Miracle-dz/UniFAR-FTUs huggingface dataset:
Harnessing Music-Related Visual Stereotypes for Music Information Retrieval

Over decades, music labels have shaped easily identifiable genres to improve recognition value and subsequently market sales of new music acts. Referring to print magazines and later to music television as important distribution channels, the visual representation thus played and still plays a significant role in music marketing. Visual stereotypes developed over decades that enable us to quickly identify referenced music only by sight without listening. Despite the richness of music-related visual information provided by music videos and album covers as well as T-shirts, advertisements, and magazines, research towards harnessing this information to advance existing or approach new problems of music retrieval or recommendation is scarce or missing. In this article, we present our research on visual music computing that aims to extract stereotypical music-related visual information from music videos. To provide comprehensive and reproducible results, we present the Music Video Dataset, a thoroughly assembled suite of datasets with dedicated evaluation tasks that are aligned to current Music Information Retrieval tasks. Based on this dataset, we provide evaluations of conventional low-level image processing and affect-related features to provide an overview of the expressiveness of fundamental visual properties such as color, illumination, and contrasts. Further, we introduce a high-level approach based on visual concept detection to facilitate visual stereotypes. This approach decomposes the semantic content of music video frames into concrete concepts such as vehicles, tools, and so on, defined in a wide visual vocabulary. Concepts are detected using convolutional neural networks and their frequency distributions as semantic descriptions for a music video. Evaluations showed that these descriptions show good performance in predicting the music genre of a video and even outperform audio-content descriptors on cross-genre thematic tags. Further, highly significant performance improvements were observed by augmenting audio-based approaches through the introduced visual approach.
Learning and Optimization with Submodular Functions

In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In cases where exact solutions cannot be computed tractably, it is beneficial to have strong guarantees on the tractable approximate solutions. In order operate under these criterion most optimization problems are cast under the umbrella of convexity or submodularity. In this report we will study design and optimization over a common class of functions called submodular functions. Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications. Informally, the property of submodularity of set functions concerns the intuitive "principle of diminishing returns. This property states that adding an element to a smaller set has more value than adding it to a larger set. Common examples of submodular monotone functions are entropies, concave functions of cardinality, and matroid rank functions; non-monotone examples include graph cuts, network flows, and mutual information. In this paper we will review the formal definition of submodularity; the optimization of submodular functions, both maximization and minimization; and finally discuss some applications in relation to learning and reasoning using submodular functions.
A Review of the Security and Usability Features of Different Graphical Password Authentication Schemes

User Authentication Schemes play a significant role in today's information security. With the reduction in the cost of computer system, there is increased demand and high reliance on the use of computer in many applications such as data transfer, data sharing, login to email or Internet. Due to increased number of users, it can be inferred that there is increase in the amount of data stored in databases. Strong text-based passwords have been typically used to offer some assurance of security, but users end-up writing them down on pieces of papers because of the problem of memorability. Thus the introduction of graphical-based password becomes imperative, because Psychologist opined that human beings have natural inclination to remember images more easily than text. Generally passwords have two essential aspects: usability and security. This paper presents a review of the security and usability features of different graphical password authentication schemes and presents what to be considered in having a balance between usability and security of graphical password as a future work.
Pseudorandom generators in propositional proof complexity

We call a pseudorandom generator G/sub n/:{0,1}/sup n//spl rarr/{0,1}/sup m/ hard for a propositional proof system P if P can not efficiently prove the (properly encoded) statement G/sub n/(x/sub 1/,.,x/sub n/)/spl ne/b for any string b/spl epsiv/{0,1}/sup m/. We consider a variety of "combinatorial" pseudorandom generators inspired by the Nisan-Wigderson generator on one hand, and by the construction of Tseitin tautologies on the other. We prove that under certain circumstances these generators are hard for such proof systems as resolution, polynomial calculus and polynomial calculus with resolution (PCR).
Thermal modeling of the module integrated DC-DC converter for flexible thin-film PV modules

Recently there is a trend in PV systems toward a more granular processing of the PV array power by means of distributed maximum power point tracking (DMPPT). This is achieved by connecting a DC-DC converter to each PV module in a PV system and thus performing power processing on a module level as opposed to centralized power processing in traditional PV system architectures. In order to decrease the manufacturing costs and to improve the level of integration, the converter can be directly integrated into the PV module. This on the other hand brings tight thermal coupling between the PV module and the integrated converter and introduces additional heat to the PV module which may deteriorate its performance. It is therefore important to model the thermal behavior of the system in order to estimate reached temperatures and to test the effectiveness of different converter thermal management strategies. This paper presents an investigation into the thermal behavior of a DC-DC converter integrated into a flexible PV module. The goal of this work is to develop analytical thermal models of the PV module and the integrated converter that will allow to quickly predict the reached system temperatures for a set of PV module and converter specifications and boundary conditions. Finally, the results obtained from the developed thermal models show satisfying accuracy when compared to the experimental results and the results obtained using computational fluid dynamics (CFD) simulations.
Clinical Pathophysiology of Human T-Lymphotropic Virus-Type 1-Associated Myelopathy/Tropical Spastic Paraparesis

