Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjective evaluation of transmission errors in IPTV and 3DTV

The increase of multimedia services delivered over packet-based networks has entailed greater quality expectations of the end-users. This has led to an intensive research on techniques for evaluating the quality of experience perceived by the viewers of audiovisual content, considering the different degradations that it could suffer along the broadcasting system. In this paper, a comprehensive study of the impact of transmission errors affecting video and audio in IPTV is presented. With this aim, subjective assessment tests were carried out proposing a novel methodology trying to keep as close as possible home environment viewing conditions. Also 3DTV content in side-by-side format has been used in the experiments to compare the impact of the degradations. The results provide a better understanding of the effects of transmission errors, and show that the QoE related to the first approach of 3DTV is acceptable, but the visual discomfort that it causes should be reduced.
Executive attention in schizophrenic males and the impact of COMT Val108/158Met genotype on performance on the attention network test.

BACKGROUND Executive control of attention in schizophrenia has recently been assessed by means of the Attention Network Test (ANT). In the past, for tasks assessing executive attention, findings in schizophrenia have been contradictory, among others suggesting a lack of increased stimulus interference effects. Attention and executive functioning are substantially influenced by candidate genes of schizophrenia, including the functional single-nucleotide polymorphism catechol-o-methyltransferase (COMT) Val108/158Met, with task-dependent, specific effects of Met allele load on cognitive function. Therefore, we aimed at investigating executive attention in schizophrenic patients (SZP) as compared with healthy controls (HC), and to assess the specific impact of COMT Val108/158Met on executive attention, using ANT. METHODS We applied ANT to 63 SZP and 40 HC. We calculated a general linear model to investigate the influence of affection status and the COMT Val108/158Met genotype on executive attention as assessed by the ANT. RESULTS Multivariate analysis of variance revealed a significant effect of group on executive attention. SZP exhibited smaller conflict effects in the ANT. Met allele load significantly modulated executive attention efficiency, with homozygous Met individuals showing low overall reaction time but increased effects conflicting stimulus information in executive attention. CONCLUSIONS Our data suggest a disease-related dissociation of executive attention with reduced conflict effects in SZP. Furthermore, they support the hypothesis of differential tonic-phasic dopamine activation and specific dopamine level effects in different cognitive tasks, which helps interpreting contradictory findings of Met allele load on cognitive performance. Disease status seems to modulate the impact of COMT Val108/158Met on cognitive performance.
Title of Thesis: Modeling Spatial Correlations for Effective Discriminative Clas- Sifiers Modeling Spatial Correlations for Effective Discriminative Classifiers

Classification — i.e. categorizing data instances into pre-defined categories — is an interesting and challenging task. Many real world problems involve classification, in domains such as medical informatics, image analysis, and text tagging. We consider the challenge of learning a classifier from data. This is especially challenging when data instances are correlated. Here, we focus on learning an image segmenter – e.g. a system that classifies each pixel of a magnetic resonance (MR) image of a brain as either tumor or non-tumor. Here the labels of neighboring pixels are correlated. By contrast, discriminative approaches that assume the data instances are independent and identically distributed (i.i.d.), such as Logistic Regression (LR) and Support Vector Machines (SVM), take a single pixel as an input to a fitted decision function and make a decision for that individual pixel that ignores the continuity of labels of neighboring pixels. To be effective here, it is important to also consider the spatial correlations of labels: that is, neighboring pixels tend to have same labels. This has led to the now-standard random field approach (eg, Conditional Random Fields, CRFs), which involves learning and using two potential functions: one for estimating relevant characteristics of the individual pixel, and the other that deals with interactions between adjacent pixels. This dissertation presents extensions to CRFs to address the following three challenging issues: (1) Modeling spatial correlations more effectively by using a variant of support vector machines for the random field potential, leading to Support Vector Random Fields (SVRFs). (2) Using both unlabelled and labelled data in a supervised learning framework, leading to Semi-Supervised Discriminative Random Fields (SSDRFs) that produce more accurate model parameters. (3) Modeling spatial correlations more efficiently, leading to both Decoupled Conditional Random Fields (DCRFs) that decouple learning of the two potentials of a random field, and Pseudo Conditional Random Fields (PCRFs) that explicitly model spatial correlation only in inference. Our empirical evaluations on complex tasks (such as segmenting brain tumors) show these systems perform statistically significantly better than existing methods and promise wide practical applications.
Radio access selection approaches in heterogeneous wireless networks

