Computer Science Community Research Articles

Time-optimal symbolic control of a changeover process based on an approximately bisimilar symbolic model

Many process control problems with complex qualitative specifications cannot be addressed via conventional control design methods. Examples of such specifications include logic specifications expressed in the design of start-up, shut-down, changeover, and emergency shutdown operating procedures. In recent years, it has been shown in the control systems and computer science communities that symbolic models provide convenient and powerful mechanisms to synthesize controllers enforcing such qualitative specifications. The use of symbolic models reduces the synthesis of the controllers to a fixed-point computation problem over a finite-state abstract system. In this paper, after explaining the notion of approximate bisimulation for incrementally globally asymptotically stable (δ-GAS) nonlinear control systems, the construction of approximately bisimilar symbolic models for such systems is presented. Then synthesis of time-optimal symbolic controller for this class of systems is performed based on results from the computer science literature. As a benchmark chemical process control problem, an approximately bisimilar symbolic model is constructed for a safe changeover process. Then a symbolic controller is designed and it is refined to a controller to be applied to the original process. Simulation results show the effectiveness of the symbolic controller. Although the construction of the symbolic model may be complex, the synthesis of the controller in a finite-state space is fast and most importantly the error bounds are adjustable as design parameters.

NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING

This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.

Decolonising Technological Futures: A dialogical tryptich between Te Haumoana White, Ruth Irwin, and Tegmark’s Artificial Intelligence

Colonialism has wrought great injustices across many indigenous people at the during the modern period. Maori exercise unique negotiating skills to productively engage with the power dynamics of colonialism and modernity. This dialogue between Te Haumoana White and Ruth Irwin illuminates the ongoing pressures of modernity and colonialism, unique Maori approaches to ownership, and a deep understanding of power and politics. This dialogue traces historic injustice and contemporary innovations that seek to resuscitate the landscape and the community in, through, and around modern legal and technological onto-epistemologies. At the cutting edge of contemporary technology, AI is using algorithms to aggregate and sift political communities online. Te Haumoana White’s insights on land, environment, people and law are reflected on the AI tendency to advocate for the manipulation of popular opinion in the aggregation of AI algorithms. The computer science community who advocate AI seem to have little understanding of political theory, political economics outside the tired and outdated neoliberal paradigm, nor how voice constitutes politics. Thus, Tegmark and others in the AI genre, tend to advocate a ‘benign dictatorship’ model, based on a combination of neoliberalism and algorithms that are vulnerable to exacerbation and manipulation of popularist trends.

Materials Design in Digital Era: Challenges and Opportunities

The role of materials in our daily lives vis-a-vis applications in areas like energy, environment, manufacturing and healthcare can hardly be undermined. Yet, transitioning a new material from its initial discovery to practical use frequently involves significant cost and time. However, of late, materials community is witnessing a fundamental change in the way novel materials are being designed. Recent advancements in computational capabilities coupled with improved quantum mechanical algorithms have paved path for accelerated design and screening of materials. This article briefly describes how we are leveraging modeling and simulation to design/screen materials for various applications such as transdermal drug delivery, high-strength alloys, lithium-ion batteries, corrosion inhibition, mineral processing and recovery of rare-earth elements. Each of the aforementioned examples make use of high-performance computing-based first-principle simulations (ranging from electronic scale to molecular scale) to arrive at a promising material, generating huge amount of ‘data’ in the process. The quantity of data accumulated as a result of these simulations is so large, that the word ‘data’ is no longer a cliche only for the computer science community. Moreover, at this point it seems imperative to substantiate how big data has just scraped research in the materials domain.

Connecting the dots in high-energy physics

Rebuilding particle trajectories from high-energy proton collisions is an essential step in processing the petabytes of data generated by the Large Hadron Collider at CERN. In search of an order of magnitude speed-up, physicists reached out to the computer science community.

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

AbstractIn the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high‐frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long‐term and high‐frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long‐term.

In Memoriam

We write to celebrate the life of a colleague and friend, Ker-I Ko, who passed away peacefully from lung failure on December 13, 2018 in New York, with his wife and all three children by his side. His passing is a great loss to the theoretical computer science community.

Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies

The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the ‘smartification’ of public services and the national infrastructure will be much more significant in comparison to any other sector given government's function and importance to every institution and individual. Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major ‘client’ and also ‘public champion’ for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants.

Situating science in Africa: The dynamics of computing research in Nairobi and Kampala.

Since the turn of the century, both Kampala and Nairobi have experienced a dramatic growth of computer science research, challenging accepted views of science in Africa. We deploy qualitative methods to follow active computer science researchers, graduate students, policy makers, administrators and entrepreneurs, in order to understand how computer science is enacted in these two cities. Our analysis focuses on four interrelated areas of labor, institutions, identities and scale. We illustrate the dynamics and frictions of computer science research across these areas, revealing the interlacing of moral economies of science and the political economy of higher education, the management of precarious professional lives and desire to get research done, and the pluralistic imaginations and multiple scales of computer science. Urban centers in East Africa are increasingly active in supporting granular and connective research communities that are socially transformative in ways that challenge conventional views of Africa as technologically dry. In this way, the computer science communities of Nairobi and Kampala are instructive for thinking about new geographies of science and technology studies.

