Computer Science Community Research Articles

An Introduction to Temporal Graphs: An Algorithmic Perspective*

A \emph{temporal graph} is, informally speaking, a graph that changes with time. When time is discrete and only the relationships between the participating entities may change and not the entities themselves, a temporal graph may be viewed as a sequence $G_1,G_2\ldots,G_l$ of static graphs over the same (static) set of nodes $V$. Though static graphs have been extensively studied, for their temporal generalization we are still far from having a concrete set of structural and algorithmic principles. Recent research shows that many graph properties and problems become radically different and usually substantially more difficult when an extra time dimension in added to them. Moreover, there is already a rich and rapidly growing set of modern systems and applications that can be naturally modeled and studied via temporal graphs. This, further motivates the need for the development of a temporal extension of graph theory. We survey here recent results on temporal graphs and temporal graph problems that have appeared in the Computer Science community.

The multifaceted impact of Ada Lovelace in the digital age

Ada Lovelace (1815–1852), the Victorian-era mathematician daughter of the Romantic poet Lord Byron, is famous for her work with Charles Babbage on the Analytic Engine and is widely celebrated as the first computer programmer. Her work has been recognized over the years, and even though the bearing of her contribution has often been questioned, she has always been acknowledged as a pioneering figure by the Computer Science community. Recently she has been chosen as a symbol of the achievements of women in Science, Technology, Engineering and Mathematics (STEM).Ada was worldwide celebrated on December 10th 2015, on the occasion of her 200th birthday, with workshops, meetings, and publications. In particular, ACM contributed with a book: an interdisciplinary collection of papers inspired by Ada's life, work, and legacy. The book covers Ada's collaboration with Babbage, her position in the Victorian and steampunk literature, her representation in contemporary art and comics, and her increasing relevance in promoting women in science and technology.I here review the book, focusing in particular on Ada's visionary ideas of software, on her relation with Alan Turing and the inception of Artificial Intelligence.

Approximation Algorithms for Stochastic Combinatorial Optimization Problems

Stochastic optimization has established itself as a major method to handle uncertainty in various optimization problems by modeling the uncertainty by a probability distribution over possible realizations. Traditionally, the main focus in stochastic optimization has been various stochastic mathematical programming (such as linear programming, convex programming). In recent years, there has been a surge of interest in stochastic combinatorial optimization problems from the theoretical computer science community. In this article, we survey some of the recent results on various stochastic versions of classical combinatorial optimization problems. Since most problems in this domain are NP-hard (or #P-hard, or even PSPACE-hard), we focus on the results which provide polynomial time approximation algorithms with provable approximation guarantees. Our discussions are centered around a few representative problems, such as stochastic knapsack, stochastic matching, multi-armed bandit etc. We use these examples to introduce several popular stochastic models, such as the fixed-set model, 2-stage stochastic optimization model, stochastic adaptive probing model etc, as well as some useful techniques for designing approximation algorithms for stochastic combinatorial optimization problems, including the linear programming relaxation approach, boosted sampling, content resolution schemes, Poisson approximation etc. We also provide some open research questions along the way. Our purpose is to provide readers a quick glimpse to the models, problems, and techniques in this area, and hopefully inspire new contributions .

Research collaboration and topic trends in Computer Science based on top active authors

Academic publication metadata can be used to analyze the collaboration, productivity and hot topic trends of a research community. In this paper, we study a specific group of authors, namely the top active authors. They are defined as the top 1% authors with uninterrupted and continuous presence in scientific publications over a time window. We take the top active authors in the Computer Science (CS) community over different time windows in the past 50 years, and use them to analyze collaboration, productivity and topic trends. We show that (a) the top active authors are representative of the overall population; (b) the community is increasingly moving in the direction of Team Research, with increased level and degree of collaboration; and (c) the research topics are increasingly inter-related. By focusing on the top active authors, it helps visualize these trends better. Besides, the observations from top active authors also shed light on design of better evaluation framework and resource management for policy makers in academia.

Computing and Climate

This guest editors' introduction to the special issue on computing and climate can be viewed as a call to action for the broader computer science community to get involved in this very exciting area of societal importance. Although the issue focuses primarily on data-driven opportunities for computer science contributions, the guest editors hope that future research will broaden this scope.

Interior-Point Based Online Stochastic Bin Packing

Bin packing is an algorithmic problem that arises in diverse applications such as remnant inventory systems, shipping logistics, and appointment scheduling. In its simplest variant, a sequence of T items (e.g., orders for raw material, packages for delivery) is revealed one at a time, and each item must be packed on arrival in an available bin (e.g., remnant pieces of raw material in inventory, shipping containers). The sizes of items are i.i.d. samples from an unknown distributions, but the sizes are known when the items arrive. The goal is to minimize the number of non­empty bins (equivalently waste, defined to be the total unused space in non­empty bins). This problem has been extensively studied in the Operations Research and Theoretical Computer Science communities, yet all existing heuristics either rely on learning the distribution or exhibit ​o(T) additive suboptimality compared to the optimal offline algorithm only for certain classes of distributions (those with sublinear optimal expected waste). In this paper, we propose a family of algorithms which are the first truly distribution-­oblivious algorithms for stochastic bin packing, and achieve ​O(√T) additive suboptimality for all item size distributions. Our algorithms are inspired by approximate interior­-point algorithms for convex optimization. In addition to regret guarantees for i.i.d. sequences, we also prove a family of novel regret bounds for general non­i.i.d. input sequences, including guarantees for locally adversarially perturbed i.i.d. sequences. To the best of our knowledge these are the first such results for non-­i.i.d. and non­-random-­permutation input sequences for online stochastic packing.

Automatic Geometry Generation from Point Clouds for BIM

The need for better 3D documentation of the built environment has come to the fore in recent years, led primarily by city modelling at the large scale and Building Information Modelling (BIM) at the smaller scale. Automation is seen as desirable as it removes the time-consuming and therefore costly amount of human intervention in the process of model generation. BIM is the focus of this paper as not only is there a commercial need, as will be shown by the number of commercial solutions, but also wide research interest due to the aspiration of automated 3D models from both Geomatics and Computer Science communities. The aim is to go beyond the current labour-intensive tracing of the point cloud to an automated process that produces geometry that is both open and more verifiable. This work investigates what can be achieved today with automation through both literature review and by proposing a novel point cloud processing process. We present an automated workflow for the generation of BIM data from 3D point clouds. We also present quality indicators for reconstructed geometry elements and a framework in which to assess the quality of the reconstructed geometry against a reference.

Privacy in the Genomic Era.

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward.

Usability and User Experience

Human – Computer Interaction (HCI) should be a basic part of the formative process of all Computer Science (CS) professionals. Usability and User Experience (UX) were (re)defined by many authors and well recognized standards. UX is usually considered as an extension of usability. To move from usability to UX seems to be a tendency lately. The lack of generally agreed formal definitions of HCI/usability/UX may have consequences on their development and recognition among CS communities, especially in regions where HCI is poorly developed, as Latin America. Practical activities are fundamental in complementing the theoretical foundations of HCI/usability/UX. The practice is usually more appealing and persuasive than the theory. The gap between HCI/usability/UX research and practice may be reduced by applied research, problem – oriented, or at least based on real case studies.

Trust Evolution: Modeling and Its Applications

Trust plays a crucial role in helping online users collect reliable information and it has gained increasing attention from the computer science community in recent years. Traditionally, research about online trust assumes static trust relations between users. However, trust, as a social concept, evolves as people interact. Most existing studies about trust evolution are from sociologists in the physical world while little work exists in an online world. Studying online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this work, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we identify the differences of trust evolution study in physical and online worlds and propose a framework, eTrust, to study trust evolution using online data from passive observation in the context of product review sites by exploiting the dynamics of user preferences. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as trust prediction, rating prediction and ranking evolution.

BLIS: A Framework for Rapidly Instantiating BLAS Functionality

The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in ISO C99 so that it can be reused and/or reparameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL).

Visual Computing and Machine Learning Techniques for Digital Forensics

It is impressive how fast science has improved day by day in so many different fields. In special, technology advances are shocking so many people bringing to their reality facts that previously were beyond their imagination. Inspired by methods earlier presented in scientific fiction shows, the computer science community has created a new research area named Digital Forensics, which aims at developing and deploying methods for fighting against digital crimes such as digital image forgery.This work presents some of the main concepts associated with Digital Forensics and, complementarily, presents some recent and powerful techniques relying on Computer Graphics, Image Processing, Computer Vision and Machine Learning concepts for detecting forgeries in photographs. Some topics addressed in this work include: sourceattribution, spoofing detection, pornography detection, multimedia phylogeny, and forgery detection. Finally, this work highlights the challenges and open problems in Digital Image Forensics to provide the readers with the myriad opportunities available for research.

The bottom-up formation and maintenance of a Twitter community

Purpose – The purpose of this paper is to explore the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure applies to online communities. Design/methodology/approach – The research adopted Big Data methodological approaches in tracking user-generated contents over a series of months and mapped online Twitter interactions as a multimodal, longitudinal “social information landscape”. Centrality measures were employed to gauge the importance of particular user nodes within the complete network and time-series analysis were used to track ego centralities in order to see if this particular online communities were maintained by specific egos. Findings – The case study shows that communities with distinct boundaries and memberships can form and exist within Twitter’s limited user content and sequential policies, which unlike other social media services, do not support formal groups, demonstrating the resilience of desperate online users when their ideology overcome social media limitations. Analysis in this paper using social networks approaches also reveals that communities are formed and maintained from the bottom-up. Research limitations/implications – The research data is based on a particular data set which occurred within a specific time and space. However, due to the rapid, polarising group behaviour, growth, disintegration and decline of the online community, the data set presents a “laboratory” case from which many other online community can be compared with. It is highly possible that the case can be generalised to a broader range of communities and from which online community theories can be proved/disproved. Practical implications – The paper showed that particular group of egos with high activities, if removed, could entirely break the cohesiveness of the community. Conversely, strengthening such egos will reinforce the community strength. The questions mooted within the paper and the methodology outlined can potentially be applied in a variety of social science research areas. The contribution to the understanding of a complex social and political arena, as outlined in the paper, is a key example of such an application within an increasingly strategic research area – and this will surely be applied and developed further by the computer science and security community. Originality/value – The majority of researches that cover these domains have not focused on communities that are multimodal and longitudinal. This is mainly due to the challenges associated with the collection and analysis of continuous data sets that have high volume and velocity. Such data sets are therefore unexploited with regards to cyber-community research.

