nils-herrmann/clean_scirepeval huggingface dataset:
LOMAC: Low Water-Mark Integrity Protection for COTS Environments

We hypothesizethat a form of kernel-residentaccesscontrol-based integrity protection can gain widespread acceptancein Commer cial Off-The-Shelf(COTS)environmentsprovidedthat it couplessomeusefulprotectionwith a high degreeof compatibilitywith existing software, configurations,andpractices.To testthis hypothesis, wehave developeda highly-compatiblefreeopen-sour ceprototype calledLOMAC, andreleasedit on theInternet.LOMAC is a dynamicallyloadableextensionfor COTSLinux kernels thatprovidesintegrity protectionbasedonLowWater-Mark accesscontrol. We presenta classificationof existing accesscontrol modelswith regard tocompatibility, concluding thatmodelssimilar to LowWater-Mark areespeciallywellsuitedto high-compatibilitysolutions.Wealsodescribeour practical strategiesfor dealingwith thepathological cases in the Low Water-Mark model’s behavior , which includea smallextensionof themodel,andanunusualapplicationof
Joint tracking and segmentation of multiple targets

Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios [e.g. 40, 53, 55]. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these “dots” over time. An obvious short-coming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain a video segmentation in addition to the classical bounding-box representation in unconstrained, real-world videos. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.
Parameter extraction for a physics-based circuit simulator IGBT model

A practical parameter extraction method is presented for the Fourier-based-solution physics-based IGBT model. In the extraction procedure, only one simple clamped inductive load test is needed for the extraction of the eleven and thirteen parameters required for the NPT and PT IGBT models, respectively. Validation with experimental results from various structure IGBTs demonstrates the accuracy of the proposed IGBT model and the robustness of the parameter extraction method.
Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates

Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying participants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.
Hair animation with collision detection

We propose an efficient method for hair animation. The motion of hairs is modeled by a simplified physical simulation. Collision between hair and a human body or other objects is used to make a realistic hair animation. Fast collision detection is required because the number of hairs is very large. Therefore, a cylindrical representation of human body or other objects is created for efficient collision detection. Because of the cylindrical representation, collision is detected with table look-up and interpolation, and computation complexity is independent of the complexity of the body. Reaction constraint method is applied for the collision response to simulate inelastic contact. This method is successfully applied to generate hair animation.
The Allen Brain Atlas: 5 years and beyond

The Allen Brain Atlas, a Web-based, genome-wide atlas of gene expression in the adult mouse brain, was an experiment on a massive scale. The development of the atlas faced a combination of great technical challenges and a non-traditional open research model, and it encountered many hurdles on the path to completion and community adoption. Having overcome these challenges, it is now a fundamental tool for neuroscientists worldwide and has set the stage for the creation of other similar open resources. Nevertheless, there are many untapped opportunities for exploration.
Three-phase single-stage four-switch PFC buck-boost-type rectifier

This paper proposes a new three-phase single-stage power-factor corrector buck-boost-type rectifier topology. The typical topology uses a bridge configuration with six switches. This new topology only requires four switches, improving the rectifier efficiency as only one reverse-blocking power semiconductor conducts at any time. A vector-based sliding-mode control method for the three-phase input currents is also proposed. This fast and robust technique uses sliding mode to generate /spl alpha//spl beta/ space-vector modulation, which forces the input line currents to track a suitable sinusoidal reference. A near-unity power-factor operation of the rectifier is obtained using a sinusoidal reference in phase with the input source voltages. A proportional-integral controller is adopted to regulate the output voltage of the converter. This external voltage controller modulates the amplitude of the current references. The characteristics of the new rectifier are verified with experimental results.
Prioritized multi-task compliance control of redundant manipulators

We propose a new approach for dynamic control of redundant manipulators to deal with multiple prioritized tasks at the same time by utilizing null space projection techniques. The compliance control law is based on a new representation of the dynamics wherein specific null space velocity coordinates are introduced. These allow to efficiently exploit the kinematic redundancy according to the task hierarchy and lead to a dynamics formulation with block-diagonal inertia matrix. The compensation of velocity-dependent coupling terms between the tasks by an additional passive feedback action facilitates a stability analysis for the complete hierarchy based on semi-definite Lyapunov functions. No external forces have to be measured. Finally, the performance of the control approach is evaluated in experiments on a torque-controlled robot.
Spectrum Survey in Singapore: Occupancy Measurements and Analyses

We study the 24-hour spectrum usage pattern in Singapore in the frequency bands ranging from 80 MHz to 5850 MHz. The objectives are to find how the scarce radio spectrum allocated to different services is utilized in Singapore and identify the bands that could be accessed for future opportunistic use due to their low or no active utilization. The results from the spectrum measurements taken over 12 weekday periods reveal that a significant amount of spectrum in Singapore has very low occupancy all the time. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold. The outcome of this study suggests that Singapore has a great potential for employing emerging spectrum sharing technology such as the cognitive radio technology to accommodate enormous demands for future wireless services. However, this study of spectrum survey is preliminary in its nature and future long term studies need to be performed to determine any potential secondary usage on those channels that have low or no active utilization.
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image

In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusion, and combines estimates from the color image and surface normals with learned attention maps to improve the depth accuracy especially for distant areas. Extensive experiments demonstrate that our model improves upon the state-of-the-art performance on KITTI depth completion benchmark. Ablation study shows the positive impact of each model components to the final performance, and comprehensive analysis shows that our model generalizes well to the input with higher sparsity or from indoor scenes.
Multiphase Bidirectional Flyback Converter Topology for Hybrid Electric Vehicles

For hybrid electric vehicles, the batteries and the drive dc link may be at different voltages. The batteries are at low voltage to obtain higher volumetric efficiencies, and the dc link is at higher voltage to have higher efficiency on the motor side. Therefore, a power interface between the batteries and the drive's dc link is essential. This power interface should handle power flow from battery to motor, motor to battery, external genset to battery, and grid to battery. This paper proposes a multi-power-port topology which is capable of handling multiple power sources and still maintains simplicity and features like obtaining high gain, wide load variations, lower output-current ripple, and capability of parallel-battery energy due to the modular structure. The scheme incorporates a transformer winding technique which drastically reduces the leakage inductance of the coupled inductor. The development and testing of a bidirectional flyback dc-dc converter for hybrid electric vehicle is described in this paper. Simple hysteresis voltage control is used for dc-link voltage regulation. The experimental results are presented to show the working of the proposed converter.
Design and Vlsi Implementation of Anti- Collision Enabled Robot Processor Using Rfid Technology

RFID is a low power wireless emerging technology which has given rise to highly promising applications in real life. It can be employed for robot navigation. In multi-robot environment, when many robots are moving in the same workspace, there is a possibility of their physical collision with themselves as well as with physical objects. In the present work, we have proposed and developed a processor incorporating smart algorithm for avoiding such collisions with the help of RFID technology and implemented it by using VHDL. The design procedure and the simulated results are very useful in designing and implementing a practical RFID system. The RTL schematic view of the processor is achieved by successfully synthesizing the proposed design.
Development and comparison of homography based estimation techniques for camera to road surface orientation

This paper focuses on dynamic orientation estimation of a vehicle-mounted mono camera. In particular, the pitch and roll angles of the camera relative to the road surface. Information about the orientation angles is included in the homography (projective transformation) between two images of a planar road surface. The extraction of angles from a homography matrix is possible but not recommended due to parameter ambiguities. For this reason we do not estimate a full homography matrix but reduce the parameter space to two parameters. In this area of parameter estimation there are mainly two different approaches: The optical flow based approach and the image registration based approach. In order to decide which of these approaches is more favorable for the angle estimation problem, we develop one optical flow based and one image registration based angle estimation algorithm. We are the first directly evaluating and comparing both approaches with each other with the help of an artificial image sequence as well as real-world driving scenarios. In addition, this paper lifts the common limitation of roll angle of zero degree for dynamic camera orientation estimation. Our research finds that there is only a small difference between the parameter estimation results of the optical flow and image registration based approach.
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)

In this shared task, we present evaluations on two related tasks Paraphrase Identification (PI) and Semantic Textual Similarity (SS) systems for the Twitter data. Given a pair of sentences, participants are asked to produce a binary yes/no judgement or a graded score to measure their semantic equivalence. The task features a newly constructed Twitter Paraphrase Corpus that contains 18,762 sentence pairs. A total of 19 teams participated, submitting 36 runs to the PI task and 26 runs to the SS task. The evaluation shows encouraging results and open challenges for future research. The best systems scored a F1-measure of 0.674 for the PI task and a Pearson correlation of 0.619 for the SS task respectively, comparing to a strong baseline using logistic regression model of 0.589 F1 and 0.511 Pearson; while the best SS systems can often reach >0.80 Pearson on well-formed text. This shared task also provides insights into the relation between the PI and SS tasks and suggests the importance to bringing these two research areas together. We make all the data, baseline systems and evaluation scripts publicly
The evolution to 4G cellular systems: LTE-Advanced

This paper provides an in-depth view on the technologies being considered for Long Term Evolution-Advanced (LTE-Advanced). First, the evolution from third generation (3G) to fourth generation (4G) is described in terms of performance requirements and main characteristics. The new network architecture developed by the Third Generation Partnership Project (3GPP), which supports the integration of current and future radio access technologies, is highlighted. Then, the main technologies for LTE-Advanced are explained, together with possible improvements, their associated challenges, and some approaches that have been considered to tackle those challenges.
ANN Model to Predict Stock Prices at Stock Exchange Markets

Stock exchanges are considered major players in financial sectors of many countries. Most Stockbrokers, who execute stock trade, use technical, fundamental or time series analysis in trying to predict stock prices, so as to advise clients. However, these strategies do not usually guarantee good returns because they guide on trends and not the most likely price. It is therefore necessary to explore improved methods of prediction. The research proposes the use of Artificial Neural Network that is feedforward multi-layer perceptron with error backpropagation and develops a model of configuration 5:21:21:1 with 80% training data in 130,000 cycles. The research develops a prototype and tests it on 2008-2012 data from stock markets e.g. Nairobi Securities Exchange and New York Stock Exchange, where prediction results show MAPE of between 0.71% and 2.77%. Validation done with Encog and Neuroph realized comparable results. The model is thus capable of prediction on typical stock markets.
Automated Data Structure Generation: Refuting Common Wisdom

Common wisdom in the automated data structure generation community states that declarative techniques have better usability than imperative techniques, while imperative techniques have better performance. We show that this reasoning is fundamentally flawed: if we go to the declarative limit and employ constraint logic programming (CLP), the CLP data structure generation has orders of magnitude better performance than comparable imperative techniques. Conversely, we observe and argue that when it comes to realistically complex data structures and properties, the CLP specifications become more obscure, indirect, and difficult to implement and understand than their imperative counterparts. We empirically evaluate three competing generation techniques, CLP, Korat, and UDITA, to validate these observations on more complex and interesting data structures than any prior work in this area. We explain why these observations are true, and discuss possible techniques for attaining the best of both worlds.
Towards a Methodology for Building Ontologies

We outline some requirements for a comprehensive methodology for building ontologies, and review some important work that has been done in the area which could contribute to this goal. We describe our own experiences in constructing a signi cant ontology, emphasising the ontology capture phase. We rst consider the very general issue of categorisation in modelling, and relate it to the process of ontology capture. We then describe the procedure that we used to identify the terms and produce de nitions. We describe a successful way to handle ambiguous terms, which can be an enormous obstacle to reaching a shared understanding. Other important ndings include: it may not be necessary to identify competency questions before building the ontology; the meta-ontology can be chosen after detailed text de nitions are produced; de ning terms which are 'cognitively basic' rst can lead to less re-work. AIAI-TR-183 Page 1 of 13
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

State-of-the-art sequence labeling systems traditionally require large amounts of taskspecific knowledge in the form of handcrafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both wordand character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data preprocessing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks — Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both datasets — 97.55% accuracy for POS tagging and 91.21% F1 for NER.
3D deep convolutional neural networks for amino acid environment similarity analysis

Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures. Our deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions. End-to-end trained deep learning networks consistently outperform methods using hand-engineered features, suggesting that the 3DCNN framework is well suited for analysis of protein microenvironments and may be useful for other protein structural analyses.
Eleos: ExitLess OS Services for SGX Enclaves

Intel Software Guard extensions (SGX) enable secure and trusted execution of user code in an isolated enclave to protect against a powerful adversary. Unfortunately, running I/O-intensive, memory-demanding server applications in enclaves leads to significant performance degradation. Such applications put a substantial load on the in-enclave system call and secure paging mechanisms, which turn out to be the main reason for the application slowdown. In addition to the high direct cost of thousands-of-cycles long SGX management instructions, these mechanisms incur the high indirect cost of enclave exits due to associated TLB flushes and processor state pollution.
 We tackle these performance issues in Eleos by enabling exit-less system calls and exit-less paging in enclaves. Eleos introduces a novel Secure User-managed Virtual Memory (SUVM) abstraction that implements application-level paging inside the enclave. SUVM eliminates the overheads of enclave exits due to paging, and enables new optimizations such as sub-page granularity of accesses.
 We thoroughly evaluate Eleos on a range of microbenchmarks and two real server applications, achieving notable system performance gains. memcached and a face verification server running in-enclave with Eleos, achieves up to 2.2× and 2.3× higher throughput respectively while working on datasets up to 5× larger than the enclave's secure physical memory.
Design and Implementation of a True Random Number Generator Based on Digital Circuit Artifacts

There are many applications for true, unpredictable random numbers. For example the strength of numerous cryptographic operations is often dependent on a source of truly random numbers. Sources of random information are available in nature but are often hard to access in integrated circuits. In some specialized applications, analog noise sources are used in digital circuits at great cost in silicon area and power consumption. These analog circuits are often influenced by periodic signal sources that are in close proximity to the random number generator. We present a random number generator comprised entirely of digital circuits, which utilizes electronic noise. Unlike earlier work [11], only standard digital gates without regard to precise layout were used.