OpenRaiser/Intern-Atlas huggingface dataset:
Bring Your Own Devices Classroom: Issues of Digital Divides in Teaching and Learning Contexts
Venue: arXiv cs.CY 2016
Year: 2016

Technology mediated learning provide potentially valuable resources for learners' academic and social development. However, according to recent researches, as the adoption stages of ICTs advance there arise further levels of digital divides in terms of equity of information literacy and learning outcomes. For the last three years we have been working with the first secondary school in New Zealand to introduce a Bring Your Own Device (BYOD) policy. Our research has included a number of methods, including surveys, interviews and classroom observations. In this paper we present the findings from the investigation into BYOD project, which gave us insights into the digital divide issues in the context of technology mediated learning.
Examining the social aspects of Enterprise Architecture Implementation: A Morphogenetic Approach
Venue: arXiv cs.CY 2016
Year: 2016

This paper argues that existing research in Enterprise Architecture (EA) tends to emphasise the technical aspects of implementation and neglects the role of 'people'. The paper uses Archer's Morphogenetic Approach (MA) to elaborate the social context of EA. This approach examines the relationships between agency, structure and culture to understand how stakeholders affect and are affected by EA implementation. A university just commencing an EA program is used as a case example. The case example describes the challenging university environment and provides an illustration of the important situational logics that direct agency action within the complex social context of a university.
The Development Strategy of IT Capability: A Contingency Perspective
Venue: arXiv cs.CY 2016
Year: 2016

This study proposes a conceptual model to link IT capabilities, industry types, and value implications. We attempt to use a contingency analysis to theorize that which types of IT capabilities (e.g., externally-focused, internally-focused, and aggregate IT capability) should a firm develop and then what benefits (e.g., firm value and firm performance) it will gain according to its industry's value creation logic (e.g., value chain-based, value shop-based, and value network-based industry). The empirical findings show that a value network-based firm should develop externally-focused IT capabilities to create its firm value and a value chain-based firm should develop aggregate IT capabilities to improve its firm performance and create its firm value.
Assessment Issues in Mathematics: Design Science Approach for developing an Expert-System Based Solution
Venue: arXiv cs.CY 2016
Year: 2016

Information and communication technology (ICT) tools are ineffective when assessing solutions of questions with more than one step. ICT tools assessing these types of questions are paralleled to solving complex problems. This conceptual paper describes the method that will be adopted to create rule based expert system (ES) prototype implementing method marking concept (MMC) in assessing multi-step questions (MSQ) solutions. Pragmatist views and mixed methods will be used to conduct document analysis using undergraduate students' solutions to past examination papers and related findings from previous research. Design science research paradigm and ES development phases will be used to design, construct and evaluate an artifact based on ES. The expected functioning ES artifact will enhance the authenticity of assessment, because evaluation of solution strategies in MSQ solution reveals how much the comprehension of the domain under evaluation is shown. The artifact and the conceptual framework can be applied to a similar domain with modifications or as it is.
Exploring Practitioner Perspectives of Sourcing Risks: Towards the Development of an Integrated Risk and Control Framework
Venue: arXiv cs.CY 2016
Year: 2016

Outsourcing of information and communication technologies (ICT) and related services is an established and growing industry. Recent trends, such as the move toward multi-sourcing have increased the complexity and risk of these outsourcing arrangements. There is a critical research need to identify the risks faced by both the organisations that outsource ICT and the vendors that provide it in this changing landscape. To address growing concerns regarding the best way to deal with risk and control in this environment, our research focuses on establishing a Sourcing Risk and Control Framework to assist organisations identify these risks and develop effective mitigation strategies. In this paper we report on the first stage of our research that sought to document how sourcing risk is represented and considered in practice. To date, limited empirical research has been conducted in an Australian context. Using a series of workshops involving client and vendor representatives, we identified a broad range of risks and developed a cohesive categorisation scheme that incorporates functional and multi-stakeholder perspectives.
Protector Control PC-AODV-BH in The Ad Hoc Networks
Venue: arXiv cs.CR 2016
Year: 2016

In this paper we deal with the protector control that which we used to secure AODV routing protocol in Ad Hoc networks. The considered system can be vulnerable to several attacks because of mobility and absence of infrastructure. While the disturbance is assumed to be of the black hole type, we purpose a control named "PC-AODV-BH" in order to neutralize the effects of malicious nodes. Such a protocol is obtained by coupling hash functions, digital signatures and fidelity concept. An implementation under NS2 simulator will be given to compare our proposed approach with SAODV protocol, basing on three performance metrics and taking into account the number of black hole malicious nodes
Help, Anyone? A User Study For Modeling Robotic Behavior To Mitigate Malfunctions With The Help Of The User
Venue: arXiv cs.RO 2016
Year: 2016

Service robots for the domestic environment are intended to autonomously provide support for their users. However, state-of-the-art robots still often get stuck in failure situations leading to breakdowns in the interaction flow from which the robot cannot recover alone. We performed a multi-user Wizard-of-Oz experiment in which we manipulated the robot's behavior in such a way that it appeared unexpected and malfunctioning, and asked participants to help the robot in order to restore the interaction flow. We examined how participants reacted to the robot's error, its subsequent request for help and how it changed their perception of the robot with respect to perceived intelligence, likability, and task contribution. As interaction scenario we used a game of building Lego models performed by user dyads. In total 38 participants interacted with the robot and helped in malfunctioning situations. We report two major findings: (1) in user dyads, the user who gave the last command followed by the user who is closer is more likely to help (2) malfunctions that can be actively fixed by the user seem not to negatively impact perceived intelligence and likability ratings. This work offers insights in how far user support can be a strategy for domestic service robots to recover from repeating malfunctions.
DISCO Nets: DISsimilarity COefficient Networks
Venue: arXiv cs.CV 2016
Year: 2016

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks.
A Joint Model for Word Embedding and Word Morphology
Venue: arXiv cs.CL 2016
Year: 2016

This paper presents a joint model for performing unsupervised morphological analysis on words, and learning a character-level composition function from morphemes to word embeddings. Our model splits individual words into segments, and weights each segment according to its ability to predict context words. Our morphological analysis is comparable to dedicated morphological analyzers at the task of morpheme boundary recovery, and also performs better than word-based embedding models at the task of syntactic analogy answering. Finally, we show that incorporating morphology explicitly into character-level models help them produce embeddings for unseen words which correlate better with human judgments.
Efficient Estimation of k for the Nearest Neighbors Class of Methods
Venue: arXiv cs.LG 2016
Year: 2016

The k Nearest Neighbors (kNN) method has received much attention in the past decades, where some theoretical bounds on its performance were identified and where practical optimizations were proposed for making it work fairly well in high dimensional spaces and on large datasets. From countless experiments of the past it became widely accepted that the value of k has a significant impact on the performance of this method. However, the efficient optimization of this parameter has not received so much attention in literature. Today, the most common approach is to cross-validate or bootstrap this value for all values in question. This approach forces distances to be recomputed many times, even if efficient methods are used. Hence, estimating the optimal k can become expensive even on modern systems. Frequently, this circumstance leads to a sparse manual search of k. In this paper we want to point out that a systematic and thorough estimation of the parameter k can be performed efficiently. The discussed approach relies on large matrices, but we want to argue, that in practice a higher space complexity is often much less of a problem than repetitive distance computations.
Feedback Scheduling for Energy-Efficient Real-Time Homogeneous Multiprocessor Systems
Venue: arXiv cs.OS 2016
Year: 2016

Real-time scheduling algorithms proposed in the literature are often based on worst-case estimates of task parameters. The performance of an open-loop scheme can be degraded significantly if there are uncertainties in task parameters, such as the execution times of the tasks. Therefore, to cope with such a situation, a closed-loop scheme, where feedback is exploited to adjust the system parameters, can be applied. We propose an optimal control framework that takes advantage of feeding back information of finished tasks to solve a real-time multiprocessor scheduling problem with uncertainty in task execution times, with the objective of minimizing the total energy consumption. Specifically, we propose a linear programming based algorithm to solve a workload partitioning problem and adopt McNaughton's wrap around algorithm to find the task execution order. The simulation results illustrate that our feedback scheduling algorithm can save energy by as much as 40% compared to an open-loop method for two processor models, i.e. a PowerPC 405LP and an XScale processor.
Time Optimal Spectrum Sensing
Venue: arXiv cs.IT 2016
Year: 2016

