OpenRaiser/Intern-Atlas huggingface dataset:
From Word Embeddings to Item Recommendation
Venue: arXiv cs.LG 2016
Year: 2016

Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techniques, such as neighborhood based methods, machine-learning based methods and matrix-factorization based methods. In this work, a set of well known methods from natural language processing domain, namely Word2Vec, is applied to recommendation systems domain. Unlike previous works that use Word2Vec for recommendation, this work uses non-textual features, the check-ins, and it recommends venues to visit/check-in to the target users. For the experiments, a Foursquare check-in dataset is used. The results show that use of continuous vector space representations of items modeled by techniques of Word2Vec is promising for making recommendations.
State-Of-The-Art In Empirical Validation Of Software Metrics For Fault Proneness Prediction: Systematic Review
Venue: arXiv cs.SE 2016
Year: 2016

With the sharp rise in software dependability and failure cost, high quality has been in great demand. However, guaranteeing high quality in software systems which have grown in size and complexity coupled with the constraints imposed on their development has become increasingly difficult, time and resource consuming activity. Consequently, it becomes inevitable to deliver software that have no serious faults. In this case, object-oriented (OO) products being the de facto standard of software development with their unique features could have some faults that are hard to find or pinpoint the impacts of changes. The earlier faults are identified, found and fixed, the lesser the costs and the higher the quality. To assess product quality, software metrics are used. Many OO metrics have been proposed and developed. Furthermore, many empirical studies have validated metrics and class fault proneness (FP) relationship. The challenge is which metrics are related to class FP and what activities are performed. Therefore, this study bring together the state-of-the-art in fault prediction of FP that utilizes CK and size metrics. We conducted a systematic literature review over relevant published empirical validation articles. The results obtained are analysed and presented. It indicates that 29 relevant empirical studies exist and measures such as complexity, coupling and size were found to be strongly related to FP.
Delay-Aware Dynamic Resource Management for High-Speed Railway Wireless Communications
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we investigate the delay-aware dynamic resource management problem for multi-service transmission in high-speed railway wireless communications, with a focus on resource allocation among the services and power control along the time. By taking account of average delay requirements and power constraints, the considered problem is formulated into a stochastic optimization problem, rather than pursuing the traditional convex optimization means. Inspired by Lyapunov optimization theory, the intractable stochastic optimization problem is transformed into a tractable deterministic optimization problem, which is a mixed-integer resource management problem. By exploiting the specific problem structure, the mixed-integer resource management problem is equivalently transformed into a single variable problem, which can be effectively solved by the golden section search method with guaranteed global optimality. Finally, we propose a dynamic resource management algorithm to solve the original stochastic optimization problem. Simulation results show the advantage of the proposed dynamic algorithm and reveal that there exists a fundamental tradeoff between delay requirements and power consumption.
Congestion Aware Spray And Wait Protocol: A Congestion Control Mechanism For The Vehicular Delay Tolerant Network
Venue: arXiv cs.NI 2016
Year: 2016

In the last few years, the Vehicular Ad-hoc Network (VANET) has come to be an important area of research. Significant research has been conducted to improve the performance of VANETS. One output of further research conducted on VANET is the Vehicular Delay Tolerant Network (VDTN). It is an application of the mobile DTN where nodes relay messages in the network using a store-carry-forward approach. Due to its high mobility, it suffers frequent disconnections and also congestions at nodes which leads to message drops. To minimize the rate of message drops and so optimize the probability of message delivery so that drivers are increasingly aware of the situation of the road, we propose a congestion control mechanism: Congestion Aware Spray and Wait (CASaW) protocol in this work so as to optimize the rate of message delivery to its destination and so increase the awareness of drivers in the vehicular environment thereby improve road safety. The results have shown that our proposition performed better than other classical VDTN protocols in terms of message delivery probability and rate of packet drops performance measures. We used the Opportunistic Networking Environment (ONE) simulator to implement the classical VDTN protocols: the PROPHET protocol, the Epidemic protocol, the MaxProp protocol and the Spray and Wait Protocol. The simulation scenarios shows a better performance for the congestion control mechanism we propose as it maintains a good message delivery rate as well as minimize the rate of packet losses thereby optimizing the chances of messages getting to their destinations and so improve road safety.
k-Nearest Neighbors on Road Networks: A Journey in Experimentation and In-Memory Implementation
Venue: arXiv cs.DS 2016
Year: 2016

