Miracle-dz/UniFAR-FTUs huggingface dataset:
Old fashioned state-of-the-art image classification

In this paper we present a statistical learning scheme for image classification based on a mixture of old fashioned ideas and state of the art learning tools. We represent input images through large dimensional and usually sparse histograms which, depending on the task, are either color histograms or co-occurrence matrices. Support vector machines are trained on these sparse inputs directly, to solve problems like indoor/outdoor classification and cityscape retrieval from image databases. The experimental results indicate that the use of a kernel function derived from the computer vision literature leads to better recognition results than off the shelf kernels. According to our findings, it appears that image classification problems can be addressed with no need of explicit feature extraction or dimensionality reduction stages. We argue that this might be used as the starting point for developing image classification systems which can be easily tuned to a number of different tasks.
Perceptual object tracking

This paper presents an improved kernel-based target tracking that uses new and effective features able to describe the target appearance. The key idea consists of adopting features that are related to the visual perception of the target in place of its color histogram. The change of the feature space is twofold advantageous. It allows us to faithfully follow the target and to considerably reduce the computational cost of the whole tracking algorithm thanks to the reduced dimension of the perceptual feature space. Preliminary tests on some video sequences are encouraging. The proposed tracker is able to follow the target even in case of complete occlusion for some consecutive frames. Moreover, it is more stable than the algorithm that uses just the luminance histogram as target feature. Finally, it is robust to the presence of other moving targets.
Towards Peer-to-Peer Solution for Utilization of Volunteer Resources to Provide Computation-as-a-Service

In last few years, cloud computing has emerged as a popular alternative to satisfy the computing requirement of users. To support large number of users which is increasing very fast, the cloud service providers are forced to deploy more number of machines continuously. On the other hand, it has been observed that there are large number of machines indifferent organizations, educational institutions etc. which are underutilized. The whole scenario is against the basic principlesof green computing. In this paper, we present a peer-to-peerframework to aggregate the underutilized computing resourcesof an organization in order to provide computation-as-a-service. Our approach tackles the issue of dynamic leaving and joining ofcomputing nodes which is prevalent in peer-to-peer architectures. Simulation results establish the applicability of our proposal.
Iterative Parallax Mapping with Slope Information

This paper improves per-pixel displacement mapping algorithms. First we review the previous work including bump, parallax, and relief mapping, and also sphere tracing. Then we propose a new iterative algorithm based on parallax mapping, which takes into account the surface slope represented by the stored normal vector. We show that the new method is much better than parallax mapping, however a little poorer than relief mapping, in terms of image quality. However, it is much faster than relief mapping, and is thus a good candidate for implementation in games.
Slide-KWIC Browser : Snippet Generation Based on Semantic Relations and Document Structure for Presentation Slides

Currently, presentation contents are useful and valuable to students for e-Learning. However, there is still a lack of support for self-learners browsing through slides containing information that might be irrelevant to their query. We propose a snippet-generation method, and discuss how to present the retrieval results to users by considering what portions of the slides are relevant to a user query, on the basis of the relationships between slides that enable the browsing of slide retrieval at the conceptual level. This method uses the keyword conceptual structure of the semantic relations, and the document structure of the indent levels in the slides. We also present a prototype system and evaluate its effectiveness through experiments. Keyword Multimedia, E-Learning, Presentation content retrieval, Snippet generation, Semantic relation
Training Asynchronous Input / Output Hidden Markov ModelsYoshua

In learning tasks in which input sequences are mapped to output sequences, it is often the case that the input and output sequences are not synchronous. For example, in speech recognition , acoustic sequences are longer than phoneme sequences. Input/Output Hidden Markov Models have already been proposed to represent the distribution of an output sequence given an input sequence of the same length. We extend here this model to the case of asynchronous sequences, and show an Expectation-Maximization algorithm for training such models .
Improved Genetic Programming Algorithm

The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first investigates initialising population, the second investigates repuduction operator, the third investigates crossover operator, the fourth investigates mutation operation. This approach is examined on two experiments about symbolic regression. The results suggest that the new approach is more effective and more efficient than the canonical one.
Coloring Algorithms On Subcubic Graphs

