Miracle-dz/UniFAR-FTUs huggingface dataset:
Embracing conflicts: exploiting inconsistencies in distributed schedules using simple temporal network representations

Simple Temporal Networks (STNs) provide key advantages in dealing with dynamic, uncertain schedule execution environments. However, they have received scant attention in distributed scheduling domains A key limitation has been that STNs require consistency of all temporal information for continued operation. Unfortunately, consistency cannot always be guaranteed, as agents frequently have conflicting information due to a variety of factors. This thesis investigates three important issues surrounding the use of Simple Temporal Networks (STNs) in a distributed scheduling environment. We first present strategies to recover from the inconsistent information that naturally arises during schedule execution. We leverage the conflict analysis tools presented by the STN framework to detect, explain and resolve these inconsistencies. The conflict explanation produces an analysis of the inconsistency, identifying the conflicting temporal constraints. Using this analysis, we present a set of conflict resolution actions that an agent can use to restore its STN to a consistent state. Conflict explanation can also be used on inconsistencies encountered during the scheduling process to enhance the multi-agent schedule. This thesis develops strategies that increase the robustness of the schedule, and enable agents to update previous commitments they had made to each other. To enhance the fault-tolerance of a multi-agent schedule, we have developed Just-in-Time Backfilling (JIT-BF), a strategy for agents with a model of durational uncertainty to monitor their "close to execution" scheduled activities for potential failures, and take action to prevent these failures by scheduling redundant activities. We conclude this thesis by presenting a conflict-driven coordination approach to updating existing temporal commitments between agents. When an agent fails to schedule a new activity because of conflicts with the scheduled activities of other agents, the resulting inconsistency can be analyzed to discover what these conflicting activities are, and by how much they need to move to allow the new activity to be scheduled. We present two alternative strategies to perform these coordinated schedule updates. The strategies provide a trade-off between the amount of information an agent needs to initiate a coordination session, and the time needed for coordination.
Near the horizon of 5D black rings

For the five dimensional = 2 black rings, we study the supersymmetry enhancement and identify the global supergroup of the near horizon geometry. We show that the global part of the supergroup is OSp(4*|2) × U(1) which is similar to the small black string. We show that results obtained by applying the entropy function formalism, the c-extremization approach and the Brown-Henneaux method to the black ring solution are in agreement with the microscopic entropy calculation.
Colored pencil filter with custom colors

This paper presents a new technique for automatically converting digital images into colored pencil drawings. In real colored pencil drawing, artists use different colors for different regions, and add pure colors directly onto paper to build the target color through optical blending. To support such effect, our technique extends the existing technique for reproducing color image with custom inks to automatically select the best color set for individual regions in a source image. Then layers of stroke image for each color are generated and superimposed with the Kubelka-Munk optical compositing model. We also allow users to specify regions and to customize the color set for a specified region interactively. The proposed technique can be easily extended to simulate other artistic media featured with optical color blending, such as pastel and wax crayons.
Adversary Resistant Deep Neural Networks with an Application to Malware Detection

Outside the highly publicized victories in the game of Go, there have been numerous successful applications of deep learning in the fields of information retrieval, computer vision, and speech recognition. In cybersecurity, an increasing number of companies have begun exploring the use of deep learning (DL) in a variety of security tasks with malware detection among the more popular. These companies claim that deep neural networks (DNNs) could help turn the tide in the war against malware infection. However, DNNs are vulnerable to adversarial samples, a shortcoming that plagues most, if not all, statistical and machine learning models. Recent research has demonstrated that those with malicious intent can easily circumvent deep learning-powered malware detection by exploiting this weakness. To address this problem, previous work developed defense mechanisms that are based on augmenting training data or enhancing model complexity. However, after analyzing DNN susceptibility to adversarial samples, we discover that the current defense mechanisms are limited and, more importantly, cannot provide theoretical guarantees of robustness against adversarial sampled-based attacks. As such, we propose a new adversary resistant technique that obstructs attackers from constructing impactful adversarial samples by randomly nullifying features within data vectors. Our proposed technique is evaluated on a real world dataset with 14,679 malware variants and 17,399 benign programs. We theoretically validate the robustness of our technique, and empirically show that our technique significantly boosts DNN robustness to adversarial samples while maintaining high accuracy in classification. To demonstrate the general applicability of our proposed method, we also conduct experiments using the MNIST and CIFAR-10 datasets, widely used in image recognition research.
Multiple factor analysis: principal component analysis for multitable and multiblock data sets