A study of publication trajectories of the Brazilian Computer Science community.

The average faculty productivity have been described as a rapid rise-short peak-gradual decline pattern. Way et al. (2017) have studied this pattern for faculty careers in Computer Science in North America using a piecewise linear model. In this paper, we use a similar methodology and study trajectories (N = 20655) of the Brazilian Computer Science community. First, we have evaluated how the median publication count of researchers is related to institution prestige and public vs. private administration. Second, we have studied how the annual publication rates have increased and its variation according to prestige ranks of institutions. Third, we have found the average trajectory can indeed be described as the canonical rapid increase and slower decrease in productivity. For individual trajectories of senior researchers we have observed only 4.5% of trajectories are well explained by the conventional narrative of rapid rise and gradual decline model. We also have found polynomial models of degrees 1 to 3 explain almost 63.1% of trajectories. The rest of trajectories are considered unstable and not well explained by neither of approaches.

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

BackgroundThe Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences. However, it may become impracticable in some contexts due to its high computational demands. Consequently, the computer science community has focused on the use of modern parallel architectures such as Graphics Processing Units (GPUs), Xeon Phi accelerators and Field Programmable Gate Arrays (FGPAs) to speed up large-scale workloads.ResultsThis paper presents and evaluates SWIFOLD: a Smith-Waterman parallel Implementation on FPGA with OpenCL for Long DNA sequences. First, we evaluate its performance and resource usage for different kernel configurations. Next, we carry out a performance comparison between our tool and other state-of-the-art implementations considering three different datasets. SWIFOLD offers the best average performance for small and medium test sets, achieving a performance that is independent of input size and sequence similarity. In addition, SWIFOLD provides competitive performance rates in comparison with GPU-based implementations on the latest GPU generation for the large dataset.ConclusionsThe results suggest that SWIFOLD can be a serious contender for accelerating the SW alignment of DNA sequences of unrestricted size in an affordable way reaching on average 125 GCUPS and almost a peak of 270 GCUPS.

A simulation provenance data management system for efficient job execution on an online computational science engineering platform

In the past few years an online simulation service platform (named EDISON) has been applauded by several computational science and engineering communities in several countries. Though armed with multiple computing clusters and high-end storage resources, the platform has suffered from handling a huge amount of CPU-/IO-bound simulations that are most duplicated. Such intense simulations are normally admitted with no duplicate elimination and thus can adversely affect the performance of the platform. To address this performance concern, we propose a novel system, termed SuperMan, to seamlessly record and retrieve the provenances of previously executed simulations, and so prevent users from initiating duplicate and/or similar simulations using the limited computing resources. The system collects the simulation provenances based on a variant of a de-facto standard form, thereby offering interoperability. Based on the stored provenances, the system can provide useful simulation run statistics for users that need assistance. SuperMan also applies a hash-based duplicate elimination technique, resulting in making more efficient simulations on the platform. Finally, we show that the proposed proposed system could remove slightly over half of duplicate simulations on a variety of simulation software while obtaining about overall elapsed time savings of 30% and queuing time savings of 25%.

A Comparative Analysis of Tree-Based Ensemble Methods for Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments

Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the decision-making process associated with lane changing makes such maneuvers prone to driving errors, and hence, increases the possibility of car crashes. Thus, researchers have been investigating ways to model and predict lane changing maneuvers for optimally designed crash avoidance systems. Such systems rely on the accuracy of detecting the onset of lane-change maneuvers, which requires comprehensive vehicle trajectory data. Connected Vehicles (CV) data provide opportunities for accurate modeling of lane changing maneuvers, especially with the variety of advanced tools available nowadays. The review of the literature indicates that most of the implemented modeling tools do not achieve reliable accuracy for such critical safety application of lane-change prediction. Recently, eXtreme Gradient Boosting (XGB) became a well-recognized algorithm among the computer science community in solving classification problems due to its accuracy, scalability, and speed. This study implements the XGB in predicting the onset of lane changing maneuvers using CV trajectory data. The performance of XGB is compared to three other tree-based algorithms namely, decision trees, gradient boosting, and random forests. The Next Generation SIMulation trajectory data are used to represent the high-resolution CV data. The results indicate that XGB is superior to the other algorithms with a high accuracy value of 99.7%. This outstanding accuracy is achieved when considering vehicle trajectory data two seconds prior to a potential lane change maneuver. The findings of this study are promising for detection of lane change maneuvers in CV environments.

On the role of statistics in the era of big data: A computer science perspective

Statistics and computer science are facing remarkably similar discussions on the role of big data. In this article, I advocate that the computer science community has taken advantage of big data since about five decades, thereby building the main commercial companies of today’s computer industry, and specifically I describe the new emphasis on data as the emergence of the so-called Fourth Paradigm. Then, I draw a parallel between the debates on big data occurring within the statistics and computer science community; and finally I advocate for a joint, new and pervasive approach to data science, in which both communities can capitalize on each other’s skills.

