Reviewerly/exHarmony huggingface dataset:
Intelligent vehicle knowledge representation and anomaly detection using neural knowledge DNA

The knowledge expression and anomaly detection of the intelligent vehicle are studied. Firstly, an Intelligent Vehicle Knowledge Management Framework (IVKMF) is proposed to describe the whole knowledge of Intelligent vehicle in a unified structure. Then, the detection rules are divided into simple rules based on features or statistical characteristics and complex sequence rules based on neural networks. The former can effectively detect the specific CAN command and flooding, replay attacks, the latter uses neural networks to learn the characteristics of CAN commands, which can effectively detect the complex attacks. The simple detection rules are standardized by SOEKS knowledge expression and the complex detection rules storage in Neural Knowledge DNA framework. With this unified knowledge expression, the detection rules can be shared and inherited easily. A secure gateway for intelligent vehicle is also designed. The gateway is placed between the external network and the vehicle bus network and it prevent all suspicious external data. The simulations of real car data prove the feasibility of the methods.
Effectiveness Evaluation of Uavs to Detect Maritime Targets in Marine Environment

Gu, T. and Chen, Y., 2018. Effectiveness evaluation of UAVs to detect maritime targets in marine environment. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 621–626. Coconut Creek (Florida), ISSN 0749-0208.The traditional effectiveness evaluation model of maritime targets has the disadvantages of low evaluation effectiveness of uplink and downlink, and low air interface rate of target determination. In order to solve this problem effectively, on the basis of retaining the advantages of the original model, the deficiencies are improved and a new model for the effectiveness evaluation of the UAVs' detection of maritime targets in the marine environment is designed. Through the relationship between gray correlation degree and effectiveness evaluation targets, the evaluation factors and process are determined and the evaluation target is confirmed. Through the effectiveness evaluation method of maritime targets, the effectiveness evaluation of the UAVs' detection of maritime targets is accomplished. The environment is simulated for the model and comparison experiments are designed. Experimental results show that the new model can indeed increase the effectiveness of the uplink and downlink evaluation, and augment the air interface rate of target determination.
Type qualifiers as composable language extensions for code analysis and generation

The work presented here reformulates type qualifiers as composable language extensions that can be automatically and reliably composed by the end-user programmer. Type expressions can be annotated with type qualifiers to specify new subtyping relations that are expressive enough to detect many kinds of programming errors. Type qualifiers, as illustrated in our ableC extensible language framework for C, can also introduce rich forms of concrete syntax, can generate dynamic checks on data when static checks are infeasible or not appropriate, and can inject generated code that affects program behavior, for example to log or display program execution or for the run-time conversion of data. The ableC framework and extensions to it are implemented using context-free grammars and attribute grammars. This provides an expressive mechanism for type qualifier implementations to check for additional errors, e.g.dereferences to pointers not qualified by a “nonnull” qualifier, and generate custom and informative error messages. This approach distinguishes programmers that use language extensions from language engineers that develop extensions. The framework provides modular analyses that extension developers use to ensure that their extension will compose with other extensions that all pass these analyses. Thus, when a programmer selects a set of extensions to use they will automatically and reliably compose to form a working translator for the extended language.
QED: Out-of-the-Box Datasets for SPARQL Query Evaluation

In this paper, we present SPARQL QED, a system generating out-of-the-box datasets for SPARQL queries over linked data. QED distinguishes the queries according to the different SPARQL features and creates, for each query, a small but exhaustive dataset comprising linked data and the query answers over this data. These datasets can support the development of applications based on SPARQL query answering in various ways. For instance, they may serve as SPARQL compliance tests or can be used for learning in query-by-example systems. We ensure that the created datasets are diverse and cover various practical use cases and, of course, that the sets of answers included are the correct ones. Example tests generated based on queries and data from DBpedia have shown bugs in Jena and Virtuoso.
Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation

Most decisions in aviation regarding systems and operation are currently taken under uncertainty, relaying in limited measurable information, and with little assistance of formal methods and tools to help decision makers to cope with all those uncertainties. This chapter illustrates how Bayesian analysis can constitute a systematic approach for dealing with uncertainties in aviation and air transport. The chapter addresses the three main ways in which Bayesian networks are currently employed for scientific or regulatory decision-making purposes in the aviation industry, depending on the extent to which decision makers rely totally or partially on formal methods. These three alternatives are illustrated with three aviation case studies that reflect research work carried out by the authors.
Security and Trust Assessment, and Design for Security

