Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobipedia: Mobile Applications Linked Data

We present Mobipedia, an integrated knowledge base with information about 1 million mobile applications (apps) such as their category, meta-data (author, reviews, rating, release date), permissions and libraries used, and similar apps. The goal of Mobipedia is to integrate unstructured and semi-structured data about mobile apps from publicly available data sources and publish it as Linked Data using RDF. We describe the extraction process for facts, access mechanisms to the knowledge base, and an overview of applications facilitated by Mobipedia.
Enforcing Routing Consistency in Structured Peer-to-Peer Overlays: Should We and Could We?

In this paper, we argue that enforcing routing consistency in keybased routing (KBR) protocols can simplify P2P application design and make structured P2P overlays suitable for more applications. We define two levels of routing consistency semantics, namely weakly consistent KBR and strongly consistent KBR. We focus on an algorithm that provides strong consistency based on group membership service and weakly consistent KBR. The algorithm provides a continuum of consistency levels for applications with a
A Malware Classification Method Based on Similarity of Function Structure

Malicious software (Malware) in form of Internet worms, computer viruses, and trojan horses poses a major threat to the security of network systems. Identification of malware variants provides great benefit in early detection. Taking into account that variants of malware families share similar functions reflecting its origin and purpose, we propose a method focusing on the features of functions that a malware program consists of. In our method, the feature database is created based on the analysis of known malware programs, and functions in unknown programs are compared to the content of the database to determine the program belong to what family. To decrease the cost of the calculation of similarity, we use a filtering algorithm based on one-class SVM to filter out functions which have small influence in determining the family. We evaluated the approach using 32 categorized malware samples and 113 malware samples to be classified. In the experiment, it is shown that our approach effectively reduce the time for calculation while the accuracy is not deteriorated too much.
Feature selection for classification of gene expression data

This paper presents a gene selection method for classification of gene expression data. First, a fFeature selection techniques based on t-test statistic is used applied in order to select the n top-ranked significant genes. Then, a combination of Genetic Algorithm (GA) and /Support Vector Machines (SVM) is used to further select significant genes to the resulting set of genes obtained from t-test selection.improve the selection of t-test. SVM classifier is utilized implemented to evaluate performance of the feature selection approaches. the final gene. Results of comparative studies are provided, demonstrating that effective feature selection is essential to the development of classifiers intended for to be used in for high-dimensionality data problems. The result of SVM classifier with all genes compared with t-test/SVM and t-test/GA/SVM procedure are reported. This research also shows that feature selection helps increase computational efficiency while improving classification accuracy.
A Study on Terminology Extraction Based on Classified Corpora

Algorithms for automatic term extraction in a specific domain should consider at least two issues, namely Unithood and Termhood (Kageura, 1996). Unithood refers to the degree of a string to occur as a word or a phrase. Termhood (Chen Yirong, 2005) refers to the degree of a word or a phrase to occur as a domain specific concept. Unlike unithood, study on termhood is not yet widely reported. In classified corpora, the class information provides the cue to the nature of data and can be used in termhood calculation. Three algorithms are provided and evaluated to investigate termhood based on classified corpora. The three algorithms are based on lexicon set computing, term frequency and document frequency, and the strength of the relation between a term and its document class respectively. Our objective is to investigate the effects of these different termhood measurement features. After evaluation, we can find which features are more effective and also, how we can improve these different features to achieve the best performance. Preliminary results show that the first measure can effectively filter out independent terms or terms of general use.
Asymmetric NoC Architectures for GPU Systems

While both Chip MultiProcessors (CMPs) and Graphics Processing Units (GPUs) are many-core systems, they exhibit different memory access patterns. CMPs execute threads in parallel, where threads communicate and synchronize through the memory hierarchy (without any coalescing). GPUs on the other hand execute a large number of independent thread blocks and their accesses to memory are frequent and coalesced, resulting in a completely different access pattern. NoC designs for GPUs have not been extensively explored. In this paper, we first evaluate several NoC designs for GPUs to determine the most power/performance efficient NoCs. To improve NoC energy efficiency, we explore an asymmetric NoC design tailored for a GPU's memory access pattern, providing one network for L1-to-L2 communication and a second for L2-to-L1 traffic. Our analysis shows that an asymmetric multi-network Cmesh provides the most energy-efficient communication fabric for our target GPU system.
Becoming an Engineering Education Researcher: Finding Pathways toward Interdisciplinarity. Research Brief.

