nils-herrmann/clean_scirepeval huggingface dataset:
A 10.5-b ENOB 645 nW 100kS/s SAR ADC with statistical estimation based noise reduction

This paper presents a power-efficient SNR enhancement technique for SAR ADCs. By accurately estimating the conversion residue, it can suppress both comparator noise and quantization error. Thus, it allows the use of a noisy low-power comparator and a relatively low resolution DAC to achieve high resolution. The proposed technique has low hardware complexity, requiring no change to the standard ADC operation except for repeating the LSB comparisons. A prototype ADC is designed in 65nm CMOS. Its SNR is improved by 7dB with the proposed technique. Overall, it achieves 10.5-b ENOB while operating at 100kS/s and consuming 645nW from a 0.7V power supply.
Development of quadruped walking robot TITAN-VIII

In this paper, we discuss about the development of quadruped walking robot TITAN-VIII , especially about its leg mechanism and results of experiments using one-leg model. In the design of TITAN-VIII , we have been considering not only about “high performance of the movement” but also about “low price”, “Simplicity of the treatment”, “simplicity of the functional extension” etc so that many researchers can get walking robots easily. For example, we introduce driving system using wires which is helpful to achieve these objectives. We made one-leg model of TITAN-VIII at first. By using this one-leg model, we did experiments and made remarks about feature of response,velocity,force and energy consumption. From these results, we estimate that the limitation of walking velocity of TITAN-VIII in standard walking posture is O.S[m/s](crawl gait of /3 = 0.75) ,0.9[m/s](trot gait of /3 = O S ) , the payload is 5 N 7 [ k g ] and it with 2.5[Ah] battery can walk 2000 steps. And we discuss about standard walking posture of TITAN-VIII based on energy efficiency.
A recommendation mechanism for contextualized mobile advertising

Mobile advertising complements the Internet and interactive television advertising and makes it possible for advertisers to create tailormade campaigns targeting users according to where they are, their needs of the moment and the devices they are using (i.e. contextualized mobile advertising). Therefore, it is necessary that a fully personalized mobile advertising infrastructure be made. In this paper, we present such a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities. We name this infrastructure MALCR, in which the primary ingredient is a recommendation mechanism that is supported by the following concepts: (1) minimize users’ inputs (a typical interaction metaphor for mobile devices) for implicit browsing behaviors to be best utilized; (2) implicit browsing behaviors are then analyzed with a view to understanding the users’ interests in the values of features of advertisements; (3) having understood the users’ interests, Mobile Ads relevant to a designated location are subsequently scored and ranked; (4) Top-N scored advertisements are recommended. The recommendation mechanism is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering. This recommendation mechanism is also justified (by thorough evaluations) to show its ability in furnishing effective personalized contextualized mobile advertising. q 2003
Development and Navigation of a Mobile Robot for Floating Production Storage and Offloading Ship Hull Inspection

This paper describes the current development status of a mobile robot designed to inspect the outer surface of large oil ship hulls and floating production storage and offloading platforms. These vessels require a detailed inspection program, using several nondestructive testing techniques. A robotic crawler designed to perform such inspections is presented here. Locomotion over the hull is provided through magnetic tracks, and the system is controlled by two networked PCs and a set of custom hardware devices to drive motors, video cameras, ultrasound, inertial platform, and other devices. Navigation algorithm uses an extended-Kalman-filter (EKF) sensor-fusion formulation, integrating odometry and inertial sensors. It was shown that the inertial navigation errors can be decreased by selecting appropriate Q and R matrices in the EKF formulation.
How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)

Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm’s effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling the change of the layers’ input distributions during training to reduce the so-called “internal covariate shift”. In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm. Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. These findings bring us closer to a true understanding of our DNN training toolkit.
InTml: a description language for VR applications

We present the Interaction Technique Markup Language (InTml), a profile on top of the core X3D that describes 3D interaction techniques (InTs) and hardware platforms. InTml makes 3D InTs easier to understand, compare, and integrate in complete virtual reality (VR) applications. InTml can be used as a front end for any VR toolkit, so InTml documents that plug together 3D InTs, VR objects, and devices can be fully described and executed.
Handling contingency in temporal constraint networks: from consistency to controllabilities

