OpenRaiser/Intern-Atlas huggingface dataset:
Penerapan teknik web scraping pada mesin pencari artikel ilmiah
Venue: arXiv cs.IR 2014
Year: 2014

Search engines are a combination of hardware and computer software supplied by a particular company through the website which has been determined. Search engines collect information from the web through bots or web crawlers that crawls the web periodically. The process of retrieval of information from existing websites is called "web scraping." Web scraping is a technique of extracting information from websites. Web scraping is closely related to Web indexing, as for how to develop a web scraping technique that is by first studying the program makers HTML document from the website will be taken to the information in the HTML tag flanking the aim is for information collected after the program makers learn navigation techniques on the website information will be taken to a web application mimicked the scraping that we will create. It should also be noted that the implementation of this writing only scraping involves a free search engine such as: portal garuda, Indonesian scientific journal databases (ISJD), google scholar.
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Venue: arXiv cs.CY 2014
Year: 2014

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
Ergodic Capacity Analysis of Remote Radio Head Associations in Cloud Radio Access Networks
Venue: arXiv cs.IT 2014
Year: 2014

Characterizing user to Remote Radio Head (RRH) association strategies in cloud radio access networks (C-RANs) is critical for performance optimization. In this letter, the single nearest and N--nearest RRH association strategies are presented, and the corresponding impact on the ergodic capacity of C-RANs is analyzed, where RRHs are distributed according to a stationary point process. Closed-form expressions for the ergodic capacity of the proposed RRH association strategies are derived. Simulation results demonstrate that the derived uplink closed-form capacity expressions are accurate. Furthermore, the analysis and simulation results show that the ergodic capacity gain is not linear with either the RRH density or the number of antenna per RRH. The ergodic capacity gain from the RRH density is larger than that from the number of antennas per RRH,which indicates that the association number of the RRH should not be bigger than 4 to balance the performance gain and the implementation cost.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Venue: arXiv cs.CV 2014
Year: 2014

Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we formulate saliency map computation as a regression problem. Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score. Saliency scores across multiple levels are finally fused to produce the saliency map. The contributions lie in two-fold. One is that we propose a discriminate regional feature integration approach for salient object detection. Compared with existing heuristic models, our proposed method is able to automatically integrate high-dimensional regional saliency features and choose discriminative ones. The other is that by investigating standard generic region properties as well as two widely studied concepts for salient object detection, i.e., regional contrast and backgroundness, our approach significantly outperforms state-of-the-art methods on six benchmark datasets. Meanwhile, we demonstrate that our method runs as fast as most existing algorithms.
AbPress: Flexing Partial-Order Reduction and Abstraction
Venue: arXiv cs.LO 2014
Year: 2014

Partial-order reduction (POR) and lazy abstraction with interpolants are two complementary techniques that have been successfully employed to make model checking tools for concurrent programs effective. In this work, we present AbPress - Abstraction-based Partial-order Reduction with Source-Sets - an algorithm that fuses a recently proposed and powerful dynamic POR technique based on source-sets and lazy abstraction to obtain an efficient software model checker for multi-threaded programs. It trims the inter- leaving space by taking the abstraction and source-sets into account. We amplify the effectiveness of AbPress with a novel solution that summarizes the accesses to shared variables over a collection of interleavings. We have implemented AbPress in a tool that analyzes concurrent programs using lazy abstraction, viz., Impara. Our evaluation on the effectiveness of the presented approach has been encouraging. AbPress compares favorably to existing state-of-the-art tools in the landscape.
On the Average-case Complexity of Parameterized Clique
Venue: arXiv cs.DS 2014
Year: 2014

The k-Clique problem is a fundamental combinatorial problem that plays a prominent role in classical as well as in parameterized complexity theory. It is among the most well-known NP-complete and W[1]-complete problems. Moreover, its average-case complexity analysis has created a long thread of research already since the 1970s. Here, we continue this line of research by studying the dependence of the average-case complexity of the k-Clique problem on the parameter k. To this end, we define two natural parameterized analogs of efficient average-case algorithms. We then show that k-Clique admits both analogues for Erdős-Rényi random graphs of arbitrary density. We also show that k-Clique is unlikely to admit neither of these analogs for some specific computable input distribution.
Initialization of multilayer forecasting artifical neural networks
Venue: arXiv cs.NE 2014
Year: 2014

