nils-herrmann/clean_scirepeval huggingface dataset:
A quasi-Z-source integrated multi-port power converter with reduced capacitance for switched reluctance motor drives

This paper presents a quasi Z-source integrated multiport converter (ZIMPC) for switched reluctance motor (SRM) drives to reduce the dc link capacitance. In conventional SRM drives, employing multi-phase asymmetrical H-bridge (ASHB) topology, large capacitors are necessary to absorb the transient energy during phase current commutation. However, electrolytic capacitors would affect the lifetime, cost and power density of the drive system. With switch multiplexing technique, a Z-source integrated multiport power converter is derived to achieve power ripple reduction using relatively small capacitance. Corresponding control method is designed and developed for the proposed SRM drive. Also, the ZIMPC can boost the equivalent phase exciting voltage and widen the constant power speed range (CPSR). At last, simulation and experimental results verify the feasibility of the proposed ZIMPC and its superior performance with smaller capacitance as compared with ASHB.
Geometry and Meaning

Geometry and Meaning is an interesting book about a relationship between geometry and logic defined on certain types of abstract spaces and how that intimate relationship might be exploited when applied in computational linguistics. It is also about an approach to information retrieval, because the analysis of natural language, especially negation, is applied to problems in IR, and indeed illustrated throughout the book by simple examples using search engines. It is refreshing to see IR issues tackled from a different point of view than the standard vector space (Salton, 1968). It is an enjoyable read, as intended by the author, and succeeds as a sort of tourist guide to the subject in hand. The early part of the book concentrates on the introduction of a number of elementary concepts from mathematics: graph theory, linear algebra (especially vector spaces), lattice theory, and logic. These concepts are well motivated and illustrated with good examples, mostly of a classificatory or taxonomic kind. One of the major goals of the book is to argue that non-classical logic, in the form of a quantum logic, is a candidate for analyzing language and its underlying logic, with a promise that such an approach could lead to improved search engines. The argument for this is aided by copious references to early philosophers, scientists, and mathematicians, creating the impression that when Aristotle, Descartes, Boole, and Grassmann were laying the foundations for taxonomy, analytical geometry, logic, and vector spaces, they had a more flexible and broader view of these subjects than is current. This is especially true of logic. Thus the historical approach taken to introducing quantum logic (chapter 7) is to show that this particular kind of logic and its interpretation in vector space were inherent in some of the ideas of these earlier thinkers. Widdows claims that Aristotle was never respected for his mathematics and that Grassmann’s Ausdehnungslehre was largely ignored and left in obscurity. Whether Aristotle was never admired for his mathematics I am unable to judge, but certainly Alfred North Whitehead (1925) was not complimentary when he said:
Transparent Runtime Shadow Stack : Protection against malicious return address modifications

Exploitation of buffer overflow vulnerabilities constitutes a significant portion of security attacks in computer systems. One of the most common types of buffer overflow attacks is the hijacking of the program counter by overwriting function return addresses in the process’ stack so as to redirect the program’s control flow to some malicious code injected into the process’ memory. Previous solutions to this problem are based either on hardware or the compiler. The former requires special hardware while the latter requires the source code of the software. In this paper we introduce the use of a T ransparent RUntime Shadow Stack (TRUSS) to protect against function return address modification. Our proposed scheme is built on top of DynamoRIO, a dynamic binary rewriting framework. DynamoRIO is implemented on both Windows and Linux. Hence, our scheme is able to protect applications on both operating systems. We have successfully tested our implementation on the SPECINT 2000 benchmark programs on both Windows and Linux, John Wilander’s “Dynamic testbed for twenty buffer overflow attacks” as well as Microsoft Access, Powerpoint and Word 2002. This paper will discuss the implementation details of our scheme as well as provide a performance evaluation. The latter shows that TRUSS is able to operate with an average overhead of about 20% to 50% which we believe is acceptable.
KD-tree acceleration structures for a GPU raytracer

Modern graphics hardware architectures excel at compute-intensive tasks such as ray-triangle intersection, making them attractive target platforms for raytracing. To date, most GPU-based raytracers have relied upon uniform grid acceleration structures. In contrast, the kd-tree has gained widespread use in CPU-based raytracers and is regarded as the best general-purpose acceleration structure. We demonstrate two kd-tree traversal algorithms suitable for GPU implementation and integrate them into a streaming raytracer. We show that for scenes with many objects at different scales, our kd-tree algorithms are up to 8 times faster than a uniform grid. In addition, we identify load balancing and input data recirculation as two fundamental sources of inefficiency when raytracing on current graphics hardware.
Ship Surveillance With TerraSAR-X

