nils-herrmann/clean_scirepeval huggingface dataset:
SOFTWARE CONSIDERATIONS IN AIRBORNE SYSTEMS AND EQUIPMENT CERTIFICATION

R euse has been defined variously: Definitions range from " the systematic practice of developing software from a stock of building blocks, so that similarities in requirements and/or architecture between applications can be exploited to achieve substantial benefits in productivity , quality, and business performance "  to " the process of creating software systems from predefined software components " . The first definition is seemingly more complete, but the second definition is less restrictive and more useful. Too often in literature, a purist attitude is taken toward reuse. For example, often the term reuse is applied to only those elements that can be used without change or to only those elements that have been designed and configured for reuse. For this article, therefore , reuse is defined simply as using previously existing software artifacts. Artifacts include all products of a certification development process and include plan-Reuse Factors Functional Alignment Two aspects of functional alignment can affect the reuse strategy adopted. The first aspect, applicability, is a determination of how well the existing require-ments/functionality align with the requirements of the target application. Do the artifacts serve the intended pur-pose? How much must the artifacts be modified to accommodate any new func-tionality? The second aspect also concerns the alignment of new functionality to existing functionality, although in the opposite respect. Does the existing configuration contain more functionality than is needed for the targeted application? What must be done to accommodate this additional functionality? The issues surrounding extra func-tionality are prevalent with design-for-reuse component libraries. These libraries are designed to include all possible future functionality needs and, by their very nature, include additional func-tionality. These existing configurations typically contain more functionality than is needed for the targeted application. How, then, must this additional functionality be accommodated in a certifiable software system? A variety of strategies are available to handle extra functionality. Seemingly, the most simple is to strip the unnecessary functionality from the configuration (from requirements through verification arti-facts). Conceptually, this is the simplest approach, but this may not be the most cost-effective approach. Additional unused functionality may be retained in the code as long as the mechanism by which such code could be inadvertently executed is prevented, isolated , or eliminated  is verified. In other words, although the code is present, its non-availability within a specific application must be demonstrated. Typically, this means that the unused functional interface must be …
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.
Exploration in relational domains for model-based reinforcement learning

A fundamental problem in reinforcement learning is balancing exploration and exploitation. We address this problem in the context of model-based reinforcement learning in large stochastic relational domains by developing relational extensions of the concepts of the E and R-max algorithms. Efficient exploration in exponentially large state spaces needs to exploit the generalization of the learned model: what in a propositional setting would be considered a novel situation and worth exploration may in the relational setting be a wellknown context in which exploitation is promising. To address this we introduce relational count functions which generalize the classical notion of state and action visitation counts. We provide guarantees on the exploration efficiency of our framework using count functions under the assumption that we had a relational KWIK learner and a near-optimal planner. We propose a concrete exploration algorithm which integrates a practically efficient probabilistic rule learner and a relational planner (for which there are no guarantees, however) and employs the contexts of learned relational rules as features to model the novelty of states and actions. Our results in noisy 3D simulated robot manipulation problems and in domains of the international planning competition demonstrate that our approach is more effective than existing propositional and factored exploration techniques.
Software Technology Maturation

We have reviewed the growth and propagation of a variety of software technologies in an attempt to discover natural characteristics of the process as well as principles and techniques useful in transitioning modern software technology into widespread use. What we have looked at is the technology maturation process, the process by which a piece of technology is first conceived, then shaped into something usable, and finally “marketed” to the point that it is found in the repertoire of a majority of professionals.
A major interest is the time required for technology maturation — and our conclusion is that technology maturation generally takes much longer than popularly thought, especially for major technology areas. But our prime interest is in determining what actions, if any, can accelerate the maturation of technology, in particular that part of maturation that has to do with transitioning the technology into widespread use. Our observations concerning maturation facilitators and inhibitors are the major subject of this paper.
BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1, 000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.
Exposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains

A wide range of malicious activities rely on the domain name service (DNS) to manage their large, distributed networks of infected machines. As a consequence, the monitoring and analysis of DNS queries has recently been proposed as one of the most promising techniques to detect and blacklist domains involved in malicious activities (e.g., phishing, spam, botnets command-and-control, etc.). EXPOSURE is a system we designed to detect such domains in real time, by applying 15 unique features grouped in four categories.
 We conducted a controlled experiment with a large, real-world dataset consisting of billions of DNS requests. The extremely positive results obtained in the tests convinced us to implement our techniques and deploy it as a free, online service. In this article, we present the Exposure system and describe the results and lessons learned from 17 months of its operation. Over this amount of time, the service detected over 100K malicious domains. The statistics about the time of usage, number of queries, and target IP addresses of each domain are also published on a daily basis on the service Web page.
VisTrails: visualization meets data management

Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypothesis, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Often, insight comes from comparing the results of multiple visualizations. Unfortunately, today this process is far from interactive and contains many error-prone and time-consuming tasks. As a result, the generation and maintenance of visualizations is a major bottleneck in the scientific process, hindering both the ability to mine scientific data and the actual use of the data. The VisTrails system represents our initial attempt to improve the scientific discovery process and reduce the time to insight. In VisTrails, we address the problem of visualization from a data management perspective: VisTrails manages the data and metadata of a visualization product. In this demonstration, we show the power and flexibility of our system by presenting actual scenarios in which scientific visualization is used and showing how our system improves usability, enables reproducibility, and greatly reduces the time required to create scientific visualizations.
Theory of Credit Card Networks : A Survey of the Literature

Credit cards provide benefits to consumers and merchants not provided by other payment instruments as evidenced by their explosive growth in the number and value of transactions over the last 20 years. Recently, credit card networks have come under scrutiny from regulators and antitrust authorities around the world. The costs and benefits of credit cards to network participants are discussed. Focusing on interrelated bilateral transactions, several theoretical models have been constructed to study the implications of several business practices of credit card networks. The results and implications of these economic models along with future research topics are discussed.
Analysis on credit card fraud detection methods

Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.
Robust Lane Detection Using Multiple Features

Lane marker detection is a crucial challenge in developing self-driving cars. Despite significant research, large gaps remain between research and needs for fully autonomous driving. We highlight the limitations of present work and present a unified approach for robust and real-time lane marker detection. We present a multi-feature lane detection algorithm and give evidence why relying on one type of features can be harmful. We design a lane model using geometric constraints on lane shape and fit the lane model to the visual cues extracted. We improve the robustness of our algorithm by tracking lane markers temporally. We test our algorithm on KITTI dataset and show results that our algorithm can detect lane markers in presence of occlusions, sharp curves, and shadows.
Adding Chinese Captions to Images

This paper extends research on automated image captioning in the dimension of language, studying how to generate Chinese sentence descriptions for unlabeled images. To evaluate image captioning in this novel context, we present Flickr8k-CN, a bilingual extension of the popular Flickr8k set. The new multimedia dataset can be used to quantitatively assess the performance of Chinese captioning and English-Chinese machine translation. The possibility of re-using existing English data and models via machine translation is investigated. Our study reveals to some extent that a computer can master two distinct languages, English and Chinese, at a similar level for describing the visual world. Data is publicly available at http://tinyurl.com/flickr8kcn
Branch-and-bound processing of ranked queries

Despite the importance of ranked queries in numerous applications involving multi-criteria decision making, they are not efficiently supported by traditional database systems. In this paper, we propose a simple yet powerful technique for processing such queries based on multi-dimensional access methods and branch-and-bound search. The advantages of the proposed methodology are: (i) it is space efficient, requiring only a single index on the given relation (storing each tuple at most once), (ii) it achieves significant (i.e., orders of magnitude) performance gains with respect to the current state-of-theart, (iii) it can efficiently handle data updates, and (iv) it is applicable to other important variations of ranked search (including the support for non-monotone preference functions), at no extra space overhead. We confirm the superiority of the proposed methods with a detailed experimental study.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, CURB, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.
Single image tree modeling

In this paper, we introduce a simple sketching method to generate a realistic 3D tree model from a single image. The user draws at least two strokes in the tree image: the first crown stroke around the tree crown to mark up the leaf region, the second branch stroke from the tree root to mark up the main trunk, and possibly few other branch strokes for refinement. The method automatically generates a 3D tree model including branches and leaves. Branches are synthesized by a growth engine from a small library of elementary subtrees that are pre-defined or built on the fly from the recovered visible branches. The visible branches are automatically traced from the drawn branch strokes according to image statistics on the strokes. Leaves are generated from the region bounded by the first crown stroke to complete the tree. We demonstrate our method on a variety of examples.
VizLens: A Robust and Interactive Screen Reader for Interfaces in the Real World

