nils-herrmann/clean_scirepeval huggingface dataset:
PhishNet: Predictive Blacklisting to Detect Phishing Attacks

Phishing has been easy and effective way for trickery and deception on the Internet. While solutions such as URL blacklisting have been effective to some degree, their reliance on exact match with the blacklisted entries makes it easy for attackers to evade. We start with the observation that attackers often employ simple modifications (e.g., changing top level domain) to URLs. Our system, PhishNet, exploits this observation using two components. In the first component, we propose five heuristics to enumerate simple combinations of known phishing sites to discover new phishing URLs. The second component consists of an approximate matching algorithm that dissects a URL into multiple components that are matched individually against entries in the blacklist. In our evaluation with real-time blacklist feeds, we discovered around 18,000 new phishing URLs from a set of 6,000 new blacklist entries. We also show that our approximate matching algorithm leads to very few false positives (3%) and negatives (5%).
Security Issues in OAuth 2.0 SSO Implementations

Many Chinese websites (relying parties) use OAuth 2.0 as the basis of a single sign-on service to ease password management for users. Many sites support five or more different OAuth 2.0 identity providers, giving users choice in their trust point. However, although OAuth 2.0 has been widely implemented (particularly in China), little attention has been paid to security in practice. In this paper we report on a detailed study of OAuth 2.0 implementation security for ten major identity providers and 60 relying parties, all based in China. This study reveals two critical vulnerabilities present in many implementations, both allowing an attacker to control a victim user’s accounts at a relying party without knowing the user’s account name or password. We provide simple, practical recommendations for identity providers and relying parties to enable them to mitigate these vulnerabilities. The vulnerabilities have been reported to the parties concerned.
A V-Band Beam-Steering Antenna on a Thin-Film Substrate With a Flip-Chip Interconnection

We propose a planar V-band beam-steering antenna based on a millimeter-wave (mm-wave) system-on-package technology using advanced thin-film technology on a silicon mother board. The thin-film substrate has the capability of integrated passive elements and flip-chip interconnection. Space-consuming components such as a microstrip Rotman lens and patch antennas are implemented on a low-loss, low-cost, and low-dielectric-constant material, benzocyclobutane. V-band monolithic microwave integrated circuits, a power amplifier, and a SP4T switch, are flip-chipped on the thin-film substrate while minimizing parasitic effects. The fabricated antenna module shows if-plane beam steering at four angles, plusmn6deg and plusmn20deg. The measured effective isotropic radiated power is in the range of 17.3-18 dBm. To our knowledge, this is the first demonstration of a planar mm-wave beam-steering antenna module on a thin-film substrate incorporating integrated terminating loads and flip-chip interconnections.
Play and Learn: Using Video Games to Train Computer Vision Models

Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world images to improve the performance of computer vision models in practice. We present experiments assessing the effectiveness on real-world data of systems trained on synthetic RGB images that are extracted from a video game. We collected over 60,000 synthetic samples from a modern video game with similar conditions to the real-world CamVid and Cityscapes datasets. We provide several experiments to demonstrate that the synthetically generated RGB images can be used to improve the performance of deep neural networks on both image segmentation and depth estimation. These results show that a convolutional network trained on synthetic data achieves a similar test error to a network that is trained on real-world data for dense image classification. Furthermore, the synthetically generated RGB images can provide similar or better results compared to the real-world datasets if a simple domain adaptation technique is applied. Our results suggest that collaboration with game developers for an accessible interface to gather data is potentially a fruitful direction for future work in computer vision.
Bandwidth optimal all-reduce algorithms for clusters of workstations

We consider an efficient realization of the all-reduce operation with large data sizes in cluster environments, under the assumption that the reduce operator is associative and commutative. We derive a tight lower bound of the amount of data that must be communicated in order to complete this operation and propose a ring-based algorithm that only requires tree connectivity to achieve bandwidth optimality. Unlike the widely used butterfly-like all-reduce algorithm that incurs network contention in SMP/multi-core clusters, the proposed algorithm can achieve contention-free communication in almost all contemporary clusters including SMP/multi-core clusters and Ethernet switched clusters with multiple switches. We demonstrate that the proposed algorithm is more efficient than other algorithms on clusters with different nodal architectures and networking technologies when the data size is sufficiently large.
Planning for Crowdsourcing Hierarchical Tasks