A k nearest neighbor (kNN) query on road networks retrieves the k closest points of interest (POIs) by their network distances from a given location. Today, in the era of ubiquitous mobile computing, this is a highly pertinent query. While Euclidean distance has been used as a heuristic to search for the closest POIs by their road network distance, its efficacy has not been thoroughly investigated. The most recent methods have shown significant improvement in query performance. Earlier studies, which proposed disk-based indexes, were compared to the current state-of-the-art in main memory. However, recent studies have shown that main memory comparisons can be challenging and require careful adaptation. This paper presents an extensive experimental investigation in main memory to settle these and several other issues. We use efficient and fair memory-resident implementations of each method to reproduce past experiments and conduct additional comparisons for several overlooked evaluations. Notably we revisit a previously discarded technique (IER) showing that, through a simple improvement, it is often the best performing technique.
Automatic Calibration of a Robot Manipulator and Multi 3D Camera System
Venue: arXiv cs.RO 2016
Year: 2016

With 3D sensing becoming cheaper, environment-aware and visually-guided robot arms capable of safely working in collaboration with humans will become common. However, a reliable calibration is needed, both for camera internal calibration, as well as Eye-to-Hand calibration, to make sure the whole system functions correctly. We present a framework, using a novel combination of well proven methods, allowing a quick automatic calibration for the integration of systems consisting of the robot and a varying number of 3D cameras by using a standard checkerboard calibration grid. Our approach allows a quick camera-to-robot recalibration after any changes to the setup, for example when cameras or robot have been repositioned. Modular design of the system ensures flexibility regarding a number of sensors used as well as different hardware choices. The framework has been proven to work by practical experiments to analyze the quality of the calibration versus the number of positions of the checkerboard used for each of the calibration procedures.
A Prefixed-Itemset-Based Improvement For Apriori Algorithm
Venue: arXiv cs.DS 2016
Year: 2016

Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
On Which Skills do Indian Universities Evaluate Software Engineering Students?
Venue: arXiv cs.SE 2016
Year: 2016

Universities conduct examinations to evaluate acquired skills and knowledge gained by students. An assessment of skills and knowledge levels evaluated during Software Engineering examinations is presented in this paper. The question items asked during examinations are analyzed from three dimensions that are cognitive levels, knowledge levels and knowledge areas. The Revised Bloom's Taxonomy is used to classify question items along the dimensions of cognitive levels and knowledge levels. Question items are also classified in various knowledge areas specified in ACM/IEEE's Computer Science Curricula. The analysis presented in this paper will be useful for software engineering educators to devise corrective interventions and employers of fresh graduates to design pre-induction training programs.
Dynamic Environments for Virtual Machine Placement considering Elasticity and Overbooking
Venue: arXiv cs.DC 2016
Year: 2016

Cloud computing datacenters provide millions of virtual machines in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. Considering the on-demand model of cloud computing, the VMP problem should be solved dynamically to efficiently attend typical workload of modern applications. This work proposes a taxonomy in order to understand possible challenges for Cloud Service Providers (CSPs) in dynamic environments, based on the most relevant dynamic parameters studied so far in the VMP literature. Based on the proposed taxonomy, several unexplored environments have been identified. To further study those research opportunities, sample workload traces for each particular environment are required; therefore, basic examples illustrate a preliminary work on dynamic workload trace generation.
Delivery Time Reduction for Order-Constrained Applications using Binary Network Codes
Venue: arXiv cs.IT 2016
Year: 2016

Consider a radio access network wherein a base-station is required to deliver a set of order-constrained messages to a set of users over independent erasure channels. This paper studies the delivery time reduction problem using instantly decodable network coding (IDNC). Motivated by time-critical and order-constrained applications, the delivery time is defined, at each transmission, as the number of undelivered messages. The delivery time minimization problem being computationally intractable, most of the existing literature on IDNC propose sub-optimal online solutions. This paper suggests a novel method for solving the problem by introducing the delivery delay as a measure of distance to optimality. An expression characterizing the delivery time using the delivery delay is derived, allowing the approximation of the delivery time minimization problem by an optimization problem involving the delivery delay. The problem is, then, formulated as a maximum weight clique selection problem over the IDNC graph wherein the weight of each vertex reflects its corresponding user and message's delay. Simulation results suggest that the proposed solution achieves lower delivery and completion times as compared to the best-known heuristics for delivery time reduction.
The Essence of Inheritance
Venue: arXiv cs.PL 2016
Year: 2016

Programming languages serve a dual purpose: to communicate programs to computers, and to communicate programs to humans. Indeed, it is this dual purpose that makes programming language design a constrained and challenging problem. Inheritance is an essential aspect of that second purpose: it is a tool to improve communication. Humans understand new concepts most readily by first looking at a number of concrete examples, and later abstracting over those examples. The essence of inheritance is that it mirrors this process: it provides a formal mechanism for moving from the concrete to the abstract.
Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier
Venue: arXiv cs.CV 2016
Year: 2016

Multiview learning problem refers to the problem of learning a classifier from multiple view data. In this data set, each data points is presented by multiple different views. In this paper, we propose a novel method for this problem. This method is based on two assumptions. The first assumption is that each data point has an intact feature vector, and each view is obtained by a linear transformation from the intact vector. The second assumption is that the intact vectors are discriminative, and in the intact space, we have a linear classifier to separate the positive class from the negative class. We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices. Moreover, we also propose a linear classifier in the intact space, and learn it jointly with the intact vectors. The learning problem is modeled by a minimization problem, and the objective function is composed of a Cauchy error estimator-based view-conditional reconstruction term over all data points and views, and a classification error term measured by hinge loss over all the intact vectors of all the data points. Some regularization terms are also imposed to different variables in the objective function. The minimization problem is solve by an iterative algorithm using alternate optimization strategy and gradient descent algorithm. The proposed algorithm shows it advantage in the compression to other multiview learning algorithms on benchmark data sets.
A FIRM Approach to Software-Defined Service Composition
Venue: arXiv cs.DC 2016
Year: 2016

Service composition is an aggregate of services often leveraged to automate the enterprise business processes. While Service Oriented Architecture (SOA) has been a forefront of service composition, services can be realized as efficient distributed and parallel constructs such as MapReduce, which are not typically exploited in service composition. With the advent of Software\-Defined Networking (SDN), global view and control of the entire network is made available to the networking controller, which can further be leveraged in application level. This paper presents FIRM, an approach for Software-Defined Service Composition by leveraging SDN and MapReduce. FIRM comprises Find, Invoke, Return, and Manage, as the core procedures in achieving a QoS-Aware Service Composition.
Possible values: exploring a concept for concurrency
Venue: arXiv cs.LO 2016
Year: 2016

An important issue in concurrency is interference. This issue manifests itself in both shared-variable and communication-based concurrency --- this paper focusses on the former case where interference is caused by the environment of a process changing the values of shared variables. Rely/guarantee approaches have been shown to be useful in specifying and reasoning compositionally about concurrent programs. This paper explores the use of a "possible values" notation for reasoning about variables whose values can be changed multiple times by interference. Apart from the value of this concept in providing clear specifications, it offers a principled way of avoiding the need for some auxiliary (or ghost) variables whose unwise use can destroy compositionality.
Modeling and Analysis of Cooperative Relaying in Spectrum-Sharing Cellular Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, spectrum-sharing technology is integrated into cellular systems to improve spectrum efficiency. Macrocell users are primary users (PUs) while those within local cells, e.g., femtocell users, or desiring cost-effective services, e.g., roamers, are identified as secondary users (SUs). The SUs share the spectrum resources of the PUs in a underlay way, thus the transmit power of a secondary is strictly limited by the primary's tolerable interference power. Given such constraints, a cooperative relaying transmission between a SU and the macrocell base station (BS) is necessary. In order to guarantee the success of dual-hop relaying and avoid multi-hop relaying, a new cooperative paradigm is proposed, where an idle PU (instead of a secondary as assumed in general) in the vicinity of a target SU is chosen to serve as a relaying node, thanks to the fact that any PU can always transmit to the macrocell BS directly. Moreover, two-way relaying strategy is applied at the chosen relaying node so as to further improve the spectral efficiency. Our results demonstrate that the proposed system is particularly suitable for delay-tolerant wireless services with asymmetric downlink/uplink traffics, such as e-mail checking, web browsing, social networking and data streaming, which are the most popular applications for SUs in spectrum-sharing cellular networks.
Compressed Shattering
Venue: arXiv cs.IT 2016
Year: 2016

The central idea of compressed sensing is to exploit the fact that most signals of interest are sparse in some domain and use this to reduce the number of measurements to encode. However, if the sparsity of the input signal is not precisely known, but known to lie within a specified range, compressed sensing as such cannot exploit this fact and would need to use the same number of measurements even for a very sparse signal. In this paper, we propose a novel method called Compressed Shattering to adapt compressed sensing to the specified sparsity range, without changing the sensing matrix by creating shattered signals which have fixed sparsity. This is accomplished by first suitably permuting the input spectrum and then using a filter bank to create fixed sparsity shattered signals. By ensuring that all the shattered signals are utmost 1-sparse, we make use of a simple but efficient deterministic sensing matrix to yield very low number of measurements. For a discrete-time signal of length 1000, with a sparsity range of $5 - 25$, traditional compressed sensing requires $175$ measurements, whereas Compressed Shattering would only need $20 - 100$ measurements.
Joint Object-Material Category Segmentation from Audio-Visual Cues
Venue: arXiv cs.CV 2016
Year: 2016

It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials. In this paper, we therefore present an approach that augments the available dense visual cues with sparse auditory cues in order to estimate dense object and material labels. Since estimates of object class and material properties are mutually informative, we optimise our multi-output labelling jointly using a random-field framework. We evaluate our system on a new dataset with paired visual and auditory data that we make publicly available. We demonstrate that this joint estimation of object and material labels significantly outperforms the estimation of either category in isolation.
Extended Capability Models for Carbon Fiber Composite (CFC) Panels in the Unstructured Transmission Line Modelling (UTLM) Method
Venue: arXiv cs.CE 2016
Year: 2016

An effective model of single and multilayered thin panels, including those formed using carbon fiber composite (CFC) materials, is incorporated into the Transmission Line Modeling (TLM) method. The thin panel model is a one-dimensional (1D) one based on analytical expansions of cotangent and cosecant functions that are used to describe the admittance matrix in the frequency domain; these are then converted into the time domain by using digital filter theory and an inverse Z transform. The model, which is extended to allow for material anisotropy, is executed within 1D TLM codes. And, for the first time, the two-dimensional (2D) thin surface model is embedded in unstructured three-dimensional (3D) TLM codes. The approach is validated by using it to study some canonical structures with analytic solutions, and against results taken from the literature. It is then used to investigate shielding effectiveness of carbon fiber composite materials in a practical curved aerospace-related structure.
Sublinear scaling of country attractiveness observed from Flickr dataset
Venue: arXiv cs.SI 2016
Year: 2016

The number of people who decide to share their photographs publicly increases every day, consequently making available new almost real-time insights of human behavior while traveling. Rather than having this statistic once a month or yearly, urban planners and touristic workers now can make decisions almost simultaneously with the emergence of new events. Moreover, these datasets can be used not only to compare how popular different touristic places are, but also predict how popular they should be taking into an account their characteristics. In this paper we investigate how country attractiveness scales with its population and size using number of foreign users taking photographs, which is observed from Flickr dataset, as a proxy for attractiveness. The results showed two things: to a certain extent country attractiveness scales with population, but does not with its size; and unlike in case of Spanish cities, country attractiveness scales sublinearly with population, and not superlinearly.
On the sum-of-squares degree of symmetric quadratic functions
Venue: arXiv cs.CC 2016
Year: 2016

We study how well functions over the boolean hypercube of the form $f_k(x)=(|x|-k)(|x|-k-1)$ can be approximated by sums of squares of low-degree polynomials, obtaining good bounds for the case of approximation in $\ell_{\infty}$-norm as well as in $\ell_1$-norm. We describe three complexity-theoretic applications: (1) a proof that the recent breakthrough lower bound of Lee, Raghavendra, and Steurer on the positive semidefinite extension complexity of the correlation and TSP polytopes cannot be improved further by showing better sum-of-squares degree lower bounds on $\ell_1$-approximation of $f_k$; (2) a proof that Grigoriev's lower bound on the degree of Positivstellensatz refutations for the knapsack problem is optimal, answering an open question from his work; (3) bounds on the query complexity of quantum algorithms whose expected output approximates such functions.
A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
Venue: arXiv cs.IR 2016
Year: 2016

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.