nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing Sensor Networks in the Energy-Latency-Density Design Space

ÐIn wireless sensor networks, energy efficiency is crucial to achieving satisfactory network lifetime. To reduce the energy consumption significantly, a node should turn off its radio most of the time, except when it has to participate in data forwarding. We propose a new technique, called Sparse Topology and Energy Management (STEM), which efficiently wakes up nodes from a deep sleep state without the need for an ultra low-power radio. The designer can trade the energy efficiency of this sleep state for the latency associated with waking up the node. In addition, we integrate STEM with approaches that also leverage excess network density. We show that our hybrid wakeup scheme results in energy savings of over two orders of magnitude compared to sensor networks without topology management. Furthermore, the network designer is offered full flexibility in exploiting the energy-latency-density design space by selecting the appropriate parameter settings of our protocol. Index TermsÐSensor networks, energy efficiency, wakeup, topology.
Revisiting the JDL Data Fusion Model II

This paper suggests refinements and extensions of the JDL Data Fusion Model, the standard process model used for a multiplicity of community purposes. However, this Model has not been reviewed in accordance with (a) the dynamics of world events and (b) the changes, discoveries, and new methods in both the data fusion research and development community and related IT technologies. This paper suggests ways to revise and extend this important model. Proposals are made regarding (a) improvements in the understanding of internal processing within a fusion node and (b) extending the model to include (1) remarks on issues related to quality control, reliability, and consistency in DF processing, (2) assertions about the need for co-processing of abductive/ inductive and deductive inferencing processes, (3) remarks about the need for and exploitation of an onto logicallybased approach to DF process design, and ( 4) extensions to account for the case of Distributed Data Fusion (DDF).
Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs

The rapid growth in the volume of many real-world graphs (e.g., social networks, web graphs, and spatial networks) has led to the development of various vertex-centric distributed graph computing systems in recent years. However, real-world graphs from different domains have very different characteristics, which often create bottlenecks in vertex-centric parallel graph computation. We identify three such important characteristics from a wide spectrum of real-world graphs, namely (1)skewed degree distribution, (2)large diameter, and (3)(relatively) high density. Among them, only (1) has been studied by existing systems, but many real-world powerlaw graphs also exhibit the characteristics of (2) and (3). In this paper, we propose a block-centric framework, called Blogel, which naturally handles all the three adverse graph characteristics. Blogel programmers may think like a block and develop efficient algorithms for various graph problems. We propose parallel algorithms to partition an arbitrary graph into blocks efficiently, and blockcentric programs are then run over these blocks. Our experiments on large real-world graphs verified that Blogel is able to achieve orders of magnitude performance improvements over the state-ofthe-art distributed graph computing systems.
Five dimensions of information security awareness

Until the era of the information society, information was a concern mainly for organizations whose line of business demanded a high degree of security. However, the growing use of information technology is affecting the status of information security so that it is gradually becoming an area that plays an important role in our everyday lives. As a result, information security issues should now be regarded on a par with other security issues. Using this assertion as the point of departure, this paper outlines the dimensions of information security awareness, namely its organizational, general public, socio-political, computer ethical and institutional education dimensions, along with the categories (or target groups) within each dimension.
Multi-Beam Multi-Layer Leaky-Wave SIW Pillbox Antenna for Millimeter-Wave Applications

This work proposes a novel multi-beam leaky-wave pillbox antenna. The antenna system is based on three main parts: feeding part (integrated horns), quasi-optical system and radiating part. The radiating and input parts are placed in two different stacked substrates connected by an optimized quasi-optical system. In contrast to conventional pillbox antennas, the quasi-optical system is made by a pin-made integrated parabola and several coupling slots whose sizes and positions are used to efficiently transfer the energy coming from the input part to the radiating part. The latter consists of a printed leaky-wave antenna, namely an array of slots etched on the uppermost metal layer. Seven pin-made integrated horns are placed in the focal plane of the integrated parabola to radiate seven beams in the far field. Each part of the antenna structure can be optimized independently, thus facilitating and speeding up the complete antenna design. The antenna concept has been validated by measurements (around 24 GHz) showing a scanning capability over ±30° in azimuth and more than 20° in elevation thanks to the frequency scanning behavior of the leaky-wave radiating part. The proposed antenna is well suited to low-cost printed circuit board fabrication process, and its low profile and compactness make it a very promising solution for applications in the millimeter-wave range.
Patch antenna using EBG structure for ISM band wearable applications

