nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Latency of Blockchain-Based Systems Using Architectural Modelling and Simulation

Blockchain is an emerging technology for sharing transactional data and computation without using a central trusted third party. It is an architectural choice to use a blockchain instead of traditional databases or protocols, and this creates trade-offs between non-functional requirements such as performance, cost, and security. However, little is known about predicting the behaviour of blockchain-based systems. This paper shows the feasibility of using architectural performance modelling and simulation tools to predict the latency of blockchain-based systems. We use established tools and techniques, but explore new blockchain-specific issues such as the configuration of the number of confirmation blocks and inter-block times. We report on a lab-based experimental study using an incident management system, showing predictions of median system level response time with a relative error mostly under 10%. We discuss how the approach can be used to support architectural decision-making, during the design of blockchain-based systems.
Multicell Interference Management in Device to Device Underlay Cellular Networks

In this paper, the problem of interference mitigation in a multicell Device to Device (D2D) underlay cellular network is addressed. In this type of network architectures, cellular users and D2D users share common Resource Blocks (RBs). Though such paradigms allow potential increase in the number of supported users, the latter comes at the cost of interference increase that in turn calls for the design of efficient interference mitigation methodologies. To treat this problem efficiently, we propose a two step approach, where the first step concerns the efficient RB allocation to the users and the second one the transmission power allocation. Specifically, the RB allocation problem is formulated as a bilateral symmetric interaction game. This assures the existence of a Nash Equilibrium (NE) point of the game, while a distributed algorithm, which converges to it, is devised. The power allocation problem is formulated as a linear programming problem per RB, and the equivalency between this problem and the total power minimization problem is shown. Finally, the operational effectiveness of the proposed approach is evaluated via numerical simulations, while its superiority against state of the art approaches existing in the recent literature is shown in terms of increased number of supported users, interference reduction and power minimization.
Last Line of Defense: A Novel IDS Approach Against Advanced Threats in Industrial Control Systems

Industrial control systems are becoming increasingly interconnected, and with it their vulnerability to malicious actors. While intrusion detection systems are suited to detect network-based attacks, they remain unable to detect more sophisticated attacks against control systems, for example a compromise of the PLCs. This paper makes the case that the evolving landscape of threats such as the Stuxnet malware requires an alternative approach to intrusion detection in industrial control systems. We argue that effective control of such advanced threats needs to happen in the last link of the control network, hence building a last line of defense. A proof of concept of this new paradigm was implemented for the control system of a dredging vessel, and we describe main lessons learned and pose open research questions we find based on these experiences for ICS intrusion detection.
Conditional image generation using feature-matching GAN

Generative Adversarial Net is a frontier method of generative models for images, audios and videos. In this paper, we focus on conditional image generation and introduce conditional Feature-Matching Generative Adversarial Net to generate images from category labels. By visualizing state-of-art discriminative conditional generative models, we find these networks do not gain clear semantic concepts. Thus we design the loss function in the light of metric learning to measure semantic distance. The proposed model is evaluated on several well-known datasets. It is shown to be of higher perceptual quality and better diversity then existing generative models.
The Marketing Mix Revisited : Towards the 21 st Century Marketing

University of Twente The paper assesses the current standing of the 4Ps Marketing Mix framework as the dominant marketing management paradigm and identifies market developments, environmental changes, and trends, as well as changing academic attitudes likely to affect the future of the Mix as theoretical concept and also the favourite management tool of marketing practitioners. It reviews the criticism on the 4P’s emanating from five “traditional” marketing areas Consumer Marketing, Relationship Marketing, Services Marketing, Retail Marketing, Industrial Marketing and the emerging field of Electronic Marketing. The paper identifies two main limitations of the Marketing Mix as management tool, common in all examined domains, namely the model’s internal orientation and lack of personalisation. It also identifies several area-specific limitations and underlines the need for further research on the issue. The weaknesses identified in the study seem to support the frequently expressed suggestion that marketing scholars should focus their efforts in formulating the conceptual foundations and marketing methodologies that better address the needs of today’s and tomorrow’s marketer.
Occlusion-capable multiview volumetric three-dimensional display.

