nils-herrmann/clean_scirepeval huggingface dataset:
Directed Information Graphs

We propose a graphical model for representing networks of stochastic processes, the minimal generative model graph. It is based on reduced factorizations of the joint distribution over time. We show that under appropriate conditions, it is unique and consistent with another type of graphical model, the directed information graph, which is based on a generalization of Granger causality. We demonstrate how directed information quantifies Granger causality in a particular sequential prediction setting. We also develop efficient methods to estimate the topological structure from data that obviate estimating the joint statistics. One algorithm assumes upper bounds on the degrees and uses the minimal dimension statistics necessary. In the event that the upper bounds are not valid, the resulting graph is nonetheless an optimal approximation in terms of Kullback-Leibler (KL) divergence. Another algorithm uses near-minimal dimension statistics when no bounds are known, but the distribution satisfies a certain criterion. Analogous to how structure learning algorithms for undirected graphical models use mutual information estimates, these algorithms use directed information estimates. We characterize the sample-complexity of two plug-in directed information estimators and obtain confidence intervals. For the setting when point estimates are unreliable, we propose an algorithm that uses confidence intervals to identify the best approximation that is robust to estimation error. Last, we demonstrate the effectiveness of the proposed algorithms through the analysis of both synthetic data and real data from the Twitter network. In the latter case, we identify which news sources influence users in the network by merely analyzing tweet times.
Matrix Completion on Graphs

The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Although the problem under the standard low rank assumption is NP-hard, Candès and Recht showed that it can be exactly relaxed if the number of observed entries is sufficiently large. In this work, we introduce a novel matrix completion model that makes use of proximity information about rows and columns by assuming they form communities. This assumption makes sense in several real-world problems like in recommender systems, where there are communities of people sharing preferences, while products form clusters that receive similar ratings. Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs. We borrow ideas from manifold learning to constrain our solution to be smooth on these graphs, in order to implicitly force row and column proximities. Our matrix recovery model is formulated as a convex non-smooth optimization problem, for which a well-posed iterative scheme is provided. We study and evaluate the proposed matrix completion on synthetic and real data, showing that the proposed structured low-rank recovery model outperforms the standard matrix completion model in many situations.
Modeling, Identification, and Control of Tendon-Based Actuation Systems

In this paper, we deal with several aspects related to the control of tendon-based actuation systems for robotic devices. In particular, the problems that are considered in this paper are related to the modeling, identification, and control of tendons sliding on curved pathways, subject to friction and viscoelastic effects. Tendons made in polymeric materials are considered, and therefore, hysteresis in the transmission system characteristic must be taken into account as an additional nonlinear effect because of the plasticity and creep phenomena typical of these materials. With the aim of reproducing these behaviors, a viscoelastic model is used to model the tendon compliance. Particular attention has been given to the friction effects arising from the interaction between the tendon pathway and the tendon itself. This phenomenon has been characterized by means of a LuGre-like dynamic friction model to consider the effects that cannot be reproduced by employing a static friction model. A specific setup able to measure the tendon's tension in different points along its path has been designed in order to verify the tension distribution and identify the proper parameters. Finally, a simple control strategy for the compensation of these nonlinear effects and the control of the force that is applied by the tendon to the load is proposed and experimentally verified.
Biological identifications through DNA barcodes.

Although much biological research depends upon species diagnoses, taxonomic expertise is collapsing. We are convinced that the sole prospect for a sustainable identification capability lies in the construction of systems that employ DNA sequences as taxon 'barcodes'. We establish that the mitochondrial gene cytochrome c oxidase I (COI) can serve as the core of a global bioidentification system for animals. First, we demonstrate that COI profiles, derived from the low-density sampling of higher taxonomic categories, ordinarily assign newly analysed taxa to the appropriate phylum or order. Second, we demonstrate that species-level assignments can be obtained by creating comprehensive COI profiles. A model COI profile, based upon the analysis of a single individual from each of 200 closely allied species of lepidopterans, was 100% successful in correctly identifying subsequent specimens. When fully developed, a COI identification system will provide a reliable, cost-effective and accessible solution to the current problem of species identification. Its assembly will also generate important new insights into the diversification of life and the rules of molecular evolution.
Visual Similarity Based 3D Shape Retrieval Using Bag-of-Features

This paper presents a novel 3D shape retrieval method, which uses Bag-of-Features and an efficient multi-view shape matching scheme. In our approach, a properly normalized object is first described by a set of depth-buffer views captured on the surrounding vertices of a given unit geodesic sphere. We then represent each view as a word histogram generated by the vector quantization of the view’s salient local features. The dissimilarity between two 3D models is measured by the minimum distance of their all (24) possible matching pairs. This paper also investigates several critical issues including the influence of the number of views, codebook, training data, and distance function. Experiments on four commonly-used benchmarks demonstrate that: 1) Our approach obtains superior performance in searching for rigid models. 2) The local feature and global feature based methods are somehow complementary. Moreover, a linear combination of them significantly outperforms the state-of-the-art in terms of retrieval accuracy.
MUTEC: Mutation-based testing of Cross Site Scripting

Cross Site Scripting (XSS) is one of the worst vulnerabilities that allow malicious attacks such as cookie thefts and Web page defacements. Testing an implementation against XSS vulnerabilities (XSSVs) can avoid these consequences. Obtaining an adequate test data set is essential for testing of XSSVs. An adequate test data set contains effective test cases that can reveal XSSVs. Unfortunately, traditional testing techniques for XSSVs do not address the issue of adequate testing. In this work, we apply the idea of mutation-based testing technique to generate adequate test data sets for testing XSSVs. Our work addresses XSSVs related to Web-applications that use PHP and JavaScript code to generate dynamic HTML contents. We propose 11 mutation operators to force the generation of adequate test data set. A prototype mutation-based testing tool named MUTEC is developed to generate mutants automatically. The proposed operators are validated by using five open source applications having XSSVs. The results indicate that the proposed operators are effective for testing XSSVs.
Adaptive Web Sites: an AI Challenge

The creation of a complex web site is a thorny problem in user interface design. First, di erent visitors have distinct goals. Second, even a single visitor may have di erent needs at different times. Much of the information at the site may also be dynamic or time-dependent. Third, as the site grows and evolves, its original design may no longer be appropriate. Finally, a site may be designed for a particular purpose but used in unexpected ways. Web servers record data about user interactions and accumulate this data over time. We believe that AI techniques can be used to examine user access logs in order to automatically improve the site. We challenge the AI community to create adaptive web sites: sites that automatically improve their organization and presentation based on user access data. Several unrelated research projects in plan recognition, machine learning, knowledge representation, and user modeling have begun to explore aspects of this problem. We hope that posing this challenge explicitly will bring these projects together and stimulate fundamental AI research. Success would have a broad and highly visible impact on the web and the AI community.
Semi-Supervised Learning with Very Few Labeled Training Examples

In semi-supervised learning, a number of labeled examples are usually required for training an initial weakly useful predictor which is in turn used for exploiting the unlabeled examples. However, in many real-world applications there may exist very few labeled training examples, which makes the weakly useful predictor difficult to generate, and therefore these semisupervised learning methods cannot be applied. This paper proposes a method working under a two-view setting. By taking advantages of the correlations between the views using canonical component analysis, the proposed method can perform semi-supervised learning with only one labeled training example. Experiments and an application to content-based image retrieval validate the effectiveness of the proposed method.
Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

In this paper, we address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions. Using linguistic context and visual features, our method is able to efficiently hypothesize the semantic meaning of new words and add them to its word dictionary so that they can be used to describe images which contain these novel concepts. Our method has an image captioning module based on with several improvements. In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task. We propose methods to prevent overfitting the new concepts. In addition, three novel concept datasets are constructed for this new task, and are publicly available on the project page. In the experiments, we show that our method effectively learns novel visual concepts from a few examples without disturbing the previously learned concepts. The project page is: www.stat.ucla.edu/junhua. mao/projects/child_learning.html.
Ice-Breaking: Mitigating Cold-Start Recommendation Problem by Rating Comparison

Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the coldstart problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RAPARE strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.
Hierarchical Monte-Carlo Planning

Monte-Carlo Tree Search, especially UCT and its POMDP version POMCP, have demonstrated excellent performance on many problems. However, to efficiently scale to large domains one should also exploit hierarchical structure if present. In such hierarchical domains, finding rewarded states typically requires to search deeply; covering enough such informative states very far from the root becomes computationally expensive in flat non-hierarchical search approaches. We propose novel, scalable MCTS methods which integrate a task hierarchy into the MCTS framework, specifically leading to hierarchical versions of both, UCT and POMCP. The new method does not need to estimate probabilistic models of each subtask, it instead computes subtask policies purely sample-based. We evaluate the hierarchical MCTS methods on various settings such as a hierarchical MDP, a Bayesian model-based hierarchical RL problem, and a large hierarchi-
Real exhibitions in a virtual museum