Multiple factor analysis (MFA, also called multiple factorial analysis) is an extension of principal component analysis (PCA) tailored to handle multiple data tables that measure sets of variables collected on the same observations, or, alternatively, (in dual‐MFA) multiple data tables where the same variables are measured on different sets of observations. MFA proceeds in two steps: First it computes a PCA of each data table and ‘normalizes’ each data table by dividing all its elements by the first singular value obtained from its PCA. Second, all the normalized data tables are aggregated into a grand data table that is analyzed via a (non‐normalized) PCA that gives a set of factor scores for the observations and loadings for the variables. In addition, MFA provides for each data table a set of partial factor scores for the observations that reflects the specific ‘view‐point’ of this data table. Interestingly, the common factor scores could be obtained by replacing the original normalized data tables by the normalized factor scores obtained from the PCA of each of these tables. In this article, we present MFA, review recent extensions, and illustrate it with a detailed example. WIREs Comput Stat 2013, 5:149–179. doi: 10.1002/wics.1246
Generic NLP technologies: language, knowledge and information extraction

We have witnessed significant progress in NLP applications such as information extraction (IE), summarization, machine translation, cross-lingual information retrieval (CLIR), etc. The progress will be accelerated by advances in speech technology, which not only enables us to interact with systems via speech but also to store and retrieve texts input via speech.
Microsoft Research Asia at the Web Track of TREC 2009

In TREC 2009, we participate in the Web track, and focus on the diversity task. We propose to diversify web search results by first mining subtopics, and then rank results based on mined subtopics. We propose a model to diversify search results by considering both relevance of documents and richness of mined subtopics. Our experimental results show that the model improves diversity of search results in terms of α-NDCG, and combining subtopics from multiple data sources helps further improve result diversity.
Optimizing NUCA Organizations and Wiring Alternatives for Large Caches with CACTI 6.0

A significant part of future microprocessor real estate will be dedicated to 12 or 13 caches. These on-chip caches will heavily impact processor performance, power dissipation, and thermal management strategies. There are a number of interconnect design considerations that influence power/performance/area characteristics of large caches, such as wire models (width/spacing/repeaters), signaling strategy (RC/differential/transmission), router design, etc. Yet, to date, there exists no analytical tool that takes all of these parameters into account to carry out a design space exploration for large caches and estimate an optimal organization. In this work, we implement two major extensions to the CACTI cache modeling tool that focus on interconnect design for a large cache. First, we add the ability to model different types of wires, such as RC-based wires with different power/delay characteristics and differential low-swing buses. Second, we add the ability to model non-uniform cache access (NUCA). We not only adopt state-of-the-art design space exploration strategies for NUCA, we also enhance this exploration by considering on-chip network contention and a wider spectrum of wiring and routing choices. We present a validation analysis of the new tool (to be released as CACTI 6.0) and present a case study to showcase how the tool can improve architecture research methodologies.
Extremal traffic and bounds for the mean delay of multiplexed regulated traffic streams

In this paper, we present simple performance bounds for multiplexed regulated traffic streams, which are leaky-bucket regulated with peak, mean rate and burst size constraints. We consider independent, heterogeneous streams, which are multiplexed in a common buffer. We derive bounds on the mean delay in the deterministic context and we then obtain a simple stochastic bound, which is exact when the number of sources increases. A byproduct is a characterization of the worst case sources for mean delay, when they are leaky bucket regulated.
AODV , TORA and DSR with the Analysis of Monte Carlo Localization Scheme

The position information is usually needed when wireless sensor networks are applied for surveillance within certain region. In this manner the confinement of hubs is basic for WSNs. Be that as it may, the present confinement conspires for the most part concentrate on static sensor systems, and in versatile WSNs, these plans won't function admirably. This paper proposes a without range Monte Carlo confinement calculation TSBMCL, which is stretched out from the Monte Carlo Boxed (MCB) scheme[1]. We likewise concentrated three steering conventions AODV, DSR and TORA. It researches the elements influencing the execution criteria and working design of Ad hoc on-request Distance Vector (AODV), Dynamic Source Routing (DSR) and Temporally Ordered Routing Algorithm (TORA).
Reliability, Validity, and Responsiveness of a Modified International Knee Documentation Committee Subjective Knee Form (Pedi-IKDC) in Children With Knee Disorders