This paper is about designing Microstrip Patch Antenna with wearable Substrate with additional EBG structure for better gain to be operated in ISM band (2.4 GHz). A Wearable substrate is used in textile garments so this antenna structure is designed specifically for wearing garments, that's why SAR (Specific Absorption Rate) has been measured in this paper. For designing EBG structure help in improving gain and better results for SAR. CST studio software has been used for simulation of this antenna.
An Extended Banker's Algorithm for Deadlock Avoidance

We describe a natural extension of the banker’s algorithm for deadlock avoidance in operating systems. Representing the control flow of each process as a rooted tree of nodes corresponding to resource requests and releases, we propose a quadratic-time algorithm which decomposes each flow graph into a nested family of regions, such that all allocated resources are released before the control leaves a region. Also, information on the maximum resource claims for each of the regions can be extracted prior to process execution. By inserting operating system calls when entering a new region for each process at runtime, and applying the original banker’s algorithm for deadlock avoidance, this method has the potential to achieve better resource utilization because information on the “localized approximate maximum claims” is used for testing system safety.
Hybrid PS full bridge and LLC half bridge DC-DC converter for low-voltage and high-current output applications

A hybrid phase shifted full bridge (PSFB) and LLC half bridge (HB) dc-dc converter for low-voltage and high-current output applications is proposed in this paper. The PSFB shares its lagging leg with the LLC-HB and their outputs are parallel connected. When the output current is small, the energy of LLC circuit in combination with the energy stored in the leakage inductance of PSFB's transformer can help the lagging leg switches to realize ZVS turn on, which can reduce voltage stress and avoid annoying voltage spikes over switches. For the power distribution at rated load, the PSFB converter undergoes most of the power while the LLC-HB converter working as an auxiliary part converts only a small portion of the total power. To improve the conversion efficiency, synchronous rectification technique for the PSFB dc-dc converter is implemented. The design principle is given in view of ZVS for lagging leg switches and low transconductance of LLC converter. The validity of the proposed converter has been verified by experimental results of a 2.5kW prototype.
A robust image fingerprinting system using the Radon transform

With the ever-increasing use of multimedia contents through electronic commerce and on-line services, the problems associated with the protection of intellectual property, management of large database and indexation of content are becoming more prominent. Watermarking has been considered as efficient means to these problems. Although watermarking is a powerful tool, there are some issues with the use of it, such as the modification of the content and its security. With respect to this, identifying content itself based on its own features rather than watermarking can be an alternative solution to these problems. The aim of fingerprinting is to provide fast and reliable methods for content identification. In this paper, we present a new approach for image fingerprinting using the Radon transform to make the fingerprint robust against affine transformations. Since it is quite easy with modern computers to apply affine transformations to audio, image and video, there is an obvious necessity for affine transformation resilient fingerprinting. Experimental results show that the proposed fingerprints are highly robust against most signal processing transformations. Besides robustness, we also address other issues such as pairwise independence, database search efficiency and key dependence of the proposed method.
Advancing NLP via a distributed-messaging approach

Natural Language Processing (NLP) constitutes a fundamental module for a plethora of domains where unstructured text is a predominant source. Despite the keen interest of both industry and research community in developing NLP tools, current industrial solutions still suffer from two main cons. First, the architectures underlying existing systems do not satisfy critical requirements of large-scale processing, completeness, and versatility. Second, the algorithms typically employed for entity recognition and disambiguation — a core task common to all modern NLP systems — are still not well-suited for deployment in a real industrial environment, for evident issues of efficiency and result interpretability. In this paper we present Hermes, a novel NLP tool that overcomes the two main limitations of existing solutions. By employing an efficient and extendable distributed-messaging architecture, Hermes achieves the critical requirements of large-scale processing, completeness, and versatility. Moreover, our tool includes an entity-disambiguation algorithm enhanced with a two-level hashing-based approximation technique to considerably improve efficiency, as a well as a densest-subgraph-extraction method to increase result interpretability.
Automatic Hair Detection in the Wild

