nils-herrmann/clean_scirepeval huggingface dataset:
DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks

Camera-based lane detection algorithms are one of the key enablers for many semi-autonomous and fullyautonomous systems, ranging from lane keep assist to level-5 automated vehicles. Positioning a vehicle between lane boundaries is the core navigational aspect of a self-driving car. Even though this should be trivial, given the clarity of lane markings on most standard roadway systems, the process is typically mired with tedious pre-processing and computational effort. We present an approach to estimate lane positions directly using a deep neural network that operates on images from laterally-mounted down-facing cameras. To create a diverse training set, we present a method to generate semi-artificial images. Besides the ability to distinguish whether there is a lane-marker present or not, the network is able to estimate the position of a lane marker with sub-centimeter accuracy at an average of 100 frames/s on an embedded automotive platform, requiring no pre-or post-processing. This system can be used not only to estimate lane position for navigation, but also provide an efficient way to validate the robustness of driver-assist features which depend on lane information.
An Analysis of the Switching Performance and Robustness of Power MOSFETs Body Diodes: A Technology Evaluation

The tradeoff between the switching energy and electro-thermal robustness is explored for 1.2-kV SiC MOSFET, silicon power MOSFET, and 900-V CoolMOS body diodes at different temperatures. The maximum forward current for dynamic avalanche breakdown is decreased with increasing supply voltage and temperature for all technologies. The CoolMOS exhibited the largest latch-up current followed by the SiC MOSFET and silicon power MOSFET; however, when expressed as current density, the SiC MOSFET comes first followed by the CoolMOS and silicon power MOSFET. For the CoolMOS, the alternating p and n pillars of the superjunctions in the drift region suppress BJT latch-up during reverse recovery by minimizing lateral currents and providing low-resistance paths for carriers. Hence, the temperature dependence of the latch-up current for CoolMOS was the lowest. The switching energy of the CoolMOS body diode is the largest because of its superjunction architecture which means the drift region have higher doping, hence more reverse charge. In spite of having a higher thermal resistance, the SiC MOSFET has approximately the same latch-up current while exhibiting the lowest switching energy because of the least reverse charge. The silicon power MOSFET exhibits intermediate performance on switching energy with lowest dynamic latching current.
On the semantic annotation of places in location-based social networks

In this paper, we develop a semantic annotation technique for location-based social networks to automatically annotate all places with category tags which are a crucial prerequisite for location search, recommendation services, or data cleaning. Our annotation algorithm learns a binary support vector machine (SVM) classifier for each tag in the tag space to support multi-label classification. Based on the check-in behavior of users, we extract features of places from i) explicit patterns (EP) of individual places and ii) implicit relatedness (IR) among similar places. The features extracted from EP are summarized from all check-ins at a specific place. The features from IR are derived by building a novel network of related places (NRP) where similar places are linked by virtual edges. Upon NRP, we determine the probability of a category tag for each place by exploring the relatedness of places. Finally, we conduct a comprehensive experimental study based on a real dataset collected from a location-based social network, Whrrl. The results demonstrate the suitability of our approach and show the strength of taking both EP and IR into account in feature extraction.
Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals

Long-term continuous systolic blood pressure (SBP) and heart rate (HR) monitors are of tremendous value to medical (cardiovascular, circulatory and cerebrovascular management), wellness (emotional and stress tracking) and fitness (performance monitoring) applications, but face several major impediments, such as poor wearability, lack of widely accepted robust SBP models and insufficient proofing of the generalization ability of calibrated models.



This paper proposes a wearable cuff-less electrocardiography (ECG) and photoplethysmogram (PPG)-based SBP and HR monitoring system and many efforts are made focusing on above challenges. Firstly, both ECG/PPG sensors are integrated into a single-arm band to provide a super wearability. A highly convenient but challenging single-lead configuration is proposed for weak single-arm-ECG acquisition, instead of placing the electrodes on the chest, or two wrists. Secondly, to identify heartbeats and estimate HR from the motion artifacts-sensitive weak arm-ECG, a machine learning-enabled framework is applied. Then ECG-PPG heartbeat pairs are determined for pulse transit time (PTT) measurement. Thirdly, a PTT&HR-SBP model is applied for SBP estimation, which is also compared with many PTT-SBP models to demonstrate the necessity to introduce HR information in model establishment. Fourthly, the fitted SBP models are further evaluated on the unseen data to illustrate the generalization ability. A customized hardware prototype was established and a dataset collected from ten volunteers was acquired to evaluate the proof-of-concept system.



