nils-herrmann/clean_scirepeval huggingface dataset:
The Price of a Smile : Hedging and Spanning in Option Markets

¤Andrea Buraschi is from London Business School, Sussex Place, Regent's Park, London NW1 4SA, United Kingdom. Tel: +44-20-7262-5050, fax: +44-20-7724-3317,  Tel: 608-262-6032, Fax: 608-265-4195, jjackwerth@bus.wisc.edu. For helpful comments on this paper we wish to thank Sudipto Bhattacharya, Mark Britten-Jones, Ian Cooper, the editor Bernard Dumas, Julian Franks, Erzo Luttmer, Davide Menini, Anthony Neuberger, Stephen Schaefer, an anonymous referee, and seminar participants at EFMA Lisbon, WFA Conference, 1998 Computational Finance Conference New York, The University of Chicago, HEC Business School, London Business School, London School of Economics, City University of London, Oxford Business School, Konstanz University, University of Wisconsin at Madison, Georgetown University.
LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.
Crime Mapping in Nigeria Using GIS

This paper first examines crime situation in Benin metropolis using questionnaire to elicit information from the public and the police. Result shows that crime is on the rise and that the police are handicapped in managing it because of the obsolete methods and resources at their disposal. It also reveals that members of the public have no confidence in the police force as 80% do not report cases for fear of exposure to the informant to the criminal. In the light of these situations, the second part of the paper looks at the possibility of utilizing GIS for effective management of crime in Nigeria. This option was explored by showing the procedural method of creating 1) digital landuse map showing the crime locations, 2) crime geo-spatial database, and 3) spatial analysis such as query and buffering using ILWIS and ArcGIS software and GPS. The result of buffering analysis shows crime hotspots, areas deficient in security outfit, areas of overlap and areas requiring constant police patrol. The study proves that GIS can give a better synoptic perspective to crime study, analysis, mapping, proactive decision making and prevention of crime. It however suggests that migrating from traditional method of crime management to GIS demands capacity building in the area of personnel, laboratory and facilities backed up with policy statement.
Sample-Efficient Imitation Learning via Generative Adversarial Nets

GAIL is a recent successful imitation learning architecture that exploits the adversarial training procedure introduced in GANs. Albeit successful at generating behaviours similar to those demonstrated to the agent, GAIL suffers from a high sample complexity in the number of interactions it has to carry out in the environment in order to achieve satisfactory performance. We dramatically shrink the amount of interactions with the environment necessary to learn well-behaved imitation policies, by up to several orders of magnitude. Our framework, operating in the model-free regime, exhibits a significant increase in sample-efficiency over previous methods by simultaneously a) learning a self-tuned adversarially-trained surrogate reward and b) leveraging an off-policy actor-critic architecture. We show that our approach is simple to implement and that the learned agents remain remarkably stable, as shown in our experiments that span a variety of continuous control tasks. Video visualisation available at: https://streamable.com/42l01
Handwriting recognition on form document using convolutional neural network and support vector machines (CNN-SVM)

In this paper, we propose a workflow and a machine learning model for recognizing handwritten characters on form document. The learning model is based on Convolutional Neural Network (CNN) as a powerful feature extraction and Support Vector Machines (SVM) as a high-end classifier. The proposed method is more efficient than modifying the CNN with complex architecture. We evaluated some SVM and found that the linear SVM using L1 loss function and L2 regularization giving the best performance both of the accuracy rate and the computation time. Based on the experiment results using data from NIST SD 192nd edition both for training and testing, the proposed method which combines CNN and linear SVM using L1 loss function and L2 regularization achieved a recognition rate better than only CNN. The recognition rate achieved by the proposed method are 98.85% on numeral characters, 93.05% on uppercase characters, 86.21% on lowercase characters, and 91.37% on the merger of numeral and uppercase characters. While the original CNN achieves an accuracy rate of 98.30% on numeral characters, 92.33% on uppercase characters, 83.54% on lowercase characters, and 88.32% on the merger of numeral and uppercase characters. The proposed method was also validated by using ten folds cross-validation, and it shows that the proposed method still can improve the accuracy rate. The learning model was used to construct a handwriting recognition system to recognize a more challenging data on form document automatically. The pre-processing, segmentation and character recognition are integrated into one system. The output of the system is converted into an editable text. The system gives an accuracy rate of 83.37% on ten different test form document.
Survey and analysis of current mobile learning applications and technologies

