nils-herrmann/clean_scirepeval huggingface dataset:
How Do Drivers Behave in a Highly Automated Car ?

This paper outlines the results of a driving simulator study conducted for the European CityMobil project, which was designed to investigate the effect of a highly automated driving scenario on driver behaviour. Drivers’ response to a number of ‘critical’ scenarios was compared in manual driving with that in automated driving. Drivers were in full control of the vehicle and its manoeuvres in the manual driving condition, whilst control of the vehicle was transferred to an ‘automated system’ in the automated driving condition. Automated driving involved the engagement of lateral and longitudinal controllers, which kept the vehicle in the centre of the lane and at a speed of 40 mph, respectively. Drivers were required to regain control of the driving task if the automated system was unable to handle a critical situation. An auditory alarm forewarned drivers of an imminent collision in such critical situations. Drivers’ response to all critical events was found to be much later in the automated driving condition, compared to manual driving. This is thought to be because drivers’ situation awareness was reduced during automated driving, with response only produced after drivers heard the alarm. Alternatively, drivers may have relied too heavily on the system, waiting for the auditory alarm before responding in a critical situation. These results suggest that action must be taken when implementing fully automated driving to ensure that the driver is kept in the loop at all times and is able to respond in time and appropriately during critical situations.
Uncalibrated Near-Light Photometric Stereo

In this work we solve the uncalibrated photometric stereo problem with lights placed near the scene. Although the devised model is more complex than its far-light counterpart, we show that under a global linear ambiguity the reconstruction is possible up to a rotation and scaling, which can be easily fixed. We also propose a solution for reconstructing the normal map, the albedo, the light positions and the light intensities of a scene given only a sequence of near-light images. This is done in an alternating minimization framework which first estimates both the normals and the albedo, and then the light positions and intensities. We validate our method on real world experiments and show that a near-light model leads to a significant improvement in the surface reconstruction compared to the classic distant illumination case.
COMMUNICATION PROBLEMS IN REQUIREMENTS ENGINEERING : A FIELD STUDY By

The requirements engineering phase of software development projects is characterised by the intensity and importance of communication activities. During this phase, the various stakeholders must be able to communicate their requirements to the analysts, and the analysts need to be able to communicate the specifications they generate back to the stakeholders for validation. This paper describes a field investigation into the problems of communication between disparate communities involved in the requirements specification activities. The results of this study are discussed in terms of their relation to three major communication barriers : 1) ineffectiveness of the current communication channels; 2) restrictions on expressiveness imposed by notations; and 3) social and organisational barriers. The results confirm that organisational and social issues have great influence on the effectiveness of communication. They also show that in general, endusers find the notations used by software practitioners to model their requirements difficult to understand and validate.
Query-by-Example Image Retrieval using Visual Dependency Representations

Image retrieval models typically represent images as bags-of-terms, a representation that is wellsuited to matching images based on the presence or absence of terms. For some information needs, such as searching for images of people performing actions, it may be useful to retain data about how parts of an image relate to each other. If the underlying representation of an image can distinguish between images where objects only co-occur from images where people are interacting with objects, then it should be possible to improve retrieval performance. In this paper we model the spatial relationships between image regions using Visual Dependency Representations, a structured image representation that makes it possible to distinguish between object co-occurrence and interaction. In a query-by-example image retrieval experiment on data set of people performing actions, we find an 8.8% relative increase in MAP and an 8.6% relative increase in Precision@10 when images are represented using the Visual Dependency Representation compared to a bag-of-terms baseline.
Elastic energy storage in leaf springs for a lever-arm based Variable Stiffness Actuator

The increasing use of Variable Stiffness Actuators (VSAs) in robotic joints is helping robots to meet the demands of human-robot interaction, requiring high safety and adaptability. The key feature of a VSA is the ability to exploit internal elastic elements to obtain a variable output stiffness. These allow the joints to store mechanical energy supplied through interaction with the environment and make the system more robust, efficient, and safe. This paper discusses the design of leaf springs for a sub-class of VSAs that use variable lever arm ratios as means to change their output stiffness. Given the trade-off between compactness and the maximum energy storage capacity, the internal springs' dimensions and material choice are assessed through a theoretical analysis and practical experiments.
EsdRank: Connecting Query and Documents through External Semi-Structured Data

This paper presents EsdRank, a new technique for improving ranking using external semi-structured data such as controlled vocabularies and knowledge bases. EsdRank treats vocabularies, terms and entities from external data, as objects connecting query and documents. Evidence used to link query to objects, and to rank documents are incorporated as features between query-object and object-document correspondingly. A latent listwise learning to rank algorithm, Latent-ListMLE, models the objects as latent space between query and documents, and learns how to handle all evidence in a unified procedure from document relevance judgments. EsdRank is tested in two scenarios: Using a knowledge base for web search, and using a controlled vocabulary for medical search. Experiments on TREC Web Track and OHSUMED data show significant improvements over state-of-the-art baselines.
A form-based dialogue manager for spoken language applications

