nils-herrmann/clean_scirepeval huggingface dataset:
A Survey of Blockchain: Techniques, Applications, and Challenges

Blockchain, as a mechanism to decentralize services, security, and verifiability, offers a peer-to-peer system in which distributed nodes collaboratively affirm transaction provenance. In particular, blockchain enforces continuous storage of transaction history, secured via digital signature, and affirmed through consensus. In this study, we consider the recent surge in blockchain interest as an alternative to traditional centralized systems, and consider the emerging applications thereof. In particular, we assess the key techniques required for blockchain implementation, offering a primer to guide research practitioners. We first outline the blockchain framework in general, and then provide a detailed review of the component data and network structures. Additionally, we consider the breadth of applications to which blockchain has been applied, broadly implicating Internet of Things (IoT), Big Data, and Cloud and Edge computing paradigms, along with many other emerging applications. Finally, we assess the various challenges to blockchain implementation for widespread practical use, considering the security vulnerabilities to majority attacks, selfish mining, and privacy leakage, as well as performance limitations of blockchain platforms in terms of scalability and availability.
TripletGAN: Training Generative Model with Triplet Loss

As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts. The main innovation of triplet loss is using feature map to replace softmax in the classification task. Inspired by this concept, we propose here a new adversarial modeling method by substituting the classification loss of discriminator to triplet loss. Theoretical proof based on IPM (Integral probability metric) demonstrates that such setting will help generator converge to the given distribution theoretically under some conditions. Moreover, since triplet loss requires the generator to maximize distance within a class, we justify tripletGAN is also helpful to prevent mode collapse through both theory and experiment.
Bimodal Distribution and Co-Bursting in Review Spam Detection

Online reviews play a crucial role in helping consumers evaluate and compare products and services. This critical importance of reviews also incentivizes fraudsters (or spammers) to write fake or spam reviews to secretly promote or demote some target products and services. Existing approaches to detecting spam reviews and reviewers employed review contents, reviewer behaviors, star rating patterns, and reviewer-product networks for detection. In this research, we further discovered that reviewers’ posting rates (number of reviews written in a period of time) also follow an interesting distribution pattern, which has not been reported before. That is, their posting rates are bimodal. Multiple spammers also tend to collectively and actively post reviews to the same set of products within a short time frame, which we call co-bursting. Furthermore, we found some other interesting patterns in individual reviewers’ temporal dynamics and their co-bursting behaviors with other reviewers. Inspired by these findings, we first propose a two-mode Labeled Hidden Markov Model to model spamming using only individual reviewers’ review posting times. We then extend it to the Coupled Hidden Markov Model to capture both reviewer posting behaviors and co-bursting signals. Our experiments show that the proposed model significantly outperforms state-of-the-art baselines in identifying individual spammers. Furthermore, we propose a cobursting network based on co-bursting relations, which helps detect groups of spammers more effectively than existing approaches.
Classification of Time-Series Images Using Deep Convolutional Neural Networks

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.
Set-based Similarity Search for Time Series

A fundamental problem of time series is k nearest neighbor (k-NN) query processing. However, existing methods are not fast enough for large dataset. In this paper, we propose a novel approach, STS3, to process k-NN queries by transforming time series to sets and measure the similarity under Jaccard metric. Our approach is more accurate than Dynamic Time Warping(DTW) in our suitable scenarios and it is faster than most of the existing methods, due to the efficient similarity search for sets. Besides, we also developed an index, a pruning and an approximation technique to improve the k-NN query procedure. As shown in the experimental results, all of them could accelerate the query processing effectively.
Segregating Spammers and Unsolicited Bloggers from Genuine Experts on Twitter

Online Social Networks (OSNs) have not only significantly reformed the social interaction pattern but have also emerged as an effective platform for recommendation of services and products. The upswing in use of the OSNs has also witnessed growth in unwanted activities on social media. On the one hand, the spammers on social media can be a high risk towards the security of legitimate users and on the other hand some of the legitimate users, such as bloggers can pollute the results of recommendation systems that work alongside the OSNs. The polluted results of recommendation systems can be precarious to the masses that track recommendations. Therefore, it is necessary to segregate such type of users from the genuine experts. We propose a framework that separates the spammers and unsolicited bloggers from the genuine experts of a specific domain. The proposed approach employs modified Hyperlink Induced Topic Search (HITS) to separate the unsolicited bloggers from the experts on Twitter on the basis of tweets. The approach considers domain specific keywords in the tweets and several tweet characteristics to identify the unsolicited bloggers. Experimental results demonstrate the effectiveness of the proposed methodology as compared to several state-of-the-art approaches and classifiers.
Towards social user profiling: unified and discriminative influence model for inferring home locations

Users' locations are important to many applications such as targeted advertisement and news recommendation. In this paper, we focus on the problem of profiling users' home locations in the context of social network (Twitter). The problem is nontrivial, because signals, which may help to identify a user's location, are scarce and noisy. We propose a unified discriminative influence model, named as UDI, to solve the problem. To overcome the challenge of scarce signals, UDI integrates signals observed from both social network (friends) and user-centric data (tweets) in a unified probabilistic framework. To overcome the challenge of noisy signals, UDI captures how likely a user connects to a signal with respect to 1) the distance between the user and the signal, and 2) the influence scope of the signal. Based on the model, we develop local and global location prediction methods. The experiments on a large scale data set show that our methods improve the state-of-the-art methods by 13%, and achieve the best performance.
Incentivizing Blockchain Forks via Whale Transactions

Bitcoin’s core innovation is its solution to double-spending, called Nakamoto consensus. This mechanism provides a probabilistic guarantee that transactions will not be reversed once they are sufficiently deep in the blockchain, assuming an attacker controls a bounded fraction of mining power in the network. We show, however, that when miners are rational this guarantee can be undermined by a whale attack in which an attacker issues an off-theblockchain whale transaction with an anomalously large transaction fee in an effort to convince miners to fork the current chain. We carry out a game-theoretic analysis and simulation of this attack, and show conditions under which it yields an expected positive payoff for the attacker.
Simulation of Network Attacks on SCADA Systems

Network security is a major issue affecting SCADA systems designed and deployed in the last decade. Simulation of network attacks on a SCADA system presents certain challenges, since even a simple SCADA system is composed of models in several domains and simulation environments. Here we demonstrate the use of C2WindTunnel to simulate a plant and its controller, and the Ethernet network that connects them, in different simulation environments. We also simulate DDOS-like attacks on a few of the routers to observe and analyze the effec ts of a network attack on such a system. I. I NTRODUCTION Supervisory Control And Data Acquisition (SCADA) systems are computer-based monitoring tools that are used to manage and control critical infrastructure functions in re al time, like gas utilities, power plants, chemical plants, tr affic control systems, etc. A typical SCADA system consists of a SCADA Master which provides overall monitoring and control for the system, local process controllers called Re mot Terminal Units (RTUs), sensors and actuators and a network which provides the communication between the Master and the RTUs. A. Security of SCADA Systems SCADA systems are designed to have long life spans, usually in decades. The SCADA systems currently installed and used were designed at a time when security issues were not paramount, which is not the case today. Furthermore, SCADA systems are now connected to the Internet for remote monitoring and control making the systems susceptible to network security problems which arise through a connection to a public network. Despite these evident security risks, SCADA systems are cumbersome to upgrade for several reasons. Firstly, adding security features often implies a large downtime, which is not desirable in systems like power plants and traffic contro l. Secondly, SCADA devices with embedded codes would need to be completely replaced to add new security protocols. Lastly, the networks used in a SCADA system are usually customized for that system and cannot be generalized. Security of legacy SCADA systems and design of future systems both thus rely heavily on the assessment and rectification of security vulnerabilities of SCADA implementatio ns in realistic settings. B. Simulation of SCADA Systems In a SCADA system it is essential to model and simulate communication networks in order to study mission critical situations such as network failures or attacks. Even a simpl e SCADA system is composed of several units in various domains like dynamic systems, networks and physical environments, and each of these units can be modeled using a variety of available simulators and/or emulators. An example system could include simulating controller and plant dynamics in Simulink or Matlab, network architecture and behavior in a network simulator like OMNeT++, etc. An adequate simulation of such a system necessitates the use of an underlying software infrastructure that connects and re lates the heterogeneous simulators in a logically and temporally coherent framework.
1 TOWARDS OPTIMAL DESIGN OF SHIP HULL SHAPES

The recent increase in information technologies dedicated to optimal design, associated with the progress of the numerical tools for predicting ship hydrodynamic performances, allows significant improvement in ship design. A consortium of fourteen European partners – bringing together ship yards, model basins, consultants, research centres and universities – has therefore conducted a three years European R&D project (FANTASTIC) with the goal to improve the functional design of ship hull shapes. The following key issues were thus considered: parametric shape modelling was worked on through several complementary approaches, CFD tools and associated interfaces were enhanced to meet efficiency and robustness requirements, appropriate design space exploration and optimisation techniques were investigated. The resulting procedures where then implemented, for practical assessment purposes, in some end-users design environments, and a number of applications were undertaken.. Significant gains can be expected from this approach in design, in term of time used for performance analysis and explored range of design variations.
Modeling Under-dispersed Count Data Using Generalized Poisson Regression Approach

This paper models household fertility decisions by using a generalized Poisson regression model. Since the fertility data used in the paper exhibit under-dispersion, the generalized Poisson regression model has statistical advantages over both standard Poisson regression and negative binomial regression models, and is suitable for analysis of count data that exhibit either over-dispersion or under-dispersion. The model is estimated by maximum likelihood procedure. Approximate tests for the dispersion and goodness-of-fit measures for comparing alternative models are discussed. Based on observations from the Bangladesh Demographic Health Survey 2011, the empirical results support the generalized Poisson regression to model the under dispersed data.
Pixelwise Instance Segmentation with a Dynamically Instantiated Network

Semantic segmentation and object detection research have recently achieved rapid progress. However, the former task has no notion of different instances of the same object, and the latter operates at a coarse, bounding-box level. We propose an Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label. Most approaches adapt object detectors to produce segments instead of boxes. In contrast, our method is based on an initial semantic segmentation module, which feeds into an instance subnetwork. This subnetwork uses the initial category-level segmentation, along with cues from the output of an object detector, within an end-to-end CRF to predict instances. This part of our model is dynamically instantiated to produce a variable number of instances per image. Our end-to-end approach requires no post-processing and considers the image holistically, instead of processing independent proposals. Therefore, unlike some related work, a pixel cannot belong to multiple instances. Furthermore, far more precise segmentations are achieved, as shown by our substantial improvements at high APr thresholds.
Chapter 4. High Frequency Llc Resonant Converter 4.1 Introduction Figure 4-1. 1200w F Ront-end Ac/ Dc Converter

With the development of power conversion technology, power density becomes the major challenge for front-end AC/DC converters. Although increasing switching frequency can dramatically reduce the passive component size, its effectiveness is limited by the converter efficiency and thermal management. In present industry implementations, due to the low efficiency PWM type topologies, DC/DC stage switching frequency is limited to 100 kHz range. Therefore, passive components take large portion of converter volume. As shown in Figure 4-1, DC/DC stage transformer and inductor take more than 30% for total converter space.
The WEKA data mining software: an update

More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Subjectivity and Sentiment Analysis of Arabic: A Survey

Subjectivity and sentiment analysis (SSA) has recently gained considerable attention, but most of the resources and systems built so far are tailored to English and other Indo-European languages. The need for designing systems for other languages is increasing, especially as blogging and micro-blogging websites become popular throughout the world. This paper surveys different techniques for SSA for Arabic. After a brief synopsis about Arabic, we describe the main existing techniques and test corpora for Arabic SSA that have been introduced in the literature.
Very deep convolutional neural networks for LVCSR

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance when embedded in large vocabulary continuous speech recognition (LVCSR) systems due to its capability of modeling local correlations and reducing translational variations. In all previous related works for ASR, only up to two convolutional layers are employed. In light of the recent success of very deep CNNs in image classification, it is of interest to investigate the deep structure of CNNs for speech recognition in detail. In contrast to image classification, the dimensionality of the speech feature, the span size of input feature and the relationship between temporal and spectral domain are new factors to consider while designing very deep CNNs. In this work, very deep CNNs are introduced for LVCSR task, by extending depth of convolutional layers up to ten. The contribution of this work is two-fold: performance improvement of very deep CNNs is investigated under different configurations; further, a better way to perform convolution operations on temporal dimension is proposed. Experiments showed that very deep CNNs offer a 8-12% relative improvement over baseline DNN system, and a 4-7% relative improvement over baseline CNN system, evaluated on both a 15-hr Callhome and a 51-hr Switchboard LVCSR tasks.
Effect of plantar intrinsic muscle training on medial longitudinal arch morphology and dynamic function.

A specific training program emphasizing the neuromuscular recruitment of the plantar intrinsic foot muscles, colloquially referred to as "short foot" exercise (SFE) training, has been suggested as a means to dynamically support the medial longitudinal arch (MLA) during functional tasks. A single-group repeated measures pre- and post-intervention study design was utilized to determine if a 4-week intrinsic foot muscle training program would impact the amount of navicular drop (ND), increase the arch height index (AHI), improve performance during a unilateral functional reaching maneuver, or the qualitative assessment of the ability to hold the arch position in single limb stance position in an asymptomatic cohort. 21 asymptomatic subjects (42 feet) completed the 4-week SFE training program. Subject ND decreased by a mean of 1.8 mm at 4 weeks and 2.2 mm at 8 weeks (p < 0.05). AHI increased from 28 to 29% (p < 0.05). Intrinsic foot muscle performance during a static unilateral balancing activity improved from a grade of fair to good (p < 0.001) and subjects experienced a significant improvement during a functional balance and reach task in all directions with the exception of an anterior reach (p < 0.05). This study offers preliminary evidence to suggest that SFE training may have value in statically and dynamically supporting the MLA. Further research regarding the value of this exercise intervention in foot posture type or pathology specific patient populations is warranted.
DISPERSION MODELING IN WAVEGUIDE PIANO SYNTHESIS USING TUNABLE ALLPASS FILTERS

This paper extends a previously proposed method for designing filters simulating the dispersion phenomenon occurring in string instruments. In digital waveguide synthesis, the phenomenon is traditionally modeled by inserting an allpass filter to the string model feedback loop. In this paper, the concept of tunable dispersion filter design, which provides a closed-form formula to design a dispersion filter, is applied to a cascade of first-order allpass filters. Moreover, the method is extended to design a filter cascade including an arbitrary number of first-order filters. In addition, it is shown how the designed dispersion filter can be used in a waveguide piano synthesis model.
Developmental dyslexia and specific language impairment: same or different?

Developmental dyslexia and specific language impairment (SLI) were for many years treated as distinct disorders but are now often regarded as different manifestations of the same underlying problem, differing only in severity or developmental stage. The merging of these categories has been motivated by the reconceptualization of dyslexia as a language disorder in which phonological processing is deficient. The authors argue that this focus underestimates the independent influence of semantic and syntactic deficits, which are widespread in SLI and which affect reading comprehension and impair attainment of fluent reading in adolescence. The authors suggest that 2 dimensions of impairment are needed to conceptualize the relationship between these disorders and to capture phenotypic features that are important for identifying neurobiologically and etiologically coherent subgroups.
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study

Several recent papers investigate Active Learning (AL) for mitigating the datadependence of deep learning for natural language processing. However, the applicability of AL to real-world problems remains an open question. While in supervised learning, practitioners can try many different methods, evaluating each against a validation set before selecting a model, AL affords no such luxury. Over the course of one AL run, an agent annotates its dataset exhausting its labeling budget. Thus, given a new task, an active learner has no opportunity to compare models and acquisition functions. This paper provides a largescale empirical study of deep active learning, addressing multiple tasks and, for each, multiple datasets, multiple models, and a full suite of acquisition functions. We find that across all settings, Bayesian active learning by disagreement, using uncertainty estimates provided either by Dropout or Bayes-by-Backprop significantly improves over i.i.d. baselines and usually outperforms classic uncertainty sampling.
Processes and patterns in transitions and system innovations : Refining the coevolutionary multi-level perspective

This article investigates transitions at the level of societal functions (e.g., transport, communication, housing). Societal functions are fulfilled by sociotechnical systems, which consist of a cluster of aligned elements, e.g., artifacts, knowledge, markets, regulation, cultural meaning, infrastructure, maintenance networks and supply networks. Transitions are conceptualised as system innovations, i.e., a change from one sociotechnical system to another. The article describes a co-evolutionary multi-level perspective to understand how system innovations come about through the interplay between technology and society. The article makes a new step as it further refines the multi-level perspective by distinguishing characteristic patterns: (a) two transition routes, (b) fit–stretch pattern, and (c) patterns in breakthrough. D 2005 Elsevier Inc. All rights reserved.