nils-herrmann/clean_scirepeval huggingface dataset:
Making Contextual Decisions with Low Technical Debt

Applications and systems are constantly faced with decisions that require picking from a set of actions based on contextual information. Reinforcement-based learning algorithms such as contextual bandits can be very effective in these settings, but applying them in practice is fraught with technical debt, and no general system exists that supports them completely. We address this and create the first general system for contextual learning, called the Decision Service. Existing systems often suffer from technical debt that arises from issues like incorrect data collection and weak debuggability, issues we systematically address through our ML methodology and system abstractions. The Decision Service enables all aspects of contextual bandit learning using four system abstractions which connect together in a loop: explore (the decision space), log, learn, and deploy. Notably, our new explore and log abstractions ensure the system produces correct, unbiased data, which our learner uses for online learning and to enable real-time safeguards, all in a fully reproducible manner. The Decision Service has a simple user interface and works with a variety of applications: we present two live production deployments for content recommendation that achieved click-through improvements of 25-30%, another with 18% revenue lift in the landing page, and ongoing applications in tech support and machine failure handling. The service makes real-time decisions and learns continuously and scalably, while significantly lowering technical debt.
Planar elliptical element ultra-wideband dipole antennas

This note introduces a new class of planar ultra-wideband (UWB) dipoles that use elliptical elements. These antennas offer good dipole performance over nearly two octaves in frequency. Unlike more traditional broadband dipole elements that must be around a quarter-wavelength to radiate efficiently, planar elliptical UWB dipoles still exhibit a -10 dB return loss for a 0.20L element size, and remain 50% efficient (-3 dB retum loss) for a 0.14h element size. A wide variety of techniques including exponential and Klopfenstein .tapers, and an energy flow analysis all converge to an element axial ratio of about 1.5: 1 being optimal.
Single-Feed Circularly Polarized Aperture-Coupled Stack Antenna With Dual-Mode Square Loop Radiator

The authors propose a compact single-feed stack antenna consisting of a square loop radiator with perturbation, an aperture-coupled structure, and a straight-strip feed line for circular polarization (CP) and unidirectional radiation applications. This perturbation applies both dual-mode and orthogonal-mode effects simultaneously in the square loop resonator to present bandwidth and CP characteristics. The stack antenna presents the desired axial ratio (AR) bands of 2.42 GHz with 3-dB bandwidth (BW) = 62 MHz (2.5%). The circular polarization is demonstrated with AR spectrum and orthogonal modes distributions. The proposed antenna is successfully simulated and measured with frequency responses, radiation patterns, and current distributions.
A Faster R-CNN Based Method for Comic Characters Face Detection

Face detection of comic characters is a necessary step in most applications, such as comic character retrieval, automatic character classification and comic analysis. However, the existing methods were developed for simple cartoon images or small size comic datasets, and detection performance remains to be improved. In this paper, we propose a Faster R-CNN based method for face detection of comic characters. Our contribution is twofold. First, for the binary classification task of face detection, we empirically find that the sigmoid classifier shows a slightly better performance than the softmax classifier. Second, we build two comic datasets, JC2463 and AEC912, consisting of 3375 comic pages in total for characters face detection evaluation. Experimental results have demonstrated that the proposed method not only performs better than existing methods, but also works for comic images with different drawing styles.
Achieving Native GPU Performance for Out-of-Card Large Dense Matrix Multiplication

In this paper, we illustrate the possibility of developing strategies to carry out matrix computations on heterogeneous platforms which achieve native GPU performance on very large data sizes up to the capacity of the CPU memory. More specifically, we present a dense matrix multiplication strategy on a heterogeneous platform, specifically tailored for the case when the input is too large to fit on the device memory, which achieves near peak GPU performance. Our strategy involves the development of CUDA stream based software pipelines that effectively overlap PCIe data transfers with kernel executions. As a result, we are able to achieve over 1 and 2 TFLOPS performance on a single node using 1 and 2 GPUs respectively.
Maximizing Speedup through Self-Tuning of Processor Allocation

