nils-herrmann/clean_scirepeval huggingface dataset:
Improving Performances of MSER Features in Matching and Retrieval Tasks

MSER features are redefined to improve their performances in matching and retrieval tasks. The proposed SIMSER features (i.e. scale-insensitive MSERs) are the extremal regions which are maximally stable not only under the threshold changes (like MSERs) but, additionally, under image rescaling (smoothing). Theoretical advantages of such a modification are discussed. It is also preliminarily verified experimentally that such a modification preserves the fundamental properties of MSERs, i.e. the average numbers of features, repeatability, and computational complexity (which is only multiplicatively increased by the number of scales used), while performances (measured by typical CBVIR metrics) can be significantly improved. In particular, results on benchmark datasets indicate significant increments in recall values, both for descriptor-based matching and word-based matching. In general, SIMSERs seem particularly suitable for a usage with large visual vocabularies, e.g. they can be prospectively applied to improve quality of BoW pre-retrieval operations in large-scale databases.
Validity in Quantitative Content Analysis

Over the past 15 years, educational technologists have been dabbling with a research technique known as quantitative content analysis (QCA). Although it is characterized as a systematic and objective procedure for describing communication, readers find insufficient evidence of either quality in published reports. In this paper, it is argued that QCA should be conceived of as a form of testing and measurement. If this argument is successful, it becomes possible to frame many of the problems associated with QCA studies under the well-articulated rubric of test validity. Two sets of procedures for developing the validity of a QCA coding protocol are provided, (a) one for developing a protocol that is theoretically valid and (b) one for establishing its validity empirically. The paper is concerned specifically with the use of QCA to study educational applications of computer-mediated communication. The primary role of networked computers in higher education has shifted from presenting structured, preprogrammed learning materials to facilitating communication. In turn, the role of educational technology researchers has expanded to include the role of communication researcher. In the late 1980s, studies began to appear that incorporated new perspectives, new methods, and new techniques. One of the most promising was quantitative content analysis (QCA). Berelson (1952) defined QCA as “a research technique for the systematic, objective, and quantitative description of the manifest content of communication” (p. 18). In this context, description is a process that includes segmenting communication content into units, assigning each unit to a category, and providing tallies for each category. Bullen (1998), for instance, studied participation and critical thinking in an online discussion by counting the number of times each student contributed to the discussion and by assigning each contribution to one of three categories of critical thinking. By 1999, enough of these types of studies had been conducted in the field of educational technology that we were able to prepare a literature review (Rourke, Anderson, Garrison, & Archer, 2001). We used published reports to illustrate many of the basic issues of QCA, including objectivity, reliability, units of analysis, types of content, research designs, and ethics. Our intent was to summarize the experiences of researchers like us who were struggling to apply this unfamiliar technique in a disciplined and efficient manner. We found the technique promising but chided researchers on the rigor of their reports, particularly on the lack of reliability data. Those who persisted with the technique found their own way to these conclusions, and today
Influence of pole-arc width on cogging torque in permanent magnet motors

This work investigates the effect of the pole-arc width on the cogging torque of the permanent magnet motors based on the assumption of distributed force instead of the previous concentrated one. On the basis of the motors' mechanical and electrical symmetry, the superposition method is employed to examine the above effect. And the phase tuning relationships between the pole-arc width and the cogging torque is obtained. This study presents a tuning method for the prediction and suppression of the magnetically induced vibration, which in turn provides a theoretical foundation for the dynamic design of the permanent magnet motors. The theoretical conclusions are verified by the Finite Element method.
Efficient Multipattern Event Processing Over High-Speed Train Data Streams

Big data is becoming a key basis for productivity growth, innovation, and consumer surplus, but also bring us great challenges in its volume, velocity, variety, value, and veracity. The notion of event is an important cornerstone to manage big data. High-speed railway is one of the most typical application domains for event-based system, especially for the train onboard system. There are usually numerous complex event patterns subscribed in system sharing the same prefix, suffix, or subpattern; consequently, multipattern complex event detection often results in plenty of redundant detection operations and computations. In this paper, we propose a multipattern complex event detection model, multipattern event processing (MPEP), constructed by three parts: 1) multipattern state transition; 2) failure transition; and 3) state output. Based on MPEP, an intelligent onboard system for high-speed train is preliminarily implemented. The system logic is described using our proposed complex event description model and compiled into a multipattern event detection model. Experimental results show that MPEP can effectively optimize the complex event detection process and improve its throughput by eliminating duplicate automata states and redundant computations. This intelligent onboard system also provides better detection ability than other models when processing real-time events stored in high-speed train Juridical Recording Unit (JRU).
3D fully convolutional network for vehicle detection in point cloud

