nils-herrmann/clean_scirepeval huggingface dataset:
Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture

In physical Human-Robot Interaction, the basic problem of fast detection and safe robot reaction to unexpected collisions has been addressed successfully on advanced research robots that are torque controlled, possibly equipped with joint torque sensors, and for which an accurate dynamic model is available. In this paper, an end-user approach to collision detection and reaction is presented for an industrial manipulator having a closed control architecture and no additional sensors. The proposed detection and reaction schemes have minimal requirements: only the outer joint velocity reference to the robot manufacturer's controller is used, together with the available measurements of motor currents and joint positions. No a priori information on the robot dynamic model and existing low-level joint controllers is strictly needed. A suitable on-line processing of the motor currents allows to distinguish between accidental collisions and intended human-robot contacts, so as to switch the robot to a collaboration mode when needed. Two examples of reaction schemes for collaboration are presented, with the user pushing/pulling the robot at any point of its structure (e.g., for manual guidance) or with a compliant-like robot behavior in response to forces applied by the human. The actual performance of the methods is illustrated through experiments on a KUKA KR5 manipulator.
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intrasession authentication, 2%-3% for intersession authentication, and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multimodal biometric authentication system.
Development of a Funicular Flexible Crawler for Colonoscopy*

Recently, the number of colorectal cancer and polyp patients has been increasing. Colonoscopies are often used for diagnosis and treatment of rectal and colon diseases. Passing an endoscope into the large intestine is an extremely difficult operation in conventional large intestine endoscopy. Therefore, many robots to facilitate that operation have been developed recently. Nevertheless, they cannot move independently or quickly. We propose a simple and compact crawler mechanism for colonoscopy. It is suitable for propulsion through narrow spaces because it is driven by a geared motor arranged outside of the body through a flexible shaft. Through experimentation with a large intestine phantom, we confirmed that the proposed crawler can run quickly from the anus to the cecum. As described herein, this propulsion mechanism is applicable for self-traveling colonoscopy.
GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking

Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art.
Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes

Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things. Smart Cities; Smart Towns; Smart Homes; Internet of Things; Smart Objects; Computer Vision; Surveillance; Security;
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density

To understand the dynamics of optimization in deep neural networks, we develop a tool to study the evolution of the entire Hessian spectrum throughout the optimization process. Using this, we study a number of hypotheses concerning smoothness, curvature, and sharpness in the deep learning literature. We then thoroughly analyze a crucial structural feature of the spectra: in non-batch normalized networks, we observe the rapid appearance of large isolated eigenvalues in the spectrum, along with a surprising concentration of the gradient in the corresponding eigenspaces. In batch normalized networks, these two effects are almost absent. We characterize these effects, and explain how they affect optimization speed through both theory and experiments. As part of this work, we adapt advanced tools from numerical linear algebra that allow scalable and accurate estimation of the entire Hessian spectrum of ImageNet-scale neural networks; this technique may be of independent interest in other applications.
Computing Visual Attention from Scene Depth

Visual attention is the ability to rapidly detect the interesting partsof a given scene. Inspired by biological vision, the principle of visual attention is used with a similar goal in computer vision. Several previousworks deal with the computation of visual attention from images provided by standard video cameras, but little attention has been devoted so far to scene depth as source for visual attention. The investigation presented in this paper aims at an extensionof thevisual attentionmodel to thescenedepth component. A first part of thepaper is devoted to the integration of depth in thecomputationalmodel build aroundconspicuity and saliency maps. A second part is devoted to experimental work in which results of visual attention,obtained fromtheextended model andfor various3Dscenes, arepresented. Theresultsspeak for theusefulnessof theenhanced computationalmodel.
Columbia-IBM news video story segmentation in trecvid 2004

In this technical report, we give an overview our technical developments in the story segmentation task in TRECVID 2004. Among them, we propose an information-theoretic framework, visual cue cluster construction (VC), to automatically discover adequate mid-level features. The problem is posed as mutual information maximization, through which optimal cue clusters are discovered to preserve the highest information about the semantic labels. We extend the Information Bottleneck framework to high-dimensional continuous features and further propose a projection method to map each video into probabilistic memberships over all the cue clusters. The biggest advantage of the proposed approach is to remove the dependence on the manual process in choosing the mid-level features and the huge labor cost involved in annotating the training corpus for training the detector of each mid-level feature. When tested in TRECVID 2004 news video story segmentation, the proposed approach achieves promising performance gain over representations derived from conventional clustering techniques and even the mid-level features selected manually; meanwhile, it achieved one of the top performances, F1=0.65, close to the highest performance, F1=0.69, by other groups. We also experiment with other promising visual features and continue investigating effective prosody features. The introduction of post-processing also provides practical improvements. Furthermore, the fusion from other modalities, such as speech prosody features and ASR-based segmentation scores are significant and have been confirmed again in this experiment.
LOGO MATCHING ALGORITHM FEATURE‐BASED MATCHING RESULTS IMRESTAURANT(): MATLAB FOR FEATURE‐BASED RESTAURANT LOGO RECOGNITION

