nils-herrmann/clean_scirepeval huggingface dataset:
Towards a critical geopolitics of China ’ s engagement with African development

China, in its quest for a closer strategic partnership with Africa, has increasingly dynamic economic, political and diplomatic activities on the continent. Chinese leaders and strategists believe that China’s historical experience and vision of economic development resonates powerfully with African counterparts and that the long standing history of friendly political linkages and development co-operation offers a durable foundation for future partnership. In doing so, we see from the Chinese side and amongst some western commentators a form of exceptionalism and generalisation regarding both China and Africa. In this paper instead we seek to develop theoretical tools for examining China as a geopolitical and geoeconomic actor that is both different and similar to other industrial powers intervening in Africa. This is premised on a political economy approach that ties together material interests with a deconstruction of the discursive or ‘extra-economic’ ways by which Chinese capitalism internationalises. From there we use this framework to analyse contemporary Chinese engagement in Africa. We examine the changing historical position of Africa within Beijing’s foreign policy strategy and China’s vision of the evolving international political system, looking in particular at China’s bilateral and state-centric approach to working with African ‘partners’. Chinese practice is uncomfortable and unfamiliar with the notion of ‘development’ as an independent policy field of the kind that emerged among Western nations in the course of the 1950s and increasingly China has come to be viewed as a ‘rogue creditor’ and a threat to the international aid industry. Rather than highlighting one strand of Chinese relations with African states (such as aid or governance) we propose here that it is necessary to critically reflect on the wider geopolitics of China-Africa relations (past and present) in order to understand how China is opening up new ‘choices’ and altering the playing field for African development for the first time since the neo-liberal turn of the 1980s. Marcus Power is a Reader in Human Geography at the University of Durham (marcus.power@dur.ac.uk). Giles Mohan is a Reader in the Politics of International Development at the Open University (g.mohan@open.ac.uk 1 The authors would like to acknowledge the support of the UK’s Economic and Social Research Council in funding the research project (Ref: RES-062-23-0487) from which this paper derives. More details are available at www.geography.dur.ac.uk/projects/china-africa/.
How to Write Fast Numerical Code: A Small Introduction

The complexity of modern computing platforms has made it extremely difficult to write numerical code that achieves the best possible performance. Straightforward implementations based on algorithms that minimize the operations count often fall short in performance by at least one order of magnitude. This tutorial introduces the reader to a set of general techniques to improve the performance of numerical code, focusing on optimizations for the computer’s memory hierarchy. Further, program generators are discussed as a way to reduce the implementation and optimization effort. Two running examples are used to demonstrate these techniques: matrix-matrix multiplication and the discrete Fourier transform.
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter

The Pacific Northwest National Laboratory, located in southeastern Washington State, is a U.S. Department of Energy Office of Science laboratory that solves complex problems in energy, national security, and the environment, and advances scientific frontiers in the chemical, biological, materials, environmental, and computational sciences. The Laboratory employs nearly 5,000 staff members, has an annual budget in excess of $1 billion, and has been managed by Ohio-based Battelle since 1965.
Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network

Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.
Bitcoin-NG: A Scalable Blockchain Protocol

Cryptocurrencies, based on and led by Bitcoin, have shown promise as infrastructure for pseudonymous online payments, cheap remittance, trustless digital asset exchange, and smart contracts. However, Bitcoin-derived blockchain protocols have inherent scalability limits that trade-off between throughput and latency and withhold the realization of this potential. This paper presents Bitcoin-NG, a new blockchain protocol designed to scale. Based on Bitcoin’s blockchain protocol, Bitcoin-NG is Byzantine fault tolerant, is robust to extreme churn, and shares the same trust model obviating qualitative changes to the ecosystem. In addition to Bitcoin-NG, we introduce several novel metrics of interest in quantifying the security and efficiency of Bitcoin-like blockchain protocols. We implement Bitcoin-NG and perform large-scale experiments at 15% the size of the operational Bitcoin system, using unchanged clients of both protocols. These experiments demonstrate that Bitcoin-NG scales optimally, with bandwidth limited only by the capacity of the individual nodes and latency limited only by the propagation time of the network.
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.
Software-based self-testing methodology for processor cores

At-speed testing of gigahertz processors using external testers may not be technically and economically feasible. Hence, there is an emerging need for low-cost high-quality self-test methodologies that can be used by processors to test themselves at-speed. Currently, built-in self-test (BIST) is the primary self-test methodology available. While memory BIST is commonly used for testing embedded memory cores, complex logic designs such as microprocessors are rarely tested with logic BIST. In this paper, we first analyze the issues associated with current hardware-based logic-BIST methodologies by applying a commercial logic-BIST tool to two processor cores. We then propose a new software-based self-testing methodology for processors, which uses a software tester embedded in the processor memory as a vehicle for applying structural tests. The software tester consists of programs for test generation and test application. Prior to the test, structural tests are prepared for processor components in the form of self-test signatures. During the process of self-test, the test generation program expands the self-test signatures into test sets and the test application program applies the tests to the components under test at the speed of the processor. Application of the novel software-based self-test method demonstrates its significant cost/fault coverage benefits and its ability to apply at-speed test while alleviating the need for high-speed testers.
The Development and Validation of the Online Shopping Addiction Scale

We report the development and validation of a scale to measure online shopping addiction. Inspired by previous theories and research on behavioral addiction, the Griffiths's widely accepted six-factor component model was referred to and an 18-item scale was constructed, with each component measured by three items. The results of exploratory factor analysis, based on Sample 1 (999 college students) and confirmatory factor analysis, based on Sample 2 (854 college students) showed the Griffiths's substantive six-factor structure underlay the online shopping addiction scale. Cronbach's alpha suggested that the resulting scale was highly reliable. Concurrent validity, based on Sample 3 (328 college students), was also satisfactory as indicated by correlations between the scale and measures of similar constructs. Finally, self-perceived online shopping addiction can be predicted to a relatively high degree. The present 18-item scale is a solid theory-based instrument to empirically measure online shopping addiction and can be used for understanding the phenomena among young adults.
Social Network Hate Speech Detection for Amharic Language

The anonymity of social networks makes it attractive for hate speech to mask their criminal activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing volume of social media data, hate speech identification becomes a challenge in aggravating conflict between citizens of nations. The high rate of production, has become difficult to collect, store and analyze such big data using traditional detection methods. This paper proposed the application of apache spark in hate speech detection to reduce the challenges. Authors developed an apache spark based model to classify Amharic Facebook posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation, the model based on word2vec embedding performed best with 79.83%accuracy. The proposed method achieve a promising result with unique feature of spark for big data.
Intra-layer nonuniform quantization of convolutional neural network

Deep convolutional neural network (DCNN) has achieved remarkable performance on object detection and speech recognition in recent years. However, the excellent performance of a DCNN incurs high computational complexity and large memory requirement In this paper, an equal distance nonuniform quantization (ENQ) scheme and a K-means clustering nonuniform quantization (KNQ) scheme are proposed to reduce the required memory storage when low complexity hardware or software implementations are considered. For the VGG-16 and the AlexNet, the proposed nonuniform quantization schemes reduce the number of required memory storage by approximately 50% while achieving almost the same or even better classification accuracy compared to the state-of-the-art quantization method. Compared to the ENQ scheme, the proposed KNQ scheme provides a better tradeoff when higher accuracy is required.
Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of Visual SLAM for the past fifteen years has yielded workable systems that found their way into various applications, such as robotics and augmented reality. Although filter-based (e.g., Kalman Filter, Particle Filter) Visual SLAM systems were common at some time, non-filter based (i.e., akin to SfM solutions), which are more efficient, are becoming the de facto methodology for building a Visual SLAM system. This paper presents a survey that covers the various non-filter based Visual SLAM systems in the literature, detailing the various components of their implementation, while critically assessing the specific strategies made by each proposed system in implementing its components.
3D Urban Scene Modeling Integrating Recognition and Reconstruction

