OpenRaiser/Intern-Atlas huggingface dataset:
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Venue: arXiv cs.NE 2018
Year: 2018

Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum. In order to maintain diversity throughout the process of evolution, various means exist in literature. We analyze approaches to diversity that (a) have an explicit and quantifiable influence on fitness at the individual level and (b) require no (or very little) additional domain knowledge such as domain-specific distance functions. We also introduce the concept of genealogical diversity in a broader study. We show that employing these approaches can help evolutionary algorithms for global optimization in many cases.
A Framework for Probabilistic Generic Traffic Scene Prediction
Venue: arXiv cs.LG 2018
Year: 2018

In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approaches aimed to predict multiple vehicles, they either predicted each vehicle independently with no considerations on possible interaction with surrounding entities or generated discretized joint motions which cannot be directly used in decision making and motion planning for autonomous vehicle. In this paper, we present a probabilistic framework that is able to jointly predict continuous motions for multiple interacting road participants under any driving scenarios and is capable of forecasting the duration of each interaction, which can enhance the prediction performance and efficiency. The proposed traffic scene prediction framework contains two hierarchical modules: the upper module and the lower module. The upper module forecasts the intention of the predicted vehicle, while the lower module predicts motions for interacting scene entities. An exemplar real-world scenario is used to implement and examine the proposed framework.
Gated Hierarchical Attention for Image Captioning
Venue: arXiv cs.CV 2018
Year: 2018

Attention modules connecting encoder and decoders have been widely applied in the field of object recognition, image captioning, visual question answering and neural machine translation, and significantly improves the performance. In this paper, we propose a bottom-up gated hierarchical attention (GHA) mechanism for image captioning. Our proposed model employs a CNN as the decoder which is able to learn different concepts at different layers, and apparently, different concepts correspond to different areas of an image. Therefore, we develop the GHA in which low-level concepts are merged into high-level concepts and simultaneously low-level attended features pass to the top to make predictions. Our GHA significantly improves the performance of the model that only applies one level attention, for example, the CIDEr score increases from 0.923 to 0.999, which is comparable to the state-of-the-art models that employ attributes boosting and reinforcement learning (RL). We also conduct extensive experiments to analyze the CNN decoder and our proposed GHA, and we find that deeper decoders cannot obtain better performance, and when the convolutional decoder becomes deeper the model is likely to collapse during training.
Emotionalism within People-Oriented Software Design
Venue: arXiv cs.SE 2018
Year: 2018

In designing most software applications, much effort is placed upon the functional goals, which make a software system useful. However, the failure to consider emotional goals, which make a software system pleasurable to use, can result in disappointment and system rejection even if utilitarian goals are well implemented. Although several studies have emphasized the importance of people's emotional goals in developing software, there is little advice on how to address these goals in the software system development process. This paper proposes a theoretically-sound and practical method by combining the theories and techniques of software engineering, requirements engineering, and decision making. The outcome of this study is the Emotional Goal Systematic Analysis Technique (EG-SAT), which facilitates the process of finding software system capabilities to address emotional goals in software design. EG-SAT is easy to learn and easy to use technique that helps analysts to gain insights into how to address people's emotional goals. To demonstrate the method in use, a two-part evaluation is conducted. First, EG-SAT is used to analyze the emotional goals of potential users of a mobile learning application that provides information about low carbon living for tradespeople and professionals in the building industry in Australia. The results of using EG-SAT in this case study are compared with a professionally-developed baseline. Second, we ran a semi-controlled experiment in which 12 participants were asked to apply EG-SAT and another technique on part of our case study. The outcomes show that EG-SAT helped participants to both analyse emotional goals and gain valuable insights about the functional and non-functional goals for addressing people's emotional goals.
On tit for tat: Franceschini and Maisano versus ANVUR regarding the Italian research assessment exercise VQR 2011-2014
Venue: arXiv cs.DL 2018
Year: 2018

