nils-herrmann/clean_scirepeval huggingface dataset:
Limits and sustainable interaction design: Obsolescence in a future of collapse and resource scarcity

Electronic waste, caused by the advancements of technology and its rapidly increasing obsolescence, represents a major threat to environmental sustainability. Research in Sustainable HCI has proposed a variety of solutions to tackle this issue, but has yet to create a major impact in product design. While currently industry’s goals are opposed to research’s concepts of addressing obsolescence, a future of collapse and resource scarcity requires a revisit of those contributions: changes in society at large, such as a decrease of resource availability, different needs, requirements, and desires of the consumer, but also new directions of industry and marketing might enable researchers to bring their old concepts into practice. We take a look at a variety of obsolescence-related research in Sustainable HCI and foreshadow its potential for such a future of collapse and resource scarcity.
Stacked Graphs – Geometry & Aesthetics

In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility.
Multiple Object Tracking Using K-Shortest Paths Optimization

Multi-object tracking can be achieved by detecting objects in individual frames and then linking detections across frames. Such an approach can be made very robust to the occasional detection failure: If an object is not detected in a frame but is in previous and following ones, a correct trajectory will nevertheless be produced. By contrast, a false-positive detection in a few frames will be ignored. However, when dealing with a multiple target problem, the linking step results in a difficult optimization problem in the space of all possible families of trajectories. This is usually dealt with by sampling or greedy search based on variants of Dynamic Programming which can easily miss the global optimum. In this paper, we show that reformulating that step as a constrained flow optimization results in a convex problem. We take advantage of its particular structure to solve it using the k-shortest paths algorithm, which is very fast. This new approach is far simpler formally and algorithmically than existing techniques and lets us demonstrate excellent performance in two very different contexts.
Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity

In this paper, an attempt at summarizing the evolution and the state of the art in the field of robot hands is made. In such exposition, a critical evaluation of what in the author’s view are the leading ideas and emerging trends is privileged with respect to exhaustiveness of citations. The survey is focused mainly on three types of functional requirements a machine hand can be assigned in an artificial system, namely, manipulative dexterity, grasp robustness, and human operability. A basic distinction is made between hands designed for mimicking the human anatomy and physiology, and hands designed to meet restricted, practical requirements. In the latter domain, arguments are presented in favor of a “minimalistic” attitude in the design of hands for practical applications, i.e., use the least number of actuators, the simplest set of sensors, etc., for a given task. To achieve this rather obvious engineering goal is a challenge to our community. The paper illustrates some of the new, sometimes difficult, problems that are brought about by building and controlling simpler, more practical devices.
Intent Discovery Through Unsupervised Semantic Text Clustering

Conversational systems need to understand spoken language to be able to converse with a human in a meaningful coherent manner. This understanding (Spoken Language understanding SLU) of the human language is operationalized through identifying intents and entities. While classification methods that rely on labeled data are often used for SLU, creating large supervised data sets is extremely tedious and time consuming. This paper presents a practical approach to automate the process of intent discovery on unlabeled data sets of human language text through clustering techniques. We explore a range of representations for the texts and various clustering methods to validate the clustering stability through quantitative metrics like Adjusted Random Index (ARI). A final alignment of the clusters to the semantic intent is determined through consensus labelling. Our experiments on public datasets demonstrate the effectiveness of our approach generating homogeneous clusters with 89% cluster accuracy, leading to better semantic intent alignments. Furthermore, we illustrate that the clustering offer an alternate and effective way to mine sentence variants that can aid the bootstrapping of SLU models.
Snubberless Bidirectional DC–DC Converter With New CLLC Resonant Tank Featuring Minimized Switching Loss

A bidirectional DC-DC converter (BDC) with a new CLLC-type resonant tank, which features zero-voltage switching (ZVS) for the input inverting choppers and zero-current switching (ZCS) for the output rectifier switches, regardless of the direction of the power flow, is proposed in this paper. Possessing the very optimal ZVS +ZCS soft-switching feature, this proposed converter will have a minimized switching loss if all of the main switches are implemented with metal-oxide-semiconductor field-effect transistors, and thereby, the proposed converter is fully soft switched and totally snubberless. The detail operation principles, as well as the design considerations, are presented. The methodologies to develop a unidirectional ZVS+ZCS dc-dc converter for the corresponding pulsewidth modulation and frequency modulation converters are proposed. The approach on how to construct a fully soft-switched BDC has also been proposed and analyzed. Finally, a topology extension is made, and another fully soft-switched BDC is derived. A prototype, which interfaces the 400-48-V dc buses for the uninterrupted power supply system with a power rating of 500 VA, was developed to verify the validity and applicability of this proposed converter. The highest applicable conversion efficiencies for the bidirectional operational modes are exceeding 96%.
Grounding Language for Interactive Task Learning

This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agent's perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain, along with test versions of the world model and ontology, and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.
Detection and prevention system against cyber attacks and botnet malware for information systems and Internet of Things

The explosion of interconnected devices and the Internet of Things has triggered new important challenges in the area of internet security, due to the various device vulnerabilities and increased potential for cyber-attacks. This paper touches on the areas of Cybersecurity, intrusion detection, prevention systems and artificial intelligence. Our aim is to create a system capable of understanding, detecting and preventing malicious connections using applied concepts of machine learning. We emphasize the importance of selecting and extracting features that can lead to an accurate decision of classification for malware and intrusion attacks. We propose a solution that combines features that extract correlations from the packet history for the same and different services and hosts, based on the rate of REJ, SYN and ACK flags and connection states, with HTTP features extracted from URI and RESTful methods. Our proposed solution is able to detect network intrusions and botnet communications with a precision of 98.4% on the binary classification problem.
Pornography and attitudes supporting violence against women: revisiting the relationship in nonexperimental studies.

