nils-herrmann/clean_scirepeval huggingface dataset:
A secure light weight scheme for user authentication and key agreement in multi-gateway based wireless sensor networks

Wireless sensor networks can be deployed in any attended or unattended environments like environmental monitoring, agriculture, military, health care etc., where the sensor nodes forward the sensing data to the gateway node. As the sensor node has very limited battery power and cannot be recharged after deployment, it is very important to design a secure, effective and light weight user authentication and key agreement protocol for accessing the sensed data through the gateway node over insecure networks. Most recently, Turkanovic et al. proposed a light weight user authentication and key agreement protocol for accessing the services of the WSNs environment and claimed that the same protocol is efficient in terms of security and complexities than related existing protocols. In this paper, we have demonstrated several security weaknesses of the Turkanovic et al. protocol. Additionally, we have also illustrated that the authentication phase of the Turkanovic et al. is not efficient in terms of security parameters. In order to fix the above mentioned security pitfalls, we have primarily designed a novel architecture for the WSNs environment and basing upon which a proposed scheme has been presented for user authentication and key agreement scheme. The security validation of the proposed protocol has done by using BAN logic, which ensures that the protocol achieves mutual authentication and session key agreement property securely between the entities involved. Moreover, the proposed scheme has simulated using well popular AVISPA security tool, whose simulation results show that the protocol is SAFE under OFMC and CL-AtSe models. Besides, several security issues informally confirm that the proposed protocol is well protected in terms of relevant security attacks including the above mentioned security pitfalls. The proposed protocol not only resists the above mentioned security weaknesses, but also achieves complete security requirements including specially energy efficiency, user anonymity, mutual authentication and user-friendly password change phase. Performance comparison section ensures that the protocol is relatively efficient in terms of complexities. The security and performance analysis makes the system so efficient that the proposed protocol can be implemented in real-life application.
Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices

We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing hierarchy, a DDNN can scale up in neural network size and scale out in geographical span. Due to its distributed nature, DDNNs enhance sensor fusion, system fault tolerance and data privacy for DNN applications. In implementing a DDNN, we map sections of a DNN onto a distributed computing hierarchy. By jointly training these sections, we minimize communication and resource usage for devices and maximize usefulness of extracted features which are utilized in the cloud. The resulting system has built-in support for automatic sensor fusion and fault tolerance. As a proof of concept, we show a DDNN can exploit geographical diversity of sensors to improve object recognition accuracy and reduce communication cost. In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.
Deep Pyramid Convolutional Neural Networks for Text Categorization

This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent longrange associations in text. In the literature, several deep and complex neural networks have been proposed for this task, assuming availability of relatively large amounts of training data. However, the associated computational complexity increases as the networks go deeper, which poses serious challenges in practical applications. Moreover, it was shown recently that shallow word-level CNNs are more accurate and much faster than the state-of-the-art very deep nets such as character-level CNNs even in the setting of large training data. Motivated by these findings, we carefully studied deepening of word-level CNNs to capture global representations of text, and found a simple network architecture with which the best accuracy can be obtained by increasing the network depth without increasing computational cost by much. We call it deep pyramid CNN. The proposed model with 15 weight layers outperforms the previous best models on six benchmark datasets for sentiment classification and topic categorization.
Deep Reinforcement Learning

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multiagent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.
Who are the Devils Wearing Prada in New York City?

Fashion is a perpetual topic in human social life, and the mass has the penchant to emulate what large city residents and celebrities wear. Undeniably, New York City is such a bellwether large city with all kinds of fashion leadership. Consequently, to study what the fashion trends are during this year, it is very helpful to learn the fashion trends of New York City. Discovering fashion trends in New York City could boost many applications such as clothing recommendation and advertising. Does the fashion trend in the New York Fashion Show actually influence the clothing styles on the public? To answer this question, we design a novel system that consists of three major components: (1) constructing a large dataset from the New York Fashion Shows and New York street chic in order to understand the likely clothing fashion trends in New York, (2) utilizing a learning-based approach to discover fashion attributes as the representative characteristics of fashion trends, and (3) comparing the analysis results from the New York Fashion Shows and street-chic images to verify whether the fashion shows have actual influence on the people in New York City. Through the preliminary experiments over a large clothing dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on fashion trends and fashion influence.
An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
Detecting Spam in Chinese Microblogs - A Study on Sina Weibo

Sina Weibo is the most popular and fast growing microblogging social network in China. However, more and more spam messages are also emerging on Sina Weibo. How to detect these spam is essential for the social network security. While most previous studies attempt to detect the microblogging spam by identifying spammers, in this paper, we want to exam whether we can detect the spam by each single Weibo message, because we notice that more and more spam Weibos are posted by normal users or even popular verified users. We propose a Weibo spam detection method based on machine learning algorithm. In addition, different from most existing microblogging spam detection methods which are based on English microblogs, our method is designed to deal with the features of Chinese microblogs. Our extensive empirical study shows the effectiveness of our approach.
LOAM: Lidar Odometry and Mapping in Real-time