We address the problem of maximizing application speedup through runtime, self-selection of an appropriate number of processors on which to run. Automatic, runtime selection of processor allocations is important because many parallel applications exhibit peak speedups at allocations that are data or time dependent. We propose the use of a runtime system that: (a) dynamically measures job efficiencies at different allocations, (b) uses these measurements to calculate speedups, and (c) automatically adjusts a job’s processor allocation to maximize its speedup. Using a set of 10 applications that includes both hand-coded parallel programs and compiler-parallelized sequential programs, we show that our runtime system can reliably determine dynamic allocations that match the best possible static allocation, and that it has the potential to find dynamic allocations that outperform any static allocation.
A semiotic metrics suite for assessing the quality of ontologies

The continued growth of the World Wide Web makes the retrieval of relevant information for a user’s query increasingly difficult. Current search engines provide the user with many web pages, but varying levels of relevancy. In response, the Semantic Web has been proposed to retrieve and use more semantic information from the web. Our prior research has developed a Semantic Retrieval System to automate the processing of a user’s query while taking into account the query’s context. The system uses WordNet and the DARPA Agent Markup Language (DAML) ontologies to act as surrogates for understanding the context of terms in a user’s query. Like other applications that use ontologies, our system relies on using ‘good’ ontologies. This research draws upon semiotic theory to develop a suite of metrics that assess the syntactic, semantic, pragmatic, and social aspects of ontology quality. We operationalize the metrics and implement them in a prototype tool called the “Ontology Auditor.” An initial validation of the Ontology Auditor on the DAML library of domain ontologies indicates that the metrics are feasible and highlight the wide variations in quality among ontologies in the library. Acknowledgments The authors wish to thank Xinlin Tang and Sunyoung Cho for comments on a previous draft. This research was supported by Oakland University and by Georgia State University.
ISO 24617-2: A semantically-based standard for dialogue annotation

This paper summarizes the latest, final version of ISO standard 24617-2 “Semantic annotation framework, Part 2: Dialogue acts”. Compared to the preliminary version ISO DIS 24617-2:2010, described in Bunt et al. (2010), the final version additionally includes concepts for annotating rhetorical relations between dialogue units, defines a full-blown compositional semantics for the Dialogue Act Markup Language DiAML (resulting, as a side-effect, in a different treatment of functional dependence relations among dialogue acts and feedback dependence relations); and specifies an optimally transparent XML-based reference format for the representation of DiAML annotations, based on the systematic application of the notion of ‘ideal concrete syntax’. We describe these differences and briefly discuss the design and implementation of an incremental method for dialogue act recognition, which proves the usability of the ISO standard for automatic dialogue annotation.
Scheduling and Routing Algorithms for AGVs : a Survey

Automated Guided Vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even containers handling applications at seaports. In the past two decades, much research and many papers have been devoted to various aspects of the AGV technology and rapid progress has been witnessed. As one of the enabling technologies, scheduling and routing of AGVs have attracted considerable attention; many algorithms about scheduling and routing of AGVs have been proposed. However, most of the existing results are applicable to systems with small number of AGVs, offering low degree of concurrency. With drastically increased number of AGVs in recent applications (e.g. in the order of a hundred in a container terminal), efficient scheduling and routing algorithms are needed to resolve the increased contention of resources (e.g. path, loading and unloading buffers) among AGVs. Because they often employ regular route topologies, the new applications also demand innovative strategies to increase system performance. This survey paper first gives an account of the emergence of the problems of AGV scheduling and routing. It then differentiates them from several related problems, and surveys and classifies major existing algorithms for the problems. Noting the similarities with known problems in parallel and distributed systems, it suggests to apply analogous ideas in routing and scheduling AGVs. It concludes by pointing out fertile areas for future study.
A reference governor-based controller for a cable robot under input constraints

Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-effector but not push it. From a scientific point of view, this feature makes feedback control of cable-suspended robots lot more challenging than their counterpart parallel actuated robots. In this brief, we look into the control design for a nonredundant cable-suspended robot under positive input constraints. The design is based on feedback linearization controllers augmented with a reference governor (RG). This RG operates in accordance with the receding horizon strategy, by generating admissible reference signals, that do not violate the input constraints. An important issue in implementing such an algorithm for nonlinear systems is to predict the system behavior in a computationally efficient way. We show that feedback linearization controllers with the RG can offer an efficient way to predict the system’s future states, using the error dynamics of inner feedback loop. Finally, the effectiveness of the proposed method is illustrated by numerical simulation and laboratory experiments on a 6-degree-of-freedom cable suspended robot.
Theory and Algorithms for the Localized Setting of Learning Kernels

We analyze the localized setting of learning kernels also known as localized multiple kernel learning. This problem has been addressed in the past using rather heuristic approaches based on approximately optimizing non-convex problem formulations, of which up to now no theoretical learning bounds are known. In this paper, we show generalization error bounds for learning localized kernel classes where the localities are coupled using graph-based regularization. We propose a novel learning localized kernels algorithm based on this hypothesis class that is formulated as a convex optimization problem using a pre-obtained cluster structure of the data. We derive dual representations using Fenchel conjugation theory, based on which we give a simple yet efficient wrapper-based optimization algorithm. We apply the method to problems involving multiple heterogeneous data sources, taken from domains of computational biology and computer vision. The results show that the proposed convex approach to learning localized kernels can achieve higher prediction accuracies than its global and non-convex local counterparts.
GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function

Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time. We use a fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation. Our algorithm is efficient enough to be deployed on a modern webserver and is as accurate as, or more so than, the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark; it is robust to redundant and irrelevant data and requires, on average, less than ten seconds of computation time on tasks from these benchmarks. GeneMANIA is fast enough to predict gene function on-the-fly while achieving state-of-the-art accuracy. A prototype version of a GeneMANIA-based webserver is available at http://morrislab.med.utoronto.ca/prototype .
How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint

Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
TMSUI: A Trust Management Scheme of USB Storage Devices for Industrial Control Systems

The security of sensitive data and the safety of control signal are two core issues in industrial control system (ICS). However, the prevalence of USB storage devices brings a great challenge on protecting ICS in those respects. Unfortunately, there is currently no solution especially for ICS to provide a complete defense against data transmission between untrusted USB storage devices and critical equipment without forbidding normal USB device function. This paper proposes a trust management scheme of USB storage devices for ICS (TMSUI). By fully considering the background of application scenarios, TMSUI is designed based on security chip to achieve authoring a certain USB storage device to only access some exact protected terminals in ICS for a particular period of time. The issues about digital forensics and revocation of authorization are discussed. The prototype system is finally implemented and the evaluation on it indicates that TMSUI effectively meets the security goals with high compatibility and good performance.
Autonomous staircase detection and stair climbing for a tracked mobile robot using fuzzy controller

Theoretical analysis and implementation of autonomous staircase detection and stair climbing algorithms on a novel rescue mobile robot are presented in this paper. The main goals are to find the staircase during navigation and to implement a fast, safe and smooth autonomous stair climbing algorithm. Silver is used here as the experimental platform. This tracked mobile robot is a tele-operative rescue mobile robot with great capabilities in climbing obstacles in destructed areas. Its performance has been demonstrated in rescue robot league of international RoboCup competitions. A fuzzy controller is applied to direct the robot during stair climbing. Controller inputs are generated by processing the range data from two LASER range finders which scan the environment one horizontally and the other vertically. The experimental results of stair detection algorithm and stair climbing controller are demonstrated at the end.
Self-compassion and PTSD symptom severity.

Neff's (2003a, 2003b) notion of self-compassion emphasizes kindness towards one's self, a feeling of connectedness with others, and mindful awareness of distressing experiences. Because exposure to trauma and subsequent posttraumatic stress symptoms (PSS) may be associated with self-criticism and avoidance of internal experiences, the authors examined the relationship between self-compassion and PSS. Out of a sample of 210 university students, 100 endorsed experiencing a Criterion A trauma. Avoidance symptoms significantly correlated with self-compassion, but reexperiencing and hyperarousal did not. Individuals high in self-compassion may engage in less avoidance strategies following trauma exposure, allowing for a natural exposure process.
Learning to detect malicious executables in the wild

In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the most relevant n-grams for prediction, we evaluated a variety of inductive methods, including naive Bayes, decision trees, support vector machines, and boosting. Ultimately, boosted decision trees outperformed other methods with an area under the roc curve of 0.996. Results also suggest that our methodology will scale to larger collections of executables. To the best of our knowledge, ours is the only fielded application for this task developed using techniques from machine learning and data mining.
Chic or Social: Visual Popularity Analysis in Online Fashion Networks

From Flickr to Facebook to Pinterest, pictures are increasingly becoming a core content type in social networks. But, how important is this visual content and how does it influence behavior in the network? In this paper we study the effects of visual, textual, and social factors on popularity in a large real-world network focused on fashion. We make use of state of the art computer vision techniques for clothing representation, as well as network and text information to predict post popularity in both in-network and out-of-network scenarios. Our experiments find significant statistical evidence that social factors dominate the in-network scenario, but that combinations of content and social factors can be helpful for predicting popularity outside of the network. This in depth study of image popularity in social networks suggests that social factors should be carefully considered for research involving social network photos.
Reconfigurable Antennas for Wireless and Space Applications

Reconfigurable antennas, with the ability to radiate more than one pattern at different frequencies and polarizations, are necessary in modern telecommunication systems. The requirements for increased functionality (e.g., direction finding, beam steering, radar, control, and command) within a confined volume place a greater burden on today's transmitting and receiving systems. Reconfigurable antennas are a solution to this problem. This paper discusses the different reconfigurable components that can be used in an antenna to modify its structure and function. These reconfiguration techniques are either based on the integration of radio-frequency microelectromechanical systems (RF-MEMS), PIN diodes, varactors, photoconductive elements, or on the physical alteration of the antenna radiating structure, or on the use of smart materials such as ferrites and liquid crystals. Various activation mechanisms that can be used in each different reconfigurable implementation to achieve optimum performance are presented and discussed. Several examples of reconfigurable antennas for both terrestrial and space applications are highlighted, such as cognitive radio, multiple-input-multiple-output (MIMO) systems, and satellite communication.
A Hybrid AMOLED Driver IC for Real-Time TFT Nonuniformity Compensation

An active matrix organic light emitting diode (AMOLED) display driver IC, enabling real-time thin-film transistor (TFT) nonuniformity compensation, is presented with a hybrid driving method to satisfy fast driving speed, high TFT current accuracy, and a high aperture ratio. The proposed hybrid column-driver IC drives a mobile UHD (3840 × 2160) AMOLED panel, with one horizontal time of 7.7 μs at a scan frequency of 60 Hz, simultaneously senses the TFT current for back-end TFT variation compensation. Due to external compensation, a simple 3T1C pixel circuit is employed in each pixel. Accurate current sensing and high panel noise immunity is guaranteed by a proposed current-sensing circuit. By reusing the hybrid column-driver circuitries, the driver embodies an 8 bit current-mode ADC to measure OLED V -I transfer characteristic for OLED luminance-degradation compensation. Measurement results show that the hybrid driving method reduces the maximum current error between two emulated TFTs with a 60 mV threshold voltage difference under 1 gray-level error of 0.94 gray level (37 nA) in 8 bit gray scales from 12.82 gray level (501 nA). The circuit-reused current-mode ADC achieves 0.56 LSB DNL and 0.75 LSB INL.
Analog-to-Information Conversion via Random Demodulation

Many problems in radar and communication signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to use a high-rate analog-to-digital converter (ADC) to sample these signals, as prescribed by the Shannon/Nyquist sampling theorem. In these situations, however, the information level of the signal is often far lower than the actual bandwidth, which prompts the question of whether more efficient schemes can be developed for measuring such signals. In this paper we propose a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements. Our "analog-to-information converter" (AIC) is inspired by the theory of compressive sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. We generalize the CS theory to continuous-time sparse signals, explain our proposed AIC system in the CS context, and discuss practical issues regarding implementation