A meta-analysis was conducted to determine whether nonexperimental studies revealed an association between men's pornography consumption and their attitudes supporting violence against women. The meta-analysis corrected problems with a previously published meta-analysis and added more recent findings. In contrast to the earlier meta-analysis, the current results showed an overall significant positive association between pornography use and attitudes supporting violence against women in nonexperimental studies. In addition, such attitudes were found to correlate significantly higher with the use of sexually violent pornography than with the use of nonviolent pornography, although the latter relationship was also found to be significant. The study resolves what appeared to be a troubling discordance in the literature on pornography and aggressive attitudes by showing that the conclusions from nonexperimental studies in the area are in fact fully consistent with those of their counterpart experimental studies. This finding has important implications for the overall literature on pornography and aggression.
Hand-drawn animation with self-shaped canvas

Although 3D CG tools produce similar style animations with conventional 2D animation, conventional hand-drawn animation has advantages that cannot be substituted in animation produced by those tools.
 This paper introduces a new method to assist animators in creating 2D keyframe animation, which utilizes the self-shaped canvas hidden from the user. Drawing keyframes is the main input. Because the canvas has a three-dimensional shape deformed according to keyframes, inbetween animation with them mapped on it obtains depth-aware motion.
Development of a neuro-feedback game based on motor imagery EEG

Electroencephalogram (EEG) has widely been used to monitor subjects/patients’ mental states. Using the monitor results as feedback, neuro-feedback enables patients to learn to regulate their physiological and psychological states so that improvements in physical and psychological subjects/patients’ states could be achieved. By analyzing EEG components generated by motor imagery, a mind-controlled game based on motor imagery is developed, including the design of BCI and the design of the video game. In the game, neuro-feedback is realized to in a visual manner, through which the users could learn to improve attention span. Based on motor imagery, EEG signal is classified into two categories, the left and right hand motor imagery. The accuracy of classification is up to 70%. The bandpower analysis results show that users’ attention level improves during the experiment. In this neuro-feedback game system, EEG signal is not only used for monitoring but also used for game control. The game provides an attention state measurements for users. With the neuro-feedback in the BCI, the user and the game form a close loop interactively. The proposed BCI video game could not only be used for entertainment and relaxation purpose, but attention-span training purpose.
Curved Folded Plate Timber Structures

Current research at the laboratory for timber construction IBOIS at EPFL focuses on shell structures. Two main families are investigated: shells made of linear elements, as timber rib shells, and shells made of surface elements, typically folded plate structures. With regard to timber rib shells, bent linear timber elements are used to create the shape of a shell. With folded plate structures, new timber derived products are used to create structures made of planar panels, where the panel simultaneously covers space and acts as load bearing element. Indeed, timber industry has developed in the last fifteen years new large-size timber panels. Composition and dimensions of these panels and the possibility of milling them with Computer Numerical Controlled machines shows great potential for folded plate structures. In the present paper we investigate to combine both approaches using bent timber panels for curved folded plate structures.
Towards a Shared Ledger Business Collaboration Language Based on Data-Aware Processes

Shared ledger technologies, as exemplified by Blockchain, provide a new framework for supporting business collaborations that is based on having a high-reliability, shared, trusted, privacy-preserving, nonrepudiable data repository that includes programmable logic in the form of “smart contracts”. The framework has the potential to dramatically transform business collaboration across numerous industry sectors, including finance, supply chain, food production, pharmaceuticals, and healthcare. Widespread adoption of this technology will be accelerated by the development of business-level languages for specifying smart contracts. This paper proposes that data-aware business processes, and in particular the Business Artifact paradigm, can provide a robust basis for a shared ledger Business Collaboration Language (BCL). The fundamental rationale for adopting data-aware processes is that shared ledgers focus on both data and process in equal measure. The paper examines potential advantages of the artifact-based approach from two perspectives: conceptual modeling, and opportunities for formal reasoning (verification). Broad research challenges for the development, understanding, and usage of a shared ledger BCL are highlighted.
Detecting Faulty Nodes with Data Errors for Wireless Sensor Networks

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.
Secure Deduplication with Efficient and Reliable Convergent Key Management