Background: The International Knee Documentation Committee (IKDC) Subjective Knee Form is a knee-specific measure of symptoms, function, and sports activity. A modified IKDC Subjective Knee Form (pedi-IKDC) has been developed for use in children and adolescents. The purpose of this study was to determine the psychometric characteristics of the pedi-IKDC in children and adolescents with knee disorders. Hypothesis: The pedi-IKDC is a reliable, valid, and responsive patient-administered outcome instrument in the pediatric population with knee disorders. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Test-retest reliability, content validity, criterion validity, construct validity, and responsiveness to change were determined for the pedi-IKDC in patients aged 10 to 18 years with a variety of knee disorders. Test-retest reliability was measured in a group of 72 patients with a stable knee disorder. Validity was measured in a group of 589 patients with the Child Health Questionnaire to determine criterion validity. Responsiveness was measured in a group of 98 patients undergoing a variety of knee surgical procedures. Results: The overall pedi-IKDC had acceptable test-retest reliability (intraclass correlation coefficient, .91) and excellent internal consistency (Cronbach alpha, .91). The form also demonstrated acceptable floor (0%) and ceiling (6%) effects. There was acceptable criterion validity with significant (P < .01) correlation between the overall pedi-IKDC and 9 relevant domains of the Child Health Questionnaire. Construct validity was acceptable, with all 11 hypotheses demonstrating significance (P < .0001). Responsiveness to change was acceptable (effect size, 1.39; standardized response mean, 1.35). Conclusion: The pedi-IKDC demonstrated overall acceptable psychometric performance for outcome assessment of children and adolescents with various disorders of the knee.
Ant Colony Optimization-Scholarpedia

In ACO, a set of software agents called artificial ants search for good solutions to a given optimization problem. To apply ACO, the optimization problem is transformed into the problem of finding the best path on a weighted graph. The artificial ants (hereafter ants) incrementally build solutions by moving on the graph. The solution construction process is stochastic and is biased by a pheromone model, that is, a set of parameters associated with graph components (either nodes or edges) whose values are modified at runtime by the ants.
A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm

In multi-instance multi-label learning (i.e. MIML), each example is not only represented by multiple instances but also associated with multiple labels. Most existing algorithms solve MIML problem via the intuitive way of identifying its equivalence in degenerated version of MIML. However, this identification process may lose useful information encoded in training examples and therefore be harmful to the learning algorithm's performance. In this paper, a novel algorithm named MIML-kNN is proposed for MIML by utilizing the popular k-nearest neighbor techniques. Given a test example, MIML-kNN not only considers its neighbors, but also considers its citers which regard it as their own neighbors. The label set of the test example is determined by exploiting the labeling information conveyed by its neighbors and citers. Experiments on two real-world MIML tasks, i.e. scene classification and text categorization, show that MIML-kNN achieves superior performance than some existing MIML algorithms.
A Lexicalized Tree Kernel for Open Information Extraction

In contrast with traditional relation extraction, which only considers a fixed set of relations, Open Information Extraction (Open IE) aims at extracting all types of relations from text. Because of data sparseness, Open IE systems typically ignore lexical information, and instead employ parse trees and Part-of-Speech (POS) tags. However, the same syntactic structure may correspond to different relations. In this paper, we propose to use a lexicalized tree kernel based on the word embeddings created by a neural network model. We show that the lexicalized tree kernel model surpasses the unlexicalized model. Experiments on three datasets indicate that our Open IE system performs better on the task of relation extraction than the stateof-the-art Open IE systems of Xu et al. (2013) and Mesquita et al. (2013).
Enhancing Security of Embedded Linux on a Multi-core Processor

While recent embedded systems start to own more and more functionalities, security requirements become more and more important. In this paper we propose an approach to enhance the security of embedded systems. In this approach SPUMONE, a thin virtualization layer, is selected to build a multi-OS environment for its low overhead. Xv6 runs as trusted OS for executing the monitoring service that detects the violation of the integrity of the Linux kernel while Linux as a general purpose OS. The monitoring service checks whether Linux is compromised or not. A secure pager that offers the spatial isolation based on the core-local memory is proposed to protect the integrity of the xv6 kernel located in the main memory, which can make Linux and xv6 run in high security level.
Investigation of the influence of processing conditions on the thermal, rheological and mechanical behavior of polypropylene nanocomposites

The use of polypropylene (PP)-layered silicate nanocomposite has attracted great interest in the polymer industry over the last years. On one hand, PP is widely used in many fields of applications because of good performance and cost. On the other hand, the major advantage of layered silicate nanofiller in the polymer matrix is the small amount of filler (<5 wt%) needed to enhance various properties such as Young's modulus. The most commonly used layered silicates are organomodified montmorillonite (MMT). In this study, a PP/organoclay nanocomposite, filled with layered silicate Nanofil SE3010 (1 and 5 wt%), provided by Rockwood Additives, USA, was used. The polymer nanocomposites were prepared via melt intercalation in a laboratory kneader. The compatibilizer (PP grafted with maleic acid anhydride) admixture content relative to the organoclay content (1 and 5 wt%) was chosen at a ratio of 1:1 (clay:compatibilizer). The influence of different processing conditions (rotation speed and residence time) on the thermal, rheological, and mechanical properties and the interlayer distance was investigated. It was the target to determine whether a short and intensive or a long and soft process performs better. Different properties prefer different states of dispersion of MMT in the polymer matrix. POLYM. ENG. SCI., 2013. © 2012 Society of Plastics Engineers
Hardware synthesis from coarse-grained dataflow specification for fast HW/SW cosynthesis

