OpenRaiser/Intern-Atlas huggingface dataset:
Do Personality Profiles Differ in the Pakistani Software Industry and Academia - A Case Study
Venue: arXiv cs.SE 2015
Year: 2015

Effects of personality profiles and human factors in software engineering (SE) have been studied from different perspectives, such as: software life cycle, team performance, software quality attributes, and so on. This study intends to compare personality profiles of software engineers in academia and industry. In this survey we have collected personality profiles of software engineers from academia and the local industry in Pakistan. According to the Myers- Briggs Type Indicator (MBTI) instrument, the most prominent personality type among Pakistani academicians is a combination of Introversion, Sensing, Thinking, and Judging (ISTJ). However the most dominant personality type among software engineers in the Pakistani software industry is a combination of Extroversion, Sensing, Thinking, and Judging (ESTJ). The results of study establish that software engineers working in industry are mostly Extroverts as compared to those in academia who tend to be Introverts. The dimensions: Sensing, Thinking, and Judging (STJ), however, remain common in the dominant personality types of software engineers, both in Pakistani software industry and academia.
A Business Maturity Model of Software Product Line Engineering
Venue: arXiv cs.SE 2015
Year: 2015

In the recent past, software product line engineering has become one of the most promising practices in software industry with the potential to substantially increase the software development productivity. Software product line engineering approach spans the dimensions of business, architecture, software engineering process and organization. The increasing popularity of software product line engineering in the software industry necessitates a process maturity evaluation methodology. Accordingly, this paper presents a business maturity model of software product line, which is a methodology to evaluate the current maturity of the business dimension of a software product line in an organization. This model examines the coordination between product line engineering and the business aspects of software product line. It evaluates the maturity of the business dimension of software product line as a function of how a set of business practices are aligned with product line engineering in an organization. Using the model presented in this paper, we conducted two case studies and reported the assessment results. This research contributes towards establishing a comprehensive and unified strategy for a process maturity evaluation of software product lines.
C# Traceability System
Venue: arXiv cs.SE 2015
Year: 2015

Traceability information is a valuable asset that software development teams can leverage to minimise their risk during production and maintenance of software projects. When maintainers are added to a software project post-production, they have to learn the system from scratch and understand its dynamics before they can begin making appropriate modifications to the source code. The system outlined in this paper extracts traceability information directly from the source code of C# projects, and presents it in such a way that it can be easily used to understand the logic and validate changes to the system.
The Challenges of CASE Design Integration in the Telecommunication Application Domain
Venue: arXiv cs.SE 2015
Year: 2015

The magnitude of the problems facing the telecommunication software industry is presently at a point at which software engineers should become deeply involved. This paper presents a research project on advanced telecommunication technology carried out in Europe, called BOOST (Broadband Object-Oriented Service Technology). The project involved cooperative work among telecommunication companies, research centres and universities from several countries. The challenges to integrate CASE tools to support software development within the telecommunication application domain are discussed. A software process model that encourages component reusability, named the X model, is described as part of a software life cycle model for the telecommunication software industry.
Weighted cumulative entropies: An extension of CRE and CE
Venue: arXiv cs.IT 2015
Year: 2015

We generalize the weighted cumulative entropies (WCRE and WCE), introduced in [5], for a system or component lifetime. Representing properties of cumulative entropies, several bounds and inequalities for the WCRE is proposed
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Practical face recognition has been studied in the past decades, but still remains an open challenge. Current prevailing approaches have already achieved substantial breakthroughs in recognition accuracy. However, their performance usually drops dramatically if face samples are severely misaligned. To address this problem, we propose a highly efficient misalignment-robust locality-constrained representation (MRLR) algorithm for practical real-time face recognition. Specifically, the locality constraint that activates the most correlated atoms and suppresses the uncorrelated ones, is applied to construct the dictionary for face alignment. Then we simultaneously align the warped face and update the locality-constrained dictionary, eventually obtaining the final alignment. Moreover, we make use of the block structure to accelerate the derived analytical solution. Experimental results on public data sets show that MRLR significantly outperforms several state-of-the-art approaches in terms of efficiency and scalability with even better performance.
SINR Constrained Beamforming for a MIMO Multi-user Downlink System
Venue: arXiv cs.IT 2015
Year: 2015

