this end, we use a Flush+Reload cache side-channel technique to measure the time it takes to call (load) a cryptographic library function. Shorter loading times are indicative of the library already residing in memory and shared by the VM manager through deduplication. We demonstrate the viability of the proposed technique by detecting and distinguishing various cryptographic libraries, including MatrixSSL, PolarSSL, GnuTLS, OpenSSL and CyaSSL along with the IP of the VM running these libraries. In addition, we show how to differentiate between various versions of libraries to better select an attack target as well as the applicable exploit. Our experiments show a complete attack setup scenario with single-trial success rates of up to 90% under light load and up to 50% under heavy load for libraries running in KVM.
Automatic Vocal Segments Detection in Popular Music

We propose a technique for the automatic vocal segments detection in an acoustical polyphonic music signal. We use a combination of several characteristics specific to singing voice as the feature and employ a Gaussian Mixture Model (GMM) classifier for vocal and non-vocal classification. We have employed a pre-processing of spectral whitening and archived a performance of 81.3% over the RWC popular music dataset.
A New Method for Traffic Forecasting Based on the Data Mining Technology with Artificial Intelligent Algorithms

This study aims to investigate the traffic information forecasting based on the data mining technology. As well known, useful knowledge in traffic management system often hides in a large amount of traffic data. Generally, prior data pattern labels have been used to train the Artificial Neural Network (ANN) to identify the traffic conditions in the traffic information forecasting. The performance of the ANN models suffers from the prior information of the experts. To relieve this impact in the traffic information forecasting, a new ANN model is proposed based on the data mining technology in this study. The Self-Organized Feature Map (SOFM) is firstly employed to cluster the traffic data through an unsupervised learning and provide the labels for these data. Then the labeled data were used to train the GA-Chaos optimized RBF neural network. Herein, the GA-Chaos algorithm is used to train the RBF parameters. Experimental tests use practical data sets from the Intelligent Transportation Systems (ITS) were implemented to validate the performance of the proposed ANN model. The analyses results demonstrate that the proposed method can extract the potential patterns hidden in the traffic data and can accurately predict the future traffic state. The prediction accuracy is beyond 95%. Hence, the new data mining model can provide practical application for traffic information forecasting in the ITS system.
Auditory and Visual Processing in Children With Dyslexia

This study investigated the temporal stability and longitudinal replicability of visual and auditory sensory processes found to be poor in children with dyslexia. Seventy children with dyslexia and 52 normal readers were tested twice, 9 months apart, on measures of visual and auditory sensory processing and on phonological and orthographic skills. About 30% of children with dyslexia were found to have sensory deficits. Associations were found between sensory and cognitive skills. Based on analyses of agreement, the temporal stability of the sensory tasks was poor. Future research should develop sensory measures with high temporal stability that can control for maturation.
Sam68 Is Required for DNA Damage Responses via Regulating Poly(ADP-ribosyl)ation

The rapid and robust synthesis of polymers of adenosine diphosphate (ADP)-ribose (PAR) chains, primarily catalyzed by poly(ADP-ribose) polymerase 1 (PARP1), is crucial for cellular responses to DNA damage. However, the precise mechanisms through which PARP1 is activated and PAR is robustly synthesized are not fully understood. Here, we identified Src-associated substrate during mitosis of 68 kDa (Sam68) as a novel signaling molecule in DNA damage responses (DDRs). In the absence of Sam68, DNA damage-triggered PAR production and PAR-dependent DNA repair signaling were dramatically diminished. With serial cellular and biochemical assays, we demonstrated that Sam68 is recruited to and significantly overlaps with PARP1 at DNA lesions and that the interaction between Sam68 and PARP1 is crucial for DNA damage-initiated and PARP1-conferred PAR production. Utilizing cell lines and knockout mice, we illustrated that Sam68-deleted cells and animals are hypersensitive to genotoxicity caused by DNA-damaging agents. Together, our findings suggest that Sam68 plays a crucial role in DDR via regulating DNA damage-initiated PAR production.
Privacy-preserving content-based recommender system

By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely, also poses a serious privacy risk for the customers: ratings may be very personal or revealing, and thus highly privacy sensitive. Service providers could process the collected rating data for other purposes, sell them to third parties or fail to provide adequate physical security. In this paper, we propose technological mechanisms to protect the privacy of individuals in a recommender system. Our proposal is founded on homomorphic encryption, which is used to obscure the private rating information of the customers from the service provider. While the user's privacy is respected by the service provider, by generating recommendations using encrypted customer ratings, the service provider's commercially valuable item-item similarities are protected against curious entities, in turn. Our proposal explores simple and efficient cryptographic techniques to generate private recommendations using a server-client model, which neither relies on (trusted) third parties, nor requires interaction with peer users. The main strength of our contribution lies in providing a highly efficient solution without resorting to unrealistic assumptions.
Using Session Identifiers as Authentication Tokens