Analytical Computation of the Epidemic Threshold on Temporal Networks

The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the Susceptible-Infectious-Susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is both of fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.

A Quantity–Quality Composite Ranking of Indian Institutions in CS Research

ABSTRACTThis paper presents our proposed framework and experimental results on a quantity–quality composite performance assessment and ranking of Indian institutions in computer science (CS) research. We have tried to rank the 100 most productive Indian institutions in CS research on a composite single value rank derived from both ‘quantity’ and ‘quality’ parameters. Our work follows a standard scientometric methodology and uses data obtained from Scopus for 25 year period (1989–2013). The data are computationally analysed on relevant primary and secondary indicators and a composite quantity–quality ranking of research performance of Indian institutions in CS domain is computed. The proposed framework and the composite ranks obtained are analysed, evaluated and correlated with individual indicators and other recent work. The results obtained present a comprehensive analysis of research competitiveness of Indian institutions in CS research, both relative to each other and with the world top, and present inferences useful for policy makers, funding agencies, prospective students, and the CS community at large.

Optimal Positions Selection for Watermark Inclusion based on a Nature Inspired Algorithm

One of the powerful optimization tools that has been exploited in the computer world are nature inspired algorithms (NIAs), they are also used to solve problems in the computer programming world. For many years new algorithms have been developed regarding computer science and engineering communities such algorithms concentrates on NIAs which has proven their capabilities in many aspect, in some situation rapid solutions are needed to solve some problems these algorithms provides a versatile robust solutions for many of these situations. This paper presents a watermark inclusion based on a recently presented nature inspired algorithm to enhance the digital image watermarking procedure to be used for copyright protection. The nature inspired algorithm in focus is used to perfectly identify optimal positions in the discrete wavelet transform domain (DWT) for watermark inclusion in the gray scale image, The obtained results are shown in the experimental results section clarifying the superiority of using the algorithm in focus for the watermarking technique, In addition, showing how the algorithm optimum positions are obtained with lowest effect to the PSNR value of the produced watermark included images.

Apt

Repeating research in computer science requires more than just code and data: it requires an appropriate environment in which to run experiments. In some cases, this environment appears fairly straightforward: it consists of a particular operating system and set of required libraries. In many cases, however, it is considerably more complex: the execution environment may be an entire network, may involve complex and fragile configuration of the dependencies, or may require large amounts of resources in terms of computation cycles, network bandwidth, or storage. Even the "straightforward" case turns out to be surprisingly intricate: there may be explicit or hidden dependencies on compilers, kernel quirks, details of the ISA, etc. The result is that when one tries to repeat published results, creating an environment sufficiently similar to one in which the experiment was originally run can be troublesome; this problem only gets worse as time passes. What the computer science community needs, then, are environments that have the explicit goal of enabling repeatable research. This paper outlines the problem of repeatable research environments, presents a set of requirements for such environments, and describes one facility that attempts to address them.

Quantitative Bioimaging: Signal Processing in Light Microscopy [From the Guest Editors

The articles in this special section focus on the interaction among researchers from the biological, optical, computer science, and signal processing communities by 1) presenting cutting-edge signal processing research in quantitative bioimaging and 2) bringing the vast scope of ongoing open problems and novel applications to the attention of the signal processing community.

An Exposition of Sanders' Quasi-Polynomial Freiman-Ruzsa Theorem

The polynomial Freiman-Ruzsa conjecture is one of the most important conjec- tures in additive combinatorics. It asserts that one can switch between combinatorial and algebraic notions of approximate subgroups with only a polynomial loss in the underlying pa- rameters. This conjecture has also found several applications in theoretical computer science. Recently, Tom Sanders proved a weaker version of the conjecture, with a quasi-polynomial loss in parameters. The aim of this note is to make his proof accessible to the theoretical computer science community, and in particular to readers who are less familiar with additive combinatorics.

Parallel Networks

Synthetic biology has emerged as an important technology for engineering cells to behave in controllable and predictable ways. The promise of this modern technology is dependent on our understanding of cellular complexity to allow us to engineer cells with novel function. In this regard, the fields of computer science and synthetic biology are critical for accelerating both our understanding of biological systems, and our ability to quantitatively engineer cells. Thus, advances in biology and biotechnology are arising at the intersection of computer science and synthetic biology approaches. This review seeks to introduce the field of synthetic biology to the computer science community, and to ignite a curiosity and interest in fostering a unique synergy for possible collaborations between synthetic biologists and computer scientists.