We show how machine vision, learning, and planning can be combined to solve hierarchical consensus tasks. Hierarchical consensus tasks seek correct answers to a hierarchy of subtasks, where branching depends on answers at preceding levels of the hierarchy. We construct a set of hierarchical classification models that aggregate machine and human effort on different subtasks and use these inferences in planning. Optimal solution of hierarchical tasks is intractable due to the branching of task hierarchy and the long horizon of these tasks. We study Monte Carlo planning procedures that can exploit task structure to constrain the policy space for tractability. We evaluate the procedures on data collected from Galaxy Zoo II in allocating human effort and show that significant gains can be achieved.
Enhancing reliability to boost the throughput over screen-camera links

With the rapid proliferation of camera-equipped smart devices (e.g., smartphones, pads, tablets), visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication. Such communication via smart devices is highly competitive for its user-friendliness, security, and infrastructure-less (i.e., no dependency on WiFi or cellular infrastructure). However, existing approaches mostly focus on improving the transmission speed and ignore the transmission reliability. Considering the interplay between the transmission speed and reliability towards effective end-to-end communication, in this paper, we aim to boost the throughput over screen-camera links by enhancing the transmission reliability. To this end, we propose RDCode, a robust dynamic barcode which enables a novel packet-frame-block structure. Based on the layered structure, we design different error correction schemes at three levels: intra-blocks, inter-blocks and inter-frames, in order to verify and recover the lost blocks and frames. Finally, we implement RDCode and experimentally show that RDCode reaches a high level of transmission reliability (e.g., reducing the error rate to 10%) and yields a at least doubled transmission rate, compared with the existing state-of-the-art approach COBRA.
Image Aesthetic Assessment: An experimental survey

This article reviews recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality from low-quality photos based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature to try to solve this challenging problem. In this article, we summarize these approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (data set characteristics and evaluation metrics). The main contributions and novelties of the reviewed approaches are highlighted and discussed. In addition, following the emergence of deep-learning techniques, we systematically evaluate recent deep-learning settings that are useful for developing a robust deep model for aesthetic scoring.
Conceptual Spaces - The Geometry of Thought

Our online web service was launched by using a want to function as a full online digital collection that offers access to large number of PDF file document selection. You may find many kinds of e-book and other literatures from your papers data source. Distinct preferred subjects that spread out on our catalog are famous books, answer key, test test question and answer, guideline example, skill guide, quiz example, end user handbook, owners manual, service instructions, maintenance guide, etc.
A Unified Framework for Multi-Modal Isolated Gesture Recognition

In this article, we focus on isolated gesture recognition and explore different modalities by involving RGB stream, depth stream, and saliency stream for inspection. Our goal is to push the boundary of this realm even further by proposing a unified framework that exploits the advantages of multi-modality fusion. Specifically, a spatial-temporal network architecture based on consensus-voting has been proposed to explicitly model the long-term structure of the video sequence and to reduce estimation variance when confronted with comprehensive inter-class variations. In addition, a three-dimensional depth-saliency convolutional network is aggregated in parallel to capture subtle motion characteristics. Extensive experiments are done to analyze the performance of each component and our proposed approach achieves the best results on two public benchmarks, ChaLearn IsoGD and RGBD-HuDaAct, outperforming the closest competitor by a margin of over 10% and 15%, respectively. Our project and codes will be released at https://davidsonic.github.io/index/acm_tomm_2017.html.
Accurate Uncertainties for Deep Learning Using Calibrated Regression

Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate — for example, a 90% credible interval may not contain the true outcome 90% of the time. Here, we propose a simple procedure for calibrating any regression algorithm; when applied to Bayesian and probabilistic models, it is guaranteed to produce calibrated uncertainty estimates given enough data. Our procedure is inspired by Platt scaling and extends previous work on classification. We evaluate this approach on Bayesian linear regression, feedforward, and recurrent neural networks, and find that it consistently outputs well-calibrated credible intervals while improving performance on time series forecasting and model-based reinforcement learning tasks.
The EPFL jumpglider: A hybrid jumping and gliding robot with rigid or folding wings

Recent work suggests that wings can be used to prolong the jumps of miniature jumping robots. However, no functional miniature jumping robot has been presented so far that can successfully apply this hybrid locomotion principle. In this publication, we present the development and characterization of the ‘EPFL jumpglider’, a miniature robot that can prolong its jumps using steered hybrid jumping and gliding locomotion over varied terrain. For example, it can safely descend from elevated positions such as stairs and buildings and propagate on ground with small jumps. The publication presents a systematic evaluation of three biologically inspired wing folding mechanisms and a rigid wing design. Based on this evaluation, two wing designs are implemented and compared1.
A biologically inspired soft exosuit for walking assistance

We present the design and evaluation of a multi-articular soft exosuit that is portable, fully autonomous, and provides assistive torques to the wearer at the ankle and hip during walking. Traditional rigid exoskeletons can be challenging to perfectly align with a wearer’s biological joints and can have large inertias, which can lead to the wearer altering their natural motion patterns. Exosuits, in comparison, use textiles to create tensile forces over the body in parallel with the muscles, enabling them to be light and not restrict the wearer’s kinematics. We describe the biologically inspired design and function of our exosuit, including a simplified model of the suit’s architecture and its interaction with the body. A key feature of the exosuit is that it can generate forces passively due to the body’s motion, similar to the body’s ligaments and tendons. These passively-generated forces can be supplemented by actively contracting Bowden cables using geared electric motors, to create peak forces in the suit of up to 200N. We define the suit-human series stiffness as an important parameter in the design of the exosuit and measure it on several subjects, and we perform human subjects testing to determine the biomechanical and physiological effects of the suit. Results from a five-subject study showed a minimal effect on gait kinematics and an average best-case metabolic reduction of 6.4%, comparing suit worn unpowered vs powered, during loaded walking with 34.6kg of carried mass including the exosuit and actuators (2.0kg on both legs, 10.1kg total).
Bayesian reinforcement learning in continuous POMDPs with application to robot navigation

We consider the problem of optimal control in continuous and partially observable environments when the parameters of the model are not known exactly. Partially observable Markov decision processes (POMDPs) provide a rich mathematical model to handle such environments but require a known model to be solved by most approaches. This is a limitation in practice as the exact model parameters are often difficult to specify exactly. We adopt a Bayesian approach where a posterior distribution over the model parameters is maintained and updated through experience with the environment. We propose a particle filter algorithm to maintain the posterior distribution and an online planning algorithm, based on trajectory sampling, to plan the best action to perform under the current posterior. The resulting approach selects control actions which optimally trade-off between 1) exploring the environment to learn the model, 2) identifying the system's state, and 3) exploiting its knowledge in order to maximize long-term rewards. Our preliminary results on a simulated robot navigation problem show that our approach is able to learn good models of the sensors and actuators, and performs as well as if it had the true model.
Survey on User Interface Programming

This paper reports on the results of a survey of user interface programming. The survey was widely distributed, and we received 74 responses. The results show that in today's applications, an average of 48% of the code is devoted to the user interface portion. The average time spent on the user interface portion is 45% during the design phase, 50% during the implementation phase, and 37% during the maintenance phase. 34% of the systems were implemented using a toolkit, 27% used a UIMS, 14% used an interface builder, and 26% used no tools. This appears to be because the toolkit systems had more sophisticated user interfaces. The projects using UIMSs or interface builders spent the least percent of time and code on the user interface (around 41%) suggesting that these tools are effective. In general, people were happy with the tools they used, especially the graphical interface builders. The most common problems people reported when developing a user interface included getting users' requirements, writing help text, achieving consistency, learning how to use the tools, getting acceptable performance, and communicating among various parts of the program.
Thermal management of lithium-ion battery pack with liquid cooling

