nils-herrmann/clean_scirepeval huggingface dataset:
An efficient encoder-decoder CNN architecture for reliable multilane detection in real time

Multilane detection system is a vital prerequisite for realizing higher ADAS functionality of autonomous navigation. In this work, we present an efficient convolutional neural network (CNN) architecture for real time detection of multiple lane boundaries using a camera sensor. Our network has a simple encoder-decoder architecture and is a special two class semantic segmentation network designed to segment lane boundaries. Efficacy of our network stems from two key insights which are at the foundation of all our design decisions. Firstly, we term a lane boundary as a weak class object in the context of semantic segmentation. We show that the weak class objects which occupy relatively few pixels in the scene, also have a relatively low detection accuracy among the know segmentation methods. We present novel design choices and intuitions to improve the segmentation accuracy of weak class objects, which in turn reduces computation time. Our second insight lies in the manner we depict the ground truth information in our derived dataset. Instead of annotating just the visible lane markers, we accurately delineate the lane boundaries in the ground truth for challenging scenarios like occlusions, low light and degraded lane markings. We then leverage the CNN's ability to concisely summarize the global and local context in an image, for accurately inferring lane boundaries in these challenging cases. We evaluate our network against ENet and FCN-8, and found it performing notably better in terms of speed and accuracy. Our network achieves an encouraging 46 FPS performance on NVIDIA Drive PX2 platform and it has been validated on our test vehicle in highway driving conditions.
Value of Trichoscopy Versus Trichogram for Diagnosis of Female Androgenetic Alopecia

Female androgenetic alopecia (FAGA) is a frequent cause of hair loss in women. Standard diagnostic methods are clinical inspection, pull test, and trichogram. It has been suggested that scalp dermoscopy (trichoscopy) revealing diversity of hair shaft diameter >20% is diagnostic of FAGA.



To evaluate the value of trichoscopy as compared to the trichogram for the diagnosis of FAGA.



Retrospective case study of 162 women with the complaint of hair loss who underwent trichoscopic examination and trichograms.



Of all women diagnosed FAGA (55%), 62% were diagnosed by trichogram, 72% by trichoscopy with a cut-off point of 20%, and 100% irrespective of the degree of diversity of hair shaft diameter.



Trichoscopy is a valuable and superior method to the trichogram for diagnosis of FAGA, especially in early cases, with the highest yield irrespective of the suggested cut-off of 20% diversity of hair shaft.
Contour and Texture Analysis for Image Segmentation

This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.
Single-Image Depth Perception in the Wild

This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset “Depth in the Wild” consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild. Deep Network with Pixel-wise Prediction Metric Depth RGB-D Data Relative Depth
Secure cloud computing: Benefits, risks and controls

Cloud computing presents a new model for IT service delivery and it typically involves over-a-network, on-demand, self-service access, which is dynamically scalable and elastic, utilising pools of often virtualized resources. Through these features, cloud computing has the potential to improve the way businesses and IT operate by offering fast start-up, flexibility, scalability and cost efficiency. Even though cloud computing provides compelling benefits and cost-effective options for IT hosting and expansion, new risks and opportunities for security exploits are introduced. Standards, policies and controls are therefore of the essence to assist management in protecting and safeguarding systems and data. Management should understand and analyse cloud computing risks in order to protect systems and data from security exploits. The focus of this paper is on mitigation for cloud computing security risks as a fundamental step towards ensuring secure cloud computing environments.
A Hierarchical Cluster Synchronization Framework of Energy Internet

The concept of “Energy Internet” has been recently proposed to improve the efficiency of renewable energy resources. Accordingly, this paper investigates the synchronization of the Energy Internet system. Specifically, an appropriate hierarchical cluster synchronization framework is firstly developed for the Energy Internet, which includes primary synchronization, secondary synchronization, and tertiary synchronization. Furthermore, a secondary synchronization strategy (cluster synchronization) is proposed for the synchronization of multiple microgrid clusters in the Energy Internet, which is based on the consensus algorithm of multiagent networks. The detailed synchronization framework and control scheme are presented in this paper. Finally, some simulation results are provided and discussed to validate the performance and effectiveness of the proposed strategy.
Massively parallel Monte Carlo for many-particle simulations on GPUs

Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computi ng. Parallel algorithms for Monte Carlo simulations of thermodynamic en s mbles of particles have received little attention becaus e of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys det ailed balance and implement it for a system of hard disks on the GPU. We repro duce results of serial high-precision Monte Carlo runs to ve rify the method. This is a good test case because the hard disk equa tion of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a Tesla K20, our GPU implementation e xecutes over one billion trial moves per second, which is 148 times fa ster than on a single Intel Xeon E5540 CPU core, enables 27 tim es better performance per dollar, and cuts energy usage by a fac tor of 13. With this improved performance we are able to calcu l te the equation of state for systems of up to one million hard dis ks. These large system sizes are required in order to probe th e nature of the melting transition, which has been debated for the las t forty years. In this paper we present the details of our comp utational method, and discuss the thermodynamics of hard disks separa t ly in a companion paper.
Collecting and analyzing qualitative data for system dynamics: methods and models

System dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most influential authors. Data gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This paper describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate
Data Encryption Scheme Based on Rules of Cellular Automata and Chaotic Map Function for Information Security

Cryptography has recently played a significant role in secure data transmissions and storages. Most conventional data encryption schemes are relatively complicated and complexity in encrypted keys is insufficient, resulting in long computational time and low degree of security against all types of attacks. Consequently, a highlysecured and robust data encryption scheme is necessary. This paper therefore presents the data encryption scheme based on a combination of Cellular Automata (CA) and a robust chaotic system that employs absolutevalue piecewise-linear nonlinearity. The proposed encryption scheme is not only applicable to image encryption but also extendable to text and Excel files. Simulation results reveal that the entropy of the encrypted image is nearly 8 corresponding to the image entropy theory, and the correlation coefficients of before-and-after encrypted images are close to zero. Besides, the histogram of the encrypted image of pixel values in the range (0-255) is apparently flat, indicating that the color in image is fully distributed. Such results indicate that the proposed data encryption scheme offers a high level of security with fast processing time duration. The implementation of image encryption Android application is demonstrated. Comparisons to other related approaches are also included.
Continuous Learning in a Hierarchical Multiscale Neural Network

We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework. We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion. We use elastic weights consolidation as a higher-level to prevent catastrophic forgetting in our continuous learning framework.
Sequence-to-Sequence Learning as Beam-Search Optimization

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important generalpurpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word distributions. In this work, we introduce a model and beamsearch training scheme, based on the work of Daumé III and Marcu (2005), that extends seq2seq to learn global sequence scores. This structured approach avoids classical biases associated with local training and unifies the training loss with the test-time usage, while preserving the proven model architecture of seq2seq and its efficient training approach. We show that our system outperforms a highlyoptimized attention-based seq2seq system and other baselines on three different sequence to sequence tasks: word ordering, parsing, and machine translation.
Occlusion-Aware Real-Time Object Tracking

The online learning methods are popular for visual tracking because of their robust performance for most video sequences. However, the drifting problem caused by noisy updates is still a challenge for most highly adaptive online classifiers. In visual tracking, target object appearance variation, such as deformation and long-term occlusion, easily causes noisy updates. To overcome this problem, a new real-time occlusion-aware visual tracking algorithm is introduced. First, we learn a novel two-stage classifier with circulant structure with kernel, named integrated circulant structure kernels (ICSK). The first stage is applied for transition estimation and the second is used for scale estimation. The circulant structure makes our algorithm realize fast learning and detection. Then, the ICSK is used to detect the target without occlusion and build a classifier pool to save these classifiers with noisy updates. When the target is in heavy occlusion or after long-term occlusion, we redetect it using an optimal classifier selected from the classifier-pool according to an entropy minimization criterion. Extensive experimental results on the full benchmark demonstrate our real-time algorithm achieves better performance than state-of-the-art methods.
A 21-Gb/s 87-mW transceiver with FFE/DFE/linear equalizer in 65-nm CMOS technology

This paper presents an ultra low-power transceiver for 20-Gb/s backplane communications. Incorporating half-rate, power-saving transmitter and full-rate, high-speed receiver with 2-stage equalization, this work achieves 21 Gb/s with BER≪10−12 over a 40-cm (16-inch) regular FR4 channel, while consuming a total power of only 87 mW from a 1.2-V supply.
Linked REST APIs: A Middleware for Semantic REST API Integration

Over the last decade, an exponentially increasing number of REST services have been providing a simple and straightforward syntax for accessing rich data resources. To use these services, however, developers have to understand "information-use contracts" specified in natural language, and, to build applications that benefit from multiple existing services they have to map the underlying resource schemas in their code. This process is difficult and error-prone, especially as the number and overlap of the underlying services increases, and the mappings become opaque, difficult to maintain, and practically impossible to reuse. The more recent advent of the Linked Data formalisms can offer a solution to the challenge. In this paper, we propose a conceptual framework for REST-service integration based on Linked Data models. In this framework, the data exposed by REST services is mapped to Linked Data schemas, based on these descriptions, we have developed a middleware that can automatically compose API calls to respond to data queries (in SPARQL). Furthermore, we have developed a RDF model for characterizing the access-control protocols of these APIs and the quality of the data they expose, so that our middleware can develop "legal" compositions with desired qualities. We report our experience with the implementation of a prototype that demonstrates the usefulness of our framework in the context of a research-data management application.
Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox

Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models. The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the function. The inference can then be conducted directly in the function space by evaluating or approximating the posterior process. Despite their attractive theoretical properties GPs provide practical challenges in their implementation. GPstuff is a versatile collection of computational tools for GP models compatible with Linux and Windows MATLAB and Octave. It includes, among others, various inference methods, sparse approximations and tools for model assessment. In this work, we review these tools and demonstrate the use of GPstuff in several models.
How to Increase Users' Social Commerce Engagement? A Technology Attractiveness Model