This chapter presents pre-silicon and post-silicon security and trust assessment techniques. Pre-silicon assessment techniques analyze vulnerabilities of a hardware design and assess its security at various stages of design process, including register-transfer level (RTL), gate-level netlist, design-for-test (DFT) insertion, and physical design. On the other hand, post-silicon assessment techniques aims to identify security and trust issues in the fabricated integrated circuit (IC). The design for security and trust techniques, which provide additional resources in the hardware to make it inherently resilient to different attacks, for example, Trojan insertion, fault injection, and side-channel attacks, are discussed next.
The SFS summer research study at UMBC: Project-based learning inspires cybersecurity students

May 30–June 2, 2017, Scholarship for Service (SFS) scholars at the University of Maryland, Baltimore County (UMBC) analyzed the security of a targeted aspect of the UMBC computer systems. During this hands-on study, with complete access to source code, students identified vulnerabilities, devised and implemented exploits, and suggested mitigations. As part of a pioneering program at UMBC to extend SFS scholarships to community colleges, the study helped initiate six students from two nearby community colleges, who transferred to UMBC in fall 2017 to complete their 4-year degrees in computer science and information systems. The study examined the security of a set of “NetAdmin” custom scripts that enable UMBC faculty and staff to open the UMBC firewall to allow external access to machines they control for research purposes. Students discovered vulnerabilities stemming from weak architectural design, record overflow, and failure to sanitize inputs properly. For example, they implemented a record-overflow and code-injection exploit that exfiltrated the vital application programming interface (API) key of the UMBC firewall. This report summarizes student activities and findings and reflects on lessons learned for students, educators, and system administrators. Our students found the collaborative experience inspirational; students and educators appreciated the authentic case study; and IT administrators gained access to future employees and received free recommendations for improving the security of their systems. We hope that other universities can benefit from our motivational and educational strategy of teaming educators and system administrators to engage students in active project-based learning centering on focused questions about their university computer systems.
Uncertainty Relations and Sparse Signal Recovery

This chapter provides an introduction to uncertainty relations underlying sparse signal recovery. We start with the seminal work by Donoho and Stark (1989), which defines uncertainty relations as upper bounds on the operator norm of the band-limitation operator followed by the time-limitation operator, generalize this theory to arbitrary pairs of operators, and then develop, out of this generalization, the coherence-based uncertainty relations due to Elad and Bruckstein (2002), plus uncertainty relations in terms of concentration of the 1-norm or 2-norm. The theory is completed with set-theoretic uncertainty relations which lead to best possible recovery thresholds in terms of a general measure of parsimony, the Minkowski dimension. We also elaborate on the remarkable connection between uncertainty relations and the “large sieve,” a family of inequalities developed in analytic number theory. We show how uncertainty relations allow one to establish fundamental limits of practical signal recovery problems such as inpainting, declipping, super-resolution, and denoising of signals corrupted by impulse noise or narrowband interference.
Generalized modeling of body response of acoustic guitar for all frets using a response for single fret

Analysis of music notes with their mathematical behavior has been the topic of interest since ages. This work is focused on the analysis of acoustic guitar notes to calculate and rather separate the guitar body response or impulse response and the excitation signal. The cepstral domain approach is used for estimation of guitar body response. The adaptive cepstral domain windowing (ACDW) is adopted to estimate the guitar body response. The guitar notes for two plucking styles, finger plucking, and plectrum plucking were recorded for all the twenty frets. This resulted in the database of two hundred and forty sound notes including both plucking styles. The complete analysis of the behavior of the guitar body on the coarse level, that is, string level and on fine level, that is, individual fret level, is carried out. This paper discusses the extraction of impulse response of acoustic guitar for all strings and all frets. This present work is extended to put forth a generalized mathematical model for the impulse response of acoustic guitar for all frets using a response for a single fret as a base. It discusses the formulation of a mathematical equation for finding the body response of one fret using the body response of the last fret. The body response so obtained is convolved with the excitation for the required fret using mathematical equation. The result is verified with the subjective listening test by the experts and with the objective measure as the correlation coefficient, the statistical parameter to indicate the degree of matching. It is observed that the synthesized fret note shows the correlation of more than 0.84 with the actual fret note. The listening tests results by the experts also give a score of more than 0.9 for the synthesized notes.
A multi-cluster time aggregation approach for Markov chains