Vehicles (EVs) are projected as the most sustainable solutions for future transportation. EVs have many advantages over conventional hydrocarbon internal combustion engines including energy efficiency, environmental friendliness, noiselessness and less dependence on fossil fuels. However, there are also many challenges which are mainly related to the battery pack, such as battery cost, driving range, reliability, safety, battery capacity, cycle life, and recharge time. The performance of EVs is greatly dependent on the battery pack. Temperatures of the cells in a battery pack need to be maintained within its optimum operating temperature range in order to achieve maximum performance, safety and reliability under various operating conditions. Poor thermal management will affect the charging and discharging power, cycle life, cell balancing, capacity and fast charging capability of the battery pack. Hence, a thermal management system is needed in order to enhance the performance and to extend the life cycle of the battery pack. In this study, the effects of temperature on the Li-ion battery are investigated. Heat generated by LiFePO4 pouch cell was characterized using an EV accelerating rate calorimeter. Computational fluid dynamic analyses were carried out to investigate the performance of a liquid cooling system for a battery pack. The numerical simulations showed promising results and the design of the battery pack thermal management system was sufficient to ensure that the cells operated within their temperature limits.
Approach , Avoidance , and the Self-Regulation of Affect and Action

Several literatures converge on the idea that approach and avoidance/withdrawal behaviors are managed by two partially distinct self-regulatory system. The functions of these systems also appear to be embodied in discrepancyreducing and -enlarging feedback loops, respectively. This article describes how the feedback construct has been used to address these two classes of action and the affective experiences that relate to them. Further discussion centers on the development of measures of individual differences in approach and avoidance tendencies, and how these measures can be (and have been) used as research tools, to investigate whether other phenomena have their roots in approach or avoidance.
Transmission Expansion Planning : A Mixed-Integer LP Approach

This paper presents a mixed-integer LP approach to the solution of the long-term transmission expansion planning problem. In general, this problem is large-scale, mixed-integer, nonlinear, and nonconvex. We derive a mixed-integer linear formulation that considers losses and guarantees convergence to optimality using existing optimization software. The proposed model is applied to Garver’s 6-bus system, the IEEE Reliability Test System, and a realistic Brazilian system. Simulation results show the accuracy as well as the efficiency of the proposed solution technique.
Assessing the Spoofing Threat : Development of a Portable GPS Civilian Spoofer

A portable civilian GPS spoofer is implemented on a digital signal processor and used to characterize spoofing effects and develop defenses against civilian spoofing. This work is intended to equip GNSS users and receiver manufacturers with authentication methods that are effective against unsophisticated spoofing attacks. The work also serves to refine the civilian spoofing threat assessment by demonstrating the challenges involved in mounting a spoofing attack.
Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model

Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valencearousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. This study proposes a regional CNN-LSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses an individual sentence as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and long-distance dependency across sentences can be considered in the prediction process. Experimental results show that the proposed method outperforms lexicon-based, regression-based, and NN-based methods proposed in previous studies.
Comparative Performance Analysis of Average Max Round Robin Scheduling Algorithm ( AMRR ) using Dynamic Time Quantum with Round Robin Scheduling Algorithm using static Time Quantum

Round Robin Scheduling algorithm is designed especially for time sharing Operating system (OS).It is a preemptive CPU scheduling algorithm which switches between the processes when static time Quantum expires. The Round Robin Scheduling algorithm has its disadvantages that is its longer average waiting time, higher context switches, higher turnaround time .In this paper a new algorithm is presented called Average Max Round Robin (AMRR) scheduling algorithm .In this scheduling algorithm the main idea is to adjust the time Quantum dynamically so that (AMRR) perform better performance than simple Round Robin scheduling algorithm. Operating System, Round Robin, Average Max Round Robin, Turnaround time, Waiting time,
Adaptive Service Composition in Flexible Processes

In advanced service oriented systems, complex applications, described as abstract business processes, can be executed by invoking a number of available Web services. End users can specify different preferences and constraints and service selection can be performed dynamically identifying the best set of services available at runtime. In this paper, we introduce a new modeling approach to the Web service selection problem that is particularly effective for large processes and when QoS constraints are severe. In the model, the Web service selection problem is formalized as a mixed integer linear programming problem, loops peeling is adopted in the optimization, and constraints posed by stateful Web services are considered. Moreover, negotiation techniques are exploited to identify a feasible solution of the problem, if one does not exist. Experimental results compare our method with other solutions proposed in the literature and demonstrate the effectiveness of our approach toward the identification of an optimal solution to the QoS constrained Web service selection problem