nils-herrmann/clean_scirepeval huggingface dataset:
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Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or “particle”) representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.
The hydrodynamics of water-walking arthropods

We present the results of a combined experimental and theoretical investigation of the dynamics of water-walking insects and spiders. Using high-speed videography, we describe their numerous gaits, some analogous to those of their terrestrial counterparts, others specialized for life at the interface. The critical role of the rough surface of these water walkers in both floatation and propulsion is demonstrated. Their waxy, hairy surface ensures that their legs remain in a water-repellent state, that the bulk of their leg is not wetted, but rather contact with the water arises exclusively through individual hairs. Maintaining this water-repellent state requires that the speed of their driving legs does not exceed a critical wetting speed. Flow visualization reveals that the wakes of most water walkers are characterized by a series of coherent subsurface vortices shed by the driving stroke. A theoretical framework is developed in order to describe the propulsion in terms of the transfer of forces and momentum between the creature and its environment. The application of the conservation of momentum to biolocomotion at the interface confirms that the propulsion of water walkers may be rationalized in terms of the subsurface flows generated by their driving stroke. The two principal modes of propulsion available to small water walkers are elucidated. At driving leg speeds in excess of the capillary wave speed, macroscopic curvature forces are generated by deforming the meniscus, and the surface behaves effectively as a trampoline. For slower speeds, the driving legs need not substantially deform the surface but may instead simply brush it: the resulting contact or viscous forces acting on the leg hairs crossing the interface serve to propel the creature forward.
A Simple Named Entity Extractor using AdaBoost

This paper presents a Named Entity Extraction (NEE) system for the CoNLL-2003 shared task competition. As in the past year edition (Carreras et al., 2002a), we have approached the task by treating the two main sub–tasks of the problem, recognition (NER) and classification (NEC), sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary and multiclass AdaBoost classifiers. Named Entity recognition is performed as a greedy sequence tagging procedure under the well–known BIO labelling scheme. This tagging process makes use of three binary classifiers trained to be expertson the recognition of B, I, and O labels, respectively. Named Entity classification is viewed as a 4–class classification problem (with LOC, PER, ORG, andMISC class labels), which is straightforwardly addressed by the use of a multiclass learning algorithm. The system presented here consists of a replication, with some minor changes, of the system that obtained the best results in the CoNLL-2002 NEE task. Therefore, it can be considered as a benchmark of the state–of–the– art technology for the current edition, and will allow also to make comparisons about the training corpora of both editions.
A Prototype Navigation System for Guiding Blind People Indoors using NXT Mindstorms

People with visual impairment face enormous difficulties in terms of their mobility as they do not have enough information about their location and orientation with respect to traffic and obstacles on their route. Visually impaired people can navigate unknown areas by relying on the assistance of canes, other people, or specially trained guide dogs. The traditional ways of using guide dogs and long-cane only help to avoid obstacles, not to know where they are. The research presented in this paper introduces a mobile assistant navigation prototype to locate and direct blind people indoors. Since most of the existing navigation systems developed so far for blind people employ a complex conjunction of positioning systems, video cameras, locationbased and image processing algorithms, we designed an affordable low-cost prototype navigation system for orienting and tracking the position of blind people in complex environments. The prototype system is based on the inertial navigation system and experiments have been performed on NXT Mindstorms platform.
Synchronous Bidirectional Inference for Neural Sequence Generation

In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs in which left parts are much more accurate than right ones. In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. In addition, we propose two strategies for parameter optimization. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.
High-Resolution Side Channels for Untrusted Operating Systems

Feature-rich mass-market operating systems have large trusted computing bases (TCBs) and a long history of vulnerabilities. Systems like Overshadow, InkTag or Haven attempt to remove the operating system (OS) from the TCB of applications while retaining its functionality. However, the untrusted OS’s control of most physical resources puts it in a much better position to launch sidechannel attacks than traditional unprivileged side-channel attackers. Initial attacks focused on the page-fault channel, demonstrating significant information leakage for three legacy applications. We present two new side channels for an untrusted OS which use timer interrupts and cache misses to achieve higher temporal and spatial resolution than the page-fault channel. We leverage the untrusted OS’s control over hardware to reduce noise in the side channels to enable successful attacks in just a single run of the target. We demonstrate that our side channels enable attacks against new SGX applications such as VC3 that were designed not to trust the OS. We also show a new attack against libjpeg that extracts images with two orders of magnitude more information than the page-fault channel attack.
How Important is Weight Symmetry in Backpropagation?

Gradient backpropagation (BP) requires symmetric feedforward and feedback connections—the same weights must be used for forward and backward passes. This “weight transport problem”  is thought to be one of the main reasons of BP’s biological implausibility. Using 15 different classification datasets, we systematically study to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.’s demonstration  but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter—the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN)  and/or a “Batch Manhattan” (BM) update rule. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. ar X iv :1 51 0. 05 06 7v 3 [ cs .L G ] 2 D ec 2 01 5
Attribute-based encryption for fine-grained access control of encrypted data

As more sensitive data is shared and stored by third-party sites on the Internet, there will be a need to encrypt data stored at these sites. One drawback of encrypting data, is that it can be selectively shared only at a coarse-grained level (i.e., giving another party your private key). We develop a new cryptosystem for fine-grained sharing of encrypted data that we call Key-Policy Attribute-Based Encryption (KP-ABE). In our cryptosystem, ciphertexts are labeled with sets of attributes and private keys are associated with access structures that control which ciphertexts a user is able to decrypt. We demonstrate the applicability of our construction to sharing of audit-log information and broadcast encryption. Our construction supports delegation of private keys which subsumesHierarchical Identity-Based Encryption (HIBE).
An Improved Lexicographical Sort Algorithm of Copy-move Forgery Detection

As a technique for digital image tampering, copy-move forgery is frequently used. In this paper, an improved lexicographical sort algorithm based on discrete cosine transform (DCT) is developed to detect copy-move forgery. Firstly, the image is split into 8*8 blocks, and the image data undergoes a DCT. Then the DCT coefficients were grouped to reduce the dimension according to the frequency property. Finally, the distance of eigenvectors, instead of the DCT coefficients, was taken as the eigenvalue to fulfill the block matching. Experiments results showed that the false matching ratio of the proposed algorithm was reduced while the detecting ability was maintained.
A Survey on Pre-Processing in Image Matting

Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask (Trimap). This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories: static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods.
Modeling and Reasoning with Bayesian Networks

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
New features and insights for pedestrian detection

Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that motion features derived from optic flow yield substantial improvements on image sequences, if implemented correctly — even in the case of low-quality video and consequently degraded flow fields. Furthermore, we introduce a new feature, self-similarity on color channels, which consistently improves detection performance both for static images and for video sequences, across different datasets. In combination with HOG, these two features outperform the state-of-the-art by up to 20%. Finally, we report two insights concerning detector evaluations, which apply to classifier-based object detection in general. First, we show that a commonly under-estimated detail of training, the number of bootstrapping rounds, has a drastic influence on the relative (and absolute) performance of different feature/classifier combinations. Second, we discuss important intricacies of detector evaluation and show that current benchmarking protocols lack crucial details, which can distort evaluations.
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO with Lens Antenna Array

Beamspace channel estimation is essential for wideband millimeter-wave (mmWave) MIMO with lens antenna array to achieve substantial increase in data rates with considerably reduced number of radio-frequency chains. However, most of existing beamspace channel estimation schemes are designed for narrowband mmWave systems, while rather scarce wideband schemes ideally assume beamspace channel enjoys the common support in frequency-domain. In this paper, inspired by the classical successive interference cancellation for multi-user detection, we propose an efficient successive support detection (SSD) based scheme without the assumption of common support. Specifically, we first demonstrate that each path component of the wideband beamspace channel exhibits a unique frequency-varying sparse structure. Based on this, we then successively estimate all sparse path components. For each path component, its supports at different frequencies are jointly estimated to improve the estimation accuracy, and then its influence is removed to estimate the remained path components. Once all path components have been estimated, the wideband beamspace channel can be recovered at a low complexity. Simulation results verify that the proposed SSD based beamspace channel estimation scheme achieves higher accuracy than existing wideband schemes.
Intersection-priority based optimal RSU allocation for VANET