With the proliferation of social networking and electronic commerce, social commerce helps people engage in various forms of online social commercial activities through sharing their product or service knowledge and experiences. A better understanding of users’ engagement in social commerce websites thus become increasingly important. Based on the attractiveness theory, this study proposes a research model that highlights the unique role of technology attractiveness, including task, social, and physical attractiveness, in promoting user involvement, which in turn affects social commerce engagement. Results demonstrate that users’ perceptions of technology attractiveness are positively associated with their involvement with social commerce websites, and further stimulate engagement. In addition, website involvement partially and fully mediates the effects of social and physical attractiveness, respectively, on social commerce engagement. The limitations and implications of this study for research and practice are further discussed.
Towards Agile Implementation of Test Maturity Model Integration (TMMI) Level 2 using Scrum Practices

The software industry has invested the substantial effort to improve the quality of its products like ISO, CMMI and TMMI. Although applying of TMMI maturity criteria has a positive impact on product quality, test engineering productivity, and cycle-time effort. But it requires heavy software development processes and large investments in terms of cost and time that medium/small companies do not deal with it. At the same time, agile methods deal with changing requirements and continuous delivery of valuable software by short time iterations. So that the aim of this paper is to improve the testing process by applying a detailed mapping between TMMI and Scrum practices and verifying this mapping with a study providing empirical evidence of the obtained results. The research has been evaluated on a sample of 2 large-sized TMMI certified companies. In conclusion, the experimental results show the effectiveness of using this integrated approach compared to other existing approaches. Agile software development; Scrum; TMMI; Software Testing;
PHASE LOCKED LOOPS-I 1 ECE-599 : Low Power , Adaptive Bandwidth Tracking Phase Locked Loop Design

A low power, adaptive bandwidth tracking Phase Locked Loop (PLL) is presented in this paper. Designed PLL operates over a wide frequency range of 250MHz to 1GHz with a fixed multiplication factor of 8. PLL is optimized for 0.2% UI of r.m.s. jitter over the whole frequency range with bandwidth tracking to keep the power consumption minimum. In addition, a multiplier block is used to increase the input frequency by two, which further increases the power efficiency of the PLL. The circuit is designed and simulated using 0.18μm process with supply voltage of 1.8V.
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our model indeed learns to predict features of the visual context given phonetically transcribed image descriptions, and show that it represents linguistic information in a hierarchy of levels: lower layers in the stack are comparatively more sensitive to form, whereas higher layers are more sensitive to meaning.
CuriOS: Improving Reliability through Operating System Structure

An error that occurs in a microkernel operating system service can potentially result in state corruption and service failure. A simple restart of the failed service is not always the best solution for reliability. Blindly restarting a service which maintains client-related state such as session information results in the loss of this state and affects all clients that were using the service. CuriOS represents a novel OS design that uses lightweight distribution, isolation and persistence of OS service state to mitigate the problem of state loss during a restart. The design also significantly reduces error propagation within client-related state maintained by an OS service. This is achieved by encapsulating services in separate protection domains and granting access to client-related state only when required for request processing. Fault injection experiments show that it is possible to recover from between 87% and 100% of manifested errors in OS services such as the file system, network, timer and scheduler while maintaining low performance overheads.
Ontologies used in robotics: A survey with an outlook for automated driving

Full autonomy of road vehicles is a major goal of the automotive industry. To reach such high autonomy it is necessary to provide an accurate and comprehensible situation description for the environment and the vehicle itself. A consistent depiction is essential to facilitate data exchange and communication between internal modules, e.g. collision check and environment model, as well as communication with further information sources like traffic participants vehicle to vehicle (V2V) or the infrastructure, e.g. smart traffic lights, road signs or radio traffic service (V2I). One necessary tool to create such a model could be an ontology which represents the given information and its dependencies. Initially this work provides a summary of given approaches in the literature for use of ontologies in robotics in general. Therefore approaches are stated sorted by application and task. Further approaches with focus on autonomous robots and in particular on autonomous vehicles are listed and described. Finally we give an outlook for further research topics in the domain of ontologies.
Pathfinding in Computer Games

One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. This report will highlight pathfinding algorithms used presently in games and their shortcomings especially when dealing with real-time pathfinding. With the advances being made in other components, such as physics engines, it is AI that is impeding the next generation of computer games. This report will focus on how machine learning techniques such as Artificial Neural Networks and Genetic Algorithms can be used to enhance an agents ability to handle pathfinding in real-time.
Investigating Information Systems with Action Research

Action research is an established research method in use in the social and medical sciences since the mid-twentieth century, and has increased in importance for information systems toward the end of the 1990s. Its particular philosophic context is couched in strongly post-positivist assumptions such as idiographic and interpretive research ideals. Action research has developed a history within information systems that can be explicitly linked to early work by Lewin and the Tavistock Institute. Action research varies in form, and responds to particular problem domains. The most typical form is a participatory method based on a five-step model, which is exemplified by published IS research.