Ship detection is an important application of global monitoring of environment and security. In order to overcome the limitations by other systems, surveillance with satellite synthetic aperture radar (SAR) is used because of its possibility to provide ship detection at high resolution over wide swaths and in all weather conditions. A new X-band radar onboard the TerraSAR-X (TS-X) satellite gives access to spatial resolution as fine as 1 m. In this paper, first results on the combined use of TS-X ship detection, automatic identification system (AIS), and satellite AIS (SatAIS) is presented. The AIS system is an effective terrestrial method for tracking vessels in real time typically up to 40 km off the coast. SatAIS, as a space-based system, allows almost global coverage for monitoring of ships since not all ships operate their AIS and smaller ships are not equipped with AIS. The system is considered to be of cooperative nature. In this paper, the quality of TS-X images with respect to ship detection is evaluated, and a first assessment of its performance for ship detection is given. The velocity of a moving ship is estimated using complex TS-X data. As test cases, images were acquired over the North Sea, Baltic Sea, Atlantic Ocean, and Pacific Ocean in Stripmap mode with a resolution of 3 m at a coverage of 30 km 100 km. Simultaneous information on ship positions was available from TS-X and terrestrial as well as SatAIS. First results on the simultaneous superposition of SatAIS and high-resolution radar images are presented.
Authorship attribution based on Life-Like Network Automata

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks.
The stress-reducing effect of music listening varies depending on the social context

Given that music listening often occurs in a social context, and given that social support can be associated with a stress-reducing effect, it was tested whether the mere presence of others while listening to music enhances the stress-reducing effect of listening to music.



A total of 53 participants responded to questions on stress, presence of others, and music listening five times per day (30min after awakening, 1100h, 1400h, 1800h, 2100h) for seven consecutive days. After each assessment, participants were asked to collect a saliva sample for the later analysis of salivary cortisol (as a marker for the hypothalamic-pituitary-adrenal axis) and salivary alpha-amylase (as a marker for the autonomic nervous system).



Hierarchical linear modeling revealed that music listening per se was not associated with a stress-reducing effect. However, listening to music in the presence of others led to decreased subjective stress levels, attenuated secretion of salivary cortisol, and higher activity of salivary alpha-amylase. When listening to music alone, music that was listened to for the reason of relaxation predicted lower subjective stress.



The stress-reducing effect of music listening in daily life varies depending on the presence of others. Music listening in the presence of others enhanced the stress-reducing effect of music listening independently of reasons for music listening. Solitary music listening was stress-reducing when relaxation was stated as the reason for music listening. Thus, in daily life, music listening can be used for stress reduction purposes, with the greatest success when it occurs in the presence of others or when it is deliberately listened to for the reason of relaxation.
A New Approach of Digital Forensic Model for Digital Forensic Investigation

The research introduces a structured and consistent approach for digital forensic investigation. Digital forensic science provides tools, techniques and scientifically proven methods that can be used to acquire and analyze digital evidence. The digital forensic investigation must be retrieved to obtain the evidence that will be accepted in the court. This research focuses on a structured and consistent approach to digital forensic investigation. This research aims at identifying activities that facilitate and improves digital forensic investigation process. Existing digital forensic framework will be reviewed and then the analysis will be compiled. The result from the evaluation will produce a new model to improve the whole investigation process.
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization

1. Fig. 1 (Experiment I): the average precision plots and the success plots for the compared trackers (LSHT , LSST  and SPLTT ), which are not included in . For the other 29 compared trackers, readers are referred to . 2. Fig. 2 (Experiment I): the average precision plot ranking scores and the AUC ranking scores of the five leading trackers on different subsets of test sequences. 3. Fig. 3 and 4 (Experiment I): the precision plot ranking score tables and the AUC ranking score tables for the top five trackers. 4. Fig. 5 and 6 (Experiment I): the precision plot ranking score tables and the AUC ranking score tables for MEEM and its baselines. 5. Fig. 7 (Experiment II): the precision plot ranking score table and the AUC ranking score table for MEEM and the compared algorithms on our newly collected sequences.
An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams

An important problem in processing large data streams is detecting changes in the underlying distribution that generates the data. The challenge in designing change detection schemes is making them general, scalable, and statistically sound. In this paper, we take a general, information-theoretic approach to the change detection problem, which works for multidimensional as well as categorical data. We use relative entropy, also called the Kullback-Leibler distance, to measure the difference between two given distributions. The KL-distance is known to be related to the optimal error in determining whether the two distributions are the same and draws on fundamental results in hypothesis testing. The KL-distance also generalizes traditional distance measures in statistics, and has invariance properties that make it ideally suited for comparing distributions. Our scheme is general; it is nonparametric and requires no assumptions on the underlying distributions. It employs a statistical inference procedure based on the theory of bootstrapping, which allows us to determine whether our measurements are statistically significant. The scheme is also quite flexible from a practical perspective; it can be implemented using any spatial partitioning scheme that scales well with dimensionality. In addition to providing change detections, our method generalizes Kulldorff’s spatial scan statistic, allowing us to quantitatively identify specific regions in space where large changes have occurred. We provide a detailed experimental study that demonstrates the generality and efficiency of our approach with different kinds of multidimensional datasets, both synthetic and real.
A Novel Term Weighting Scheme Model

The use of textual data has increased exponentially in recent years due to the networking infrastructure such as Facebook, Twitter, Wikipedia, Blogs, and so one. Analysis of this massive textual data can help to automatically categorize and label new content. Before classification process, term weighting scheme is the crucial step for representing the documents in a way suitable for classification algorithms. In this paper, we are conducting a survey on the term weighting schemes and we propose an efficient term weighting scheme that provide a better classification accuracy than those obtained with the famous TF-IDF, the recent IF-IGM and the others term weighting schemes in the literature.
Planning-based prediction for pedestrians

