nils-herrmann/clean_scirepeval huggingface dataset:
A humanitarian supply chain process reference model

Humanitarian operations comprise a wide variety of activities. These activities differ in temporal and spatial scope, as well as objectives, target population and with respect to the delivered goods and services. Despite a notable variety of agendas of the humanitarian actors, the requirements on the supply chain and supporting logistics activities remain similar to a large extent. This motivates the development of a suitably generic reference model for supply chain processes in the context of humanitarian operations. Reference models have been used in commercial environments for a range of purposes, such as analysis of structural, functional, and behavioural properties of supply chains. Our process reference model aims to support humanitarian organisations when designing appropriately adapted supply chain processes to support their operations, visualising their processes, measuring their performance and thus, improving communication and coordination of organisations. A top-down approach is followed in which modular process elements are developed sequentially and relevant performance measures are identified. This contribution is conceptual in nature and intends to lay the foundation for future research.
Image Colorization with Neural Networks

We propose a method for colorizing photos, this is, providing a color version of a given gray scale image. The method does not depend on human input, and is completely automatic. It does not depend on segmentation, scribbling or sophisticated image processing techniques. It is based on training a simple classifier using back propagation over a training set of color and corresponding gray scale pictures. The classifier predicts the color of a pixel based on the gray level of the pixels surrounding it. This small patch captures a local texture. To keep the domain for the predictor small, the colors are reduced using Self Organizing Maps. This reduction produces a small set of chroma values with enough variation as to generate good approximations for all colors in the training set.
Multi-Source Social Feedback of Online News Feeds

The profusion of user generated content caused by the rise of social media platforms has enabled a surge in research relating to fields such as information retrieval, recommender systems, data mining and machine learning. However, the lack of comprehensive baseline data sets to allow a thorough evaluative comparison has become an important issue. In this paper we present a large data set of news items from well-known aggregators such as Google News and Yahoo! News, and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The data collected relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation..
Design of a Multipolarized RFID Reader Antenna for UHF Near-Field Applications

This paper presents our investigation into a novel ultrahigh-frequency (UHF) radio frequency identification (RFID) multipolarized reader antenna based on a pair of symmetrical meandering open-ended microstrip lines for near-field applications. The near-field and multipolarization operation is achieved by introducing a 90° phase shift between the currents flowing along the opposite side of two branches. The proposed antenna is shown to generate a uniform and strong electric field in its near-field region within a reading volume: <inline-formula> <tex-math notation="LaTeX">$450~\text {mm} \times 450~\text {mm} \times 350$ </tex-math></inline-formula> mm (<inline-formula> <tex-math notation="LaTeX">$\text {width} \times \text {length} \times \text {height}$ </tex-math></inline-formula>). The simulated and measured impedance bandwidths (−10 dB) agree very well, ranging from 825 to 965 MHz and covering the UHF RFID standard. In addition, it exhibits a low far-field gain, avoiding to misreading the tags outside the near-field region. The fabricated antenna was fully tested with multiple tag antennas that are placed in different orientations and even in a conveyor system, demonstrating a 100% reading rate of arbitrarily oriented tags within the reading zone.
PRE-CRASH SENSOR FOR PRE-CRASH SAFETY

Improvement of vehicle safety performance is one of the targets of ITS development. A pre-crash safety system has been developed that utilizes ITS technologies. The Pre-crash Safety system reduces collision injury by estimating TTC(time-tocollision) to preemptively activate safety devices, which consist of “Pre-crash Seatbelt” system and “Pre-crash Brake Assist” system. The key technology of these systems is a “Pre-crash Sensor” to detect obstacles and estimate TTC. In this paper, the Pre-crash Sensor is presented. The Pre-crash Sensor uses millimeter-wave radar to detect preceding vehicles, oncoming vehicles, roadside objects, etc. on the road ahead. Furthermore, by using a phased array system as a vehicle radar for the first time, a compact electronically scanned millimeter-wave radar with high recognition performance has been achieved. With respect to the obstacle determination algorithm, a crash determination algorithm has been newly developed, taking into account estimation of the direction of advance of the vehicle, in addition to the distance, relative speed and direction of the object.
IoTPOT: Analysing the Rise of IoT Compromises

We analyze the increasing threats against IoT devices. We show that Telnet-based attacks that target IoT devices have rocketed since 2014. Based on this observation, we propose an IoT honeypot and sandbox, which attracts and analyzes Telnet-based attacks against various IoT devices running on different CPU architectures such as ARM, MIPS, and PPC. By analyzing the observation results of our honeypot and captured malware samples, we show that there are currently at least 4 distinct DDoS malware families targeting Telnet-enabled IoT devices and one of the families has quickly evolved to target more devices with as many as 9 different CPU architectures.
Weighing the Pros and Cons of Engaging in Open Innovation

The advantages of open innovation projects are widely discussed in innovation management research and practice (e.g., Man & Duysters, 2005). Particularly, small and medium-sized enterprises (SMEs) are expected to gain most from open innovation collaborations due to their inherently limited capabilities (Lee et al., 2010; van de Vrande 2009). However, these enterprises also face manifold challenges in open innovation practice, leading to uncertainty and even renunciation of open innovation project participation. Thus, SMEs often deal with the decision dilemma of having to cooperate with external partners in order to improve their own innovation capacity, regardless of their ability to cope with the correlated risks. Although it is essential for SMEs to find the right balance between positive effects and possible negative consequences (i.e., the “dark sides” of open innovation, cf. Huizingh, 2011) of open innovation project participation, appropriate methods of finding this balance are still lacking. The research project “Open Darkness” was initiated with the goal of enabling SMEs to weigh the risks and benefits of open innovation participation by developing: i) a weighing and decision process framework and ii) a software tool that automatically applies this framework and allows self-assessment for SMEs. Both solutions aim to structure and support the decision process regarding potential engagement in open innovation projects. In order to tackle these targeted outcomes, an interdisciplinary consortium facilitates a multi-perspective and an integrated holistic research approach. Besides several SMEs, which function as requirement authority and implementer, the consortium consists of three German research institutions: the Chair of Economic Law (University of Paderborn), the Chair of Technology and Innovation Management (University of Aachen), and the Chair of Business Informatics (University of Potsdam).
Analytical Modeling of Human Choice Complexity in a Mixed Model Assembly Line Using Machine Learning-Based Human in the Loop Simulation

Despite the recent advances in manufacturing automation, the role of human involvement in manufacturing systems is still regarded as a key factor in maintaining higher adaptability and flexibility. In general, however, modeling of human operators in manufacturing system design still considers human as a physical resource represented in statistical terms. In this paper, we propose a human in the loop (HIL) approach to investigate the operator’s choice complexity in a mixed model assembly line. The HIL simulation allows humans to become a core component of the simulation, therefore influencing the outcome in a way that is often impossible to reproduce via traditional simulation methods. At the initial stage, we identify the significant features affecting the choice complexity. The selected features are in turn used to build a regression model, in which human reaction time with regard to different degree of choice complexity serves as a response variable used to train and test the model. The proposed method, along with an illustrative case study, not only serves as a tool to quantitatively assess and predict the impact of choice complexity on operator’s effectiveness, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.
Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform

The rise in popularity of permissioned blockchain platforms in recent time is significant. Hyperledger Fabric is one such permissioned blockchain platform and one of the Hyperledger projects hosted by the Linux Foundation. The Fabric comprises various components such as smart-contracts, endorsers, committers, validators, and orderers. As the performance of blockchain platform is a major concern for enterprise applications, in this work, we perform a comprehensive empirical study to characterize the performance of Hyperledger Fabric and identify potential performance bottlenecks to gain a better understanding of the system. We follow a two-phased approach. In the first phase, our goal is to understand the impact of various configuration parameters such as block size, endorsement policy, channels, resource allocation, state database choice on the transaction throughput & latency to provide various guidelines on configuring these parameters. In addition, we also aim to identify performance bottlenecks and hotspots. We observed that (1) endorsement policy verification, (2) sequential policy validation of transactions in a block, and (3) state validation and commit (with CouchDB) were the three major bottlenecks. In the second phase, we focus on optimizing Hyperledger Fabric v1.0 based on our observations. We introduced and studied various simple optimizations such as aggressive caching for endorsement policy verification in the cryptography component (3x improvement in the performance) and parallelizing endorsement policy verification (7x improvement). Further, we enhanced and measured the effect of an existing bulk read/write optimization for CouchDB during state validation & commit phase (2.5x improvement). By combining all three optimizations1, we improved the overall throughput by 16x (i.e., from 140 tps to 2250 tps).
A Taxonomy of Replay Attacks

