nils-herrmann/clean_scirepeval huggingface dataset:
A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids

Mining grid data is an interesting research field which aims at analyzing grid systems with data mining techniques in order to efficiently discover new meaningful knowledge to enhance grid management. In this paper, we focus particularly on how extracted knowledge enables enhancing data replication and replica selection strategies which are important data management techniques commonly used in data grids. Indeed, relevant knowledge such as file access patterns, file correlations, user or job access behavior, prediction of future behavior or network performance, and so on, can be efficiently discovered. These findings are then used to enhance both data replication and replica selection strategies. Various works in this respect are then discussed along with their merits and demerits. In addition, we propose a new guideline to data mining application in the context of data replication and replica selection strategies. & 2015
Real time detection of lane markers in urban streets

We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.
Towards a Welsh Semantic Annotation System

Automatic semantic annotation of natural language data is an important task in Natural Language Processing, and a variety of semantic taggers have been developed for this task, particularly for English. However, for many languages, particularly for low-resource languages, such tools are yet to be developed. In this paper, we report on the development of an automatic Welsh semantic annotation tool (named CySemTagger) in the CorCenCC Project, which will facilitate semantic-level analysis of Welsh language data on a large scale. Based on Lancaster’s USAS semantic tagger framework, this tool tags words in Welsh texts with semantic tags from a semantic classification scheme, and is designed to be compatible with multiple Welsh POS taggers and POS tagsets by mapping different tagsets into a core shared POS tagset that is used internally by CySemTagger. Our initial evaluation shows that the tagger can cover up to 91.78% of words in Welsh text. This tagger is under continuous development, and will provide a critical tool for Welsh language corpus and information processing at semantic level.
How To Rigorously Develop Process Theory Using Case Research

Dynamic phenomena are key concerns of IS researchers. However, the methodological approaches usually selected to investigate IS phenomena often rely on variance theory. Underlying factor models represent a rather static approach to the phenomenon by focusing on independent and dependent variables and explaining the degree of the relationships between them. Process theory has been suggested to overcome this problem. Process theory provides a complementary, dynamic perspective on IS phenomena by explaining how independent and dependent variables are linked in terms of event sequences. Although applying both approaches provides more complete pictures of IS phenomena, a lack of research methods focusing on process theories may hinder this goal. This article seeks to help closing this gap by examining how case research can be applied to develop process theory. We analyzed IS case research as well as process research literature and consolidated inputs from both sources toward a single methodology. The results highlight that the development of process theory benefits from a consistent methodology and quality measures that have been suggested in general case research. However, we also found that each step requires specific consideration of process theory characteristics in order to develop rigorous process theory.
A Double-Tail Latch-Type Voltage Sense Amplifier with 18ps Setup+Hold Time

A latch-type voltage sense amplifier in 90nm CMOS is designed with a separated input and cross-coupled stage. This separation enables fast operation over a wide common-mode and supply voltage range. With a 1-sigma offset of 8mV, the circuit consumes 92fJ/decision with a 1.2V supply. It has an input equivalent noise of 1.5mV and requires 18ps setup-plus-hold time
FastCap: a multipole accelerated 3-D capacitance extraction program

In this paper a fast algorithm for computing the capacitance of a complicated 3-D geometry of ideal conductors in a uniform dielectric is described and its performance in the capacitance extractor FastCap is examined. The algorithm is an acceleration of the boundary-element technique for solving the integral equation associated with the multiconductor capacitance extraction problem. Boundary-element methods become slow when a large number of elements are used because they lead to dense matrix problems, which are typically solved with some form of Gaussian elimination. This implies that the computation grows as n3, where n is the number of panels or tiles needed to accurately discretize the conductor surface charges. In this paper we present a generalized conjugate residual iterative algorithm with a multipole approximation to compute the iterates. This combination reduces the complexity so that accurate multiconductor capacitance calculations grow nearly as nm, where m is the number of conductors. Performance comparisons on integrated circuit bus crossing problems show that for problems with as few as 12 conductors the multipole accelerated boundary element method can be nearly 500 times faster than Gaussian elimination based algorithms, and five to ten times faster than the iterative method alone, depending on required accuracy.
Clustering of time series data - a survey

Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research.
Body mass index reference curves for the UK, 1990.

Reference curves for stature and weight in British children have been available for the past 30 years, and have recently been updated. However weight by itself is a poor indicator of fatness or obesity, and there has never been a corresponding set of reference curves to assess weight for height. Body mass index (BMI) or weight/height has been popular for assessing obesity in adults for many years, but its use in children has developed only recently. Here centile curves for BMI in British children are presented, from birth to 23 years, based on the same large representative sample as used to update the stature and weight references. The charts were derived using Cole's LMS method, which adjusts the BMI distribution for skewness and allows BMI in individual subjects to be expressed as an exact centile or SD score. Use of the charts in clinical practice is aided by the provision of nine centiles, where the two extremes identify the fattest and thinnest four per 1000 of the population.
Deep neural networks for recognizing online handwritten mathematical symbols

This paper presents application of deep learning to recognize online handwritten mathematical symbols. Recently various deep learning architectures such as Convolution neural network (CNN), Deep neural network (DNN) and Long short term memory (LSTM) RNN have been applied to fields such as computer vision, speech recognition and natural language processing where they have been shown to produce state-of-the-art results on various tasks. In this paper, we apply max-out-based CNN and BLSTM to image patterns created from online patterns and to the original online patterns, respectively and combine them. We also compare them with traditional recognition methods which are MRF and MQDF by carrying out some experiments on CROHME database.
C-ICAMA, a centralized intelligent channel assigned multiple access for multi-layer ad-hoc wireless networks with UAVs

Multi-layer ad hoc wireless networks with UAVs is an ideal infrastructure to establish a rapidly deployable wireless communication system any time any where in the world for military applications. In this tactical environment, information traflc is quite asynimetric. Ground jighting units are information consumers and receive jar more data than they transmit. The up-link is used for sending requests for information and some networking configuration overhead with a f a 0 kilobits, while the down-link is used to return the data requested with megabits size (e.g. multimedia file of images and charts). Centralized Intelligent Channel Assigned Multiple Access(C-ICAMA) is a MAC layer protocol proposed for ground backbone nodes to access UAV (Unmanned Aerie1 Vehicle) to solve the highly asymmetric data trafic in this tactical environment. With it’s intelligent scheduling algorithm, it can dynamically allocate bandwidth for up-link and downlink to jit the instantaneous status of symmetric trafJic. The results of C-ICAMA is very promising, due to the dynamic bandwidth allocation of asymmetric i.ip-link and down-link, the access delay is tremendously reduced,
Pose estimation of texture-less cylindrical objects in bin picking using sensor fusion

We propose an approach for emptying of bin using a combination of Image and Range sensor. Offering a complete solution: calibration, segmentation and pose estimation, along with approachability analysis for the estimated pose. The work is novel in the sense that the objects to be picked are featureless and uniformly black in colour, hence existing approaches are not directly applicable. A key point involves optimal utilization of range data acquired from the laser scanner for 3-D segmentation using localized geometric information. This information guides segmentation of the image for better object pose estimation, used for pick-and-drop. We analytically assure the approachability of the object to avoid collision of the manipulator with the bin. Disturbance of objects caused during pick up has been modelled, which allows pickup of multiple pellets based on information from a single range scan. This eliminates the necessity of repeated scanning and data conditioning. The proposed method offers high object detection rate and pose estimation accuracy. The innovative techniques aimed at reducing the average pickup time makes it suitable for robust industrial operation.
Mashup Service Recommendation Based on User Interest and Social Network

With the rapid development of Web2.0 and its related technologies, Mashup services (i.e., Web applications created by combining two or more Web APIs) are becoming a hot research topic. The explosion of Mashup services, especially the functionally similar or equivalent services, however, make services discovery more difficult than ever. In this paper, we present an approach to recommend Mashup services to users based on user interest and social network of services. This approach firstly extracts users' interests from their Mashup service usage history and builds a social network based on social relationships information among Mashup services, Web APIs and their tags. The approach then leverages the target user's interest and the social network to perform Mashup service recommendation. Large-scale experiments based on a real-world Mashup service dataset show that our proposed approach can effectively recommend Mashup services to users with excellent performance. Moreover, a Mashup service recommendation prototype system is developed.
Global Ridge Orientation Modeling for Partial Fingerprint Identification

Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. First, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification.
Acceptance and Commitment Therapy and the Third Wave of Behavior Therapy

Acceptance and Commitment Therapy (ACT) is a mindfulness, acceptance, and values-based psychotherapy, grounded in the behavioral and cognitive behavioral traditions. ACT attempts to alter the normal impact of human cognition in order to increase the capacity for behavior change. The present paper briefly discusses philosophical and theoretical positions of ACT along with empirical supports for ACT theory. An overview of ACT intervention is given, followed by ACT outcome data in a variety of psychological disorders. Finally, an ACT case report is provided.  Acceptance and Commitment Therapy; Cognitive Defusion; Mindfulness; Relational Frame Theory; Values;
Social interaction detection using a multi-sensor approach

In the context of a social gathering, such as a cocktail party, the memorable moments are often captured by professional photographers or the participants. The latter case is generally undesirable because many participants would rather enjoy the event instead of being occupied by the tedious photo capturing task. Motivated by this scenario, we propose an automated social event photo-capture framework for which, given the multiple sensor data streams and the information from the Web as input, will output the visually appealing photos of the social event. Our proposal consists of three components: (1) social attribute extraction from both the physical space and the cyberspace; (2) social attribute fusion; and (3) active camera control. Current work is presented and we conclude with expected contributions as well as future direction.
Network Sampling: From Static to Streaming Graphs

Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete understanding of network sampling is critical to support the field of network science. In this paper, we outline a framework for the general problem of network sampling by highlighting the different objectives, population and units of interest, and classes of network sampling methods. In addition, we propose a spectrum of computational models for network sampling methods, ranging from the traditionally studied model based on the assumption of a static domain to a more challenging model that is appropriate for streaming domains. We design a family of sampling methods based on the concept of graph induction that generalize across the full spectrum of computational models (from static to streaming) while efficiently preserving many of the topological properties of the input graphs. Furthermore, we demonstrate how traditional static sampling algorithms can be modified for graph streams for each of the three main classes of sampling methods: node, edge, and topology-based sampling. Experimental results indicate that our proposed family of sampling methods more accurately preserve the underlying properties of the graph in both static and streaming domains. Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms.
MagicFuzzer: Scalable deadlock detection for large-scale applications

We present MagicFuzzer, a novel dynamic deadlock detection technique. Unlike existing techniques to locate potential deadlock cycles from an execution, it iteratively prunes lock dependencies that each has no incoming or outgoing edge. Combining with a novel thread-specific strategy, it dramatically shrinks the size of lock dependency set for cycle detection, improving the efficiency and scalability of such a detection significantly. In the real deadlock confirmation phase, it uses a new strategy to actively schedule threads of an execution against the whole set of potential deadlock cycles. We have implemented a prototype and evaluated it on large-scale C/C++ programs. The experimental results confirm that our technique is significantly more effective and efficient than existing techniques.
Automatic detection and verification of rumors on Twitter

The spread of malicious or accidental misinformation in social media, especially in timesensitive situations such as real-world emergencies can have harmful effects on individuals and society. This thesis develops models for automated detection and verification of rumors (unverified information) that propagate through Twitter. Detection of rumors about an event is achieved through classifying and clustering assertions made about that event. Assertions are classified through a speech-act classifier for Twitter developed for this thesis. The classifier utilizes a combination of semantic and syntactic features to identify assertions with 91% accuracy. To predict the veracity of rumors, we identify salient features of rumors by examining three aspects of information spread: linguistic style used to express rumors, characteristics of people involved in propagating information, and network propagation dynamics. The predicted veracity of a time series of these features extracted from a rumor (a collection of tweets) is generated using Hidden Markov Models. The verification algorithm was tested on 209 rumors representing 938,806 tweets collected from real-world events including the 2013 Boston Marathon bombings, the 2014 Ferguson unrest and the 2014 Ebola epidemic, and many other rumors reported on popular websites that document public rumors. The algorithm is able to predict the veracity of rumors with an accuracy of 75%. The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter.  Deb K.  Associate Professor, Program in Media Arts and Sciences
What Shape Are Dolphins? Building 3D Morphable Models from 2D Images

3D morphable models are low-dimensional parameterizations of 3D object classes which provide a powerful means of associating 3D geometry to 2D images. However, morphable models are currently generated from 3D scans, so for general object classes such as animals they are economically and practically infeasible. We show that, given a small amount of user interaction (little more than that required to build a conventional morphable model), there is enough information in a collection of 2D pictures of certain object classes to generate a full 3D morphable model, even in the absence of surface texture. The key restriction is that the object class should not be strongly articulated, and that a very rough rigid model should be provided as an initial estimate of the “mean shape.” The model representation is a linear combination of subdivision surfaces, which we fit to image silhouettes and any identifiable key points using a novel combined continuous-discrete optimization strategy. Results are demonstrated on several natural object classes, and show that models of rather high quality can be obtained from this limited information.
Stochastic Kronecker Graphs

A random graph model based on Kronecker products of probability matrices has been recently proposed as a generative model for large-scale real-world networks such as the web. This model simultaneously captures several well-known properties of real-world networks; in particular, it gives rise to a heavy-tailed degree distribution, has a low diameter, and obeys the densification power law. Most properties of Kronecker products of graphs (such as connectivity and diameter) are only rigorously analyzed in the deterministic case. In this paper, we study the basic properties of stochastic Kronecker products based on an initiator matrix of size two (which is the case that is shown to provide the best fit to many real-world networks). We will show a phase transition for the emergence of the giant component and another phase transition for connectivity, and prove that such graphs have constant diameters beyond the connectivity threshold, but are not searchable using a decentralized algorithm.
A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo

Nanoscale robots have potential as intelligent drug delivery systems that respond to molecular triggers. Using DNA origami we constructed an autonomous DNA robot programmed to transport payloads and present them specifically in tumors. Our nanorobot is functionalized on the outside with a DNA aptamer that binds nucleolin, a protein specifically expressed on tumor-associated endothelial cells, and the blood coagulation protease thrombin within its inner cavity. The nucleolin-targeting aptamer serves both as a targeting domain and as a molecular trigger for the mechanical opening of the DNA nanorobot. The thrombin inside is thus exposed and activates coagulation at the tumor site. Using tumor-bearing mouse models, we demonstrate that intravenously injected DNA nanorobots deliver thrombin specifically to tumor-associated blood vessels and induce intravascular thrombosis, resulting in tumor necrosis and inhibition of tumor growth. The nanorobot proved safe and immunologically inert in mice and Bama miniature pigs. Our data show that DNA nanorobots represent a promising strategy for precise drug delivery in cancer therapy.