The response by Benedetto, Checchi, Graziosi & Malgarini (2017) (hereafter "BCG&M"), past and current members of the Italian Agency for Evaluation of University and Research Systems (ANVUR), to Franceschini and Maisano's ("F&M") article (2017), inevitably draws us into the debate. BCG&M in fact complain "that almost all criticisms to the evaluation procedures adopted in the two Italian research assessments VQR 2004-2010 and 2011-2014 limit themselves to criticize the procedures without proposing anything new and more apt to the scope". Since it is us who raised most criticisms in the literature, we welcome this opportunity to retrace our vainly "constructive" recommendations, made with the hope of contributing to assessments of the Italian research system more in line with the state of the art in scientometrics. We see it as equally interesting to confront the problem of the failure of knowledge transfer from R&D (scholars) to engineering and production (ANVUR's practitioners) in the Italian VQRs. We will provide a few notes to help the reader understand the context for this failure. We hope that these, together with our more specific comments, will also assist in communicating the reasons for the level of scientometric competence expressed in BCG&M's heated response to F&M's criticism.
The dispersion of the citation distribution of top scientists' publications
Venue: arXiv cs.DL 2018
Year: 2018

This work explores the distribution of citations for the publications of top scientists. A first objective is to find out whether the 80-20 Pareto rule applies, that is if 80% of the citations to a top scientist's work concern 20% of their publications. Observing that the rule does not apply, we also measure the dispersion of the citation distribution by means of the Gini coefficient. Further, we investigate the question of what share of a top scientist' publications go uncited. Finally, we study the relation between the dispersion of the citation distribution and the share of uncited publications. As well as the overall level, the analyses are carried out at the field and discipline level, to assess differences across them.
The ratio of top scientists to the academic staff as an indicator of the competitive strength of universities
Venue: arXiv cs.DL 2018
Year: 2018

The ability to attract and retain talented professors is a distinctive competence of world-class universities and a source of competitive advantage. The ratio of top scientists to academic staff could therefore be an indicator of the competitive strength of the universities. This work identifies the Italian top scientists in over 200 fields, by their research productivity. It then ranks the relative universities by the ratio of top scientists to overall faculty. Finally, it contrasts this list with the ranking list by average productivity of the overall faculty. The analysis is carried out at the field, discipline, and overall university levels. The paper also explores the secondary question of whether the ratio of top scientists to faculty is related to the size of the university.
Accounting for gender research performance differences in ranking universities
Venue: arXiv cs.DL 2018
Year: 2018

The literature on the theme of gender differences in research performance indicates a quite evident gap in favor of men over women. Beyond the understanding of the factors that could be at the basis of this phenomenon, it is worthwhile understanding if it would be appropriate to conduct the evaluation per population in a manner distinguished by gender. In fact if there is some factor that structurally determines a penalization of performance by women researchers compared to men then the comparative evaluation of organizations' performance that does not take gender into account will lead to an advantage for those that employ more men, under parity in the capacities of their staffs. In this work we measure the differences of the performance and the rank of research institutions as observed when gender is taken into account compared to when it is ignored. The study population consists of all Italian universities and the performance measured in the hard sciences for the period 2006-2010.
Mediation Analysis in Online Experiments at Booking.com: Disentangling Direct and Indirect Effects
Venue: arXiv cs.HC 2018
Year: 2018

Online experimentation is at the core of Booking.com's customer-centric product development. While randomised controlled trials are a powerful tool for estimating the overall effects of product changes on business metrics, they often fall short in explaining the mechanism of change. This becomes problematic when decision-making depends on being able to distinguish between the direct effect of a treatment on some outcome variable and its indirect effect via a mediator variable. In this paper, we demonstrate the need for mediation analyses in online experimentation, and use simulated data to show how these methods help identify and estimate direct causal effect. Failing to take into account all confounders can lead to biased estimates, so we include sensitivity analyses to help gauge the robustness of estimates to missing causal factors.
Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving
Venue: arXiv cs.LG 2018
Year: 2018

