nils-herrmann/clean_scirepeval huggingface dataset:
Blind path obstacle detector using smartphone camera and line laser emitter

Visually impaired people find navigating within unfamiliar environments challenging. Many smart systems have been proposed to help blind people in these difficult, often dangerous, situations. However, some of them are uncomfortable, difficult to obtain or simply too expensive. In this paper, a low-cost wearable system for visually impaired people was implemented which allows them to detect and locate obstacles in their locality. The proposed system consists of two main hardware components, a laser pointer ($12) and an android smart phone, making our system relatively cheap and accessible. The collision avoidance algorithm uses image processing to measure distances to objects in the environment. This is based on laser light triangulation. This obstacle detection is enhanced by edge detection within the captured image. An additional feature of the system is to recognize and warn the user when stairs are present in the camera's field of view. Obstacles are brought to the user's attention using an acoustic signal. Our system was shown to be robust, with only 5 % false alarm rate and a sensitivity of 90 % for 1 cm wide obstacles.
An Intelligent Dual-Voltage Driving Method and Circuit For a Common Rail Injector for Heavy-Duty Diesel Engines

The control accuracies of the injection time and fuel quantity are the most important factors affecting the fuel economy and emissions of an engine equipped with a high-pressure common-rail fuel injection system. This paper presents an intelligent dual-voltage driving control that can simultaneously reduce the response time of the injector and improve the accuracy of the injected fuel quantity. The designed method employs a novel boost dc–dc circuit and does not require an additional independent dc–dc module that includes an inductor switch MOSFET and a controller. During the time interval between two continuous injections, the low-side MOSFET serves as a dc–dc switch, and the software in the controller controls the dual-voltage dc–dc, which uses the injector as a charging inductor of the dc–dc module. The operation principle of this intelligent driving circuit is described. Experimental results show that this method can not only significantly decrease the response time of the injector but also improve the stability of the fuel injection process.
Falcons Concept Search: A Practical Search Engine for Web Ontologies

Web ontologies provide shared concepts for describing domain entities and thus enable semantic interoperability between applications. To facilitate concept sharing and ontology reusing, we developed Falcons Concept Search, a novel keyword-based ontology search engine. In this paper, we illustrate how the proposed mode of interaction helps users quickly find ontologies that satisfy their needs and present several supportive techniques including a new method of constructing virtual documents of concepts for keyword search, a popularity-based scheme to rank concepts and ontologies, and a way to generate query-relevant structured snippets. We also report the results of a usability evaluation as well as user feedback.
A Homogeneous Relational Model and Query Languages for Temporal Databases

In a temporal database, time values are associated with data item to indicate their periods of validity. We propose a model for temporal databases within the framework of the classical database theory. Our model is realized as a temporal parameterization of static relations. We do not impose any restrictions upon the schemes of temporal relations. The classical concepts of normal forms and dependencies are easily extended to our model, allowing a suitable design for a database scheme. We present a relational algebra and a tuple calculus for our model and prove their equivalence. Our data model is homogeneous in the sense that the periods of validity of all the attributes in a given tuple of a temporal relation are identical. We discuss how to relax the homogeneity requirement to extend the application domain of our approach.
Digital LDO with analog-assisted dynamic reference correction for fast and accurate load regulation

Low-dropout voltage regulators (LDOs) have been extensively used on-chip to supply voltage for various circuit blocks. Digital LDOs (DLDO) have recently attracted circuit designers for their low voltage operating capability and load current scalability. Existing DLDO techniques suffer from either poor transient performance due to slow digital control loop or poor DC load regulation due to low loop gain. A dual-loop architecture to improve the DC load regulation and transient performance is proposed in this work. The proposed regulator uses a fast control loop for improved transient response and an analog assisted dynamic reference correction loop for an improved DC load regulation. The design achieved a DC load regulation of 0.005mV/mA and a settling time of 139ns while regulating loads up to 200mA. The proposed DLDO is designed in 28nm FD-SOI technology with a 0.027mm2 active area.
Body mapping of sweating patterns in male athletes in mild exercise-induced hyperthermia