As authentication provides crucial online identity, it is the basis of data security. In this paper, a session based authentication is proposed and the long unique un-guessable session identifier is used as a parameter of an authentication token. It has the advantages of one-timeness, short-lived and no prior knowledge requirement. The session model is established with detailed implementation of communication protocol. The security of this protocol is then analyzed formally and the results show that the protocol can resist various attacks, e.g. session hijacking, message replay and pharming attacks etc. Finally, a case is studied and the performance of the application is evaluated, which indicates that the proposed scheme is simpler and more efficient than the existing schemes.
The CourseMaster Automated Assessment System – a next generation Ceilidh

While teaching in all its forms can sometimes be fun, for most people marking student work is tedious, boring and in general hard work. A courseware system is presented that not only provides on-line support for courses, but importantly can automatically assess student work. CourseMaster can mark several types of coursework in a non-trivial manner. That is, criteria can be set against which the work is thoroughly assessed. Specifically, the system is particularly good at marking computer programs in several languages including Java and C++. It can also mark diagrams and assist in the marking of essays. The marking of multiple choice questions is also supported. In addition to marking, the system also supports the provision of lecture notes and web pages and links. It can be used to collect any on-line work and enforce deadlines. Finally, it provides a suite of web based tools that allow the easy management of courses. CourseMaster has been used "live" at Nottingham with great success for two years and is based on the earlier Ceilidh system which was developed and used for over the preceding ten years.
A Smooth Transition Multivariate GARCH Approach to Contagion

This paper proposes a multivariate GARCH model with nonlinear conditional variances as a new approach to modeling contagion and interdependence between international financial markets. The conditional variances are allowed to change smoothly between two extreme regimes and the transition is governed by past volatility. Changes in volatility interactions are thus indicative of structural changes in market linkages with implications to contagion and interdependence. The model is derived both under normal and student-t distributional assumptions. A LM test of linearity in conditional variances is also derived. Simulation studies point to model robustness to different variance and correlation specifications. An empirical example is provided.
LRNC: Lifetime-aware routing using network coding in wireless sensor network

This paper proposes a lifetime-aware routing using network coding scheme that provides the solution of lifetime maximization problems in the sensor networks with multiple sources and multiple destinations. In our proposed scheme, the relaying nodes select the route of each flow based on the node condition including remaining energy of adjacent nodes and achieve reduced flow bottleneck in the multicast system via network coding scheme. With lots of random topology simulations, we show that our proposed scheme obtains increased network lifetime performance compared to the conventional maximal lifetime routing scheme and the network coding based minimum distance routing scheme.
Untreated aortic valve stenosis identified at the time of coronary artery bypass grafting: thresholds associated with adverse prognosis.

OBJECTIVES Aortic valve replacement (AVR) for severe aortic valve stenosis (AS) is a Class I indication at the time of coronary artery bypass grafting (CABG). Management of less-than-severe AS in patients undergoing CABG is uncertain however, because the thresholds at which untreated AS impacts long-term outcome are unclear. METHODS We identified 312 patients who underwent isolated CABG between 1993 and 2006 with mild or moderate AS [aortic valve area (AVA) 1-2 cm(2)], and matched them to patients undergoing CABG alone during the same period with similar characteristics but without AS (AVA >2 cm(2)). Long-term survival after CABG and its determinants were analysed using Cox proportional hazards models with AVR as a time-dependent covariate. RESULTS Late survival was lower in patients with untreated moderate AS (12 years 23 ± 5.1%) versus mild (42 ± 3.8%) or no AS (38 ± 3.3%) (P = 0.01). Adjusting for age, ejection fraction, heart failure, creatinine, diabetes, peripheral vascular disease (PVD) and interval AVR, moderate AS independently predicted higher mortality [hazard rate (HR) 2.01, 95% confidence interval (CI) 1.49-2.73; P < 0.001]; whereas incremental risk was insignificant for patients with mild AS (HR 1.09, 95% CI 0.85-1.66; P = 0.33). Further stratification showed that highest late postoperative mortality occurred with an AVA of 1-1.25 cm(2) (adjusted HR 2.45, 95% CI 1.57-3.82; P < 0.001), while risk was intermediate with an AVA of 1.25-1.5 cm(2) (HR 1.83, 95% CI 1.28-2.61; P = 0.001). CONCLUSIONS Untreated moderate AS is an independent determinant of excess late mortality following isolated CABG, and mortality risk increases with decreasing AVA. Those with moderate-to-severe AS (AVA 1-1.25 cm(2)) have more than 2-fold greater long-term mortality compared with those without AS. These data define AS severity thresholds for clinical trials aimed at defining whether valve intervention might mitigate this risk.
The Art of Software Design, a Video Game for Learning Software Design Principles

This paper introduces our gamification of a part of our software design curriculum. Based on typical design principles a motivating learning game is developed to train students in software design. We use Bloom's taxonomy to determine learning objectives. We keep the player engaged with direct feedback in a challenging level based game with increasing complexity. Players can evaluate their design actions with the help of the visualisation of control and data flows. The main learning objective: applying design principles, fits the game's main activity. This supports the learning by doing approach of lecturers. A user test indicates possible learning effects and a playable game.
Ribonucleotide reductase is not limiting for mitochondrial DNA copy number in mice

