nils-herrmann/clean_scirepeval huggingface dataset:
Recent progress in road and lane detection: a survey

The problem of road or lane perception is a crucial enabler for advanced driver assistance systems. As such, it has been an active field of research for the past two decades with considerable progress made in the past few years. The problem was confronted under various scenarios, with different task definitions, leading to usage of diverse sensing modalities and approaches. In this paper we survey the approaches and the algorithmic techniques devised for the various modalities over the last 5 years. We present a generic break down of the problem into its functional building blocks and elaborate the wide range of proposed methods within this scheme. For each functional block, we describe the possible implementations suggested and analyze their underlying assumptions. While impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps and suggest research directions that may bridge them.
Identifying Emotional Characteristics from Short Blog Texts

Emotion is at the core of understanding ourselves and others, and the automatic expression and detection of emotion could enhance our experience with technologies. In this paper, we explore the use of computational linguistic tools to derive emotional features. Using 50 and 200 word samples of naturally-occurring blog texts, we find that some emotions are more discernible than others. In particular automated content analysis shows that authors expressing anger use the most affective language and also negative affect words; authors expressing joy use the most positive emotion words. In addition we explore the use of co-occurrence semantic space techniques to classify texts via their distance from emotional concept exemplar words: This demonstrated some success, particularly for identifying author expression of fear and joy emotions. This extends previous work by using finer-grained emotional categories and alternative linguistic analysis techniques. We relate our finding to human emotion perception and note potential applications.
Hidden Markov Model Based Events Detection in Soccer Video

This paper presents an algorithm developed based on hidden Markov model for cues fusion and event inference in soccer video. Four events, shoot, foul, offside and normal playing, are defined to be detected. The states of the events are employed to model the observations of the five cues, which are extracted from the shot sequences directly. The experimental results show the algorithm is effective and robust in inferring events from roughly extracted cues.
Target simulator concept for chirp modulated 77 GHz automotive radar sensors

Automotive radar sensors are key components for driver assistant systems. In order to handle complex traffic scenarios an advanced separability is required with respect to object angle, distance and velocity. In this contribution a highly integrated automotive radar sensor enabling chirp sequence modulation will be presented and discussed. Furthermore, the development of a target simulator which is essential for the characterization of such radar sensors will be introduced including measurements demonstrating the performance of our system.
Text Detection, Tracking and Recognition in Video: A Comprehensive Survey

The intelligent analysis of video data is currently in wide demand because a video is a major source of sensory data in our lives. Text is a prominent and direct source of information in video, while the recent surveys of text detection and recognition in imagery focus mainly on text extraction from scene images. Here, this paper presents a comprehensive survey of text detection, tracking, and recognition in video with three major contributions. First, a generic framework is proposed for video text extraction that uniformly describes detection, tracking, recognition, and their relations and interactions. Second, within this framework, a variety of methods, systems, and evaluation protocols of video text extraction are summarized, compared, and analyzed. Existing text tracking techniques, tracking-based detection and recognition techniques are specifically highlighted. Third, related applications, prominent challenges, and future directions for video text extraction (especially from scene videos and web videos) are also thoroughly discussed.
Spherical nanosized focal spot unravels the interior of cells

The resolution of any linear imaging system is given by its point spread function (PSF) that quantifies the blur of an object point in the image. The sharper the PSF, the better the resolution is. In standard fluorescence microscopy, however, diffraction dictates a PSF with a cigar-shaped main maximum, called the focal spot, which extends over at least half the wavelength of light (λ = 400–700 nm) in the focal plane and >λ along the optical axis (z). Although concepts have been developed to sharpen the focal spot both laterally and axially, none of them has reached their ultimate goal: a spherical spot that can be arbitrarily downscaled in size. Here we introduce a fluorescence microscope that creates nearly spherical focal spots of 40–45 nm (λ/16) in diameter. Fully relying on focused light, this lens-based fluorescence nanoscope unravels the interior of cells noninvasively, uniquely dissecting their sub-λ–sized organelles.
Combining classifiers to identify online databases

We address the problem of identifying the domain of onlinedatabases. More precisely, given a set F of Web forms automaticallygathered by a focused crawler and an online databasedomain D, our goal is to select from F only the formsthat are entry points to databases in D. Having a set ofWebforms that serve as entry points to similar online databasesis a requirement for many applications and techniques thataim to extract and integrate hidden-Web information, suchas meta-searchers, online database directories, hidden-Webcrawlers, and form-schema matching and merging.We propose a new strategy that automatically and accuratelyclassifies online databases based on features that canbe easily extracted from Web forms. By judiciously partitioningthe space of form features, this strategy allows theuse of simpler classifiers that can be constructed using learningtechniques that are better suited for the features of eachpartition. Experiments using real Web data in a representativeset of domains show that the use of different classifiersleads to high accuracy, precision and recall. This indicatesthat our modular classifier composition provides an effectiveand scalable solution for classifying online databases.
Outdoor Mapping and Navigation Using Stereo Vision

We consider the problem of autonomous navigation in an unstr ctu ed outdoor environment. The goal is for a small outdoor robot to come into a ne w area, learn about and map its environment, and move to a given goal at modest spe ed (1 m/s). This problem is especially difficult in outdoor, off-road enviro nments, where tall grass, shadows, deadfall, and other obstacles predominate. Not su rpri ingly, the biggest challenge is acquiring and using a reliable map of the new are a. Although work in outdoor navigation has preferentially used laser rangefi d rs [13, 2, 6], we use stereo vision as the main sensor. Vision sensors allow us to u e more distant objects as landmarks for navigation, and to learn and use color and te xture models of the environment, in looking further ahead than is possible with range sensors alone. In this paper we show how to build a consistent, globally corr ect map in real time, using a combination of the following vision-based tec hniques:
Design of improved quad-ridged orthomode transducer

A high performance quad-ridged orthomode transducer (OMT) with small size was designed based on the traditional quad-ridged OMT for C band receiver of shanghai 65m radio telescope. The structure and working principle of the improved OMT were introduced, detail size and main performance characteristics were given, and FEM and FIT methods were used to verify the reliability of the improved quad-ridged OMT. Finally, the tolerance of the OMT is analyzed.
Automatic song composition from the lyrics exploiting prosody of Japanese language

Automatic composition techniques are important in sense of upgrading musical applications for amateur musicians such as composition support systems. In this paper, we present an algorithm that can automatically generate songs from Japanese lyrics. The algorithm is designed by considering composition as an optimal-solution search problem under constraints given by the prosody of the lyrics. To verify the algorithm, we launched Orpheus which composes with the visitor’s lyrics on the web-site, and 56,000 songs were produced within a year. Evaluation results on the generated songs are also reported, indicating that Orpheus can help users to compose their own original Japanese songs.
Large scale visual recommendations from street fashion images

We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose two classes of data driven models in the Deterministic Fashion Recommenders (DFR) and Stochastic Fashion Recommenders (SFR) for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. The industrial applicability of proposed models is in the context of mobile fashion shopping. Finally, we also outline a large-scale annotated data set of fashion images Fashion-136K) that can be exploited for future research in data driven visual fashion.
Estimating 3D shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior

We present a novel algorithm aiming to estimate the 3D shape, the texture of a human face, along with the 3D pose and the light direction from a single photograph by recovering the parameters of a 3D morphable model. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the estimation process. This was previously achieved using either a simple model, such as the Lambertian reflectance model, leading to a linear fitting algorithm. Alternatively, this problem was addressed using a more precise model and minimizing a non-convex cost function with many local minima. One way to reduce the local minima problem is to use a stochastic optimization algorithm. However, the convergence properties (such as the radius of convergence) of such algorithms, are limited. Here, as well as the pixel intensity, we use various image features such as the edges or the location of the specular highlights. The 3D shape, texture and imaging parameters are then estimated by maximizing the posterior of the parameters given these image features. The overall cost function obtained is smoother and, hence, a stochastic optimization algorithm is not needed to avoid the local minima problem. This leads to the multi-features fitting algorithm that has a wider radius of convergence and a higher level of precision. This is shown on some example photographs, and on a recognition experiment performed on the CMU-PIE image database.
Tables of linear congruential generators of different sizes and good lattice structure

