nils-herrmann/clean_scirepeval huggingface dataset:
Increased block size and Bitcoin blockchain dynamics

Bitcoin is a peer to peer electronic payment system where payment transactions are stored in a data structure named the blockchain which is maintained by a community of participants. The Bitcoin Core protocol limits blocks to 1 MB in size. Each block contains at most some 4,000 transactions. Blocks are added to the blockchain on average every 10 minutes, therefore the transaction rate is limited to some 7 transactions per second (TPS). This is much less than the transaction rate offered by competing financial transaction processing systems. The Bitcoin TPS can be increased by increasing the block size and/or by decreasing the block discovery interval. Both of these interventions will increase the end-to-end block transmission delay, which in turn will increase the probability that different participants momentarily record different versions of the blockchain, so that the consensus protocol will discard an increasing number of blocks. The net effect is that the real increase in the TPS is not proportional to the increase (decrease) in the block size (block discovery rate). Our simulation experiments show that large block sizes, if accompanied by large end-to-end block transmission delays, give rise to the frequent appearance of inconsistent blockchain copies, to the detriment of the TPS. We present a simulation analysis of Bitcoin-Next Generation where blocks (keyblocks) stripped of transactions propagate rapidly through the peer-to-peer network. Once a keyblock is mined, only the miner of the keyblock is entitled to broadcast small microblocks of transactions until the next keyblock is mined and another miner is selected to broadcast microblocks. Initial simulation experiments show that Bitcoin-NG can sustain substantially larger transaction rates than Bitcoin Core.
An improvement on the Euler number computing algorithm used in MATLAB

Computation of the Euler number of a binary image is often necessary in image matching, image database retrieval, image analysis, pattern recognition, and computer vision. This paper proposes an improvement on the Euler number computing algorithm used in the famous image processing tool MATLAB. By use of the information obtained during processing the previous pixel, the number of times of checking the neighbor pixels for processing a pixel decrease from 4 to 2. Our method is very simple in principle, and easily implemented. The experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms.
RELIABLE ISLANDING DETECTION WITH ACTIVE MV NETWORK MANAGEMENT

Active management of distribution networks and different controllable resources will play a key role in future Smart Grids. This paper proposes that centralized active network management functionalities at MV level could also include an algorithm which in real time confirms reliable islanding detection with local measurements based passive method. The proposed algorithm continuously ensures that there is such reactive power unbalance, so that the operation point remains constantly outside the non-detection zone of the used passive islanding detection method. The effect of recent grid code requirements on the proposed scheme, like the P/f -droop control of DG units, has been also considered.
Designing wireless transceiver blocks for LoRa application

The Internet of Things enables connected objects to communicate over distant fields, and trade-offs are often observed with the battery life and range. Long Range Radio, or LoRa, provides transmission for up to 15 km with impressive battery life, while sacrificing throughput. In order to design the LoRa system, there is a need to design and implement the LoRa transceiver. 1 V, 900 MHz, LoRa-capable transceiver blocks were designed in 65 nm process. The receiver front end consists of the narrowband low noise amplifier, common gate-common source balun, and double balanced mixer. The power consumption of this system is 2.63 mW. The overall gain of the receiver front end is approximately 51 dB. The transmitter implemented in this project utilizes a class-D amplifier alongside an inverter chain driver stage and a transformer based power combiner. The transmitter outputs 21.57 dBm of power from the given supply at 900 MHz, operating at 21% drain efficiency.
A multiperiod set covering location model for dynamic redeployment of ambulances

Emergency medical service (EMS) providers continually seek ways to improve system performance particularly the response time to incidents. The demand for ambulances fluctuate throughout the week, depending on the day of week, and even the time of day, therefore EMS operators can improve system performance by dynamic relocation/redeployment of ambulances in response to fluctuating demand patters. The objective of the model is to determine the minimum number of ambulances and their locations for each time cluster in which significant changes in demand pattern occur while meeting coverage requirement with a predetermined reliability. The model is further enhanced by calculating ambulance specific busy probabilities and validated by a comprehensive simulation model. Computational results on experimental data sets and data from an EMS agency are provided.
Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical representations without any label information. The network, optimized by layer-wise training, is constructed by stacking layers of denoising auto-encoders in a convolutional way. In each layer, high dimensional feature maps are generated by convolving features of the lower layer with kernels learned by a denoising auto-encoder. The auto-encoder is trained on patches extracted from feature maps in the lower layer to learn robust feature detectors. To better train the large network, a layer-wise whitening technique is introduced into the model. Before each convolutional layer, a whitening layer is embedded to sphere the input data. By layers of mapping, raw images are transformed into high-level feature representations which would boost the performance of the subsequent support vector machine classifier. The proposed algorithm is evaluated by extensive experimentations and demonstrates superior classification performance to state-of-the-art unsupervised networks.
FOUNDATIONS OF THE PARAFAC PROCEDURE: MODELS AND CONDITIONS FOR AN "EXPLANATORY" MULTIMODAL FACTOR ANALYSIS

Simple structure and other common principles of factor rotation do not in general provide strong grounds for attributing explanatory significance to the factors which they select. In contrast, it is shown that an extension of Cattell's principle of rotation to Proportional Profiles (PP) offers a basis for determining explanatory factors for three-way or higher order multi-mode data. Conceptual models are developed for two basic patterns of multi-mode data variation, systemand object-variation, and PP analysis is found to apply in the system-variation case. Although PP was originally formulated as a principle of rotation to be used with classic two-way factor analysis, it is shown to embody a latent three-mode factor model, which is here made explicit and generalized frown two to N "parallel occasions". As originally formulated, PP rotation was restricted to orthogonal factors. The generalized PP model is demonstrated to give unique "correct" solutions with oblique, non-simple structure, and even non-linear factor structures. A series of tests, conducted with synthetic data of known factor composition, demonstrate the capabilities of linear and non-linear versions of the model, provide data on the minimal necessary conditions of uniqueness, and reveal the properties of the analysis procedures when these minimal conditions are not fulfilled. In addition, a mathematical proof is presented for the uniqueness of the solution given certain conditions on the data. Three-mode PP factor analysis is applied to a three-way set of real data consisting of the fundamental and first three formant frequencies of 11 persons saying 8 vowels. A unique solution is extracted, consisting of three factors which are highly meaningful and consistent with prior knowledge and theory concerning vowel quality. The relationships between the three-mode PP model and Tucker's multi-modal model, McDonald's non-linear model and Carroll and Chang's multi-dimensional scaling model are explored.
Compact Antenna With U-Shaped Open-End Slot Structure for Multi-Band Handset Applications

This communication proposes a compact U-shaped slot antenna with a coupling feed for 4G handset applications. The antenna is composed of a simple open-end U-shaped slot and a feeding line with an antenna area of 58 mm × 12 mm. Two types of resonant modes are excited, including slot mode and monopole mode. Parametric studies on the slot geometry have been demonstrated. By controlling the related parameters, the bandwidth of this antenna can potentially cover the mobile bands of LTE Band 12 (698-742 MHz)/DCS (1710-1880 MHz)/PCS (1850-1990 MHz)/UMTS (1920-2170 MHz)/LTE Band 40 (2300-2400 MHz)/Band 41 (2496-2690 MHz)/Band 42 (3400-3600 MHz)/Band 43 (3600-3800 MHz). Good radiation characteristics, like gain and radiation efficiency are obtained over these operating bands. This antenna is suitable for the metal casing of handheld devices.
DIZK: A Distributed Zero Knowledge Proof System

Recently there has been much academic and industrial interest in practical implementations of zero knowledge proofs. These techniques allow a party to prove to another party that a given statement is true without revealing any additional information. In a Bitcoin-like system, this allows a payer to prove validity of a payment without disclosing the payment’s details. Unfortunately, the existing systems for generating such proofs are very expensive, especially in terms of memory overhead. Worse yet, these systems are “monolithic”, so they are limited by the memory resources of a single machine. This severely limits their practical applicability. We describe DIZK, a system that distributes the generation of a zero knowledge proof across machines in a compute cluster. Using a set of new techniques, we show that DIZK scales to computations of up to billions of logical gates (100× larger than prior art) at a cost of 10 μs per gate (100× faster than prior art). We then use DIZK to study various security applications.
Subcortical and cortical brain activity during the feeling of self-generated emotions

In a series of [15O]PET experiments aimed at investigating the neural basis of emotion and feeling, 41 normal subjects recalled and re-experienced personal life episodes marked by sadness, happiness, anger or fear. We tested the hypothesis that the process of feeling emotions requires the participation of brain regions, such as the somatosensory cortices and the upper brainstem nuclei, that are involved in the mapping and/or regulation of internal organism states. Such areas were indeed engaged, underscoring the close relationship between emotion and homeostasis. The findings also lend support to the idea that the subjective process of feeling emotions is partly grounded in dynamic neural maps, which represent several aspects of the organism's continuously changing internal state.
Definition and Multidimensionality of Security Awareness: Close Encounters of the Second Order

This study proposes and examines a multidimensional definition of information security awareness. We also investigate its antecedents and analyze its effects on compliance with organizational information security policies. The above research goals are tested through the theoretical lens of technology threat avoidance theory and protection motivation theory. Information security awareness is defined as a second-order construct composed of the elements of threat and coping appraisals supplemented by the responsibilities construct to account for organizational environment. The study was executed in two stages. First, the participants (employees of a municipality) were exposed to a series of phishing messages. Second, the same individuals were asked to participate in a survey designed to examine their security awareness. The research model was tested using PLS-SEM approach. The results indicate that security awareness is in fact a second-order formative construct composed of six components. There are significant differences in security awareness levels between the victims of the phishing experiment and the employees who maintain compliance with security policies. Our study extends the theory by proposing and validating a general, yet practical definition of security awareness. It also bridges the gap between theory and practice - our contextualization of security awareness draws heavily on both fields.
Multipath TCP path schedulers for streaming video

Modern internet traffic is heavily composed of some form of video communication. In addition, video streaming is slowly replacing regular phone connections. Given that most video transport utilizes Video over Hypertext Transfer Protocol/Transmission Control Protocol (HTTP/TCP), it is important to understand TCP performance in transporting video streams. Recently, multipath transport protocols have allowed video streaming over multiple paths to become a reality. In this paper, we investigate packet scheduling disciplines for injecting video stream packets into multiple paths at the video server. We study video streaming performance when subjected to these schedulers in conjunction with various TCP variants. We utilize network performance measures, as well as video quality metrics, to characterize the performance and interactions between network and application layers of video streams for various network scenarios.
Optimization of the internal grid of an offshore wind farm using Genetic algorithm

Offshore wind energy is a promising solution thanks to its best performance of production. However its development leads to many technical and especially economic challenges among them the electrical grid topology attracts a large investment. In this paper, our objective is to minimize a part of this total investment which represents the initial cost of the middle voltage cables in the internal network with different cross sections. An approach based on Genetic Algorithm is developed to find the best topology to connect all wind turbines and substations. The proposed model initially accepts all possible configurations: radial, star, ring, and tree. The results prove that the optimization model can be used for designing the electrical architecture of the internal network of an offshore wind farm.
Knowledge management technologies and applications - literature review from 1995 to 2002

This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period. Based on the scope of 234 articles of knowledge management applications, this paper surveys and classifies KM technologies using the seven categories as: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling, together with their applications for different research and problem domains. Some discussion is presented, indicating future development for knowledge management technologies and applications as the followings: (1) KM technologies tend to develop towards expert orientation, and KM applications development is a problem-oriented domain. (2) Different social studies methodologies, such as statistical method, are suggested to implement in KM as another kind of technology. (3) Integration of qualitative and quantitative methods, and integration of KM technologies studies may broaden our horizon on this subject. (4) The ability to continually change and obtain new understanding is the power of KM technologies and will be the application of future works. q 2003
Mining Social Media for Newsgathering

Social media is becoming an increasingly important data source for learning about and tracking breaking news. This is possible thanks to mobile devices connected to the Internet, which allow anyone to post updates from anywhere, leading in turn to a growing presence of citizen journalism. Consequently, social media has become a go-to resource for journalists during newsgathering. Use of social media for newsgathering is however challenging, and suitable tools are needed in order to facilitate access to useful information for reporting. In this paper, we provide an overview of research in data mining and natural language processing for mining social media for newsgathering. We discuss seven different tasks that researchers have worked on to mitigate the challenges inherent to social media newsgathering: event detection, summarisation, news recommenders, content verification, finding information sources, development of newsgathering dashboards and other tasks. We outline the progress made so far in the field, summarise the current challenges as well as discuss future directions in the use of computational journalism to assist with social media newsgathering. This survey paper is relevant to computer scientists researching news in social media as well as for interdisciplinary researchers interested in the intersection of computer science and journalism.
Constraint-Based Design-Space Exploration and Model Synthesis

An important bottleneck in model-based design of embedded systems is the cost of constructing models. This cost can be significantly decreased by increasing the reuse of existing model components in the design process. This paper describes a tool suite, which has been developed for component-based model synthesis. The DESERT tool suite can be interfaced to existing modeling and analysis environments and can be inserted in various, domain specific design flows. The modeling component of DESERT supports the modeling of design spaces and the automated search for designs that meet structural requirements. DESERT has been introduced in automotive applications and proved to be useful in increasing design productivity.
Dual-Band Filter Design With Flexible Passband Frequency and Bandwidth Selections

In this paper, improved dual-band filter design is studied. The dual-band resonators are composed of shunt open- and short-circuited stubs. In order to fulfil the requirements of dual-band inverters, a structure of stepped-impedance asymmetric coupled lines is proposed and its equivalent circuit is also derived. The dual-band filter is then designed based on this equivalent circuit. This type of filter can achieve relatively large practical passband center frequency ratios (in theory infinite), and it has more freedom of bandwidth ratio. The circuit size is also reduced. Detailed design procedure is presented and, finally, a filter example is given to validate the theoretical study
Gravitational Search Algorithm and Recent Trends

A R T I C L E I N F O A B S T R A C T Article history: Received 20 March 2017 Received in revised form 22 March 2017  The inability of conventional optimization algorithms to handle exponential increase in the size of the search space has paved way towards capturing the behavior of natural organisms for solving optimization problems.  Inspired Algorithms have mostly been associated with the swarm behavior of organisms. A rather new idea in this respect was proposed by Rashedi et al in 2009 by the name ‘Gravitational Search Algorithm’. The basis of their proposal, as clear from the name, is the Newtonian gravity and interactions between populations of masses. The algorithm has been widely used since then in many applications of science. Since its proposal, researches have been continuously headed in extending GSA for different scenarios and applications. The working of GSA and some such popular and recent proposals in the direction of extending GSA are discussed in this paper.
Postmenopausal frontal fibrosing alopecia: a frontal variant of lichen planopilaris.

Lichen planopilaris usually produces multifocal areas of scarring alopecia. Recently, a condition in postmenopausal women characterized by progressive frontal hairline recession associated with scarring has been described.



Our purpose was to study the clinical and histopathologic features and results of treatment in a group of women with the frontal variant of lichen planopilaris and to compare the immunohistochemical profile of scalp biopsy specimens from this subset with that found in the multifocal variant of lichen planopilaris.



The clinical data as well as the histopathologic findings in 16 women with frontal fibrosing alopecia were collated. The immunohistochemical profile of six scalp biopsy specimens from the frontal hairline were compared with six specimens from women with multifocal lichen planopilaris.



In addition to the progressive frontal fibrosing alopecia in all 16 women, total loss or a marked decrease of the eyebrows was observed in 13. No evidence of lichen planus was observed at other sites. In one patient multifocal areas of lichen planopilaris developed in the scalp. The frontal fibrosing alopecia was slowly progressive but has stabilized in five patients. Biopsy specimens from the frontal hairline showed histologic changes identical to lichen planopilaris. Immunophenotyping failed to reveal any significant differences between the frontal and multifocal variants. No effective treatments emerged although oral steroids and antimalarials may temporarily slow the course. Hormone replacement therapy did not appear to influence the course of the alopecia.



Progressive frontal fibrosing alopecia is a clinically distinct variant of lichen planopilaris that affects in particular elderly women and frequently involves the eyebrows. The basis for this lichenoid tissue reaction targeting frontal scalp follicles and eyebrows is unknown.
Bias in Wikipedia

While studies have shown that Wikipedia articles exhibit quality that is comparable to conventional encyclopedias, research still proves that Wikipedia, overall, is prone to many different types of Neutral Point of View (NPOV) violations that are explicitly or implicitly caused by bias from its editors. Related work focuses on political, cultural and gender bias. We are developing an approach for detecting both explicit and implicit bias in Wikipedia articles and observing its evolution over time. Our approach is based on different factors of bias, with the most important ones being language style, editors, and citations. In this paper we present the approach, methodology and a first analysis.
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!

Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at http://github.com/UKPLab/ coling2018-xling_argument_mining.