This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour. The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique that yields the desired (complete) hair region. We demonstrate the applicability of our method on several real images.
Design and Test of a High-Power High-Efficiency Loosely Coupled Planar Wireless Power Transfer System

In this paper, a high-power high-efficiency wireless-power-transfer system using the class-E operation for transmitter via inductive coupling has been designed and fabricated using the proposed design approach. The system requires no complex external control system but relies on its natural impedance response to achieve the desired power-delivery profile across a wide range of load resistances while maintaining high efficiency to prevent any heating issues. The proposed system consists of multichannels with independent gate drive to control power delivery. The fabricated system is compact and capable of 295 W of power delivery at 75.7% efficiency with forced air cooling and of 69 W of power delivery at 74.2% efficiency with convection cooling. This is the highest power and efficiency of a loosely coupled planar wireless-power-transfer system reported to date.
3.2 A 320mW 32Gb/s 8b ADC-based PAM-4 analog front-end with programmable gain control and analog peaking in 28nm CMOS

Multilevel modulation formats, such as PAM-4, have been introduced in recent years for next generation wireline communication systems for more efficient use of the available link bandwidth. High-speed ADCs with digital signal processing (DSP) can provide robust performance for such systems to compensate for the severe channel impairment as the data rate continues to increase.
A Simple But Effective Method to Incorporate Trusted Neighbors in Recommender Systems

Providing high quality recommendations is important for online systems to assist users who face a vast number of choices in making effective selection decisions. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. To address these issues, in this paper, we propose a simple but effective method, namely "Merge", to incorporate social trust information (i.e. trusted neighbors explicitly specified by users) in providing recommendations. More specifically, ratings of a user's trusted neighbors are merged to represent the preference of the user and to find similar other users for generating recommendations. Experimental results based on three real data sets demonstrate that our method is more effective than other approaches, both in accuracy and coverage of recommendations.
SMT-based Task- and Network-level Static Schedule Generation for Time-Triggered Networked Systems

In Ethernet-based time-triggered networks, like TTEthernet, a global communication scheme, for which the schedule synthesis is known to be an NP-complete problem, establishes contention-free windows for the exchange of messages with guaranteed low latency and minimal jitter. However, in order to achieve end-to-end determinism at the application level, software tasks running on the end-system nodes need to obey a similar execution scheme with tight dependencies towards the network domain. In this paper we address the simultaneous co-synthesis of network as well as application schedules for preemptive time-triggered tasks communicating in a switched multi-speed time-triggered network. We use Satisfiability Modulo Theories (SMT) to formulate the scheduling constraints and solve the resulting problem using a state-of-the-art SMT solver. Furthermore, we introduce a novel incremental scheduling approach, based on the demand bound test for asynchronous constrained-deadline periodic tasks, which significantly improves scalability for the average case without sacrificing schedulability. We demonstrate the performance of our approach using synthetic network topologies and system configurations.
Sequence classification using the high-level features extracted from deep neural networks

The recent success of deep neural networks (DNNs) in speech recognition can be attributed largely to their ability to extract a specific form of high-level features from raw acoustic data for subsequent sequence classification or recognition tasks. Among the many possible forms of DNN features, what forms are more useful than others and how effective these DNN features are in connection with the different types of downstream sequence recognizers remained unexplored and are the focus of this paper. We report our recent work on the construction of a diverse set of DNN features, including the vectors extracted from the output layer and from various hidden layers in the DNN. We then apply these features as the inputs to four types of classifiers to carry out the identical sequence classification task of phone recognition. The experimental results show that the features derived from the top hidden layer of the DNN perform the best for all four classifiers, especially for the autoregressive-moving-average (ARMA) version of a recurrent neural network. The feature vector derived from the DNN's output layer performs slightly worse but better than any of the hidden layers in the DNN except the top one.
Optimal Charging Strategies for Unidirectional Vehicle-to-Grid

Vehicle-to-grid (V2G) has been proposed as a way to increase the adoption rate of electric vehicles (EVs). Unidirectional V2G is especially attractive because it requires little if any additional infrastructure other than communication between the EV and an aggregator. The aggregator in turn combines the capacity of many EVs to bid into energy markets. In this work an algorithm for unidirectional regulation is developed for use by an aggregator. Several smart charging algorithms are used to set the point about which the rate of charge varies while performing regulation. An aggregator profit maximization algorithm is formulated with optional system load and price constraints analogous to the smart charging algorithms. Simulations on a hypothetical group of 10 000 commuter EVs in the Pacific Northwest verify that the optimal algorithms increase aggregator profits while reducing system load impacts and customer costs.
Use of Fillers in Rhinoplasty.

