nils-herrmann/clean_scirepeval huggingface dataset:
Learning to match ontologies on the Semantic Web

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible on the Web scale. Hence the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies and describe experiments that show the promise of the approach.
Junction detection in handwritten documents and its application to writer identification

In this paper, we propose a novel junction detection method in handwritten images, which uses the stroke-length distribution in every direction around a reference point inside the ink of texts. Our proposed junction detection method is simple and efficient, and yields a junction feature in a natural manner, which can be considered as a local descriptor. We apply our proposed junction detector to writer identification by Junclets which is a codebook-based representation trained from the detected junctions. A new challenging data set which contains multiple scripts (English and Chinese) written by the same writers is introduced to evaluate the performance of the proposed junctions for cross-script writer identification. Furthermore, two other common data sets are used to evaluate our junction-based descriptor. Experimental results show that our proposed junction detector is stable under rotation and scale changes, and the performance of writer identification indicates that junctions are important atomic elements to characterize the writing styles. The proposed junction detector is applicable to both historical documents and modern handwritings, and can be used as well for junction retrieval. & 2015
Design of Vedic-multiplier using area-efficient Carry Select Adder

In this paper, area-efficient Vedic multiplier is designed using modified Carry Select Adder (CSLA). As the multiplication is nothing but subsequent addition process, adder is important block in the design of multiplier. Simple Ripple Carry adder (RCA) can be used for implementing multiplier. Digital adder has problem of carry propagation, thus carry select adder is used instead. Carry select adder is known to be one of the fastest adder structures. Here novel technique that is Vedic multiplier is implemented instead of normal multipliers like add and shift multiplier, array multiplier etc. Here multiplier is designed based on ancient Vedic multiplication technique. The goal of this paper is to design area-efficient Vedic multiplier based on crosswise and vertical algorithms. Conventional CSLA designs are compared with proposed design to prove its efficiency. On an average, modified CSLA has 29 % less area than Binary to Excess one Converter (BEC) based CSLA for different bit widths. Proposed Vedic multiplier is designed using modified CSLA, which has approximately 10 % less area than BEC based Vedic multiplier. It shows improved performance in terms of area. Proposed design is also compared with the Booth multiplier. Proposed multiplier showed more excellent results than Booth multiplier.
Universal Semantic Parsing

Universal Dependencies (UD) provides a cross-linguistically uniform syntactic representation, with the aim of advancing multilingual applications of parsing and natural language understanding. Reddy et al. (2016) recently developed a semantic interface for (English) Stanford Dependencies, based on the lambda calculus. In this work, we introduce UDEPLAMBDA, a similar semantic interface for UD, which allows mapping natural language to logical forms in an almost language-independent framework. We evaluate our approach on semantic parsing for the task of question answering against Freebase. To facilitate multilingual evaluation, we provide German and Spanish translations of the WebQuestions and GraphQuestions datasets. Results show that UDEPLAMBDA outperforms strong baselines across languages and datasets. For English, it achieves the strongest result to date on GraphQuestions, with competitive results on WebQuestions.
Integrated task and motion planning in belief space

We describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains based on planning in the belief space of probability distributions over states using hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of logical expressions that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators can give rise to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.
Adversarial Logit Pairing

In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting—an important open scientific question (Athalye et al., 2018). Next, we introduce enhanced defenses using a technique we call logit pairing, a method that encourages logits for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, logit pairing improves accuracy on adversarial examples over vanilla adversarial training; we also find that logit pairing on clean examples only is competitive with adversarial training in terms of accuracy on two datasets. Finally, we show that adversarial logit pairing achieves the state of the art defense on Imagenet against PGD white box attacks, with an accuracy improvement from 1.5% to 27.9%. Adversarial logit pairing also successfully damages the current state of the art defense against black box attacks on Imagenet (Tramèr et al., 2018), dropping its accuracy from 66.6% to 47.1%. With this new accuracy drop, adversarial logit pairing ties with Tramèr et al. (2018) for the state of the art on black box attacks on ImageNet.
Challenges in Open Source Intelligence

A host of tools and techniques are now available for data mining on the Internet. The explosion in social media usage and people reporting brings a new range of problems related to trust and credibility. Traditional media monitoring systems have now reached such sophistication that real time situation monitoring is possible. The challenge though is deciding what reports to believe, how to index them and how to process the data. Vested interests allow groups to exploit both social media and traditional media reports for propaganda purposes. The importance of collecting reports from all sides in a conflict and of balancing claims and counter-claims becomes more important as ease of publishing increases. Today the challenge is no longer accessing open source information but in the tagging, indexing, archiving and analysis of the information. This requires the development of general-purpose and domain specific knowledge bases. Intelligence tools are needed which allow an analyst to rapidly access relevant data covering an evolving situation, ranking sources covering both facts and opinions.
Quantum Annealing and Analog Quantum Computation

We review here the recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations. The concept is introduced in successive steps through the studies of mapping of such computationally hard problems to the classical spin glass problems. The quantum spin glass problems arise with the introduction of quantum fluctuations, and the annealing behavior of the systems as these fluctuations are reduced slowly to zero. This provides a general framework for realizing analog quantum computation.
Cementum formation around a titanium implant: a case report.