When creating a virtual environment open to the public a number of challenges have to be addressed. The equipment has to be chosen carefully in order to be be able to withstand hard everyday usage, and the application has not only to be robust and easy to use, but has also to be appealing to the user, etc. The current paper presents findings gathered from the creation of a multi-thematic virtual museum environment to be offered to visitors of real world museums. A number of design and implementation aspects are described along with an experiment designed to evaluate alternative approaches for implementing the navigation in a virtual museum environment. The paper is concluded with insights gained from the development of the virtual museum and portrays future research plans.
Enumerating possible Sudoku grids

In some of these boxes, the setter puts some of the digits 1–9; the aim of the solver is to complete the grid by filling in a digit in every box in such a way that each row, each column, and each 3 × 3 box contains each of the digits 1–9 exactly once. In this note, we discuss the problem of enumerating all possible Sudoku grids. This is a very natural problem, but, perhaps surprisingly, it seems unlikely that the problem should have a simple combinatorial answer. Indeed, Sudoku grids are simply special cases of Latin squares, and the enumeration of Latin squares is itself a difficult problem, with no general combinatorial formulae known. Latin squares of sizes up to 11 × 11 have been enumerated, and the methods are broadly brute force calculations, much like the approach we sketch for Sudoku grids below. See ,  and  for more details. It is known that the number of 9 × 9 Latin squares is 5524751496156892842531225600 ≈ 5.525 × 10. Since this answer is enormous, we need to refine our search considerably in order to be able to get an answer in a sensible amount of computing time.
Social snapshots: digital forensics for online social networks

Recently, academia and law enforcement alike have shown a strong demand for data that is collected from online social networks. In this work, we present a novel method for harvesting such data from social networking websites. Our approach uses a hybrid system that is based on a custom add-on for social networks in combination with a web crawling component. The datasets that our tool collects contain profile information (user data, private messages, photos, etc.) and associated meta-data (internal timestamps and unique identifiers). These social snapshots are significant for security research and in the field of digital forensics. We implemented a prototype for Facebook and evaluated our system on a number of human volunteers. We show the feasibility and efficiency of our approach and its advantages in contrast to traditional techniques that rely on application-specific web crawling and parsing. Furthermore, we investigate different use-cases of our tool that include consensual application and the use of sniffed authentication cookies. Finally, we contribute to the research community by publishing our implementation as an open-source project.
3D Reconstruction of Transparent Objects with Position-Normal Consistency

Estimating the shape of transparent and refractive objects is one of the few open problems in 3D reconstruction. Under the assumption that the rays refract only twice when traveling through the object, we present the first approach to simultaneously reconstructing the 3D positions and normals of the object's surface at both refraction locations. Our acquisition setup requires only two cameras and one monitor, which serves as the light source. After acquiring the ray-ray correspondences between each camera and the monitor, we solve an optimization function which enforces a new position-normal consistency constraint. That is, the 3D positions of surface points shall agree with the normals required to refract the rays under Snell's law. Experimental results using both synthetic and real data demonstrate the robustness and accuracy of the proposed approach.
Word Embeddings through Hellinger PCA

Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming to train. Instead, we propose to drastically simplify the word embeddings computation through a Hellinger PCA of the word cooccurence matrix. We compare those new word embeddings with some well-known embeddings on named entity recognition and movie review tasks and show that we can reach similar or even better performance. Although deep learning is not really necessary for generating good word embeddings, we show that it can provide an easy way to adapt embeddings to specific tasks.
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio

Rhythmic patterns are an important structural element in music. This paper investigates the use of rhythmic pattern modeling to infer metrical structure in musical audio recordings. We present a Hidden Markov Model (HMM) based system that simultaneously extracts beats, downbeats, tempo, meter, and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al [20], which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. For learning rhythmic patterns and evaluating beat and downbeat tracking, 697 ballroom dance pieces were annotated with beat and measure information. The results showed that explicitly modeling rhythmic patterns of dance styles drastically reduces octave errors (detection of half or double tempo) and substantially improves downbeat tracking.
A Three-Phase Delta Switch Rectifier for Use in Modern Aircraft

