nils-herrmann/clean_scirepeval huggingface dataset:
RoboCup Rescue: A Grand Challenge for Multiagent and Intelligent Systems

issues that involves very large numbers of heterogeneous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain at various levels, involving multiagent teamwork coordination, physical agents for search and rescue, information infrastructures, personal digital assistants, a standard simulator and decision-support systems, evaluation benchmarks for rescue strategies, and robotic systems that are all integrated into a comprehensive system in the future. For this effort, which was built on the success of the RoboCup Soccer project, we will provide forums of technical discussions and competitive evaluations for researchers and practitioners. Although the rescue domain is intuitively appealing as a large-scale multiagent and intelligent system domain, analysis has not yet revealed its domain characteristics. The first research evaluation meeting will be held at RoboCup-2001, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), as part of the RoboCup Rescue Simulation League and RoboCup/AAAI Rescue Robot Competition. In this article, we present a detailed analysis of the task domain and elucidate characteristics necessary for multiagent and intelligent systems for this domain. Then, we present an overview of the RoboCup Rescue project.
Clustered Deferred and Forward Shading

This paper presents and investigates Clustered Shading for deferred and forward rendering. In Clustered Shading, view samples with similar properties (e.g. 3D-position and/or normal) are grouped into clusters. This is comparable to tiled shading, where view samples are grouped into tiles based on 2D-position only. We show that Clustered Shading creates a better mapping of light sources to view samples than tiled shading, resulting in a significant reduction of lighting computations during shading. Additionally, Clustered Shading enables using normal information to perform per-cluster back-face culling of lights, again reducing the number of lighting computations. We also show that Clustered Shading not only outperforms tiled shading in many scenes, but also exhibits better worst case behaviour under tricky conditions (e.g. when looking at high-frequency geometry with large discontinuities in depth). Additionally, Clustered Shading enables real-time scenes with two to three orders of magnitudes more lights than previously feasible (up to around one million light sources).
Shape-based hand recognition

The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a preprocessing stage of the algorithm, the silhouettes of hand images are registered to a fixed pose, which involves both rotation and translation of the hand and, separately, of the individual fingers. Two feature sets have been comparatively assessed, Hausdorff distance of the hand contours and independent component features of the hand silhouette images. Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme.
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models

Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retraining the model. By post-hoc learning latent constraints, value functions that identify regions in latent space that generate outputs with desired attributes, we can conditionally sample from these regions with gradient-based optimization or amortized actor functions. Combining attribute constraints with a universal “realism” constraint, which enforces similarity to the data distribution, we generate realistic conditional images from an unconditional variational autoencoder. Further, using gradient-based optimization, we demonstrate identity-preserving transformations that make the minimal adjustment in latent space to modify the attributes of an image. Finally, with discrete sequences of musical notes, we demonstrate zero-shot conditional generation, learning latent constraints in the absence of labeled data or a differentiable reward function. Code with dedicated cloud instance has been made publicly available .
Effect of social commerce factors on user purchase behavior: An empirical investigation from renren.com

As a new type of e-commerce, social commerce is an emerging marketing form in which business is conducted via social networking platforms. It is playing an increasingly important role in influencing consumers’ purchase intentions. Social commerce uses friendships on social networking platforms, such as Facebook and Twitter, as the vehicle for social sharing about products or sellers to induce interest in a product, thereby increasing the purchase intention. In this paper, we develop and validate a conceptual model of how social factors, such as social support, seller uncertainty, and product uncertainty, influence onsumer purchasing intentions ocial support eller uncertainty roduct uncertainty hird-party infomediaries users’ purchasing behaviors in social commerce. This study aims to provide an understanding of the relationship between user behavior and social factors on social networking platforms. Using the largest social networking website in China, renren.com, this study finds that social support, seller uncertainty, and product uncertainty affect user behaviors. The results further show that social factors can significantly enhance users’ purchase intentions in social shopping.
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts

We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selection cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge of the properties of individual object classes, by solving a sequence of Constrained Parametric Min-Cut problems (CPMC) on a regular image grid. In a subsequent step, we learn to rank the corresponding segments by training a continuous model to predict how likely they are to exhibit real-world regularities (expressed as putative overlap with ground truth) based on their mid-level region properties, then diversify the estimated overlap score using maximum marginal relevance measures. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC 2009 and 2010 data sets. In our companion papers , , we show that the algorithm can be used, successfully, in a segmentation-based visual object category recognition pipeline. This architecture ranked first in the VOC2009 and VOC2010 image segmentation and labeling challenges.
Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper.
POLY: Mining Relational Paraphrases from Multilingual Sentences

Language resources that systematically organize paraphrases for binary relations are of great value for various NLP tasks and have recently been advanced in projects like PATTY, WiseNet and DEFIE. This paper presents a new method for building such a resource and the resource itself, called POLY. Starting with a very large collection of multilingual sentences parsed into triples of phrases, our method clusters relational phrases using probabilistic measures. We judiciously leverage fine-grained semantic typing of relational arguments for identifying synonymous phrases. The evaluation of POLY shows significant improvements in precision and recall over the prior works on PATTY and DEFIE. An extrinsic use case demonstrates the benefits of POLY for question answering.
Wideband Planar Array With Integrated Feed and Matching Network for Wide-Angle Scanning

It is traditionally known that wideband apertures lose bandwidth when placed over a ground plane. To overcome this issue, this paper introduces a new non-symmetric tightly coupled dipole element for wideband phased arrays. The proposed array antenna incorporates additional degrees of freedom to control capacitance and cancel the ground plane inductance. Specifically, each arm on the dipole is different than the other (or non-symmetric). The arms are identical near the center feed section but dissimilar towards the ends, forming a ball-and-cup. It is demonstrated that the non-symmetric qualities achieve wideband performance. Concurrently, a design example for planar installation with balun and matching network is presented to cover X-band. The balun avoids extraneous radiation, maintains the array's low-profile height and is printed on top of the ground plane connecting to the array aperture with 180° out of phase vertical twin-wire transmission lines. To demonstrate the concept, a 64-element array with integrated feed and matching network is designed, fabricated and verified experimentally. The array aperture is placed λ/7 (at 8 GHz) above the ground plane and shown to maintain a active VSWR less than 2 from 8-12.5 GHz while scanning up to 70° and 60° in E- and H-plane, respectively. The array's simulated diagonal plane cross-polarization is approximately 10 dB below the co-polarized component during 60° diagonal scan and follows the theoretical limit for an infinite current sheet.
Category Specific Post Popularity Prediction

Social media have become dominant in everyday life during the last few years where users share their thoughts and experiences about their enjoyable events in posts. Most of these posts are related to different categories related to: activities, such as dancing, landscapes, such as beach, people, such as a selfie, and animals such as pets. While some of these posts become popular and get more attention, others are completely ignored. In order to address the desire of users to create popular posts, several researches have studied post popularity prediction. Existing works focus on predicting the popularity without considering the category type of the post. In this paper we propose category specific post popularity prediction using visual and textual content for action, scene, people and animal categories. In this way we aim to answer the question What makes a post belonging to a specific action, scene, people or animal category popular? To answer to this question we perform several experiments on a collection of 65K posts crawled from Instagram.
Wisdom of Artificial Crowds—A Metaheuristic Algorithm for Optimization

Finding optimal solutions to NP-Hard problems requires exponential time with respect to the size of the problem. Consequently, heuristic methods are usually utilized to obtain approximate solutions to problems of such difficulty. In this paper, a novel swarm-based nature-inspired metaheuristic algorithm for optimization is proposed. Inspired by human collective intelligence, Wisdom of Artificial Crowds (WoAC) algorithm relies on a group of simulated intelligent agents to arrive at independent solutions aggregated to produce a solution which in many cases is superior to individual solutions of all participating agents. We illustrate superior performance of WoAC by comparing it against another bio-inspired approach, the Genetic Algorithm, on one of the classical NP-Hard problems, the Travelling Salesperson Problem. On average a 3-10% improvement in quality of solutions is observed with little computational overhead.
Transforming the Balanced Scorecard from Performance Measurement to Strategic Management : Part I

Several years ago we introduced the Balanced Scorecard (Kaplan and Norton 1992). We began with the premise that an exclusive reliance on financial measures in a management system is insufficient. Financial measures are lag indicators that report on the outcomes from past actions. Exclusive reliance on financial indicators could promote behavior that sacrifices long-term value creation for short-term performance (Porter 1992; AICPA 1994). The Balanced Scorecard approach retains measures of financial performance-the lagging outcome indicators-but supplements these with measures on the drivers, the lead indicators, of future financial performance.
The red one!: On learning to refer to things based on discriminative properties

As a first step towards agents learning to communicate about their visual environment, we propose a system that, given visual representations of a referent (CAT) and a context (SOFA), identifies their discriminative attributes, i.e., properties that distinguish them (has_tail). Moreover, although supervision is only provided in terms of discriminativeness of attributes for pairs, the model learns to assign plausible attributes to specific objects (SOFA-has_cushion). Finally, we present a preliminary experiment confirming the referential success of the predicted discriminative attributes.
A Harmonic Suppressed Bandpass Filter and its Application in Diplexer

A multilayer bandpass filter (BPF) with harmonic suppression using meander line inductor and interdigital capacitor (MLI-IDC) resonant structure is presented in this letter. The BPF is fabricated with three unit cells and its measured passband center frequency is 2.56 GHz with a bandwidth of 0.38 GHz and an insertion loss of 1.5 dB. The harmonics are suppressed up to 11 GHz. A diplexer using the proposed BPF is also presented. The proposed diplexer consists of 4.32 mm sized unit cells to couple 2.5 GHz signal into port 2, and 3.65 mm sized unit cells to couple 3.7 GHz signal into port 3. The notch circuit is placed on the output lines of the diplexer to improve isolation. The proposed diplexer has demonstrated insertion loss of 1.35 dB with 0.45 GHz bandwidth in port 2 and 1.73 dB insertion loss with 0.44 GHz bandwidth in port 3. The isolation is better than 18 dB in the first passband with 38 dB maximum isolation at 2.5 GHz. The isolation in the second passband is better than 26 dB with 45 dB maximum isolation at 3.7 GHz.
iSOAMM: An Independent SOA Maturity Model