This work focuses on the computation of the steady state distribution of a Markov chain, making use of an embedding algorithm. In this regard, a well-known approach dubbed time aggregation has been proposed in Cao et al., (2002). Roughly, the idea hinges on the partition of the state space into two subsets. The linchpin in this partitioning process is a small subset of states, selected to be the state space of the aggregated process, which will account for the state space of the embedded semi-Markov process. Although this approach has provided an interesting body of theoretical results and advanced in the study of the so-called curse of dimensionality, one is still left with a high-dimensional problem to be solved. In this paper we investigate the possibility to remedy this problem by proposing a time aggregation approach with multiple subsets. This is achieved by devising a decomposition algorithm which makes use of a partition scheme to evaluate the steady state probabilities of the chain. Besides the convergence proof of the algorithm, we prove also a result for the cardinality of the partition, vis-à-vis the computational effort of the algorithm, for the case in which the state space is partitioned in a collection of subsets of the same cardinality.
Exploring effects of fully probing sequence on solving nonogram puzzles

This paper presents our experimental studies on improving the speed of solving nonogram puzzles. Our approach is based on the algorithm developed by Wu et al. A puzzle solving program, named LalaFrogKK, implementing the algorithm has won several nonogram tournaments since 2011. The algorithm consists of three major parts: propagation based on line solving, fully probing, and backtracking. Fully probing, running before backtracking, contributes to solving a nonogram puzzle in two ways. The first is to paint more pixels after propagation. The second is testing each unpainted pixel for providing backtracking with more accurate guidance when choosing pixels to guess. However, we found that different probing sequences can lead to significant differences in the numbers of painted pixels after fully probing. Therefore, in this paper, we investigate into the effects of different fully probing sequences, and propose several new fully probing mechanisms. Experimental results show that the new mechanisms can significantly outperform the original fully probing method. One of the new fully probing mechanisms achieves a speedup of more than two times in solving nonogram puzzles.
Research on Recommendation Algorithm Based on Ranking Learning

After analyzing the logistic regression and support vector machine's limitation, the author has chosen the learning to rank method to solve the problem of news recommendations. The article proposes two news recommendation methods which were based on Bayesian optimization criterion and RankSVM. In addition, the article also proposes two methods to solve the dynamic change of user interest and recommendation novelty and diversity. The experimental results show that the two methods can get ideal results, and the overall performance of the method based on Bayesian optimization criterion is better than that based on RankSVM.
A GPU-Based Automatic Approach for Guide Wire Tracking in Fluoroscopic Sequences

Guide wire tracking in fluoroscopic images has done a significant task in assisting the physicians during radiology-aided interventions. Many groups have tried to detect the guide wire from the fluoroscopic images based on the image properties. The main challenge is that manual intervention is required during the detection. Other groups try to introduce localizers to track guide wires during intervention, which requires additional hardware equipment, and may intervene with the traditional clinical routines. Machine learning methods are also exploited. Although such methods may provide accurate tracking, they often require large amount of data and training time. In this paper, we propose a GPU-based fast and automatic approach to track guide wires in fluoroscopic sequences. We propose a multi-scale filtering and gradient vector field-based real-time tracking method for guide wire tracking from fluoroscopic images. To improve calculation efficiency and meet real-time application requirement, we propose a GPU-based acceleration scheme, and also a Bayesian filter-like motion tracking method to limit the guide wire tracking to a smaller range to improve calculation efficiency. We test our proposed method on two test data sets of fluoroscopic sequences of 102 frames and 72 frames. We achieve an average guide wire detection rate of 96.7%, a false detection rate of 0.0011% and an error distance measure of 0.83 pixels for the first sequence, and 98.8%, 0.000069% and 0.85 pixels, respectively, for the second sequence. With the proposed acceleration method, we finish calculation for the first sequence in nine seconds, thus, efficiency is enhanced by 100 times with the unaccelerated algorithm.
A reversible and secure electronic patient record embedding technique using histogram bin shifting and RC6 encryption

Multimedia communication in today's world has changed the way we work and handle day-to-day business. The exponential growth of multimedia technology has affected all fields. Conventional healthcare has been replaced by electronic healthcare and is now commonly referred to as eHealth. For successful implementation of the electronic healthcare set-up, the security of the electronic patient record (EPR) is a major concern. Though many approaches are being used for ensuring the security of the EPR, data hiding in medical images has flourished as an effective technique in the last decade. For hiding EPR in medical images, reversible data hiding is generally used, due to various advantages associated with it. This chapter presents a reversible EPR hiding technique in general and medical images. The proposed scheme utilizes a lossless information hiding technique that uses the concept of histogram bin shifting. We make use of a block division technique, thus taking advantage of multiple embedding points so as to increase the payload. For providing an extra layer of security to the EPR, we encrypt the information using an RC6 encryption algorithm prior to embedding. We have extensively tested our scheme in terms of payload, reversibility, and imperceptivity. A comparison of our work with existing work shows that proposed system outperforms the techniques under comparison.
Economic Governance in Gabon: Assessment of the &lt;i&gt; Plan Strat&#233;gique Gabon Emergent &lt;/i&gt; on the Business Environment