Human T-lymphotropic virus type 1 (HTLV-1), a human retrovirus, is the causative agent of a progressive neurological disease termed HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). HAM/TSP is a chronic inflammatory disease of the central nervous system and is characterized by unremitting myelopathic symptoms such as spastic paraparesis, lower limb sensory disturbance, and bladder/bowel dysfunction. Approximately 0.25–3.8% of HTLV-1-infected individuals develop HAM/TSP, which is more common in women than in men. Since the discovery of HAM/TSP, significant advances have been made with respect to elucidating the virological, molecular, and immunopathological mechanisms underlying this disease. These findings suggest that spinal cord invasion by HTLV-1-infected T cells triggers a strong virus-specific immune response and increases proinflammatory cytokine and chemokine production, leading to chronic lymphocytic inflammation and tissue damage in spinal cord lesions. However, little progress has been made in the development of an optimal treatment for HAM/TSP, more specifically in the identification of biomarkers for predicting disease progression and of molecular targets for novel therapeutic strategies targeting the underlying pathological mechanisms. This review summarizes current clinical and pathophysiological knowledge on HAM/TSP and discusses future focus areas for research on this disease.
ViRAT: An advanced multi-robots platform

In this paper we present the architecture of ViRAT (Virtual Reality for Advanced Tele-operation), the hardware-software platform we developed to support researches and studies dealing with multi-robotics. The achieved architecture is multi-purposes and as such, allows a wide spectrum of operation modes ranging from full tele-operated controls to full autonomous exploitation. From the hardware point of view, the platform offers the possibility to integrate heterogeneous robots having different mobility (wheeled, Humanoids, etc) as well as embedded sensing capabilities (video, laser-scanner, IMU, etc.). From the software side, the developed architecture is modular for more flexibility and adaptability: it includes human-robots interfaces (HRI) bricks, together with supervision and low/high level controls. After presenting the architecture of the ViRAT platform, we give some examples and some use cases to illustrate the performances of ViRAT and the easiness of developing around it.
Multi-modal nonlinear feature reduction for the recognition of handwritten numerals

A novel method of multi-modal nonlinear feature reduction is proposed for the recognition of handwritten numerals. In order to find an effective decision boundary, each class is divided into several clusters. Then the k-NN sorting algorithm is applied to each cluster to get the training data along the effective decision boundary. Optimal discriminant analysis is implemented by multimodal nonlinear mapping to generate a between-class scatter matrix, which requires less CPU time than other nonparametric approaches. Experiments demonstrated that our proposed method could achieve a high feature reduction without sacrificing much discriminant ability. As a result, this new method can reduce ANN training complexity and make the ANN classifier more reliable. Its feature dimensionality reduction outperforms the PCA and mono-modal nonparametric analysis.
Evolving Issues in Working Memory

This chapter summarizes the main issues that have divided researchers in the field of working memory and provides an integrated account of what we have learned about working memory. The main issues of contention include the capacity of working memory, the gateway hypothesis, and the phonological loop. The findings of the study in this book reveal that working memory is a complex system responsible for the temporary storage and processing of information, and that it is structurally and functionally distinct from different forms of permanent or long-term memory.
Fuel-efficient heavy-duty vehicle platooning by look-ahead control

The operation of groups of heavy-duty vehicles at close intervehicular distances (known as platoons) has been shown to be an effective way of reducing fuel consumption. For single vehicles, it is also known that the availability of preview information on the road topography can be exploited to obtain fuel savings. The current paper aims at the inclusion of preview information in platooning by introducing a two-layer control system architecture for so-called look-ahead platooning. Here, the layers are responsible for the inclusion of preview information and real-time vehicle control for platooning, respectively. Within this framework, a control strategy is presented, where dynamic programming is used for the calculation of fuel-optimal speed profiles, while a model predictive control approach is exploited for the real-time vehicle control. The feasibility of this approach is illustrated by means of the simulation of relevant scenarios.
Energy efficient data gathering using prediction-based filtering in wireless sensor networks