The semi-customized prototype successfully acquired from the left upper arm the PPG signal, and the weak ECG signal, the amplitude of which is only around 10% of that of the chest-ECG. The HR estimation has a mean absolute error (MAE) and a root mean square error (RMSE) of only 0.21 and 1.20 beats per min, respectively. Through the comparative analysis, the PTT&HR-SBP models significantly outperform the PTT-SBP models. The testing performance is 1.63 ± 4.44, 3.68, 4.71 mmHg in terms of mean error ± standard deviation, MAE and RMSE, respectively, indicating a good generalization ability on the unseen fresh data.



The proposed proof-of-concept system is highly wearable, and its robustness is thoroughly evaluated on different modeling strategies and also the unseen data, which are expected to contribute to long-term pervasive hypertension, heart health and fitness management.
A 1.2 V 30 nm 3.2 Gb/s/pin 4 Gb DDR4 SDRAM With Dual-Error Detection and PVT-Tolerant Data-Fetch Scheme

A 1.2 V 4 Gb DDR4 SDRAM is presented in a 30 nm CMOS technology. DDR4 SDRAM is developed to raise memory bandwidth with lower power consumption compared with DDR3 SDRAM. Various functions and circuit techniques are newly adopted to reduce power consumption and secure stable transaction. First, dual error detection scheme is proposed to guarantee the reliability of signals. It is composed of cyclic redundancy check (CRC) for DQ channel and command-address (CA) parity for command and address channel. For stable reception of high speed signals, a gain enhanced buffer and PVT tolerant data fetch scheme are adopted for CA and DQ respectively. To reduce the output jitter, the type of delay line is selected depending on data rate at initial stage. As a result, test measurement shows 3.3 Gb/s DDR operation at 1.14 V.
Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model

Real-time marker-less hand tracking is of increasing importance in human-computer interaction. Robust and accurate tracking of arbitrary hand motion is a challenging problem due to the many degrees of freedom, frequent self-occlusions, fast motions, and uniform skin color. In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time. The main contributions include a new generative tracking method which employs an implicit hand shape representation based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is smooth and analytically differentiable making fast gradient based pose optimization possible. This shape representation, together with a full perspective projection model, enables more accurate hand modeling than a related baseline method from literature. Our method achieves better accuracy than previous methods and runs at 25 fps. We show these improvements both qualitatively and quantitatively on publicly available datasets.
Semi-supervised feature selection for graph classification

The problem of graph classification has attracted great interest in the last decade. Current research on graph classification assumes the existence of large amounts of labeled training graphs. However, in many applications, the labels of graph data are very expensive or difficult to obtain, while there are often copious amounts of unlabeled graph data available. In this paper, we study the problem of semi-supervised feature selection for graph classification and propose a novel solution, called gSSC, to efficiently search for optimal subgraph features with labeled and unlabeled graphs. Different from existing feature selection methods in vector spaces which assume the feature set is given, we perform semi-supervised feature selection for graph data in a progressive way together with the subgraph feature mining process. We derive a feature evaluation criterion, named gSemi, to estimate the usefulness of subgraph features based upon both labeled and unlabeled graphs. Then we propose a branch-and-bound algorithm to efficiently search for optimal subgraph features by judiciously pruning the subgraph search space. Empirical studies on several real-world tasks demonstrate that our semi-supervised feature selection approach can effectively boost graph classification performances with semi-supervised feature selection and is very efficient by pruning the subgraph search space using both labeled and unlabeled graphs.
Kefir ? a complex probiotic

Kefir is a fermented milk drink produced by the actions of bacteria and yeasts contained in kefir grains, and is reported to have a unique taste and unique properties. During fermentation, peptides and exopolysaccharides are formed that have been shown to have bioactive properties. Moreover, in vitro and animal trials have shown kefir and its constituents to have anticarcinogenic, antimutagenic, antiviral and antifungal properties. Although kefir has been produced and consumed in Eastern Europe for a long period of time, few clinical trials are found in the scientific literature to support the health claims attributed to kefir. The large number of microorganisms in kefir, the variety of possible bioactive compounds that could be formed during fermentation, and the long list of reputed benefits of eating kefir make this fermented dairy product a complex
GenERRate: Generating Errors for Use in Grammatical Error Detection

This paper explores the issue of automatically generated ungrammatical data and its use in error detection, with a focus on the task of classifying a sentence as grammatical or ungrammatical. We present an error generation tool called GenERRate and show how GenERRate can be used to improve the performance of a classifier on learner data. We describe initial attempts to replicate Cambridge Learner Corpus errors using GenERRate.
Modeling Website Design across Cultures : Relationships to Trust , Satisfaction and E-loyalty ( Forthcoming Journal of Management Information Systems )

Despite rapidly increasing numbers of diverse online shoppers the relationship of website design to trust, satisfaction, and loyalty has not previously been modeled across cultures. In the current investigation three components of website design (Information Design, Navigation Design, and Visual Design) are considered for their impact on trust and satisfaction. In turn, relationships of trust and satisfaction to online loyalty are evaluated. Utilizing data collected from 571 participants in Canada, Germany, and China various relationships in the research model are tested using PLS analysis for each country separately. In addition the overall model is tested for all countries combined as a control and verification of earlier research findings, although this time with a mixed country sample. All paths in the overall model are confirmed. Differences are determined for separate country samples concerning whether Navigation Design, Visual Design, and Information Design result in trust, satisfaction, and ultimately loyalty suggesting design characteristics should be a central consideration in website design across cultures.
Data Storage in DNA

Encoding information into synthetic DNA is a novel approach for data storage. Due to its natural robustness and size in molecular dimensions, it can be used for long-term and very high-density archiving of data. Since the DNA molecules can be corrupted by thermal process and the writing/reading process of DNA molecules can be faulty, it is necessary to encode the data using error-correcting codes. In this thesis, the student analyzes errors that occur in such a storage system and designs coding schemes that can be used for error correction.
An ontology-based Enterprise Architecture

0957-4174/$ see front matter 2009  Corresponding author.  dwkang@kt.com (D. Kang), jsr blissray@postech.ac.kr (S. Choi), kskim@postech.ac.kr The Enterprise Architecture refers to a comprehensive description of all of the key elements and relationships that make up an organization [Harmon, P. (2003). Developing an Enterprise Architecture, Business process trends: Whitepaper]. Through the Enterprise Architecture, enterprises can implement enterprise integration to cope with dynamically changing business environment. Existing Enterprise Architectures, however, lack of semantics for humans and systems to understand them exactly and commonly, which causes communication problems between humans or between systems or between human and system. These communication problems keep enterprises from implementing integration and collaborating with other enterprises. In order to solve this problem, the ontology-based Enterprise Architecture is suggested in this paper. The Enterprise Architecture ontology is composed of ontologies in three levels. Ontologies of business terms are in the first level, ontologies of Enterprise Architecture components are in the second level, and ontologies of relationships among Enterprise Architecture components are in the top level. The ontologies of business terms are defined in the approach of the WordNet, and the ontologies of Enterprise Architecture components and relationships of them are defined in the approach of the SBVR. Through these ontologies, it is expected that humans and systems can understand Enterprise Architectures exactly and commonly, which supports integrations in enterprises and collaborations between enterprises.
Faster Greedy MAP Inference for Determinantal Point Processes

Determinantal point processes (DPPs) are popular probabilistic models that arise in many machine learning tasks, where distributions of diverse sets are characterized by matrix determinants. In this paper, we develop fast algorithms to find the most likely configuration (MAP) of large-scale DPPs, which is NP-hard in general. Due to the submodular nature of the MAP objective, greedy algorithms have been used with empirical success. Greedy implementations require computation of log-determinants, matrix inverses or solving linear systems at each iteration. We present faster implementations of the greedy algorithms by utilizing the complementary benefits of two log-determinant approximation schemes: (a) first-order expansions to the matrix log-determinant function and (b) high-order expansions to the scalar log function with stochastic trace estimators. In our experiments, our algorithms are orders of magnitude faster than their competitors, while sacrificing marginal accuracy.
Addressing challenges in promoting healthy lifestyles: the al-chatbot approach

Healthy lifestyles promotion is the main objective of primary care interventions, starting from the pediatric age, were overweight is nowadays exposing about one third of children to the risk of developing chronic diseases, such as diabetes. Recent years have seen a blast of mHealth apps for health promotion, targeting in particular nutrition and dietary behaviour change. However, reviews show difficulties in the adoption and effective usage of these applications in telemedicine and by the population in general, due to a lack of evidence-based content and strategies provided (e.g., by commercial apps) or lack of sufficient user engagement with the apps. Nutrition apps typically require self-reporting of food intake by the user which is often seen as a burden and a cause of abandonment of the app. However, current wave of research has taken up the challenge of promoting healthy lifestyles with advances in artificial intelligence (AI). This paper focus on AI chatbots as an innovative approach offering more simplicity and facilitating long-term adherence to health promotion interventions. Conversational assistants provide the advantage of being deployed in smartphones and laptops within a wide variety of applications. We will particularly focus on harnessing the power of intelligent chatbot systems to provide behaviour change interventions in telemedicine for healthy lifestyle promotion. We describe an application scenario for an AI-chatbot delivering support to nutrition education that could help to overcome current limitations of similar mHealth solutions provided for healthy lifestyles and contribute to more effective public health interventions in this application domain.
Doing More with the Dew: A New Approach to Cloud-Dew Architecture

