nils-herrmann/clean_scirepeval huggingface dataset:
Representing Knowledge within the Situation Calculus Using Interval-Valued Epistemic Fluents

The ability of interval arithmetic to provide a finite (and succinct) way to represent uncertainty about a large, possibly uncountable, set of alternatives turns out to be useful in building “intelligent” autonomous agents. In particular, consider the two important issues of reasoning and sensing in intelligent control for autonomous agents. Developing a principled way to combine the two raises complicated issues in knowledge representation. In this paper we describe a solution to the problem. The idea is to incorporate interval arithmetic into the situation calculus. The situation calculus is a well known formalism for describing changing worlds using sorted first-order logic. It can also be used to describe how an agent’s knowledge of its world changes. Potentially, this provides a sound basis for incorporating sensing into logic programming. Previous work has relied on a possible worlds approach to knowledge. This leads to an elegant mathematical specification language. Unfortunately, there have been no proposals on how to implement the approach. This is because the number of possible worlds is potentially uncountable. We propose an alternative formalization of knowledge within the situation calculus. Our approach is based on intervals. The advantage is that it is straightforward to implement. Moreover, we can prove that it is sound and (sometimes) complete with respect to the previous possible worlds approach.
Designing culturally situated technologies for the home

As digital technologies proliferate in the home, the Human Computer Interaction (HCI) community has turned its attention from the workplace and productivity tools towards domestic design environments and non-utilitarian activities. In the workplace, applications tend to focus on productivity and efficiency and involve relatively well-understood requirements and methodologies, but in domestic design environments we are faced with the need to support new classes of activities. While usability is still central to the field, HCI is beginning to address considerations such as pleasure, fun, emotional effect, aesthetics, the experience of use, and the social and cultural impacts of new technologies. These considerations are particularly relevant to the home, where technologies are situated or embedded within an ecology that is rich with meaning and nuance.The aim of this workshop is to explore ways of designing domestic technology by incorporating an awareness of cultural context, accrued social meanings, and user experience.
Assessing elementary students' science competency with text analytics

Real-time formative assessment of student learning has become the subject of increasing attention. Students' textual responses to short answer questions offer a rich source of data for formative assessment. However, automatically analyzing textual constructed responses poses significant computational challenges, and the difficulty of generating accurate assessments is exacerbated by the disfluencies that occur prominently in elementary students' writing. With robust text analytics, there is the potential to accurately analyze students' text responses and predict students' future success. In this paper, we present WriteEval, a hybrid text analytics method for analyzing student-composed text written in response to constructed response questions. Based on a model integrating a text similarity technique with a semantic analysis technique, WriteEval performs well on responses written by fourth graders in response to short-text science questions. Further, it was found that WriteEval's assessments correlate with summative analyses of student performance.
High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content

In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images due to the growing popularity of HDR in applications, such as photography and rendering via Image-Based Lighting, and the imminent arrival of HDR displays to the consumer market. LDR content expansion is required due to the lack of fast and reliable consumer level HDR capture for still images and videos. Furthermore, LDR content expansion, will allow the re-use of legacy LDR stills, videos and LDR applications created, over the last century and more, to be widely available. The use of certain LDR expansion methods, those that are based on the inversion of tone mapping operators, has made it possible to create novel compression algorithms that tackle the problem of the size of HDR content storage, which remains one of the major obstacles to be overcome for the adoption of HDR. These methods are used in conjunction with traditional LDR compression methods and can evolve accordingly. The goal of this report is to provide a comprehensive overview on HDR Imaging, and an in depth review on these emerging topics.
Fast Algorithms for Computing the Smallest k-Enclosing Circle

For a set $P$ of $n$ points in the plane and an integer $k \leq n$, consider the problem of finding the smallest circle enclosing at least $k$ points of $P$. We present a randomized algorithm that computes in $O( n k )$ expected time such a circle, improving over previously known algorithms. Further, we present a linear time $\delta$-approximation algorithm that outputs a circle that contains at least $k$ points of $P$ and has radius less than $(1+\delta)r_{opt}(P,k)$, where $r_{opt}(P,k)$ is the radius of the minimum circle containing at least $k$ points of $P$. The expected running time of this approximation algorithm is $O(n + n \cdot\min((1/k\delta^3) \log^2 (1/\delta), k))$.
An empirical study of identifier splitting techniques