Interdisciplinary thinking is gaining momentum as an important topic for empirical investigation, particularly in regard to how crossing disciplinary boundaries can enrich teaching and learning across fields. This paper focuses on one particular interdisciplinary setting: engineering education. Using data from semi-structured interviews with engineering education researchers, this study explores what it means to work in an interdisciplinary space and the process of becoming an interdisciplinary scholar. Findings emerged regarding the nature of the pathway to expertise in engineering education research, including entry points, factors that facilitate working in this field, strategies for successful interdisciplinarity, and the nature and role of community throughout scholars’ pathways. Given the growth of interdisciplinary work, we anticipate that our findings will have applications beyond our context, in other interdisciplinary fields.
Parallelization for Multi-dimensional Loops with Non-uniform Dependences

This paper presents two enhanced partitioning algorithms in order to improve parallelization of multidimensional loops with flow and anti-dependences Using distance of the first dependence, we show the first general enhanced algorithm of single-loops with both flow and anti-dependences. Using the first algorithm, loop interchanging method, and cycle shrinking method, we present parallelization of nested loops with simple subscripts. Using the second algorithm, we also present parallelization to multi-dimensional loops with both flow and anti-dependences. Our two presented algorithms show enhanced loop parallelization of multi-dimensional loops with both dependences. We will improve our proposed algorithms for multi-dimensional space with multiple dependences.
Wet Adhesion and Adhesive Locomotion of Snails on Anti-Adhesive Non-Wetting Surfaces

Creating surfaces capable of resisting liquid-mediated adhesion is extremely difficult due to the strong capillary forces that exist between surfaces. Land snails use this to adhere to and traverse across almost any type of solid surface of any orientation (horizontal, vertical or inverted), texture (smooth, rough or granular) or wetting property (hydrophilic or hydrophobic) via a layer of mucus. However, the wetting properties that enable snails to generate strong temporary attachment and the effectiveness of this adhesive locomotion on modern super-slippy superhydrophobic surfaces are unclear. Here we report that snail adhesion overcomes a wide range of these microscale and nanoscale topographically structured non-stick surfaces. For the one surface which we found to be snail resistant, we show that the effect is correlated with the wetting response of the surface to a weak surfactant. Our results elucidate some critical wetting factors for the design of anti-adhesive and bio-adhesion resistant surfaces.
On Multi-Link Failure Localization in All-Optical Networks

Link failure localization has been an important and challenging problem for all-optical networks. The most general monitoring structure, called path-trial, is a light-path into which optical signals are launched and monitored. How to minimize the number of required path-trials is critical to the expense of this technique. Existing solutions are limited to localizing single link failure or handling only small networks. Moreover, some practical constraints, like lacking of knowledge of the failure quantity, are ignored. To overcome these limitations is prospective but quite challenging. To this end, we address the multi-link failure localization problem. On one hand, for moderate-size networks, we provide a tree-decomposition based centralized algorithm; on the other hand, a random walk based localized algorithm for large-scale networks is proposed. In addition, we further adapt these two algorithms to cope with three practical constraints. Theoretical analysis and simulation results are included to prove the correctness and efficiency of the proposed solution.
Vision-Based Realtime Animation Synthesis

This paper introduces the design of our real time vision-based motion performance animation system. The system requires user to wear a small set of markers. The low-dimensional control signals from user's performance are first used to construct a series of local models, which could be later used to animation synthesis. When constructing local models, we preprocess motion capture data to K-nearest neighborhood graph and store these data in KD-tree to ensure model building is real-time. In animation synthesis, we use an approach named locally weighted linear regression to synthesis the animation data closest to current pose. The performance of the system is tested by having users do three kinds of motion: running, walking and jumping.
Automatic Language Identification using Support Vector Machines

As powerful theoretical and computational tools, support vector machines (SVMs) have been widely used in pattern classification of many areas. A key issue of applying SVMs to language identification of speech signals is to find a SVM kernel that compares a sequence of feature vectors with others efficiently. In this paper, we introduce a sequence kernel used in language identification, and develop a Gaussian mixture model to do the sequence mapping task, which maps a variable length sequence of vectors to a fixed dimensional space. Experiment results demonstrate that the new system not only yields performance superior to those of a GMM classifier but also outperforms the system using generalized linear discriminant sequence (GLDS) kernel
Meta-Learning Evolutionary Artificial Neural Networks: by Means of Cellular Automata

In this paper, we introduce meta-learning evolutionary artificial neural network by means of cellular automata (MLEANN-CA). It is an adaptive computational framework based on evolutionary computation with indirect encoding methods (cellular automata) for automatic design of optimal artificial neural networks wherein the neural network architecture, activation function, connection weights, and the learning algorithm with its parameters are adapted according to the problem. We explored, experimentally, the performance of the MLEANN-CA framework using the Matlab and two famous chaotic time series. We compared it with a previous MLEANN framework, which used the direct encoding methods, and with the conventional design of ANNs. We demonstrated how effective is the proposed MLEANN-CA framework to obtain a design of feed-forward neural network that is smaller, faster and with better generalization performance
A hierarchical Bayesian network for face recognition using 2D and 3D facial data