In wireless sensor network, sensor readings generated by nearby nodes are redundant and highly correlated, both in space and time domains. Since transmitting redundant and highly correlated data incurs a huge waste of energy and bandwidth, spatial and temporal correlation should be exploited in order to reduce redundant data transmission. In this paper, we propose an energy efficient data gathering protocol that uses a prediction-based filtering EEDGPF mechanism to solve the problem of redundant data transmissions. Our data gathering protocol organises a WSN into clusters, using data similarity that exists in readings of sensor nodes and cluster heads and uses a GARCH 1, 1 model-based non-linear predictor to exploit the temporal correlation of sensor readings. Experimental results over real dataset show that our protocol significantly outperforms linear predictor AR3-based protocol proposed in Jiang et al. 2011, in terms of number of data packets delivered, number of successful predictions and average energy consumption.
Reconstructing Human Intelligence within Computational Sciences: An Introductory Essay

This chapter outlines a possible research program for computational systems representing humanlike intelligence. After a short historical introduction, a possible theoretical framework is described showing how it is possible to integrate heterogeneous disciplines like neurobiology, psychology and phenomenology within one and the same computational framework. Concrete examples are given by reconstructing behavioural (Morris) and phenomenal semiotics (Peirce) with the aid of formal theories. The author hopes to improve the interdisciplinary discussion about adaptive computational models of humanlike intelligence through a unified theoretical framework.
Automatic performance evaluation of parallel programs

Traditional parallel programming forces the programmer, apart from designing the application, to analyse the performance of this recently built application. This difficult task of testing the behaviour of the program can be avoided with the use of an automatic performance analysis tool. Users are released from having to understand the enormous amount of performance information obtained from the execution of a program. The automatic analysis bases its work on the use of a predefined list of logical rules of production of performance problems. These rules form the "knowledge base" of the tool. When the tool analyses an application, it looks for the occurrence of an element in the list of performance problems recorded in the "knowledge base". When one of the problems is found (a "match" in the list), the tool analyses the cause of the performance problem and builds a recommendation to the user to direct the possible modifications the code of the application.
SPECIAL RELATIVISTIC SIMULATIONS OF MAGNETICALLY DOMINATED JETS IN COLLAPSING MASSIVE STARS

We perform a series of two-dimensional magnetohydrodynamic core-collapse simulations of rapidly rotating and strongly magnetized massive stars. To study the properties of magnetic explosions for a longer time stretch of postbounce evolution, we develop a new code under the framework of special relativity including a realistic equation of state with a multiflavor neutrino leakage scheme. Our results show the generation of the magnetically dominated jets in the two ways. One is launched just after the core bounce in a prompt way and another is launched at ∼100 ms after the stall of the prompt shock. We find that the shock-revival occurs when the magnetic pressure becomes strong enough, due to the field wrapping, to overwhelm the ram pressure of the accreting matter. The critical toroidal magnetic fields for the magnetic shock-revival are found to be universal of ∼1015 G behind the jets. We point out that the time difference before the shock-revival has a strong correlation with the explosion energies. Our results suggest that the magnetically dominated jets are accompanied by the formation of the magnetars. Since the jets are mildly relativistic, we speculate that they might be the origin of some observed X-ray flashes.
A Graphical Chunk Production Model: Evaluation Using Graphical Protocol Analysis with Artificial Sentences

A model of the processes involved in the graphical production of chunks form memory is proposed. The primary processing constraints of the model are (a) serial processing (b) over a hierarchical structure of chunks (c) in a depth first manner. The model makes predictions about the relative durations of each individual pause that occurs between successive writing or drawing actions based on the particular chunk structure of each individual stimulus. An experiment is also described that tests the model using artificial sentences. The overall results of the experiment were consistent with main predictions of the model.
Multi-wavelength observations of PKS 2155-304 with HESS

The High Energy Stereoscopic System (HESS) has observed the high-frequency peaked BL Lac object PKS 2155−304 in 2003 between October 19 and November 26 in Very High Energy (VHE) γ-rays (E ≥ 160 GeV for these observations). Observations were carried out simultaneously with the Proportional Counter Array (PCA) on board the Rossi X-ray Timing Explorer satellite (RXTE), the Robotic Optical Transient Search Experiment (ROTSE) and the Nancay decimetric radiotelescope (NRT). Intra-night variability is seen in the VHE band, the source being detected with a high significance on each night it was observed. Variability is also found in the X-ray and optical bands on kilosecond timescales, along with flux-dependent spectral changes in the X-rays. A transient X-ray event with a 1500 s timescale is detected, making this the fastest X-ray flare seen in this object. No correlation can be established between the X-ray and the γ-ray fluxes, or any of the other wavebands, over the small range of observed variability. The average HESS spectrum shows a very soft power law shape with a photon index of 3.37 ± 0.07stat ± 0.10sys. The energy outputs in the 2–10 keV and in the VHE γ-ray range are found to be similar, with the X-rays and the optical fluxes at a level comparable to some of the lowest historical measurements, indicating that PKS 2155−304 was in a low or quiescent state during the observations. Both a leptonic and a hadronic model are used to derive source parameters from these observations. These parameters are found to be sensitive to the model of Extragalactic Background Light (EBL) that attenuates the VHE signal at this source’s redshift (z = 0.117).
Centralized BOINC resources manager for institutional networks