Consider a multi-input multi-output (MIMO) downlink multi-user channel. A well-studied problem in such system is the design of linear beamformers for power minimization with the quality of service (QoS) constraints. The most representative algorithms for solving this class of problems are the so-called MMSE-SOCP algorithm [11-12] and the UDD algorithm [9]. The former is based on alternating optimization of the transmit and receive beamformers, while the latter is based on the well-known uplink-dowlink duality theory. Despite their wide applicability, the convergence (to KKT solutions) of both algorithms is still open in the literature. In this paper, we rigorously establish the convergence of these algorithms for QoS-constrained power minimization (QCPM) problem with both single stream and multiple streams per user cases. Key to our analysis is the development and analysis of a new MMSE-DUAL algorithm, which connects the MMSE-SOCP and the UDD algorithm. Our numerical experiments show that 1) all these algorithms can almost always reach points with the same objective value irrespective of initialization, 2) the MMSE-SOCP/MMSE-DUAL algorithm works well while the UDD algorithm may fail with an infeasible initialization.
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing
Venue: arXiv cs.LG 2015
Year: 2015

Task selection (picking an appropriate labeling task) and worker selection (assigning the labeling task to a suitable worker) are two major challenges in task assignment for crowdsourcing. Recently, worker selection has been successfully addressed by the bandit-based task assignment (BBTA) method, while task selection has not been thoroughly investigated yet. In this paper, we experimentally compare several task selection strategies borrowed from active learning literature, and show that the least confidence strategy significantly improves the performance of task assignment in crowdsourcing.
Capturing the Dynamics of Pedestrian Traffic Using a Machine Vision System
Venue: arXiv cs.CV 2015
Year: 2015

We developed a machine vision system to automatically capture the dynamics of pedestrians under four different traffic scenarios. By considering the overhead view of each pedestrian as a digital object, the system processes the image sequences to track the pedestrians. Considering the perspective effect of the camera lens and the projected area of the hallway at the top-view scene, the distance of each tracked object from its original position to its current position is approximated every video frame. Using the approximated distance and the video frame rate (30 frames per second), the respective velocity and acceleration of each tracked object are later derived. The quantified motion characteristics of the pedestrians are displayed by the system through 2-dimensional graphs of the kinematics of motion. The system also outputs video images of the pedestrians with superimposed markers for tracking. These visual markers were used to visually describe and quantify the behavior of the pedestrians under different traffic scenarios.
Face Search at Scale: 80 Million Gallery
Venue: arXiv cs.CV 2015
Year: 2015

Due to the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to process and search for persons of interest among the billions of shared photos on these websites. Facebook revealed in a 2013 white paper that its users have uploaded more than 250 billion photos, and are uploading 350 million new photos each day. Due to this humongous amount of data, large-scale face search for mining web images is both important and challenging. Despite significant progress in face recognition, searching a large collection of unconstrained face images has not been adequately addressed. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top-k most similar faces using deep features generated from a convolutional neural network. The k candidates are re-ranked by combining similarities from deep features and the COTS matcher. We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images. Experimental results demonstrate that the deep features are competitive with state-of-the-art methods on unconstrained face recognition benchmarks (LFW and IJB-A). Further, the proposed face search system offers an excellent trade-off between accuracy and scalability on datasets consisting of millions of images. Additionally, in an experiment involving searching for face images of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed face search system could find the younger brother's (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a 5M gallery and at rank 8 in 7 seconds on an 80M gallery.
On the Expressive Power of Kleene Algebra with Domain
Venue: arXiv cs.LO 2015
Year: 2015

It is shown that antidomain semirings are more expressive than test semirings and that Kleene algebras with domain are more expressive than Kleene algebras with tests. It is also shown that Kleene algebras with domain are expressive for propositional Hoare logic whereas Kleene algebras with tests are not.
Stable Support Recovery of Stream of Pulses with Application to Ultrasound Imaging
Venue: arXiv cs.IT 2015
Year: 2015

This paper considers the problem of estimating the delays of a weighted superposition of pulses, called stream of pulses, in a noisy environment. We show that the delays can be estimated using a tractable convex optimization problem with a localization error proportional to the square root of the noise level. Furthermore, all false detections produced by the algorithm have small amplitudes. Numerical and in-vitro ultrasound experiments corroborate the theoretical results and demonstrate their applicability for the ultrasound imaging signal processing.
Likelihood Ratio Based Scheduler for Secure Detection in Cyber Physical Systems
Venue: arXiv cs.SY 2015
Year: 2015

This paper is concerned with a binary detection problem over a non-secure network. To satisfy the communication rate constraint and against possible cyber attacks, which are modeled as deceptive signals injected to the network, a likelihood ratio based (LRB) scheduler is designed in the sensor side to smartly select sensor measurements for transmission. By exploring the scheduler, some sensor measurements are successfully retrieved from the attacked data at the decision center. We show that even under a moderate communication rate constraint of secure networks, an optimal LRB scheduler can achieve a comparable asymptotic detection performance to the standard N-P test using the full set of measurements, and is strictly better than the random scheduler. For non-secure networks, the LRB scheduler can also maintain the detection functionality but suffers graceful performance degradation under different attack intensities. Finally, we perform simulations to validate our theoretical results.
A Mixed-ADC Receiver Architecture for Massive MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Motivated by the demand for energy-efficient communication solutions in the next generation cellular network, a mixed-ADC receiver architecture for massive multiple input multiple output (MIMO) systems is proposed, which differs from previous works in that herein one-bit analog-to-digital converters (ADCs) partially replace the conventionally assumed high-resolution ADCs. The information-theoretic tool of generalized mutual information (GMI) is exploited to analyze the achievable data rates of the proposed system architecture and an array of analytical results of engineering interest are obtained. For deterministic single input multiple output (SIMO) channels, a closed-form expression of the GMI is derived, based on which the linear combiner is optimized. Then, the asymptotic behaviors of the GMI in both low and high SNR regimes are explored, and the analytical results suggest a plausible ADC assignment scheme. Finally, the analytical framework is applied to the multi-user access scenario, and the corresponding numerical results demonstrate that the mixed system architecture with a relatively small number of high-resolution ADCs is able to achieve a large fraction of the channel capacity without output quantization.
Learning API Usages from Bytecode: A Statistical Approach
Venue: arXiv cs.SE 2015
Year: 2015

When developing mobile apps, programmers rely heavily on standard API frameworks and libraries. However, learning and using those APIs is often challenging due to the fast-changing nature of API frameworks for mobile systems, the complexity of API usages, the insufficiency of documentation, and the unavailability of source code examples. In this paper, we propose a novel approach to learn API usages from bytecode of Android mobile apps. Our core contributions include: i) ARUS, a graph-based representation of API usage scenarios; ii) HAPI, a statistical, generative model of API usages; and iii) three algorithms to extract ARUS from apps' bytecode, to train HAPI based on method call sequences extracted from ARUS, and to recommend method calls in code completion engines using the trained HAPI. Our empirical evaluation suggests that our approach can learn useful API usage models which can provide recommendations with higher levels of accuracy than the baseline n-gram model.
A note on the security of the hHB protocol
Venue: arXiv cs.CR 2015
Year: 2015

We propose a polynomial-time attack on the hHB protocol, showing that the protocol does not attain the claimed security. Our attack is based on the GRS attack.
Almost Optimal Cover-Free Families
Venue: arXiv cs.DM 2015
Year: 2015

Roughly speaking, an $(n,(r,s))$-Cover Free Family (CFF) is a small set of $n$-bit strings such that: "in any $d:=r+s$ indices we see all patterns of weight $r$". CFFs have been of interest for a long time both in discrete mathematics as part of block design theory, and in theoretical computer science where they have found a variety of applications, for example, in parametrized algorithms where they were introduced in the recent breakthrough work of Fomin, Lokshtanov and Saurabh under the name `lopsided universal sets'. In this paper we give the first explicit construction of cover-free families of optimal size up to lower order multiplicative terms, {for any $r$ and $s$}. In fact, our construction time is almost linear in the size of the family. Before our work, such a result existed only for $r=d^{o(1)}$. and $r= ω(d/(\log\log d\log\log\log d))$. As a sample application, we improve the running times of parameterized algorithms from the recent work of Gabizon, Lokshtanov and Pilipczuk.
Almost universal codes achieving ergodic MIMO capacity within a constant gap
Venue: arXiv cs.IT 2015
Year: 2015

This work addresses the question of achieving capacity with lattice codes in multi-antenna block fading channels when the number of fading blocks tends to infinity. A design criterion based on the normalized minimum determinant is proposed for division algebra multiblock space-time codes over fading channels; this plays a similar role to the Hermite invariant for Gaussian channels. It is shown that this criterion is sufficient to guarantee transmission rates within a constant gap from capacity both for slow fading channels and ergodic fading channels. This performance is achieved both under maximum likelihood decoding and naive lattice decoding. In the case of independent identically distributed Rayleigh fading, it is also shown that the error probability vanishes exponentially fast. In contrast to the standard approach in the literature which employs random lattice ensembles, the existence results in this paper are derived from number theory. First the gap to capacity is shown to depend on the discriminant of the chosen division algebra; then class field theory is applied to build families of algebras with small discriminants. The key element in the construction is the choice of a sequence of division algebras whose centers are number fields with small root discriminants.
A Combinatorial Approximation Algorithm for Graph Balancing with Light Hyper Edges
Venue: arXiv cs.DS 2015
Year: 2015

Makespan minimization in restricted assignment $(R|p_{ij}\in \{p_j, \infty\}|C_{\max})$ is a classical problem in the field of machine scheduling. In a landmark paper in 1990 [8], Lenstra, Shmoys, and Tardos gave a 2-approximation algorithm and proved that the problem cannot be approximated within 1.5 unless P=NP. The upper and lower bounds of the problem have been essentially unimproved in the intervening 25 years, despite several remarkable successful attempts in some special cases of the problem [2,4,12] recently. In this paper, we consider a special case called graph-balancing with light hyper edges, where heavy jobs can be assigned to at most two machines while light jobs can be assigned to any number of machines. For this case, we present algorithms with approximation ratios strictly better than 2. Specifically, Two job sizes: Suppose that light jobs have weight $w$ and heavy jobs have weight $W$, and $w < W$. We give a $1.5$-approximation algorithm (note that the current 1.5 lower bound is established in an even more restrictive setting [1,3]). Indeed, depending on the specific values of $w$ and $W$, sometimes our algorithm guarantees sub-1.5 approximation ratios. Arbitrary job sizes: Suppose that $W$ is the largest given weight, heavy jobs have weights in the range of $(βW, W]$, where $4/7\leq β< 1$, and light jobs have weights in the range of $(0,βW]$. We present a $(5/3+β/3)$-approximation algorithm. Our algorithms are purely combinatorial, without the need of solving a linear program as required in most other known approaches.
Achieving Fair Network Equilibria with Delay-based Congestion Control Algorithms
Venue: arXiv cs.NI 2015
Year: 2015

Delay-based congestion control algorithms provide higher throughput and stability than traditional loss-based AIMD algorithms, but they are inherently unfair against older connections when the queuing and the propagation delay cannot be measured accurately and independently. This paper presents a novel measurement algorithm whereby fairness between old and new connections is preserved. The algorithm does not modify the dynamics of congestion control, and runs entirely in the server host using locally available information.
Training recurrent networks online without backtracking
Venue: arXiv cs.NE 2015
Year: 2015

We introduce the "NoBackTrack" algorithm to train the parameters of dynamical systems such as recurrent neural networks. This algorithm works in an online, memoryless setting, thus requiring no backpropagation through time, and is scalable, avoiding the large computational and memory cost of maintaining the full gradient of the current state with respect to the parameters. The algorithm essentially maintains, at each time, a single search direction in parameter space. The evolution of this search direction is partly stochastic and is constructed in such a way to provide, at every time, an unbiased random estimate of the gradient of the loss function with respect to the parameters. Because the gradient estimate is unbiased, on average over time the parameter is updated as it should. The resulting gradient estimate can then be fed to a lightweight Kalman-like filter to yield an improved algorithm. For recurrent neural networks, the resulting algorithms scale linearly with the number of parameters. Small-scale experiments confirm the suitability of the approach, showing that the stochastic approximation of the gradient introduced in the algorithm is not detrimental to learning. In particular, the Kalman-like version of NoBackTrack is superior to backpropagation through time (BPTT) when the time span of dependencies in the data is longer than the truncation span for BPTT.
A Hierarchical Rate Splitting Strategy for FDD Massive MIMO under Imperfect CSIT
Venue: arXiv cs.IT 2015
Year: 2015

In a multiuser MIMO broadcast channel, the rate performance is affected by the multiuser interference when the Channel State Information at the Transmitter (CSIT) is imperfect. To tackle the interference problem, a Rate-Splitting (RS) approach has been proposed recently, which splits one user's message into a common and a private part, and superimposes the common message on top of the private messages. The common message is drawn from a public codebook and should be decoded by all users. In this paper, we propose a novel and general framework, denoted as Hierarchical Rate Splitting (HRS), that is particularly suited to FDD massive MIMO systems. HRS simultaneously transmits private messages intended to each user and two kinds of common messages that can be decoded by all users and by a subset of users, respectively. We analyse the asymptotic sum rate of HRS under imperfect CSIT. A closed-form power allocation is derived which provides insights into the effects of system parameters. Finally, simulation results validate the significant sum rate gain of HRS over various baselines.
The role of concurrency in an evolutionary view of programming abstractions
Venue: arXiv cs.PL 2015
Year: 2015

In this paper we examine how concurrency has been embodied in mainstream programming languages. In particular, we rely on the evolutionary talking borrowed from biology to discuss major historical landmarks and crucial concepts that shaped the development of programming languages. We examine the general development process, occasionally deepening into some language, trying to uncover evolutionary lineages related to specific programming traits. We mainly focus on concurrency, discussing the different abstraction levels involved in present-day concurrent programming and emphasizing the fact that they correspond to different levels of explanation. We then comment on the role of theoretical research on the quest for suitable programming abstractions, recalling the importance of changing the working framework and the way of looking every so often. This paper is not meant to be a survey of modern mainstream programming languages: it would be very incomplete in that sense. It aims instead at pointing out a number of remarks and connect them under an evolutionary perspective, in order to grasp a unifying, but not simplistic, view of the programming languages development process.