In this paper, a new method was developed for initialising artificial neural networks predicting dynamics of time series. Initial weighting coefficients were determined for neurons analogously to the case of a linear prediction filter. Moreover, to improve the accuracy of the initialization method for a multilayer neural network, some variants of decomposition of the transformation matrix corresponding to the linear prediction filter were suggested. The efficiency of the proposed neural network prediction method by forecasting solutions of the Lorentz chaotic system is shown in this paper.
Density-Based Region Search with Arbitrary Shape for Object Localization
Venue: arXiv cs.CV 2014
Year: 2014

Region search is widely used for object localization. Typically, the region search methods project the score of a classifier into an image plane, and then search the region with the maximal score. The recently proposed region search methods, such as efficient subwindow search and efficient region search, %which localize objects from the score distribution on an image are much more efficient than sliding window search. However, for some classifiers and tasks, the projected scores are nearly all positive, and hence maximizing the score of a region results in localizing nearly the entire images as objects, which is meaningless. In this paper, we observe that the large scores are mainly concentrated on or around objects. Based on this observation, we propose a method, named level set maximum-weight connected subgraph (LS-MWCS), which localizes objects with arbitrary shapes by searching regions with the densest score rather than the maximal score. The region density can be controlled by a parameter flexibly. And we prove an important property of the proposed LS-MWCS, which guarantees that the region with the densest score can be searched. Moreover, the LS-MWCS can be efficiently optimized by belief propagation. The method is evaluated on the problem of weakly-supervised object localization, and the quantitative results demonstrate the superiorities of our LS-MWCS compared to other state-of-the-art methods.
Parallel Prefix Polymorphism Permits Parallelization, Presentation & Proof
Venue: arXiv cs.PL 2014
Year: 2014

Polymorphism in programming languages enables code reuse. Here, we show that polymorphism has broad applicability far beyond computations for technical computing: parallelism in distributed computing, presentation of visualizations of runtime data flow, and proofs for formal verification of correctness. The ability to reuse a single codebase for all these purposes provides new ways to understand and verify parallel programs.
Foreground-Background Segmentation Based on Codebook and Edge Detector
Venue: arXiv cs.CV 2014
Year: 2014

Background modeling techniques are used for moving object detection in video. Many algorithms exist in the field of object detection with different purposes. In this paper, we propose an improvement of moving object detection based on codebook segmentation. We associate the original codebook algorithm with an edge detection algorithm. Our goal is to prove the efficiency of using an edge detection algorithm with a background modeling algorithm. Throughout our study, we compared the quality of the moving object detection when codebook segmentation algorithm is associated with some standard edge detectors. In each case, we use frame-based metrics for the evaluation of the detection. The different results are presented and analyzed.
Reengineering GSM/GPRS Towards a Dedicated Network for Massive Smart Metering
Venue: arXiv cs.IT 2014
Year: 2014

GSM is a synonym for a major success in wireless technology, achieving widespread use and high technology ma- turity. However, its future is questionable, as many stakeholders indicate that the GSM spectrum should be refarmed for LTE. On the other hand, the advent of smart grid and the ubiquity of smart meters will require reliable, long-lived wide area connections. This motivates to investigate the potential of GSM to be evolved into a dedicated network for smart metering. We introduce simple mechanisms to reengineer the access control in GSM. The result is a system that offers excellent support for smart metering, as well as the other massive machine-to-machine traffic patterns that are envisioned in 3GPP.
Computing an Evolutionary Ordering is Hard
Venue: arXiv cs.CC 2014
Year: 2014

We prove that computing an evolutionary ordering of a family of sets, i.e. an ordering where each set intersects with --but is not included in-- the union earlier sets, is NP-hard.
Analyzing the Fault-Containment Time of Self-Stabilizing Algorithms - A Case Study for Graph Coloring
Venue: arXiv cs.DC 2014
Year: 2014

The paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms in the message passing model. For a new Delta+1-coloring algorithm we analytically derive a bound for the mean time to recover and show that the variance is bounded by a small constant independent of the network's size. For the class of bounded-independence graphs (e.g. unit disc graphs) all containment metrics are in O(1).
Load & Backhaul Aware Decoupled Downlink/Uplink Access in 5G Systems
Venue: arXiv cs.NI 2014
Year: 2014