Ribonucleotide reductase (RNR) is the rate-limiting enzyme in deoxyribonucleoside triphosphate (dNTP) biosynthesis, with important roles in nuclear genome maintenance. RNR is also essential for maintenance of mitochondrial DNA (mtDNA) in mammals. The mechanisms regulating mtDNA copy number in mammals are only being discovered. In budding yeast, RNR overexpression resulted in increased mtDNA levels, and rescued the disease phenotypes caused by a mutant mtDNA polymerase. This raised the question of whether mtDNA copy number increase by RNR induction could be a strategy for treating diseases with mtDNA mutations. We show here that high-level overexpression of RNR subunits (Rrm1, Rrm2 and p53R2; separately or in different combinations) in mice does not result in mtDNA copy number elevation. Instead, simultaneous expression of two RNR subunits leads to imbalanced dNTP pools and progressive mtDNA depletion in the skeletal muscle, without mtDNA mutagenesis. We also show that endogenous RNR transcripts are downregulated in response to large increases of mtDNA in mice, which is indicative of nuclear-mitochondrial crosstalk with regard to mtDNA copy number. Our results establish that RNR is not limiting for mtDNA copy number in mice, and provide new evidence for the importance of balanced dNTP pools in mtDNA maintenance in postmitotic tissues.
A study of mining certain itemsets from uncertain data

Association rule mining is an important data analysis method for discovering associations within data. Recently, some researchers have extended association rule mining techniques to imprecise or uncertain data. However, the question arises as to how we can mine relevant and interesting patterns from uncertain data. Additionally, using the Σ-count, the summation of a large number of itemsets with very small support may induce irrelevant associations. To this end, this study proposes a new approach to discover relevant patterns from uncertain data. This approach is based on the α-cut method allowing us to filter out the irrelevant patterns with small support. Furthermore, a correlation measure, also known as lift, is used to augment the support-confidence framework for association rules. Next, we develop an algorithm to discover relevant and interesting association rules from uncertain data. Experimental results from the survey data show that the proposed approach can help us to discover interesting and valuable patterns with high correlation.
On designing approximate inference algorithms for multiply sectioned Bayesian networks

An increasing number of applications require cooperative agents to reason about the state of an distributed uncertainty domain. However, inference process of such system could become overly slow for practical applications, and there has been significant interest in developing faster approximation techniques. In this paper, we focus on the existing MSBN models for cooperative reasoning in multi-agent environments. We show that, while the MSBNs provide a framework for exact inference, existing algorithms are usually not feasible in larger problem domains. Therefore, we investigate the issues related to the design of efficient inference algorithm for the MSBN model. We then propose a suite of algorithms for approximate multi-agent probabilistic reasoning in MSBNs. Our approach includes an MSBN subnet calibration process and distributed stochastic sampling on MSBN LJFs.
Intracellular interaction of collagen-specific stress protein HSP47 with newly synthesized procollagen

Heat shock protein 47 (HSP47), a collagen-specific stress protein, has been postulated to be a collagen-specific molecular chaperone localized in the ER. We previously demonstrated that HSP47 transiently associated with newly synthesized procollagen in the ER (Nakai, A., M. Satoh, K. Hirayoshi, and K. Nagata. 1992. J. Cell Biol. 117:903-914). In the present work, we examined the location where HSP47 binds to and dissociates from newly synthesized procollagen within the cells, and whether HSP47 associates with nascent single procollagen polypeptide chains and/or with mature triple-helix procollagen. This was accomplished by biochemical coprecipitation with anti-HSP47 and anticollagen antibodies, combined with pulse-label and chase experiments in the presence or absence of various inhibitors for protein secretion, as well as by confocal laser microscopic observation of the cells double stained with both antibodies. We further examined whether the RDEL (Arg-Asp-Glu-Leu) sequence at the COOH terminus of HSP47 can act as an ER-retention signal, as the KDEL sequence does. When the secretion of procollagen was inhibited by the presence of alpha, alpha'-dipyridyl, an iron chelator that inhibits procollagen triple-helix formation, or by the presence of brefeldin A. which inhibits protein transport between the ER and the Golgi apparatus, procollagen was found to be bound to HSP47 during the chase period in the intermediate compartment. In contrast, the dissociation of procollagen chains from HSP47 was not inhibited when procollagen secretion was inhibited by monensin or bafilomycin A1, both of which are known to be inhibitors of post-cis-Golgi transport. These findings suggest that HSP47 and procollagen dissociated between the post-ER and the cis-Golgi compartments. HSP47 was shown to bind to nascent, single- polypeptide chains of newly synthesized procollagen, as well as to the mature triple-helix form of procollagen. HSP47 with the RDEL sequence deleted was secreted out of the cells, which suggests that the RDEL sequence actually acts as an ER-retention signal, as the KDEL sequence does. This secreted HSP47 did not acquire endoglycosidase H resistance. The biological significance of the interaction between HSP47 and procollagen in the central secretory pathway, as well as possible mechanisms for this pathway, will be discussed.