In this paper, we tackle the problem of face classification and verification. We present a novel face representation method based on a Bayesian network. The model captures dependencies between 2D salient facial regions and the full 3D geometrical model of the face, which makes it robust to pose variations, and useable in unconstrained environments. We present experiments on the challenging databases FERET and LFW, which show a significant advantage over state-of-the-art methods.
Composite Pavement Systems: Synthesis of Design and Construction Practices

Composite pavement systems have shown the potential for becoming a cost-effective pavement alternative for highways with high and heavy traffic volumes, especially in Europe. This study investigated the design and performance of composite pavement structures composed of a flexible layer (top-most layer) over a rigid base. The report compiles (1) a literature review of composite pavement systems in the U.S. and worldwide; (2) an evaluation of the state-of-the-practice in the U.S. obtained using a survey; (3) an investigation of technical aspects of various alternative composite pavement systems designed using available methodologies and mechanistic-empirical pavement distress models (fatigue, rutting, and reflective cracking); and (4) a preliminary life cycle cost analysis (LCCA) to study the feasibility of the most promising composite pavement systems. Composite pavements, when compared to traditional flexible or rigid pavements, have the potential to become a cost-effective alternative because they may provide better levels of performance, both structurally and functionally, than the traditional flexible and rigid pavement designs. Therefore, they can be viable options for high volume traffic corridors. Countries, such as the U.K. and Spain, which have used composite pavement systems in their main road networks, have reported positive experiences in terms of functional and structural performance. Composite pavement structures can provide long-life pavements that offer good serviceability levels and rapid, cost-effective maintenance operations, which are highly desired, especially for high-volume, high-priority corridors. Composite pavements mitigate various structural and functional problems that typical flexible or rigid pavements tend to present, such as hot-mix asphalt (HMA) fatigue cracking, subgrade rutting, portland cement concrete (PCC) erosion, and PCC loss of friction, among others. At the same time, though, composite systems are potentially more prone to other distresses, such as reflective cracking and rutting within the HMA layer. Premium HMA surfaces and/or reflective cracking mitigation techniques may be required to mitigate these potential problems. At the economic level, the results of the deterministic agency-cost LCCA suggest that the use of a composite pavement with a cement-treated base (CTB) results in a cost-effective alternative for a typical interstate traffic scenario. Alternatively, a composite pavement with a continuously reinforced concrete pavement (CRCP) base may become more cost-effective for very high volumes of traffic. Further, in addition to savings in agency cost, road user cost savings could also be important, especially for the HMA over CRCP composite pavement option because it would not require any lengthy rehabilitation actions, as is the case for the typical flexible and rigid pavements.
自動運転のための LiDAR を用いた リアルタイム周辺環境認識システムの開発 Development of Real-time Environment Recognition System using LiDAR for Autonomous Driving

Development of Real-time Environment Recognition System using LiDAR for Autonomous Driving 徳留 成亮, 鮎川 秀平, 二宮 舜 Naruaki Tokudome, Shuhei Ayukawa, Shun Ninomiya 榎田 修一, ⃝ 西田 健 Shuichi Enokida, ⃝Takeshi Nishida 九州工業大学 Kyushu Institute of Technology Abstract: LiDAR (light detection and ranging) sensors are installed in many autonomous driving cars for detection of the surrounding environment and situations. It is possible to measure the shape, distance, and infrared reflection intensity of the objects in the range of 100 m distance around the whole circumference, and the performance deterioration is small against the change of the natural environment. In this research, we developed a novel real-time environment recognition system for autonomous driving by using a LiDAR sensor. Moreover, we developed an improving method of long distance recognition precision of LiDAR by superimposing of measurement data in time-series. In this paper, we describe the developed systems and the methods. Furthermore, the effectiveness of them, such as the recognition precision and processing time confirmed by several experiments with a car installed the developed LiDAR system and methods is described.
Towards a Visual Perception System for Pipe Inspection: Monocular Visual Odometry