This paper investigates the vision-based autonomous driving with deep learning and reinforcement learning methods. Different from the end-to-end learning method, our method breaks the vision-based lateral control system down into a perception module and a control module. The perception module which is based on a multi-task learning neural network first takes a driver-view image as its input and predicts the track features. The control module which is based on reinforcement learning then makes a control decision based on these features. In order to improve the data efficiency, we propose visual TORCS (VTORCS), a deep reinforcement learning environment which is based on the open racing car simulator (TORCS). By means of the provided functions, one can train an agent with the input of an image or various physical sensor measurement, or evaluate the perception algorithm on this simulator. The trained reinforcement learning controller outperforms the linear quadratic regulator (LQR) controller and model predictive control (MPC) controller on different tracks. The experiments demonstrate that the perception module shows promising performance and the controller is capable of controlling the vehicle drive well along the track center with visual input.
How do you define and measure research productivity?
Venue: arXiv cs.DL 2018
Year: 2018

Productivity is the quintessential indicator of efficiency in any production system. It seems it has become a norm in bibliometrics to define research productivity as the number of publications per researcher, distinguishing it from impact. In this work we operationalize the economic concept of productivity for the specific context of research activity and show the limits of the commonly accepted definition. We propose then a measurable form of research productivity through the indicator "Fractional Scientific Strength (FSS)", in keeping with the microeconomic theory of production. We present the methodology for measure of FSS at various levels of analysis: individual, field, discipline, department, institution, region and nation. Finally, we compare the ranking lists of Italian universities by the two definitions of research productivity.
Inefficiency in selecting products for submission to national research assessment exercises
Venue: arXiv cs.DL 2018
Year: 2018

One of the critical issues in national research assessment exercises concerns the choice of whether to evaluate the entire scientific portfolio of the institutions or a subset composed of the best products. Under the second option, the capacities of the institutions to select the appropriate researchers and their best products (the UK case) or simply the best products of every researcher (the Italian case) becomes critical, both for purposes of correct assessment of the real quality of research in the institutions evaluated, and for the selective funding that follows. In this work, through case studies of three Italian universities, we analyze the efficiency of the product selection that is intended to maximize the universities' scores in the current national research assessment exercise, the results of which will be the basis for assigning an important share of public financing over the coming years.
Submodular Maximization Under A Matroid Constraint: Asking more from an old friend, the Greedy Algorithm
Venue: arXiv cs.DS 2018
Year: 2018

The classical problem of maximizing a submodular function under a matroid constraint is considered. Defining a new measure for the increments made by the greedy algorithm at each step, called the discriminant, improved approximation ratio guarantees are derived for the greedy algorithm. At each step, discriminant measures the multiplicative gap in the incremental valuation between the item chosen by the greedy algorithm and the largest potential incremental valuation for eligible items not selected by it. The new guarantee subsumes all the previous known results for the greedy algorithm, including the curvature based ones, and the derived guarantees are shown to be tight via constructing specific instances. More refined approximation guarantee is derived for a special case called the submodular welfare maximization/partition problem that is also tight, for both the offline and the online case.
Finding Cryptocurrency Attack Indicators Using Temporal Logic and Darkweb Data
Venue: arXiv cs.CR 2018
Year: 2018

With the recent prevalence of darkweb/deepweb (D2web) sites specializing in the trade of exploit kits and malware, malicious actors have easy-access to a wide-range of tools that can empower their offensive capability. In this study, we apply concepts from causal reasoning, itemset mining, and logic programming on historical cryptocurrency-related cyber incidents with intelligence collected from over 400 D2web hacker forums. Our goal was to find indicators of cyber threats targeting cryptocurrency traders and exchange platforms from hacker activity. Our approach found interesting activities that, when observed together in the D2web, subsequent cryptocurrency-related incidents are at least twice as likely to occur than they would if no activity was observed. We also present an algorithmic extension to a previously-introduced algorithm called APT-Extract that allows to model new semantic structures that are specific to our application.
Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata
Venue: arXiv cs.NI 2018
Year: 2018

