nils-herrmann/clean_scirepeval huggingface dataset:
Indoor and Outdoor Depth Imaging of Leaves With Time of Flight and stereo vision Sensors : Analysis and Comparison

In this article we analyze the response of Time of Flight cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. Time of Flight sensors are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce some metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposures of the sensors. Performance of three different time of flight cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo-correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancellation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. stereo vision is more robust to ambient illumination and provides high resolution depth data but it is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of the ToF cameras for a scene involving both shadow and sunlight exposures at the same time using camera flags (PMD) or confidence matrix (SwissRanger).
Curved Crease Folding a Review on Art , Design and Mathematics

In this paper we review masterpieces of curved crease folding, the deployed design methods and geometric studies on this special kind of paper folding. Our goal is to make this work and its techniques accessible to enable further development. By exploring masterpieces of the past and present of this still underexplored field, this paper aims to contribute to the development of novel design methods to achieve shell structures and deployable structures that can accommodate structural properties and design intention for a wide range of materials.
Secure Distributed Deduplication Systems with Improved Reliability

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.
Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing

Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.
HKUST/MTS: A Very Large Scale Mandarin Telephone Speech Corpus

The paper describes the design, collection, transcription and analysis of 200 hours of HKUST  (HKUST/MTS) from over 2100 Mandarin speakers in mainland China under the DARPA EARS framework. The corpus includes speech data, transcriptions and speaker demographic information. The speech data include 1206 ten-minute natural Mandarin conversations between either strangers or friends. Each conversation focuses on a single topic. All calls are recorded over public telephone networks. All calls are manually annotated with standard Chinese characters (GBK) as well as specific mark-ups for spontaneous speech. A file with speaker demographic information is also provided. The corpus is the largest and first of its kind for Mandarin conversational telephone speech, providing abundant and diversified samples for Mandarin speech recognition and other applicationdependent tasks, such as topic detection, information retrieval, keyword spotting, speaker recognition, etc. In a 2004 evaluation test by NIST, the corpus is found to improve system performance quite significantly.
An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment

Interval type-2 fuzzy set (IT2FS) offers interesting avenue to handle high order information and uncertainty in decision support system (DSS) when dealing with both extrinsic and intrinsic aspects of uncertainty. Recently, multiple attribute decision making (MADM) problems with interval type-2 fuzzy information have received increasing attentions both from researchers and practitioners. As a result, a number of interval type-2 fuzzy MADM methods have been developed. In this paper, we extend the VIKOR (VlseKriterijumska Optimizacijia I Kompromisno Resenje, in Serbian) method based on the prospect theory to accommodate interval type-2 fuzzy circumstances. First, we propose a new distance measure for IT2FS, which is comes as a sound alternative when being compared with the existing interval type-2 fuzzy distance measures. Then, a decision model integrating VIKOR method and prospect theory is proposed. A case study concerning a high-tech risk evaluation is provided to illustrate the applicability of the proposed method. In addition, a comparative analysis with interval type-2 fuzzy TOPSIS method is also presented.
Development of grip amplified glove using bi-articular mechanism with pneumatic artificial rubber muscle

In this paper, a grip amplified glove using pneumatic artificial rubber muscles (PARMs) which are covered with a exoskeleton structure is developed. A bi-articular mechanism with a PARM that is suitable for bending finger is realized. The glove has totally 10 DOFs consist of four units. To achieve power-assist motion properly, the PI control, which is based on the pressure value from the balloon sensor, is performed. This sensor makes the applied part free from electricity. To evaluate the validity of glove, the skin surface electromyography (EMG) signals of muscles are measured during grasping. These results demonstrate that the effectiveness of the power amplified glove.
Analyzing In-Game Movements of Soccer Players at Scale

Quantitative performance analysis in sports has become mainstream in the last decade. The focus of the analyses is shifting towards more sport-speci ic metrics due to novel technologies. These systems measure the movements of the players and the events happening during trainings and games. This allows for a more detailed evaluation of professional athletes with implications on areas such as opponent scouting, planning of training sessions, or player scouting. Previousworks that analyze soccer-related logs focus on the game-relatedperformanceof theplayers and teams. Vast majority of these methodologies concentrate on descriptive statistics that capture some part of the players’ strategy. For example, in case of soccer, the average number of shots, goals, fouls, passes are derived both for the teams and for the players [1, 5]. Other works identify and analyze the outcome of the strategies that teams apply [18, 16, 13, 11, 9, 24, 14]. However, the physical performance and in particular the movements of players has not received detailed attention yet. It is challenging to get access to datasets related to the physical performance of soccer players. The teams consider such information highly con idential, especially if it covers in-game performance. Despite the fact that numerous teams deployed player tracking systems in their stadiums, datasets of this nature are not available for research or for public usage. It is nearly impossible to havequantitative information on the physical performance of all the teams of a competition. Hence, most of the analysis and evaluation of the players’ performance do not contain much information on the physical aspect of the game, creating a blindspot in performance analysis. We propose a novelmethod to solve this issue by derivingmovement characteristics of soccer players. We use event-based datasets from data provider companies covering 50+ soccer leagues allowing us to analyze the movement pro iles of potentially tens of thousands of players without any major investment. Our methodology does not require expensive, dedicated player tracking system deployed in the stadium. Instead, if the game is broadcasted, our methodology can be used. As a consequence, our technique does not require the consent of the involved teams yet it can provide insights on the physical performance of many players in different teams. The main contribution of our work is threefold:
Indonesian News Classification using Support Vector Machine

Digital news with a variety topics is abundant on the internet. The problem is to classify news based on its appropriate category to facilitate user to find relevant news rapidly. Classifier engine is used to split any news automatically into the respective category. This research employs Support Vector Machine (SVM) to classify Indonesian news. SVM is a robust method to classify binary classes. The core processing of SVM is in the formation of an optimum separating plane to separate the different classes. For multiclass problem, a mechanism called one against one is used to combine the binary classification result. Documents were taken from the Indonesian digital news site, www.kompas.com. The experiment showed a promising result with the accuracy rate of 85%. This system is feasible to be implemented on Indonesian news classification. classification, Indonesian news, text processing, support vector machine
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks

Assume we were to come up with a family of distributions parameterized by θ in order to approximate the posterior, qθ(ω). Our goal is to set θ such that qθ(ω) is as similar to the true posterior p(ω|D) as possible. For clarity, qθ(ω) is a distribution over stochastic parameters ω that is determined by a set of learnable parameters θ and some source of randomness. The approximation is therefore limited by our choice of parametric function qθ(ω) as well as the  Given ω and an input x, an output distribution p(y|x,ω) = p(y|fω(x)) = fω(x,y) is induced by observation noise (the conditionality of which is omitted for brevity).
Decoupled sampling for graphics pipelines

We propose a generalized approach to decoupling shading from visibility sampling in graphics pipelines, which we call decoupled sampling. Decoupled sampling enables stochastic supersampling of motion and defocus blur at reduced shading cost, as well as controllable or adaptive shading rates which trade off shading quality for performance. It can be thought of as a generalization of multisample antialiasing (MSAA) to support complex and dynamic mappings from visibility to shading samples, as introduced by motion and defocus blur and adaptive shading. It works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. Decoupled sampling is inspired by the Reyes rendering architecture, but like traditional graphics pipelines, it shades fragments rather than micropolygon vertices, decoupling shading from the geometry sampling rate. Also unlike Reyes, decoupled sampling only shades fragments after precise computation of visibility, reducing overshading.
 We present extensions of two modern graphics pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications of decoupled sampling and blur, and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion and defocus blur, as well as variable and adaptive shading rates.
Using The Canny Edge Detector for Feature Extraction and Enhancement of Remote Sensing Images

Edges are important features in an image since they represent significant local intensity changes. They provide important clues to separate regions within an object or to identify changes in illumination. point noise. The real problem is how to enhance noisy remote sensing images and simultaneously extract the edges. Using the implemented Canny edge detector for features extraction and as an enhancement tool for remote sensing images, the result was robust with a very high enhancement level.
StirTraceV2.0: Enhanced Benchmarking and Tuning of Printed Fingerprint Detection

In this paper, we address the problem of assessing the overall quality of forgery detection approaches for artificial sweat printed latent fingerprints placed at crime scenes. It is very important to have reliable detection mechanisms tested on manifold characteristics caused for example by different surfaces, printers and during acquisition, avoiding misleading crime scene investigations. Today only a limited number of detection methods exist in the literature and test sets are still limited in size and quality covering all different conditions (influence factors). Based on the recently introduced publicly available StirTrace tool, we enhance the functionality to simulate complex and realistic test sets and discuss how detection approaches can be tuned by further preprocessing and feature selection. Our contributions here are twofold. First, we suggest a benchmarking design in 16-bit domain working in full bit-depth of today's nanometer sensory and propose enhancements for further simulations of sensor and substrate characteristics as well as single and combined scan artifacts (simulated, novel experimental data set of in sum 1.254.000 samples). Second, we benchmark exemplarily two known feature sets on nonsimulated and simulated data and compare findings with additional preprocessing and feature selection. Finally, we summarize lessons learned how good today's detection works and which challenges exist for achieving a high reliability. For the community we provide a tool, which can be used as fundamental basis to simulate influence factors allowing a systematic comparison and benchmarking of results. We also want to motivate further research in the design and tuning of forgery detection approaches.
The Liminal Magic Circle : Boundaries , Frames , and Participation in Pervasive Mobile Games

