OpenRaiser/Intern-Atlas huggingface dataset:
Remarks to the paper "Control system for reducing energy consumption in backbone computer network" from "Concurrency and Computation: Practice and Experience" journal
Venue: arXiv cs.DC 2016
Year: 2016

This paper indicates two errors in the formulation of the main optimization model in the article "Control system for reducing energy consumption in backbone computer network" by Niewiadomska-Szynkiewicz et al. and shows how to fix them.
Similarity Learning for Time Series Classification
Venue: arXiv cs.LG 2016
Year: 2016

Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these structured data. In this context, dynamic time warping (DTW) is probably the most common comparison measure. However, not much research effort has been put into improving it by learning. In this paper, we propose a novel method for learning similarities based on DTW, in order to improve time series classification. Making use of the uniform stability framework, we provide the first theoretical guarantees in the form of a generalization bound for linear classification. The experimental study shows that the proposed approach is efficient, while yielding sparse classifiers.
Coupled and decoupled impedance models compared in power electronics systems
Venue: arXiv cs.SY 2016
Year: 2016

This paper provides a comparative analysis of impedance models for power electronic converters and systems for the purpose of stability investigations. Such models can be divided into either decoupled models or matrix models. A decoupled impedance model is highly appealing since the Single-Input-Single-Output (SISO) structure makes the analysis and result interpretation very simple. On the other hand, matrix impedance models are more accurate, and in some cases necessary. Previous works have applied various approximations to obtain decoupled models, and both the dq- and sequence domains have been used. This paper introduces the terms decoupled and semi-decoupled impedance models in order to have a clear classification of the available approximations. The accuracy of 4 decoupled impedance models are discussed based on the concept of Mirror Frequency Coupling (MFC). By definition the decoupled models based on sequence domain impedances will be exact for systems without MFC. In the general case, they are expected to be more accurate than the decoupled dq-impedance models. The paper defines a norm $ε$ to measure the degree of coupling in the impedance matrices. This norm equals the error in the eigenvalue loci between the matrix and semi-decoupled models. This can also be viewed as the error in the semi-decoupled Nyquist plot. An example case study consisting of a grid-connected VSC with current controller and PLL is used to compare the different methods. It is found that decoupled and semi-decoupled models in the dq-domain are only applicable in grids with very low X/R-ratio. Furthermore, it is concluded that the decoupled model in the sequence domain gives close to equal results as the semi-decoupled model.
Achieving while maintaining: A logic of knowing how with intermediate constraints
Venue: arXiv cs.LO 2016
Year: 2016

In this paper, we propose a ternary knowing how operator to express that the agent knows how to achieve $ϕ$ given $ψ$ while maintaining $χ$ in-between. It generalizes the logic of goal-directed knowing how proposed by Yanjing Wang 2015 'A logic of knowing how'. We give a sound and complete axiomatization of this logic.
Gene Tree Construction and Correction using SuperTree and Reconciliation
Venue: arXiv cs.DS 2016
Year: 2016

The supertree problem asking for a tree displaying a set of consistent input trees has been largely considered for the reconstruction of species trees. Here, we rather explore this framework for the sake of reconstructing a gene tree from a set of input gene trees on partial data. In this perspective, the phylogenetic tree for the species containing the genes of interest can be used to choose among the many possible compatible "supergenetrees", the most natural criteria being to minimize a reconciliation cost. We develop a variety of algorithmic solutions for the construction and correction of gene trees using the supertree framework. A dynamic programming supertree algorithm for constructing or correcting gene trees, exponential in the number of input trees, is first developed for the less constrained version of the problem. It is then adapted to gene trees with nodes labeled as duplication or speciation, the additional constraint being to preserve the orthology and paralogy relations between genes. Then, a quadratic time algorithm is developed for efficiently correcting an initial gene tree while preserving a set of "trusted" subtrees, as well as the relative phylogenetic distance between them, in both cases of labeled or unlabeled input trees. By applying these algorithms to the set of Ensembl gene trees, we show that this new correction framework is particularly useful to correct weaklysupported duplication nodes. The C++ source code for the algorithms and simulations described in the paper are available at https://github.com/UdeM-LBIT/SuGeT.
Employing Game theory and Multilevel Analysis to Predict the Factors that affect Collaborative Learning Outcomes: An Empirical Study
Venue: arXiv cs.CY 2016
Year: 2016

The purpose of this study is to propose a model that predicts the social and psychological factors that affect the individuals collaborative learning outcome in group projects. The model is established on the basis of two theories, namely, the multilevel analysis and the cooperative game theory (CGT). In CGT, a group of players form a coalition and a set of payoffs for each member in the coalition. Shapely values is one of the most important solution concepts in CGT. It represents a fair and efficient distribution of payoffs among members of a coalition. The proposed approach was applied on a sample that consisted of 78 freshman students, in their first semester, who were studying philosophical thinking course and instructed by the same professor. Tools for the data collection included self-assessments, peer assessments, quizzes and observations. The research concluded that learning outcome and contribution are best prophesied by the extent of engagement the content is purveying. Whereas personality traits, as well as, learning styles have the least impact on contribution. In addition, results show that Shapley values can be used as good vaticinators for individuals learning outcomes. These results indicate that CGT can be used as a good engine for analyzing interactions that recur in collaborative learning.
CapriDB - Capture, Print, Innovate: A Low-Cost Pipeline and Database for Reproducible Manipulation Research
Venue: arXiv cs.RO 2016
Year: 2016

We present a novel approach and database which combines the inexpensive generation of 3D object models via monocular or RGB-D camera images with 3D printing and a state of the art object tracking algorithm. Unlike recent efforts towards the creation of 3D object databases for robotics, our approach does not require expensive and controlled 3D scanning setups and enables anyone with a camera to scan, print and track complex objects for manipulation research. The proposed approach results in highly detailed mesh models whose 3D printed replicas are at times difficult to distinguish from the original. A key motivation for utilizing 3D printed objects is the ability to precisely control and vary object properties such as the mass distribution and size in the 3D printing process to obtain reproducible conditions for robotic manipulation research. We present CapriDB - an extensible database resulting from this approach containing initially 40 textured and 3D printable mesh models together with tracking features to facilitate the adoption of the proposed approach.
Dynamic Buffer Management for Multimedia QoS in Beyond 3G Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

This paper investigates a dynamic buffer management scheme for QoS control of multimedia services in beyond 3G wireless systems. The scheme is studied in the context of the state-of-the-art 3.5G system i.e. the High Speed Downlink Packet Access (HSDPA) which enhances 3G UMTS to support high-speed packet switched services. Unlike earlier systems, UMTS-evolved systems from HSDPA and beyond incorporate mechanisms such as packet scheduling and HARQ in the base station necessitating data buffering at the air interface. This introduces a potential bottleneck to end-to-end communication. Hence, buffer management at the air interface is crucial for end-to-end QoS support of multimedia services with multiplexed parallel diverse flows such as video and data in the same end-user session. The dynamic buffer management scheme for HSDPA multimedia sessions with aggregated real-time and non real-time flows is investigated via extensive HSDPA simulations. The impact of the scheme on end-to-end traffic performance is evaluated with an example multimedia session comprising a real-time streaming flow concurrent with TCP-based non real-time flow. Results demonstrate that the scheme can guarantee the end-to-end QoS of the real-time streaming flow, whilst simultaneously protecting the non real-time flow from starvation resulting in improved end-to-end throughput performance.
How Well Do Local Algorithms Solve Semidefinite Programs?
Venue: arXiv cs.DM 2016
Year: 2016

Several probabilistic models from high-dimensional statistics and machine learning reveal an intriguing --and yet poorly understood-- dichotomy. Either simple local algorithms succeed in estimating the object of interest, or even sophisticated semi-definite programming (SDP) relaxations fail. In order to explore this phenomenon, we study a classical SDP relaxation of the minimum graph bisection problem, when applied to Erdős-Renyi random graphs with bounded average degree $d>1$, and obtain several types of results. First, we use a dual witness construction (using the so-called non-backtracking matrix of the graph) to upper bound the SDP value. Second, we prove that a simple local algorithm approximately solves the SDP to within a factor $2d^2/(2d^2+d-1)$ of the upper bound. In particular, the local algorithm is at most $8/9$ suboptimal, and $1+O(1/d)$ suboptimal for large degree. We then analyze a more sophisticated local algorithm, which aggregates information according to the harmonic measure on the limiting Galton-Watson (GW) tree. The resulting lower bound is expressed in terms of the conductance of the GW tree and matches surprisingly well the empirically determined SDP values on large-scale Erdős-Renyi graphs. We finally consider the planted partition model. In this case, purely local algorithms are known to fail, but they do succeed if a small amount of side information is available. Our results imply quantitative bounds on the threshold for partial recovery using SDP in this model.
Probabilistic Safety Programs
Venue: arXiv cs.RO 2016
Year: 2016