We present efficient algorithms for three coloring problems on subcubic graphs (ones with maximum degree 3). These algorithms are based on a simple decomposition principle for subcubic graphs. The first algorithm is for 4-edge coloring, or more generally, 4-list-edge coloring. Our algorithm runs in linear time, and appears to be simpler than previous ones. As evidence we give the first randomized EREW PRAM algorithm that uses O(n/log n) processors and runs in O(log n) time with high probability, where n is the number of vertices of the input graph. The second algorithm is the first linear-time algorithm to 5-total-color subcubic graphs. The third algorithm generalizes this to the first linear-time algorithm to 5-list-total-color subcubic graphs.
Social and spatial proactive caching for mobile data offloading

The surge in video traffic and shift toward on-demand content consumption is straining mobile operators' networks to a breaking point. In this article, we investigate the problem of mobile data offloading for beyond 4G networks from a caching perspective. Leveraging notions of prediction, storage, and social networking, it is shown that peak traffic demands can be substantially reduced by proactively serving predictable user demands, through caching at the network edge (i.e., base stations and users' devices). Notably, we focus on two caching scenarios which exploit the spatial and social structure of the network. Firstly, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak demands based on file popularity and correlations among users-files patterns. Secondly, leveraging social networks and device-to-device (D2D) communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to cache strategic contents and disseminate them among their social ties. Numerical results show that important gains are incurred, with backhaul savings and a higher ratio of satisfied users of up to 22% and 26%, respectively. Higher gains can further be obtained by increasing the storage capability at the network edge.
Distributed Data Acquisition and Storage Architecture for the SuperNova Acceleration Probe

The Supernova Acceleration Probe (SNAP) instrument is being designed to collect image and spectroscopic data for the study of dark energy in the universe. In this paper, we describe a distributed architecture for the data acquisition system which interfaces to visible light and infrared imaging detectors. The architecture includes the use of NAND flash memory for the storage of exposures in a file system. Also described is an FPGA-based lossless data compression algorithm with a configurable pre-scaler based on a novel square root data compression method to improve compression performance. The required interactions of the distributed elements with an instrument control unit will be described as well.
Automatic regions of interest identification and classification in CT images: Application to kidney cysts

Recently, we proposed an original approach, in a statistical framework, for fully automatic detection of pathological kidneys in 2D CT images. In this paper, we propose some important improvements of our previous work and an attempt to classify the identified regions into pathological vs non pathological. To this end, we propose two indexing methods to construct the signatures coding the relevant information. The index is then used in a supervised classification technique to discriminate the kidney images. These approaches are tested on more than 500 clinically acquired images and promising results are obtained.
Non-Deterministic Timers for Hardware Trojan Activation (or How a Little Randomness Can Go the Wrong Way)

The security of digital Integrated Circuits (ICs) is essential to the security of a computer system that comprises them. A particularly pernicious attack is the insertion of a hardware backdoor, that is triggered in the field using a timer that is also inserted in the hardware. Prior work has addressed deterministic timer-based triggers--those that are designed to trigger at a specific time with probability 1. We address open questions related to the feasibility of realizing non-deterministic timer-based triggers in hardware -- those that are designed with a random component. We show that such timers can be realized in hardware in a manner that is impractical to detect or disable using existing countermeasures of which are aware. We discuss our design, implementation and analysis of such a timer. We show that the attacker can have surprisingly fine-grained control over the time-window within which the timer triggers. Our timer has several other appealing features as well, from the attacker's standpoint. For example, it is practical and effective with only a few bits of Non-Volatile (NV) memory and a small time-window within which volatile state needs to be maintained. Our work raises the bar considerably for defense mechanisms for hardware security.
An Improved Lda Model in Micro-Blog Tags Extracting Based on Multi-Tags

This article mainly discusses how to extract the interested information from massive amounts of micro-blogs and recommend right information to user, which is a hot research area in recommendation systems and social networks, too. To solve this problem, a model called Multi-tags Latent Dirichlet Allocation is proposed. Using this model, topics paid attention by users can be mined effectively and the defect of low degree of differentiation for the short blog content is settled. Experiments showed that the tags of user's micro-blog can be figured out with this model which makes users manage their resources at their convenience and others find their needed resources through tags. The results, experimented on real micro-blog data set, indicate that this model works better than traditional model on extracting tags. Standard measuring index Perplexity is applied to this model to estimate the likelihood of new text. If the number of topics is se- lected appropriately, the accuracy will be raised to almost 10%.
Propositional Versus Dual-Process Accounts of Evaluative Conditioning

