OpenRaiser/Intern-Atlas huggingface dataset:
Which Adaptation Logic? An Objective and Subjective Performance Evaluation of HTTP-based Adaptive Media Streaming Systems
Venue: arXiv cs.MM 2016
Year: 2016

Multimedia content delivery over the Internet is predominantly using the Hypertext Transfer Protocol (HTTP) as its primary protocol and multiple proprietary solutions exits. The MPEG standard Dynamic Adaptive Streaming over HTTP (DASH) provides an interoperable solution and in recent years various adaptation logics/algorithms have been proposed. However, to the best of our knowledge, there is no comprehensive evaluation of the various logics/algorithms. Therefore, this paper provides a comprehensive evaluation of ten different adaptation logics/algorithms, which have been proposed in the past years. The evaluation is done both objectively and subjectively. The former is using a predefined bandwidth trajectory within a controlled environment and the latter is done in a real-world environment adopting crowdsourcing. The results shall provide insights about which strategy can be adopted in actual deployment scenarios. Additionally, the evaluation methodology described in this paper can be used to evaluate any other/new adaptation logic and to compare it directly with the results reported here.
Scaling Submodular Maximization via Pruned Submodularity Graphs
Venue: arXiv cs.LG 2016
Year: 2016

We propose a new random pruning method (called "submodular sparsification (SS)") to reduce the cost of submodular maximization. The pruning is applied via a "submodularity graph" over the $n$ ground elements, where each directed edge is associated with a pairwise dependency defined by the submodular function. In each step, SS prunes a $1-1/\sqrt{c}$ (for $c>1$) fraction of the nodes using weights on edges computed based on only a small number ($O(\log n)$) of randomly sampled nodes. The algorithm requires $\log_{\sqrt{c}}n$ steps with a small and highly parallelizable per-step computation. An accuracy-speed tradeoff parameter $c$, set as $c = 8$, leads to a fast shrink rate $\sqrt{2}/4$ and small iteration complexity $\log_{2\sqrt{2}}n$. Analysis shows that w.h.p., the greedy algorithm on the pruned set of size $O(\log^2 n)$ can achieve a guarantee similar to that of processing the original dataset. In news and video summarization tasks, SS is able to substantially reduce both computational costs and memory usage, while maintaining (or even slightly exceeding) the quality of the original (and much more costly) greedy algorithm.
How to morph planar graph drawings
Venue: arXiv cs.CG 2016
Year: 2016

Given an $n$-vertex graph and two straight-line planar drawings of the graph that have the same faces and the same outer face, we show that there is a morph (i.e., a continuous transformation) between the two drawings that preserves straight-line planarity and consists of $O(n)$ steps, which we prove is optimal in the worst case. Each step is a unidirectional linear morph, which means that every vertex moves at constant speed along a straight line, and the lines are parallel although the vertex speeds may differ. Thus we provide an efficient version of Cairns' 1944 proof of the existence of straight-line planarity-preserving morphs for triangulated graphs, which required an exponential number of steps.
Analysis of applications suitable for mobile learning of preschool children
Venue: arXiv cs.CY 2016
Year: 2016

This article considers the use of mobile learning in Bulgarian education by young children. The most used mobile operating systems are analyzed. Also some of the most used existing applications suitable for mobile learning of preschool children are presented and classified. Keywords: Mobile applications for preschool children, mobile learning.
Attack resilient architecture to replace embedded Flash with STTRAM in homogeneous IoTs
Venue: arXiv cs.ET 2016
Year: 2016

Spin-Transfer Torque RAM (STTRAM) is an emerging Non-Volatile Memory (NVM) technology that provides better endurance, write energy and performance than traditional NVM technologies such as Flash. In embedded application such as microcontroller SoC of Internet of Things (IoT), embedded Flash (eFlash) is widely employed. However, eFlash is also associated with cost. Therefore, replacing eFlash with STTRAM is desirable in IoTs for power-efficiency. Although promising, STTRAM brings several new security and privacy challenges that pose a significant threat to sensitive data in memory. This is inevitable due to the underlying dependency of this memory technology on environmental parameters such as temperature and magnetic fields that can be exploited by an adversary to tamper with the program and data. In this paper, we investigate these attacks and propose a novel memory architecture for attack resilient IoT network. The information redundancy present in a homogeneous peer-to-peer connected IoT network is exploited to restore the corrupted memory of any IoT node when under attack. We are able to build a failsafe IoT system with STTRAM based program memory which allows guaranteed execution of all the IoT nodes without complete shutdown of any node under attack. Experimental results using commercial IoT boards demonstrate the latency and energy overhead of the attack recovery process.
Uplink and Downlink Rate Analysis of a Full-Duplex C-RAN with Radio Remote Head Association
Venue: arXiv cs.IT 2016
Year: 2016