We provide sets of parameters for multiplicative linear congruential generators (MLCGs) of different sizes and good performance with respect to the spectral test. For ` = 8, 9, . . . , 64, 127, 128, we take as a modulus m the largest prime smaller than 2`, and provide a list of multipliers a such that the MLCG with modulus m and multiplier a has a good lattice structure in dimensions 2 to 32. We provide similar lists for power-of-two moduli m = 2`, for multiplicative and non-multiplicative LCGs.
RIOT: I/O-Efficient Numerical Computing without SQL

tems. File modes and some partitioning schemes are the only means a user has to specify desired access patterns. These are not sufficient for the user to specify the file structure matching the access patterns of particular applications since they were developed under the abstraction of a one-dimensional file. This paper presents the design of UPIO, a software for User-controllable Parallel Input and Output, and our experience in producing high-performance external computation codes using UPIO. UPIO is designed to maximize 1/0 performance for scientific applications on MIMD multicomputers. It allows users to determine the file structure by considering the access pat terns of particular applications and the distribution of data for parallel access, and do 1 / 0 collectively. This enables users l o produce high-performance external computation codes by planning I/O, computations, communication, and the reuse of data effectively in the codes. We show how well UPIO produces highperformance external computation codes by designing 1 / 0 arid rrierriory-efficient external matrix multiplication algorithms and exploring the effects of UPIO with the codes. The rest of this paper is organized as follows. In Section 2 we briefly describe the functionality of UPIO, including our architecture model. 1 / 0 and memory-efficient matrix multiplication algorithm using UP10 and the experimental results are presented in Section 3 and 4. Finally, we make some concluding remarks.
Random Spray Retinex: A New Retinex Implementation to Investigate the Local Properties of the Model

In order to investigate the local filtering behavior of the Retinex model, we propose a new implementation in which paths are replaced by 2-D pixel sprays, hence the name "random spray Retinex." A peculiar feature of this implementation is the way its parameters can be controlled to perform spatial investigation. The parameters' tuning is accomplished by an unsupervised method based on quantitative measures. This procedure has been validated via user panel tests. Furthermore, the spray approach has faster performances than the path-wise one. Tests and results are presented and discussed
Competition Between Local and Electronic Markets: How the Benefit of Buying Online Depends on Where You Live

O paper shows that the parameters in existing theoretical models of channel substitution such as offline transportation cost, online disutility cost, and the prices of online and offline retailers interact to determine consumer choice of channels. In this way, our results provide empirical support for many such models. In particular, we empirically examine the trade-off between the benefits of buying online and the benefits of buying in a local retail store. How does a consumer’s physical location shape the relative benefits of buying from the online world? We explore this problem using data from Amazon.com on the top-selling books for 1,497 unique locations in the United States for 10 months ending in January 2006. We show that when a store opens locally, people substitute away from online purchasing, even controlling for product-specific preferences by location. These estimates are economically large, suggesting that the disutility costs of purchasing online are substantial and that offline transportation costs matter. We also show that offline entry decreases consumers’ sensitivity to online price discounts. However, we find no consistent evidence that the breadth of the product line at a local retail store affects purchases.
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting

This paper proposes a crowd counting method. Crowd counting is difficult because of large appearance changes of a target which caused by density and scale changes. Conventional crowd counting methods generally utilize one predictor (e.g. regression and multi-class classifier). However, such only one predictor can not count targets with large appearance changes well. In this paper, we propose to predict the number of targets using multiple CNNs specialized to a specific appearance, and those CNNs are adaptively selected according to the appearance of a test image. By integrating the selected CNNs, the proposed method has the robustness to large appearance changes. In experiments, we confirm that the proposed method can count crowd with lower counting error than a CNN and integration of CNNs with fixed weights. Moreover, we confirm that each predictor automatically specialized to a specific appearance.
Character-Level Chinese Dependency Parsing

Recent work on Chinese analysis has led to large-scale annotations of the internal structures of words, enabling characterlevel analysis of Chinese syntactic structures. In this paper, we investigate the problem of character-level Chinese dependency parsing, building dependency trees over characters. Character-level information can benefit downstream applications by offering flexible granularities for word segmentation while improving wordlevel dependency parsing accuracies. We present novel adaptations of two major shift-reduce dependency parsing algorithms to character-level parsing. Experimental results on the Chinese Treebank demonstrate improved performances over word-based parsing methods.
FEXT Crosstalk Cancellation for High-Speed Serial Link Design

We have proposed and verified an efficient architecture for a high-speed I/O transceiver design that implements far-end crosstalk (FEXT) cancellation. In this design, TX pre-emphasis, used traditionally to reduce ISI, is combined with FEXT cancellation at the transmitter to remove crosstalk-induced jitter and interference. The architecture has been verified via simulation models based on channel measurement. A prototype implementation of a 12.8Gbps source-synchronous serial link transmitter has been developed in TSMC's 0.18mum CMOS technology. The proposed design consists of three 12.8Gbps data lines that uses a half-rate PLL clock of 6.4GHz. The chip includes a PRBS generator to simplify multi-lane testing. Simulation results show that, even with a 2times reduction in line separation, FEXT cancellation can successfully reduce jitter by 51.2 %UI and widen the eye by 14.5%. The 2.5 times 1.5 mm2 core consumes 630mW per lane at 12.8Gbps with a 1.8V supply
The End is Nigh: Generic Solving of Text-based CAPTCHAs

Over the last decade, it has become well-established that a captcha’s ability to withstand automated solving lies in the difficulty of segmenting the image into individual characters. The standard approach to solving captchas automatically has been a sequential process wherein a segmentation algorithm splits the image into segments that contain individual characters, followed by a character recognition step that uses machine learning. While this approach has been effective against particular captcha schemes, its generality is limited by the segmentation step, which is hand-crafted to defeat the distortion at hand. No general algorithm is known for the character collapsing anti-segmentation technique used by most prominent real world captcha schemes. This paper introduces a novel approach to solving captchas in a single step that uses machine learning to attack the segmentation and the recognition problems simultaneously. Performing both operations jointly allows our algorithm to exploit information and context that is not available when they are done sequentially. At the same time, it removes the need for any hand-crafted component, making our approach generalize to new captcha schemes where the previous approach can not. We were able to solve all the real world captcha schemes we evaluated accurately enough to consider the scheme insecure in practice, including Yahoo (5.33%) and ReCaptcha (33.34%), without any adjustments to the algorithm or its parameters. Our success against the Baidu (38.68%) and CNN (51.09%) schemes that use occluding lines as well as character collapsing leads us to believe that our approach is able to defeat occluding lines in an equally general manner. The effectiveness and universality of our results suggests that combining segmentation and recognition is the next evolution of catpcha solving, and that it supersedes the sequential approach used in earlier works. More generally, our approach raises questions about how to develop sufficiently secure captchas in the future.
How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions

A novel paradigm that changes the scene for the modern communication and computation systems is the Edge Computing. It is not a coincidence that terms like Mobile Cloud Computing, Cloudlets, Fog Computing, and Mobile-Edge Computing are gaining popularity both in academia and industry. In this paper, we embrace all these terms under the umbrella concept of “Edge Computing” to name the trend where computational infrastructures hence the services themselves are getting closer to the end user. However, we observe that bringing computational infrastructures to the proximity of the user does not magically solve all technical challenges. Moreover, it creates complexities of its own when not carefully handled. In this paper, these challenges are discussed in depth and categorically analyzed. As a solution direction, we propose that another major trend in networking, namely software-defined networking (SDN), should be taken into account. SDN, which is not proposed specifically for Edge Computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of Edge Computing be achieved. To fully demonstrate our ideas, initially, we put forward a clear collaboration model for the SDN-Edge Computing interaction through practical architectures and show that SDN related mechanisms can feasibly operate within the Edge Computing infrastructures. Then, we provide a detailed survey of the approaches that comprise the Edge Computing domain. A comparative discussion elaborates on where these technologies meet as well as how they differ. Later, we discuss the capabilities of SDN and align them with the technical shortcomings of Edge Computing implementations. We thoroughly investigate the possible modes of operation and interaction between the aforementioned technologies in all directions and technically deduce a set of “Benefit Areas” which is discussed in detail. Lastly, as SDN is an evolving technology, we give the future directions for enhancing the SDN development so that it can take this collaboration to a further level.
Cu/Adhesive Hybrid Bonding at 180 °C in H-Containing HCOOH Vapor Ambient for 2.5D/3D Integration

Bump-less Cu/adhesive hybrid bonding is a promising technology for 2.5D/3D integration. The remaining issues of this technology include high Cu–Cu bonding temperature, long thermal-compression time (low throughput), and large thermal stress. In this paper, we investigate a Cu-first hybrid bonding process in hydrogen(H)-containing formic acid (HCOOH) vapor ambient, lowering the bonding temperature to 180 °C and shortening the thermal-compression time to 600 s. We find that the H-containing HCOOH vapor pre-bonding treatment is effective for Cu surface activation and friendly to adhesives at treatment temperature of 160–200 °C. The effects of surface activation (temperature and time) on Cu–Cu bonding and cyclo-olefin polymer (COP) adhesive bonding are studied by shear tests, fracture surface observations, and interfacial observations. Cu/adhesive hybrid bonding was successfully demonstrated at a bonding temperature of 180 °C with post-bonding adhesive curing at 200 °C.