2D fully convolutional network has been recently successfully applied to the object detection problem on images. In this paper, we extend the fully convolutional network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows significant performance improvement over the previous point cloud based detection approaches.
Microbiome and metabolic disease: revisiting the bacterial phylum Bacteroidetes

Bacterial species composition in the gut has emerged as an important factor in obesity and its related metabolic diseases such as type 2 diabetes. Out of thousands of bacterial species-level phylotypes inhabiting the human gut, the majority belong to two dominant phyla, the Bacteroidetes and Firmicutes. Members of the Bacteroidetes in particular have been associated with human metabolic diseases. However, their associations with disease are not always consistent between studies. Delving deeper into the diversity within the Bacteroidetes reveals a vast diversity in genomes and capacities, which partly explain how not all members respond equally to similar environmental conditions in their hosts. Here, we discuss the Bacteroidetes phylum, associations of its members with metabolic phenotypes, and efforts to characterize functionally their interactions with their hosts. Harnessing the Bacteroidetes to promote metabolic health will require a nuanced understanding of how specific strains interact with their microbial neighbors and their hosts under various conditions.
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article's results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.
Testing Django Configurations Using Combinatorial Interaction Testing

Combinatorial Interaction Testing (CIT) is important because it tests the interactions between the many parameters that make up the configuration space of software systems. We apply this testing paradigm to a Python-based framework for rapid development of webbased applications called Django. In particular, we automatically create a CIT model for Django website configurations and run a state-of-the-art tool for CIT test suite generation to obtain sets of test configurations. Our automatic CIT-based approach is able to efficiently detect invalid configurations.
Superpixel-Based Tracking-by-Segmentation Using Markov Chains

We propose a simple but effective tracking-by-segmentation algorithm using Absorbing Markov Chain (AMC) on superpixel segmentation, where target state is estimated by a combination of bottom-up and top-down approaches, and target segmentation is propagated to subsequent frames in a recursive manner. Our algorithm constructs a graph for AMC using the superpixels identified in two consecutive frames, where background superpixels in the previous frame correspond to absorbing vertices while all other superpixels create transient ones. The weight of each edge depends on the similarity of scores in the end superpixels, which are learned by support vector regression. Once graph construction is completed, target segmentation is estimated using the absorption time of each superpixel. The proposed tracking algorithm achieves substantially improved performance compared to the state-of-the-art segmentation-based tracking techniques in multiple challenging datasets.
The Effect of a Low-Carbohydrate, Ketogenic Diet on Nonalcoholic Fatty Liver Disease: A Pilot Study

Nonalcoholic fatty liver disease is an increasingly common condition that may progress to hepatic cirrhosis. This pilot study evaluated the effects of a low-carbohydrate, ketogenic diet on obesity-associated fatty liver disease. Five patients with a mean body mass index of 36.4 kg/m2 and biopsy evidence of fatty liver disease were instructed to follow the diet (<20 g/d of carbohydrate) with nutritional supplementation for 6 months. Patients returned for group meetings biweekly for 3 months, then monthly for the second 3 months. The mean weight change was −12.8 kg (range 0 to −25.9 kg). Four of 5 posttreatment liver biopsies showed histologic improvements in steatosis (P=.02) inflammatory grade (P=.02), and fibrosis (P=.07). Six months of a low-carbohydrate, ketogenic diet led to significant weight loss and histologic improvement of fatty liver disease. Further research is into this approach is warranted.
VEGF-A stimulates lymphangiogenesis and hemangiogenesis in inflammatory neovascularization via macrophage recruitment.

Lymphangiogenesis, an important initial step in tumor metastasis and transplant sensitization, is mediated by the action of VEGF-C and -D on VEGFR3. In contrast, VEGF-A binds VEGFR1 and VEGFR2 and is an essential hemangiogenic factor. We re-evaluated the potential role of VEGF-A in lymphangiogenesis using a novel model in which both lymphangiogenesis and hemangiogenesis are induced in the normally avascular cornea. Administration of VEGF Trap, a receptor-based fusion protein that binds and neutralizes VEGF-A but not VEGF-C or -D, completely inhibited both hemangiogenesis and the outgrowth of LYVE-1(+) lymphatic vessels following injury. Furthermore, both lymphangiogenesis and hemangiogenesis were significantly reduced in mice transgenic for VEGF-A(164/164) or VEGF-A(188/188) (each of which expresses only one of the three principle VEGF-A isoforms). Because VEGF-A is chemotactic for macrophages and we demonstrate here that macrophages in inflamed corneas release lymphangiogenic VEGF-C/VEGF-D, we evaluated the possibility that macrophage recruitment plays a role in VEGF-A-mediated lymphangiogenesis. Either systemic depletion of all bone marrow-derived cells (by irradiation) or local depletion of macrophages in the cornea (using clodronate liposomes) prior to injury significantly inhibited both hemangiogenesis and lymphangiogenesis. We conclude that VEGF-A recruitment of monocytes/macrophages plays a crucial role in inducing inflammatory neovascularization by supplying/amplifying signals essential for pathological hemangiogenesis and lymphangiogenesis.
A behavioral planning framework for autonomous driving

In this paper, we propose a novel planning framework that can greatly improve the level of intelligence and driving quality of autonomous vehicles. A reference planning layer first generates kinematically and dynamically feasible paths assuming no obstacles on the road, then a behavioral planning layer takes static and dynamic obstacles into account. Instead of directly commanding a desired trajectory, it searches for the best directives for the controller, such as lateral bias and distance keeping aggressiveness. It also considers the social cooperation between the autonomous vehicle and surrounding cars. Based on experimental results from both simulation and a real autonomous vehicle platform, the proposed behavioral planning architecture improves the driving quality considerably, with a 90.3% reduction of required computation time in representative scenarios.
Area-Efficient ESD-Transient Detection Circuit With Smaller Capacitance for On-Chip Power-Rail ESD Protection in CMOS ICs

The RC-based power-rail electrostatic-discharge (ESD) clamp circuit with big field-effect transistor (BigFET) layout style in the main ESD clamp n-channel metal-oxide-semiconductor (NMOS) transistor was widely used to enhance the ESD robustness of a CMOS IC fabricated in advanced CMOS processes. To further reduce the occupied layout area of the RC in the power-rail ESD clamp circuit, a new ESD-transient detection circuit realized with smaller capacitance has been proposed and verified in a 0.13-mum CMOS process. From the experimental results, the power-rail ESD clamp circuit with the new proposed ESD-transient detection circuit can achieve a long-enough turn-on duration and higher ESD robustness under ESD stress condition, as well as better immunity against mis-trigger and latch-on event under the fast-power-on condition.
A Foundation for Multi-dimensional Databases

We present a multi-dimensional database model, which we believe can serve as a conceptual model for On-Line Analytical Processing (OLAP)-based applications. Apart from providing the functionalities necessary for OLAP-based applications, the main feature of the model we propose is a clear separation between structural aspects and the contents. This separation of concerns allows us to define data manipulation languages in a reasonably simple, transparent way. In particular, we show that the data cube operator can be expressed easily. Concretely, we define an algebra and a calculus and show them to be equivalent. We conclude by comparing our approach to related work. The conceptual multi-dimensional database model developed here is orthogonal to its implementation, which is not a subject of the present paper.
Reproducibility of quantitative tractography methods applied to cerebral white matter

Tractography based on diffusion tensor imaging (DTI) allows visualization of white matter tracts. In this study, protocols to reconstruct eleven major white matter tracts are described. The protocols were refined by several iterations of intra- and inter-rater measurements and identification of sources of variability. Reproducibility of the established protocols was then tested by raters who did not have previous experience in tractography. The protocols were applied to a DTI database of adult normal subjects to study size, fractional anisotropy (FA), and T2 of individual white matter tracts. Distinctive features in FA and T2 were found for the corticospinal tract and callosal fibers. Hemispheric asymmetry was observed for the size of white matter tracts projecting to the temporal lobe. This protocol provides guidelines for reproducible DTI-based tract-specific quantification.
Speech and Language Processing An Introduction to Natural Language Processing , Computational Linguistics , and Speech Recognition Second Edition

Open the pod bay doors, HAL. HAL: I'm sorry Dave, I'm afraid I can't do that. The idea of giving computers the ability to process human language is as old as the idea of computers themselves. This book is about the implementation and implications of that exciting idea. We introduce a vibrant interdisciplinary field with many names corresponding to its many facets, names like speech and language processing, human language technology, natural language processing, computational linguistics, and speech recognition and synthesis. The goal of this new field is to get computers to perform useful tasks involving human language, tasks like enabling human-machine communication, improving human-human communication, or simply doing useful processing of text or speech. One example of a useful such task is a conversational agent. The HAL 9000 com-Conversational agent puter in Stanley Kubrick's film 2001: A Space Odyssey is one of the most recognizable characters in 20th century cinema. HAL is an artificial agent capable of such advanced language behavior as speaking and understanding English, and at a crucial moment in the plot, even reading lips. It is now clear that HAL's creator, Arthur C. Clarke, was a little optimistic in predicting when an artificial agent such as HAL would be available. But just how far off was he? What would it take to create at least the language-related parts of HAL? We call programs like HAL that converse with humans in natural language conversational agents or dialogue systems. In this text we study the vari-Dialogue system ous components that make up modern conversational agents, including language input (automatic speech recognition and natural language understanding) and language output (dialogue and response planning and speech synthesis). Let's turn to another useful language-related task, that of making available to non-English-speaking readers the vast amount of scientific information on the Web in En-glish. Or translating for English speakers the hundreds of millions of Web pages written in other languages like Chinese. The goal of machine translation is to automatically Machine translation translate a document from one language to another. We introduce the algorithms and mathematical tools needed to understand how modern machine translation works. Machine translation is far from a solved problem; we cover the algorithms currently used in the field, as well as important component tasks. Many other language processing tasks are also related to the Web. Another such task is Web-based question answering. This is a generalization …
A Multichannel High-Precision CMOS Time-to-Digital Converter for Laser-Scanner-Based Perception Systems

A multichannel time-to-digital converter (TDC) implemented with 0.35-μm complementary metal-oxide-semiconductor technology that uses a low-frequency crystal as reference and measures the time intervals with counter and delay line interpolation techniques is described. The multichannel measurement architecture provides information on the time intervals between several timing signals. The circuit can be used for laser time-of-flight distance measurements, e.g., where it can determine time intervals between a transmitted laser pulse and several reflected pulses and also pulsewidths or rise times, to compensate for the timing walk error. This paper shows how several measurement channels can be integrated into one TDC without losing the measurement performance. The circuit offers a measurement precision that is better than 8 ps and a measurement range of up to 74 μs. In terms of laser distance measurement, its performance is equivalent to millimeter-level precision within an 11-km range.
Searching for build debt: Experiences managing technical debt at Google

With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed.
Advanced Safe PIN-Entry Against Human Shoulder-Surfing Ms

When users insert their passwords in a common area, they might be at risk of aggressor stealing their password. The PIN entry can be perceived by close by adversaries, more effectually in a crowded place. A new technique has been established to cope with this problem that is cryptography prevention techniques. Instead, there have been alternative approaches among them, the PIN entry was elegant because of its simplicity and accessibility. The basic BW method is focused to withstand a human shoulder surfing attack. In every round, a well ordered numeric keypad is colored at odd. A user who knows the accurate PIN digit can enter by pressing the separate color key. The IBW method is examined to be confidential against human nemesis due to the restricted cognitive abilities of humans. Also the IBW method is proven to be robust against any hacking attacks.
Digital Dead Time Auto-Tuning for Maximum Efficiency Operation of Isolated DC-DC Converters

The rapidly growing use of digital control in Distributed Power Architectures (DPA) has been primarily driven by the increasing need of sophisticated Power Management functions. However, specifically designed control algorithms can also be exploited for optimizing the system performance. In this paper we present a new auto-tuning method and system that makes it possible to operate an isolated DC-DC converter at maximum efficiency. The auto-tuning performs an optimum adjustment of both primary side dead time and secondary side conduction times based on the measurement of the input current. It does not require any additional external components since current sensing functions are already implemented for power management purposes. Experimental measurements performed on an asymmetrical driven half-bridge DC-DC converter demonstrate the effectiveness of our solution and its robustness to component variations.