nils-herrmann/clean_scirepeval huggingface dataset:
Seamless patches for GPU-based terrain rendering

In this paper we present a novel approach for interactive rendering of large terrain datasets. Our approach is based on subdividing a terrain into rectangular patches at different resolutions. Each patch is represented by four triangular tiles that are selected form different resolutions, and four strips which are used to stitch the four tiles in a seamless manner. Such a scheme maintains resolution changes within patches through the stitching strips, and not across patches. At runtime, these patches are used to construct a level-of-detail representation of the input terrain based on view-parameters. A selected level of detail only includes the layout of the patches and their boundary edges resolutions. The layout includes the location and dimension of each patch. Within the graphics hardware, the GPU generates the meshes of the patches by using scaled instances of cached tiles and assigns elevation for each vertex from cached textures. Since adjacent rectangular patches agree on the resolution of the common edges, the resulted mesh does not include cracks or degenerate triangles. Our algorithm manages to achieve quality images at high frame rates while providing seamless transition between different levels of detail.
A Multi-aspect Analysis of Automatic Essay Scoring for Brazilian Portuguese

While several methods for automatic essay scoring (AES) for the English language have been proposed, systems for other languages are unusual. To this end, we propose in this paper a multi-aspect AES system for Brazilian Portuguese which we apply to a collection of essays, which human experts evaluated according to the five aspects defined by the Brazilian Government for the National High School Exam (ENEM). These aspects are skills that student must master and every skill is assessed separately from one another. In addition to prediction, we also performed feature analysis for each aspect. The proposed AES system employs several features already used by AES systems for the English language. Our results show that predictions for some aspects performed well with the employed features, while predictions for other aspects performed poorly. Furthermore, the detailed feature analysis we performed made it possible to note their independent impacts on each of the five aspects. Finally, aside from these contributions, our work reveals some challenges and directions for future research, related, for instance, to the fact that the ENEM has over eight million yearly enrollments.
Text-Based Deep Learning for Artificial Jazz Improvisation

Deep learning has exploded in popularity in the past several years. Picking up from recent projects using recurrent neural networks to generate text, we attempted to use the same principles to generate jazz-like improvisations. We provide an RNN using torch-rnn, trained on freely available MIDI files of jazz transcriptions, which can generate music that captures rhythms, and to a lesser extend harmony and melody. Listener feedback shows that our generated music is easily recognizable as “jazz”, though lacking macro-level structure as can be expected for a basic RNN generating entirely sequential patterns. We also provide a new ASCII-based file format capable of storing music in a compact but not totally opaque way. Our format allows for close to a 7-times size decrease from existing MIDI-to-CSV converters. We show the musical principles behind the information we chose to include and leave out in converting MIDI to our new format.
A comprehensive review of firefly algorithms

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations. & 2013 Elsevier Ltd. All rights reserved.
DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380,000 associations between >16,000 genes and 13,000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners.
A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes

Internet of things is the trending direction when it comes to designing smart living environment. One of the popular applications of Internet of things is the smart home system. A smart home system usually consists of various types of connected sensors, actuators, appliances and a controller. The controller analyzes the data reported by the sensors and sends out messages to electric appliances and other sensors to ask them to behave accordingly. There are a lot of smart home designs proposed in the literature to make the systems smarter and more comfortable. However, little work has considered the security and privacy issues in smart home systems. In this paper, we analyze the differences of security and privacy issues that lie in the smart home systems, smart grid, and wireless sensor networks. Further, we propose our own solutions that achieves privacy preservation during the communications between end sensors and appliances and the controller.
Expanding the Limits of Vision-based Localization for Long-term Route-following Autonomy

Vision-based, autonomous, route-following algorithms enable robots to autonomously repeat manually driven routes over long distances. Through the use of inexpensive, commercial vision sensors, these algorithms have the potential to enable robotic applications across multiple industries. However, in order to extend these algorithms to long-term autonomy, they must be able to operate over long periods of time. This poses a difficult challenge for vision-based systems in unstructured and outdoor environments, where appearance is highly variable. While many techniques have been developed to perform localization across extreme appearance change, most are not suitable or untested for vision-in-the-loop systems such as autonomous route following, which requires continuous metric localization to keep the robot driving. In this paper, we present a vision-based, autonomous, route-following algorithm that combines multiple channels of information during localization to increase robustness against daily appearance change such as lighting. We explore this multichannel visual teach and repeat framework by adding the following channels of information to the basic single-camera, gray-scale, localization pipeline: images that are resistant to lighting change and images from additional stereo cameras to increase the algorithm’s field of view. Using these methods, we demonstrate robustness against appearance change through extensive field deployments spanning over 26 km with an autonomy rate greater than 99.9%. We furthermore discuss the limits of this system when subjected to harsh environmental conditions by investigating keypoint match degradation through time.
CREDAL: Towards Locating a Memory Corruption Vulnerability with Your Core Dump

After a program has crashed and terminated abnormally, it typically leaves behind a snapshot of its crashing state in the form of a core dump. While a core dump carries a large amount of information, which has long been used for software debugging, it barely serves as informative debugging aids in locating software faults, particularly memory corruption vulnerabilities. A memory corruption vulnerability is a special type of software faults that an attacker can exploit to manipulate the content at a certain memory. As such, a core dump may contain a certain amount of corrupted data, which increases the difficulty in identifying useful debugging information (e.g. , a crash point and stack traces). Without a proper mechanism to deal with this problem, a core dump can be practically useless for software failure diagnosis. In this work, we develop CREDAL, an automatic tool that employs the source code of a crashing program to enhance core dump analysis and turns a core dump to an informative aid in tracking down memory corruption vulnerabilities. Specifically, CREDAL systematically analyzes a core dump potentially corrupted and identifies the crash point and stack frames. For a core dump carrying corrupted data, it goes beyond the crash point and stack trace. In particular, CREDAL further pinpoints the variables holding corrupted data using the source code of the crashing program along with the stack frames. To assist software developers (or security analysts) in tracking down a memory corruption vulnerability, CREDAL also performs analysis and highlights the code fragments corresponding to data corruption.
 To demonstrate the utility of CREDAL, we use it to analyze 80 crashes corresponding to 73 memory corruption vulnerabilities archived in Offensive Security Exploit Database. We show that, CREDAL can accurately pinpoint the crash point and (fully or partially) restore a stack trace even though a crashing program stack carries corrupted data. In addition, we demonstrate CREDAL can potentially reduce the manual effort of finding the code fragment that is likely to contain memory corruption vulnerabilities.
Smart data pricing models for the internet of things: a bundling strategy approach

The Internet of Things (IoT) has emerged as a new paradigm for the future Internet. In IoT, devices are connected to the Internet and thus are a huge data source for numerous applications. In this article, we focus on addressing data management in IoT through using a smart data pricing (SDP) approach. With SDP, data can be managed flexibly and efficiently through intelligent and adaptive incentive mechanisms. Moreover, data is a major source of revenue for providers and partners. We propose a new pricing scheme for IoT service providers to determine the sensing data buying price and IoT service subscription fee offered to sensor owners and service users, respectively. Additionally, we adopt the bundling strategy that allows multiple providers to form a coalition and offer their services as a bundle, attracting more users and achieving higher revenue. Finally, we outline some important open research issues for SDP and IoT.
A Progressive Search Paradigm for the Internet of Things

As the Internet of Things (IoT) expands, IoT search has tremendous potential applications but also presents many challenges. To solve these challenges, we propose a progressive search paradigm to reduce the search space that has three main strategies: coarse-to-fine search in the feature space, near-to-distant search in the spatial-temporal space, and low-to-high permission search in the security space. Two case studies from a multimedia-based urban sensing network demonstrate that our approach can significantly improve IoT search speed and accuracy.
Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks

This study introduces a novel conditional recycle generative adversarial network for facial attribute transformation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.
Statistical Stylometrics and the Marlowe-Shakespeare Authorship Debate

I. Authorship Attribution Paradigm Historians, literary scholars, psychologists, and – more recently – computational linguists have long sought a reliable methodology for analyzing texts to determine the identity of their author. Since at least the late nineteenth century (see Mendenhall, 1887; Mascol, 1888a/b), one tool used in the investigation of authorship has been the extraction of statistical tendencies from the documents and comparison of these data in order to group the documents appropriately. Underlying the search for such a methodology is the critical assumption that some statistically quantifiable characteristic or set of characteristics inherent in a single author’s use of written language could be isolated that would be consistent across works by that author, but differ between different authors. Thus, the feature(s) could be used as a sort of fingerprint to distinguish between works by distinct authors and to identify the likely author of an anonymously published work. This endeavor is generally referred to as stylometry. With the advent of natural language processing and machine learning techniques in recent decades, the field has advanced greatly, and numerous researchers have addressed many specific
Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity

A Brain-Computer Interface (BCI) acquires brain signals, analyzes and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, pre-processing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning based Selective Attention Mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified Long Short-Term Memory (LSTM) to distinguish the inter-dimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines. Two practical real-time human-thing cognitive interaction applications are presented to validate the feasibility of our approach.
Atrous Convolutional Neural Network (ACNN) for Semantic Image Segmentation with full-scale Feature Maps

Deep Convolutional Neural Networks (DCNNs) are used extensively in biomedical image segmentation. However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to semantic image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, different atrous blocks, shortcut connections and normalization methods are explored to select the optimal network structure setting. These lead to a new and full-scale DCNN Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Fine Group Normalization (FGN). Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve comparable segmentation Dice Similarity Coefficients (DSCs) to U-Net, optimized U-Net and hybrid network, but with significantly reduced trainable parameters due to the use of fullscale feature maps and therefore computationally is much more efficient for both the training and inference.
Biomechanical reevaluation of orthodontic asymmetric headgear.