Regional variation in sweating over the body is widely recognised. However, most studies only measured a limited number of regions, with the use of differing thermal states across studies making a good meta-analysis to obtain a whole body map problematic. A study was therefore conducted to investigate regional sweat rates (RSR) and distributions over the whole body in male athletes. A modified absorbent technique was used to collect sweat at two exercise intensities [55% (I1) and 75% (I2) $$ {\dot{\text{V}}\text{O}}_{{2{ \max }}} $$ ] in moderately warm conditions (25°C, 50% rh, 2 m s−1 air velocity). At I1 and I2, highest sweat rates were observed on the central (upper and mid) and lower back, with values as high as 1,197, 1,148, and 856 g m−2 h−1, respectively, at I2. Lowest values were observed on the fingers, thumbs, and palms, with values of 144, 254, and 119 g m−2 h−1, respectively at I2. Sweat mapping of the head demonstrated high sweat rates on the forehead (1,710 g m−2 h−1 at I2) compared with low values on the chin (302 g m−2 h−1 at I2) and cheeks (279 g m−2 h−1 at I2). Sweat rate increased significantly in all regions from the low to high exercise intensity, with exception of the feet and ankles. No significant correlation was present between RSR and regional skin temperature (T sk), nor did RSR correspond to known patterns of regional sweat gland density. The present study has provided detailed regional sweat data over the whole body and has demonstrated large intra- and inter-segmental variation and the presence of consistent patterns of regional high versus low sweat rate areas in Caucasians male athletes. This data may have important applications for clothing design, thermophysiological modelling and thermal manikin design.
Dynamic Load Balancing for High-Performance Graph Processing on Hybrid CPU-GPU Platforms

Graph analysis is becoming increasingly important in many research fields - biology, social sciences, data mining - and daily applications - path finding, product recommendation. Many different large-scale graph-processing systems have been proposed for different platforms. However, little effort has been placed on designing systems for hybrid CPU-GPU platforms.In this work, we present HyGraph, a novel graph-processing systems for hybrid platforms which delivers performance by using CPUs and GPUs concurrently. Its core feature is a specialized data structure which enables dynamic scheduling of jobs onto both the CPU and the GPUs, thus (1) supersedes the need for static workload distribution, (2) provides load balancing, and (3) minimizes inter-process communication overhead by overlapping computation and communication.Our preliminary results demonstrate that HyGraph outperforms CPU-only and GPU-only solutions, delivering close-to-optimal performance on the hybrid system. Moreover, it supports large-scale graphs which do not fit into GPU memory, and it is competitive against state-of-the-art systems.
Recovering traceability links between an API and its learning resources

Large frameworks and libraries require extensive developer learning resources, such as documentation and mailing lists, to be useful. Maintaining these learning resources is challenging partly because they are not explicitly linked to the frameworks' API, and changes in the API are not reflected in the learning resources. Automatically recovering traceability links between an API and learning resources is notoriously difficult due to the inherent ambiguity of unstructured natural language. Code elements mentioned in documents are rarely fully qualified, so readers need to understand the context in which a code element is mentioned. We propose a technique that identifies code-like terms in documents and links these terms to specific code elements in an API, such as methods. In an evaluation study with four open source systems, we found that our technique had an average recall and precision of 96%.
Reduction of English function words in switchboard

The causes of pronunciation reduction in 8458 occurrences o f ten frequent English function words in a four-hour sample from c onversations from the Switchboard corpus were examined. Usin g ordinary linear and logistic regression models, we examine d th length of the words, the form of their vowel (basic, full, or r educed), and final obstruent deletion. For all of these we foun d strong, independent effects of speaking rate, predictabil ity, the form of the following word, and planning problem disfluencie s. The results bear on issues in speech recognition, models of s peech production, and conversational analysis.
High-Quality Face Image SR Using Conditional Generative Adversarial Networks

We propose a novel single face image superresolution method, which named Face Conditional Generative Adversarial Network(FCGAN), based on boundary equilibrium generative adversarial networks. Without taking any facial prior information, our method can generate a high-resolution face image from a low-resolution one. Compared with existing studies, both our training and testing phases are end-toend pipeline with little pre/post-processing. To enhance the convergence speed and strengthen feature propagation, skiplayer connection is further employed in the generative and discriminative networks. Extensive experiments demonstrate that our model achieves competitive performance compared with state-of-the-art models.
Analysis of a bounding box heuristic for object intersection

Bounding boxes are commonly used in computer graphics and other fields to improve the performance of algorithms that should process only the intersecting objects. A bounding-box-based heuristic avoids unnecessary intersection processing by eliminating the pairs whose bounding boxes are disjoint. Empirical evidence suggests that the heuristic works well in many practical applications, although its worst-case performance can be bad for certain pathological inputs. What is a pathological input, however, is not well understood, and consequently there is no guarantee that the heuristic will always work well in a specific application. In this paper, we analyze the performance of bounding box heuristic in terms of two natural shape parameters, aspect ratio and scale factor. These parameters can be used to realistically measure the degree to which the objects are pathologically shaped. We derive tight worst-case bounds on the performance for bounding box heuristic. One of the significant contributions of our paper is that we only require that objects be well shaped on average. Somewhat surprisingly, the bounds are significantly different from the case when all objects are well shaped.
Trapa bispinosa Roxb.: A Review on Nutritional and Pharmacological Aspects

Trapa bispinosa Roxb. which belongs to the family Trapaceae is a small herb well known for its medicinal properties and is widely used worldwide. Trapa bispinosa or Trapa natans is an important plant of Indian Ayurvedic system of medicine which is used in the problems of stomach, genitourinary system, liver, kidney, and spleen. It is bitter, astringent, stomachic, diuretic, febrifuge, and antiseptic. The whole plant is used in gonorrhea, menorrhagia, and other genital affections. It is useful in diarrhea, dysentery, ophthalmopathy, ulcers, and wounds. These are used in the validated conditions in pitta, burning sensation, dipsia, dyspepsia, hemorrhage, hemoptysis, diarrhea, dysentery, strangely, intermittent fever, leprosy, fatigue, inflammation, urethrorrhea, fractures, erysipelas, lumbago, pharyngitis, bronchitis and general debility, and suppressing stomach and heart burning. Maybe it is due to photochemical content of Trapa bispinosa having high quantity of minerals, ions, namely, Ca, K, Na, Zn, and vitamins; saponins, phenols, alkaloids, H-donation, flavonoids are reported in the plants. Nutritional and biochemical analyses of fruits of Trapa bispinosa in 100 g showed 22.30 and 71.55% carbohydrate, protein contents were 4.40% and 10.80%, a percentage of moisture, fiber, ash, and fat contents were 70.35 and 7.30, 2.05 and 6.35, 2.30 and 8.50, and 0.65 and 1.85, mineral contents of the seeds were 32 mg and 102.85 mg calcium, 1.4 and 3.8 mg Iron, and 121 and 325 mg phosphorus in 100 g, and seeds of Trapa bispinosa produced 115.52 and 354.85 Kcal of energy, in fresh and dry fruits, respectively. Chemical analysis of the fruit and fresh nuts having considerable water content citric acid and fresh fruit which substantiates its importance as dietary food also reported low crude lipid, and major mineral present with confirming good amount of minerals as an iron and manganese potassium were contained in the fruit. Crude fiber, total protein content of the water chestnut kernel, Trapa bispinosa are reported. In this paper, the recent reports on nutritional, phytochemical, and pharmacological aspects of Trapa bispinosa Roxb, as a medicinal and nutritional food, are reviewed.
Cell biology: Detached membrane bending

& 10 0 YE A RS A G O 50 YEARS AGO A Congress was held in Singapore during December 2–9 to celebrate “the Centenary of the formulation of the theory of Evolution by Charles Darwin and Alfred Russel Wallace and the Bicentenary of the publication of the tenth edition of the ‘Systema Naturae’ by Linnaeus”. It was particularly fitting that this Congress should have been held in Singapore for ... it directed special attention to the work of Wallace, who was one of the greatest biologists ever to have worked in south-east Asia ... Prof. Haldane then delivered his presidential address ... The president emphasised the stimuli gained by Linnaeus, Darwin and Wallace through working in peripheral areas where lack of knowledge was a challenge. He suggested that the next major biological advance may well come for similar reasons from peripheral places such as Singapore, or Calcutta, where this challenge still remains and where the lack of complex scientific apparatus drives biologists into different and long-neglected fields of research.
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation

Learning to reliably perceive and understand the scene is an integral enabler for robots to operate in the realworld. This problem is inherently challenging due to the multitude of object types as well as appearance changes caused by varying illumination and weather conditions. Leveraging complementary modalities can enable learning of semantically richer representations that are resilient to such perturbations. Despite the tremendous progress in recent years, most multimodal convolutional neural network approaches directly concatenate feature maps from individual modality streams rendering the model incapable of focusing only on the relevant complementary information for fusion. To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modalityspecific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner. Specifically, we propose an architecture consisting of two modality-specific encoder streams that fuse intermediate encoder representations into a single decoder using our proposed self-supervised model adaptation fusion mechanism which optimally combines complementary features. As intermediate representations are not aligned across modalities, we introduce an attention scheme for better correlation. In addition, we propose a computationally efficient unimodal segmentation architecture termed AdapNet++ that incorporates a new encoder with multiscale residual units and an efficient atrous spatial pyramid pooling that has a larger effective receptive field with more than 10× fewer parameters, complemented with a strong decoder with a multiresolution supervision scheme that recovers high-resolution details. Comprehensive empirical evaluations on Cityscapes, Synthia, SUN RGB-D, ScanNet and Freiburg Forest benchAbhinav Valada · Rohit Mohan ·Wolfram Burgard University of Freiburg, Germany E-mail: {valada,mohan,burgard}@cs.uni-freiburg.de marks demonstrate that both our unimodal and multimodal architectures achieve state-of-the-art performance while simultaneously being efficient in terms of parameters and inference time as well as demonstrating substantial robustness in adverse perceptual conditions.
Logic Programming for Robot Control