A popular approach to dialogue management is based on a finitestate model, where user utterances trigger transitions between the dialogue states, and these states, in turn, determine the system’s response. This paper describes an alternative dialogue planning algorithm based on the notion of filling in an electronic form, or “Eform.” Each slot has associated prompts that guide the user through the dialogue, and a priority that determines the order in which the system tries to acquire information. These slots can be optional or mandatory. However, the user is not restricted to follow the system’s lead, and is free to ignore the prompts and take the initiative in the dialogue. The E-form-based dialogue planner has been used in an application to search a database of used car advertisements. The goal is to assist the user in selecting, from this database, a small list of cars which match their constraints. For a large number of dialogues collected from over 600 naive users, we found over 70% compliance in answering specific system prompts.
Evolving Behaviour Trees for the Commercial Game DEFCON

Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game’s original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.
Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents

The advent of software agents gave rise to much discussion of just what such an agent is, and of how they differ from programs in general. Here we propose a formal definition of an autonomous agent which clearly distinguishes a software agent from just any program. We also offer the beginnings of a natural kinds taxonomy of autonomous agents, and discuss possibilities for further classification. Finally, we discuss subagents and multiagent systems.
Performance analysis of stochastic behavior trees

This paper presents a mathematical framework for performance analysis of Behavior Trees (BTs). BTs are a recent alternative to Finite State Machines (FSMs), for doing modular task switching in robot control architectures. By encoding the switching logic in a tree structure, instead of distributing it in the states of a FSM, modularity and reusability are improved. In this paper, we compute performance measures, such as success/failure probabilities and execution times, for plans encoded and executed by BTs. To do this, we first introduce Stochastic Behavior Trees (SBT), where we assume that the probabilistic performance measures of the basic action controllers are given. We then show how Discrete Time Markov Chains (DTMC) can be used to aggregate these measures from one level of the tree to the next. The recursive structure of the tree then enables us to step by step propagate such estimates from the leaves (basic action controllers) to the root (complete task execution). Finally, we verify our analytical results using massive Monte Carlo simulations, and provide an illustrative example of the results for a complex robotic task.
Security enhancement in image steganography for medical integrity verification system

Nowadays image steganography has major role in confidential medical image communication. When the medical image is transmitted through insecure public network, there is a chance for tampering medical images. Therefore, it is crucial to check the integrity of medical images to prevent any unauthorized modification. To check the integrity we calculate cryptographic hash function of ROI (Region Of Interest) by using SHA algorithm. The hash value (H1) will be embedded in the RONI using discrete wavelet transform. By comparing the hash value at receiver side, we can check the integrity of medical image. If any tampering occurs, the hash function does not match. This paper proposes a new method to improve the security. The modified medical image is embedded in an ordinary looking image by spatial reversible steganography method. It helps to conceal the existence of secret medical data. It ensures that the eavesdroppers will not have any suspicion that medical image is hidden in that image. This combined approach will give enhanced security.
OPUS: An Efficient Admissible Algorithm for Unordered Search

OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm’s search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.
A Lightweight Organic X-Band Active Receiving Phased Array With Integrated SiGe Amplifiers and Phase Shifters

This paper presents for the first time an X-band antenna array with integrated silicon germanium low noise amplifiers (LNA) and 3-bit phase shifters (PS). LNAs and PSs were successfully integrated onto an 8 × 2 lightweight antenna utilizing a multilayer liquid crystal polymer (LCP) feed substrate laminated with a duroid antenna layer. A baseline passive 8×2 antenna is measured along with a SiGe integrated 8×2 receive antenna for comparison of results. The active antenna array weighs only 3.5 ounces and consumes 53 mW of dc power. Successful comparisons of the measured and simulated results verify a working phased array with a return loss better than 10 dB across the frequency band of 9.25 GHz-9.75 GHz. A comparison of radiation patterns for the 8×2 baseline antenna and the 8×2 SiGe integrated antenna show a 25 dB increase in gain (ΔG). The SiGe integrated antenna demonstrated a predictable beam steering capability of ±41°. Combined antenna and receiver performance yielded a merit G/T of -9.1 dB/K and noise figure of 5.6 dB.
The role of conscious reasoning and intuition in moral judgment: testing three principles of harm.

Is moral judgment accomplished by intuition or conscious reasoning? An answer demands a detailed account of the moral principles in question. We investigated three principles that guide moral judgments: (a) Harm caused by action is worse than harm caused by omission, (b) harm intended as the means to a goal is worse than harm foreseen as the side effect of a goal, and (c) harm involving physical contact with the victim is worse than harm involving no physical contact. Asking whether these principles are invoked to explain moral judgments, we found that subjects generally appealed to the first and third principles in their justifications, but not to the second. This finding has significance for methods and theories of moral psychology: The moral principles used in judgment must be directly compared with those articulated in justification, and doing so shows that some moral principles are available to conscious reasoning whereas others are not.
The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark. Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of complete robotic systems — including perception and manipulation — instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.
Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification

It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.
Gossip training for deep learning