This project deals with online simultaneous localization and mapping (SLAM) problem without taking any assistance from Global Positioning System (GPS) and Inertial Measurement Unit (IMU). The main aim of this project is to perform online odometry and mapping in real time using a 2-axis lidar mounted on a robot. This involves use of two algorithms, the first of which runs at a higher frequency and uses the collected data to estimate velocity of the lidar which is fed to the second algorithm, a scan registration and mapping algorithm, to perform accurate matching of point cloud data.
Understanding Twitter Usage: What Drive People Continue to Tweet

In this study, we proposed a research model to investigate the factors influencing users’ continuance intention to use Twitter. Building on the uses and gratification framework, we have proposed four types of gratifications for Twitter usage, including content gratification, technology gratification, process gratification, and social gratification. We conducted an online survey and collected 124 responses. The data was analyzed using Partial Least Squares. Our results showed that content gratifications and new technology gratification are the two key types of gratifications affecting the continuance intention to use Twitter. We conclude with a discussion of theoretical and practical implications. We believe that this study will provide important insights for future research on Twitter.
A Weighted Coding in a Genetic Algorithm for the Degree-Constrained Minimum Spanning Tree Problem

The coding by which chromosomes represent candidate solutions is a fundamental design choice in a genetic algorithm. This paper describes a novel coding of spanning trees in a genetic algorithm for the degree-constrained minimum spanning tree problem. For a connected, weighted graph, this problem seeks to identify the shortest spanning tree whose degree does not exceed an upper bound k > 2. In the coding, chromosomes are strings of numerical weights associated with the target graph's vertices. The weights temporarily bias the graph's edge costs, and an extension of Prim's algorithm, applied to the biased costs, identifies the feasible spanning tree a chromosome represents. This decoding algorithm enforces the degree constraint, so that all chromosomes represent valid solutions and there is no need to discard, repair, or penalize invalid chromosomes. On a set of hard graphs whose unconstrained minimum spanning trees are of high degree, a genetic algorithm that uses this coding identifies degree-constrained minimum spanning trees that are on average shorter than those found by several competing algorithms.
Partial discharge and noise separation by means of spectral-power clustering techniques

Partial Discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. The measurement of PDs is useful in the diagnosis of electrical equipment because PDs activity is related to different ageing mechanisms. Classical Phase-Resolved Partial Discharge (PRPD) patterns are able to identify PD sources when they are related to a clear degradation process and when the noise level is low compared to the amplitudes of the PDs. However, real insulation systems usually exhibit several PD sources and the noise level is high, especially if measurements are performed on-line. High-frequency (HF) sensors and advanced signal processing techniques have been successfully applied to identify these phenomena in real insulation systems. In this paper, spectral power analyses of PD pulses and the spectral power ratios at different frequencies were calculated to classify PD sources and noise by means of a graphical representation in a plane. This technique is a flexible tool for noise identification and will be useful for pulse characterization.
The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform’s profit relative to contracts that have a fixed price or fixed wage (or both), and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.
A visual analytics process for maritime resource allocation and risk assessment

In this paper, we present our collaborative work with the U.S. Coast Guard's Ninth District and Atlantic Area Commands where we developed a visual analytics system to analyze historic response operations and assess the potential risks in the maritime environment associated with the hypothetical allocation of Coast Guard resources. The system includes linked views and interactive displays that enable the analysis of trends, patterns and anomalies among the U.S. Coast Guard search and rescue (SAR) operations and their associated sorties. Our system allows users to determine the potential change in risks associated with closing certain stations in terms of response time, potential lives and property lost and provides optimal direction as to the nearest available station. We provide maritime risk assessment tools that allow analysts to explore Coast Guard coverage for SAR operations and identify regions of high risk. The system also enables a thorough assessment of all SAR operations conducted by each Coast Guard station in the Great Lakes region. Our system demonstrates the effectiveness of visual analytics in analyzing risk within the maritime domain and is currently being used by analysts at the Coast Guard Atlantic Area.
An Ultra-Low Power QRS Complex Detection Algorithm Based on Down-Sampling Wavelet Transform

Low-power design has become a key technology for battery-power biomedical devices in Wireless Body Area Network. In order to meet the requirement of low-power dissipation for electrocardiogram related applications, a down-sampling QRS complex detection algorithm is proposed. Based on Wavelet Transform (WT), this letter characterizes the energy distribution of QRS complex corresponding to the frequency band of WT. Then this letter details for the first time the process of down-sampled filter design, and presents the time and frequency response of the filter. The algorithm is evaluated in fixed point on MIT-BIH and QT database. Compared with other existing results, our work reduces the power dissipation by 23%, 61%, and 72% for 1 ×, 2 ×, and 3 × down-sampling rate, respectively, while maintaining almost constant detection performance.
On Random Graphs I