We characterize the uplink (UL) and downlink (DL) rates of a full-duplex cloud radio access network (C-RAN) with all participate and single best remote radio head (RRH) association schemes. Specifically, multi-antenna equipped RRHs distributed according to a Poisson point process is assumed. The UL and DL sum rate of the single best RRH association scheme is maximized using receive and transmit beamformer designs at the UL and DL RRHs, respectively. In the case of the single best strategy, we study both optimum and sub-optimum schemes based on maximum ratio combining/maximal ratio transmission (MRC/MRT) and zero-forcing/MRT (ZF/MRT) processing. Numerical results show that significant performance improvements can be achieved by using the full-duplex mode as compared to the half-duplex mode. Moreover, the choice of the beamforming design and the RRH association scheme have a major influence on the achievable full-duplex gains.
A Formal Graph Model for RDF and Its Implementation
Venue: arXiv cs.DB 2016
Year: 2016

Formalizing an RDF abstract graph model to be compatible with the RDF formal semantics has remained one of the foundational problems in the Semantic Web. In this paper, we propose a new formal graph model for RDF datasets. This model allows us to express the current model-theoretic semantics in the form of a graph. We also propose the concepts of resource path and triple path as well as an algorithm for traversing the new graph. We demonstrate the feasibility of this graph model through two implementations: one is a new graph engine called GraphKE, and the other is extended from RDF-3X to show that existing systems can also benefit from this model. In order to evaluate the empirical aspect of our graph model, we choose the shortest path algorithm and implement it in the GraphKE and the RDF-3X. Our experiments on both engines for finding the shortest paths in the YAGO2S-SP dataset give decent performance in terms of execution time. The empirical results show that our graph model with well-defined semantics can be effectively implemented.
Dynamic Control of Interference Limited Underlay D2D Network
Venue: arXiv cs.NI 2016
Year: 2016

Device-to-Device (D2D) communication appears as a key communication paradigm to realizing the vision of Internet of Things (IoT) into reality by supporting heterogeneous objects interconnection in a large scale network. These devices may be many types of objects with embedded intelligence and communication capabilities, e.g., smart phones, cars, or home appliances. The issue in in this type of communication is the interference to cellular communication caused by D2D communication. Thus, proper power control and resource allocation should be coordinated in D2D network to prevent excessive interference and drastic decrease in the throughput of the cellular system. In this paper, we consider the problem of cross-layer resource allocation in time-varying cellular wireless networks with D2D communication and incorporate average interference to cellular system as a quality-of-service constraint. Specifically, each D2D pair in the network injects packets to its queue, at rates chosen in order to maximize a global utility function, subject to network stability and interference constraints. The interference constraint enforces an arbitrarily low interference to the cellular system caused by D2D communication. We first obtain the stability region for the multiuser systems assuming that the nodes have full channel state information (CSI) of their neighbors. Then, we provide a joint flow control and scheduling scheme, which is proven to achieve a utility arbitrarily close to the maximum achievable utility. Finally, we address the consequences of practical implementation issue such as distributed scheduling by a designing algorithm, which is capable of taking advantage of diversity gain introduced by fading channels. We demonstrate the efficacy of our policies by numerical studies under various network conditions.
Mining Software Components from Object-Oriented APIs
Venue: arXiv cs.SE 2016
Year: 2016

Object-oriented Application Programing Interfaces (APIs) support software reuse by providing pre-implemented functionalities. Due to the huge number of included classes, reusing and understanding large APIs is a complex task. Otherwise, software components are admitted to be more reusable and understandable entities than object-oriented ones. Thus, in this paper, we propose an approach for reengineering object-oriented APIs into component-based ones. We mine components as a group of classes based on the frequency they are used together and their ability to form a quality-centric component. To validate our approach, we experimented on 100 Java applications that used Android APIs.
Low-dimensional Query Projection based on Divergence Minimization Feedback Model for Ad-hoc Retrieval
Venue: arXiv cs.IR 2016
Year: 2016

