nils-herrmann/clean_scirepeval huggingface dataset:
Recovering the spatial layout of cluttered rooms

In this paper, we consider the problem of recovering the spatial layout of indoor scenes from monocular images. The presence of clutter is a major problem for existing single-view 3D reconstruction algorithms, most of which rely on finding the ground-wall boundary. In most rooms, this boundary is partially or entirely occluded. We gain robustness to clutter by modeling the global room space with a parameteric 3D “box” and by iteratively localizing clutter and refitting the box. To fit the box, we introduce a structured learning algorithm that chooses the set of parameters to minimize error, based on global perspective cues. On a dataset of 308 images, we demonstrate the ability of our algorithm to recover spatial layout in cluttered rooms and show several examples of estimated free space.
Projected Newton methods for optimization problems with simple constraints

We consider the problem min {f(x)|x ¿ 0} and algorithms of the form xk+1 = [xk - ¿k Dk¿f(xk)]+ where [¿]+ denotes projection on the positive orthant, ¿k is a stepsize chosen by an Armijolike rule, and Dk is a positive definite symmetric matrix which is partly diagonal. We show that Dk can be calculated simply on the basis of second derivatives of f so that the resulting Newton-like algorithm has a typically superlinear rate of convergence. With other choices of Dk convergence at a typically linear rate is obtained. The algorithms are almost as simple as their unconstrained counterparts. They are well suited for problems of large dimension such as those arising in optimal control while being competitive with existing methods for low-dimensional problems.
Sparse Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms

The problem of signal recovery from its Fourier transform magnitude is of paramount importance in various fields of engineering and has been around for more than 100 years. Due to the absence of phase information, some form of additional information is required in order to be able to uniquely identify the signal of interest. In this paper, we focus our attention on discrete-time sparse signals (of length <inline-formula><tex-math notation="LaTeX">$n$ </tex-math></inline-formula>). We first show that if the discrete Fourier transform dimension is greater than or equal to <inline-formula><tex-math notation="LaTeX">$2n$</tex-math></inline-formula>, then almost all signals with <italic> aperiodic</italic> support can be uniquely identified by their Fourier transform magnitude (up to time shift, conjugate flip, and global phase). Then, we develop an efficient two-stage sparse-phase retrieval algorithm (TSPR), which involves: identifying the support, i.e., the locations of the nonzero components, of the signal using a combinatorial algorithm; and identifying the signal values in the support using a convex algorithm. We show that TSPR can <italic> provably</italic> recover most <inline-formula><tex-math notation="LaTeX">$O(n^{1/2-{\epsilon }})$</tex-math> </inline-formula>-sparse signals (up to a time shift, conjugate flip, and global phase). We also show that, for most <inline-formula><tex-math notation="LaTeX">$O(n^{1/4-{\epsilon }})$</tex-math></inline-formula>-sparse signals, the recovery is <italic>robust</italic> in the presence of measurement noise. These recovery guarantees are asymptotic in nature. Numerical experiments complement our theoretical analysis and verify the effectiveness of TSPR.
Detection and Classification of Plant Leaf Diseases using ANN

Plant diseases problem can cause significant reduction in both quality and quantity of agricultural products. Automatic detection of plant leaf diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for automatic detection and computation of plant leaf diseases. The developed processing scheme consists of five main steps, first a color transformation structure for the input RGB image is created, then the noise i.e. unnecessary part is removed using specific threshold value, then the image is segmented with connected component labeling and the useful segments are extracted, finally the ANN classification is computed by giving different features i.e. size, color, proximity and average centroid distance. Experimental results on a database of 4 different diseases confirms the robustness of the proposed approach. KeywordsANN, Color, Plant Leaf Diseases, RGB ————————————————————
Discriminative acoustic model for improving mispronunciation detection and diagnosis in computer-aided pronunciation training (CAPT)

In this study, we propose a discriminative training algorithm to jointly minimize mispronunciation detection errors (i.e., false rejections and false acceptances) and diagnosis errors (i.e., correctly pinpointing mispronunciations but incorrectly stating how they are wrong). An optimization procedure, similar to Minimum Word Error (MWE) discriminative training, is developed to refine the ML-trained HMMs. The errors to be minimized are obtained by comparing transcribed training utterances (including mispronunciations) with Extended Recognition Networks  which contain both canonical pronunciations and explicitly modeled mispronunciations. The ERN is compiled by handcrafted rules, or data-driven rules. Several conclusions can be drawn from the experiments: (1) data-driven rules are more effective than hand-crafted ones in capturing mispronunciations; (2) compared with the ML training baseline, discriminative training can reduce false rejections and diagnostic errors, though false acceptances increase slightly due to a small number of false-acceptance samples in the training set.
Simultaneous Traffic Sign Detection and Boundary Estimation Using Convolutional Neural Network

We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using convolutional neural network (CNN). Estimating the precise boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3-D landmarks for road environment. Previous traffic sign detection systems, including recent methods based on CNN, only provide bounding boxes of traffic signs as output, and thus requires additional processes such as contour estimation or image segmentation to obtain the precise boundary of signs. In this paper, the boundary estimation of traffic sign is formulated as 2-D pose and shape class prediction problem, and this is effectively solved by a single CNN. With the predicted 2-D pose and the shape class of a target traffic sign in the input, we estimate the actual boundary of the target sign by projecting the boundary of a corresponding template sign image into the input image plane. By formulating the boundary estimation problem as a CNN-based pose and shape prediction task, our method is end-to-end trainable, and more robust to occlusion and small targets than other boundary estimation methods that rely on contour estimation or image segmentation. With our architectural optimization of the CNN-based traffic sign detection network, the proposed method shows a detection frame rate higher than seven frames/second while providing highly accurate and robust traffic sign detection and boundary estimation results on a low-power mobile platform.
Time-varying social networks in a graph database: a Neo4j use case

Representing and efficiently querying time-varying social network data is a central challenge that needs to be addressed in order to support a variety of emerging applications that leverage high-resolution records of human activities and interactions from mobile devices and wearable sensors. In order to support the needs of specific applications, as well as general tasks related to data curation, cleaning, linking, post-processing, and data analysis, data models and data stores are needed that afford efficient and scalable querying of the data. In particular, it is important to design solutions that allow rich queries that simultaneously involve the topology of the social network, temporal information on the presence and interactions of individual nodes, and node metadata. Here we introduce a data model for time-varying social network data that can be represented as a property graph in the Neo4j graph database. We use time-varying social network data collected by using wearable sensors and study the performance of real-world queries, pointing to strengths, weaknesses and challenges of the proposed approach.
Retrospective on Aurora

This experience paper summarizes the key lessons we learned throughout the design and implementation of the Aurora stream-processing engine. For the past 2 years, we have built five stream-based applications using Aurora. We first describe in detail these applications and their implementation in Aurora. We then reflect on the design of Aurora based on this experience. Finally, we discuss our initial ideas on a follow-on project, called Borealis, whose goal is to eliminate the limitations of Aurora as well as to address new key challenges and applications in the stream-processing domain.
Optimal decision making and the anterior cingulate cortex

Learning the value of options in an uncertain environment is central to optimal decision making. The anterior cingulate cortex (ACC) has been implicated in using reinforcement information to control behavior. Here we demonstrate that the ACC's critical role in reinforcement-guided behavior is neither in detecting nor in correcting errors, but in guiding voluntary choices based on the history of actions and outcomes. ACC lesions did not impair the performance of monkeys (Macaca mulatta) immediately after errors, but made them unable to sustain rewarded responses in a reinforcement-guided choice task and to integrate risk and payoff in a dynamic foraging task. These data suggest that the ACC is essential for learning the value of actions.
What is beautiful is usable

An experiment was conducted to test the relationships between users' perceptions of a computerized system's beauty and usability. The experiment used a computerized application as a surrogate for an Automated Teller Machine (ATM). Perceptions were elicited before and after the participants used the system. Pre-experimental measures indicate strong correlations between system's perceived aesthetics and perceived usability. Post-experimental measures indicated that the strong correlation remained intact. A multivariate analysis of covariance revealed that the degree of system's aesthetics affected the post-use perceptions of both aesthetics and usability, whereas the degree of actual usability had no such effect. The results resemble those found by social psychologists regarding the effect of physical attractiveness on the valuation of other personality attributes. The ®ndings stress the importance of studying the aesthetic aspect of human±computer interaction (HCI) design and its relationships to other design dimensions. q 2000 Elsevier Science B.V. All rights reserved.
DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome

Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at
Binpac: a Yacc for Writing Application Protocol Parsers

A key step in the semantic analysis of network traffic is to parse the traffic stream according to the high-level protocols it contains. This process transforms raw bytes into structured, typed, and semantically meaningful data fields that provide a high-level representation of the traffic. However, constructing protocol parsers by hand is a tedious and error-prone affair due to the complexity and sheer number of application protocols.This paper presents binpac, a declarative language and compiler designed to simplify the task of constructing robust and efficient semantic analyzers for complex network protocols. We discuss the design of the binpac language and a range of issues in generating efficient parsers from high-level specifications. We have used binpac to build several protocol parsers for the "Bro" network intrusion detection system, replacing some of its existing analyzers (handcrafted in C++), and supplementing its operation with analyzers for new protocols. We can then use Bro's powerful scripting language to express application-level analysis of network traffic in high-level terms that are both concise and expressive. binpac is now part of the open-source Bro distribution.
Blockstack: A Global Naming and Storage System Secured by Blockchains

Blockchains like Bitcoin and Namecoin and their respective P2P networks have seen significant adoption in the past few years and show promise as naming systems with no trusted parties. Users can register human meaningful names and securely associate data with them, and only the owner of the particular private keys that registered them can write or update the name-value pair. In theory, many decentralized systems can be built using these blockchain networks, such as new, decentralized versions of DNS and PKI. As the technology is relatively new and evolving rapidly, however, little production data or experience is available to guide design tradeoffs. In this paper, we describe our experiences operating a large deployment of a decentralized PKI service built on top of the Namecoin blockchain. We present various challenges pertaining to network reliability, throughput, and security that we needed to overcome while registering and updating over 33,000 entries and 200,000 transactions on the Namecoin blockchain. Further, we discuss how our experience informed the design of a new blockchain-based naming and storage system called Blockstack. We detail why we switched from the Namecoin network to the Bitcoin network for the new system, and present operational lessons from this migration. Blockstack is released as open source software and currently powers a production PKI system for 55,000 users.
Deeply Learning the Messages in Message Passing Inference

Deep structured output learning shows great promise in tasks like semantic image segmentation. We proffer a new, efficient deep structured model learning scheme, in which we show how deep Convolutional Neural Networks (CNNs) can be used to estimate the messages in message passing inference for structured prediction with Conditional Random Fields (CRFs). With such CNN message estimators, we obviate the need to learn or evaluate potential functions for message calculation. This confers significant efficiency for learning, since otherwise when performing structured learning for a CRF with CNN potentials it is necessary to undertake expensive inference for every stochastic gradient iteration. The network output dimension for message estimation is the same as the number of classes, in contrast to the network output for general CNN potential functions in CRFs, which is exponential in the order of the potentials. Hence CNN message learning has fewer network parameters and is more scalable for cases that a large number of classes are involved. We apply our method to semantic image segmentation on the PASCAL VOC 2012 dataset. We achieve an intersection-over-union score of 73.4 on its test set, which is the best reported result for methods using the VOC training images alone. This impressive performance demonstrates the effectiveness and usefulness of our CNN message learning method.
Image completion using planar structure guidance

We propose a method for automatically guiding patch-based image completion using mid-level structural cues. Our method first estimates planar projection parameters, softly segments the known region into planes, and discovers translational regularity within these planes. This information is then converted into soft constraints for the low-level completion algorithm by defining prior probabilities for patch offsets and transformations. Our method handles multiple planes, and in the absence of any detected planes falls back to a baseline fronto-parallel image completion algorithm. We validate our technique through extensive comparisons with state-of-the-art algorithms on a variety of scenes.
A Framework for Behavioural Cloning

This paper describes recent experiments in automatically constructing reactive agents. The method used is behavioural cloning, where the logged data from skilled, human operators are input to an induction program which outputs a control strategy for a complex control task. Initial studies were able to successfully construct such behavioural clones, but suffered from several drawbacks, namely, that the clones were brittle and difficult to understand. Current research is aimed at solving these problems by learning in a framework where there is a separation between an agent’s goals and its knowledge of how to achieve them.
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis

The Self-Organizing Map (SOM) is one of the best known and most popular neural network-based data analysis tools. Many variants of the SOM have been proposed, like the Neural Gas by Martinetz and Schulten, the Growing Cell Structures by Fritzke, and the Tree-Structured SOM by Koikkalainen and Oja. The purpose of such variants is either to make a more flexible topology, suitable for complex data analysis problems or to reduce the computational requirements of the SOM, especially the time-consuming search for the best-matching unit in large maps. We propose here a new variant called the Evolving Tree which tries to combine both of these advantages. The nodes are arranged in a tree topology that is allowed to grow when any given branch receives a lot of hits from the training vectors. The search for the best matching unit and its neighbors is conducted along the tree and is therefore very efficient. A comparison experiment with high dimensional real world data shows that the performance of the proposed method is better than some classical variants of SOM.
Personalization in distributed e-learning environments

Personalized support for learners becomes even more important, when e-Learning takes place in open and dynamic learning and information networks. This paper shows how to realize personalized learning support in distributed learning environments based on Semantic Web technologies. Our approach fills the existing gap between current adaptive educational systems with well-established personalization functionality, and open, dynamic learning repository networks. We propose a service-based architecture for establishing personalized e-Learning, where personalization functionality is provided by various web-services. A Personal Learning Assistant integrates personalization services and other supporting services, and provides the personalized access to learning resources in an e-Learning network.
What do people do with porn ? qualitative research into the consumption , use and experience of pornography and other sexually explicit media

ATTWOOD, F. (2005). What do people do with porn? qualitative research into the consumption, use and experience of pornography and other sexually explicit media. Sexuality and culture, 9 (2), 65-86. one copy of any article(s) in SHURA to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain.
Sit-to-Stand and Stand-to-Sit Transfer Support for Complete Paraplegic Patients with Robot Suit HAL

Physical support of lower limbs during sit-to-stand and stand-to-sit transfers is important for an independent life of paraplegic patients. The purpose of this study is, therefore, to realize the control method of complete paraplegic patients during sit-to-stand and stand-to-sit transfers by using a ‘robot suit HAL’. It is the most challenging issue because the HAL should start supporting the wearer’s motions synchronizing his/her intention. Our proposed algorithm infers the intention based on a preliminary motion that is observed just before a desired motion so the patient could start the sit-to-stand or stand-to-sit transfers without any operation. When the HAL detects the intention to stand up and sit down, the HAL starts to support the wearer’s weight and to control their body posture for stability during their transfer. The proposed algorithms embedded in the HAL were applied to a complete spinal cord injury patient in a clinical trial to confirm the effectiveness. The experimental results indicate that the proposed algorithms could support his sit-to-stand and stand-to-sit transfers safely and conveniently by keeping his stability and by reflecting his intentions. Consequently, we confirmed that the proposed method successfully supported the sit-to-stand and stand-to-sit transfers of the complete paraplegic patient with the HAL.
PM wind generator topologies

The objective of this paper is to provide a comparison among permanent magnet (PM) wind generators of different topologies. Seven configurations are chosen for the comparison, consisting of both radial-flux and axial-flux machines. The comparison is done at seven power levels ranging from 1 to 200 kW. The basis for the comparison is discussed and implemented in detail in the design procedure. The criteria used for comparison are considered to be critical for the efficient deployment of PM wind generators. The design data are optimized and verified by finite-element analysis and commercial generator test results. For a given application, the results provide an indication of the best-suited machine.
Indoor human localization with orientation using WiFi fingerprinting

Localization in indoor environment poses a fundamental challenge in ubiquitous computing compared to its well-established GPS-based outdoor environment counterpart. This study investigated the feasibility of a WiFi-based indoor positioning system to localize elderly in an elderly center focusing on their orientation. The fingerprinting method of Received Signal Strength Indication (RSSI) from WiFi Access Points (AP) has been employed to discriminate and uniquely identify a position. The discrimination process of the reference points with its orientation have been analyzed with 0.9, 1.8, and 2.7 meter resolution. The experimental result shows that the WiFi-based RSSI fingerprinting method can discriminate the location and orientation of a user within 1.8 meter resolution.