Until the 4th Generation (4G) cellular 3GPP systems, a user equipment's (UE) cell association has been based on the downlink received power from the strongest base station. Recent work has shown that - with an increasing degree of heterogeneity in emerging 5G systems - such an approach is dramatically suboptimal, advocating for an independent association of the downlink and uplink where the downlink is served by the macro cell and the uplink by the nearest small cell. In this paper, we advance prior art by explicitly considering the cell-load as well as the available backhaul capacity during the association process. We introduce a novel association algorithm and prove its superiority w.r.t. prior art by means of simulations that are based on Vodafone's small cell trial network and employing a high resolution pathloss prediction and realistic user distributions. We also study the effect that different power control settings have on the performance of our algorithm.
Steganography in Modern Smartphones and Mitigation Techniques
Venue: arXiv cs.MM 2014
Year: 2014

By offering sophisticated services and centralizing a huge volume of personal data, modern smartphones changed the way we socialize, entertain and work. To this aim, they rely upon complex hardware/software frameworks leading to a number of vulnerabilities, attacks and hazards to profile individuals or gather sensitive information. However, the majority of works evaluating the security degree of smartphones neglects steganography, which can be mainly used to: i) exfiltrate confidential data via camouflage methods, and ii) conceal valuable or personal information into innocent looking carriers. Therefore, this paper surveys the state of the art of steganographic techniques for smartphones, with emphasis on methods developed over the period 2005 to the second quarter of 2014. The different approaches are grouped according to the portion of the device used to hide information, leading to three different covert channels, i.e., local, object and network. Also, it reviews the relevant approaches used to detect and mitigate steganographic attacks or threats. Lastly, it showcases the most popular software applications to embed secret data into carriers, as well as possible future directions.
Lost in Space: Improving Inference of IPv4 Address Space Utilization
Venue: arXiv cs.NI 2014
Year: 2014

One challenge in understanding the evolution of Internet infrastructure is the lack of systematic mechanisms for monitoring the extent to which allocated IP addresses are actually used. In this paper we try to advance the science of inferring IPv4 address space utilization by analyzing and correlating results obtained through different types of measurements. We have previously studied an approach based on passive measurements that can reveal used portions of the address space unseen by active approaches. In this paper, we study such passive approaches in detail, extending our methodology to four different types of vantage points, identifying traffic components that most significantly contribute to discovering used IPv4 network blocks. We then combine the results we obtained through passive measurements together with data from active measurement studies, as well as measurements from BGP and additional datasets available to researchers. Through the analysis of this large collection of heterogeneous datasets, we substantially improve the state of the art in terms of: (i) understanding the challenges and opportunities in using passive and active techniques to study address utilization; and (ii) knowledge of the utilization of the IPv4 space.
On the use of Stress information in Speech for Speaker Recognition
Venue: arXiv cs.SD 2014
Year: 2014

The performance of a speaker recognition system decreases when the speaker is under stress or emotion. In this paper we explore and identify a mechanism that enables use of inherent stress-in-speech or speaking style information present in speech of a person as additional cues for speaker recognition. We quantify the the inherent stress present in the speech of a speaker mainly using 3 features, namely, pitch, amplitude and duration (together called PAD) We experimentally observe that the PAD vectors of similar phones in different words of a speaker are close to each other in the three dimensional (PAD) space confirming that the way a speaker stresses different syllables in their speech is unique to them, thus we propose the use of PAD based speaking style of a speaker as an additional feature for speaker recognition applications.
Confusion in the Church-Turing Thesis
Venue: arXiv cs.LO 2014
Year: 2014

The Church-Turing Thesis confuses numerical computations with symbolic computations. In particular, any model of computability in which equality is not definable, such as the lambda-models underpinning higher-order programming languages, is not equivalent to the Turing model. However, a modern combinatory calculus, the SF-calculus, can define equality of its closed normal forms, and so yields a model of computability that is equivalent to the Turing model. This has profound implications for programming language design.
Toward Sequentializing Overparallelized Protocol Code
Venue: arXiv cs.PL 2014
Year: 2014

In our ongoing work, we use constraint automata to compile protocol specifications expressed as Reo connectors into efficient executable code, e.g., in C. We have by now studied this automata based compilation approach rather well, and have devised effective solutions to some of its problems. Because our approach is based on constraint automata, the approach, its problems, and our solutions are in fact useful and relevant well beyond the specific case of compiling Reo. In this short paper, we identify and analyze two such rather unexpected problems.
Satisfiability and Model Checking of CTL* with Graded Path Modalities
Venue: arXiv cs.LO 2014
Year: 2014

Graded path modalities count the number of paths satisfying a property, and generalize the existential (E) and universal (A) path modalities of CTL*. The resulting logic is called GCTL*. We settle the complexity of satisfiability of GCTL*, i.e., 2ExpTime-Complete, and the complexity of the model checking problem for GCTL*, i.e., PSpace-Complete. The lower bounds already hold for CTL*, and so, using the automata-theoretic approach we supply the upper bounds. The significance of this work is two-fold: GCTL* is more expressive than CTL* at no extra cost in computational complexity, and GCTL* has all the advantages over GCTL (CTL with graded path modalities) that CTL* has over CTL, e.g., the ability to express fairness.
Fast Algorithms for Online Stochastic Convex Programming
Venue: arXiv cs.LG 2014
Year: 2014

We introduce the online stochastic Convex Programming (CP) problem, a very general version of stochastic online problems which allows arbitrary concave objectives and convex feasibility constraints. Many well-studied problems like online stochastic packing and covering, online stochastic matching with concave returns, etc. form a special case of online stochastic CP. We present fast algorithms for these problems, which achieve near-optimal regret guarantees for both the i.i.d. and the random permutation models of stochastic inputs. When applied to the special case online packing, our ideas yield a simpler and faster primal-dual algorithm for this well studied problem, which achieves the optimal competitive ratio. Our techniques make explicit the connection of primal-dual paradigm and online learning to online stochastic CP.
Andlantis: Large-scale Android Dynamic Analysis
Venue: arXiv cs.CR 2014
Year: 2014

Analyzing Android applications for malicious behavior is an important area of research, and is made difficult, in part, by the increasingly large number of applications available for the platform. While techniques exist to perform static analysis on a large number of applications, dynamic analysis techniques are relatively limited in scale due to the computational resources required to emulate the full Android system to achieve accurate execution. We present Andlantis, a scalable dynamic analysis system capable of processing over 3000 Android applications per hour. During this processing, the system is able to collect valuable forensic data, which helps reverse-engineers and malware researchers identify and understand anomalous application behavior. We discuss the results of running 1261 malware samples through the system, and provide examples of malware analysis performed with the resulting data.
A First Look at Firefox OS Security
Venue: arXiv cs.CR 2014
Year: 2014

With Firefox OS, Mozilla is making a serious push for an HTML5-based mobile platform. In order to assuage security concerns over providing hardware access to web applications, Mozilla has introduced a number of mechanisms that make the security landscape of Firefox OS distinct from both the desktop web and other mobile operating systems. From an application security perspective, the two most significant of these mechanisms are the the introduction of a default Content Security Policy and code review in the market. This paper describes how lightweight static analysis can augment these mechanisms to find vulnerabilities which have otherwise been missed. We provide examples of privileged applications in the market that contain vulnerabilities that can be automatically detected. In addition to these findings, we show some of the challenges that occur when desktop software is repurposed for a mobile operating system. In particular, we argue that the caching of certificate overrides across applications--a known problem in Firefox OS--generates a counter-intuitive user experience that detracts from the security of the system.
Towards a Virtual Data Centre for Classics
Venue: arXiv cs.DL 2014
Year: 2014

The paper presents some of our work on integrating datasets in Classics. We present the results of various projects we had in this domain. The conclusions from LaQuAT concerned limitations to the approach rather than solutions. The relational model followed by OGSA-DAI was more effective for resources that consist primarily of structured data (which we call data-centric) rather than for largely unstructured text (which we call text-centric), which makes up a significant component of the datasets we were using. This approach was, moreover, insufficiently flexible to deal with the semantic issues. The gMan project, on the other hand, addressed these problems by virtualizing data resources using full-text indexes, which can then be used to provide different views onto the collections and services that more closely match the sort of information organization and retrieval activities found in the humanities, in an environment that is more interactive, researcher-focused, and researcher-driven.
Energy-Aware Lease Scheduling in Virtualized Data Centers
Venue: arXiv cs.DC 2014
Year: 2014

Energy efficiency has become an important measurement of scheduling algorithms in virtualized data centers. One of the challenges of energy-efficient scheduling algorithms, however, is the trade-off between minimizing energy consumption and satisfying quality of service (e.g. performance, resource availability on time for reservation requests). We consider resource needs in the context of virtualized data centers of a private cloud system, which provides resource leases in terms of virtual machines (VMs) for user applications. In this paper, we propose heuristics for scheduling VMs that address the above challenge. On performance evaluation, simulated results have shown a significant reduction on total energy consumption of our proposed algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling algorithm with the same fulfillment of performance requirements. We also discuss the improvement of energy saving when additionally using migration policies to the above mentioned algorithms.