This paper discusses the implementation of imrestaurant(), a MATLAB function for feature-based restaurant logo recognition. The algorithm works by first applying SIFT to a logo database and then creating a hierarchical vocabulary tree using K-means clustering. The input to the algorithm is a photograph of an image logo, which is first filtered to reduce noise. Then SIFT is applied to the input image, and the output descriptors are pushed to the vocabulary tree. Each image in the logo database is scored using TF-IDF weights and the ten highest scoring database logos are pairwise matched using the ratio test and RANSAC. The restaurant database image with the largest consensus set is searched for on yelp.com. This algorithm was shown to be robust against input image size, background noise, and shearing. Finally, future work in increasing the accuracy of feature detection as well as optical character recognition is discussed.
Determinants of a sustainable competitive advantage due to an IT-enabled strategy

Information technology (IT) has been asserted to be a source of sustainable competitive advantage. Empirical evidence has shown that IT can improve a company’s performance and competitive position. We examine the factors that are believed to lead to a sustainable competitive advantage due to an IT-enabled strategy, and test these factors empirically. Our findings show that managerial IT skills are positively related to sustainability, and competitor’s knowledge of competitive advantage is negatively related to sustainability. There was no support for technical IT skills or IT infrastructure as a source of sustainable competitive advantage. q 2003 Elsevier Science B.V. All rights reserved.
A Decentralised Sharing App running a Smart Contract on the Ethereum Blockchain

The sharing economy, the business of collectively using privately owned objects and services, has fuelled some of the fastest growing businesses of the past years. However, popular sharing platforms like Airbnb or Uber exhibit several drawbacks: a cumbersome sign up procedure, lack of participant privacy, overbearing terms and conditions, and significant fees for users. We demonstrate a Decentralised App (DAPP) for the sharing of everyday objects based on a smart contract on the Ethereum blockchain. This contract enables users to register and rent devices without involvement of a Trusted Third Party (TTP), disclosure of any personal information or prior sign up to the service. With increasing distribution of cryptocurrencies the use of smart contracts such as proposed in this paper has the potential to revolutionise the sharing economy.
Reverse Engineering of the Interaction Diagrams from C++ Code

In object oriented programming, the functionalities of a system result from the interactions (message exchanges) among the objects allocated by the system. While designing object interactions is far more complex than designing the object structure in forward engineering, the problem of understanding object interactions during code evolution is even harder, because the related information is spread across the code. In this paper, a technique for the automatic extraction of UML interaction diagrams from C++ code is proposed. The algorithm is based on a static, conservative flow analysis, that approximates the behavior of the system in any execution and for any possible input. Applicability of the approach to large software is achieved by means of two mechanisms: partial analysis and focusing. Usage of our method on a real world, large C++ system confirmed its viability.
Geographical Routing With Location Service in Intermittently Connected MANETs

Combining mobile platforms such as manned or unmanned vehicles and peer-assisted wireless communication is an enabler for a vast number of applications. A key enabler for the applications is the routing protocol that directs the packets in the network. Routing packets in fully connected mobile ad hoc networks (MANETs) has been studied to a great extent, but the assumption on full connectivity is generally not valid in a real system. This case means that a practical routing protocol must handle intermittent connectivity and the absence of end-to-end connections. In this paper, we propose a geographical routing algorithm called location-aware routing for delay-tolerant networks (LAROD), enhanced with a location service, location dissemination service (LoDiS), which together are shown to suit an intermittently connected MANET (IC-MANET). Because location dissemination takes time in IC-MANETs, LAROD is designed to route packets with only partial knowledge of geographic position. To achieve low overhead, LAROD uses a beaconless strategy combined with a position-based resolution of bids when forwarding packets. LoDiS maintains a local database of node locations, which is updated using broadcast gossip combined with routing overhearing. The algorithms are evaluated under a realistic application, i.e., unmanned aerial vehicles deployed in a reconnaissance scenario, using the low-level packet simulator ns-2. The novelty of this paper is the illustration of sound design choices in a realistic application, with holistic choices in routing, location management, and the mobility model. This holistic approach justifies that the choice of maintaining a local database of node locations is both essential and feasible. The LAROD-LoDiS scheme is compared with a leading delay-tolerant routing algorithm (spray and wait) and is shown to have a competitive edge, both in terms of delivery ratio and overhead. For spray and wait, this case involved a new packet-level implementation in ns-2 as opposed to the original connection-level custom simulator.
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.
Defining a NoSQL Document Database of Accessible Transport Routes

Transport is one the aspects that most critically affects the population's life conditions, particularly in the case of those people with various special mobility needs. In spite of the existence of numerous Websites that provide information about public transport services, there is a lack of open data regarding accessible public transportation systems. We are, therefore, working on the development of a technological framework for the processing, management and exploitation of open data, with the goal of promoting accessibility to city public transport, which is taking place within the framework of the Access@city project. In this paper we specifically focus on the problem of designing the NoSQL document database with which to manage the information concerning accessible routes obtained by means of crowdsourcing techniques.
Components for immersion

A person is immersed when they feel part of an environment they experience and influence. While virtual immersion systems are usually designed on a case by case basis, and are not easily reusable or scalable, OUT goal is to specify and develop a framework for the design and integration of immersive systems. We address the issues raised in the design and implementation of the middleware components necessary for immersion, in a generic, extensible, modular architecture for Integrated Media Systems we have developed for efficient generic concurrent processing of data streams. Design principles are illustrated on specific visual immersion components. Utilization of these components is demonstrated with a real-time immersive interactive application.
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging

Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec—two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple k-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.
Heterogeneity and plasticity of T helper cells

CD4 T helper (Th) cells play critical roles in adaptive immune responses. They recruit and activate other immune cells including B cells, CD8 T cells, macrophages, mast cells, neutrophils, eosinophils and basophils. Based on their functions, their pattern of cytokine secretion and their expression of specific transcription factors, Th cells, differentiated from naïve CD4 T cells, are classified into four major lineages, Th1, Th2, Th17 and T regulatory (Treg) cells, although other Th lineages may exist. Subsets of the same lineage may express different effector cytokines, reside at different locations or give rise to cells with different fates, whereas cells from different lineages may secrete common cytokines, such as IL-2, IL-9 and IL-10, resulting in massive heterogeneity of the Th cell population. In addition, the pattern of cytokine secretion may switch from that of one lineage toward another under certain circumstances, suggesting that Th cells are plastic. Tregs are also more heterogeneous and plastic than were originally thought. In this review, we summarize recent reports on heterogeneity and plasticity of Th cells, and discuss potential mechanisms and implications of such features that Th cells display.
Intrinsically Motivated Affordance Discovery and Modeling

In this chapter, we argue that a single intrinsic motivation function for affordance discovery can guide long-term learning in robot systems. To these ends, we provide a novel definition of “affordance” as the latent potential for the closed-loop control of environmental stimuli perceived by sensors. Specifically, the proposed intrinsic motivation function rewards the discovery of such control affordances. We will demonstrate how this function has been used by a humanoid robot to learn a number of general purpose control skills that address many different tasks. These skills, for example, include strategies for finding, grasping, and placing simple objects. We further show how this same intrinsic reward function is used to direct the robot to build stable models of when the environment affords these skills.
OPC Unified Architecture: A Service-oriented Architecture for Smart Grids

In this paper, the OPC UA is introduced as a key technology for realizing a variety of Smart Grid use cases enabling relevant tasks of automation and control. OPC UA is the successor of the established Classic OPC specification and state of the art regarding information exchange in the industrial automation branch. One of its key improvements over the Classic OPC is that the area of application is no longer restricted to industrial automation but OPC UA can be applied almost in every domain facing challenges in automated control. This improvement stems from a more generic and object-oriented approach. For the adoption of OPC UA to Smart Grids, two of the most important data models -- the Common Information Model (CIM) and the IEC 61850 -- have been identified to be integrated into OPC UA communication. In this contribution, basic OPC UA features and functionalities (information modeling, communication services, and information security) are introduced and discussed in the context of Smart Grids.