Surgical rhinoplasty is the one of the most common cosmetic procedures in Asians. But there are limitations, such as down time, high cost, and a steep learning curve. Most complications are implant related. A safer and less invasive procedure is rhinoplasty using fillers. Good knowledge of the nasal anatomy is essential for rhinoplasty using fillers. Knowledge of nerves, blood supply, and injection plane allows avoiding complications. There are several planes in the nose. The deep fatty layer is recommended for injection, because it is wide and loose and there are less important neurovascular structures. Botulinum toxin also can be used for noninvasive rhinoplasty.
Millimeter-Wave Transmitarrays for Wavefront and Polarization Control

Two separate transmitarrays that operate at 77 GHz are designed and fabricated. The first transmitarray acts as a quarter-wave plate that transforms a linearly polarized incident wave into a circularly polarized transmitted wave. The second transmitarray acts as both a quarter-wave plate and a beam refracting surface to provide polarization and wavefront control. When the second transmittarray is illuminated with a normally incident, linearly polarized beam, the transmitted field is efficiently refracted to 45 °, and the polarization is converted to circular. The half-power bandwidth was measured to be 17%, and the axial ratio of the transmitted field remained below 2.5 dB over the entire bandwidth. Both designs have a subwavelength thickness of 0.4 mm (λ°/9.7). The developed structures are fabricated with low-cost printed-circuit-board processes on flexible substrates. The transmitarrays are realized by cascading three patterned metallic surfaces (sheet admittances) to achieve complete phase control, while maintaining high transmission. Polarization conversion is accomplished with anisotropic sheets that independently control the field polarized along the two orthogonal axes. The structures are analyzed with both circuit- and fields-based approaches.
Project massive: a study of online gaming communities

Massively Multiplayer Online Games (MMOGs) continue to be a popular and lucrative sector of the gaming market. Project Massive was created to assess MMOG players' social experiences both inside and outside of their gaming environments and the impact of these activities on their everyday lives. The focus of Project Massive has been on the persistent player groups or "guilds" that form in MMOGs. The survey has been completed online by 1836 players, who reported on their play patterns, commitment to their player organizations, and personality traits like sociability, extraversion and depression. Here we report our cross-sectional findings and describe our future longitudinal work as we track players and their guilds across the evolving landscape of the MMOG product space.
Effective emotion recognition in movie audio tracks

This paper addresses the problem of speech emotion recognition from movie audio tracks. The recently collected Acted Facial Expression in the Wild 5.0 database is used. The aim is to discriminate among angry, happy, and neutral. We extract a relatively small number of features, a subset of which is not commonly used for the emotion recognition task. Those features are fed as input to an ensemble classifier that combines random forests with support vector machines. An accuracy of 65.63% is reported, outperforming a baseline system that uses the K-nearest neighbor classifier and has an accuracy of 56.88%. To verify the suitability of the exploited features, the same ensemble classification schema is applied on the feature set similar those employed in Audio/Visual Emotion Challenge 2011. In the latter case, an accuracy of 61.25% is achieved using a large set of 1582 features, as opposed to just 86 features in our case that lead to a relative improvement of 7.15% in accuracy.
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state.
A simple approach to Bayesian network computations

The general problem of computing poste rior probabilities in Bayesian networks is NP hard Cooper However e cient algorithms are often possible for particular applications by exploiting problem struc tures It is well understood that the key to the materialization of such a possibil ity is to make use of conditional indepen dence and work with factorizations of joint probabilities rather than joint probabilities themselves Di erent exact approaches can be characterized in terms of their choices of factorizations We propose a new approach which adopts a straightforward way for fac torizing joint probabilities In comparison with the clique tree propagation approach our approach is very simple It allows the pruning of irrelevant variables it accommo dates changes to the knowledge base more easily it is easier to implement More importantly it can be adapted to utilize both intercausal independence and condi tional independence in one uniform frame work On the other hand clique tree prop agation is better in terms of facilitating pre computations