This paper proposes logic programs as a specification for robot control. These provide a formal specification of what an agent should do depending on what it senses, and its previous sensory inputs and actions. We show how to axiomatise reactive agents, events as an interface between continuous and discrete time, and persistence, as well as axiomatising integration and differentiation over time (in terms of the limit of sums and differences). This specification need not be evaluated as a Prolog program; we use can the fact that it will be evaluated in time to get a more efficient agent. We give a detailed example of a nonholonomic maze travelling robot, where we use the same language to model both the agent and the environment. One of the main motivations for this work is that there is a clean interface between the logic programs here and the model of uncertainty embedded in probabilistic Horn abduction. This is one step towards building a decisiontheoretic planning system where the output of the planner is a plan suitable for actually controlling a robot.
Growing Story Forest Online from Massive Breaking News

We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion. Our real-world system has distinct requirements in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we 1) need to accurately and quickly extract distinguishable events from massive streams of long text documents that cover diverse topics and contain highly redundant information, and 2) must develop the structures of event stories in an online manner, without repeatedly restructuring previously formed stories, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest, a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. We conducted extensive evaluation based on 60 GB of real-world Chinese news data, although our ideas are not language-dependent and can easily be extended to other languages, through detailed pilot user experience studies. The results demonstrate the superior capability of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers, compared to multiple existing algorithm frameworks.
Answer Set Programming for Procedural Content Generation: A Design Space Approach

Procedural content generators for games produce artifacts from a latent design space. This space is often only implicitly defined, an emergent result of the procedures used in the generator. In this paper, we outline an approach to content generation that centers on explicit description of the design space, using domain-independent procedures to produce artifacts from the described space. By concisely capturing a design space as an answer set program, we can rapidly define and expressively sculpt new generators for a variety of game content domains. We walk through the reimplementation of a reference evolutionary content generator in a tutorial example, and review existing applications of answer set programming to generative-content design problems in and outside of a game context.
Better Protection of SS7 Networks with Machine Learning

Deregulation and migration to IP have made SS7 vulnerable to serious attacks such as location tracking of subscribers, interception of calls and SMS, fraud, and denial of services. Unfortunately, current protection measures such as firewalls, filters, and blacklists are not able to provide adequate protection of SS7. In this paper, a method for detection of SS7 attacks using machine learning is proposed. The paper clarifies the vulnerabilities of SS7 networks and explains how machine learning techniques can help improve SS7 security. A proof- of- concept SS7 protection system using machine learning is also described thoroughly.
FPGA switch block layout and evaluation

This paper presents abstract layout techniques for a variety of FPGA switch block architectures. We evaluate the relative density of subset, universal, and Wilton switch block architectures. For subset switch blocks of small size, we find the optimal implementations using a simple metric. We also develop a tractable heuristic that returns the optimal results for small switch blocks, and good results for large switch blocks. For switch blocks with general connectivity, we develop a representation and a layout evaluation technique. We use these techniques to compare a variety of small switch blocks. We find that the traditional Xilinx-style, subset switch block is superior to the other proposed architectures. Finally, we have hand-designed some small switch blocks to confirm our results.
Holographic-laser-drawing volumetric display

We propose a holographic-laser-drawing volumetric display using a computer-generated hologram displayed on a liquid crystal spatial light modulator and multilayer fluorescent screen. The holographic-laser-drawing technique has enabled three things; (i) increasing the number of voxels of the volumetric graphics per unit time; (ii) increasing the total input energy to the volumetric display because the maximum energy incident at a point in the multilayer fluorescent screen is limited by the damage threshold; (iii) controlling the size, shape and spatial position of voxels. In this paper, we demonstrated (i) and (ii). The multilayer fluorescent screen was newly developed to display colored voxels. The thin layer construction of the multilayer fluorescent screen minimized the axial length of the voxels. A two-color volumetric display with blue-green voxels and red voxels were demonstrated.