GraphDBLP: a system for analysing networks of computer scientists through graph databases

This paper presents GraphDBLP, a system that models the DBLP bibliography as a graph database for performing graph-based queries and social network analyses. GraphDBLP also enriches the DBLP data through semantic keyword similarities computed via word-embedding. In this paper, we discuss how the system was formalized as a multi-graph, and how similarity relations were identified through word2vec. We also provide three meaningful queries for exploring the DBLP community to (i) investigate author profiles by analysing their publication records; (ii) identify the most prolific authors on a given topic, and (iii) perform social network analyses over the whole community. To date, GraphDBLP contains 5+ million nodes and 24+ million relationships, enabling users to explore the DBLP data by referencing more than 3.3 million publications, 1.7 million authors, and more than 5 thousand publication venues. Through the use of word-embedding, more than 7.5 thousand keywords and related similarity values were collected. GraphDBLP was implemented on top of the Neo4j graph database. The whole dataset and the source code are publicly available to foster the improvement of GraphDBLP in the whole computer science community.

SparseX

The Sparse Matrix-Vector Multiplication (SpMV) kernel ranks among the most important and thoroughly studied linear algebra operations, as it lies at the heart of many iterative methods for the solution of sparse linear systems, and often constitutes a severe performance bottleneck. Its optimization, which is intimately associated with the data structures used to store the sparse matrix, has always been of particular interest to the applied mathematics and computer science communities and has attracted further attention since the advent of multicore architectures. In this article, we present SparseX, an open source software package for SpMV targeting multicore platforms, that employs the state-of-the-art Compressed Sparse eXtended (CSX) sparse matrix storage format to deliver high efficiency through a highly usable “BLAS-like” interface that requires limited or no tuning. Performance results indicate that our library achieves superior performance over competitive libraries on large-scale problems.

Space Weather in the Machine Learning Era: A Multidisciplinary Approach

AbstractThe workshop entitled Space Weather: A Multidisciplinary Approach took place at the Lorentz Center, University of Leiden, Netherlands, on 25–29 September 2017. The aim of this workshop was to bring together members of the Space Weather, Mathematics, Statistics, and Computer Science communities to address the use of advanced techniques such as Machine Learning, Information Theory, and Deep Learning, to better understand the Sun‐Earth system and to improve space weather forecasting. Although individual efforts have been made toward this goal, the community consensus is that establishing interdisciplinary collaborations is the most promising strategy for fully utilizing the potential of these advanced techniques in solving Space Weather‐related problems.

Reflections of Practical Implementation of the academic course Analysis and Design of Algorithms taught in the Universities of Pakistan

This Analysis and Design of Algorithm is considered as a compulsory course in the field of Computer Science. It increases the logical and problem solving skills of the students and make their solutions efficient in terms of time and space. These objectives can only be achieved if a student practically implements what he or she has studied throughout the course. But if the contents of this course are merely studied and rarely practiced then the actual goals of the course is not fulfilled.
 This article will explore the extent of practical implementation of the course of analysis and design of algorithm. Problems faced by the computer science community and major barriers in the field are also enumerated. Finally, some recommendations are made to overcome the obstacles in the practical implementation of analysis and design of algorithms.

A multi-granularity collective behavior analysis approach for online social networks

Online social networks play more and more important roles in the modern society in terms of the rapid and large scale information spread. Many efforts have been made to understand these phenomena in the computer science communities and other relative fields, ranging from popular topic detection to information diffusion modeling. In this article, a multi-granularity collective behavior modeling approach has been proposed, which describes the collective behavior at a node level, a neighbor level, a community level, and a social level. How collective behaviors on the largest online social network Weibo are formed is analyzed by this model. The results demonstrate that individuals can rationally make their decisions when they are isolated from others. However, when neighbors exist, their actions will be irrational; the phenomena will be more obvious when social reinforcement is taken into consideration. Members in the same community can easily reach a group consensus while holding different opinions in different communities.

Heat transfer prediction of supercritical water with artificial neural networks

Supercritical fluids have been under intensive investigation due to their broad applications in the domain of energy conversion. They are able to significantly increase the efficiency of thermal cycles. However, abrupt changes of thermophysical properties of the supercritical fluids have been observed near the critical point, causing heat transfer deterioration that is challenging to predict. This largely thwarts the technology development with the supercritical fluids, making accurate prediction of heat transfer the fundamental issue to address. In this paper, we propose to train an artificial neural network (ANN) based on 5280 data points collected from published experimental results, for the heat transfer prediction of supercritical water. Validation (strictly separated from training) shows that the mean error percentage and its standard deviation are both below 0.5%. Furthermore, a series of tests, including operational conditions out of the training and validation data, are performed in comparison with four well-established correlations. The results demonstrate that the performance of the ANN is considerably better than the correlations. Training of the ANN takes less than an hour on a regular computer, and the prediction takes several milliseconds. This is the first time that ANNs are trained for general heat transfer prediction of supercritical water. This paper should open further opportunities in the supercritical fluids research to be pursued jointly by the fluid dynamics community and the computer science community.