Researchers have shown that program analyses that drive software development and maintenance tools supporting search, traceability and other tasks can benefit from leveraging the natural language information found in identifiers and comments. Accurate natural language information depends on correctly splitting the identifiers into their component words and abbreviations. While conventions such as camel-casing can ease this task, conventions are not well-defined in certain situations and may be modified to improve readability, thus making automatic splitting more challenging. This paper describes an empirical study of state-of-the-art identifier splitting techniques and the construction of a publicly available oracle to evaluate identifier splitting algorithms. In addition to comparing current approaches, the results help to guide future development and evaluation of improved identifier splitting approaches.
When visual perception causes feeling: Enhanced cross-modal processing in grapheme-color synesthesia

In synesthesia, stimulation of one sensory modality (e.g., hearing) triggers a percept in another, non-stimulated sensory modality (e.g., vision). Likewise, perception of a form (e.g., a letter) may induce a color percept (i.e., grapheme-color synesthesia). To date, the neural mechanisms underlying synesthesia remain to be elucidated. We disclosed by fMRI, while controlling for surface color processing, enhanced activity in the left intraparietal cortex during the experience of grapheme-color synesthesia (n = 9). In contrast, the perception of surface color per se activated the color centers in the fusiform gyrus bilaterally. The data support theoretical accounts that grapheme-color synesthesia may originate from enhanced cross-modal binding of form and color. A mismatch of surface color and grapheme induced synesthetically felt color additionally activated the left dorsolateral prefrontal cortex (DLPFC). This suggests that cognitive control processes become active to resolve the perceptual conflict resulting from synesthesia.
A Content-Based Indexing Scheme for Large-Scale Unstructured Data

The sheer volume of multimedia contents generated by today's Internet services are stored in the cloud. The traditional indexing method associating the user-generated metadata with the content is vulnerable to the inaccuracy caused by the low quality of the metadata. While the content-based indexing does not depend on the error-prone metadata. However, the state-of-the-art research focuses on developing descriptive features and miss the system-oriented considerations when incorporating these features into the practical cloud computing systems. We propose an Update-Efficient and Parallel-Friendly content-based multimedia indexing system, called Partitioned Hash Forest (PHF). The PHF system incorporates the state-of-the-art content-based indexing models and multiple system-oriented optimizations. PHF contains an approximate content-based index and leverages the hierarchical memory system to support the high volume of updates. Additionally, the content-aware data partitioning and lock-free concurrency management module enable the parallel processing of the concurrent user requests. We evaluate PHF in terms of indexing accuracy and system efficiency by comparing it with the state-of-the-art content-based indexing algorithm and its variances. We achieve the significantly better accuracy with less resource consumption, around 37% faster in update processing and up to 2.5X throughput speedup in a multi-core platform comparing to other parallel-friendly designs.
Providing low vision rehabilitation services with occupational therapy and ophthalmology: a program description.

This article describes a low vision rehabilitation program operating within a hospital-based outpatient rehabilitation clinic. The program uses a team approach combining ophthalmology and occupational therapy services. Patients are referred to the program by their primary care physician for a low vision evaluation completed jointly by the ophthalmologist and occupational therapist. The ophthalmology portion of the evaluation includes assessment of visual acuity, contrast sensitivity function, and macular perimetry with a scanning laser ophthalmoscope. The occupational therapy evaluation focuses on assessing the functional limitations experienced by the patient due to the vision loss and determining how the patient is best able to use remaining vision to complete daily activities. Occupational therapy treatment emphasizes training the patient to use remaining vision as efficiently and effectively as possible to complete daily activities and includes training in use of optical devices. Because of the specialized nature of the service provided, additional postgraduate preparation is needed to enable occupational therapists to provide effective low vision rehabilitation.
Validation of the MOS Social Support Survey 6-item (MOS-SSS-6) measure with two large population-based samples of Australian women

This study aimed to validate a 6-item 1-factor global measure of social support developed from the Medical Outcomes Study Social Support Survey (MOS-SSS) for use in large epidemiological studies. Data were obtained from two large population-based samples of participants in the Australian Longitudinal Study on Women’s Health. The two cohorts were aged 53–58 and 28–33 years at data collection (N = 10,616 and 8,977, respectively). Items selected for the 6-item 1-factor measure were derived from the factor structure obtained from unpublished work using an earlier wave of data from one of these cohorts. Descriptive statistics, including polychoric correlations, were used to describe the abbreviated scale. Cronbach’s alpha was used to assess internal consistency and confirmatory factor analysis to assess scale validity. Concurrent validity was assessed using correlations between the new 6-item version and established 19-item version, and other concurrent variables. In both cohorts, the new 6-item 1-factor measure showed strong internal consistency and scale reliability. It had excellent goodness-of-fit indices, similar to those of the established 19-item measure. Both versions correlated similarly with concurrent measures. The 6-item 1-factor MOS-SSS measures global functional social support with fewer items than the established 19-item measure.
An Artificial intelligence technique for the prediction of persistent asthma in children

The prediction of asthma that persists throughout childhood and into adulthood, in early life of a child has practical, clinical and prognostic implications and sets the basis for the future prevention. Artificial Neural Networks (ANNs) seems to be a superior tool for analyzing data sets where nonlinear relationships are existing between the input data and the predicted output. This study presents an effective machine-learning approach based on Multi-Layer Perceptron (MLP) neural networks, for the prediction of persistent asthma in children. Through a feature reduction, 10 high importance prognostic factors correlated to persistent asthma have been discovered. The feature selection approach results in 89.8% reduction of the initial number of features. Afterwards, a feature reduced classifier is constructed, which achieves 100% accuracy on the training and test data sets. Experimental results are presenting and verify this statement.
Neural correlates of motor memory consolidation.

Computational studies suggest that acquisition of a motor skill involves learning an internal model of the dynamics of the task, which enables the brain to predict and compensate for mechanical behavior. During the hours that follow completion of practice, representation of the internal model gradually changes, becoming less fragile with respect to behavioral interference. Here, functional imaging of the brain demonstrates that within 6 hours after completion of practice, while performance remains unchanged, the brain engages new regions to perform the task; there is a shift from prefrontal regions of the cortex to the premotor, posterior parietal, and cerebellar cortex structures. This shift is specific to recall of an established motor skill and suggests that with the passage of time, there is a change in the neural representation of the internal model and that this change may underlie its increased functional stability.
StreamGlobe: Processing and Sharing Data Streams in Grid-Based P2P Infrastructures

Data stream processing is currently gaining importance due to the developments in novel application areas like escience, e-health, and e-business (considering RFID, for example). Focusing on e-science, it can be observed that scientific experiments and observations in many fields, e. g., in physics and astronomy, create huge volumes of data which have to be interchanged and processed. With experimental and observational data coming in particular from sensors, online simulations, etc., the data has an inherently streaming nature. Furthermore, continuing advances will result in even higher data volumes, rendering storing all of the delivered data prior to processing increasingly impractical. Hence, in such e-science scenarios, processing and sharing of data streams will play a decisive role. It will enable new possibilities for researchers, since they will be able to subscribe to interesting data streams of other scientists without having to set up their own devices or experiments. This results in much better utilization of expensive equipment such as telescopes, satellites, etc. Further, processing and sharing data streams on-the-fly in the network helps to reduce network traffic and to avoid network congestion. Thus, even huge streams of data can be handled efficiently by removing unnecessary parts early on, e. g., by early filtering and aggregation, and by sharing previously generated data streams and processing results. To enable these optimizations, we use Peer-to-Peer (P2P) networking techniques. P2P has gained a lot of attention in the context of exchanging persistent data—in particular for file sharing. In contrast to that, we apply P2P networks for the dissemination of individually subscribed and transformed data streams, allowing for data stream sharing. By using the computational capabilities of peers in the P2P network, we can push data stream transforming operators into the network, thus enabling efficient in-network
A survey of vision-based methods for action representation, segmentation and recognition

Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human–computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions.
A New Collision Differential For MD5 With Its Full Differential Path

【Abstract】Since the first collision differential with its full differential path was presented for MD5 function by Wang et al. in 2004, renewed interests on collision attacks for the MD family of hash functions have surged over the world of cryptology. To date, however, no cryptanalyst can give a second computationally feasible collision differential for MD5 with its full differential path, even no improved differential paths based on Wang’s MD5 collision differential have appeared in literature. Firstly in this paper, a new differential cryptanalysis called signed difference is defined, and some principles or recipes on finding collision differentials and designing differential paths are proposed, the signed difference generation or elimination rules which are implicit in the auxiliary functions, are derived. Then, based on these newly found properties and rules, this paper comes up with a new computationally feasible collision differential for MD5 with its full differential path, which is simpler thus more understandable than Wang’s, and a set of sufficient conditions considering carries that guarantees a full collision is derived from the full differential path. Finally, a multi-message modification-based fast collision attack algorithm for searching collision messages is specialized for the full differential path, resulting in a computational complexity of 36 2 and 32 2 MD5 operations, respectively for the first and second blocks. As for examples, two collision message pairs with different first blocks are obtained.
AVID: A global alignment program.

In this paper we describe a new global alignment method called AVID. The method is designed to be fast, memory efficient, and practical for sequence alignments of large genomic regions up to megabases long. We present numerous applications of the method, ranging from the comparison of assemblies to alignment of large syntenic genomic regions and whole genome human/mouse alignments. We have also performed a quantitative comparison of AVID with other popular alignment tools. To this end, we have established a format for the representation of alignments and methods for their comparison. These formats and methods should be useful for future studies. The tools we have developed for the alignment comparisons, as well as the AVID program, are publicly available. See Web Site References section for AVID Web address and Web addresses for other programs discussed in this paper.
Diffusion LMS Strategies for Distributed Estimation

We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate some parameter of interest from noisy measurements. The problem is useful in several contexts including wireless and sensor networks, where scalability, robustness, and low power consumption are desirable features. Diffusion cooperation schemes have been shown to provide good performance, robustness to node and link failure, and are amenable to distributed implementations. In this work we focus on diffusion-based adaptive solutions of the LMS type. We motivate and propose new versions of the diffusion LMS algorithm that outperform previous solutions. We provide performance and convergence analysis of the proposed algorithms, together with simulation results comparing with existing techniques. We also discuss optimization schemes to design the diffusion LMS weights.
Mutual Component Analysis for Heterogeneous Face Recognition

Heterogeneous face recognition, also known as cross-modality face recognition or intermodality face recognition, refers to matching two face images from alternative image modalities. Since face images from different image modalities of the same person are associated with the same face object, there should be mutual components that reflect those intrinsic face characteristics that are invariant to the image modalities. Motivated by this rationality, we propose a novel approach called Mutual Component Analysis (MCA) to infer the mutual components for robust heterogeneous face recognition. In the MCA approach, a generative model is first proposed to model the process of generating face images in different modalities, and then an Expectation Maximization (EM) algorithm is designed to iteratively learn the model parameters. The learned generative model is able to infer the mutual components (which we call the hidden factor, where hidden means the factor is unreachable and invisible, and can only be inferred from observations) that are associated with the person’s identity, thus enabling fast and effective matching for cross-modality face recognition. To enhance recognition performance, we propose an MCA-based multiclassifier framework using multiple local features. Experimental results show that our new approach significantly outperforms the state-of-the-art results on two typical application scenarios: sketch-to-photo and infrared-to-visible face recognition.
A Smooth Path Tracking Algorithm for Wheeled Mobile Robots with Dynamic Constraints

In order to avoid wheel slippage or mechanical damage during the mobile robot navigation, it is necessary to smoothly change driving velocity or direction of the mobile robot. This means that dynamic constraints of the mobile robot should be considered in the design of path tracking algorithm. In the study, a path tracking problem is formulated as following a virtual target vehicle which is assumed to move exactly along the path with specified velocity. The driving velocity control law is designed basing on bang-bang control considering the acceleration bounds of driving wheels. The steering control law is designed by combining the bang-bang control with an intermediate path called the landing curve which guides the robot to smoothly land on the virtual target’s tangential line. The curvature and convergence analyses provide sufficient stability conditions for the proposed path tracking controller. A series of path tracking simulations and experiments conducted for a two-wheel driven mobile robot show the validity of the proposed algorithm.
Lecture Video Indexing and Analysis Using Video OCR Technology

The text displayed in a lecture video is closely related to the lecture content. Therefore, it provides a valuable source for indexing and retrieving lecture video contents. Textual content can be detected, extracted and analyzed automatically by video OCR (Optical Character Recognition) techniques. In this paper, we present an approach for automated lecture video indexing based on video OCR technology: Firstly, we developed a novel video segmenter for an automated slide video structure analysis. Having adopted a localization and verification scheme, we perform text detection secondly. We employ SWT (stroke width transform) not only to remove false alarms from the text detection, but also to analyze the slide structure further. To recognize texts, a multi-hypotheses framework is adopted, that consists of multiple text segments, OCR, spell checking and result merging processes. Finally, we implemented a novel algorithm for slide structure analysis and extraction by using the geometrical information of detected text lines. The accuracy of the proposed approach is proven by evaluation.