Along with the rapid growth of mobile broadband traffic, multiple radio access technologies (RATs) are being integrated and jointly managed. To optimize heterogeneous network performance, efficient Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the access technology selection - a key CRRM functionality - and proposes a hybrid approach that combines benefits from both network-centric and user-centric methods. Network information, that is periodically broadcasted, assists mobile users in their decisions. By broadcasting appropriate decisional information, the network tries to globally control users decision in a way to meet operator objectives. On the other hand, mobiles also integrate their needs and preferences to select their access technology so as to maximize their own utility. In comparison with other RAT selection techniques, including network-centric, hybrid and user-centric methods, simulation results prove the efficiency of our hybrid approach in enhancing resource utilization and maximizing user satisfaction.
If “it” were “then”, then when was “it”? Establishing the anaphoric role of “then”

The adverb "then" is among the most frequent Englishtemporal adverbs, being also capable of filling a variety of semantic roles. The identification of anaphoric usages of "then"is important for temporal expression resolution, while thetemporal relationship usage is important for event ordering. Given that previous work has not tackled the identification and temporal resolution of anaphoric "then", this paper presents a machine learning approach for setting apart anaphoric usages and a rule-based normaliser that resolves it with respect to an antecedent. The performance of the two modules is evaluated. The present paper also describes the construction of an annotated corpus and the subsequent derivation of training data required by the machine learning module.
Teaching Language Skills to Preschool Students with Developmental Delays and Autism Spectrum Disorder Using Language for Learning

Language intervention using Direct Instruction (DI) has shown positive results. There is a growing body of investigation of Language for Learning (LL), a DI program, on the performance of students with autism spectrum disorders (ASD) and students with developmental delays (DD). There is need for replication and extension of research to include rural settings and students with ASD and DD. The purpose of this study was to investigate the effects of LL on the language of students with DD and ASD in a rural preschool classroom. Four preschool students with DD and ASD participated in 12 weeks of instruction, resulting in improved language skills.
A Manipulator Design Optimization Based on Constrained Multi-objective Evolutionary Algorithms

In order to achieve high performance of robotic manipulators, light-weight and high manipulability are two essential indicators. In this paper, a model of UR5 (Universal Robot) manipulator is defined and analyzed with focus on optimizing the total mass and manipulability. The kinematics and dynamics for the robotic manipulator are used to calculate the joints torque and define quantitative measures of manipulability. Then, a design optimization problem is formulated for UR5 manipulator. It adopts the weight and manipulability of the manipulator as the objective functions. The drive train constraints associate with joint motors and gearboxes have also been considered. Finally, constrained multi-objective evolutionary algorithms (CMOEAs) are employed to solve the formulated problem. Several reasonable optimal combinations of geometrical parameters and type selection of motor and gearbox are provided. And compare them with the original structure of UR5.
LISFS: a logical information system as a file system

We present Logical Information Systems (LIS). A LIS can be viewed as a schema-less database whose objects are described by logical formulas. Objects are automatically organized according to their logical description, and logical formulas can be used for representing both queries and navigation links. The key feature of a LIS is that it answers a query with a set of navigation links expressed in the same logic as the query. As navigation links are dynamically computed from any query, and can be used as query increments, it follows that querying and navigation steps can be combined in any order.We then present LISFS, a file-system implementation of a LIS, where objects are files or parts of files. This has the benefit to make LIS features available right now to existing applications. This implementation can easily be extended and specialized through a plug-in mechanism.Finally, we present some applications in the field of personal databases (e.g., music, images, emails) and in the field of software engineering.
Cache Coherence Method for Improving Multi-threaded Applications on Multicore Systems

Chip-multiprocessors (CMPs) have become the mainstream parallel architecture in enterprise and scientific computing facilities. For scalability reasons, design with larger core counts tends towards with physically distributed hardware caches. This naturally results in a Non-Uniform Cache Access design, where data movement and management impacts access latency and consume power. In this work, we observed that shared data writing behavior dramatically wastes precious on-chip hardware cache resource and seriously affects the whole system performance due to the high remote access latency. Therefore, we propose a new prediction mechanism to predict the impact of shared data and a directory-based MESI cache coherence protocol with selective write-shared-data-update transition strategy instead of native write-invalidate strategy. We evaluate our proposal on a modern multi-core machine with NAS Parallel Benchmarks. Experimental results showed speedup gains of up to 21% opposed to the native write-invalidate transition strategy.
Triangle listing in massive networks