Low-dimensional word vectors have long been used in a wide range of applications in natural language processing. In this paper we shed light on estimating query vectors in ad-hoc retrieval where a limited information is available in the original query. Pseudo-relevance feedback (PRF) is a well-known technique for updating query language models and expanding the queries with a number of relevant terms. We formulate the query updating in low-dimensional spaces first with rotating the query vector and then with scaling. These consequential steps are embedded in a query-specific projection matrix capturing both angle and scaling. In this paper we propose a new but not the most effective technique necessarily for PRF in language modeling, based on the query projection algorithm. We learn an embedded coefficient matrix for each query, whose aim is to improve the vector representation of the query by transforming it to a more reliable space, and then update the query language model. The proposed embedded coefficient divergence minimization model (ECDMM) takes top-ranked documents retrieved by the query and obtains a couple of positive and negative sample sets; these samples are used for learning the coefficient matrix which will be used for projecting the query vector and updating the query language model using a softmax function. Experimental results on several TREC and CLEF data sets in several languages demonstrate effectiveness of ECDMM. The experimental results reveal that the new formulation for the query works as well as state-of-the-art PRF techniques and outperforms state-of-the-art PRF techniques in a TREC collection in terms of MAP,P@5, and P@10 significantly.
On Solving Floating Point SSSP Using an Integer Priority Queue
Venue: arXiv cs.DS 2016
Year: 2016

We address the single source shortest path planning problem (SSSP) in the case of floating point edge weights. We show how any integer based Dijkstra solution that relies on a monotone integer priority queue to create a full ordering over path lengths in order to solve integer SSSP can be used as an oracle to solve floating point SSSP with positive edge weights (floating point P-SSSP). Floating point P-SSSP is of particular interest to the robotics community. This immediately yields a handful of faster runtimes for floating point P-SSSP; for example, ${O({m + n\log \log \frac{C}δ})}$, where $C$ is the largest weight and $δ$ is the minimum edge weight in the graph. It also ensures that many future advances for integer SSSP will be transferable to floating point P-SSSP.
Investigating the Impact of Learners Learning Styles on the Acceptance of Open Learner Models for Information Sharing
Venue: arXiv cs.CY 2016
Year: 2016

Individual differences in learners learning styles can have a significant effect on their acceptance of collaboration technologies to facilitate the sharing of learning information in technology-based collaborative learning. There is, however, a lack of research in exploring the impact of learning styles on the acceptance of open learner models for information sharing in this area. This study investigates the impact of learners learning styles on their acceptance of open learner models for information sharing. A total of 240 undergraduate students in a university in Malaysia have participated in the online survey. A chi-square test is performed to explore the relationship between learning styles and the acceptance of open learner models for information sharing in a higher education setting. The result reveals that learning styles have no significant impact on learners acceptance of open learner models for information sharing. The implications of this study can assist education instructors to apply appropriate instructional strategies in adopting open learner models for improving the performance of technology-mediated learning.
A note on reductions between compressed sensing guarantees
Venue: arXiv cs.IT 2016
Year: 2016

In compressed sensing, one wishes to acquire an approximately sparse high-dimensional signal $x\in\mathbb{R}^n$ via $m\ll n$ noisy linear measurements, then later approximately recover $x$ given only those measurement outcomes. Various guarantees have been studied in terms of the notion of approximation in recovery, and some isolated folklore results are known stating that some forms of recovery are stronger than others, via black-box reductions. In this note we provide a general theorem concerning the hierarchy of strengths of various recovery guarantees. As a corollary of this theorem, by reducing from well-known results in the compressed sensing literature, we obtain an efficient $\ell_p/\ell_p$ scheme for any $0<p<1$ with the fewest number of measurements currently known amongst efficient schemes, improving recent bounds of [SomaY16].
On self-dual double negacirculant codes
Venue: arXiv cs.IT 2016
Year: 2016

Double negacirculant (DN) codes are the analogues in odd characteristic of double circulant codes. Self-dual DN codes of odd dimension are shown to be consta-dihedral. Exact counting formulae are derived for DN codes. The special class of length a power of two is studied by means of Dickson polynomials, and is shown to contain families of codes with relative distances satisfying a modified Gilbert-Varshamov bound.
Investigating the Role of Socio-organizational Factors in the Information Security Compliance in Organizations
Venue: arXiv cs.CY 2016
Year: 2016

The increase reliance on information systems has created unprecedented challenges for organizations to protect their critical information from different security threats that have direct consequences on the corporate liability, loss of credibility, and monetary damage. As a result, the security of information has become a top priority in many organizations. This study investigates the role of socio-organizational factors by drawing the insights from the organizational theory literature in the adoption of information security compliance in organizations. Based on the analysis of the survey data collected from 294 employees from different organizations, the study indicates management commitment, awareness and training, accountability, technology capability, technology compatibility, processes integration, and audit and monitoring have a significant positive impact on the adoption of information security compliance in organizations. The study contributes to the information security compliance research by exploring the criticality of socio-organizational factors at the organizational level for information security compliance.
Internal versus external recruitment the story of three consecutive project managers in an IT project
Venue: arXiv cs.CY 2016
Year: 2016