Evaluative conditioning (EC) is defined as the change in the evaluation of a conditioned stimulus (CS) due to its pairing with a valenced unconditioned stimulus (US). Expanding on the debate between dual-process and propositional accounts, two studies investigated the relative effectiveness of counter-conditioning and counter-instructions in reversing EC effects on implicit and explicit evaluations. After conditioned evaluations were acquired via CS-US pairings, participants were either (1) presented with repeated CS-US pairings of the opposite valence or (2) given instructions that the CSs will be paired with USs of the opposite valence. Although both procedures reversed previously conditioned explicit evaluations, only directly experienced CS-US pairings reversed previously conditioned implicit evaluations. The findings question the functional equivalence of counter-conditioning and counter-instructions hypothesized by single-process propositional accounts. Yet, they support dual-process accounts, suggesting that associative and propositional processes jointly contribute to EC effects.
The Influence Of Cultural Diversity On Initial Decisions To Trust In Newly Forming Teams: A Policy Capturing Approach

This study investigated the impact of diversity on the decision to trust at team formation when no history or prior relationship exists. The study consisted of two phases: 1) a selection phase and 2) a policy capturing phase. The first phase consisted of demographics, propensity to trust, and prejudice scales that were used to select participants for phase 2. The second phase consisted of a full factorial design, policy capturing study which consisted of 64 scenarios which varied the level (i.e., high and low) of 6 variables: cultural diversity, attribution, perceptions of risk, trustworthiness, third party information, and role clarity. The policy capturing study was used to identify the weights given to these variables when deciding whether or not to trust a new team member. Propensity to trust scores and prejudice ratings were used as moderators of the relationships between these 6 variables and the decision to trust. Findings showed that there was a strong moderating affect of the diversity of the simulated team member on the participant's decision to trust. However, there was no direct relationship between diversity and the decision to trust. The weight given to each variable, as well as the interaction of variables, was different based on the diversity of the new team member. Findings suggest that when forming teams, the diversity of new team members will impact what factors individuals consider in deciding to trust that other person. In addition to future research needs, the impact of these results is discussed in terms of both training and selection in teams.
Semi-supervised adaptive parzen Gentleboost algorithm for fault diagnosis

In this paper, we present a novel semi-supervised strategy for machine fault diagnosis. In the proposed method, we select parzen window as the generative classifier and Gentleboost as the discriminative classifier. Compared with SVM, boosting method has a very interesting property of relative immunity to overfitting. In addition, we propose a novel adaptive parzen window algorithm. It employs variational adaptive parzen window rather than a global optimized and fixed window, therefore, more accurate density estimates can be obtained. In experiments, artificial and machine vibration data are used to compare with other algorithms. Our proposed algorithm achieves stronger robustness and lower classification error rate.
Does Early Postsurgical Temozolomide Plus Concomitant Radiochemotherapy Regimen Have Any Benefit in Newly-diagnosed Glioblastoma Patients? A Multi-center, Randomized, Parallel, Open-label, Phase II Clinical Trial

Background: The radiochemotherapy regimen concomitantly employing temozolomide (TMZ) chemotherapy and radiotherapy (RT) 4 weeks after surgery, followed by 6 cycles of TMZ is a common treatment for glioblastoma (GBM). However, its median overall survival (OS) is only 14.6 months. This study was to explore the effectiveness and safety of early TMZ chemotherapy between surgery and chemoradiotherapy plus the standard concomitant radiochemotherapy regimen. Methods: A randomized, parallel group, open-label study of 99 newly diagnosed GBM patients was conducted at 10 independent Chinese neurosurgical departments from June 2008 to June 2012. Patients were treated with concomitant radiochemotherapy regimen plus early postsurgical temozolomide (early TMZ group) or standard concomitant radiochemotherapy regimen (control group). Overall response was assessed based on objective tumor assessments, administration of corticosteroid and neurological status test. Hematological, biochemical, laboratory, adverse event (AE), and neurological condition were measured for 24 months of follow-up. The primary efficacy endpoint of this study was overall survival (OS). The secondary endpoint was progression free survival (PFS). Results: The median OS time in the early TMZ group was 17.6 months, compared with 13.2 months in the control group (log-rank test P = 0.021). In addition, the OS rate in the early TMZ group was higher at 6, 12, and 18 months than in the control group, respectively (P < 0.05). The median PFS time was 8.7 months in the early TMZ group and 10.4 months in the control group (log-rank test P = 0.695). AEs occurred in 29 (55.8%) and 31(73.8%) patients respectively in early and control groups, including nausea (15.4% vs. 33.3%), vomiting (7.7% vs. 28.6%), fever (7.7% vs. 11.9%), and headache (3.8% vs. 23.8%). Only 30.8% and 33.3% were drug-related, respectively. Conclusions: Addition of TMZ chemotherapy in the early break of the standard concomitant radiochemotherapy regimen was well tolerated and significantly improved the OS of the GBM patients, compared with standard concomitant radiochemotherapy regimen. However, a larger randomized trial is warranted to verify these results.
TR 14-016 Feature-based Similarity Models for Top-n Recommendation of New Items

Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Non-collaborative user modeling techniques that rely on the item features can be used to recommend new items. However, they only use the past preferences of each user to provide recommendations for that user. They do not utilize information from the past preferences of other users which can potentially be ignoring useful information. More recent factor models transfer knowledge across users using their preference information in order to provide more accurate recommendations. These methods learn a low rank approximation for the preference matrix which can lead to loss of information. Moreover, they might not be able to learn useful patterns given very sparse datasets. In this work we present FSM, a method for top-n recommendation of new items given binary user preferences. FSM learns Feature-based item-Similarity Models and its strength lies in combining two points: (i) exploiting preference information across all users to learn multiple global item similarity functions, and (ii) learning user-specific weights that determine the contribution of each global similarity function in generating recommendations for each user. FSM can be considered as a sparse high-dimensional factor model where the previous preferences of each user are incorporated within his latent representation. This way FSM combines the merits of item similarity models that capture local relations among items and factor models that learn global preference patterns. A comprehensive set of experiments was conduced to compare FSM against state-of-the-art collab-orative factor models and non-collaborative user modeling techniques. Results show that FSM outperforms other techniques in terms of recommendation quality. FSM manages to yield better recommendations even with very sparse datasets. Results also show that FSM can efficiently handle high-dimensional as well as low-dimensional item feature spaces.
Model Free Subspace H Control for an Autonomous Catamaran

An autonomous surface vehicle, based on a Prindle- 19 catamaran and substituting a self-trimming vertical wing for the sail, was developed to demonstrate precision guidance and control. This vehicle, the Atlantis, was demonstrated to track straight line segments to better than 0.3 meters (one ) when already trimmed to sail along a segment, using a Linear Quadratic Gaussian (LQG) controller based on an identified plant using the Observer Kalman Identification (OKID) methods. In this work, a novel controller based on model free subspace H1 control is shown to achieve similar performance levels without building an actual model of the system. These new control methods were tested and implemented on a nonlinear simulation of the catamaran which included realistic wind and current models. The model free control architecture was applied to the simulated catamaran and using Monte Carlo simulations, demonstrated very robust tracking traversal while maintaining a cross-track error of less than one meter throughout the path.
Real-time rendering of large scale scenes based on multiresolutional strip models

In the paper, a real-time rendering method based on continuous level of detail models is proposed for large scale scenes. First, each object included in the scene is converted into a triangle strip model, and simplified using edge collapse algorithm. Then, a GPU-oriented data structure is applied to store the information of the simplification. The structure also takes advantage of temporal coherence when the viewing position moves gradually. Using this structure, the LOD model for each object according to the current view can be extracted efficiently with a little communication between the CPU and the GPU, so that a high frame rate can be achieved. In addition, an octree-based culling algorithm is used to clip the objects that are outside of the current viewing frustum for further acceleration on rendering.
Managing the complexity of inner-city scenes: An efficient situation hypotheses selection scheme

Due to the large number and the high variability of possible traffic situations, intersections are among the most accident-prone spots in inner-city traffic. To reliably assist the driving tasks elaborated risk assessment systems are needed. Current approaches are mainly based on the prediction of possible future trajectories of the involved traffic participants. However, considering the variability and combinatorics of intersection-related traffic situations, this becomes unfeasible for limited computational resources. Here, we present a general framework for an efficient situation hypotheses selection system. The selection process is based on reasoning about whether a particular situation results in a threat for the ego vehicle's behavior. Our approach combines the results of a probabilistic situation recognition and a fast risk assessment using state-of-the-art regression methods. We show that the proposed system is able to effectively reduce the number of unnecessarily considered situation hypotheses on average by over 80%.