Triangle listing is one of the fundamental algorithmic problems whose solution has numerous applications especially in the analysis of complex networks, such as the computation of clustering coefficients, transitivity, triangular connectivity, trusses, etc. Existing algorithms for triangle listing are mainly in-memory algorithms, whose performance cannot scale with the massive volume of today's fast growing networks. When the input graph cannot fit in main memory, triangle listing requires random disk accesses that can incur prohibitively huge I/O cost. Some streaming, semistreaming, and sampling algorithms have been proposed but these are approximation algorithms. We propose an I/O-efficient algorithm for triangle listing. Our algorithm is exact and avoids random disk access. Our results show that our algorithm is scalable and outperforms the state-of-the-art in-memory and local triangle estimation algorithms.
Radoop: Analyzing Big Data with RapidMiner and Hadoop

Working with large data sets is increasingly common in research and industry. There are some distributed data analytics solutions like Hadoop, that oﬀer high scalability and fault-tolerance, but they usually lack a user interface and only developers can exploit their functionalities. In this paper, we present Radoop, an extension for the RapidMiner data mining tool which provides easy-to-use operators for running distributed processes on Hadoop. We describe integration and development details and provide runtime measurements for several data transformation tasks. We conclude that Radoop is an excellent tool for big data analytics and scales well with increasing data set size and the number of nodes in the cluster.
Development and Experimental Testing of an Optical Micro-Spectroscopic Technique Incorporating True Line-Scan Excitation

Multiphoton micro-spectroscopy, employing diffraction optics and electron-multiplying CCD (EMCCD) cameras, is a suitable method for determining protein complex stoichiometry, quaternary structure, and spatial distribution in living cells using Förster resonance energy transfer (FRET) imaging. The method provides highly resolved spectra of molecules or molecular complexes at each image pixel, and it does so on a timescale shorter than that of molecular diffusion, which scrambles the spectral information. Acquisition of an entire spectrally resolved image, however, is slower than that of broad-bandwidth microscopes because it takes longer times to collect the same number of photons at each emission wavelength as in a broad bandwidth. Here, we demonstrate an optical micro-spectroscopic scheme that employs a laser beam shaped into a line to excite in parallel multiple sample voxels. The method presents dramatically increased sensitivity and/or acquisition speed and, at the same time, has excellent spatial and spectral resolution, similar to point-scan configurations. When applied to FRET imaging using an oligomeric FRET construct expressed in living cells and consisting of a FRET acceptor linked to three donors, the technique based on line-shaped excitation provides higher accuracy compared to the point-scan approach, and it reduces artifacts caused by photobleaching and other undesired photophysical effects.
High-Performance Capabilities for 1-Hop Containment of Network Attacks

Capabilities-based networks present a fundamental shift in the security design of network architectures. Instead of permitting the transmission of packets from any source to any destination, routers deny forwarding by default. For a successful transmission, packets need to positively identify themselves and their permissions to the router. A major challenge for a high-performance implementation of such a network is an efficient design of the credentials that are carried in the packet and the verification procedure on the router. We present a capabilities system that uses packet credentials based on Bloom filters. The credentials are of fixed length (independent of the number of routers that are traversed by the packet) and can be verified by routers with a few simple operations. This high-performance design of capabilities makes it feasible that traffic is verified on every router in the network, and most attack traffic can be contained within a single hop. We present an analysis of our design and a practical protocol implementation that can effectively limit unauthorized traffic with only a small per-packet overhead.
Acquiring variable length speech bases for factorisation-based noise robust speech recognition

Studies from multiple disciplines show that spectro-temporal units of natural languages and human speech perception are longer than short-time frames commonly employed in automatic speech recognition. Extended temporal context is also beneficial for separation of concurrent sound sources such as speech and noise. However, the length of patterns in speech varies greatly, making it difficult to model with fixed-length units. We propose methods for acquiring variable length speech atom bases for accurate yet compact representation of speech with a large temporal context. Bases are generated from spectral features, from assigned state labels, and as a combination of both. Results for factorisation-based speech recognition in noisy conditions show equal or better separation and recognition quality in comparison to fixed length units, while model sizes are reduced by up to 40%.
Generalised G2-structures and type IIB superstrings

The recent mathematical literature introduces generalised geometries which are defined by a reduction from the structure group SO(d,d) of the vector bundle Td⊕Td* to a special subgroup. In this article we show that compactification of IIB superstring vacua on 7-manifolds with two covariantly constant spinors leads to a generalised G2-structure associated with a reduction from SO(7,7) to G2 × G2. We also consider compactifications on 6-manifolds where analogously we obtain a generalised SU(3)-structure associated with SU(3) × SU(3), and show how these relate to generalised G2-structures.
The supermassive black hole in NGC 4486a detected with SINFONI at the Very Large Telescope

The near-infrared (IR) integral field spectrograph SINFONI at the European Southern Observatory Very Large Telescope opens a new window for the study of central supermassive black holes. With a near-IR spatial resolution similar to Hubble Space Telescope optical and the ability to penetrate dust, it provides the possibility to explore the low-mass end of the M•‐σ relation ( σ< 120 km s −1 ) where so far very few black hole masses were measured with stellar dynamics. With SINFONI, we observed the central region of the low-luminosity elliptical galaxy NGC 4486a at a spatial resolution of ≈0.1 arcsec in the K band. The stellar kinematics were measured with a maximum penalized likelihood method considering the region around the CO absorption band heads. We determined a black hole mass of M• = (1.25 +0.75 −0.79) × 10 7 M� (90 per cent confidence limit) using the Schwarzschild orbit superposition method including the full two-dimensional spatial information. This mass agrees with the predictions of the M•‐σ relation, strengthening its validity at the lower σ end.
Rescuing the Corticostriatal Synaptic Disconnection in the R6/2 Mouse Model of Huntington’s Disease: Exercise, Adenosine Receptors and Ampakines

In the R6/2 mouse model of Huntington’s disease (HD) we examined the effects of a number of behavioral and pharmacological manipulations aimed at rescuing the progressive loss of synaptic communication between cerebral cortex and striatum. Two cohorts of transgenic mice with ~110 and 210 CAG repeats were utilized. Exercise prevented the reduction in striatal medium-sized spiny neuron membrane capacitance but did not reestablish synaptic communication. Activation of adenosine A2A type receptors renormalized postsynaptic activity to some extent. Finally, the ampakine Cx614, which has been shown to prevent α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) receptor desensitization, slow deactivation, and facilitate glutamate release, induced significant increases in synaptic activity, albeit the effect was somewhat reduced in fully symptomatic, compared to control mice. With some limitations, each of these strategies can be used to delay and partially rescue phenotypic progression of HD in this model.
It's the economy stupid: comparison of proportional hazards models with economic and socio-demographic variables for estimating the purchase of financial products

Relatively there is little empirical research that has been taken to understand how the underlying economy affects customers’ subsequent financial product purchase behaviours. A better understanding of this influence and being able to predict the probability of purchasing are important for financial service industries. This paper undertakes an examination of the impacts of social-demographic and economic variables on the probability of purchasing financial products. In particular two most common, the Cox and Weibull, proportional hazard models are compared to examine their adequacy in terms of predictive ability. The results show that the change of external economic environment is an important source that drives customers’ financial products purchasing behaviours. Furthermore, the results also that indicate Cox proportional hazard models are superior to Weibull proportional hazard models.
Adaptive Control of Hybrid PSO-APGA using Neural Network for Constrained Real-Parameter Optimization

This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for self- adaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of convergence to the optimal solution and incorporates concept from neural network for self-adaptive of control parameters. It is shown to be statistically significantly superior to other Evolutionary Algorithms (EAs) on numerical benchmark problems and constrained real-parameter optimization.
A Data Pre-partitioning and Distribution Optimization Approach for Distributed Data Warehouses

The increasing volumes of relational data let us find an alternative to cope with them. The Hadoop framework an open source project based on the MapReduce paradigm is a popular choice for distributed data warehouses and big data analytics. In this paper, we propose an original approach for partitioning and collocating data in distributed file systems, especially Hadoop-based systems, and this, to overcome the default data partitioning and placement policies which does not take any data characteristics into account. The goal is to reduce the amount of data transfer required during Mapreduce’s shuffling phase. Indeed, the efficiency of many relational operations can be improved if a careful data fragmentation design and placement policy are applied, including indexing, grouping, aggregation and joins. Based on k-means clustering methode that allows to master the number of clusters through its k parameter, we investigate the performance gain for OLAP cube construction on a multi-nodes cluster with and without data organization. And this, by varying the number of clusters and data warehouse size. Our experiments suggest that defining a good data partitioning and placement schemes during the implementation of the data warehouse increase significantly the OLAP cube computation and querying performances. Keywords-Data warehouses; On-Line Analytical Processing (OLAP); Mapreduce; Data placement;
Classification of gear damage levels in planetary gearboxes

Linear discriminant analysis (LDA) is a method of feature extraction that has demonstrated successful applications. The selection of the number of discriminant directions (r) is important to LDA, yet little attention is paid in the reported literature. In this paper a method is proposed for determining the optimal r in terms of the classification accuracy of support vector machine. The method is applied to identify gear damage levels in a planetary gearbox. Planet gears with four damage levels labeled as baseline, slight, moderate, and severe were used in lab experiments for data collection. Results demonstrate that the proposed method outperforms two reported methods and is effective to address the given problem.