We address the issue of speeding up the training of convolutional networks. Here we study a distributed method adapted to stochastic gradient descent (SGD). The parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way to share information between different threads inspired by gossip algorithms and showing good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized. We compared our method to the recent EASGD in  on CIFAR-10 show encouraging results.
The local binary pattern approach to texture analysis - extensions and applications

This thesis presents extensions to the local binary pattern (LBP) texture analysis operator. The operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. It is made invariant against the rotation of the image domain, and supplemented with a rotation invariant measure of local contrast. The LBP is proposed as a unifying texture model that describes the formation of a texture with micro-textons and their statistical placement rules. The basic LBP is extended to facilitate the analysis of textures with multiple scales by combining neighborhoods with different sizes. The possible instability in sparse sampling is addressed with Gaussian low-pass filtering, which seems to be somewhat helpful. Cellular automata are used as texture features, presumably for the first time ever. With a straightforward inversion algorithm, arbitrarily large binary neighborhoods are encoded with an eight-bit cellular automaton rule, resulting in a very compact multi-scale texture descriptor. The performance of the new operator is shown in an experiment involving textures with multiple spatial scales. An opponent-color version of the LBP is introduced and applied to color textures. Good results are obtained in static illumination conditions. An empirical study with different color and texture measures however shows that color and texture should be treated separately. A number of different applications of the LBP operator are presented, emphasizing real-time issues. A very fast software implementation of the operator is introduced, and different ways of speeding up classification are evaluated. The operator is successfully applied to industrial visual inspection applications and to image retrieval.
An 80-Tile 1.28TFLOPS Network-on-Chip in 65nm CMOS

A 275mm2 network-on-chip architecture contains 80 tiles arranged as a 10 times 8 2D array of floating-point cores and packet-switched routers, operating at 4GHz. The 15-F04 design employs mesochronous clocking, fine-grained clock gating, dynamic sleep transistors, and body-bias techniques. The 65nm 100M transistor die is designed to achieve a peak performance of 1.0TFLOPS at 1V while dissipating 98W.
Visibility culling using hierarchical occlusion maps

We present hierarchical occlusion maps (HOM) for visibility culling on complex models with high depth complexity. The culling algorithm uses an object space bounding volume hierarchy and a hierarchy of image space occlusion maps. Occlusion maps represent the aggregate of projections of the occluders onto the image plane. For each frame, the algorithm selects a small set of objects from the modelas occludersand renders them to form an initial occlusion map, from which a hierarchy of occlusion maps is built. The occlusion maps are used to cull away a portion of the model not visible from the current viewpoint. The algorithm is applicable to all models and makes no assumptions about the size, shape, or type of occluders. It supports approximate culling in which small holes in or among occluders can be ignored. The algorithm has been implemented on current graphics systems and has been applied to large models composed of hundreds of thousands of polygons. In practice, it achieves significant speedup in interactive walkthroughs of models with high depth complexity.
Reputation-based incentive protocols in crowdsourcing applications

Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and etc.) emerged in recent years that allow requesters from all around the world to post tasks and seek help from an equally global pool of workers. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to strategically maximize their own benefit. In this paper, we propose to provide incentives for workers to exert effort using a novel game-theoretic model based on repeated games. As there is always a gap in the social welfare between the non-cooperative equilibria emerging when workers pursue their self-interests and the desirable Pareto efficient outcome, we propose a novel class of incentive protocols based on social norms which integrates reputation mechanisms into the existing pricing schemes currently implemented on crowdsourcing websites, in order to improve the performance of the non-cooperative equilibria emerging in such applications. We first formulate the exchanges on a crowdsourcing website as a two-sided market where requesters and workers are matched and play gift-giving games repeatedly. Subsequently, we study the protocol designer's problem of finding an optimal and sustainable (equilibrium) protocol which achieves the highest social welfare for that website. We prove that the proposed incentives protocol can make the website operate close to Pareto efficiency. Moreover, we also examine an alternative scenario, where the protocol designer aims at maximizing the revenue of the website and evaluate the performance of the optimal protocol.
Autonomous Real-time Vehicle Detection from a Medium-Level UAV

A generic and robust approach for the detection of road vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present a novel approach to the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers (a disjunctive set of cascades). Our approach facilitates the realtime detection of both static and moving vehicles independent of orientation, colour, type and configuration. The results presented show the successful detection of differing vehicle types under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. The technique is realised on aerial imagery obtained at 1Hz from an optical camera on the medium UAV B-MAV platform with results presented to include those from the MoD Grand Challenge 2008.
A flat pipeline inspection robot with two wheel chains

This paper presents a new pipeline inspection robot that has multiple sensors for inspection of 80–100mm pipelines. The special feature of this robot is realization of driving and steering capability by using only two wheel chains. Compared to popularly employed pipeline robots using three wheel chains, the new design allows simple robot control and easy user interface, specially at T-branch. As another advantage, the flat shape of this robot allows mounting additional sensors on the both sides of the robot. The kinematics and three control modes are described. Finally, the performance of this robot system is verified by experimentation.