Miracle-dz/UniFAR-FTUs huggingface dataset:
Social network ordering based on communities to reduce cache misses [Numérotation des graphes sociaux basée sur les communautés pour la réduction des défauts de cache]

ABSTRACT. One of social graph's properties is the community structure, that is, subsets where nodes belonging to the same subset have a higher link density between themselves and a low link density with nodes belonging to external subsets. Futhermore, most social network mining algorithms comprise a local exploration of the underlying graph, which consists in referencing nodes in the neighborhood of a particular node. The idea of this paper is to use the community structure during the storage of large graphs that arise in social network mining. The goal is to reduce cache misses and consequently, execution time. After formalizing the problem of social network ordering as a problem of optimal linear arrangement which is known as NP-Complet, we propose NumBaCo, a heuristic based on the community structure. We present for Katz score and Pagerank, simulations that compare classic data structures Bloc and Yale to their corresponding versions that use NumBaCo. Results on a 32 cores NUMA machine using amazon, dblp and web-google datasets show that NumBaCo allows to reduce from 62% to 80% of cache misses and from 15% to 50% of execution time.
Image compression via K-means and SLIC superpixel approaches

Image compression is a key component in the transmission and storage process of image data. The major objective of image compression is to reduce the irrelevance and redundancy of the image data in order to store and transmit them in an efficient form. One of the practical approaches is to reduce the original large color space to a considerable small scale. In detail, we implement this image compression method via two approaches: K-means, which directly clustering the colors to nearest centroids, and the SLIC superpixel approaches, which relies on generating computationally efficient and perceptually meaningful image segments. Experiment results reveal that both of our approaches could effectively compress the image size.
Development of variance reduction techniques in Monte Carlo code RMC

In the field of nuclear engineering, deterministic and stochastic methods are used to solve radiation transport problems. Deterministic methods solve the transport equation for the average particle behavior and also contain uncertainties associated with the discretization of the independent variables such as space, energy and angle of the transport equation and can admit solutions that exhibit non-physics features [1]. The Monte Carlo method obtains results by simulating individual particles and recording some aspects of their average behavior [2]. This method enables detailed, explicit geometrical, energy and angular representations and hence is considered the most accurate method presently available for solving complex radiation transport problems. One of the difficulties associated with Monte Carlo method is the amount of computer time required to obtain sufficient precision in the simulations. Despite substantial advancements in computational hardware performance and widespread availability of parallel computers, the computer time required for analog Monte Carlo is still considered exorbitant and prohibitive for the design and analysis of many relevant real-world nuclear applications especially for the problems with complex and large geometry [1]. But there are many ways other than increasing simulation time in the Monte Carlo method by which the precision can be improved. These ways are known as Variance Reduction techniques and required enabling the Monte Carlo calculation of quantities of interest with the desired statistical uncertainty. Without the use of variance reduction techniques in complex problems, Monte Carlo code should run continuously for days or weeks and still cannot obtain statistically significant reliable results. The goal of Variance Reduction techniques is to produce more accurate and precise estimate of the expected value than could be obtained in analog calculation with the same computational efforts [3]. RMC is a Monte Carlo transport code which has been being developed by Department of Engineering Physics, Tsinghua University in China since 2008 as a tool for reactor core analysis on high-performance computing platforms [4]. To meet the requirements of reactor analysis, RMC now has such functions as criticality calculation, fixed-source calculation, burnup calculation and kinetics simulations. Some techniques for geometry treatment, new burnup algorithm, source convergence acceleration, massive tally, parallel calculation, and temperature dependent cross sections processing have been implemented in RMC to improve the efficiency and functions. In this paper, we mainly present several variance reduction techniques developed and implemented in RMC code recently including geometry splitting/roulette and weight window. Based on weight window technique, a new strategy of inner iterative fixed source calculation is also developed.
WYT: Optimized Consistency for Geo-Diverse Online Social Networks

Large scale Online Social Networks (OSNs) like Facebook and Twitter are hosted out of multiple geo-diverse data centers to provide low latency and high fault tolerance. Such geo-diversity creates large amounts of WAN traffic for maintaining the consistency of replicas at different locations. Despite the dropping price of WAN bandwidth, the growth rate of OSNs combined with the incorporation of media rich long tail content (including images and videos) makes WAN traffic costs an increasing concern for OSN operators. At the heart of the problem lies a tradeoff between consistency and WAN bandwidth cost. In this paper, we propose the "Wait Your Turn"; WYT system that optimizes the tradeoff by leveraging: (i) knowledge of mapping between social relationships and geographic location, and (ii) knowledge of timing regularities in end user activity patterns. We quantify the benefits of such an OSN-aware update propagation strategy through a trace-driven analysis and show that it reduces WAN traffic by 55% compared to an immediate update of all replicas, while having minimal impact on consistency. Furthermore, for a given budget for WAN bandwidth, WYT increases consistency by several orders of magnitude compared to FIFO scheduling of updates.
Designing a general software system for musculoskeletal analysis

This paper describes the considerations that went before the development of the AnyBody Modeling System, a system for computer modeling and analysis of the musculoskeletal system. The paper explains the considerations and choices behind the system’s design and basic methodology, and it presents a number of examples to illustrate the consequences. It is concluded that much of the necessary functionality has been provided at the cost of a technical interface that might be difficult for non-experts to master.
Security Detection and Research of Intelligent Hardware System

With the rapid development of Internet of things technology, intelligent hardware equipment has become an important part of our lives. It widely exists in the smart router, network cameras, smart home and smart wearable devices. By analyzing the common features of intelligent hardware devices, our research object is the intelligent hardware devices based on embedded Linux system in this paper. Through the method of software simulation, we can reduce the hardware cost and make it possible for the intelligent hardware security analysis at scale. By combing static and dynamic analysis method, serious vulnerability is found. In the stage of static analysis, we collect the file information as many as possible, which can make dynamic analysis more effective and accurately. In the dynamic analysis stage, we mine system vulnerabilities of the firmware. Finally, testing the actual application of the 24 manufacturers of 1945 firmwares, we found 365 vulnerabilities in the 96 firmwares. The results of the test show the effectiveness of the proposed method.
Predictive Coding Strategies for Invariant Object Recognition and Volitional Motion Control in Neuromorphic Agents

Abstract : Aim #1: Learning invariant representations of environments through experience has been important area of research both in the field of machine learning as well as in computational neuroscience. In this study, we employed a novel method for the discovery of invariants from a single video input based on the learning of the predictability of spatio-temporal relationships between inputs. To this end, videos containing spatio-temporal movements of unlabeled natural objects were used. Progress 1\202 Conducted real-time invariant perception and tracking of natural images 2\202 Conducted real-time invariant perception and tracking of video objects Aim #2: Volitional movements are a hallmark for human behavior. In this project, we hypothesized that visual memory of past motion trajectories may be used for selecting future behavior. In other words: following free energy principle, apparent volitional movements can be generated by minimizing the difference between what the agent expected to see and what it effectively sees. Progress 1\202 Tested of robotic systems prediction-based pseud-volitional movements in a complex environment requiring adaptive modifications. Phase I: physics modeled in Gazebo-type of simulated environment 2\202 Tested of robotic systems prediction-based pseud-volitional movements in a complex environment requiring adaptive modifications. Phase II: tested in a real physical environment
Thor's Microsoft Security Bible: A Collection of Practical Security Techniques

Thor's Microsoft® Security Bible provides a "one-stop-shop" for Microsoft-related security techniques and procedures as applied to the typical deployment of a Microsoft-based infrastructure. The book contains detailed security concepts and methodologies described at every level: Server, Client, Organizational Structure, Platform-specific security options, application specific security (IIS, SQL, Active Directory, etc.) and also includes new, never-before-published security tools complete with source code. Named the 2011 Best Systems Administration Book by InfoSec Reviews Detailed technical information on security processes for all major Microsoft applications Unique project-based "storytelling" delivery, combining multiple security techniques and methods together for real-world solutions to security challenges in actual business use cases Reference-style content for access to specific application security techniques and methods Actual author opinion and guidance as not only HOW to go about security particular applications, but WHY to do so DVD Companion disk with source code, tools, video tutorials, and other materials Table of Contents Chapter 1. Securely Writing Web Proxy Log Data to Structured Query Language (SQL) Server and Programmatically Monitoring Web Traffic Data in Order to Automatically Inject Allow/Deny Rules into Threat Management Gateway (TMG) Chapter 2. Internet Information Server (IIS) Authentication and Authorization Models, and Locking Down File Access with Encrypting File System (EFS) and Web Distributed Authoring and Versioning (Web DAV) Chapter 3. Analyzing and Blocking Malicious Traffic Based on Geolocation Chapter 4. Creating an Externally Accessible Authenticated Proxy in a Secure Manner Chapter 5. The Creation and Maintenance of Low Privileged Service Users (with a focus on SQL) Chapter 6. Remote Security Log Collection in a Least Privilege Environment Chapter 7. Securing RDP Appendix A: List Of Acronyms Appendix B: Full list of Server 2008 logs via WEVTUTIL tool
Self-regulating interval type-2 neuro-fuzzy inference system for non-stationary EEG signal processing

Motor-imagery based Brain Computer Interface (BCI) provides a direct communication pathway between the brain and a computer based on the neural activities generated by the brain. Such a technology enables people with physical disabilities to communicate with the external world without using their peripheral nerves and muscles. Moreover, BCI systems can also be used in field of gaming, robotics and human-computer interaction, in general. A typical motor-imagery based BCI system consists of a brain activity acquisition phase to obtain ElectroEncephaloGram (EEG) data using multiple channels, a brain signal processing phase to decode the user intentions and a translation phase for transforming the decoded information to control external devices. However, the usage of multiple channels may lead to several issues: a) Longer preparation time; b) Redundant channels with noisy information. In addition, the thoughts of users vary, which may change the data distribution over time (leads to non-stationary nature of EEG). Some of the trials are also affected by the presence of artifacts in EEG data. Further, the frequency response for a motor-imagery task varies for different individuals. These issues can render the BCI system inaccurate with deteriorated performance. The main goal of this thesis is to develop a unified BCI system that address the above mentioned issues. There are four major contributions in this thesis. The first contribution is development of a Robust Common Spatial Pattern (RoCSP) feature extraction algorithm that eliminates the trials affected by artifacts and discards the redundant channels to improve the classification performance of the BCI system. Experimental results show that RoCSP is able to reduce the number of channels and produce a good classification performance. Although, RoCSP provides robust features and improves performance, it does not handle non-stationarity in the EEG data. Recently, it has been shown that interval type2 fuzzy systems are capable of handling non-stationarity. However, there is a challenge
A Practical Guide, With Theoretical Underpinnings, for Creating Effective Virtual Reality Learning Environments

With the advent of open source virtual environments, the associated cost reductions, and the more flexible options, avatar-based virtual reality environments are within reach of educators. By using and repurposing readily available virtual environments, instructors can bring engaging, community-building, and immersive learning opportunities to students. Based on many years of academic research and development within this environment, the authors suggest educationally productive, research-supported ways to create learning environments that can motivate, engage, and educate participants. Instructors can develop virtual communities as centers for meetings, collaborations, and shared experiences, moving distance experiences beyond the limitations of engagement and collaboration in nonimmersive settings. The authors explain how instructors can develop useful learning interactions, pilot their learning environments, assess learners, and evaluate the environment. Specific experiences, images, and videos from the authors’ work are shared as well as broader application that could suit multiple purposes in guiding an instructor’s development and instructional efforts.
A Secure and Efficient Conference Key Distribution Scheme

A security flaw in the conference distribution scheme, namely GDH.2, proposed by M. Steiner et al , is found out, and a new secure and efficient conference key distribution scheme suitable for users exchanging information on insecure communication networks. Compared with GDH.2, the security of this new scheme is greatly improved at the cost of a slight increase in computation and communication loads.
Human-Computer Interaction and International Public Policymaking: A Framework for Understanding and Taking Future Actions

This monograph lays out a discussion framework for understanding the role of human–computer interaction HCI in public policymaking. We take an international view, discussing potential areas for research and application, and their potential for impact. Little has been written about the intersection of HCI and public policy; existing reports typically focus on one specific policy issue or incident. To date, there has been no overarching view of the areas of existing impact and potential impact. We have begun that analysis and argue here that such a global view is needed. Our aims are to provide a solid foundation for discussion, cooperation and collaborative interaction, and to outline future programs of activity. The five sections of this report provide relevant background along with a preliminary version of what we expect to be an evolving framework. Sections 1 and 2 provides an introduction to HCI and public policy. Section 3 discusses how HCI already informs public policy, with representative examples. Section 4 discusses how public policy influences HCI and provides representative public policy areas relevant to HCI, where HCI could have even more impact in the future: i laws, regulations, and guidelines for HCI research, ii HCI research assessments, iii research funding, iv laws for interface design — accessibility and language, v data privacy laws and regulations, vi intellectual property, and vii laws and regulations in specific sectors. There is a striking difference between where the HCI community has had impact Section 3 and the many areas of potential involvement Section 4. Section 5 a framework for action by the HCI community in public policy internationally. This monograph summarizes the observations and recommendations from a daylong workshop at the CHI 2013 conference in Paris, France. The workshop invited the community's perspectives regarding the intersection of governmental policies, international and domestic standards, recent HCI research discoveries, and emergent considerations and challenges. It also incorporates contributions made after the workshop by workshop participants and by individuals who were unable to participate in the workshop but whose work and interests were highly related and relevant.
Financial Frictions and the Delay to Entry ∗