We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.
Mobile Robot Path Planning with Randomly Moving Obstacles and Goal

This article presents the dynamic path planning for a mobile robot to track a randomly moving goal with avoidance of multiple randomly moving obstacles. The main feature of the developed scheme is its capability of dealing with the situation that the paths of both the goal and the obstacles are unknown a priori to the mobile robot. A new mathematical approach that is based on the concepts of 3-D geometry is proposed to generate the path of the mobile robot. The mobile robot decides its path in real time to avoid the randomly moving obstacles and to track the randomly moving goal. The developed scheme results in faster decision-making for successful goal tracking. 3-D simulations using MATLAB validate the developed scheme.
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM

We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence — among synchronous or asynchronous methods — on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.
Validating a Deep Learning Framework by Metamorphic Testing

Deep learning has become an important tool for image classification and natural language processing. However, the effectiveness of deep learning is highly dependent on the quality of the training data as well as the net model for the learning. The training data set for deep learning normally is fairly large, and the net model is pretty complex. It is necessary to validate the deep learning framework including the net model, executing environment, and training data set before it is used for any applications. In this paper, we propose an approach for validating the classification accuracy of a deep learning framework that includes a convolutional neural network, a deep learning executing environment, and a massive image data set. The framework is first validated with a classifier built on support vector machine, and then it is tested using a metamorphic validation approach. The effectiveness of the approach is demonstrated by validating a deep learning classifier for automated classification of biology cell images. The proposed approach can be used for validating other deep learning framework for different applications.
Ako: Decentralised Deep Learning with Partial Gradient Exchange

Distributed systems for the training of deep neural networks (DNNs) with large amounts of data have vastly improved the accuracy of machine learning models for image and speech recognition. DNN systems scale to large cluster deployments by having worker nodes train many model replicas in parallel; to ensure model convergence, parameter servers periodically synchronise the replicas. This raises the challenge of how to split resources between workers and parameter servers so that the cluster CPU and network resources are fully utilised without introducing bottlenecks. In practice, this requires manual tuning for each model configuration or hardware type.
 We describe Ako, a decentralised dataflow-based DNN system without parameter servers that is designed to saturate cluster resources. All nodes execute workers that fully use the CPU resources to update model replicas. To synchronise replicas as often as possible subject to the available network bandwidth, workers exchange partitioned gradient updates directly with each other. The number of partitions is chosen so that the used network bandwidth remains constant, independently of cluster size. Since workers eventually receive all gradient partitions after several rounds, convergence is unaffected. For the ImageNet benchmark on a 64-node cluster, Ako does not require any resource allocation decisions, yet converges faster than deployments with parameter servers.
Statistical Machine Translation with Rule based Machine Translation

We have evaluated the two-stage machine translation (MT) system. The first stage is a state-of-the-art trial rule-based machine translation system. The second stage is a normal statistical machine translation system. For Japanese-English machine translation, first, we used a Japanese-English rule-based MT, and we obtained "ENGLISH" sentences from Japanese sentences. Second, we used a standard statistical machine translation. This means that we translated "ENGLISH" to English machine translation. This method has an advantages that it produces grammatically correct sentences. From the results of experiments in the JE task, we obtained a BLEU score of 0.1996 using our proposed method. In contrast, we obtained a BLEU score of 0.1436 using a standard method. And for the EJ task, we obtained a BLEU score of 0.2775 using our proposed method. In contrast, we obtained a BLEU score of 0.0831 using a standard method. This means that our proposed method was effective for the JE and EJ task. However, there is a problem. The BLEU score was not so effective to measure the translation quality.
Foundations of Cryptography: Basic Tools

If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, foundations of cryptography basic tools always becomes the most wanted book. Many people are absolutely searching for this book. It means that many love to read this kind of book.
Learning Bilingual Lexicons from Monolingual Corpora

We present a method for learning bilingual translation lexicons from monolingual corpora. Word types in each language are characterized by purely monolingual features, such as context counts and orthographic substrings. Translations are induced using a generative model based on canonical correlation analysis, which explains the monolingual lexicons in terms of latent matchings. We show that high-precision lexicons can be learned in a variety of language pairs and from a range of corpus types.
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

Face recognition has benefitted greatly from the many databases that have been produced to study it. Most of these databases have been created under controlled conditions t o facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, expression, background, camera quality, occlusion , age, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database is provided as an aid in studying the latter, unconstrained, face recognition problem. The database represents an initial attempt to provide a set of labeled face photographs spanning the range of conditions typically encountered by people in their everyday lives. The database exhibits “natural” variability in pose, lighting, focus, resolution, facial expression, age, gender, race, acces sories, make-up, occlusions, background, and photographic quality. Despite this variability, the images in the database are presented in a simple and consistent format for maximum ease of use. In addition to describing the details of the database and its acquisition, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible.