nils-herrmann/clean_scirepeval huggingface dataset:
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of a group, shrinkage of the transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Non-linear Metric Learning

In this paper, we introduce two novel metric learning algorithms, χ-LMNN and GB-LMNN, which are explicitly designed to be non-linear and easy-to-use. The two approaches achieve this goal in fundamentally different ways: χ-LMNN inherits the computational benefits of a linear mapping from linear metric learning, but uses a non-linear χ-distance to explicitly capture similarities within histogram data sets; GB-LMNN applies gradient-boosting to learn non-linear mappings directly in function space and takes advantage of this approach’s robustness, speed, parallelizability and insensitivity towards the single additional hyperparameter. On various benchmark data sets, we demonstrate these methods not only match the current state-of-the-art in terms of kNN classification error, but in the case of χ-LMNN, obtain best results in 19 out of 20 learning settings.
On the Helmholtz Principle for Data Mining

Unusual behaviour detection and information extraction in streams of short documents and files (emails, news, tweets, log files, messages, etc.) are important problems in security applications. In , , a new approach to rapid change detection and automatic summarization of large documents was introduced. This approach is based on a theory of social networks and ideas from image processing and especially on the Helmholtz Principle from the Gestalt Theory of human perception. In this article we modify, optimize and verify the approach from ,  to unusual behaviour detection and information extraction from small documents.
Temporal profiles of queries

Documents with timestamps, such as email and news, can be placed along a timeline. The timeline for a set of documents returned in response to a query gives an indication of how documents relevant to that query are distributed in time. Examining the timeline of a query result set allows us to characterize both how temporally dependent the topic is, as well as how relevant the results are likely to be. We outline characteristic patterns in query result set timelines, and show experimentally that we can automatically classify documents into these classes. We also show that properties of the query result set timeline can help predict the mean average precision of a query. These results show that meta-features associated with a query can be combined with text retrieval techniques to improve our understanding and treatment of text search on documents with timestamps.
Flexible and stretchable electronics for biointegrated devices.

Advances in materials, mechanics, and manufacturing now allow construction of high-quality electronics and optoelectronics in forms that can readily integrate with the soft, curvilinear, and time-dynamic surfaces of the human body. The resulting capabilities create new opportunities for studying disease states, improving surgical procedures, monitoring health/wellness, establishing human-machine interfaces, and performing other functions. This review summarizes these technologies and illustrates their use in forms integrated with the brain, the heart, and the skin.
Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners

Numerous studies have begun to address how the brain's gray and white matter may be shaped by meditation. This research is yet to be integrated, however, and two fundamental questions remain: Is meditation associated with altered brain structure? If so, what is the magnitude of these differences? To address these questions, we reviewed and meta-analyzed 123 brain morphology differences from 21 neuroimaging studies examining ∼300 meditation practitioners. Anatomical likelihood estimation (ALE) meta-analysis found eight brain regions consistently altered in meditators, including areas key to meta-awareness (frontopolar cortex/BA 10), exteroceptive and interoceptive body awareness (sensory cortices and insula), memory consolidation and reconsolidation (hippocampus), self and emotion regulation (anterior and mid cingulate; orbitofrontal cortex), and intra- and interhemispheric communication (superior longitudinal fasciculus; corpus callosum). Effect size meta-analysis (calculating 132 effect sizes from 16 studies) suggests a global 'medium' effect size (Cohen's d¯=0.46; r¯=.19). Publication bias and methodological limitations are strong concerns, however. Further research using rigorous methods is required to definitively link meditation practice to altered brain morphology.
Dynamic Prefetching of Data Tiles for Interactive Visualization

In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a client-server architecture, where the user interacts with a lightweight client-side interface to browse datasets, and the data to be browsed is retrieved from a DBMS running on a back-end server. We assume a detail-on-demand browsing paradigm, and optimize the back-end support for this paradigm by inserting a separate middleware layer in front of the DBMS. To improve response times, the middleware layer fetches data ahead of the user as she explores a dataset.
 We consider two different mechanisms for prefetching: (a) learning what to fetch from the user's recent movements, and (b) using data characteristics (e.g., histograms) to find data similar to what the user has viewed in the past. We incorporate these mechanisms into a single prediction engine that adjusts its prediction strategies over time, based on changes in the user's behavior. We evaluated our prediction engine with a user study, and found that our dynamic prefetching strategy provides: (1) significant improvements in overall latency when compared with non-prefetching systems (430% improvement); and (2) substantial improvements in both prediction accuracy (25% improvement) and latency (88% improvement) relative to existing prefetching techniques.
Detection and Handling of MAC Layer Misbehavior in Wireless Networks

Selfish hosts in wireless networks that fail to adhere to the MAC protocol may obtain an unfair share of the channel bandwidth. We present modifications to the IEEE 802.11 backoff mechanism to simplify detection of such selfish hosts. We also present a correction scheme for penalizing greedy misbehavior which attempts to restrict the misbehaving nodes to a fair share of the channel bandwidth. Simulation results indicate that our detection and correction schemes are fairly successful in handling MAC layer misbehavior.
A Compact Printed Antenna for Triple-Band WLAN/WiMAX Applications

In this letter, a novel compact printed antenna for triple-band WLAN/WiMAX applications is presented. The proposed antenna consists of three simple circular-arc-shaped strips, whose whole geometry looks like “ear” type. By adjusting the geometries and the sizes of these three circular-arc-shaped strips, three different resonance modes can be effectively created for three distinct frequency bands, respectively. The overall dimension of the proposed antenna can reach <formula formulatype="inline"><tex Notation="TeX">$18\times 37\times 1\ {\hbox{mm}}^{3}$</tex> </formula>. Measured results show that the presented antenna can cover three separated impedance bandwidths of 400 MHz (2.38–2.78 GHz), 480 MHz (3.28–3.76 GHz), and 1000 MHz (4.96–5.96 GHz), which are well applied for both 2.4/5.2/5.8-GHz WLAN bands and 2.5/3.5/5.5-GHz WiMAX bands.
Towards a Semantic Extract-Transform-Load (ETL) Framework for Big Data Integration

Big Data has become the new ubiquitous term used to describe massive collection of datasets that are difficult to process using traditional database and software techniques. Most of this data is inaccessible to users, as we need technology and tools to find, transform, analyze, and visualize data in order to make it consumable for decision-making. One aspect of Big Data research is dealing with the Variety of data that includes various formats such as structured, numeric, unstructured text data, email, video, audio, stock ticker, etc. Managing, merging, and governing a variety of data is the focus of this paper. This paper proposes a semantic Extract-Transform-Load (ETL) framework that uses semantic technologies to integrate and publish data from multiple sources as open linked data. This includes - creation of a semantic data model to provide a basis for integration and understanding of knowledge from multiple sources, creation of a distributed Web of data using Resource Description Framework (RDF) as the graph data model, extraction of useful knowledge and information from the combined data using SPARQL as the semantic query language.
Testing and validating machine learning classifiers by metamorphic testing

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no "test oracle" to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique "metamorphic testing", which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program.
A General Geographical Probabilistic Factor Model for Point of Interest Recommendation

The problem of point of interest (POI) recommendation is to provide personalized recommendations of places, such as restaurants and movie theaters. The increasing prevalence of mobile devices and of location based social networks (LBSNs) poses significant new opportunities as well as challenges, which we address. The decision process for a user to choose a POI is complex and can be influenced by numerous factors, such as personal preferences, geographical considerations, and user mobility behaviors. This is further complicated by the connection LBSNs and mobile devices. While there are some studies on POI recommendations, they lack an integrated analysis of the joint effect of multiple factors. Meanwhile, although latent factor models have been proved effective and are thus widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of LBSNs. To this end, in this paper, we propose a general geographical probabilistic factor model (Geo-PFM) framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user's check-in behavior. Also, user mobility behaviors can be effectively leveraged in the recommendation model. Moreover, based our Geo-PFM framework, we further develop a Poisson Geo-PFM which provides a more rigorous probabilistic generative process for the entire model and is effective in modeling the skewed user check-in count data as implicit feedback for better POI recommendations. Finally, extensive experimental results on three real-world LBSN datasets (which differ in terms of user mobility, POI geographical distribution, implicit response data skewness, and user-POI observation sparsity), show that the proposed recommendation methods outperform state-of-the-art latent factor models by a significant margin.
Fast Discrete Curvelet Transforms

This paper describes two digital implementations of a new mathematical transform, namely, the second generation curvelet transform in two and three dimensions. The first digital transformation is based on unequally spaced fast Fourier transforms, while the second is based on the wrapping of specially selected Fourier samples. The two implementations essentially differ by the choice of spatial grid used to translate curvelets at each scale and angle. Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an orientation parameter, and a spatial location parameter. And both implementations are fast in the sense that they run in O(n2 logn) flops for n by n Cartesian arrays; in addition, they are also invertible, with rapid inversion algorithms of about the same complexity. Our digital transformations improve upon earlier implementations—based upon the first generation of curvelets—in the sense that they are conceptually simpler, faster, and far less redundant. The software CurveLab, which implements both transforms presented in this paper, is available at http://www.curvelet.org.
Mining topics in documents: standing on the shoulders of big data

Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (e.g., thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have so many documents. Given a small number of documents, the classic topic model LDA generates very poor topics. Even with a large volume of data, unsupervised learning of topic models can still produce unsatisfactory results. In recently years, knowledge-based topic models have been proposed, which ask human users to provide some prior domain knowledge to guide the model to produce better topics. Our research takes a radically different approach. We propose to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning. When faced with a new task, we first mine some reliable (prior) knowledge from the past learning/modeling results and then use it to guide the model inference to generate more coherent topics. This approach is possible because of the big data readily available on the Web. The proposed algorithm mines two forms of knowledge: must-link (meaning that two words should be in the same topic) and cannot-link (meaning that two words should not be in the same topic). It also deals with two problems of the automatically mined knowledge, i.e., wrong knowledge and knowledge transitivity. Experimental results using review documents from 100 product domains show that the proposed approach makes dramatic improvements over state-of-the-art baselines.
The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices

The limitation of using low electron doses in non-destructive cryo-electron tomography of biological specimens can be partially offset via averaging of aligned and structurally homogeneous subsets present in tomograms. This type of sub-volume averaging is especially challenging when multiple species are present. Here, we tackle the problem of conformational separation and alignment with a "collaborative" approach designed to reduce the effect of the "curse of dimensionality" encountered in standard pair-wise comparisons. Our new approach is based on using the nuclear norm as a collaborative similarity measure for alignment of sub-volumes, and by exploiting the presence of symmetry early in the processing. We provide a strict validation of this method by analyzing mixtures of intact simian immunodeficiency viruses SIV mac239 and SIV CP-MAC. Electron microscopic images of these two virus preparations are indistinguishable except for subtle differences in conformation of the envelope glycoproteins displayed on the surface of each virus particle. By using the nuclear norm-based, collaborative alignment method presented here, we demonstrate that the genetic identity of each virus particle present in the mixture can be assigned based solely on the structural information derived from single envelope glycoproteins displayed on the virus surface.
Context-aware Path Ranking for Knowledge Base Completion

Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entitypairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy. C-PR learns global semantics of entities in the KB using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk. Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines.
Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner's preference tree

The explosion of the learning materials in personal learning environments has caused difficulties to locate appropriate learning materials to learners. Personalized recommendations have been used to support the activities of learners in personal learning environments and this technology can deliver suitable learning materials to learners. In order to improve the quality of recommendations, this research considers the multidimensional attributes of material, rating of learners, and the order and sequential patterns of the learner's accessed material in a unified model. The proposed approach has two modules. In the sequential-based recommendation module, latent patterns of accessing materials are discovered and presented in two formats including the weighted association rules and the compact tree structure (called Pattern-tree). In the attribute-based module, after clustering the learners using latent patterns by K-means algorithm, the learner preference tree (LPT) is introduced to consider the multidimensional attributes of materials, rating of learners, and also order of the accessed materials. The mixed, weighted, and cascade hybrid methods are employed to generate the final combined recommendations. The experiments show that the proposed approach outperforms the previous algorithms in terms of precision, recall, and intra-list similarity measure. The main contributions are improvement of the recommenda-tions' quality and alleviation of the sparsity problem by combining the contextual information, including order and sequential patterns of the accessed material, rating of learners, and the multidimensional attributes of materials. With the explosion of learning materials available on personal learning environments (PLEs), it is difficult for learners to discover the most appropriate materials according to keyword searching method. One way to address this challenge is the use of recom-mender systems [16]. In addition, up to very recent years, several researches have expressed the need for personalization in e-learning environments. In fact, one of the new forms of personalization in e-learning environments is to provide recommendations to learners to support and help them through the e-learning process [19]. According to the strategies applied, recommender systems can be segmented into three major categories: content-based, collabo-rative, and hybrid recommendation . Hybrid recommendation mechanisms attempt to deal with some of the limitations and overcome the drawbacks of pure content-based approach and pure collaborative approach by combining the two approaches. The majority of the traditional recommendation algorithms have been developed for e-commerce applications, which are unable to cover the entire requirements of learning environments. One of these drawbacks is that they do not consider the learning process in their recommendation …
Lift-Off: Using Reference Imagery and Freehand Sketching to Create 3D Models in VR

Three-dimensional modeling has long been regarded as an ideal application for virtual reality (VR), but current VR-based 3D modeling tools suffer from two problems that limit creativity and applicability: (1) the lack of control for freehand modeling, and (2) the difficulty of starting from scratch. To address these challenges, we present Lift-Off, an immersive 3D interface for creating complex models with a controlled, handcrafted style. Artists start outside of VR with 2D sketches, which are then imported and positioned in VR. Then, using a VR interface built on top of image processing algorithms, 2D curves within the sketches are selected interactively and “lifted” into space to create a 3D scaffolding for the model. Finally, artists sweep surfaces along these curves to create 3D models. Evaluations are presented for both long-term users and for novices who each created a 3D sailboat model from the same starting sketch. Qualitative results are positive, with the visual style of the resulting models of animals and other organic subjects as well as architectural models matching what is possible with traditional fine art media. In addition, quantitative data from logging features built into the software are used to characterize typical tool use and suggest areas for further refinement of the interface.