As project managers (PMs) play an important role in project success, assigning PMs with appropriate skills and personalities to projects is a crucial task. Empirical research on skill requirements for information technology (IT) PMs is limited and little information systems literature focuses on the role of internally recruited IT PMs. This paper presents a case study of a troubled IT project which was led by three consecutive PMs, all with different backgrounds, skill sets, and personalities. Lessons learned from the three PMs confirm the importance of some skills previously described in the literature, and the need for an additional focus on how an IT PMs personality encourages or inhibits IT project outcomes. Internally recruited PMs usually lack IT project management expertise and experience but were found to have advantages in understanding organisational culture and business processes. Nevertheless, IT project management expertise was found to be a necessity of project success.
Behavioural Dimensions for Discovering Knowledge Actor Roles Utilising Enterprise Social Network Metrics
Venue: arXiv cs.CY 2016
Year: 2016

The identification of distinct user roles is an important theme in social media research. However, for Enterprise Social Networks (ESN), the use of social media within organisations, research identifying such roles is still lacking. Yet, understanding user roles, in particular regarding their knowledge contributions and communication behaviour, might usefully support companies in managing critical knowledge resources. Against this backdrop, in this research-in-progress paper we derive 16 metrics characterising the participation behaviour, message content and structural position of ESN users of an Australian professional services firm. Based on a factor analysis, we identify four distinct dimensions of ESN user behaviour: Contribution & networking, information provision, contact dispersion and invisible usage. With this research we contribute to the literature by transferring concepts and methods of organisation science and social media research to an ESN context. Further, our approach forms the basis for the identification of different types of knowledge actors, which might ultimately help to improve organisational knowledge transparency.
Breaching the Human Firewall: Social engineering in Phishing and Spear-Phishing Emails
Venue: arXiv cs.CY 2016
Year: 2016

We examined the influence of three social engineering strategies on users' judgments of how safe it is to click on a link in an email. The three strategies examined were authority, scarcity and social proof, and the emails were either genuine, phishing or spear-phishing. Of the three strategies, the use of authority was the most effective strategy in convincing users that a link in an email was safe. When detecting phishing and spear-phishing emails, users performed the worst when the emails used the authority principle and performed best when social proof was present. Overall, users struggled to distinguish between genuine and spear-phishing emails. Finally, users who were less impulsive in making decisions generally were less likely to judge a link as safe in the fraudulent emails. Implications for education and training are discussed.
Evaluating the Business Value of CPOE for Cancer Care in Australia: A Resource Based View Perspective
Venue: arXiv cs.CY 2016
Year: 2016

Today, cancer is one of the leading causes of death throughout the world. This threatening disease has huge negative impacts, not only on quality of life, but also on the healthcare industry, whose resources are already scarce. Thus, finding new approaches for cancer care has been a central point of interest during the last few decades. One of these approaches is the use of computerised physician order entry (CPOE) systems. This systems have the potential to provide more effective and efficient patient-centric cancer care. This paper serves to examine the business value of an American CPOE in an Australian context. This is achieved by using our specifically designed tool to evaluate the business value of IT in the healthcare, in combination with a resource based view perspective. Our results show that the system has a number of enabling resources to generate business value subject to having other resources.
Towards a Job Title Classification System
Venue: arXiv cs.LG 2016
Year: 2016

Document classification for text, images and other applicable entities has long been a focus of research in academia and also finds application in many industrial settings. Amidst a plethora of approaches to solve such problems, machine-learning techniques have found success in a variety of scenarios. In this paper we discuss the design of a machine learning-based semi-supervised job title classification system for the online job recruitment domain currently in production at CareerBuilder.com and propose enhancements to it. The system leverages a varied collection of classification as well clustering algorithms. These algorithms are encompassed in an architecture that facilitates leveraging existing off-the-shelf machine learning tools and techniques while keeping into consideration the challenges of constructing a scalable classification system for a large taxonomy of categories. As a continuously evolving system that is still under development we first discuss the existing semi-supervised classification system which is composed of both clustering and classification components in a proximity-based classifier setup and results of which are already used across numerous products at CareerBuilder. We then elucidate our long-term goals for job title classification and propose enhancements to the existing system in the form of a two-stage coarse and fine level classifier augmentation to construct a cascade of hierarchical vertical classifiers. Preliminary results are presented using experimental evaluation on real world industrial data.
Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information
Venue: arXiv cs.IR 2016
Year: 2016

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper information of the question. Hence, we present a neural attention-based model to represent the questions dynamically according to the different focuses of various candidate answer aspects. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. And it also alleviates the out of vocabulary (OOV) problem, which helps the attention model to represent the question more precisely. The experimental results on WEBQUESTIONS demonstrate the effectiveness of the proposed approach.