nils-herrmann/clean_scirepeval huggingface dataset:
A 50V high-speed level shifter with high dv/dt immunity for multi-MHz DCDC converters

Size and cost of a switched mode power supply can be reduced by increasing the switching frequency. The maximum switching frequency and the maximum input voltage range, respectively, is limited by the minimum propagated on-time pulse, which is mainly determined by the level shifter speed. At switching frequencies above 10 MHz, a voltage conversion with an input voltage range up to 50 V and output voltages below 5 V requires an on-time of a pulse width modulated signal of less than 5 ns. This cannot be achieved with conventional level shifters. This paper presents a level shifter circuit, which controls an NMOS power FET on a high-voltage domain up to 50 V. The level shifter was implemented as part of a DCDC converter in a 180 nm BiCMOS technology. Experimental results confirm a propagation delay of 5 ns and on-time pulses of less than 3 ns. An overlapping clamping structure with low parasitic capacitances in combination with a high-speed comparator makes the level shifter also very robust against large coupling currents during high-side transitions as fast as 20 V/ns, verified by measurements. Due to the high dv/dt, capacitive coupling currents can be two orders of magnitude larger than the actual signal current. Depending on the conversion ratio, the presented level shifter enables an increase of the switching frequency for multi-MHz converters towards 100 MHz. It supports high input voltages up to 50 V and it can be applied also to other high-speed applications.
Adversarially Regularized Graph Autoencoder

Graph embedding is an eective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which oen results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. e framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.
The Major Transitions in Evolution

It's not surprisingly when entering this site to get the book. One of the popular books now is the the major transitions in evolution. You may be confused because you can't find the book in the book store around your city. Commonly, the popular book will be sold quickly. And when you have found the store to buy the book, it will be so hurt when you run out of it. This is why, searching for this popular book in this website will give you benefit. You will not run out of this book.
Denial of Service Elusion (DoSE): Keeping Clients Connected for Less

Denial of Service (DoS) attacks continue to grow in magnitude, duration, and frequency increasing the demand for techniques to protect services from disruption, especially at a low cost. We present Denial of Service Elusion (DoSE) as an inexpensive method for mitigating network layer attacks by utilizing cloud infrastructure and content delivery networks to protect services from disruption. DoSE uses these services to create a relay network between the client and the protected service that evades attack by selectively releasing IP address information. DoSE incorporates client reputation as a function of prior behavior to stop attackers along with a feedback controller to limit costs. We evaluate DoSE by modeling relays, clients, and attackers in an agent-based MATLAB simulator. The results show DoSE can mitigate a single-insider attack on 1,000 legitimate clients in 3.9 minutes while satisfying an average of 88.2% of requests during the attack.
CODE4: a unified system for managing conceptual knowledge

CODE4 is a general-purpose knowledge management system, intended to assist with the common knowledge processing needs of anyone who desires to analyse, store, or retrieve conceptual knowledge in applications as varied as the specification, design and user documentation of computer systems; the construction of term banks, or the development of ontologies for natural language understanding. This paper provides an overview of CODE4 as follows: We first describe the general philosophy and rationale of CODE4 and relate it to other systems. Next, we discuss the knowledge representation, specifically designed to meet the needs of flexible, interactive knowledge management. The highly-developed user interface, which we believe to be critical for this type of system, is explained in some detail. We finally describe how CODE4 is being used in a number of applications.
Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network

Identification and extraction of singing voice from within musical mixtures is a key challenge in sourc e separation and machine audition. Recently, deep neural network s (DNN) have been used to estimate 'ideal' binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capab le of generalizing to the discrimination of voice and non -voice in the context of musical mixtures. Here, we trained a con volutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation o f vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for 'karaoke' type applications.
Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile

This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.
Mathematical Formulation of Multilayer Networks