The implementation of an enterprise-wide Service Oriented Architecture (SOA) is a complex task. In most cases, evolutional approaches are used to handle this complexity. Maturity models are a possibility to plan and control such an evolution as they allow evaluating the current maturity and identifying current shortcomings. In order to support an SOA implementation, maturity models should also support in the selection of the most adequate maturity level and the deduction of a roadmap to this level. Existing SOA maturity models provide only weak assistance with the selection of an adequate maturity level. Most of them are developed by vendors of SOA products and often used to promote their products. In this paper, we introduce our independent SOA Maturity Model (iSOAMM), which is independent of the used technologies and products. In addition to the impacts on IT systems, it reflects the implications on organizational structures and governance. Furthermore, the iSOAMM lists the challenges, benefits and risks associated with each maturity level. This enables enterprises to select the most adequate maturity level for them, which is not necessarily the highest one.
Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
Design of Class E Resonant Rectifiers and Diode Evaluation for VHF Power Conversion

Resonant rectifiers have important applications in very-high-frequency (VHF) power conversion systems, including dc-dc converters, wireless power transfer systems, and energy recovery circuits for radio-frequency systems. In many of these applications, it is desirable for the rectifier to appear as a resistor at its ac input port. However, for a given dc output voltage, the input impedance of a resonant rectifier varies in magnitude and phase as output power changes. This paper presents a design methodology for class E rectifiers that maintain near-resistive input impedance along with the experimental demonstration of this approach. Resonant rectifiers operating at 30 MHz over 10:1 and 2:1 power ranges are used to validate the design methodology and identify its limits. Furthermore, a number of Si Schottky diodes are experimentally evaluated for VHF rectification and categorized based on performance.
Register allocation and spilling via graph coloring (with retrospective)

In a previous paper we reported the successful use of graph coloring techniques for doing global register allocation in an experimental PL/I optimizing compiler. When the compiler cannot color the register conflict graph with a number of colors equal to the number of available machine registers, it must add code to spill and reload registers to and from storage. Previously the compiler produced spill code whose quality sometimes left much to be desired, and the ad hoc techniques used took considerable amounts of compile time. We have now discovered how to extend the graph coloring approach so that it naturally solves the spilling problem. Spill decisions are now made on the basis of the register conflict graph and cost estimates of the value of keeping the result of a computation in a register rather than in storage. This new approach produces better object code and takes much less compile time.
Technical Note: Q-Learning

$$\mathcal{Q}$$ -learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for $$\mathcal{Q}$$ -learning based on that outlined in Watkins (1989). We show that $$\mathcal{Q}$$ -learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many $$\mathcal{Q}$$ values can be changed each iteration, rather than just one.
Deep reinforcement learning-based scheduling for roadside communication networks

The proper design of a vehicular network is the key expeditor for establishing an efficient Intelligent Transportation System, which enables diverse applications associated with traffic safety, traffic efficiency, and the entertainment of commuting passengers. In this paper, we address both safety and Quality-of-Service (QoS) concerns in a green Vehicle-to-Infrastructure communication scenario. Using the recent advances in training deep neural networks, we present a deep reinforcement learning model, namely deep Q-network, that learns an energy-efficient scheduling policy from high-dimensional inputs corresponding to the characteristics and requirements of vehicles residing within a RoadSide Unit's (RSU) communication range. The realized policy serves to extend the lifetime of the battery-powered RSU while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform both random and greedy scheduling benchmarks.
A formal approach to adaptive software: continuous assurance of non-functional requirements

Modern software systems are increasingly requested to be adaptive to changes in the environment in which they are embedded. Moreover, adaptation often needs to be performed automatically, through self-managed reactions enacted by the application at run time. Off-line, human-driven changes should be requested only if self-adaptation cannot be achieved successfully. To support this kind of autonomic behavior, software systems must be empowered by a rich run-time support that can monitor the relevant phenomena of the surrounding environment to detect changes, analyze the data collected to understand the possible consequences of changes, reason about the ability of the application to continue to provide the required service, and finally react if an adaptation is needed. This paper focuses on non-functional requirements, which constitute an essential component of the quality that modern software systems need to exhibit. Although the proposed approach is quite general, it is mainly exemplified in the paper in the context of service-oriented systems, where the quality of service (QoS) is regulated by contractual obligations between the application provider and its clients. We analyze the case where an application, exported as a service, is built as a composition of other services. Non-functional requirements—such as reliability and performance—heavily depend on the environment in which the application is embedded. Thus changes in the environment may ultimately adversely affect QoS satisfaction. We illustrate an approach and support tools that enable a holistic view of the design and run-time management of adaptive software systems. The approach is based on formal (probabilistic) models that are used at design time to reason about dependability of the application in quantitative terms. Models continue to exist at run time to enable continuous verification and detection of changes that require adaptation.