Roadside Unit (RSU) is an essential unit in a vehicular ad-hoc network (VANET) for collecting and analyzing traffic data given from smart vehicles. Furthermore, RSUs can take part in controlling traffic flow for vehicle's secure driving by broadcasting locally analyzed data, forwarding some important messages, and communicating with other RSUs, and soon. In order to maximize the availability of RSUs in the VANET, RSUs need to be fully distributed over an entire area. Thus, RSUs can make the best use of all traffic data gathered from every intersection. In this paper, we provide intersection-priority based RSU placement methods to find the optimal number and positions of RSUs for the full distribution providing with a maximal connectivity between RSUs while minimizing RSU setup costs. We propose three optimal algorithms: greedy, dynamic and hybrid algorithms. Finally, we provide simulated analyses of our algorithms using real urban roadmaps of JungGu/Jongrogu, YongsanGu, and GangnamGu in Seoul, each of which has a characteristic road style different than each other. We analyze how our algorithms work in such different types of roadways with real traffic data, and find the optimal number and positions of RSUs in these areas.
In Defense of the Internet: The Relationship between Internet Communication and Depression, Loneliness, Self-Esteem, and Perceived Social Support

As more people connect to the Internet, researchers are beginning to examine the effects of Internet use on users' psychological health. Due in part to a study released by Kraut and colleagues in 1998, which concluded that Internet use is positively correlated with depression, loneliness, and stress, public opinion about the Internet has been decidedly negative. In contrast, the present study was designed to test the hypothesis that Internet usage can affect users beneficially. Participants engaged in five chat sessions with an anonymous partner. At three different intervals they were administered scales measuring depression, loneliness, self-esteem, and social support. Changes in their scores were tracked over time. Internet use was found to decrease loneliness and depression significantly, while perceived social support and self-esteem increased significantly.
Encouraging user behaviour with achievements: An empirical study

Stack Overflow, a question and answer Web site, uses a reward system called badges to publicly reward users for their contributions to the community. Badges are used alongside a reputation score to reward positive behaviour by relating a user's site identity with their perceived expertise and respect in the community. A greater number of badges associated with a user profile in some way indicates a higher level of authority, leading to a natural incentive for users to attempt to achieve as many badges as possible. In this study, we examine the publicly available logs for Stack Overflow to examine three of these badges in detail. We look at the effect of one badge in context on an individual user level and at the global scope of three related badges across all users by mining user behaviour around the time that the badge is awarded. This analysis supports the claim that badges can be used to influence user behaviour by demonstrating one instance of an increase in user activity related to a badge immediately before it is awarded when compared to the period afterwards.
Cognitive Mapping and Planning for Visual Navigation

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the planner, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. Our experiments demonstrate that CMP outperforms both reactive strategies and standard memory-based architectures and performs well in novel environments. Furthermore, we show that CMP can also achieve semantically specified goals, such as go to a chair.
Consumer Attitudes Toward Online Mass Customization: An Application of Extended Technology Acceptance Model

This study investigates consumer attitudes toward the online codesign process in mass customization among Korean consumers. The extended Technology Acceptance Model (TAM) was our theoretical framework, along with the constructs of perceived control and enjoyment. Web skill and fashion involvement were integrated as individual characteristics variables in the model. Data from 749 self-administered surveys collected in South Korea were used in this study. Findings indicated that TAM was an effective conceptual framework in explaining consumer response to the online codesign process. The statistically significant results of the perceived enjoyment and control constructs in the extended TAM suggest the importance of their addition to building a model to explain consumer attitudes toward the online codesign process.
An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews

Opinion mining is a challenging task to identify the opinions or sentiments underlying user generated contents, such as online product reviews, blogs, discussion forums, etc. Previous studies that adopt machine learning algorithms mainly focus on designing effective features for this complex task. This paper presents our approach based on tree kernels for opinion mining of online product reviews. Tree kernels alleviate the complexity of feature selection and generate effective features to satisfy the special requirements in opinion mining. In this paper, we define several tree kernels for sentiment expression extraction and sentiment classification, which are subtasks of opinion mining. Our proposed tree kernels encode not only syntactic structure information, but also sentiment related information, such as sentiment boundary and sentiment polarity, which are important features to opinion mining. Experimental results on a benchmark data set indicate that tree kernels can significantly improve the performance of both sentiment expression extraction and sentiment classification. Besides, a linear combination of our proposed tree kernels and traditional feature vector kernel achieves the best performances using the benchmark data set.