Distance learning, electronic learning, and mobile learning offer content, methods, and technologies that decrease the limitations of traditional education. Mobile learning (m-learning) is an extension of distance education, supported by mobile devices equipped with wireless technologies. It is an emerging learning model and process that requires new forms of teaching, learning, contents, and dynamics between actors. In order to ascertain the current state of knowledge and research, an extensive review of the literature in m-learning has been undertaken to identify and harness potential factors and gaps in implementation. This article provides a critical analysis of m-learning projects and related literature, presenting the findings of this aforementioned analysis. It seeks to facilitate the inquiry into the following question: “What is possible in m-learning using recent technologies?” The analysis will be divided into two main parts: applications from the recent online mobile stores and operating system standalone applications.
Seaweed Bioactive Compounds against Pathogens and Microalgae: Potential Uses on Pharmacology and Harmful Algae Bloom Control

Cyanobacteria are found globally due to their adaptation to various environments. The occurrence of cyanobacterial blooms is not a new phenomenon. The bloom-forming and toxin-producing species have been a persistent nuisance all over the world over the last decades. Evidence suggests that this trend might be attributed to a complex interplay of direct and indirect anthropogenic influences. To control cyanobacterial blooms, various strategies, including physical, chemical, and biological methods have been proposed. Nevertheless, the use of those strategies is usually not effective. The isolation of natural compounds from many aquatic and terrestrial plants and seaweeds has become an alternative approach for controlling harmful algae in aquatic systems. Seaweeds have received attention from scientists because of their bioactive compounds with antibacterial, antifungal, anti-microalgae, and antioxidant properties. The undesirable effects of cyanobacteria proliferations and potential control methods are here reviewed, focusing on the use of potent bioactive compounds, isolated from seaweeds, against microalgae and cyanobacteria growth.
Deep Convolutional Neural Network Architecture With Reconfigurable Computation Patterns

Deep convolutional neural networks (DCNNs) have been successfully used in many computer vision tasks. Previous works on DCNN acceleration usually use a fixed computation pattern for diverse DCNN models, leading to imbalance between power efficiency and performance. We solve this problem by designing a DCNN acceleration architecture called deep neural architecture (DNA), with reconfigurable computation patterns for different models. The computation pattern comprises a data reuse pattern and a convolution mapping method. For massive and different layer sizes, DNA reconfigures its data paths to support a hybrid data reuse pattern, which reduces total energy consumption by 5.9~8.4 times over conventional methods. For various convolution parameters, DNA reconfigures its computing resources to support a highly scalable convolution mapping method, which obtains 93% computing resource utilization on modern DCNNs. Finally, a layer-based scheduling framework is proposed to balance DNA’s power efficiency and performance for different DCNNs. DNA is implemented in the area of 16 mm2 at 65 nm. On the benchmarks, it achieves 194.4 GOPS at 200 MHz and consumes only 479 mW. The system-level power efficiency is 152.9 GOPS/W (considering DRAM access power), which outperforms the state-of-the-art designs by one to two orders.
Automatic Detection of Region-Mura Defect in TFT-LCD

Visual defects, called mura in the field, sometimes occur during the manufacturing of the flat panel liquid crystal displays. In this paper we propose an automatic inspection method that reliably detects and quantifies TFT-LCD regionmura defects. The method consists of two phases. In the first phase we segment candidate region-muras from TFT-LCD panel images using the modified regression diagnostics and Niblack’s thresholding. In the second phase, based on the human eye’s sensitivity to mura, we quantify mura level for each candidate, which is used to identify real muras by grading them as pass or fail. Performance of the proposed method is evaluated on real TFT-LCD panel samples. key words: Machine vision, image segmentation, regression diagnostics, industrial inspection, visual perception.
TWO FIXED-POINT THEOREMS FOR MAPPINGS SATISFYING A GENERAL CONTRACTIVE CONDITION OF INTEGRAL TYPE

