nils-herrmann/clean_scirepeval huggingface dataset:
Automated Checking of Conformance to Requirements Templates Using Natural Language Processing

Templates are effective tools for increasing the precision of natural language requirements and for avoiding ambiguities that may arise from the use of unrestricted natural language. When templates are applied, it is important to verify that the requirements are indeed written according to the templates. If done manually, checking conformance to templates is laborious, presenting a particular challenge when the task has to be repeated multiple times in response to changes in the requirements. In this article, using techniques from natural language processing (NLP), we develop an automated approach for checking conformance to templates. Specifically, we present a generalizable method for casting templates into NLP pattern matchers and reflect on our practical experience implementing automated checkers for two well-known templates in the requirements engineering community. We report on the application of our approach to four case studies. Our results indicate that: (1) our approach provides a robust and accurate basis for checking conformance to templates; and (2) the effectiveness of our approach is not compromised even when the requirements glossary terms are unknown. This makes our work particularly relevant to practice, as many industrial requirements documents have incomplete glossaries.
Cover Tree Bayesian Reinforcement Learning

This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration policies in unknown environments. The flexibility and computational simplicity of the model render it suitable for many reinforcement learning problems in continuous state spaces. We demonstrate this in an experimental comparison with a Gaussian process model, a linear model and simple least squares policy iteration.
DASH fast start using HTTP/2

Dynamic Adaptive Streaming over HTTP (DASH) is broadly deployed on the Internet for live and on-demand video streaming services. Recently, a new version of HTTP was proposed, named HTTP/2. One of the objectives of HTTP/2 is to improve the end-user perceived latency compared to HTTP/1.1. HTTP/2 introduces the possibility for the server to push resources to the client. This paper focuses on using the HTTP/2 protocol and the server push feature to reduce the start-up delay in a DASH streaming session. In addition, the paper proposes a new approach for video adaptation, which consists in estimating the bandwidth, using WebSocket (WS) over HTTP/2, and in making partial adaptation on the server side. Obtained results show that, using the server push feature and WebSocket layered over HTTP/2 allow faster loading time and faster convergence to the nominal state. Proposed solution is studied in the context of a direct client-server HTTP/2 connection. Intermediate caches are not considered in this study.
Games, Gameplay, and BCI: The State of the Art

Brain-computer interfaces (BCIs) and basic computer games have been interconnected since BCI development began, exploiting gameplay elements as a means of enhancing performance in BCI training protocols and entertaining and challenging participants while training to use a BCI. By providing the BCI user with an entertaining environment, researchers hope to assist users in becoming more proficient at controlling a BCI system. BCIs have been used to enrich the experience of abled-bodied and physically impaired users in various computer applications, in particular, computer games. BCI games have been reviewed previously, yet a critical evaluation of “gameplay” within BCI games has not been undertaken. Gameplay is a key aspect of any computer game and encompasses the challenges presented to the player, the actions made available to the player by the game designer to overcome the challenges and the interaction mechanism in the game. Here, the appropriateness of game genres (a category of games characterized by a particular set of gameplay challenges) and the associated gameplay challenges for different BCI paradigms is evaluated. The gameplay mechanics employed across a range of BCI games are reviewed and evaluated in terms of the BCI control strategy's suitability, considering the genre and gameplay mechanics employed. A number of recommendations for the field relating to genre-specific BCI-games development and assessing user performance are also provided for BCI game developers.
Masked Autoregressive Flow for Density Estimation

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow. This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.
The State-of-the-Art in Predictive Visual Analytics

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. In this state-of-the-art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.
Anorexia on Tumblr: A Characterization Study

Eating disorders, such as anorexia nervosa are a major health concern affecting many young individuals. Given the extensive adoption of social media technologies in the anorexia affected demographic, we study behavioral characteristics of this population focusing on the social media Tumblr. Aligned with observations in prior literature, we find the presence of two prominent anorexia related communities on Tumblr -- pro-anorexia and pro-recovery. Empirical analyses on several thousand Tumblr posts show use of the site as a media-rich platform replete with triggering content for enacting anorexia as a lifestyle choice. Through use of common pro-anorexia tags, the pro-recovery community however attempts to "permeate" into the pro-anorexia community to educate them of the health risks of anorexia. Further, the communities exhibit distinctive affective, social, cognitive, and linguistic style markers. Compared with recover- ing anorexics, pro-anorexics express greater negative affect, higher cognitive impairment, and greater feelings of social isolation and self-harm. We also observe that these characteristics may be used in a predictive setting to detect anorexia content with 80% accuracy. Based on our findings, clinical implications of detecting anorexia related content on social media are discussed.
A Compact Quasi-Isotropic Shorted Patch Antenna