In the course of the More Electric Aircraft program frequently active three-phase rectifiers in the power range of several kilowatts are required. It is shown that the three-phase -switch rectifier (comprising three -connected bidirectional switches) is well suited for this application. The system is analyzed using space vector calculus and a novel PWM current controller modulation concept is presented, where all three phases are controlled simultaneously; the analysis shows that the proposed concept yields optimal switching sequences. Analytical relationships for calculating the power components average and rms current ratings are derived to facilitate the rectifier design. A laboratory prototype with an output power of 5 kW is built and measurements taken from this prototype confirm the operation of the proposed current controller. Finally, initial EMI-measurements of the system are also presented.
A 12-bit 20 MS/s 56.3 mW Pipelined ADC With Interpolation-Based Nonlinear Calibration

The linearity of a high-resolution pipelined analog- to-digital converter (ADC) is mainly limited by the capacitor mismatch and the finite operational amplifier (OPAMP) gain in the multiplying-digital-to-analog converter (MDAC). Therefore, high resolution pipelined ADCs usually require high-gain OPAMP and large capacitors, which causes large ADC power. In recent years, various nonlinear calibration techniques have been developed to compensate both linear and nonlinear error from MDCAs so that low-power MDACs with small capacitors and low-gain OPAMP can be used. Hence, the ADC power can be greatly reduced. This paper introduces a novel interpolation- based digital self-calibration architecture for pipelined ADC. Compared to previous techniques, the new architecture is free of adaptation. Hence, long convergence is not needed. The complexity of the digital processor is also considerably lower. The new architecture does not use backend ADC to measure MDACs. Hence, it is free of the accumulation of measurement error, which leads to more accurate calibration. A prototype ADC with the calibration architecture is fabricated in a 0.35 3.3 V CMOS process. The ADC samples at 20 MS/s. The calibration improves the ADC DNL and INL from 1.47 LSB and 7.85 LSB to 0.2 LSB and 0.27 LSB. For a 590 kHz sinusoidal signal, the calibration improves the ADC signal-to-noise-distortion ratio(SNDR) and spurious-free dynamic range (SFDR) from 41.3 dB and 52.1 dB to 72.5 dB and 84.4 dB respectively. The 11.8-ENOB 20 MS/s ADC consumes 56.3 mW power with 3.3 V supply. The 0.78 pJ/step figure-of-merit (FOM) is low for designs in 0.35 CMOS processes. At the Nyquist frequency, SNDR of the calibrated ADC drops 8 dB due to the slow settling of the first pipeline stage.
An Upper-Level Ontology for the Biomedical Domain

At the US National Library of Medicine we have developed the Unified Medical Language System (UMLS), whose goal it is to provide integrated access to a large number of biomedical resources by unifying the vocabularies that are used to access those resources. The UMLS currently interrelates some 60 controlled vocabularies in the biomedical domain. The UMLS coverage is quite extensive, including not only many concepts in clinical medicine, but also a large number of concepts applicable to the broad domain of the life sciences. In order to provide an overarching conceptual framework for all UMLS concepts, we developed an upper-level ontology, called the UMLS semantic network. The semantic network, through its 134 semantic types, provides a consistent categorization of all concepts represented in the UMLS. The 54 links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. Because of the growing number of information resources that contain genetic information, the UMLS coverage in this area is being expanded. We recently integrated the taxonomy of organisms developed by the NLM's National Center for Biotechnology Information, and we are currently working together with the developers of the Gene Ontology to integrate this resource, as well. As additional, standard, ontologies become publicly available, we expect to integrate these into the UMLS construct.
The Insecurity of the Elliptic Curve Digital Signature Algorithm with Partially Known Nonces

Nguyen and Shparlinski recently presented a polynomial-time algorithm that provably recovers the signer’s secret DSA key when a few bits of the random nonces k (used at each signature generation) are known for a number of DSA signatures at most linear in log q (q denoting as usual the small prime of DSA), under a reasonable assumption on the hash function used in DSA. The number of required bits is about log q, and can be further decreased to 2 if one assumes access to ideal lattice basis reduction, namely an oracle for the lattice closest vector problem for the infinity norm. All previously known results were only heuristic, including those of Howgrave-Graham and Smart who introduced the topic. Here, we obtain similar results for the elliptic curve variant of DSA (ECDSA).