While the emphasis in traditional game theory has been on the rule formations and zone demarcations that distinguish games from play, mobile games tend to deliberately and thoughtfully blur the lines, not just between games and play but between a game and an experience, as well as between places of play (the “magic circle”) and places of everyday life. Mobile games often create moments of liminality as they are driven by the idea of playing with and within everyday spaces, technologies, and objects. In this brief paper, I will discuss these boundaries and the liminality of mobile games to date, and focus on one particularly difficult ethical areathat of player and non-player participation. The concepts of play and game are complexly intertwined. According to Johan Huizinga (1950), games can be found on a continuum between the two poles of ludus and paidia. Ludus (frequently understood as a pure game) is characterized by the corseting of exuberance with deliberate and arbitrary conventions that require the player to demonstrate effort, skill, patience, or intellect. Chess is often upheld as a pure ludus game. Paidia (or pure play), on the other hand, is characterized by a shared, ritualistic principle that is found in gaiety and improvisation, as is the case with children’s games, with less rigorous boundaries and rules guiding moments and places of play. Confusion often arises when discussing the play of games. For the purposes of this essay, I am discussing games in the sense of ludus and play as a verb to describe people who are taking part in games. When I refer to the magic circle, I am describing the place where a particular game takes place, with
Estimating driving behavior by a smartphone

In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.
Why Robots? A Survey on the Roles and Benefits of Social Robots in the Therapy of Children with Autism

This paper reviews the use of socially interactive robots to assist in the therapy of children with autism. The extent to which the robots were successful in helping the children in their social, emotional, and communication deficits was investigated. Child-robot interactions were scrutinized with respect to the different target behaviours that are to be elicited from a child during therapy. These behaviours were thoroughly examined with respect to a child's development needs. Most importantly, experimental data from the surveyed works were extracted and analyzed in terms of the target behaviours and how each robot was used during a therapy session to achieve these behaviours. The study concludes by categorizing the different therapeutic roles that these robots were observed to play, and highlights the important design features that enable them to achieve high levels of effectiveness in autism therapy.
A hybrid AC/DC micro-grid architecture, operation and control

This paper presents a hybrid AC/DC micro grid concept to directly integrate DC/AC renewable sources and loads to DC/AC links respectively. The hybrid grid eliminates multiple DC-AC-DC&AC-DC-AC conversions in an individual AC&DC grid. The hybrid grid increases system efficiency, eliminates the embedded AC/DC and DC/DC converters in various home, office and industry facilities which can reduce size and cost of those facilities. The basic architecture of the hybrid grid is introduced in this paper. Different operation modes of the hybrid grid are discussed. The various control algorithms are investigated and proposed to harness the maximum power from various renewable sources, to store energy surplus during low peak loads, to eliminate unbalance problem in AC link, to maintain voltage stability and smooth power transfer between AC and DC links under various generation and load conditions. A prototype of the hybrid grid under construction is presented. Some simulation and test results are presented.
A dynamic over-sampling procedure based on sensitivity for multi-class problems

Classification with imbalanced datasets supposes a new challenge for researches in the framework of machine learning. This problem appears when the number of patterns that represents one of the classes of the dataset (usually the concept of interest) is much lower than in the remaining classes. Thus, the learning model must be adapted to this situation, which is very common in real applications. In this paper, a dynamic over-sampling procedure is proposed for improving the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a memetic algorithm (MA) that optimizes radial basis functions neural networks (RBFNNs). To handle class imbalance, the training data are resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to balance in part the size of the classes. Then, the MA is run and the data are oversampled in different generations of the evolution, generating new patterns of the minimum sensitivity class (the class with the worst accuracy for the best RBFNN of the population). The methodology proposed is tested using 13 imbalanced benchmark classification datasets from well-known machine learning problems and one complex problem of microbial growth. It is compared to other neural network methods specifically designed for handling imbalanced data. These methods include different over-sampling procedures in the preprocessing stage, a threshold-moving method where the output threshold is moved toward inexpensive classes and ensembles approaches combining the models obtained with these techniques. The results show that our proposal is able to improve the sensitivity in the generalization set and obtains both a high accuracy level and a good classification level for
Learning Graph-Level Representation for Drug Discovery

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of smallmolecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional networks and other graph neural networks all focus on learning node-level representation rather than graph-level representation. Previous works simply sum all feature vectors for all nodes in the graph to obtain the graph feature vector for drug predication. In this paper, we introduce a dummy super node that is connected with all nodes in the graph by a directed edge as the representation of the graph and modify the graph operation to help the dummy super node learn graph-level feature. Thus, we can handle graph-level classification and regression in the same way as node-level classification and regression. In addition, we apply focal loss to address class imbalance in drug datasets. The experiments on MoleculeNet show that our method can effectively improve the performance of molecular properties predication.
3 D Discrete Curvelet Transform

In this paper, we present the first 3D discrete curvelet transform. This transform is an extension to the 2D transform described in Candès et al.. The resulting curvelet frame preserves the important properties, such as parabolic scaling, tightness and sparse representation for singularities of codimension one. We describe three different implementations: in-core, out-of-core and MPI-based parallel implementations. Numerical results verify the desired properties of the 3D curvelets and demonstrate the efficiency of our implementations.