Supplying realistically textured 3D city models at ground level promises to be useful for pre-visualizing upcoming traffic situations in car navigation systems. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the required maneuver will be more easily understandable. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. In this paper, we present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed. Objects in the environment, such as cars and pedestrians, may however disturb the reconstruction, as they violate the simplified geometry assumptions, leading to visually unpleasant artifacts and degrading the visual realism of the resulting 3D city model. Unfortunately, such objects are prevalent in urban scenes. We therefore extend the reconstruction framework by integrating it with an object recognition module that automatically detects cars in the input video streams and localizes them in 3D. The two components of our system are tightly integrated and benefit from each other’s continuous input. 3D reconstruction delivers geometric scene context, which greatly helps improve detection precision. The detected car locations, on the other hand, are used to instantiate virtual placeholder models which augment the visual realism of the reconstructed city model.
Partisans without Constraint: Political Polarization and Trends in American Public Opinion.

Public opinion polarization is here conceived as a process of alignment along multiple lines of potential disagreement and measured as growing constraint in individuals' preferences. Using NES data from 1972 to 2004, the authors model trends in issue partisanship-the correlation of issue attitudes with party identification-and issue alignment-the correlation between pairs of issues-and find a substantive increase in issue partisanship, but little evidence of issue alignment. The findings suggest that opinion changes correspond more to a resorting of party labels among voters than to greater constraint on issue attitudes: since parties are more polarized, they are now better at sorting individuals along ideological lines. Levels of constraint vary across population subgroups: strong partisans and wealthier and politically sophisticated voters have grown more coherent in their beliefs. The authors discuss the consequences of partisan realignment and group sorting on the political process and potential deviations from the classic pluralistic account of American politics.
A survey on anti-forensics techniques

Digital forensics methodologies and tools have become a crucial part for investigation of cybercrimes and collecting digital evidences in a case. Digital forensics experts usually follow a common workflow and use known methodologies and tools while investigating a case. Attackers and cybercriminals also know which methodologies are used in an investigation and how digital forensics tools work. As a consequence, they started to find and implement a new methodology which is called anti-forensics for deceiving investigator or having a case lasts longer than expected. Anti-forensics has been acknowledged as a legitimate field of study recently, therefore it can be considered as an emerging area of interest and there is a lack of knowledge about anti-forensics techniques. This paper aims to make mention of the anti-forensics techniques such as Data Pooling; Non-Standart RAID'ed Disks; Manipulating File Signatures; Restricted Filenames; Manipulating MACE (file Modified, Accessed, Created and Entry) Times; Loop References; Hash Collisions; Dummy HDDs and proposals for mitigating these techniques.
Toward integrated motion planning and control using potential fields and torque-based steering actuation for autonomous driving

This paper proposes an integrated motion planning and control approach for autonomous car navigation. Existing approaches to autonomous vehicle navigation typically plan a trajectory and pass it on to a steering controller that commands steering wheel angle (SWA) or curvature at every timestep to minimize tracking error. However, this approach exhibits large amounts of control effort, and ignores other criteria such as smoothness or the importance of staying on plan at different times. Conversely, our proposed approach leverages the concept of potential fields to represent a driving corridor with a desired tracking error tolerance and direct torque-based steering control to smoothly steer the vehicle with a much smaller control effort. Further, using potential fields allows us to naturally incorporate obstacles in the driving corridor to circumvent them, with typically no need for explicit trajectory planning. We compare our approach to a standard steering controller in experiments with a real-world autonomous vehicle platform. Results show that our proposed approach achieves similar path tracking performance as a high-gain SWA controller, but with much less actuator effort.
A Digital Image of the City : 3-D isovists and a tribute to Kevin Lynch

New techniques to measure visual perception over urban spaces are presented in this paper. This study aims to extend the concept of ‘isovist’, i.e. the visible space from a vantage point, in three dimensions and examines how it could help in providing a quantifiable basis for Kevin Lynch's urban analysis, as outlined in his book The Image of the City. Outputs of the analysis are stored in a voxel space, i.e. a three-dimensional matrix of urban visibility measures. The analysis of what we have called ‘iso-visi-matrix’ seems to allow a more useful interpretation of visibility from a visual perception point of view.
Examining the anatomy of last-mile distribution in e- commerce omnichannel retailing: A supply network configuration approach

This paper investigates the interplay among the configuration dimensions (network structure, network flow, relationship governance, and service architecture) of LastMile Supply Networks (LMSN) and the underlying mechanisms influencing omnichannel performance.  Based on mixed-method design incorporating a multiple embedded case study, mapping, survey and archival records, this research involved undertaking in-depth withinand cross-case analyses to examine seven LMSNs, employing a configuration approach.  The existing literature in the operations management (OM) field was shown to provide limited understanding of LMSNs within the emerging omnichannel context. Case results suggest that particular configurations have intrinsic capabilities, and that these directly influence omnichannel performance. The study further proposes a taxonomy of LMSNs comprising six forms, with two hybrids, supporting the notion of equifinality in configuration theory. Propositions are developed to further explore interdependencies between configurational attributes, refining the relationships between LMSN types and factors influencing LMSN performance.  The findings provide retailers a set of design parameters for the (re)configuration of LMSNs and facilitate performance evaluation using the concept of fit between configurational attributes. The developed model sheds light on the consequential effects when certain configurational attributes are altered, providing design indications. Given the global trend in urbanization, improved LMSN performance would have positive societal impacts in terms of service and resource efficiency.  This is one of the first studies in the OM field to critically analyze LMSNs and their behaviors in omnichannel. Additionally, the paper offers several important avenues for future research.
Extending EMV Tokenised Payments to Offline-Environments

Tokenisation has been adopted by the payment industry as a method to prevent Personal Account Number (PAN) compromise in EMV (Europay MasterCard Visa) transactions. The current architecture specified in EMV tokenisation requires online connectivity during transactions. However, it is not always possible to have online connectivity. We identify three main scenarios where fully offline transaction capability is considered to be beneficial for both merchants and consumers. Scenarios include making purchases in locations without online connectivity, when a reliable connection is not guaranteed, and when it is cheaper to carry out offline transactions due to higher communication/payment processing costs involved in online approvals. In this study, an offline contactless mobile payment protocol based on EMV tokenisation is proposed. The aim of the protocol is to address the challenge of providing secure offline transaction capability when there is no online connectivity on either the mobile or the terminal. The solution also provides end-to-end encryption to provide additional security for transaction data other than the token. The protocol is analysed against protocol objectives and we discuss how the protocol can be extended to prevent token relay attacks. The proposed solution is subjected to mechanical formal analysis using Scyther. Finally, we implement the protocol and obtain performance measurements.
Ontology research and development. Part 1 - a review of ontology generation

Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next generation of Web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This timely survey is presented in two parts. This first part reviews the state-of-the-art techniques and work done on semiautomatic and automatic ontology generation, as well as the problems facing these researches. The second complimentary survey is dedicated to ontology mapping and ontology evolving. Through this survey, we identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impedance to ontology learning and applicability. Further researches are encouraged to find appropriate and efficient ways to detect or identify relations through semi-automatic automatic means.
An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease

Parkinson’s disease (PD) is a chronic disease that develops over years and varies dramatically in its clinical manifestations. A preferred strategy to resolve this heterogeneity and thus enable better prognosis and targeted therapies is to segment out more homogeneous patient sub-populations. However, it is challenging to evaluate the clinical similarities among patients because of the longitudinality and temporality of their records. To address this issue, we propose a deep model that directly learns patient similarity from longitudinal and multi-modal patient records with an Recurrent Neural Network (RNN) architecture, which learns the similarity between two longitudinal patient record sequences through dynamically matching temporal patterns in patient sequences. Evaluations on real world patient records demonstrate the promising utility and efficacy of the proposed architecture in personalized predictions.