Let us consider a " random graph " r,:l,~v having n possible (labelled) vertices and N edges; in other words, let us choose at random (with equal probabilities) one of the t 1 has no isolated points) and is connected in the ordinary sense. In the present paper we consider asymptotic statistical properties of random graphs for 11++ 30. We shall deal with the following questions: 1. What is the probability of r,,. T being completely connected? 2. What is the probability that the greatest connected component (sub-graph) of r,,, s should have effectively n-k points? (k=O, 1,. . .). 3. What is the probability that rp,N should consist of exactly kf I connected components? (k = 0, 1,. + .). 4. If the edges of a graph with n vertices are chosen successively so that after each step every edge which has not yet been chosen has the same probability to be chosen as the next, and if we continue this process until the graph becomes completely connected, what is the probability that the number of necessary sfeps v will be equal to a given number I? As (partial) answers to the above questions we prove ihe following four theorems. In Theorems 1, 2, and 3 we use the notation N,= (I-&n log n+cn 1 where c is an arbitrary fixed real number ([xl denotes the integer part of x).
What You Should Know about the Vehicle Routing Problem

In the Vehicle Routing Problem (VRP), the aim is to design a set of m minimum cost vehicle routes through n customer locations, so that each route starts and ends at a common location and some side constraints are satisfied. Common application arise in newspaper and food delivery, and in milk collection. This paper summarizes the main known results for the classical VRP in which only vehicle capacity constraints are present. The paper is structured around three main headings: exact algorithms, classical heuristics, and metaheuristics.
Design and characterization of a multi-RC-triggered MOSFET-based power clamp for on-chip ESD protection

We present a novel multi-RC-triggered MOSFET-based power clamp with up to 70% trigger circuit area reduction and improved transient HBM, MM, and CDM ESD clamping performance. A three-stage RC-trigger circuit design gives a 300 ns self-shutdown time during power-up for mistrigger leakage current control and an improved mistrigger immunity down to 1 mus power-up rise time. TLP and HBM hardware characterization data from a 90 nm CMOS technology show >5A failure current and >3 kV HBM robustness for a designed MOSFET width of 4000 mum.
From Moodle to Facebook: Exploring students' motivation and experiences in online communities

The present paper documents a qualitative study that examines the motivating and inhibiting factors that influenced students’ engagement in online discussions via Moodle and Facebook. The data was collected through individual interviews with 14 pre-service teachers. Using the Activity Theory as a lens, the study reveals a set of factors concerning the technical tools, subjective perceptions, goals of online discussion, social presence within a community, rules for participation, and roles of the participants that affect their online engagement patterns. The findings of the study inform educators and software designers of how online discussions can be better promoted among students, and how a web-based environment more conducive to learning can be created.
Consistency , Availability , and Convergence

We examine the limits of consistency in highly available and fault-tolerant distributed storage systems. We introduce a new property—convergence—to explore the these limits in a useful manner. Like consistency and availability, convergence formalizes a fundamental requirement of a storage system: writes by one correct node must eventually become observable to other connected correct nodes. Using convergence as our driving force, we make two additional contributions. First, we close the gap between what is known to be impossible (i.e. the consistency, availability, and partition-tolerance theorem) and known systems that are highly-available but that provide weaker consistency such as causal. Specifically, in an asynchronous system, we show that natural causal consistency, a strengthening of causal consistency that respects the real-time ordering of operations, provides a tight bound on consistency semantics that can be enforced without compromising availability and convergence. In an asynchronous system with Byzantine-failures, we show that it is impossible to implement many of the recently introduced forking-based consistency semantics without sacrificing either availability or convergence. Finally, we show that it is not necessary to compromise availability or convergence by showing that there exist practically useful semantics that are enforceable by available, convergent, and Byzantine-fault tolerant systems.
An efficient watershed algorithm based on connected components

In this paper, a formal de"nition and a new algorithmic technique for the watershed transformation is presented. The novelty of the approach is to adapt the connected component operator to solve the watershed segmentation problem. The resulting algorithm is independent of the number of grey-levels, employs simple data structures, requires less error prone memory management, and issues a lower complexity and a short running time. However, the algorithm does not modify the principle of the watershed segmentation; the output result is the same as that of using any traditional algorithm which does not build watershed lines. ! 2000 Pattern Recognition Society.
Automated anomaly detection and performance modeling of enterprise applications

Automated tools for understanding application behavior and its changes during the application lifecycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects, and ultimately can result in company financial loss. Significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. Our thesis is that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: (i) a regression-based transaction model that reflects a resource consumption model of the application, and (ii) an application performance signature that provides a compact model of runtime behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: It is not intrusive and is based on monitoring data that is typically available in enterprise production environments. The introduced solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments.