nils-herrmann/clean_scirepeval huggingface dataset:
1 Marine anthropogenic litter on British beaches : a 10-year nationwide assessment using citizen science

Growing evidence suggests that anthropogenic litter, particularly plastic, represents a highly pervasive and persistent threat to global marine ecosystems. Multinational research is progressing to characterise its sources, distribution and abundance so that interventions aimed at reducing future inputs and clearing extant litter can be developed. Citizen science projects, whereby members of the public gather information, offer a low-cost method of collecting large volumes of data with considerable temporal and spatial coverage. Furthermore, such projects raise awareness of environmental issues and can lead to positive changes in behaviours and attitudes. We present data collected over a decade (2005-2014 inclusive) by Marine Conservation Society (MCS) volunteers during beach litter surveys carried along the British coastline, with the aim of increasing knowledge on the composition, spatial distribution and temporal trends of coastal debris. Unlike many citizen science projects, the MCS beach litter survey programme gathers information on the number of volunteers, duration of surveys and distances covered. This comprehensive information provides an opportunity to standardise data for variation in sampling effort among surveys, enhancing the value of outputs and robustness of findings. We found that plastic is the main constituent of anthropogenic litter on British beaches and the majority of traceable items originate from land-based sources, such as public littering. We identify the coast of the Western English Channel and Celtic Sea as experiencing the highest relative litter levels. Increasing trends over the 10-year time period were detected for a number of individual item categories, yet no statistically significant change in total (effort-corrected) litter was detected. We discuss the limitations of the dataset and make recommendations for future work. The study demonstrates the value of citizen science data in providing insights that would otherwise not be possible due to logistical and financial constraints of running government-funded sampling programmes on such large scales.
GPU Acceleration of Runge-Kutta Integrators

We consider the use of commodity graphics processing units (GPUs) for the common task of numerically integrating ordinary differential equations (ODEs), achieving speedups of up to 115-fold over comparable serial CPU implementations, and 15-fold over multithreaded CPU code with SIMD intrinsics. Using Lorenz '96 models as a case study, single and double precision benchmarks are established for both the widely used DOPRI5 method and computationally tailored low-storage RK4(3)5[2R+]C. A range of configurations are assessed on each, including multithreading and SIMD intrinsics on the CPU, and GPU kernels parallelized over both the dimensionality of the ODE system and number of trajectories. On the GPU, we draw particular attention to the problem of variable task-length among threads of the same warp, proposing a lightweight strategy of assigning multiple data items to each thread to reduce the prevalence of redundant operations. A simple analysis suggests that the strategy can draw performance close to that of ideal parallelism, while empirical results demonstrate up to a 10 percent improvement over the standard approach.
Classification of Heart Disease Dataset using Multilayer Feed forward backpropogation Algorithm

In this study, for the classification of heart diseases dataset, multilayer feed forward network with backpropogation algorithm is introduced. Artificial Neural Network (ANN) is widely used data mining method to extract patterns. Data mining is the process of automating information discovery .Its main aim is to find relationships in data and to predict outcomes. Classification is one of the important data mining techniques for classifying given set of input data. Many real world problems in various fields can be solved by using classification approach such as business, science, industry and medicine. For analysis the medical data related to Heart diseases is considered and analyzed using artificial neural network (ANN). To perform classification task of medical data, the neural network is trained using back propagation algorithm with momentum. To increase the efficiency of the classification process parallel processing approach is also applies on each neuron in different layers.
Performance comparison between slotted IEEE 802.15.4 and IEEE 802.1 lah in IoT based applications

In this paper, we present a performance comparison between IEEE 802.15.4 which specifies the physical and media access control layers for low-rate wireless personal area networks (LR-WPANs) like ZigBee, and IEEE 802.11ah, a new global WLAN standard using sub-1 GHz frequency band, in terms of throughput and energy consumption. Both standards are targeting low power applications with relatively high number of nodes as in IoT and M2M applications. The simulations results demonstrate the better performance of IEEE 802.11ah throughput mainly in congested networks. However in particular cases, the IEEE 802.15.4 outperforms the IEEE 802.11ah from energy consumption point of view.
Leader-following consensus of multi-agent systems under fixed and switching topologies

The leader-following consensus problemof higher ordermulti-agent systems is considered. In the system, the dynamics of each agent and the leader is a linear system. The control of each agent using local information is designed and detailed analysis of the leader-following consensus is presented for both fixed and switching interaction topologies, which describe the information exchange between the multiagent systems. The design technique is based on algebraic graph theory, Riccati inequality and Lyapunov inequality. Simulations indicate the capabilities of the algorithms.
Towards Semantic Understanding -- An Approach Based on Information Extraction Ontologies

Information is ubiquitous, and we are flooded with more than we can process. Somehow, we must rely less on visual processing, point-and-click navigation, and manual decision making and more on computer sifting and organization of information and automated negotiation and decision making. A resolution of these problems requires software with semantic understanding—a grand challenge of our time. More particularly, we must solve problems of automated interoperability, integration, and knowledge sharing, and we must build information agents and process agents that we can trust to give us the information we want and need and to negotiate on our behalf in harmony with our beliefs and goals. This paper proffers the use of informationextraction ontologies as an approach that may lead to semantic understanding.
TL ; DR : Improving Abstractive Summarization Using LSTMs

Traditionally, summarization has been approached through extractive methods. However, they have produced limited results. More recently, neural sequence-tosequence models for abstractive text summarization have shown more promise, although the task still proves to be challenging. In this paper, we explore current state-of-the-art architectures and reimplement them from scratch. We begin with a basic sequence-to-sequence model. We upgrade the model to use our own attention mechanism and change the encoder to a bi-directional LSTM. Finally, we introduce and implement a hybrid pointergenerator model that points back to words in the source text if it encounters an outof-vocabulary (OOV) word. Our qualitative results show our models successfully working by producing very similar results to the ground truth summaries. The pointergenerator model, it correctly replaces OOV words with key words from the source, particularly for numbers. Our models also do not experiences short-comings from other models such as repeating summaries.
A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs

Sorting is at the core of many database operations, such as index creation, sort-merge joins, and user-requested output sorting. As GPUs are emerging as a promising platform to accelerate various operations, sorting on GPUs becomes a viable endeavour. Over the past few years, several improvements have been proposed for sorting on GPUs, leading to the first radix sort implementations that achieve a sorting rate of over one billion 32-bit keys per second. Yet, state-of-the-art approaches are heavily memory bandwidth-bound, as they require substantially more memory transfers than their CPU-based counterparts. Our work proposes a novel approach that almost halves the amount of memory transfers and, therefore, considerably lifts the memory bandwidth limitation. Being able to sort two gigabytes of eight-byte records in as little as 50 milliseconds, our approach achieves a 2.32-fold improvement over the state-of-the-art GPU-based radix sort for uniform distributions, sustaining a minimum speed-up of no less than a factor of 1.66 for skewed distributions. To address inputs that either do not reside on the GPU or exceed the available device memory, we build on our efficient GPU sorting approach with a pipelined heterogeneous sorting algorithm that mitigates the overhead associated with PCIe data transfers. Comparing the end-to-end sorting performance to the state-of-the-art CPU-based radix sort running 16 threads, our heterogeneous approach achieves a 2.06-fold and a 1.53-fold improvement for sorting 64 GB key-value pairs with a skewed and a uniform distribution, respectively.
Convolutional neural networks and multimodal fusion for text aided image classification

With the exponential growth of web meta-data, exploiting multimodal online sources via standard search engine has become a trend in visual recognition as it effectively alleviates the shortage of training data. However, the web meta-data such as text data is usually not as cooperative as expected due to its unstructured nature. To address this problem, this paper investigates the numerical representation of web text data. We firstly adopt convolutional neural network (CNN) for web text modeling on top of word vectors. Combined with CNN for image, we present a multimodal fusion to maximize the discriminative power of visual and textual modality data for decision level and feature level simultaneously. Experimental results show that the proposed framework achieves significant improvement in large-scale image classification on Pascal VOC-2007 and VOC-2012 datasets.
Classification of Mixed-Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks

In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed-type defect patterns, i.e., two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed-type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WBMs.
Prediction of human emergency behavior and their mobility following large-scale disaster

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, accurately predicting human emergency behavior and their mobility will become the critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. In this paper, we build up a large human mobility database (GPS records of 1.6 million users over one year) and several different datasets to capture and analyze human emergency behavior and their mobility following the Great East Japan Earthquake and Fukushima nuclear accident. Based on our empirical analysis through these data, we find that human behavior and their mobility following large-scale disaster sometimes correlate with their mobility patterns during normal times, and are also highly impacted by their social relationship, intensity of disaster, damage level, government appointed shelters, news reporting, large population flow and etc. On the basis of these findings, we develop a model of human behavior that takes into account these factors for accurately predicting human emergency behavior and their mobility following large-scale disaster. The experimental results and validations demonstrate the efficiency of our behavior model, and suggest that human behavior and their movements during disasters may be significantly more predictable than previously thought.
A Generative Entity-Mention Model for Linking Entities with Knowledge Base

Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entitymention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods.
Comparison and evaluation of stresses generated by rapid maxillary expansion and the implant-supported rapid maxillary expansion on the craniofacial structures using finite element method of stress analysis

The study aimed to evaluate and compare the stress distribution and 3-dimensional displacements along the craniofacial sutures in between the Rapid maxillary Expansion (RME) and Implant supported RME (I-RME).  Finite element model of the skull and the implants were created using ANSYS software. The finite element model thus built composed of 537692 elements and 115694 nodes in RME model & 543078 elements and 117948 nodes with implants model. The forces were applied on the palatal surface of the posterior teeth to cause 5mm of transverse displacement on either side of the palatal halves, making it a total of 10mm. The stresses and the displacement values were obtained and interpreted.



Varying pattern of stress and the displacements with both positive and negative values were seen. The maximum displacement was seen in the case of plain RME model and that too at Pterygomaxillary suture and Mid-palatal suture in descending order. In the case of I-RME maximum displacement was seen at Zygomaticomaxillary suture followed by Pterygomaxillary suture. The displacements produced in all the three planes of space for the plain RME model were greater in comparison to the Implant Supported RME model. And the stresses remained high for all the sutures in case of an I-RME.



There is a definite difference in the stress and the displacement pattern produced by RME and I-RME model and each can be used according to the need of the patient. The stresses generated in case of conventional RME were considerably less than that of the I-RME for all the sutures.
Lane identification and ego-vehicle accurate global positioning in intersections

This paper proposes a method for achieving accurate ego-vehicle global localization with respect to an approaching intersection; the method is based on the data alignment of the information from two input systems: a Sensorial Perception system, on-board of the ego-vehicle, and an a priori digital map. For this purpose an Extended Digital Map is proposed that contains the detailed information about the intersection infrastructure: detailed landmarks accurately measured and positioned on the map. The data alignment mechanism is thus based on superimposing the sensorial detected landmarks with the corresponding, correctly positioned map landmarks stored in the new Extended Digital Map. The data Alignment Algorithm requires as input, beside the information from the two input systems, the ego-vehicle driving lane. This information is inferred by using a probabilistic approach in the form of a Bayesian Network; the uncertain and noisy character of the sensorial data require such a probabilistic approach in the quest of the ego-lane.
Multidisciplinary Perspectives on Music Emotion Recognition : Implications for Content and Context-Based Models

The prominent status of music in human culture and every day life is due in large part to its striking ability to elicit emotions, which may manifest from slight variation in mood to changes in our physical condition and actions. In this paper, we first review state of the art studies on music and emotions from different disciplines including psychology, musicology and music information retrieval. Based on these studies, we then propose new insights to enhance automated music emotion recog-
Letter-based classification of Arabic scripts style in ancient Arabic manuscripts: Preliminary results

Classifying ancient Arabic manuscripts based on handwriting styles is one of the important roles in the field of paleography. Recognizing the style of handwriting in Arabic manuscripts helps in identifying the origin and date of ancient documents. In this paper we proposed using segmented letters from Arabic manuscripts to recognize handwriting style. Both Gabor Filters (GF) and Local Binary Pattern (LBP) are used to extract features from letters. The fused features are sent to Support Vector Machine (SVM) classifier. Experimental results have been implemented using manuscripts images from the Qatar National Library (QNL) and other online datasets. Better results are achieved when both GF and LBP descriptors are combined. The recognized Handwritten Arabic styles are Diwani, Kufic, Naskh, Farsi,
Mining hidden community in heterogeneous social networks

Social network analysis has attracted much attention in recent years. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only one kind of relation in the network, and moreover, the mining results are independent of the users' needs or preferences. However, in reality, there exist multiple, heterogeneous social networks, each representing a particular kind of relationship, and each kind of relationship may play a distinct role in a particular task. Thus mining networks by assuming only one kind of relation may miss a lot of valuable hidden community information and may not be adaptable to the diverse information needs from different users.In this paper, we systematically analyze the problem of mining hidden communities on heterogeneous social networks. Based on the observation that different relations have different importance with respect to a certain query, we propose a new method for learning an optimal linear combination of these relations which can best meet the user's expectation. With the obtained relation, better performance can be achieved for community mining. Our approach to social network analysis and community mining represents a major shift in methodology from the traditional one, a shift from single-network, user-independent analysis to multi-network, user-dependant, and query-based analysis. Experimental results on Iris data set and DBLP data set demonstrate the effectiveness of our method.
On the Existence of 3-Round Zero-Knowledge Protocols

In this paper, we construct a 3-round zero-knowledge protocol for any NP language. Our protocol achieves weaker notions of zero-knowledge than black-box simulation zero-knowledge. Therefore, our result does not contradict the triviality result of Goldreich and Krawczyk [GoKr96] which shows that 3-round black-box simulation zero-knowledge exist only for BPP languages. Our main contribution is to provide a non-black-box simulation technique. Whether there exists such a simulation technique was a major open problem in the theory of zero-knowledge. Our simulation technique is based on a non-standard computational assumption related to the DiffieHellman problem, which was originally proposed by Damg̊ard [Da91]. This assumption, which we call the DA-1, says that, given randomly chosen instance of the discrete logarithm problem (p, q, g, g), it is infeasible to compute (B,X) such that X = B mod p without knowing the value b satisfying B = g mod p. Our protocol achieves different notions of zero-knowledge under different variants of DA-1. All variants are augmented by an auxiliary-input. Under non-uniform DA-1 w.r.t. auxiliaryinput of restricted length, it achieves the notion of non-uniform zero-knolwedge. Furthermore, under the stronger variant called non-uniform DA-1 w.r.t. auxiliary-input of non-restricted length, it achieves the notion of auxiliary-input non-uniform zero-knowledge. Unfortunately, these variants have some kind of reverse-engineering property which seems to be unreasonable. It also achieves the standard notions called GMR zero-knowledge and auxiliary-input zeroknowledge under uniform variants of these assumptions, but they have the stronger reverseengineering property. Since the required computational assumptions seem to be unreasonable, our protocol may have no practical significance. However, our result is interesting in the sense that its zeroknowledge property can be demonstrated using the non-black-box simulation technique unlike almost all known zero-knowledge protocols. We note that it is still open whether one can construct a 3-round zero-knowledge protocol for a non-trivial language based on reasonable computational assumptions.
Hierarchical committee of deep convolutional neural networks for robust facial expression recognition

This paper describes our approach towards robust facial expression recognition (FER) for the third Emotion Recognition in the Wild (EmotiW2015) challenge. We train multiple deep convolutional neural networks (deep CNNs) as committee members and combine their decisions. To improve this committee of deep CNNs, we present two strategies: (1) in order to obtain diverse decisions from deep CNNs, we vary network architecture, input normalization, and random weight initialization in training these deep models, and (2) in order to form a better committee in structural and decisional aspects, we construct a hierarchical architecture of the committee with exponentially-weighted decision fusion. In solving a seven-class problem of static FER in the wild for the EmotiW2015, we achieve a test accuracy of 61.6 %. Moreover, on other public FER databases, our hierarchical committee of deep CNNs yields superior performance, outperforming or competing with state-of-the-art results for these databases.