This paper describes a new BOINC based approach to manage desktop grid environments belonging to institutions such as Universities or Companies. While the BOINC model delegates to users the decision of selecting the projects to which their own resources will collaborate with, we consider here the case of institutions which owns the computers provided to their staff. Our model removes users from the projects selection step and transfers this responsibility to computers' owner: the institution itself. Our solution describes a new model for handling BOINC enabled computers in a centralized way, while also provides a new software management tool. This tool allows an institution to remotely manage all its BOINC enabled resources from a centralized point of view. We have set up and tested the model in a modern University, with distributed computer labs on three different cities. We show how the resources were remotely assigned to different projects selected by the institution. Finally, we describe results supporting the effectiveness of the approach.
Isotope effects in peptide group hydrogen exchange

Kinetic and equilibrium isotope effects in peptide group hydrogen exchange reactions were evaluated. Unlike many other reactions, Kinetic isotope effects in amide hydrogen exchange are small because exchange pathways are not limited by bondbreaking steps. Rate constants for the acid‐cat‐alyzed exchange of peptide group NH, ND, and NT in H2O are essentially identical, but a solvent esotope effect doubles the rate in D2O. Rate constants for base‐catalyzed exchange in H2O decrease slowly in the order NH > ND > NT. The alkaline rate constant in D2O is very close to that in H2O when account is taken of the glass electrode pH artifact and the difference in solvent ionization constant. Small equilibrium isotope effects lead to an excess equilibrium accumulation of the heavier isotopes by the peptide group. Results obtained are expressed in terms of rate constants for the random coil polypeptide, poly‐DL‐alanine, to provide reference rates for protein hydrogen exchange studies as described in Bai et al. [preceding paper in this issue]. © 1993 Wiley‐Liss, Inc.
Foot Drop Caused by Lumbar Degenerative Disease: Clinical Features, Prognostic Factors of Surgical Outcome and Clinical Stage

Objective The purpose of this study was to analyze the clinical features and prognostic factors of surgical outcome of foot drop caused by lumbar degenerative disease and put forward the clinical stage. Methods We retrospectively reviewed 135 patients with foot drop due to lumbar degenerative disease. The clinical features and mechanism were analyzed. Age, sex, duration of palsy, preoperative muscle strength of tibialis anterior (TA), sensation defect of affected lower limb, affected foot, diagnosis and compressed nerve roots were recorded and compared with surgical outcome. Results Foot drop was observed in 8.1% of all inpatients of lumbar degenerative disease. L5 nerve root compression was observed in 126 of all 135 patients (93.3%). Single, double and triple roots compression was observed respectively in 43, 83, and 9 patients (31.9%, 61.5%, and 6.6%). But there was no significant relationship between preoperative muscle strength of TA and the number of compressed roots. The muscle strength of TA was improved in 113 (83.7%) patients after surgery, but it reached to >=4 in only 21 (15.6%) patients. Improvement of the muscle strength of TA was almost stable at the 6-month follow-up. At the last follow-up, the muscle strength of TA was 1, 2, 3, 4, 5 respectively in 28, 24, 62, 13, 8 patients. Multivariate logistic regression showed duration of palsy (p=0.0360, OR=2.543), preoperative muscle strength of TA (p=0.0064, OR=5.528) and age (p=0.0309, OR=3.208) were factors that influenced recovery following an operation. Conclusions L5 nerve root was most frequently affected. The muscle strength of TA improved in most patients after surgery, but few patients can get a good recovery from foot drop. Patients of shorter duration of palsy, better preoperative muscle strength of TA and younger age showed a better surgical outcome.
A Tabling Engine Designed to Support Mixed-Strategy Evaluation

Tabling is an implementation technique that improves the declarativeness and expressiveness of Prolog by reusing answers to subgoals. During tabled execution, there are several points where different operations can be applied. The decision on which operation to perform is determined by the scheduling strategy. Whereas a strategy can achieve very good performance for certain applications, for others it might add overheads and even lead to unacceptable inefficiency. The ability of using multiple strategies within the same evaluation can be a means of achieving the best possible performance. In this work, we present how the YapTab system was designed to support the two most successful tabling scheduling strategies: batched and local scheduling; and how it can be easily extended to support simultaneous mixed-strategy evaluation.