Achieving safe control under uncertainty is a key problem that needs to be tackled for enabling real-world autonomous robots and cyber-physical systems. This paper introduces Probabilistic Safety Programs (PSP) that embed both the uncertainty in the environment as well as invariants that determine safety parameters. The goal of these PSPs is to evaluate future actions or trajectories and determine how likely it is that the system will stay safe under uncertainty. We propose to perform these evaluations by first compiling the PSP to a graphical model then using a fast variational inference algorithm. We highlight the efficacy of the framework on the task of safe control of quadrotors and autonomous vehicles in dynamic environments.
Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management
Venue: arXiv cs.HC 2016
Year: 2016

Crowdsourcing is the primary means to generate training data at scale, and when combined with sophisticated machine learning algorithms, crowdsourcing is an enabler for a variety of emergent automated applications impacting all spheres of our lives. This paper surveys the emerging field of formally reasoning about and optimizing open-ended crowdsourcing, a popular and crucially important, but severely understudied class of crowdsourcing---the next frontier in crowdsourced data management. The underlying challenges include distilling the right answer when none of the workers agree with each other, teasing apart the various perspectives adopted by workers when answering tasks, and effectively selecting between the many open-ended operators appropriate for a problem. We describe the approaches that we've found to be effective for open-ended crowdsourcing, drawing from our experiences in this space.
VRPBench: A Vehicle Routing Benchmark Tool
Venue: arXiv cs.AI 2016
Year: 2016

The number of optimization techniques in the combinatorial domain is large and diversified. Nevertheless, there is still a lack of real benchmarks to validate optimization algorithms. In this work we introduce VRPBench, a tool to create instances and visualize solutions to the Vehicle Routing Problem (VRP) in a planar graph embedded in the Euclidean 2D space. We use VRPBench to model a real-world mail delivery case of the city of Artur Nogueira. Such scenarios were characterized as a multi-objective optimization of the VRP. We extracted a weighted graph from a digital map of the city to create a challenging benchmark for the VRP. Each instance models one generic day of mail delivery with hundreds to thousands of delivery points, thus allowing both the comparison and validation of optimization algorithms for routing problems.
A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.
Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme
Venue: arXiv cs.NI 2016
Year: 2016

This paper introduces novel schemes for indoor localization, outlier detection, and radio map interpolation using Wireless Local Area Networks (WLANs). The localization method consists of a novel multicomponent optimization technique that minimizes the squared $\ell_{2}$-norm of the residuals between the radio map and the online Received Signal Strength (RSS) measurements, the $\ell_{1}$-norm of the user's location vector, and weighted $\ell_{2}$-norms of layered groups of Reference Points (RPs). RPs are grouped using a new criterion based on the similarity between the so-called Access Point (AP) coverage vectors. In addition, since AP readings are prone to containing inordinate readings, called outliers, an augmented optimization problem is proposed to detect the outliers and localize the user with cleaned online measurements. Moreover, a novel scheme to record fingerprints from a smaller number of RPs and estimate the radio map at RPs without recorded fingerprints is developed using sparse recovery techniques. All localization schemes are tested on RSS fingerprints collected from a real environment. The overall scheme has comparable complexity with competing approaches, while it performs with high accuracy under a small number of APs and finer granularity of RPs.
Fair Representation and a Linear Shapley Rule
Venue: arXiv cs.GT 2016
Year: 2016

When delegations to an assembly or council represent differently sized constituencies, they are often allocated voting weights which increase in population numbers (EU Council, US Electoral College, etc.). The Penrose square root rule (PSRR) is the main benchmark for fair representation of all bottom-tier voters in the top-tier decision making body, but rests on the restrictive assumption of independent binary decisions. We consider intervals of alternatives with single-peaked preferences instead, and presume positive correlation of local voters. This calls for a replacement of the PSRR by a linear Shapley rule: representation is fair if the Shapley value of the delegates is proportional to their constituency sizes.
Expenditure Aware Rating Prediction for Recommendation
Venue: arXiv cs.SI 2016
Year: 2016

The rating score prediction is widely studied in recommender system, which predicts the rating scores of users on items through making use of the user-item interaction information. Besides the rating information between users and items, lots of additional information have been employed to promote recommendations, such as social relation and geographic location. Expenditure information on each transaction between users and items is widely available on e-commerce websites, often appearing next to the rating information, while there is seldom study on the correlation between expenditures and rating scores. In this paper, we first study their correlations in real data sets and propose the expenditure aware rating prediction problem. From the data sets crawled from a well-known social media platform Dianping in China, we find some insightful correlations between expenditures and rating scores: 1) transactions or experiences with higher expenditures usually lead to higher rating scores; 2) when the real expenditures are higher than users' normal spending behavior, the users usually give higher scores; and 3) there are multiple grades of expenditure behaviors. Based on these three observations, we propose an Expenditure ware RatingPrediction method (EARP), based on low-rank matrix factorization, to effectively incorporate the expenditure information. Extensive experiments on five real data sets show that EARP not only always outperforms other state-of-the-art baselines but also discovers the latent characteristics of users and businesses.
Addressing Community Question Answering in English and Arabic
Venue: arXiv cs.CL 2016
Year: 2016

This paper studies the impact of different types of features applied to learning to re-rank questions in community Question Answering. We tested our models on two datasets released in SemEval-2016 Task 3 on "Community Question Answering". Task 3 targeted real-life Web fora both in English and Arabic. Our models include bag-of-words features (BoW), syntactic tree kernels (TKs), rank features, embeddings, and machine translation evaluation features. To the best of our knowledge, structural kernels have barely been applied to the question reranking task, where they have to model paraphrase relations. In the case of the English question re-ranking task, we compare our learning to rank (L2R) algorithms against a strong baseline given by the Google-generated ranking (GR). The results show that i) the shallow structures used in our TKs are robust enough to noisy data and ii) improving GR is possible, but effective BoW features and TKs along with an accurate model of GR features in the used L2R algorithm are required. In the case of the Arabic question re-ranking task, for the first time we applied tree kernels on syntactic trees of Arabic sentences. Our approaches to both tasks obtained the second best results on SemEval-2016 subtasks B on English and D on Arabic.
A Scalable Multi-Stage Packet-Switch for Data Center Networks
Venue: arXiv cs.NI 2016
Year: 2016

The growing trends of data centers over last decades including social networking, cloud-based applications and storage technologies enabled many advances to take place in the networking area. Recent changes imply continuous demand for bandwidth to manage the large amount of packetized traffic. Cluster switches and routers make the switching fabric in a Data Center Network (DCN) environment and provide interconnectivity between elements of the same DC and inter DCs. To handle the constantly variable loads, switches need deliver outstanding throughput along with resiliency and scalability for DCN requirements. Conventional DCN switches adopt crossbars or/and blocks of memories mounted in a multistage fashion (commonly 2-Tiers or 3-Tiers). However, current multistage switches, with their space-memory variants, are either too complex to implement, have poor performance, or not cost effective. We propose a novel and highly scalable multistage switch based on Networks-on-Chip (NoC) fabrics for DCNs. In particular, we describe a three-stage Clos packet-switch with a Round Robin packets dispatching scheme where each central stage module is based on a Unidirectional NoC (UDN), instead of the conventional single-hop crossbar. The design, referred to as Clos-UDN, overcomes shortcomings of traditional multistage architectures as it (i) Obviates the need for a complex and costly input modules, by means of few, yet simple, input FIFO queues. (ii) Avoids the need for a complex and synchronized scheduling process over a high number of input-output modules and/or port pairs. (iii) Provides speedup, load balancing and path-diversity thanks to a dynamic dispatching scheme as well as the NoC based fabric nature. Simulations show that the Clos-UDN outperforms some common multistage switches under a range of input traffics, making it highly appealing for ultra-high capacity DC networks.
Structured Unit Testable Templated Code for Efficient Code Review Process
Venue: arXiv cs.SE 2016
Year: 2016

Modern software development teams are distributed across onsite and off-shore locations. Each team has developers with varying experience levels and English communication skills. In such a diverse development environment it is important to maintain the software quality, coding standards, timely delivery of features and bug fixes. It is also important to reduce testing effort, minimize side effects such as change in functionality, user experience or application performance. Code reviews are intended to control code quality. Unfortunately, many projects lack enforcement of processes and standards because of approaching deadlines, live production issues and lack of resource availability. This study examines a novel structured, unit testable templated code method to enforce code review standards with an intent to reduce coding effort, minimize revisions and eliminate functional and performance side effects on the system. The proposed method would also result in unit-testable code that can also be easily rolled back and increase team productivity. The baseline for traditional code review processes using metrics such as code review duration, bug regression rate, revision count was measured. These metrics were then compared with results from the proposed code review process that used structured unit testable templated code. The performance on 2 large enterprise level applications spanning over 2 years and 9 feature and maintenance release cycles was evaluated. The structured unit testable templated code method resulted in a decrease in total code review time, revision count and coding effort. It also decreased the number of live production issues caused by code churn or side effects of bug fix when compared to traditional code review process.