Volumetric 3D displays are frequently purported to lack the ability to reconstruct scenes with viewer-position-dependent effects such as occlusion. To counter these claims, a swept-screen 198-view horizontal-parallax-only 3D display is reported here that is capable of viewer-position-dependent effects. A digital projector illuminates a rotating vertical diffuser with a series of multiperspective 768 x 768 pixel renderings of a 3D scene. Evidence of near-far object occlusion is reported. The aggregate virtual screen surface for a stationary observer is described, as are guidelines to construct a full-parallax system and the theoretical ability of the present system to project imagery outside of the volume swept by the screen.
Ship Classification in TerraSAR-X Images With Feature Space Based Sparse Representation

Ship classification is the key step in maritime surveillance using synthetic aperture radar (SAR) imagery. In this letter, we develop a new ship classification method in TerraSAR-X images based on sparse representation in feature space, in which the sparse representation classification (SRC) method is exploited. In particular, to describe the ship more accurately and to reduce the dimension of the dictionary in SRC, we propose to employ a representative feature vector to construct the dictionary instead of utilizing the image pixels directly. By testing on a ship data set collected from TerraSAR-X images, we show that the proposed method is superior to traditional methods such as the template matching (TM), K-nearest neighbor (K-NN), Bayes and Support Vector Machines (SVM).
Towards Hardware Accelerated Reinforcement Learning for Application-Specific Robotic Control

Reinforcement Learning (RL) is an area of machine learning in which an agent interacts with the environment by making sequential decisions. The agent receives reward from the environment based on how good the decisions are and tries to find an optimal decision-making policy that maximises its longterm cumulative reward. This paper presents a novel approach which has showon promise in applying accelerated simulation of RL policy training to automating the control of a real robot arm for specific applications. The approach has two steps. First, design space exploration techniques are developed to enhance performance of an FPGA accelerator for RL policy training based on Trust Region Policy Optimisation (TRPO), which results in a 43% speed improvement over a previous FPGA implementation, while achieving 4.65 times speed up against deep learning libraries running on GPU and 19.29 times speed up against CPU. Second, the trained RL policy is transferred to a real robot arm. Our experiments show that the trained arm can successfully reach to and pick up predefined objects, demonstrating the feasibility of our approach.
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition

RECENTLY , several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches. Firstly, this dissertation will present a new framework for multi-target tracking in a single camera. We proposed a novel data association technique using Dominant set clustering (DCS) framework. We formulate the tracking task as finding dominant sets on the constructed undirected edge weighted graph. Unlike most techniques which are limited in temporal locality (i.e. few frames are considered), we utilized a pairwise relationships (in appearance and position) between different detections across the whole temporal span of the video for data association in a global manner. Meanwhile, temporal sliding window technique is utilized to find tracklets and perform further merging on them. Our robust tracklet merging step renders our tracker to long term occlusions with more robustness. DSC leads us to a more accurate approach to multi-object tracking by considering all the pairwise relationships in a batch of frames; however, it has some limitations. Firstly, it finds target trajectories(clusters) one-by-one (peel off strategy), causing change in the scale of the problem. Secondly, the algorithm used to enumerate dominant sets, i.e., replicator dynamics, have quadratic computational complexity, which makes it impractical on larger graphs. To address these problems and extend tracking problem to multiple non-overlapping cameras, we proposed a novel and unified three-layer hierarchical approach. Given a video and a set of detections (obtained by any person detector), we first solve withincamera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant set clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as
Memory versus non-linearity in reservoirs

Reservoir Computing (RC) is increasingly being used as a conceptually simple yet powerful method for using the temporal processing of recurrent neural networks (RNN). However, because fundamental insight in the exact functionality of the reservoir is as yet still lacking, in practice there is still a lot of manual parameter tweaking or brute-force searching involved in optimizing these systems. In this contribution we aim to enhance the insights into reservoir operation, by experimentally studying the interplay of the two crucial reservoir properties, memory and non-linear mapping. For this, we introduce a novel metric which measures the deviation of the reservoir from a linear regime and use it to define different regions of dynamical behaviour. Next, we study the relationship of two important reservoir parameters, input scaling and spectral radius, on two properties of an artificial task, namely memory and non-linearity.
Introduction to Fillers.

Over the last few years, injectable soft-tissue fillers have become an integral part of cosmetic therapy, with a wide array of products designed to fill lines and folds and revolumize the face.



This review describes cosmetic fillers currently approved by the Food and Drug Administration and discusses new agents under investigation for use in the United States.



Because of product refinements over the last few years-greater ease of use and longevity, the flexibility of multiple formulations within one line of products, and the ability to reverse poor clinical outcomes-practitioners have gravitated toward the use of biodegradable agents that stimulate neocollagenesis for sustained aesthetic improvements lasting up to a year or more with minimal side effects. Permanent implants provide long-lasting results but are associated with greater potential risk of complications and require the skilled hand of the experienced injector.



A variety of biodegradable and nonbiodegradable filling agents are available or under investigation in the United States. Choice of product depends on injector preference and the area to be filled. Although permanent agents offer significant clinical benefits, modern biodegradable fillers are durable and often reversible in the event of adverse effects.
A broadband compact low-loss 4×4 Butler Matrix in CMOS with stacked transformer based quadrature couplers

This paper presents an ultra-broadband ultracompact Butler Matrix design scheme. The design employs stacked transformer based couplers and lumped LC π-network phase shifters for substantial size reduction. As a proof-of-concept design, a 4×4 Butler Matrix is implemented in a standard 130nm bulk CMOS process at a center frequency of 2.0 GHz. Compared with reported fully integrated 2.0 GHz 4×4 Butler Matrix designs in CMOS, the proposed design achieves the lowest insertion loss of 1.10dB, the smallest amplitude mismatch of 0.3 dB, the largest fractional bandwidth of 34.6%, and the smallest chip core area of 0.635×1.122 mm2. Based on the measured S-parameters, the four concurrent electrical array patterns of the Butler Matrix achieve array peak-to-null ratio (PNR) of 29.5 dB at 2.0 GHz and better than 15.0 dB between 1.55 GHz and 2.50 GHz.
A survey on text mining techniques

text mining is a technique to find meaningful patterns from the available text documents. The pattern discovery from the text and document organization of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a complex task of data mining. In order to find an efficient and effective technique for text categorization, various techniques of text categorization and classification is recently developed. Some of them are supervised and some of them unsupervised manner of document arrangement. This presented paper discusses different method of text categorization and cluster analysis for text documents. In addition of that a new text mining technique is proposed for future implementation.  text mining, classification, cluster analysis, survey
Insecure attachments: Attachment, emotional regulation, sexting and condomless sex among women in relationships

Associations have been found among sexting, attachment insecurity, emotional dysregulation, and sexual and risky sexual behavior in young adults. In a sample of 92 young adult women in romantic relationships, this study aimed to examine whether emotional regulation might constitute an indirect pathway linking attachment and sexting, and the potential implications for engaging in condomless sex. Findings revealed that in young adult women in romantic relationships, attachment anxiety was associated with sexting through difficulty controlling impulses during moments of emotional distress. Also, sexting directly predicted condomless sex. Attachment avoidance and anxiety were both associated with sexting in the bivariate correlations. Findings highlight the role of digital technology in understanding how people's negotiation of needs for attachment and emotional regulation might lead to risky sexual behavior. Interventions targeting the interplay of attachment, emotional regulation and technology use may positively impact relational and behavioral health outcomes.
Path diversification for future internet end-to-end resilience and survivability

Path Diversification is a new mechanism that can be used to select multiple paths between a given ingress and egress node pair using a quantified diversity measure to achieve maximum flow reliability. The path diversification mechanism is targeted at the end-to-end layer, but can be applied at any level for which a path discovery service is available. Path diversification also takes into account service requirements for low-latency or maximal reliability in selecting appropriate paths. Using this mechanism will allow future internetworking architectures to exploit naturally rich physical topologies to a far greater extent than is possible with shortest-path routing or equal-cost load balancing. We describe the path diversity metric and its application at various aggregation levels, and apply the path diversification process to 13 real-world network graphs as well as 4 synthetic topologies to asses the gain in flow reliability. Based on the analysis of flow reliability across a range of networks, we then extend our path diversity metric to create a composJ. P. Rohrer* E-mail: jprohrer@nps.edu Tel.: +1 831 656 3196 Computer Science Department, Naval Postgraduate School Monterey, California, USA A. Jabbar* E-mail: jabbar@ge.com Advanced Communication Systems Lab, GE Global Research Niskayuna, New York, USA J. P. G. Sterbenz E-mail: jpgs@{ittc.ku.edu|comp.lancs.ac.uk} Tel.: +1 785 864 7890 Information and Telecommunication Technology Center, The University of Kansas Lawrence, Kansas, USA and Lancaster University, Lancaster, UK * Work performed while at The University of Kansas ite compensated total graph diversity metric that is representative of a particular topology’s survivability with respect to distributed simultaneous link and node failures. We tune the accuracy of this metric having simulated the performance of each topology under a range of failure severities, and present the results. The topologies used are from nationalscale backbone networks with a variety of characteristics, which we characterize using standard graph-theoretic metrics. The end result is a compensated total graph diversity metric that accurately predicts the survivability of a given network topology.
NANOFIBRILLATED CELLULOSE AS PAPER ADDITIVE IN EUCALYPTUS PULPS

In this work, the physical and mechanical properties of bleached Eucalyptus pulp reinforced with nanofibrillated cellulose (NFC) are compared with those of traditional beaten pulp used in the making of writing/printing and offset printing papers. For this purpose, three different types of hardwood slurries were prepared: beaten pulps, unbeaten pulps reinforced with NFC, and slightly beaten pulps also reinforced with NFC. Physical and mechanical tests were performed on handsheets from these different slurries. The results showed that adding NFC to unbeaten pulps results in physical and mechanical properties similar to those in pulps used for printing/writing papers. Nevertheless, the best results were obtained in slurries previously beaten at slight conditions and subsequently reinforced with NFC. These results demonstrate that the addition of NFC allows a reduction in beating intensity without decreasing the desired mechanical properties for this specific purpose.
Sparseness and Expansion in Sensory Representations

In several sensory pathways, input stimuli project to sparsely active downstream populations that have more neurons than incoming axons. Here, we address the computational benefits of expansion and sparseness for clustered inputs, where different clusters represent behaviorally distinct stimuli and intracluster variability represents sensory or neuronal noise. Through analytical calculations and numerical simulations, we show that expansion implemented by feed-forward random synaptic weights amplifies variability in the incoming stimuli, and this noise enhancement increases with sparseness of the expanded representation. In addition, the low dimensionality of the input layer generates overlaps between the induced representations of different stimuli, limiting the benefit of expansion. Highly sparse expansive representations obtained through synapses that encode the clustered structure of the input reduce both intrastimulus variability and the excess overlaps between stimuli, enhancing the ability of downstream neurons to perform classification and recognition tasks. Implications for olfactory, cerebellar, and visual processing are discussed.
Service Science

This paper is a first exploration of the relationship between service science and Grid computing. Service science is the study of value co-creation interactions among entities, known as service systems. Within the emerging service science community, service is often defined as the application of competences (resources) for the benefit of another. Grid computing is the study of resource sharing among entities, known as virtual organizations, which solve complex business, societal, scientific, and engineering problems. Within the Grid computing community, service is sometimes defined as protocols plus behavior. Both Grid computing and service science are connecting academic, industry, government, and volunteer sector collaborators on a range of projects including eScience, healthcare, environmental sustainability, and more. This paper compares and contrasts the notions of resource, entity, service, interaction, and success criteria for the two areas of study. In conclusion, new areas for collaborative inquiry are proposed.
Differentially Private Exponential Random Graphs

We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, and thus offer rigorous privacy guarantees. More specifically, we use the randomized response mechanism to release networks under -edge differential privacy. To maintain utility for statistical inference, treating the original graph as missing, we propose a way to use likelihood based inference and Markov chain Monte Carlo (MCMC) techniques to fit ERGMs to the produced synthetic networks. We demonstrate the usefulness of the proposed techniques on a real data example.
Ultra High Speed operation of BLDC motor with enhanced motor dynamics for space limited applications

This paper proposes new method of operating BLDC motor which extends operating speed beyond the limit set by conventional 120° conduction mode of operation, without field weakening and having same inverter dc bus voltage. Concepts of Phase Extension mode and Ultra High Speed mode are introduced. Also in the paper it is shown that the proposed `phase extension' mode of operation can produce additional average torque, above the conventionally available torque. Thus increasing torque-to-weight ratio of BLDC motor drive system makes it desirable for space constrained applications like Aerospace. Proposed modes of operation will enhance the motor dynamics in the wide speed range. Analysis, simulation results and pictorial descriptions are presented.