I use a tractable dynamic model of occupational choice to show that the existence of financial constraints to the creation of businesses implies a delay to entry into entrepreneurship, which has both individual and aggregate consequences on inequality and welfare. Talented individuals with low collateral would need to work for several years as wage-earners to accumulate enough savings required to start their own firm. Hence, by the time they become entrepreneurs, they are older than they would in a perfect credit market economy. Data from Cameroon are used to study the qualitative and quantitative predictions of the dynamic model. The results show that it takes on average 7 years for a prospective entrepreneur to accumulate enough wealth required to start his own firm. The related inequality and welfare losses are also found to be substantial, averaging 16% Gini and 10% of lifetime consumption, respectively. An empirical test of the model’s predictions is performed by providing a cross-country evidence that low levels of financial development are associated with large age gaps between entrepreneurs and wage-earners, a pattern often found in empirical studies of entrepreneurial choice in developing countries.
Collaborative Filtering Recommendation Based on Content and Item Rating Prediction

Traditional similarity measure methods work poor in this situation,which makes the quality of recommendation system decrease dramatically. To address this issue a novel collaborative filtering algorithm based on content and item rating prediction is proposed. This method predicts item ratings that usesr have not rated based on content prediction and then uses item-based collaborative filtering to find similar items and make a prediction. The experiment results suggeste that this method can efficiently improve the extreme sparsity of user rating data,improve accuracy of recommendation using item-based collaborative filtering,and provide better recommendation results than nearest neighborhood collaborative filtering algorithms.
Scheduling Scientific Workflows using Imperialist Competitive Algorithm

Computational grid is a growing technology in large-scale computing environment, as it provides more flexibility and easier access to distributed computing resources at a lower cost. In grid computing, applications are regarded as workflows. Scheduling complex workflows plays an important role in the performance of grid applications. In this paper we propose a meta-heuristic scheduling method based on a novel evolutionary algorithm called Imperialist Competitive Algorithm (ICA). ICA enhances the global search capability and it balances the exploration and exploitation abilities of the scheduling algorithm. Many of existing workflow scheduling algorithms can only tackle the problems with a single QoS parameter. Proposed scheduling algorithm deals with two important problems: cost optimization under deadline constraint, and execution time optimization under budget constraint. Experiment results illustrate the algorithm performance and feasibility for scheduling workflow applications and solving constrained satisfaction problems. Furthermore, solved examples represent that ICA can be effectively used to solve optimization problems with discrete variables.
Research of Integration Mode for Multi-source Spatial Data Based on GML

To solve the problem of integration of the multi-source spatial data,this paper introduced the research of integration mode of the different formats.Based on that,the author putted forward to a data integration framework for multi-source spatial data based on GML(Geography Markup Language). Then the author described a GML mode matching algorithm,and above all had a conclusion.
A review on using bioinformatics web servers, softwares and databases for prediction and analysis of metal binding sites in proteins

: Bioinformatics, as an interdisciplinary science, seeks to find various biological issues in molecular level using available techniques in computer science, mathematics, chemistry, physics and other relevant sciences. One part of this science concerns the sequences and proteins absorbing metals in which many softwares and servers are widely used for bioinformatics prediction. More than half of proteins in Protein Data Bank possess metals and thus it expected that most of these proteins are metalloproteins. Metals are necessary for proteins function and structure. Since metal ions are essential in too many biological functions, mainly in metalloproteins, finding metalloproteins is of great importance in the biological and medical fields. Metal binding site prediction is considered as a step in function assignment for new proteins. In this review, we tried to study softwares and servers which are found to be pioneer in finding metal binding sites in proteins and introduce them briefly. In this way, the importance of metal ions is explained and then the binding sites of proteins like CHDEs have been partly introduced.