To investigate the distribution of distal and lateral forces produced by orthodontic asymmetric headgear (AHG) using mathematical models to assess periodontal ligament (PDL) influence and to attempt to resolve apparent inconsistencies in the literature.



Mechanical models for AHG were constructed to calculate AHG force magnitudes and direction using the theory of elasticity. The PDL was simulated by elastic springs attached to the inner-bow terminals of the AHG. The total storage energy (E(t)) of the AHG and the supporting springs was integrated to evaluate the distal and lateral forces produced by minimizing E(t) (Castigliano's theorem). All analytical solutions were derived symbolically.



The spring-supported headgear model (SSHG) predicted the magnitude and distribution of distal forces consistent with our data and the published data of others. The SSHG model revealed that the lateral forces delivered to the inner-bow terminals were not equal, and the spring constant (stiffness of the PDL) affected the magnitude and direction of the resultant lateral forces. Changing the stiffness of the PDL produced a greater biomechanical effect than did altering the face-bow design. The PDL spring model appeared to help resolve inconsistencies in the literature between laboratory in vitro experiments and clinical in vivo studies.



Force magnitude and direction of AHG were predicted precisely using the present model and may be applied to improve the design of AHG to minimize unwanted lateral tooth movement.
Monitoring of Vital Signs with Flexible and Wearable Medical Devices.

Advances in wireless technologies, low-power electronics, the internet of things, and in the domain of connected health are driving innovations in wearable medical devices at a tremendous pace. Wearable sensor systems composed of flexible and stretchable materials have the potential to better interface to the human skin, whereas silicon-based electronics are extremely efficient in sensor data processing and transmission. Therefore, flexible and stretchable sensors combined with low-power silicon-based electronics are a viable and efficient approach for medical monitoring. Flexible medical devices designed for monitoring human vital signs, such as body temperature, heart rate, respiration rate, blood pressure, pulse oxygenation, and blood glucose have applications in both fitness monitoring and medical diagnostics. As a review of the latest development in flexible and wearable human vitals sensors, the essential components required for vitals sensors are outlined and discussed here, including the reported sensor systems, sensing mechanisms, sensor fabrication, power, and data processing requirements.
Improving deep learning performance using random forest HTM cortical learning algorithm

Deep Learning is an artificial intelligence function that imitates the mechanisms of the human mind in processing records and developing shapes to be used in selection construction. The objective of the paper is to improve the performance of the deep learning using a proposed algorithm called RFHTMC. This proposed algorithm is a merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep Learning depends on the concept of minimizing the mean absolute percentage error which is an indication of the high performance of the forecast procedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent errors by using half of the value. And increase the percentage of the overlap duty cycle with 15%.
A generic visual perception domain randomisation framework for Gazebo

The impressive results of applying deep neural networks in tasks such as object recognition, language translation, and solving digital games are largely attributed to the availability of massive amounts of high quality labelled data. However, despite numerous promising steps in incorporating these techniques in robotic tasks, the cost of gathering data with a real robot has halted the proliferation of deep learning in robotics. In this work, a plugin for the Gazebo simulator is presented, which allows rapid generation of synthetic data. By introducing variations in simulator-specific or irrelevant aspects of a task, one can train a model which exhibits some degree of robustness against those aspects, and ultimately narrow the reality gap between simulated and real-world data. To show a use-case of the developed software, we build a new dataset for detection and localisation of three object classes: box, cylinder and sphere. Our results in the object detection and localisation task demonstrate that with small datasets generated only in simulation, one can achieve comparable performance to that achieved when training on real-world images.
The TangibleK Robotics Program: Applied Computational Thinking for Young Children.

This article describes the TangibleK robotics program for young children. Based on over a decade of research, this program is grounded on the belief that teaching children about the human-made world, the realm of technology and engineering, is as important as teaching them about the natural world, numbers, and letters.  It includes a research-based, developmentally appropriate robotics kit that children can use to make robots and program their behaviors. The curriculum has been piloted in kindergarten classrooms and in summer camps and lab settings. The author presents the theoretical framework that informs TangibleK and the “powerful ideas” from computer science and robotics on which the curriculum is based, linking them to other disciplinary areas and developmental characteristics of early childhood. The article concludes with a description of classroom pedagogy and activities, along with assessment tools used to evaluate learning outcomes.