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. Promising as it is, an arising challenge is to perform secure deduplication in cloud storage. Although convergent encryption has been extensively adopted for secure deduplication, a critical issue of making convergent encryption practical is to efficiently and reliably manage a huge number of convergent keys. This paper makes the first attempt to formally address the problem of achieving efficient and reliable key management in secure deduplication. We first introduce a baseline approach in which each user holds an independent master key for encrypting the convergent keys and outsourcing them to the cloud. However, such a baseline key management scheme generates an enormous number of keys with the increasing number of users and requires users to dedicatedly protect the master keys. To this end, we propose Dekey , a new construction in which users do not need to manage any keys on their own but instead securely distribute the convergent key shares across multiple servers. Security analysis demonstrates that Dekey is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement Dekey using the Ramp secret sharing scheme and demonstrate that Dekey incurs limited overhead in realistic environments.
Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results 1

In this paper we describe initial experiments using meta-learning techniques to learn models of fraudulent credit card transactions. Our collaborators, some of the nation’s largest banks, have provided us with real-world credit card transaction data from which models may be computed to distinguish fraudulent transactions from legitimate ones, a problem growing in importance. Our experiments reported here are the first step towards a better understanding of t he advantages and limitations of current meta-learning strategies. We argue that, for the fraud detection domain, fraud catching rate (True Positive rate) and false alarm rate (False Positive rate ) are better metrics than the overall accuracy when evaluating the learned fraud classifiers. We show that given a skew ed distribution in the original data, artificially more balanced training data leads to better clas sifiers. We demonstrate how meta-learning can be used to combine different classifiers (from different lea rning algorithms) and maintain, and in some cases, improve the performance of the best classifier.
Fast and practical indexing and querying of very large graphs

Many applications work with graph-structured data. As graphs grow in size, indexing becomes essential to ensure sufficient query performance. We present the GRIPP index structure (GRaph Indexing based on Pre- and Postorder numbering) for answering reachability queries in graphs.
 GRIPP requires only linear time and space. Using GRIPP, we can answer reachability queries on graphs with 5 million nodes on average in less than 5 milliseconds, which is unrivaled by previous methods. We evaluate the performance and scalability of our approach on real and synthetic random and scale-free graphs and compare our approach to existing indexing schemes. GRIPP is implemented as stored procedure inside a relational database management system and can therefore very easily be integrated into existing graph-oriented applications.
Image Processing: Principles and Applications

This book covers the classical methods in image processing and introduces many recent applications. The described applications span a broad range of fields, including medical and dental imaging, multimedia search and retrieval, and tracking and security. The book opens with a description of the physical process of image formation and color perception. Several color spaces are defined and several deinterlacing algorithms are compared, with the authors concluding that their algorithm produces better image quality than the others. Next, there is a thorough description of image transformations, including principle
Benefit of Direct Charge Measurement (DCM) on Interconnect Capacitance Measurement

This paper discusses application of direct charge measurement (DCM) on characterizing on-chip interconnect capacitance. Measurement equipment and techniques are leveraged from Flat Panel Display testing. On-chip active device is not an essential necessity for DCM test structure and it is easy to implement parallel measurements. Femto-Farad measurement sensitivity is achieved without having on-chip active device. Measurement results of silicon and glass substrates, including parallel measurements, are presented.
Design and identification of a lumped-parameter thermal network for permanent magnet synchronous motors based on heat transfer theory and particle swarm optimisation

A lumped-parameter thermal network (LPTN) for a permanent magnet motor (PMSM) is developed to estimate the most crucial motor temperatures. In this contribution a 60 kW PMSM prototype designed for automotive traction drives is used as the investigation basis. Aiming at real-time motor monitoring well-known analytic equations from the heat transfer theory are used to model the dominant heat paths. Based on a three-dimensional approach in cylindrical coordinates a differential-algebraic state-space model with varying parameters (LPV) is proposed. Due to the chosen level of model abstraction as well as motor material data uncertainties significant estimation errors between the LPTN and experimental test bench measurements result. To improve the estimation accuracy particle swarm optimisation (PSO) is applied for strategic fitting of uncertain model parameters with respect to a maximum likelihood cost function. To avoid converging in suboptimal local minima, which is a typical problem of gradient-based standard optimisation methods, the meta-heuristic PSO is utilised for the resulting multi-variable, non-linear and constrained optimisation problem. For the identification process experimental training data is used which is statistically independent from the (cross-)validation profiles. As a result the maximum estimation error (worst-case) regarding all considered motor component temperatures can be drastically reduced to 8 °C.
Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems

We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such <i>independent </i> agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.
Comparison of Fixed and Variable Pitch Actuators for Agile Quadrotors

This paper presents the design, analysis and experimental testing of a variablepitch quadrotor. A custom in-lab built quadrotor with on-board attitude stabilization is developed and tested. An analysis of the dynamic differences in thrust output between a fixed-pitch and variable-pitch propeller is given and validated with simulation and experimental results. It is shown that variable-pitch actuation has significant advantages over the conventional fixed-pitch configuration, including increased thrust rate of change, decreased control saturation, and the ability to quickly and efficiently reverse thrust. These advantages result in improved quadrotor tracking of linear and angular acceleration command inputs in both simulation and hardware testing. The benefits should enable more aggressive and aerobatic flying with the variable-pitch quadrotor than with standard fixed-pitch actuation, while retaining much of the mechanical simplicity and robustness of the fixed-pitch quadrotor.