Animal studies have shown that a periodontal ligament may be produced around a titanium implant when it is in contact with fractured and retained roots. Formation of cementum and attachment connective tissue around titanium implants confirms that cementum progenitor cells are located in the periodontal ligament, since cementum and periodontal ligament are present at the implant-root interface, whereas the remainder of the implant, which is not in contact with the root, shows osseointegration. The aim was to evaluate histologically the characteristics of the tissue present between a titanium implant and a retained root, which were subsequently extracted as a result of peri-implantitis. The histologic examination revealed a continuous layer of cementum and numerous cementocytes on the implant surface. No blood vessel or collagen fibers were detected in the periodontal space. In contrast to experimental studies carried out on animals, the lack of connective tissue fibers and the presence of hypercementosis in this specimen could have been caused by the inflammatory process. Furthermore, the extrusive movement of the root might explain the presence of cementum hypertrophy. Further studies are required to establish whether the neoformation of cementum and collagen fibers on an implant in the presence of root residues occurs only in animal models or whether it may also occur in humans.
A vision-based formation control framework

We describe a framework for cooperative control of a group of nonholonomic mobile robots that allows us to build complex systems from simple controllers and estimators. The resultant modular approach is attractive because of the potential for reusability. Our approach to composition also guarantees stability and convergence in a wide range of tasks. There are two key features in our approach: 1) a paradigm for switching between simple decentralized controllers that allows for changes in formation; 2) the use of information from a single type of sensor, an omnidirectional camera, for all our controllers. We describe estimators that abstract the sensory information at different levels, enabling both decentralized and centralized cooperative control. Our results include numerical simulations and experiments using a testbed consisting of three nonholonomic robots.
Contextual Priming and Feedback for Faster R-CNN

The field of object detection has seen dramatic performance improvements in the last few years. Most of these gains are attributed to bottom-up, feedforward ConvNet frameworks. However, in case of humans, top-down information, context and feedback play an important role in doing object detection. This paper investigates how we can incorporate top-down information and feedback in the state-of-the-art Faster R-CNN framework. Specifically, we propose to: (a) augment Faster RCNN with a semantic segmentation network; (b) use segmentation for top-down contextual priming; (c) use segmentation to provide top-down iterative feedback using two stage training. Our results indicate that all three contributions improve the performance on object detection, semantic segmentation and region proposal generation.
A unified approach to building hybrid recommender systems

Content-based recommendation systems can provide recommendations for "cold-start" items for which little or no training data is available, but typically have lower accuracy than collaborative filtering systems. Conversely, collaborative filtering techniques often provide accurate recommendations, but fail on cold start items. Hybrid schemes attempt to combine these different kinds of information to yield better recommendations across the board.
 We describe unified Boltzmann machines, which are probabilistic models that combine collaborative and content information in a coherent manner. They encode collaborative and content information as features, and then learn weights that reflect how well each feature predicts user actions. In doing so, information of different types is automatically weighted, without the need for careful engineering of features or for post-hoc hybridization of distinct recommender systems.
 We present empirical results in the movie and shopping domains showing that unified Boltzmann machines can be used to combine content and collaborative information to yield results that are competitive with collaborative techniques in recommending items that have been seen before, and also effective at recommending cold-start items.
The Design Space of Temporal Graph Visualisation

This paper presents our work in mapping the design space of techniques for temporal graph visualisation. We identify two independent dimensions upon which the techniques can be classified: graph structural encoding and temporal encoding. Based on these dimensions, we create a matrix into which we organise existing techniques. We identify gaps in this design space which may prove interesting opportunities for the development of novel techniques. We also consider additional dimensions upon which further useful classification could be made. In organising the disparate existing approaches from a wide range of domains, our classification will assist those new to the research area, and designers and evaluators developing systems for temporal graph data by raising awareness of the range of possible approaches available, and highlighting possible directions for further research.
Predicting links in ego-networks using temporal information

Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships. As the structural information is very poor, we rely on another source of information to predict links among egos’ neighbors: the timing of interactions. We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.
Gridlock Resolution in Interbank Payment Systems

The paper analyses the severity of gridlocks in interbank payment systems operating on a real time basis and evaluates by means of simulations the merits of a gridlock resolution algorithm. Data used in the simulations consist of actual payments settled in the Danish and Finnish RTGS systems. The algorithm is found to be applicable to a real time environment and effective in reducing queuing in the systems at all levels of liquidity, but in particular when intra-day liquidity is scarce.
Maximizing text-mining performance

WITH THE ADVENT OF CENTRALized data warehouses, where data might be stored as electronic documents or as text fields in databases, text mining has increased in importance and economic value. One important goal in text mining is automatic classification of electronic documents. Computer programs scan text in a document and apply a model that assigns the document to one or more prespecified topics. Researchers have used benchmark data, such as the Reuters-21578 test collection, to measure advances in automated text categorization. Conventional methods such as decision trees have had competitive, but not optimal, predictive performance. Using the Reuters collection, we show that adaptive resampling techniques can improve decision-tree performance and that relatively small, pooled local dictionaries are effective. We’ve applied these techniques to online banking applications to enhance automated e-mail routing.
A novel architecture of the 3D stacked MRAM L2 cache for CMPs

Magnetic random access memory (MRAM) is a promising memory technology, which has fast read access, high density, and non-volatility. Using 3D heterogeneous integrations, it becomes feasible and cost-efficient to stack MRAM atop conventional chip multiprocessors (CMPs). However, one disadvantage of MRAM is its long write latency and its high write energy. In this paper, we first stackMRAM-based L2 caches directly atop CMPs and compare it against SRAM counterparts in terms of performance and energy. We observe that the direct MRAM stacking might harm the chip performance due to the aforementioned long write latency and high write energy. To solve this problem, we then propose two architectural techniques: read-preemptive write buffer and SRAM-MRAM hybrid L2 cache. The simulation result shows that our optimized MRAM L2 cache improves performance by 4.91% and reduces power by 73.5%compared to the conventional SRAM L2 cache with the similar area.
Structural Equation Modeling in the Communication Sciences , 1995 – 2000

Structural equation modeling (SEM) is a viable multivariate tool used by communication researchers for the past quarter century. Building off Cappella (1975) as well as McPhee and Babrow (1987), this study summarizes the use of this technique from 1995–2000 in 37 communication-based academic journals. We identify and critically assess 3 unique methods for testing structural relationships via SEM in terms of the specification, estimation, and evaluation of their respective structural equation models. We provide general guidelines for the use of SEM and make recommendations concerning latent variable models, sample size, reporting parameter estimates, model fit statistics, cross-sectional data, univariate normality, cross-validation, nonrecursive modeling, and the decomposition of effects (direct, indirect, and total).
Music enhances category fluency in healthy older adults and Alzheimer's disease patients.

Exposure to some music, in particular classical music, has been reported to produce transient increases in cognitive performance. The authors investigated the effect of listening to an excerpt of Vivaldi's Four Seasons on category fluency in healthy older adult controls and Alzheimer's disease patients. In a counterbalanced repeated-measure design, participants completed two, 1-min category fluency tasks whilst listening to an excerpt of Vivaldi and two, 1-min category fluency tasks without music. The authors report a positive effect of music on category fluency, with performance in the music condition exceeding performance without music in both the healthy older adult control participants and the Alzheimer's disease patients. In keeping with previous reports, the authors conclude that music enhances attentional processes, and that this can be demonstrated in Alzheimer's disease.
Link Prediction Based on Graph Topology : The Predictive Value of the Generalized Clustering Coefficient

Predicting linkages among data objects is a fundamental data mining task in various application domains, including recommender systems, information retrieval, automatic Web hyperlink generation, record linkage, and communication surveillance. In many contexts link prediction is entirely based on the linkage information itself (a prominent example is the collaborative filtering recommendation). Link-structure based link prediction is closely related to a parallel and almost separate stream of research on topological modeling of large-scale graphs. Graph topological modeling builds on random graph theory to find parsimonious graph generation models reproducing empirical topological measures that summarize the global structure of a graph, such as clustering coefficient, average path length, and degree distribution. These well-studied topological measures and graph generation models have direct implications on link prediction. This paper represents initial efforts to explore the connection between link prediction and graph topology. The focus is exclusively on the predictive value of the clustering coefficient measure. The standard clustering coefficient measure is generalized to capture higher-order clustering tendencies. The proposed framework consists of a cycle formation link probability model, a procedure for estimating model parameters based on the generalized clustering coefficients, and model-based link prediction generation. Using the Enron email dataset we demonstrate that the proposed cycle formation model corresponded closely with the actual link probabilities and the link prediction algorithm based on this model outperformed existing algorithms.
Community detection in complex networks using extremal optimization.

We propose a method to find the community structure in complex networks based on an extremal optimization of the value of modularity. The method outperforms the optimal modularity found by the existing algorithms in the literature giving a better understanding of the community structure. We present the results of the algorithm for computer-simulated and real networks and compare them with other approaches. The efficiency and accuracy of the method make it feasible to be used for the accurate identification of community structure in large complex networks.
Probabilistic query expansion using query logs

Query expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by analyzing query logs. These correlations are then used to select high-quality expansion terms for new queries. The experimental results show that our log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods.