nils-herrmann/clean_scirepeval huggingface dataset:
Mining and Sentiment Analysis : A Survey

In the past few years, a great attention has been received by web documents as a new source of individual opinions and experience. This situation is producing increasing interest in methods for automatically extracting and analyzing individual opinion from web documents such as customer reviews, weblogs and comments on news. This increase was due to the easy accessibility of documents on the web, as well as the fact that all these were already machine-readable on gaining. At the same time, Machine Learning methods in Natural Language Processing (NLP) and Information Retrieval were considerably increased development of practical methods, making these widely available corpora. Recently, many researchers have focused on this area. They are trying to fetch opinion information and analyze it automatically with computers. This new research domain is usually called Opinion Mining and Sentiment Analysis. . Until now, researchers have developed several techniques to the solution of the problem. This paper try to cover some techniques and approaches that be used in this area.
Supporting Microservice Evolution

Microservices have become a popular pattern for deploying scale-out application logic and are used at companies like Netflix, IBM, and Google. An advantage of using microservices is their loose coupling, which leads to agile and rapid evolution, and continuous re-deployment. However, developers are tasked with managing this evolution and largely do so manually by continuously collecting and evaluating low-level service behaviors. This is tedious, error-prone, and slow. We argue for an approach based on service evolution modeling in which we combine static and dynamic information to generate an accurate representation of the evolving microservice-based system. We discuss how our approach can help engineers manage service upgrades, architectural evolution, and changing deployment trade-offs.
Deep Learning

Hierarchical neural networks for object recognition have a long history. In recent years, novel methods for incrementally learning a hierarchy of features from unlabeled inputs were proposed as good starting point for supervised training. These deep learning methods—together with the advances of parallel computers—made it possible to successfully attack problems that were not practical before, in terms of depth and input size. In this article, we introduce the reader to the basic concepts of deep learning, discuss selected methods in detail, and present application examples from computer vision and speech recognition.
The APVA-TURBO Approach To Question Answering in Knowledge Base

In this paper, we study the problem of question answering over knowledge base. We identify that the primary bottleneck in this problem is the difficulty in accurately predicting the relations connecting the subject entity to the object entities. We advocate a new model architecture, APVA, which includes a verification mechanism responsible for checking the correctness of predicted relations. The APVA framework naturally supports a well-principled iterative training procedure, which we call turbo training. We demonstrate via experiments that the APVA-TUBRO approach drastically improves the question answering performance. Title and Abstract in Chinese 面向知识库问答的 APVA-TURBO方法 本文主要围绕目前基于知识库的问答方法中的存在的问题展开研究。经过对现有各类问 答方法的调查与分析,我们发现目前知识库问答的瓶颈主要体现在关系预测上,即如何 准确地预测出问题中的实体与答案实体之间的关联,是需要面临的最大挑战。因此本 文在现有问答框架的基础上,加入了负责对预测关系的可靠性进行评价检验的验证机 制,用以增强关系预测效果,并提出了一种新的知识库问答框架“APVA”。另外,文中 为 APVA 设计了一种具有良好理论基础的迭代训练过程,我们称之为“涡轮式(turbo)训 练”。通过实验证明,APVA-TUBRO方法可以在问答数据集上取得优异效果,大大提升 了目前问答方法的准确性。
A Wearable Reflectance Pulse Oximeter for Remote Physiological Monitoring

To save life, casualty care requires that trauma injuries are accurately and expeditiously assessed in the field. This paper describes the initial bench testing of a wireless wearable pulse oximeter developed based on a small forehead mounted sensor. The battery operated device employs a lightweight optical reflectance sensor and incorporates an annular photodetector to reduce power consumption. The system also has short range wireless communication capabilities to transfer arterial oxygen saturation (SpO2), heart rate (HR), body acceleration, and posture information to a PDA. It has the potential for use in combat casualty care, such as for remote triage, and by first responders, such as firefighters
Formalizing and Implementing Distributed Ledger Objects

Despite the hype about blockchains and distributed ledgers, formal abstractions of these objects are scarce1. To face this issue, in this paper we provide a proper formulation of a distributed ledger object. In brief, we de ne a ledger object as a sequence of records, and we provide the operations and the properties that such an object should support. Implemen- tation of a ledger object on top of multiple (possibly geographically dispersed) computing devices gives rise to the distributed ledger object. In contrast to the centralized object, dis- tribution allows operations to be applied concurrently on the ledger, introducing challenges on the consistency of the ledger in each participant. We provide the de nitions of three well known consistency guarantees in terms of the operations supported by the ledger object: (1) atomic consistency (linearizability), (2) sequential consistency, and (3) eventual consistency. We then provide implementations of distributed ledgers on asynchronous message passing crash- prone systems using an Atomic Broadcast service, and show that they provide eventual, sequen- tial or atomic consistency semantics respectively. We conclude with a variation of the ledger the validated ledger which requires that each record in the ledger satis es a particular validation rule.
A Robust Statistical Color Edge Detection for Noisy Images

Edge detection plays a significant role in image processing and performance of high-level tasks such as image segmentation and object recognition depends on its efficiency. It is clear that accurate edge map generation is more difficult when images are corrupted with noise. Moreover, most of edge detection methods have parameters which must be set manually. Here we propose a new color edge detector based on a statistical test, which is robust to noise. Also, the parameters of this method will be set automatically based on image content. To show the effectiveness of the proposed method, four state-of-the-art edge detectors are implemented and the results are compared. Experimental results on five of the most well-known edge detection benchmarks show that the proposed method is robust to noise. The performance of our method for lower levels of noise is very comparable to the existing approaches, whose performances highly depend on their parameter tuning stage. However, for higher levels of noise, the observed results significantly highlight the superiority of the proposed method over the existing edge detection methods, both quantitatively and qualitatively.
A 10-Bit 0.5 V 100 KS/S SAR ADC with a New rail-to-rail Comparator for Energy Limited Applications

In this paper, a 10-bit 0.5V 100 kS/s successive approximation register (SAR) analog-to-digital converter (ADC) with a new fully dynamic rail-to-rail comparator is presented. The proposed comparator enhances the input signal range to the rail-to-rail mode, and hence, improves the signal-to-noise ratio (SNR) of the ADC in low supply voltages. The e®ect of the latch o®set voltage is reduced by providing a higher voltage gain in the regenerative latch. To reduce the ADC power consumption further, the binary-weighted capacitive array with an attenuation capacitor (BWA) is employed as the digital-to-analog converter (DAC) in this design. The ADC is designed and simulated in a 90 nm CMOS process with a single 0.5V power supply. Spectre simulation results show that the average power consumption of the proposed ADC is about 400 nW and the peak signal-to-noise plus distortion ratio (SNDR) is 56 dB. By considering 10% increase in total ADC power consumption due to the parasitics and a loss of 0.22 LSB in ENOB due to the DAC capacitors mismatch, the achieved ̄gure of merit (FoM) is 11.4 fJ/conversion-step.
How to Make a Decision : The Analytic Hierarchy Process

People make three general types of judgments to express importance, preference, or likelihood and use them to choose the best among alternatives in the presence of environmental, social, political, and other influences. They base these judgments on knowledge in memory or from analyzing benefits, costs, and risks. From past knowledge, we sometimes can develop standards of excellence and poorness and use them to rate the alternatives one at a time. This is useful in such repetitive situations as student admissions and salary raises that must conform with established norms. Without norms one compares alternatives instead of rating them. Comparisons must fall in an admissible range of consistency. The analytic hierarchy process (AHP) includes both the rating and comparison methods. Rationality requires developing a reliable hierarchic structure or feedback network that includes criteria of various types of influence, stakeholders, and decision alternatives to determine the best choice.
Adoption of E-Commerce Applications in SMEs