Spectrum sensing is a fundamental operation in cognitive radio environment. It gives information about spectrum availability by scanning the bands. Usually a fixed amount of time is given to scan individual bands. Most of the times, historical information about the traffic in the spectrum bands is not used. But this information gives the idea, how busy a specific band is. Therefore, instead of scanning a band for a fixed amount of time, more time can be given to less occupied bands and less time to heavily occupied ones. In this paper we have formulated the time assignment problem as integer linear programming and source coding problems. The time assignment problem is solved using the associated stochastic optimization problem.
Transducer-based Rewriting Games for Active XML
Venue: arXiv cs.FL 2016
Year: 2016

Context-free games are two-player rewriting games that are played on nested strings representing XML documents with embedded function symbols. These games were introduced to model rewriting processes for intensional documents in the Active XML framework, where input documents are to be rewritten into a given target schema by calls to external services. This paper studies the setting where dependencies between inputs and outputs of service calls are modelled by transducers, which has not been examined previously. It defines transducer models operating on nested words and studies their properties, as well as the computational complexity of the winning problem for transducer-based context-free games in several scenarios. While the complexity of this problem is quite high in most settings (ranging from NP-complete to undecidable), some tractable restrictions are also identified.
NESTML: a modeling language for spiking neurons
Venue: arXiv cs.SE 2016
Year: 2016

Biological nervous systems exhibit astonishing complexity .Neuroscientists aim to capture this com- plexity by modeling and simulation of biological processes. Often very comple xm odels are nec- essary to depict the processes, which makes it dif fi cult to create these models. Powerful tools are thus necessary ,which enable neuroscientists to express models in acomprehensi ve and concise way and generate ef fi cient code for digital simulations. Se veral modeling languages for computational neuroscience ha ve been proposed [Gl10, Ra11]. Howe ver, as these languages seek simulator inde- pendence the ytypically only support asubset of the features desired by the modeler .Int his article, we present the modular and extensible domain speci fi cl anguage NESTML, which provides neuro- science domain concepts as fi rst-class language constructs and supports domain experts in creating neuron models for the neural simulation tool NEST .N ESTML and aset of example models are publically available on GitHub.
Apparent Age Estimation Using Ensemble of Deep Learning Models
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we address the problem of apparent age estimation. Different from estimating the real age of individuals, in which each face image has a single age label, in this problem, face images have multiple age labels, corresponding to the ages perceived by the annotators, when they look at these images. This provides an intriguing computer vision problem, since in generic image or object classification tasks, it is typical to have a single ground truth label per class. To account for multiple labels per image, instead of using average age of the annotated face image as the class label, we have grouped the face images that are within a specified age range. Using these age groups and their age-shifted groupings, we have trained an ensemble of deep learning models. Before feeding an input face image to a deep learning model, five facial landmark points are detected and used for 2-D alignment. We have employed and fine tuned convolutional neural networks (CNNs) that are based on VGG-16 [24] architecture and pretrained on the IMDB-WIKI dataset [22]. The outputs of these deep learning models are then combined to produce the final estimation. Proposed method achieves 0.3668 error in the final ChaLearn LAP 2016 challenge test set [5].
What Massive Open Online Course (MOOC) Stakeholders Can Learn From Learning Analytics?
Venue: arXiv cs.CY 2016
Year: 2016

Massive Open Online Courses (MOOCs) are the road that led to a revolution and a new era of learning environments. Educational institutions have come under pressure to adopt new models that assure openness in their education distribution. Nonetheless, there is still altercation about the pedagogical approach and the absolute information delivery to the students. On the other side with the use of Learning Analytics, powerful tools become available which mainly aim to enhance learning and improve learners performance. In this chapter, the development phases of a Learning Analytics prototype and the experiment of integrating it into a MOOC platform, called iMooX will be presented. This chapter explores how MOOC Stakeholders may benefit from Learning Analytics as well as it reports an exploratory analysis of some of the offered courses and demonstrate use cases as a typical evaluation of this prototype in order to discover hidden patterns, overture future proper decisions and to optimize learning with applicable and convenient interventions.
Enabling Secure and Usable Mobile Application: Revealing the Nuts and Bolts of software TPM in todays Mobile Devices
Venue: arXiv cs.CR 2016
Year: 2016

The emergence of mobile applications to execute sensitive operations has brought a myriad of security threats to both enterprises and users. In order to benefit from the large potential in smartphones there is a need to manage the risks arising from threats, while maintaining an easy interface for the users. In this paper we investigate the use of Trusted Platform Model (TPM) 2.0 to develop a secure application for smartphones using Windows Phone 8.1. In particular, we suggest a framework based on remote attestation as a proxy to authenticate remote services, where the device is associated to the user and replaces the users credentials. In addition, we use the TPM 2.0 to enable secured information and data storage within the device itself. We present an implementation and performance evaluation of the suggested architecture that uses our novel attestation and authentication scheme and reveal the caveats of using software TPM in todays mobile devices.
Simple epistemic planning: generalised gossiping
Venue: arXiv cs.AI 2016
Year: 2016

The gossip problem, in which information (known as secrets) must be shared among a certain number of agents using the minimum number of calls, is of interest in the conception of communication networks and protocols. We extend the gossip problem to arbitrary epistemic depths. For example, we may require not only that all agents know all secrets but also that all agents know that all agents know all secrets. We give optimal protocols for various versions of the generalised gossip problem, depending on the graph of communication links, in the case of two-way communications, one-way communications and parallel communication. We also study different variants which allow us to impose negative goals such as that certain agents must not know certain secrets. We show that in the presence of negative goals testing the existence of a successful protocol is NP-complete whereas this is always polynomial-time in the case of purely positive goals.
TransitLabel: A Crowd-Sensing System for Automatic Labeling of Transit Stations Semantics
Venue: arXiv cs.CY 2016
Year: 2016

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.
Geometry Helps to Compare Persistence Diagrams
Venue: arXiv cs.CG 2016
Year: 2016

Exploiting geometric structure to improve the asymptotic complexity of discrete assignment problems is a well-studied subject. In contrast, the practical advantages of using geometry for such problems have not been explored. We implement geometric variants of the Hopcroft--Karp algorithm for bottleneck matching (based on previous work by Efrat el al.) and of the auction algorithm by Bertsekas for Wasserstein distance computation. Both implementations use k-d trees to replace a linear scan with a geometric proximity query. Our interest in this problem stems from the desire to compute distances between persistence diagrams, a problem that comes up frequently in topological data analysis. We show that our geometric matching algorithms lead to a substantial performance gain, both in running time and in memory consumption, over their purely combinatorial counterparts. Moreover, our implementation significantly outperforms the only other implementation available for comparing persistence diagrams.
FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models
Venue: arXiv cs.CV 2016
Year: 2016

Common users have changed from mere consumers to active producers of multimedia data content. Video editing plays an important role in this scenario, calling for simple segmentation tools that can handle fast-moving and deformable video objects with possible occlusions, color similarities with the background, among other challenges. We present an interactive video segmentation method, named FOMTrace, which addresses the problem in an effective and efficient way. From a user-provided object mask in a first frame, the method performs semi-automatic video segmentation on a spatiotemporal superpixel-graph, and then estimates a Fuzzy Object Model (FOM), which refines segmentation of the second frame by constraining delineation on a pixel-graph within a region where the object's boundary is expected to be. The user can correct/accept the refined object mask in the second frame, which is then similarly used to improve the spatiotemporal video segmentation of the remaining frames. Both steps are repeated alternately, within interactive response times, until the segmentation refinement of the final frame is accepted by the user. Extensive experiments demonstrate FOMTrace's ability for tracing objects in comparison with state-of-the-art approaches for interactive video segmentation, supervised, and unsupervised object tracking.