A compact shorted patch antenna with quasi-isotropic radiation pattern is proposed in this paper. The antenna consists of a radiating patch, a small ground plane that has the same dimensions with the top patch, and a metallic sidewall which connects the former two. A coaxial probe is used to feed the antenna and excite its fundamental TEM mode, whose magnetic field generates surface electric current on the shorted sidewall and electric field generates surface magnetic current on the open-ended aperture. Due to the inherent properties of the electric and magnetic fields, the corresponding currents are found not only perpendicular but also quadrature with each other, and, therefore, the patch antenna can provide a quasi-isotropic radiation pattern without involving complex feeding circuit. To verify the theory, a prototype operating at 2.4-GHz WLAN band was designed, fabricated, and measured. Reasonable agreement between the calculated, simulated, and measured results is obtained. It has been shown that the difference between the maximum and minimum radiation power densities is ~2 dB over the entire spherical radiating surface, and the difference can be further reduced to ~0.9 dB by using a lower profile.
Protothreads: simplifying event-driven programming of memory-constrained embedded systems

Event-driven programming is a popular model for writing programs for tiny embedded systems and sensor network nodes. While event-driven programming can keep the memory overhead down, it enforces a state machine programming style which makes many programs difficult to write, maintain, and debug. We present a novel programming abstraction called protothreads that makes it possible to write event-driven programs in a thread-like style, with a memory overhead of only two bytes per protothread. We show that protothreads significantly reduce the complexity of a number of widely used programs previously written with event-driven state machines. For the examined programs the majority of the state machines could be entirely removed. In the other cases the number of states and transitions was drastically decreased. With protothreads the number of lines of code was reduced by one third. The execution time overhead of protothreads is on the order of a few processor cycles.
A compiler-level intermediate representation based binary analysis and rewriting system

This paper presents component techniques essential for converting executables to a high-level intermediate representation (IR) of an existing compiler. The compiler IR is then employed for three distinct applications: binary rewriting using the compiler's binary back-end, vulnerability detection using source-level symbolic execution, and source-code recovery using the compiler's C backend. Our techniques enable complex high-level transformations not possible in existing binary systems, address a major challenge of input-derived memory addresses in symbolic execution and are the first to enable recovery of a fully functional source-code.
 We present techniques to segment the flat address space in an executable containing undifferentiated blocks of memory. We demonstrate the inadequacy of existing variable identification methods for their promotion to symbols and present our methods for symbol promotion. We also present methods to convert the physically addressed stack in an executable (with a stack pointer) to an abstract stack (without a stack pointer). Our methods do not use symbolic, relocation, or debug information since these are usually absent in deployed executables.
 We have integrated our techniques with a prototype x86 binary framework called SecondWrite that uses LLVM as IR. The robustness of the framework is demonstrated by handling executables totaling more than a million lines of source-code, produced by two different compilers (gcc and Microsoft Visual Studio compiler), three languages (C, C++, and Fortran), two operating systems (Windows and Linux) and a real world program (Apache server).
An activity guideline for technology roadmapping implementation

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. To strive for sustainability under today's intense business competition, organisations apply technology roadmapping (TRM) as a strategic planning tool to align their technology strategies with business strategies. Many organisations desire to integrate TRM into an ongoing strategic planning process. The consequences of TRM implementation can lead to some changes in the business process, organisational structure, or even working culture. Applying a change management approach will help organisations to understand the basic elements that an individual needs so that some challenges can be addressed in advance before adopting the TRM process. This paper proposes a practical guideline to implement technology roadmapping along with a case example.
RF self-interference cancellation for full-duplex

This paper proposes two RF self-interference cancellation techniques. Their small form-factor enables full-duplex communication links for small-to-medium size portable devices and hence promotes the adoption of full-duplex in mass-market applications and next-generation standards, e.g. IEEE802.11 and 5G. Measured prototype implementations of an electrical balance duplexer and a dual-polarized antenna both achieve >50 dB self-interference suppression at RF, operating in the ISM band at 2.45GHz.
A More Powerful Two-Sample Test in High Dimensions using Random Projection

We consider the hypothesis testing problem of detecting a shift between the means of two multivariate normal distributions in the high-dimensional setting, allowing for the data dimension p to exceed the sample size n. Our contribution is a new test statistic for the two-sample test of means that integrates a random projection with the classical Hotelling T 2 statistic. Working within a high-dimensional framework that allows (p, n) → ∞, we first derive an asymptotic power function for our test, and then provide sufficient conditions for it to achieve greater power than other state-of-the-art tests. Using ROC curves generated from simulated data, we demonstrate superior performance against competing tests in the parameter regimes anticipated by our theoretical results. Lastly, we illustrate an advantage of our procedure with comparisons on a high-dimensional gene expression dataset involving the discrimination of different types of cancer.
Efficient Mining of Association Rules Using Closed Itemset Lattices

| Discovering association rules is one of the most important task in data mining. Many eecient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an eecient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very eecient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.
Quantization Resolution and Limit Cycling in Digitally Controlled PWM Converters

This paper discusses the presence of steady-state limit cycles in digitally controlled pulse-width modulation (PWM) converters, and suggests conditions on the control law and the quantization resolution for their elimination. It then introduces single-phase and multi-phase controlled digital dither as a means of increasing the effective resolution of digital PWM (DPWM) modules, allowing for the use of low resolution DPWM units in high regulation accuracy applications. Bounds on the number of bits of dither that can be used in a particular converter are derived.
Panoptic Studio: A Massively Multiview System for Social Motion Capture

We present an approach to capture the 3D structure and motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is functional and frequent, (2) subtle motion needs to be measured over a space large enough to host a social group, and (3) human appearance and configuration variation is immense. The Panoptic Studio is a system organized around the thesis that social interactions should be measured through the perceptual integration of a large variety of view points. We present a modularized system designed around this principle, consisting of integrated structural, hardware, and software innovations. The system takes, as input, 480 synchronized video streams of multiple people engaged in social activities, and produces, as output, the labeled time-varying 3D structure of anatomical landmarks on individuals in the space. The algorithmic contributions include a hierarchical approach for generating skeletal trajectory proposals, and an optimization framework for skeletal reconstruction with trajectory re-association.
Are all literature citations equally important? Automatic citation strength estimation and its applications

Literature citation analysis plays a very important role in bibliometrics and scientometrics, such as the Science Citation Index (SCI ) impact factor, h-index. Existing citation analysis methods assume that all citations in a paper are equally important, and they simply count the number of citations. Here we argue that the citations in a paper are not equally important and some citations are more important than the others. We use a strength value to assess the importance of each citation and propose to use the regression method with a few useful features for automatically estimating the strength value of each citation. Evaluation results on a manually labeled data set in the computer science field show that the estimated values can achieve good correlation with human-labeled values. We further apply the estimated citation strength values for evaluating paper influence and author influence, and the preliminary evaluation results demonstrate the usefulness of the citation strength values.
Visualization & assessment of ADS-B security for green ATM

Energy efficiency, noise and emissions footprint reduction are major “green” performance targets of next-generation air transportation systems. Automatic Dependent Surveillance Broadcast (ADS-B) is an advanced “cyber-physical” concept that can help meet such targets by enabling highly accurate air traffic surveillance. However, beneficial, confident application of ADS-B requires a foundational understanding of vulnerabilities and mitigation requirements. This paper focuses on the assessment and mitigation of threats from unauthorized access and unanticipated disruption of ADS-B communications. We present a solution to protect message integrity in ADS-B IN based airborne surveillance, discuss the accuracy and robustness of the solution in the presence of spoofed or compromised nodes and intentional jamming. To study effects of “intelligent jamming” strategies on ADS-B, visualize and analyze system-wide impact, we propose a security simulation tool concept and design.
Ambiguity Modeling in Latent Spaces

We are interested in the situation where we have two or more re presentations of an underlying phenomenon. In particular we ar e interested in the scenario where the representation are complementary. This implies that a single individual representation is not sufficient to fully dis criminate a specific instance of the underlying phenomenon, it also means that each r presentation is an ambiguous representation of the other complementary spa ce . In this paper we present a latent variable model capable of consolidating multiple complementary representations. Our method extends canonical cor relation analysis by introducing additional latent spaces that are specific to th e different representations, thereby explaining the full variance of the observat ions. These additional spaces, explaining representation specific variance, sepa rat ly model the variance in a representation ambiguous to the other. We develop a spec tral algorithm for fast computation of the embeddings and a probabilistic mode l (based on Gaussian processes) for validation and inference. The proposed mode l has several potential application areas, we demonstrate its use for multi-modal r egression on a benchmark human pose estimation data set.
A Fully Integrated 660 MHz Low-Swing Energy-Recycling DC–DC Converter

A fully integrated 0.18 mum DC-DC buck converter using a low-swing ldquostacked driverrdquo configuration is reported in this paper. A high switching frequency of 660 MHz reduces filter components to fit on chip, but this suffers from high switching losses. These losses are reduced using: 1) low-swing drivers; 2) supply stacking; and 3) introducing a charge transfer path to deliver excess charge from the positive metal-oxide semiconductor drive chain to the load, thereby recycling the charge. The working prototype circuit converts 2.2 to 0.75-1.0 V at 40-55 mA. Design and simulation of an improved circuit is also included that further improves the efficiency by enhancing the charge recycling path, providing automated zero voltage switching (ZVS) operation, and synchronizing the half-swing gating signals.
Accurate and efficient design approach of substrate integrated waveguide filter using numerical TRL calibration technique

In this paper, a numerical TRL calibration technique is used to accurately extract the equivalent circuit parameters of post-wall iris and input/output coupling structure which are used for the design of directly-coupled substrate integrated waveguide (SIW) filter with the first/last SIW cavities directly excited by 50 Ω microstrip line. On the basis of this dimensional design process, the entire procedure of filter design can meet all of the design specifications without resort to any time-consuming tuning and optimization. A K-band 5th-degree SIW filter with relative bandwidth of 6% was designed and fabricated by low-cost PCB process on Rogers RT/duroid 5880. Measured results which agree well with simulated results validate the accurate dimensional synthesis procedure.