The world is full of physical interfaces that are inaccessible to blind people, from microwaves and information kiosks to thermostats and checkout terminals. Blind people cannot independently use such devices without at least first learning their layout, and usually only after labeling them with sighted assistance. We introduce VizLens - an accessible mobile application and supporting backend that can robustly and interactively help blind people use nearly any interface they encounter. VizLens users capture a photo of an inaccessible interface and send it to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface to make subsequent computer vision easier. The VizLens application helps users recapture the interface in the field of the camera, and uses computer vision to interactively describe the part of the interface beneath their finger (updating 8 times per second). We show that VizLens provides accurate and usable real-time feedback in a study with 10 blind participants, and our crowdsourcing labeling workflow was fast (8 minutes), accurate (99.7%), and cheap ($1.15). We then explore extensions of VizLens that allow it to (i) adapt to state changes in dynamic interfaces, (ii) combine crowd labeling with OCR technology to handle dynamic displays, and (iii) benefit from head-mounted cameras. VizLens robustly solves a long-standing challenge in accessibility by deeply integrating crowdsourcing and computer vision, and foreshadows a future of increasingly powerful interactive applications that would be currently impossible with either alone.
Security Access Protocols in IoT Networks with Heterogenous Non-IP Terminals

To provide advanced services to citizens, Smart City services are enabled by a massive use of Internet of Things (IoT) technologies. The envisaged huge amount of sensors, and terminals with a great variety of typologies and applications, do not allow having a unique way to manage them, requiring for example minimal packaging and presentation overhead for many of them. In general in a network providing Smart City services, both IP and non-IP devices are present. In this paper, we tackle the security issue for non-IP devices able to connect by a short-range with a mediator gateway, forming a capillary access network, which can be seen as a short range extension of conventional access network in order to efficiently capture the IoT traffic. In particular, we propose security algorithms both for uni- and bi-directional terminals, depending on the terminal capabilities. The security algorithms are based on a local key renewal (without any exchange in air), performed just considering the local clock time. Performance are obtained respect to the maximum number of terminals that can be managed by one mediator gateway and the maximum packet delay as a function of the number of terminals in the area.
Accurate, Compact, and Power-Efficient Li-Ion Battery Charger Circuit

A novel, accurate, compact, and power-efficient lithium-ion (Li-Ion) battery charger designed to yield maximum capacity, cycle life, and therefore runtime is presented and experimentally verified. The proposed charger uses a diode to smoothly (i.e., continuously) transition between two high-gain linear feedback loops and control a single power MOS device, automatically charging the battery with constant current and then constant voltage. An adaptive power-efficient charging scheme in the form of a cascaded switching regulator supply ensures the voltage across the charging power-intensive pMOS remains low, thereby reducing its power losses and yielding up to 27% better overall power efficiency. An 83% power-efficient printed circuit board prototype was built and used to charge several Li-Ion batteries to within plusmn0.43% of their optimum full-charge voltage and therefore within a negligibly small fraction of their full capacity
JEPC: The Java Event Processing Connectivity

Today, event processing (EP) is the first choice technology for analyzing massive event streams in a timely manner. EP allows to detect user-defined situations of interest, like in streaming position events for example, in near real-time such that actions can be taken immediately. Unfortunately, each specific EP system has its very own API and query language because there are no standards. The exchange of EP systems as well as their use within a federation is challenging, error-prone, and expensive. To overcome these problems, we introduce the Java Event Processing Connectivity (JEPC) that is a middleware for uniform EP functionality in Java. JEPC provides always the same API and query language for EP completely independent of the EP system beneath. Furthermore, we show in detail how JEPC can integrate database systems besides EP systems and evaluate the performance of EP powered by databases systems.
Spoken language understanding using long short-term memory neural networks

Neural network based approaches have recently produced record-setting performances in natural language understanding tasks such as word labeling. In the word labeling task, a tagger is used to assign a label to each word in an input sequence. Specifically, simple recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have shown to significantly outperform the previous state-of-the-art - conditional random fields (CRFs). This paper investigates using long short-term memory (LSTM) neural networks, which contain input, output and forgetting gates and are more advanced than simple RNN, for the word labeling task. To explicitly model output-label dependence, we propose a regression model on top of the LSTM un-normalized scores. We also propose to apply deep LSTM to the task. We investigated the relative importance of each gate in the LSTM by setting other gates to a constant and only learning particular gates. Experiments on the ATIS dataset validated the effectiveness of the proposed models.
A Study on the Effective Permittivity of Carbon/PI Honeycomb Composites for Radar Absorbing Design

Radar absorbing honeycomb composites with different coating thicknesses are prepared by impregnation of aramid paper frame with solutions containing conductive carbon blacks (non-magnetic) and PI (polyimide). Expressions for the effective permittivity of the composites are studied and validated both in theory and experiment. It is found that a theoretical equivalent panel with given permittivity can be obtained to represent the honeycomb structure in the quasistatic approximation, which provides a feasible way to optimize the design of radar absorbing honeycomb structure by connecting the effective electromagnetic parameters with the unit cell dimensions. The effective permittivity is measured by a network analyzer system in the frequency range of 8-12 GHz and compared with the theoretical result.