The present study aims to identify the existing reforms introduced by the recent economic diversification policy of the government of Gabon called Plan Stratégique Gabon Emergent.It assesses the current stage of reforms aiming to enable the business environment.Empirical part of our research was carried out with evidence relating to the impact of regulatory reform on the business environment in Gabon.We used the World Bank Doing Business and Ibrahim Index of African Governance databases to provide indicators of the quality of regulatory governance from 2009 to 2018.Our key findings showed that the investment climate is still below the expectations.The current macroeconomic situation of the country due to the barrel price has limited the capacity of the government to finance its major reforms.Furthermore, the small size of the market weakens the potentiality of the country to attract foreign direct investments in non-extractive sectors.In brief, Gabon's diversification model shows some weaknesses due to non-existent coherent industrial policy empowering small medium-size enterprises through an effective financial framework favorable for entrepreneurship.
Exploiting Class Learnability in Noisy Data

In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged Web data. Noisy labels occur frequently in data sets harvested via these means, sometimes resulting in entire classes of data on which learned classifiers generalize poorly. For real world applications, we argue that it can be beneficial to avoid training on such classes entirely. In this work, we aim to explore the classes in a given data set, and guide supervised training to spend time on a class proportional to its learnability. By focusing the training process, we aim to improve model generalization on classes with a strong signal. To that end, we develop an online algorithm that works in conjunction with classifier and training algorithm, iteratively selecting training data for the classifier based on how well it appears to generalize on each class. Testing our approach on a variety of data sets, we show our algorithm learns to focus on classes for which the model has low generalization error relative to strong baselines, yielding a classifier with good performance on learnable classes.
An Interactive Exploration Tool for High-Dimensional Datasets: A Shock Physics Case Study

Validating simulations with experimental results is a vital component of modern materials science. Existing workflows require substantial human intervention to interpret their results. We present a statistical approach to identify physically meaningful features, and we construct a visualization system that allows users to interactively and intuitively explore their datasets.
Pitching Diablo

This monograph examines the game pitch document written by Condor, Inc. for Diablo, released in 1996 by Blizzard Entertainment, and places the game in the context of developments of Procedural Content Generation, marketing of Loot Boxes, and the impact of the collectible card game Magic: The Gathering on its development.
Two parallel algorithms for finding all minimal maximum subsequences

A minimal maximum subsequence is a minimal subsequence with maximum cumulative sum. We present two parallel algorithms that find all successive minimal maximum subsequences: one on the parallel random-access model in logarithmic time with linear work, and the other with overlapping domain decomposition on cluster systems. We confirm the efficacy and efficiency of the latter algorithm for random sequences via: (1) an application of random-walk theory that derives an approximate probabilistic length upper bound for overlapping subsequences — thus facilitating concurrent/independent computations of all minimal maximum subsequences in hosting processors, and (2) an empirical study with normally-distributed random sequences.
Unbiasedness of estimation-of-distribution algorithms

In the context of black-box optimization, black-box complexity is used for understanding the inherent difficulty of a given optimization problem. Central to our understanding of nature-inspired search heuristics in this context is the notion of unbiasedness. Specialized black-box complexities have been developed in order to better understand the limitations of these heuristics – especially of (population-based) evolutionary algorithms (EAs). In contrast to this, we focus on a model for algorithms explicitly maintaining a probability distribution over the search space: so-called estimation-of-distribution algorithms (EDAs). We consider the recently introduced n-Bernoulli-λ-EDA framework, which subsumes, for example, the commonly known EDAs PBIL, UMDA, λ-MMASIB, and cGA. We show that an n-Bernoulli-λ-EDA is unbiased if and only if its probability distribution satisfies a certain invariance property under isometric automorphisms of [0,1]n. By restricting how an n-Bernoulli-λ-EDA can perform an update, in a way common to many examples, we derive conciser characterizations, which are easy to verify. We demonstrate this by showing that our examples above are all unbiased.