Manlio De Domenico, Albert Solé-Ribalta, Emanuele Cozzo, Mikko Kivelä, Yamir Moreno, Mason A. Porter, Sergio Gómez, and Alex Arenas Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza 50018, Spain Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX1 3LB, United Kingdom Department of Theoretical Physics, University of Zaragoza, Zaragoza 50009, Spain Complex Networks and Systems Lagrange Lab, Institute for Scientific Interchange, Turin 10126, Italy Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute and CABDyN Complexity Centre, University of Oxford, Oxford OX1 3LB, United Kingdom (Received 23 July 2013; published 4 December 2013)
Line-Based Drawing Style Description for Manga Classification

Diversity of drawing styles of mangas can be easily perceived by humans, but are hard to be described in text. We design computational features derived from line segments to describe drawing styles, enabling style classification such as discriminating mangas targeting youth boys and youth girls, and discriminating artworks produced by different artists. With statistical analysis, we found that drawing styles can be effectively characterized by the proposed features such that explicit (e.g., density of line segments) or implicit (e.g., included angles between lines) observations can be made to facilitate various manga style classification.
Restricted Weighted Integer Compositions and Extended Binomial Coefficients

We prove a simple relationship between extended binomial coefficients — natural extensions of the well-known binomial coefficients — and weighted restricted integer compositions. Moreover, we give a very useful interpretation of extended binomial coefficients as representing distributions of sums of independent discrete random variables. We apply our results, e.g., to determine the distribution of the sum of k logarithmically distributed random variables, and to determining the distribution, specifying all moments, of the random variable whose values are part-products of random restricted integer compositions. Based on our findings and using the central limit theorem, we also give generalized Stirling formulae for central extended binomial coefficients. We enlarge the list of known properties of extended binomial coefficients.
COMPACT PATCH ANTENNA DESIGN FOR OUTDOOR RF ENERGY HARVESTING IN WIRELESS SENSOR NETWORKS

In this paper, two compact patch antenna designs for a new application — outdoor RF energy harvesting in powering a wireless soil sensor network — are presented. The first design is a low-profile folded shorted patch antenna (FSPA), with a small ground plane and wide impedance bandwidth. The second design is a novel FSPA structure with four pairs of slot embedded into its ground plane. Performance of both antennas was first simulated using CST Microwave Studio. Antenna prototypes were then fabricated and tested in the anechoic chamber and in their actual operating environment — an outdoor field. It was found that the FSPA with slotted ground plane achieved a comparable impedance bandwidth to the first design, with an overall size reduction of 29%. Simulations were Corresponding author: Z. W. Sim (zhiwei.sim@postgrad.manchester.ac.uk).
ATRA: Address Translation Redirection Attack against Hardware-based External Monitors

Hardware-based external monitors have been proposed as a trustworthy method for protecting the kernel integrity. We introduce the design and implementation of Address Translation Redirection Attack (ATRA) that enables complete evasion of the hardware-based external monitor that anchors its trust on a separate processor. ATRA circumvents the external monitor by redirecting the memory access to critical kernel objects into a non-monitored region. Despite the seriousness of the ATRA issue, the address translation integrity has been assumed in many hardware-based external monitors and the possibility of its exploitation has been suggested yet many considered hypothetical. We explore the intricate details of ATRA, explain major challenges in realizing ATRA in practice, and address them with two types of ATRA called Memory-bound ATRA and Register-bound ATRA. Our evaluations with benchmarks show that ATRA does not introduce a noticeable performance degradation to the host system, proving practical applicability of the attack to alert the researchers to seriously address ATRA in designing future external monitors.
Does SMS text messaging help or harm adults' knowledge of standard spelling?

The recent increase in short messaging system (SMS) text messaging, often using abbreviated, non-conventional ‘textisms’ (e.g. ‘2nite’), in school-aged children has raised fears of negative consequences of such technology for literacy. The current research used a paradigm developed by Dixon and Kaminska, who showed that exposure to phonetically plausible misspellings (e.g. ‘recieve’) negatively affected subsequent spelling performance, though this was true only with adults, not children. The current research extends this work to directly investigate the effects of exposure to textisms, misspellings and correctly spelled words on adults’ spelling. Spelling of a set of key words was assessed both before and after an exposure phase where participants read the same key words, presented either as textisms (e.g. ‘2nite’), correctly spelled (e.g. ‘tonight’) or misspelled (e.g. ‘tonite’) words.Analysis showed that scores decreased from preto post-test following exposure to misspellings, whereas performance improved following exposure to correctly spelled words and, interestingly, to textisms. Data suggest that exposure to textisms, unlike misspellings, had a positive effect on adults’ spelling. These findings are interpreted in light of other recent research suggesting a positive relationship between texting and some literacy measures in school-aged children.
A Novel Single-Stage High-Power-Factor LED Street-Lighting Driver With Coupled Inductors

This paper proposes a single-stage high-power-factor (HPF) LED driver with coupled inductors for street-lighting applications. The presented LED driver integrates a dual buck-boost power-factor-correction (PFC) ac-dc converter with coupled inductors and a half-bridge-type LLC dc-dc resonant converter into a single-stage-conversion circuit topology. The coupled inductors inside the dual buck-boost converter subcircuit are designed to be operated in the discontinuous-conduction mode for obtaining high power-factor (PF). The half-bridge-type LLC resonant converter is designed for achieving soft-switching on two power switches and output rectifier diodes, in order to reduce their switching losses. This paper develops and implements a cost-effective driver for powering a 144-W-rated LED street-lighting module with input utility-line voltage ranging from 100 to 120 V. The tested prototype yields satisfying experimental results, including high circuit efficiency (>89.5%), low input-current total-harmonic distortion (<; 5.5%), high PF (> 0.99), low output-voltage ripple (<; 7.5%), and low output-current ripple (<; 5%), thus demonstrating the feasibility of the proposed LED driver.
Visualizing timelines: evolutionary summarization via iterative reinforcement between text and image streams

We present a novel graph-based framework for timeline summarization, the task of creating different summaries for different timestamps but for the same topic. Our work extends timeline summarization to a multimodal setting and creates timelines that are both textual and visual. Our approach exploits the fact that news documents are often accompanied by pictures and the two share some common content. Our model optimizes local summary creation and global timeline generation jointly following an iterative approach based on mutual reinforcement and co-ranking. In our algorithm, individual summaries are generated by taking into account the mutual dependencies between sentences and images, and are iteratively refined by considering how they contribute to the global timeline and its coherence. Experiments on real-world datasets show that the timelines produced by our model outperform several competitive baselines both in terms of ROUGE and when assessed by human evaluators.
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading

In the last few years, machine learning has become a very popular tool for analyzing financial text data, with many promising results in stock price forecasting from financial news, a development with implications for the E cient Markets Hypothesis (EMH) that underpins much economic theory. In this work, we explore recurrent neural networks with character-level language model pre-training for both intraday and interday stock market forecasting. In terms of predicting directional changes in the Standard & Poor’s 500 index, both for individual companies and the overall index, we show that this technique is competitive with other state-of-the-art approaches.
A 1.2 V 8 Gb 8-Channel 128 GB/s High-Bandwidth Memory (HBM) Stacked DRAM With Effective I/O Test Circuits

Motivated by a graphics memory system that achieves multiplied bandwidth by the number of memories per system, HBM DRAM adopts a brand new architecture, with many technical changes and challenges. The first main change in the architecture is the stacked memory structure with TSV array, which has independent bandwidth per slice. The second is semi-independent row, column command interface, which enhances effective performance. For supporting high bandwidth, this chip has fine pitch microbump interface. Methods for testing microbump are explained. 8 Gb stacked HBM is fabricated with chip-on-wafer process and tested with high-frequency wafer probing. Using chip-on-wafer test results, 128 GB/s at 1.2 V supply voltage is achieved.
Fuzzy pre-compensated fuzzy self-tuning fuzzy PID controller of 3 DOF planar robot manipulators

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. Proportional-integral-derivative (PID)-type fuzzy controller is a well-known conventional motion control strategy for manipulators which ensures global asymptotic stability. To enhance the PID-type fuzzy controller performance for the control of rigid planar robot manipulators, in this paper, a fuzzy pre-compensation of a fuzzy self tuning fuzzy PID controller is proposed. The proposed control scheme consists of a fuzzy logic-based pre-compensator followed by a fuzzy self tuning fuzzy PID controller. In the fuzzy self tuning fuzzy PID controller, a supervisory hierarchical fuzzy controller (SHFC) is used for tuning the input scaling factors of the fuzzy PID controller according to the actual tracking position error and the actual tracking velocity error. Numerical simulations using the dynamic model of a three DOF planar rigid robot manipulator with uncertainties show the effectiveness of the approach in set point tracking problems. Our results show that the proposed controller has superior performance compared to a conventional fuzzy PID controller.
Information technology governance evaluation and processes improvement prioritization based on COBIT 5 framework at secretariat general of the Indonesian house of representatives

Information Technology (IT) within Secretariat General of The Indonesian House of Representatives has important role to support the Member of Parliaments (MPs) duties and functions and therefore needs to be well managed to become enabler in achieving organization goals. In this paper, IT governance at Secretariat General of The Indonesian House of Representatives is evaluated using COBIT 5 framework to get their current capabilities level which then followed by recommendations to improve their level. The result of evaluation shows that IT governance process of Secretariat General of The Indonesian House of Representatives is 1.1 (Performed Process), which means that IT processes have been implemented and achieved their purpose. Recommendations for process improvement are derived based on three criteria (Stakeholder's support, IT human resources, and Achievement target time) resulting three processes in COBIT 5 that need to be prioritized: APO13 (Manage Security), BAI01 (Manage Programmes and Projects), and EDM01 (Ensure Governance Framework Setting and Maintenance).
Replay Attacks on Zero Round-Trip Time: The Case of the TLS 1.3 Handshake Candidates

We investigate security of key exchange protocols supporting so-called zero round-trip time (0-RTT), enabling a client to establish a fresh provisional key without interaction, based only on cryptographic material obtained in previous connections. This key can then be already used to protect early application data, transmitted to the server before both parties interact further to switch to fully secure keys. Two recent prominent examples supporting such 0-RTT modes are Google's QUIC protocol and the latest drafts for the upcoming TLS version 1.3. We are especially interested in the question how replay attacks, enabled through the lack of contribution from the server, affect security in the 0-RTT case. Whereas the first proposal of QUIC uses state on the server side to thwart such attacks, the latest version of QUIC and TLS 1.3 rather accept them as inevitable. We analyze what this means for the key secrecy of both the preshared-key-based 0-RTT handshake in draft-14 of TLS 1.3 as well as the Diffie-Hellman-based 0-RTT handshake in TLS 1.3 draft-12. As part of this we extend previous security models to capture such cases, also shedding light on the limitations and options for 0-RTT security under replay attacks.
Authentication using graphical passwords: effects of tolerance and image choice

Graphical passwords are an alternative to alphanumeric passwords in which users click on images to authenticate themselves rather than type alphanumeric strings. We have developed one such system, called PassPoints, and evaluated it with human users. The results of the evaluation were promising with respect to rmemorability of the graphical password. In this study we expand our human factors testing by studying two issues: the effect of tolerance, or margin of error, in clicking on the password points and the effect of the image used in the password system. In our tolerance study, results show that accurate memory for the password is strongly reduced when using a small tolerance (10 x 10 pixels) around the user's password points. This may occur because users fail to encode the password points in memory in the precise manner that is necessary to remember the password over a lapse of time. In our image study we compared user performance on four everyday images. The results indicate that there were few significant differences in performance of the images. This preliminary result suggests that many images may support memorability in graphical password systems.