We establish two fixed-point theorems for mappings satisfying a general contrac-tive inequality of integral type. These results substantially extend the theorem of Branciari (2002). In a recent paper , Branciari established the following theorem. Theorem 1. Let (X, d) be a complete metric space, c ∈ [0, 1), f : X → X a mapping such that, for each x, y ∈ X, d(f x,f y) 0 ϕ(t)dt ≤ c d(x,y) 0 ϕ(t)dt, (1) where ϕ : R + → R + is a Lebesgue-integrable mapping which is summable, non-negative, and such that, for each > 0, 0 ϕ(t)dt > 0. Then f has a unique fixed point z ∈ X such that, for each x ∈ X, lim n f n x = z. In , it was mentioned that (1) could be extended to more general contrac-tive conditions. It is the purpose of this paper to make such an extension to two of the most general contractive conditions. Define m(x, y) = max d(x,y),d(x,f x),d(y,f y), d(x, f y) + d(y, f x) 2. (2) Our first result is the following theorem. Theorem 2. Let (X, d) be a complete metric space, k ∈ [0, 1), f : X → X a mapping such that, for each x, y ∈ X, d(f x,f y) 0 ϕ(t)dt ≤ k m(x,y) 0 ϕ(t)dt, (3)
Object Ordering with Bidirectional Matchings for Visual Reasoning

Visual reasoning with compositional natural language instructions, e.g., based on the newly-released Cornell Natural Language Visual Reasoning (NLVR) dataset, is a challenging task, where the model needs to have the ability to create an accurate mapping between the diverse phrases and the several objects placed in complex arrangements in the image. Further, this mapping needs to be processed to answer the question in the statement given the ordering and relationship of the objects across three similar images. In this paper, we propose a novel end-to-end neural model for the NLVR task, where we first use joint bidirectional attention to build a two-way conditioning between the visual information and the language phrases. Next, we use an RL-based pointer network to sort and process the varying number of unordered objects (so as to match the order of the statement phrases) in each of the three images and then pool over the three decisions. Our model achieves strong improvements (of 4-6% absolute) over the state-of-theart on both the structured representation and raw image versions of the dataset.
Two-Stream Convolutional Networks for Dynamic Saliency Prediction

In recent years, visual saliency estimation in images has attracted much attention in the computer vision community. However, predicting saliency in videos has received relatively little attention. Inspired by the recent success of deep convolutional neural networks based static saliency models, in this work, we study two different two-stream convolutional networks for dynamic saliency prediction. To improve the generalization capability of our models, we also introduce a novel, empirically grounded data augmentation technique for this task. We test our models on DIEM dataset and report superior results against the existing models. Moreover, we perform transfer learning experiments on SALICON, a recently proposed static saliency dataset, by finetuning our models on the optical flows estimated from static images. Our experiments show that taking motion into account in this way can be helpful for static saliency estimation.
Acid Etching as Surface Treatment Method for Luting of Glass-Ceramic Restorations, part 1: Acids, Application Protocol and Etching Effectiveness

The purpose of this review is to represent acids that can be used as surface etchant before adhesive luting of ceramic restorations, placement of orthodontic brackets or repair of chipped porcelain restorations. Chemical reactions, application protocol, and etching effect are presented as well.



Available scientific articles published in PubMed and Scopus literature databases, scientific reports and manufacturers' instructions and product information from internet websites, written in English, using following search terms: "acid etching, ceramic surface treatment, hydrofluoric acid, acidulated phosphate fluoride, ammonium hydrogen bifluoride", have been reviewed.



There are several acids with fluoride ion in their composition that can be used as ceramic surface etchants. The etching effect depends on the acid type and its concentration, etching time, as well as ceramic type. The most effective etching pattern is achieved when using hydrofluoric acid; the numerous micropores and channels of different sizes, honeycomb-like appearance, extruded crystals or scattered irregular ceramic particles, depending on the ceramic type, have been detected on the etched surfaces.



Acid etching of the bonding surface of glass - ceramic restorations is considered as the most effective treatment method that provides a reliable bond with composite cement. Selective removing of the glassy matrix of silicate ceramics results in a micromorphological three-dimensional porous surface that allows micromechanical interlocking of the luting composite.
Security Analysis of Industrial Control Systems

(SCADA), have lately gained the attention of IT security researchers as critical components of modern industrial infrastructure. One main reason for this attention is that ICS have not been built with security in mind and are thus particularly vulnerable when they are connected to computer networks and the Internet. ICS consists of SCADA, Programmable Logic Controller (PLC), Human-Machine Interfaces (HMI), sensors, and actuators such as motors. These components are connected to each other over fieldbus or IP-based protocols. In this thesis, we have developed methods and tools for assessing the security of ICSs. By applying the STRIDE threat modeling methodology, we have conducted a high level threat analysis of ICSs. Based on the threat analysis, we created security analysis guidelines for Industrial Control System devices. These guidelines can be applied to many ICS devices and are mostly vendor independent. Moreover, we have integrated support for Modbus/TCP in the Scapy packet manipulation library, which can be used for robustness testing of ICS software. In a case study, we applied our security-assessment methodology to a detailed security analysis of a demonstration ICS, consisting of current products. As a result of the analysis, we discovered several security weaknesses. Most of the discovered vulnerabilities were common IT security problems, such as web-application and software-update issues, but some are specific to ICS. For example, we show how the data visualized by the Human-Machine Interface can be altered and modified without limit. Furthermore, sensor data, such as temperature values, can be spoofed within the PLC. Moreover, we show that input validation is critical for security also in the ICS world. Thus, we disclose several security vulnerabilities in production devices. However, in the interest of responsible disclosure of security flaws, the most severe security flaws found are not detailed in the thesis. Our analysis guidelines and the case study provide a basis for conducting vulnerability assessment on further ICS devices and entire systems. In addition, we briefly describe existing solutions for securing ICSs. Acknowledgements I would like to thank Nixu Oy and the colleagues (especially Lauri Vuornos, Juhani Mäkelä and Michael Przybilski) for making it possible to conduct my thesis on Industrial Control Systems. The industrial environment enabled us to take advantage of the research and to apply it to practical projects. Moreover, without the help and involvement of Schneider Electric such an applied analysis would not have been possible. Furthermore, I would like to thank Tuomas …
Aspectual universals of temporal anaphora

It has long been recognized that temporal anaphora in French and English depends on the aspectual distinction between events and states. For example, temporal location as well as temporal update depends on the aspectual type. This paper presents a general theory of aspect-based temporal anaphora, which extends from languages with grammatical tenses (like French and English) to tenseless languages (e.g. Kalaallisut). This theory also extends to additional aspect-dependent phenomena and to non-atomic aspectual types, processes and habits, which license anaphora to proper atomic parts (cf. nominal pluralities and kinds).
CRF learning with CNN features for image segmentation

Conditional Random Rields (CRF) have been widely applied in image segmentations. While most studies rely on handcrafted features, we here propose to exploit a pre-trained large convolutional neural network (CNN) to generate deep features for CRF learning. The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels. Then the CRF parameters are learnt using a structured support vector machine (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labelling of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the promise of the proposed method. We thus provide new baselines for the segmentation performance on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC 2011 datasets.
Trace DNA: a review, discussion of theory, and application of the transfer of trace quantities of DNA through skin contact.

Advances in STR PCR DNA profiling technology allow for the analysis of minute quantities of DNA. It is frequently possible to obtain successful DNA results from cellular material transferred from the skin of an individual who has simply touched an object. Handling objects, such as weapons or other items associated with a crime, touching surfaces, or wearing clothing, may represent sufficient contact to transfer small numbers of DNA bearing cells, or trace DNA, which can be successfully analyzed. With this minimal amount of contact required to yield a suspect profile comes tremendous crime solving potential, and a number of considerations for prudent application, and the maximization of evidentiary value.  not previously considered must be recognized and preserved, and the resulting DNA type profiles interpreted in their proper forensic context.
Integrated Commonsense Reasoning and Probabilistic Planning

Commonsense reasoning and probabilistic planning are two of the most important research areas in artificial intelligence. This paper focuses on Integrated commonsense Reasoning and probabilistic Planning (IRP) problems. On one hand, commonsense reasoning algorithms aim at drawing conclusions using structured knowledge that is typically provided in a declarative way. On the other hand, probabilistic planning algorithms aim at generating an action policy that can be used for action selection under uncertainty. Intuitively, reasoning and planning techniques are good at “understanding the world” and “accomplishing the task” respectively. This paper discusses the complementary features of the two computing paradigms, presents the (potential) advantages of their integration, and summarizes existing research on this topic.
SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints

The state-of-the-art techniques for aspect-level sentiment analysis focus on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their practical performance may fall short of expectations due to semantic complexity of natural languages. Motivated by the observation that linguistic hints (e.g. explicit sentiment words and shift words) can be strong indicators of sentiment, we present a joint framework, SenHint, which integrates the output of deep neural networks and the implication of linguistic hints into a coherent reasoning model based on Markov Logic Network (MLN). In SenHint, linguistic hints are used in two ways: (1) to identify easy instances, whose sentiment can be automatically determined by machine with high accuracy; (2) to capture implicit relations between aspect polarities. We also empirically evaluate the performance of SenHint on both English and Chinese benchmark datasets. Our experimental results show that SenHint can effectively improve accuracy compared with the state-of-the-art alternatives.