While the popularity of cloud computing is exploding, a new network computing paradigm is just beginning. In this paper, we examine this exciting area of research known as dew computing and propose a new design of clouddew architecture. Instead of hosting only one dew server on a user’s PC — as adopted in the current dewsite application — our design promotes the hosting of multiple dew servers instead, one for each installed domain. Our design intends to improve upon existing cloud-dew architecture by providing significantly increased freedom in dewsite development, while also automating the chore of managing dewsite content based on the user’s interests and browsing habits. Other noteworthy benefits, all at no added cost to dewsite users, are briefly explored as well. TYPE OF PAPER AND
Conversational Gaze Aversion for Humanlike Robots

Gaze aversion-the intentional redirection away from the face of an interlocutor-is an important nonverbal cue that serves a number of conversational functions, including signaling cognitive effort, regulating a conversation's intimacy level, and managing the conversational floor. In prior work, we developed a model of how gaze aversions are employed in conversation to perform these functions. In this paper, we extend the model to apply to conversational robots, enabling them to achieve some of these functions in conversations with people. We present a system that addresses the challenges of adapting human gaze aversion movements to a robot with very different affordances, such as a lack of articulated eyes. This system, implemented on the NAO platform, autonomously generates and combines three distinct types of robot head movements with different purposes: face-tracking movements to engage in mutual gaze, idle head motion to increase lifelikeness, and purposeful gaze aversions to achieve conversational functions. The results of a human-robot interaction study with 30 participants show that gaze aversions implemented with our approach are perceived as intentional, and robots can use gaze aversions to appear more thoughtful and effectively manage the conversational floor.
Applying planning to interactive storytelling: Narrative control using state constraints

We have seen ten years of the application of AI planning to the problem of narrative generation in Interactive Storytelling (IS). In that time planning has emerged as the dominant technology and has featured in a number of prototype systems. Nevertheless key issues remain, such as how best to control the shape of the narrative that is generated (e.g., by using narrative control knowledge, i.e., knowledge about narrative features that enhance user experience) and also how best to provide support for real-time interactive performance in order to scale up to more realistic sized systems. Recent progress in planning technology has opened up new avenues for IS and we have developed a novel approach to narrative generation that builds on this. Our approach is to specify narrative control knowledge for a given story world using state trajectory constraints and then to treat these state constraints as landmarks and to use them to decompose narrative generation in order to address scalability issues and the goal of real-time performance in larger story domains. This approach to narrative generation is fully implemented in an interactive narrative based on the “Merchant of Venice.” The contribution of the work lies both in our novel use of state constraints to specify narrative control knowledge for interactive storytelling and also our development of an approach to narrative generation that exploits such constraints. In the article we show how the use of state constraints can provide a unified perspective on important problems faced in IS.
TCP covert timing channels: Design and detection

Exploiting packetspsila timing information for covert communication in the Internet has been explored by several network timing channels and watermarking schemes. Several of them embed covert information in the inter-packet delay. These channels, however, can be detected based on the perturbed traffic pattern, and their decoding accuracy could be degraded by jitter, packet loss and packet reordering events. In this paper, we propose a novel TCP-based timing channel, named TCPScript to address these shortcomings. TCPScript embeds messages in ldquonormalrdquo TCP data bursts and exploits TCPpsilas feedback and reliability service to increase the decoding accuracy. Our theoretical capacity analysis and extensive experiments have shown that TCPScript offers much higher channel capacity and decoding accuracy than an IP timing channel and JitterBug. On the countermeasure, we have proposed three new metrics to detect aggressive TCPScript channels.
Infill Optimization for Additive Manufacturing—Approaching Bone-Like Porous Structures

Porous structures such as trabecular bone are widely seen in nature. These structures are lightweight and exhibit strong mechanical properties. In this paper, we present a method to generate bone-like porous structures as lightweight infill for additive manufacturing. Our method builds upon and extends voxel-wise topology optimization. In particular, for the purpose of generating sparse yet stable structures distributed in the interior of a given shape, we propose upper bounds on the localized material volume in the proximity of each voxel in the design domain. We then aggregate the local per-voxel constraints by their p-norm into an equivalent global constraint, in order to facilitate an efficient optimization process. Implemented on a high-resolution topology optimization framework, our results demonstrate mechanically optimized, detailed porous structures which mimic those found in nature. We further show variants of the optimized structures subject to different design specifications, and we analyze the optimality and robustness of the obtained structures.