The purpose of this paper is to examine the factors within the technology-organization-environment (TOE) framework that affect the decision to adopt electronic commerce (EC) and extent of EC adoption, as well as adoption and non-adoption of different EC applications within smalland medium-sized enterprises (SMEs).  A questionnaire-based survey was conducted to collect data from 235 managers or owners of manufacturing SMEs in Iran. The data were analyzed by employing factorial analysis and relevant hypotheses were derived and tested by multiple and logistic regression analysis.  EC adoption within SMEs is affected by perceived relative advantage, perceived compatibility, CEO’s innovativeness, information intensity, buyer/supplier pressure, support from technology vendors, and competition. Similarly, description on determinants of adoption and non-adoption of different EC applications has been provided.  Cross-sectional data of this research tend to have certain limitations when it comes to explaining the direction of causality of the relationships among the variables, which will change overtime.  The findings offer valuable insights to managers, IS experts, and policy makers responsible for assisting SMEs with entering into the e-marketplace. Vendors should collaborate with SMEs to enhance the compatibility of EC applications with these businesses. To enhance the receptiveness of EC applications, CEOs, innovativeness and perception toward EC advantages should also be aggrandized.  This study is perhaps one of the first to use a wide range of variables in the light of TOE framework to comprehensively assess EC adoption behavior, both in terms of initial and post-adoption within SMEs in developing countries, as well adoption and non-adoption of simple and advanced EC applications such as electronic supply chain management systems.
A CONTRACTARIAN ARGUMENT AGAINST THE DEATH PENALTY

Opponents of the death penalty typically base their opposition on contingent features of its administration, arguing that the death penalty is applied discriminatorily, that the innocent are sometimes executed, or that there is insufficient evidence of the death penalty's deterrent efficacy. Implicit in these arguments is the suggestion that if these contingencies did not obtain, serious moral objections to the death penalty would be misplaced. In this Article, Professor Finkelstein argues that there are grounds for opposing the death penalty even in the absence of such contingent factors. She proceeds by arguing that neither of the two prevailing theories of punishment-deterrence and retributivism-is capable of justifying the death penalty. More generally, she suggests that while each theory captures an important part of the justification for punishment, each must appeal to some further limiting principle to accommodate common intuitions about appropriate punishments for crimes. Professor Finkelstein claims that contractarianism supplies this additional principle, by requiring that individuals consent to the system of punishment under whose threat they must live. Moreover, on the version of contractarianism for which she argues, they must do so based on a belief that they will benefit under the terms of that system as compared with how they would fare in its absence. While the notion of benefit is often best understood in terms of maximizing one's expected utility, Professor Finkelstein argues that with respect to choices about the basic structure of society, rational contractors will conceive of benefit in terms of a conservative, "no-gambling" decision rule. She then argues that rational contractors applying this conception of benefit would reject any system of punishment that includes the death penalty. For while contractors would recognize the death penalty's deterrent value, they must also consider the high cost they would pay in the event they end up subject to such a penalty. This Article presents both a significant new approach to the death penalty and a general theory of punishment, one that incorporates the central intuitions about deterrence and desert that have made competing theories of punishment seem compelling.
Design Principles for Industrie 4 . 0 Scenarios

The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a quantitative text analysis and a qualitative literature review, the paper identifies design principles of Industrie 4.0. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in identifying appropriate scenarios. A case study illustrates how the identified design principles support practitioners in identifying Industrie 4.0 scenarios.
Computer forensic timeline visualization tool

Forensics is mainly about investigating crime where computers have been involved. There are many tools available to aid the investigator with this task. We have created a prototype of a new type of tool called CyberForensic TimeLab where all evidence is indexed by their time variables and plotted on a timeline. We believed that this way of visualizing the evidence allows the investigators to find coherent evidence faster and more intuitively. We have performed a user test where a group of people has evaluated our prototype tool against a modern commercial computer forensic tool and the results of this preliminary test are very promising. The results show that users completed the task in shorter time, with greater accuracy and with less errors using CyberForensic TimeLab. The subjects also experienced that the prototype were more intuitive to use and that it allowed them to easier locate evidence that was coherent in time. a 2009 Digital Forensic Research Workshop.
Automatic Relation Extraction from Text: A Survey

Relation extraction is an important task for understanding text. In the big data era, automatic relation extraction from unstructured texts is urgently needed for structured information organization and information analysis. In this paper, we survey the automatic relation extraction methods, especially the traditional machine learning on closed data set and open information environment such as Web, including supervised and semi-supervised methods. And then, we discuss the applications based on relation extraction such as event extraction and QA systems.
A Survey of the Stream Processing Landscape

The continuous processing of streaming data has become an important aspect in many applications. Over the last years a variety of different streaming platforms has been developed and a number of open source frameworks is available for the implementation of streaming applications. In this report, we will survey the landscape of existing streaming platforms. Starting with an overview of the evolving developments in the recent past, we will discuss the requirements of modern streaming architectures and present the ways these are approached by the different frameworks.
Pinterest Analysis and Recommendations

Pinterest is a visual discovery tool for collecting and organizing content on the Web with over 70 million users. Users “pin” images, videos, articles, products, and other objects they find on the Web, and organize them into boards by topic. Other users can repin these and also follow other users or boards. Each user organizes things differently, and this produces a vast amount of human-curated content. For example, someone looking to decorate their home might pin many images of furniture that fits their taste. These curated collections produce a large number of associations between pins, and we investigate how to leverage these associations to surface personalized content to users. Little work has been done on the Pinterest network before due to lack of availability of data. We first performed an analysis on a representative sample of the Pinterest network. After analyzing the network, we created recommendation systems, suggesting pins that users would be likely to repin or like based on their previous interactions on Pinterest. We created recommendation systems using four approaches: a baseline recommendation system using the power law distribution of the images; a content-based filtering algorithm; and two collaborative filtering algorithms, one based on one-mode projection of a bipartite graph, and the second using a label propagation approach.
Fast R-CNN

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.
Disaster risk, climate change and international development: scope for, and challenges to, integration.

Reducing losses to weather-related disasters, meeting the Millennium Development Goals and wider human development objectives, and implementing a successful response to climate change are aims that can only be accomplished if they are undertaken in an integrated manner. Currently, policy responses to address each of these independently may be redundant or, at worst, conflicting. We believe that this conflict can be attributed primarily to a lack of interaction and institutional overlap among the three communities of practice. Differences in language, method and political relevance may also contribute to the intellectual divide. Thus, this paper seeks to review the theoretical and policy linkages among disaster risk reduction, climate change and development. It finds that not only does action within one realm affect capacity for action in the others, but also that there is much that can be learnt and shared between realms in order to ensure a move towards a path of integrated and more sustainable development.
Building Language Models for Text with Named Entities

Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a discriminative language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than the stateof-the-art language models.
Quantum-secured blockchain

Blockchain is a distributed database which is cryptographically protected against malicious modifications. While promising for a wide range of applications, current blockchain platforms rely on digital signatures, which are vulnerable to attacks by means of quantum computers. The same, albeit to a lesser extent, applies to cryptographic hash functions that are used in preparing new blocks, so parties with access to quantum computation would have unfair advantage in procuring mining rewards. Here we propose a possible solution to the quantum-era blockchain challenge and report an experimental realization of a quantum-safe blockchain platform that utilizes quantum key distribution across an urban fiber network for information-theoretically secure authentication. These results address important questions about realizability and scalability of quantum-safe blockchains for commercial and governmental applications.
RGBD-HuDaAct: A color-depth video database for human daily activity recognition

In this paper, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: 1) We have created a publicly releasable human activity video database (i.e., named as RGBD-HuDaAct), which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging more research efforts on human activity recognition based on multi-modality sensor combination (e.g., color plus depth). 2) Two multi-modality fusion schemes, which naturally combine color and depth information, have been developed from two state-of-the-art feature representation methods for action recognition, i.e., spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively and superior recognition performances over their uni-modality (e.g., color only) counterparts are demonstrated.
Towards dynamic trot gait locomotion: Design, control, and experiments with Cheetah-cub, a compliant quadruped robot

We present the design of a novel compliant quadruped robot: Cheetahcub, and a series of locomotion experiments with fast trotting gaits. The robot’s leg configuration is based on a spring-loaded, pantograph mechanism with multiple segments. A dedicated open loop locomotion controller was derived and implemented. Experiments were run in simulation and in hardware on flat terrain and with a step-down, demonstrating the robot’s self-stabilizing properties. The robot reached a running trot with short flight phases with a maximum Froude number of FR=1.30, or 6.9 body lengths per second. Morphological parameters such as the leg design also played a role. By adding distal in-series elasticity, self-stability and maximum robot speed improved. Our robot has several advantages, especially when compared to larger and stiffer quadruped robot designs. 1) It is, to the best of our knowledge, the fastest of all quadruped robots below 30 kg (in terms of Froude number and body lengths per second). 2) It shows self-stabilizing behavior over a large range of speeds with open loop control. 3) It is lightweight, compact, electrically powered. 4) It is cheap, easy to reproduce, robust, and safe to handle. This makes it an excellent tool for research of multi-segment legs in quadruped robots.