Communication-enabled devices routinely carried by individuals have become pervasive, opening unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. Our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. More importantly, it enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. By leveraging substantial real-world mobile network metadata and ground-truth information, we demonstrate that the accuracy of our solution is superior to that granted by state-of-the-art methods in practical heterogeneous urban scenarios.
Symbiotic Radio: A New Communication Paradigm for Passive Internet-of-Things
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, a novel technique, called symbiotic radio (SR), is proposed for passive Internet-of-Things (IoT), in which a backscatter device (BD) is integrated with a primary transmission. The primary transmitter is designed to assist the primary and BD transmissions, and the primary receiver decodes the information from the primary transmitter as well as the BD. We consider a multiple-input single-output (MISO) SR and the symbol period for BD transmission is designed to be either the same as or much longer than that of the primary system, resulting in parasitic or commensal relationship between the primary and BD transmissions. We first derive the achievable rates for the primary system and the BD transmission. Then, we formulate two transmit beamforming optimization problems, i.e., the weighted sum-rate maximization problem and the transmit power minimization problem, and solve these non-convex problems by applying semi-definite relaxation technique. In addition, a novel transmit beamforming structure is proposed to reduce the computational complexity of the solutions. Simulation results show that when the BD transmission rate is properly designed, the proposed SR not only enables the opportunistic transmission for the BD via energy-efficient passive backscattering, but also enhances the achievable rate of the primary system by properly exploiting the additional signal path from the BD.
Formal Verification of Neural Network Controlled Autonomous Systems
Venue: arXiv cs.AI 2018
Year: 2018

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial conditions such that a robot trajectory starting from these initial conditions is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of the safe initial states. The first technical problem in computing the finite state abstraction is to mathematically model the imaging function that maps the robot position to the LiDAR image. To that end, we introduce the notion of imaging-adapted sets as partitions of the workspace in which the imaging function is guaranteed to be affine. We develop a polynomial-time algorithm to partition the workspace into imaging-adapted sets along with computing the corresponding affine imaging functions. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) nonlinearity, the second technical challenge is to analyze the behavior of the neural network. To that end, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible segments of different ReLUs. SMC solvers then use a Boolean satisfiability solver and a convex programming solver and decompose the problem into smaller subproblems. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space feasible ReLU segments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with increasing complexity of the neural network controller.
Efficient LTL Decentralized Monitoring Framework Using Formula Simplification Table
Venue: arXiv cs.LO 2018
Year: 2018

This paper presents a new technique for optimizing formal analysis of propositional logic formulas and Linear Temporal Logic (LTL) formulas, namely the formula simplification table. A formula simplification table is a mathematical table that shows all possible simplifications of the formula under different truth assignments of its variables. The advantages of constructing a simplification table of a formula are two-fold. First, it can be used to compute the logical influence weight of each variable in the formula, which is a metric that shows the importance of the variable in affecting the outcome of the formula. Second, it can be used to identify variables that have the highest logical influences on the outcome of the formula. %The simplification table can be used to optimize %existing solutions for several interesting %LTL verification problems. We demonstrate the effectiveness of formula simplification table in the context of software verification by developing efficient framework to the well-known decentralized LTL monitoring problem.
Towards a more efficient use of process and product traceability data for continuous improvement of industrial performances
Venue: arXiv cs.CY 2018
Year: 2018

Nowadays all industrial sectors are increasingly faced with the explosion in the amount of data. Therefore, it raises the question of the efficient use of this large amount of data. In this research work, we are concerned with process and product traceability data. In some sectors (e.g. pharmaceutical and agro-food), the collection and storage of these data are required. Beyond this constraint (regulatory and / or contractual), we are interested in the use of these data for continuous improvements of industrial performances. Two research axes were identified: product recall and responsiveness towards production hazards. For the first axis, a procedure for product recall exploiting traceability data will be propose. The development of detection and prognosis functions combining process and product data is envisaged for the second axis.
Don't forget, there is more than forgetting: new metrics for Continual Learning
Venue: arXiv cs.AI 2018
Year: 2018

Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills. The lack of consensus in evaluating continual learning algorithms and the almost exclusive focus on forgetting motivate us to propose a more comprehensive set of implementation independent metrics accounting for several factors we believe have practical implications worth considering in the deployment of real AI systems that learn continually: accuracy or performance over time, backward and forward knowledge transfer, memory overhead as well as computational efficiency. Drawing inspiration from the standard Multi-Attribute Value Theory (MAVT) we further propose to fuse these metrics into a single score for ranking purposes and we evaluate our proposal with five continual learning strategies on the iCIFAR-100 continual learning benchmark.