This work concerns automatic hardware synthesis from data flow graph (DFG) specification for fast HW/SW cosynthesis. A node in DFG represents a coarse grain block such as FIR and DCT and a port in a block may consume multiple data samples per invocation, which distinguishes our approach from behavioral synthesis and complicates the problem. In the presented design methodology, a dataflow graph with specified algorithm can be mapped to various hardware structures according to the resource allocation and schedule information. This simplifies the management of the area/performance tradeoff in hardware design and widens the design space of hardware implementation of a dataflow graph compared with the previous approaches. Through experiments with some examples, the usefulness of the proposed technique is demonstrated.
An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management

This paper focuses on the role of user modeling and semantically enhanced representations for personalization. This paper presents a generic Ontology-based User Modeling framework (OntobUMf), its components, and its associated user modeling processes. This framework models the behavior of the users and classifies its users according to their behavior. The user ontology is the backbone of OntobUMf and has been designed according to the Information Management System Learning Information Package (IMS LIP). The user ontology includes a Behavior concept that extends IMS LIP specification and defines characteristics of the users interacting with the system. Concrete examples of how OntobUMf is used in the context of a Knowledge Management (KM) System are provided. This paper discusses some of the implications of ontology-based user modeling for semantically enhanced KM and, in particular, for personal KM. The results of this research may contribute to the development of other frameworks for modeling user behavior, other semantically enhanced user modeling frameworks, or other semantically enhanced information systems.
Investigation on B, Cr Doped Ni3Al Alloy Prepared by Self-Propagation High-Temperature Synthesis and Hot Extrusion

The Ni3Al and Ni3Al-B-Cr alloys were fabricated by the self-propagation high-temperature synthesis with hot extrusion method. Their microstructure and mechanical properties were studied by using combination of X-ray diffraction, optical microscopy, transmission electron microscopy and compression test. Analysis of X-ray spectra exhibited that the elemental powders had been transformed to the Ni3Al phase after the self-propagation high-temperature synthesis processing. Microstructure examination showed that the alloy without extrusion consisted of coarse and fine grains, but the subsequent hot extrusion procedure homogenized the grain size and densified the alloy obviously. Transmission electron microscopy observations on the Ni3Al alloy revealed that Ni3Al, γ-Ni and Al2O3 particles were the main phases. When the boron and chromium were added, besides the β-NiAl phase, α-Cr phase and some Cr7Ni3 particles with stacking faults inside were observed. In addition, a lot of substructure and high-density dislocation arrays were observed in the extruded part, which indicated that the subsequent extrusion had led to great deformation and partly recrystallizing in the alloy. Moreover, the subsequent extrusion procedure redistributed the Al2O3 particles and eliminated the γ-Ni. These changes were helpful to refine the microstructure and weaken the misorientation. The mechanical test showed that the self-propagation high-temperature synthesis with hot extrusion improved the mechanical properties of the Ni3Al alloy significantly. The addition of B and Cr in Ni3Al alloy increased the mechanical properties further, but the compressive strength of the alloy was still lower than that synthesized by combustion. Finally, the self-propagation high-temperature synthesis with hot extrusion was a good method to prepare Ni3Al alloy from powder.
G-ToPSS: fast filtering of graph-based metadata

RDF is increasingly being used to represent metadata. RDF Site Summary (RSS) is an application of RDF on the Web that has considerably grown in popularity. However, the way RSS systems operate today does not scale well. In this paper we introduce G-ToPSS, a scalable publish/subscribe system for selective information dissemination. G-ToPSS is particularly well suited for applications that deal with large-volume content distribution from diverse sources. RSS is an instance of the content distribution problem. G-ToPSS allows use of ontology as a way to provide additional information about the data. Furthermore, in this paper we show how G-ToPSS can support RDFS class taxonomies. We have implemented and experimentally evaluated G-ToPSS and we provide results in the paper demonstrating its scalability compared to alternatives.
A Score-Based Opportunistic Scheduler for Fading Radio Channels

While fading effects have long been combatted in 2G wireless networks, primarly devoted to voice calls, they are now seen as an opportunity to increase the capacity of 3G networks that incorporate data traffic. The packet delay tolerance of data applications, such as file transfers and Web browsing for instance, allows the system flexibility in scheduling a user’s packets. Opportunistic scheduling ensures transmission occurs when radio conditions are most favorable. This paper discusses different resource sharing strategies and presents some shortcomings of the classical Proportional Fair opportunistic scheduler. A new algorithm, called the Score-Based scheduler, is presented and shown to overcome these drawbacks.