This paper presents a taxonomy of replay attacks on cryptographic protocols in terms of message origin and destination. The taxonomy is independent of any method used to analyze or prevent such attacks. It is also complete in the sense that any replay attack is composed entirely of elements classi ed by the taxonomy. The classi cation of attacks is illustrated using both new and previously known attacks on protocols. The taxonomy is also used to discuss the appropriateness of particular countermeasures and protocol analysis methods to particular kinds of replays.
Examining the Effects of Deregulation on Retail Electricity Prices

A primary aim of deregulation is to reduce the customer cost of electricity. In this paper, we examine the degree of success in reaching that goal using a variety of methods. We examine rates for each of four customer classes; for regulated, deregulated and publicly owned utilities; and for three definitions of deregulation. We control for a variety of factors which may independently affect differences in electricity price: climate, fuel costs, and electricity generation by energy source. Taken as a whole, the results from our analysis do not support a conclusion that deregulation has led to lower electricity rates.  This paper was presented July 5, 2005 at the annual meeting of the Western Economic Association International at the session entitled “Power Market Restructuring: A Review” and September 20, 2005 at the annual North American Conference of the USAEE/IAEE at the session entitled “Electricity Market Structure, Conduct and Performance.”
Overview of Dual-Active-Bridge Isolated Bidirectional DC–DC Converter for High-Frequency-Link Power-Conversion System

High-frequency-link (HFL) power conversion systems (PCSs) are attracting more and more attentions in academia and industry for high power density, reduced weight, and low noise without compromising efficiency, cost, and reliability. In HFL PCSs, dual-active-bridge (DAB) isolated bidirectional dc-dc converter (IBDC) serves as the core circuit. This paper gives an overview of DAB-IBDC for HFL PCSs. First, the research necessity and development history are introduced. Second, the research subjects about basic characterization, control strategy, soft-switching solution and variant, as well as hardware design and optimization are reviewed and analyzed. On this basis, several typical application schemes of DAB-IBDC for HPL PCSs are presented in a worldwide scope. Finally, design recommendations and future trends are presented. As the core circuit of HFL PCSs, DAB-IBDC has wide prospects. The large-scale practical application of DAB-IBDC for HFL PCSs is expected with the recent advances in solid-state semiconductors, magnetic and capacitive materials, and microelectronic technologies.
Bilinear Attention Networks

Attention networks in multimodal learning provide an efficient way to utilize given visual information selectively. However, the computational cost to learn attention distributions for every pair of multimodal input channels is prohibitively expensive. To solve this problem, co-attention builds two separate attention distributions for each modality neglecting the interaction between multimodal inputs. In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly. BAN considers bilinear interactions among two groups of input channels, while low-rank bilinear pooling extracts the joint representations for each pair of channels. Furthermore, we propose a variant of multimodal residual networks to exploit eight-attention maps of the BAN efficiently. We quantitatively and qualitatively evaluate our model on visual question answering (VQA 2.0) and Flickr30k Entities datasets, showing that BAN significantly outperforms previous methods and achieves new state-of-the-arts on both datasets.
Energy Efficient Utilization of Resources in Cloud Computing Systems

-Abstract the energy consumption of under-utilized resources, particularly in a cloud environment, accounts for a substantial amount of the actual energy use. Inherently, a resource allocation strategy that takes into account resource utilization would lead to better energy efficiency; this, in clouds, extends further with virtualization technologies in that tasks can be easily consolidated. Task consolidation is an effective method to increase resource utilization and in turn reduces energy consumption. Recent studies identified that server energy consumption scales linearly with (processor) resource utilization. This encouraging fact further highlights the significant contribution of task consolidation to the reduction in energy consumption. However, task consolidation can also lead to the freeing up of resources that can sit idling yet still drawing power. There have been some notable efforts to reduce idle power draw, typically by putting computer resources into some form of sleep/power-saving mode. In this paper, we present two energy-conscious task consolidation heuristics, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption. Our heuristics assign each task to the resource on which the energy consumption for executing the task is explicitly or implicitly minimized without the performance degradation of that task. Based on our experimental results, our heuristics demonstrate their promising energy-saving capability.
A framework of supply chain management literature

Over the past decade, the traditional purchasing and logistics functions have evolved into a broader strategic approach to materials and distribution management known as supply chain management. This research reviews the literature base and development of supply chain management from two separate paths that eventually merged into the modern era of a holistic and strategic approach to operations, materials and logistics management. In addition, this article attempts to clearly describe supply chain management since the literature is replete with buzzwords that address elements or stages of this new management philosophy. This article also discusses various supply chain management strategies and the conditions conducive to supply chain management.
Relationship of Transformational and Transactional Leadership Style with Job Satisfaction

This paper attempts to review the important concepts of human resource management (HRM),so at first it will discuss about the necessity of job satisfaction. Then this review study will present different types of leadership style (both transaction and transformational), in o order to justify the relationship between leadership style and job satisfaction. At the end, this study proposed one framework based on the different components of transformational and transactional leadership style.
Degradable Biomaterials based on Magnesium Corrosion

Biodegradable metals are breaking the current paradigm in biomaterial science to develop only corrosion resistant metals. In particular, metals which consist of trace elements existing in the human body are promising candidates for temporary implant materials. These implants would be temporarily needed to provide mechanical support during the healing process of the injured or pathological tissue. Magnesium and its alloys have been investigated recently by many authors as a suitable biodegradable biomaterial. In this investigative review we would like to summarize the latest achievements and comment on the selection and use, test methods and the approaches to develop and produce magnesium alloys that are intended to perform clinically with an appropriate host response.
Extractive Summarization Using Multi-Task Learning with Document Classification

The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multitask learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to stateof-the-art systems.
The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz.

Three experimental techniques based on automatic swept-frequency network and impedance analysers were used to measure the dielectric properties of tissue in the frequency range 10 Hz to 20 GHz. The technique used in conjunction with the impedance analyser is described. Results are given for a number of human and animal tissues, at body temperature, across the frequency range, demonstrating that good agreement was achieved between measurements using the three pieces of equipment. Moreover, the measured values fall well within the body of corresponding literature data.
The financial market impact of UK quantitative easing 1

We measure the impact of the UK's initial 2009–10 Quantitative Easing (QE) Programme on bonds and other assets. First, we use a macro-finance yield curve both to create a counterfactual path for bond yields and to estimate the impact of QE directly. Second, we analyse the impact of individual QE operations on a range of asset prices. We find that QE significantly lowered government bond yields through the portfolio balance channel – by around 50 to 100 basis points. We also uncover significant effects of individual operations but limited pass through to other assets.
CHALET: Cornell House Agent Learning Environment

We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.
A taxonomy and catalog of runtime software-fault monitoring tools

A goal of runtime software-fault monitoring is to observe software behavior to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing additional defense against catastrophic failure. Although runtime monitoring has been in use for over 30 years, there is renewed interest in its application to fault detection and recovery, largely because of the increasing complexity and ubiquitous nature of software systems. We present taxonomy that developers and researchers can use to analyze and differentiate recent developments in runtime software fault-monitoring approaches. The taxonomy categorizes the various runtime monitoring research by classifying the elements that are considered essential for building a monitoring system, i.e., the specification language used to define properties; the monitoring mechanism that oversees the program's execution; and the event handler that captures and communicates monitoring results. After describing the taxonomy, the paper presents the classification of the software-fault monitoring systems described in the literature.
A Survey on Optical Character Recognition Techniques

This paper presents a review on Optical Character Recognition Techniques. Optical Character recognition (OCR) is a technology that allows machines to automatically recognize the characters through an optical mechanism. OCR can be described as Mechanical or electronic conversion of scanned images where images can be handwritten, typewritten or printed text. It converts the images into machine-encoded text that can be used in machine translation, text-to-speech and text mining. Various techniques are available for character recognition in optical character recognition system. This material can be useful for the researchers who wish to work in character recognition area.