Liquid Natural Gas (LNG) processing facilities contain large complex networks of pipes of varying diameter and orientation intermixed with control valves, processes and sensors. Regular inspection of these pipes for corrosion, caused by impurities in the gas processing chain, is critical for safety. Popular existing non-destructive technologies that used for corrosion inspection in LNG pipes include Magnetic Flux Leakage (MFL), radiography (X-rays), and ultrasound among others. These methods can be used to obtain measurements of pipe wall thickness, and by monitoring for changes in pipe wall thickness over time the rate of corrosion can be estimated. For LNG pipes, unlike large mainstream gas pipelines, the complex infrastructure means that these sensors are currently employed external to the pipe itself making comprehensive, regular coverage of the pipe network difficult to impossible. As a result, a sampling-based approach is taken where parts of the pipe network are sampled regularly, and the corrosion estimate is extrapolated to the remainder of the pipe using predictive corrosion models derived from metallurgical properties. We argue that a robot crawler that can move a suite of sensors inside the pipe network, can provide a mechanism to achieve more comprehensive and effective coverage. In this technical report, we explore a vision-based system for building 2D registered appearance maps of the pipe surface whilst simultaneously localizing the robot in the pipe. Such a system is essential to provide a localization estimate for overlaying other non-destructive sensors, registering changes over time, and the resulting 2D metric appearance maps may also be useful for corrosion detection. For this work, we restrict ourselves to linear pipe formations. We explore two distinct classes of algorithms that can be used to estimate this pose are investigated, both visual odometry systems which estimate motion by observing how the appearance of images change between frames. The first is a class of dense algorithms that use the greyscale intensity values and their derivatives of all pixels in adjacent images. The second class is a sparse algorithm that use the change in position (sparse optical flow) of salient point feature correspondences between adjacent images. Pose estimate results obtained using the dense and sparse algorithms are presented for a number of images sequences captured by different cameras as they moved through two pipes having diameters of 152.40mm (6”) and 406.40mm (16”), and lengths 6 and 4 meters respectively. These results show that accurate pose estimates can be obtained which consistently have errors of less than 1 percent for distance traveled down the pipe. Examples of the stitched images are also presented, which highlight the accuracy of these pose estimates.
Reconfigurable hardware SAT solvers: a survey of systems

By adapting to computations that are not so well-supported by general-purpose processors, reconfigurable systems achieve significant increases in performance. Such computational systems use high-capacity programmable logic devices and are based on processing units customized to the requirements of a particular application. A great deal of the research effort in this area is aimed at accelerating the solution of combinatorial optimization problems. Special attention in this context was given to the Boolean satisfiability (SAT) problem resulting in a considerable number of different architectures being proposed. This paper presents the state-of-the-art in reconfigurable hardware SAT satisfiers. The analysis and classification of existing systems has been performed according to such criteria as algorithmic issues, reconfiguration modes, the execution model, the programming model, logic capacity, and performance.
Role of Angiopoietin/Tie2 in Critical Illness: Promising Biomarker, Disease Mediator, and Therapeutic Target?

Critical illness is a descriptive, broad term for a serious clinical condition that can result from enormously heterogeneous etiologies. A common end feature these patients regularly suffer from is the so-called multiple organ dysfunction syndrome (MODS), often a consequence of organ hypoperfusion and ischemia, coagulopathies, overwhelming inflammatory responses, immune paralysis and mitochondrial dysfunction. Mechanistically, endothelial injury and particularly microvascular leakage is a major step in the pathophysiology of MODS and contributes to its mortality. The angiopoietin (Angpt)/Tie2 system consists of the endothelial tyrosine kinase Tie2 and its 4 circulating ligands (Angpt1-4). The balance between the agonistic ligand “Angpt-1" and the antagonistic one “Angpt-2" regulates baseline endothelial barrier function and its response to injury and is therefore considered a gatekeeper of endothelial activation. This paper provides a systematic overview of the Angpt/Tie2 system with respect to (1) its role as a global biomarker of endothelial activation in critical ill patients, (2) its contribution to MODS pathophysiology as a disease mediator, and last but not least (3) putative therapeutic applications to modify the activation state of Tie2 in mice and men.
Mining Self-Adaptive Sequence Patterns Based on the Sequence Fuzzy Concept Lattice

Traditional algorithms in mining sequence patterns only discover the frequent sequences satisfying the minimum support threshold minsup; however, these methods don't consider the importance of actual sequences. In order to mine the important sequence patterns satisfying users' many demands, this paper presents a new algorithm called SASeqP for mining self-adaptive sequence patterns based on sequence fuzzy concept lattice. The algorithm firstly introduces the weight for each item in sequences. On the basis of the weight, we define the sequence fuzzy formal context and sequence fuzzy concept lattice. After these, we define self-adaptive sequence patterns and the self-adaptive coefficient that may dynamically adjust the minimum support threshold minsup. At last, this article presents the new algorithm SASeqP for mining self-adaptive sequence patterns. The experimental results show that the algorithm SASeqP has excellent performance on the time-spatial complexity.
Colombia : decentralized education management

This study identifies common issues, benefits and constraints regarding local government's education management and institutional change in Colombia. This was accomplished through case studies of four local education systems during two administrations (1995-1997 and 1998-2000): in the Department of Antioquia, the District of Barranquilla, the Municipality of Pasto and the Municipality of Pensilvania (a small rural municipality with less than 100,000 inhabitants). The study was conducted to describe the institutional processes of decentralizing education management in Colombia and to identify key institutional and management elements of local governments' education management.