Temporal Constraint Networks (TCN) allow to express minimal and maximal durations between time-points. Though being used in many research areas, this model disregards the contingent nature of some constraints, whose e ective duration cannot be decided by the system but is provided by the external world. We propose an extension of TCN based on the de nition of the Simple Temporal Problem under Uncertainty (STPU) in which the classical network consistency property must be rede ned in terms of controllability: intuitively, we would like to say that a network is controllable i it is consistent in any situation (i.e. any assignment of the whole set of contingent intervals) that may arise in the external world. Three levels of controllability must be distinguished, namely the Strong, the Weak and the Dynamic ones. This paper provides a full characterization of those properties and their usefulness in practice, and proposes algorithms for checking them. Complexity issues and tractable equivalence classes are only partially tackled, since it is still the topic of on-going work. All the same, hardness is discussed and argued, giving evidence for the general intractability of Dynamic controllability, which is the most commonly required property in domains such as planning or scheduling. 1 Background and overview A large number of research areas in Arti cial Intelligence have to tackle the necessity of handling time in an explicit and highly expressive manner. This is especially true when one has to reason on the \activities" performed by some \agent" interacting with a so-called \external world". This de nes in rough terms all the possible target areas of this paper, though we will most of the time preferably use terms and examples inspired by the planning community. Apart from studies dealing with logics of time, researches have been carried out on temporal algebras, i.e. formalisms solely capturing the time entities (points or intervals) and the relations between them. Constraint Satisfaction Problems (Kumar, 1992) model them in terms of variables and binary constraints between them, and the main reasoning issue, on which this paper will focus, is then to check the consistency of the whole (i.e. check that one can nd a complete assignment of the variables, called a solution, that satis es the constraints). We should distinguish here between symbolic constraint algebras (Allen, 1983; Vilain et al., 1989), models dealing with numerical constraints (Dechter et al., 1991), and some advanced proposals
Isolated Swiss-Forward Three-Phase Rectifier With Resonant Reset

This paper presents a new isolated single-stage pulse-width modulation rectifier system based on the recently presented Swiss rectifier topology providing the isolation by replacing a buck with a forward converter. The principle of operation and a new modulation technique which compensates the reactive power generated by the input filter at light load and maximizes the power factor are discussed. Furthermore, the analytical equations for the stress in the semiconductor devices useful for the system optimization are derived. The proposed topology and modulation technique are experimentally validated on a 3.3-kW 115 Vac to 270 Vdc prototype demonstrator.
Linked Data Indexing of Distributed Ledgers

Searching for information in distributed ledgers is currently not an easy task, as information relating to an entity may be scattered throughout the ledger with no index. As distributed ledger technologies become more established, they will increasingly be used to represent real world transactions involving many parties and the search requirements will grow. An index providing the ability to search using domain specific terms across multiple ledgers will greatly enhance to power, usability and scope of these systems. We have implemented a semantic index to the Ethereum blockchain platform, to expose distributed ledger data as Linked Data. As well as indexing blockand transactionlevel data according to the BLONDiE ontology, we have mapped smart contracts to the Minimal Service Model ontology, to take the first steps towards connecting smart contracts with Semantic Web Services.
A New High-Gain Microstrip Yagi Array Antenna With a High Front-to-Back (F/B) Ratio for WLAN and Millimeter-Wave Applications

A new printed microstrip Yagi array antenna is proposed that can achieve a high gain and low backside radiation for various applications up to the millimeter-wave frequency range. The high front-to-back (F/B) ratio (up to 15 dB) is attributed to the constructive interference that takes place between the individual printed Yagi arrays in the design. Through the spacing of the elements, the directivity (between 9-11.5 dBi) and the F/B ratio can be altered to suit the application of interest. The operational principles of this design are discussed to give insight on the radiation mechanism of the antenna. An initial design at around 32.5 GHz is presented to show the performance capabilities of this configuration. An impedance bandwidth of 8.3% can be achieved around this frequency. Then, a parametric analysis is conducted to estimate the significance of the design parameters that affect the antenna's performance. Finally, measured return loss and radiation pattern performance at 5.2 GHz is displayed to validate the principles and simulated results of the design. The measured impedance bandwidth of 10% is achieved. The F/B ratio is 15 dB which is larger than values previously published by 5-10 dB. Additionally, a gain of 10.7 dBi is observed. To the author's knowledge, this is the first microstrip Yagi array antenna presented that has a high gain and a high F/B ratio designed using simple fabrication techniques
Enterprise logistics and supply chain structure : the role of fit

The emergence of the extended manufacturing enterprise, a globally dispersed collection of strategically aligned organizations, has brought new attention to how organizations coordinate the flow of information and materials across their w supply chains. This paper explores and develops the concept of enterprise logistics Greis, N.P., Kasarda, J.D., 1997. Ž . x Enterprise logistics in the information age. California Management Review 39 3 , 55–78 as a tool for integrating the logistics activities both within and between the strategically aligned organizations of the extended enterprise. Specifically, this paper examines the fit between an organization’s enterprise logistics integration capabilities and its supply chain structure. Using a configurations approach, we test whether globally dispersed network organizations that adopt enterprise logistics practices are able to achieve higher levels of organizational performance. Results indicate that enterprise logistics is a necessary tool for the coordination of supply chain operations that are geographically dispersed around the world. However, for a pure network structure, a high level of enterprise logistics integration alone does not guarantee improved organizational performance. The paper ends with a discussion of managerial implications and directions for future research.
Minimum-effort driven dynamic faceted search in structured databases

In this paper, we propose minimum-effort driven navigational techniques for enterprise database systems based on the faceted search paradigm. Our proposed techniques dynamically suggest facets for drilling down into the database such that the cost of navigation is minimized. At every step, the system asks the user a question or a set of questions on different facets and depending on the user response, dynamically fetches the next most promising set of facets, and the process repeats. Facets are selected based on their ability to rapidly drill down to the most promising tuples, as well as on the ability of the user to provide desired values for them. Our facet selection algorithms also work in conjunction with any ranked retrieval model where a ranking function imposes a bias over the user preferences for the selected tuples. Our methods are principled as well as efficient, and our experimental study validates their effectiveness on several application scenarios.
OpenGM: A C++ Library for Discrete Graphical Models

OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive structure are handled efficiently because (i) functions that occur repeatedly need to be stored only once, and (ii) distinct functions can be implemented differently, using different encodings alongside each other in the same model. Several parametric functions (e.g. metrics), sparse and dense value tables are provided and so is an interface for custom C++ code. Algorithms are separated by design from the representation of graphical models and are easily exchangeable. OpenGM, its algorithms, HDF5 file format and command line tools are modular and extendible.
A Novel Similarity Measure for Clustering Categorical Data Sets

Measuring similarity between two data objects is a more challenging problem for data mining and knowledge discovery tasks. The traditional clustering algorithms have been mainly stressed on numerical data, the implicit property of which can be exploited to define distance function between the data points to define similarity measure. The problem of similarity becomes more complex when the data is categorical which do not have a natural ordering of values or can be called as non geometrical attributes. Clustering on relational data sets when majority of its attributes are of categorical types makes interesting facts. No earlier work has been done on clustering categorical attributes of relational data set types making use of the property of functional dependency as parameter to measure similarity. This paper is an extension of earlier work on clustering relational data sets where domains are unique and similarity is context based and introduces a new notion of similarity based on dependency of an attribute on other attributes prevalent in the relational data set. This paper also gives a brief overview of popular similarity measures of categorical attributes. This novel similarity measure can be used to apply on tuples and their respective values. The important property of categorical domain is that they have smaller number of attribute values. The similarity measure of relational data sets then can be applied to the smaller data sets for efficient results.
Automatic Optic Disc Abnormality Detection in Fundus Images : A Deep Learning Approach

Optic disc (OD) is a key structure in retinal images. It serves as an indicator to detect various diseases such as glaucoma and changes related to new vessel formation on the OD in diabetic retinopathy (DR) or retinal vein occlusion. OD is also essential to locate structures such as the macula and the main vascular arcade. Most existing methods for OD localization are rule-based, either exploiting the OD appearance properties or the spatial relationship between the OD and the main vascular arcade. The detection of OD abnormalities has been performed through the detection of lesions such as hemorrhaeges or through measuring cup to disc ratio. Thus these methods result in complex and inflexible image analysis algorithms limiting their applicability to large image sets obtained either in epidemiological studies or in screening for retinal or optic nerve diseases. In this paper, we propose an end-to-end supervised model for OD abnormality detection. The most informative features of the OD are learned directly from retinal images and are adapted to the dataset at hand. Our experimental results validated the effectiveness of this current approach and showed its potential application.
Drivable Road Detection with 3D Point Clouds Based on the MRF for Intelligent Vehicle

In this paper, a reliable road/obstacle detection with 3D point cloud for intelligent vehicle on a variety of challenging environments (undulated road and/or uphill/ downhill) is handled. For robust detection of road we propose the followings: 1) correction of 3D point cloud distorted by the motion of vehicle (high speed and heading up and down) incorporating vehicle posture information; 2) guideline for the best selection of the proper features such as gradient value, height average of neighboring node; 3) transformation of the road detection problem into a classification problem of different features; and 4) inference algorithm based on MRF with the loopy belief propagation for the area that the LIDAR does not cover. In experiments, we use a publicly available dataset as well as numerous scans acquired by the HDL-64E sensor mounted on experimental vehicle in inner city traffic scenes. The results show that the proposed method is more robust and reliable than the conventional approach based on the height value on the variety of challenging environment. Jaemin Byun Robot and Cognitive System Research Department(RCSRD) in Electronicsand Telecommunications Research Institute (ETRI), daejeon, south korea, e-mail: jaemin.byu@etri.re.kr, Ki-in Na Robot and Cognitive System Research Department(RCSRD) in Electronicsand Telecommunications Research Institute (ETRI), daejeon, south korea,  Beom-su Seo Robot and Cognitive System Research Department(RCSRD) in Electronicsand Telecommunications Research Institute (ETRI), daejeon, south korea,  in Electronicsand Telecommunications Research Institute (ETRI), daejeon, south korea,
Combining Multiple Evidence from Different Properties of Weighting Schemes

It has been known that using different representations of either queries or documents, or different retrieval techniques retrieves different sets of documents. Recent work suggests that significant improvements in retrieval performance can be achieved by combining multiple representations or multiple retrieval techniques. In this paper we propose a simple method for retrieving different documents within a single query representation, a single document representation and a single retrieval technique. We classify the types of documents, and describe the properties of weighting schemes. Then, we explain that different properties of weighting schemes may retrieve different types of documents. Experimental results show that significant improvements can be obtained by combining the retrieval results from different properties of weighting schemes.
Smartphone-Based Measurement Systems for Road Vehicle Traffic Monitoring and Usage-Based Insurance

A framework is presented to deploy a smartphone-based measurement system for road vehicle traffic monitoring and usage-based insurance (UBI). Through the aid of a hierarchical model to modularize the description, the functionality is described as spanning from sensor-level functionality and technical specification to the topmost business model. The designer of a complex measurement system has to consider the full picture from low-level sensing, actuating, and wireless data transfer to the topmost level, including enticements for the individual smartphone owners, i.e., the end users who are the actual measurement probes. The measurement system provides two data streams: a primary stream to support road vehicle traffic monitoring and a secondary stream to support the UBI program. The former activity has a clear value for a society and its inhabitants, as it may reduce congestion and environmental impacts. The latter data stream drives the business model and parts of the revenue streams, which ensure the funding of the total measurement system and create value for the end users, the service provider, and the insurance company. In addition to the presented framework, outcome from a measurement campaign is presented, including road vehicle traffic monitoring (primary data stream) and a commercial pilot of UBI based on the driver profiles (secondary data stream). The measurement system is believed to be sustainable due to the incitements offered to the individual end users, in terms of favorable pricing for the insurance premium. The measurement campaign itself is believed to have an interest in its own right, as it includes smartphone probing of road traffic with a number of probes in the vicinity of the current state of the art, given by the Berkeley Mobile Millennium Project. During the ten-month run of the project, some 4500 driving h/250 000 km of road vehicle traffic data were collected.
Focused crawler for events

There is need for an Integrated Event Focused Crawling system to collect Web data about key events. When a disaster or other significant event occurs, many users try to locate the most up-to-date information about that event. Yet, there is little systematic collecting and archiving anywhere of event information. We propose intelligent event focused crawling for automatic event tracking and archiving, ultimately leading to effective access. We developed an event model that can capture key event information, and incorporated that model into a focused crawling algorithm. For the focused crawler to leverage the event model in predicting webpage relevance, we developed a function that measures the similarity between two event representations. We then conducted two series of experiments to evaluate our system about two recent events: California shooting and Brussels attack. The first experiment series evaluated the effectiveness of our proposed event model representation when assessing the relevance of webpages. Our event model-based representation outperformed the baseline method (topic-only); it showed better results in precision, recall, and F1-score with an improvement of 20% in F1-score. The second experiment series evaluated the effectiveness of the event model-based focused crawler for collecting relevant webpages from the WWW. Our event model-based focused crawler outperformed the state-of-the-art baseline focused crawler (best-first); it showed better results in harvest ratio with an average improvement of 40%.