nils-herrmann/clean_scirepeval huggingface dataset:
Piggyback Cooperative Repetition for Reliable Broadcasting of Safety Messages in VANETs

Vehicle safety communication applications require safety messages to be received by all the targeted vehicles within their lifetime. We propose a piggybacked cooperative repetition approach for reliably broadcasting safety messages in VANETs. Repetitions by neighbors that receive the original transmission can effectively cover the areas that are missed in the original transmissions. Moreover, the repetitions are piggybacked by the newly generated messages so that no extra messages are injected into the network. Our experiments show a significant performance improvement, achieving over 90% reception rate of safety messages at distances up to 200 meters to the sender.
Identification of actuator model in an electrical machine by prediction error method and cultural particle swarm optimization

This paper discusses identification of an actuator model, which has been built inside a two-pole induction motor in order to control rotor vibrations. The methods used for identification are prediction error method and cultural particle swarm optimization with mutation. The first-mentioned method produces a black box model with correspondence to input-output measurements. The second method is used to identify parameters of a linear time-invariant state-space model, which is based on electromechanical equations. The results are compared in time domain and in frequency domain.
Multipath Effect Mitigation by Joint Spatiotemporal Separation in Large-Scale Array Localization

The emerging technologies in 5G network not only bring revolutions in communications, but also promise new solutions in wireless localization. In this paper, we investigate how large-scale arrays impact the localization performance and derive the accuracy bound for wireless localization with multiple-input multiple-output (MIMO) systems. We then prove that when the number of array elements goes large, the multipath effects will vanish due to the asymptotic orthogonality of spatial channels in MIMO localization. The multipath mitigation can also be interpreted as the temporal and spatial separation supported by the signal bandwidth and the number of antennas, respectively. Finally, numerical results are given to validate our analysis.
A 2.2-ps Two-Dimensional Gated-Vernier Time-to-Digital Converter With Digital Calibration

This brief presents a two-dimensional (2-D) Vernier time-to-digital converter (TDC) which uses two 3-stage gated ring oscillators (GROs) in the X/Y Vernier branches. The already small Vernier quantization noise (~10.6 ps) is improved by the first-order noise shaping of the GRO. Moreover, since all the delay differences between the X and Y phases can be used (rather than only the diagonal line of the one-dimensional architecture), the intrinsic large latency time of the Vernier architecture is dramatically reduced. The TDC is implemented in a 65-nm CMOS process and consumes 2.3 mA from 1.0 V. The measured total noise integrated over a bandwidth of 1.25 MHz yields an equivalent TDC resolution of 2.2 ps, whereas the average latency time (within 2 ns) is less than 1/6 of that in a standard Vernier TDC.
Online supervised learning from multi-field documents for email spam filtering

Email spam filtering is considered as an online supervised learning task for binary text classification (TC). Normally, the previous statistical TC algorithms treat an email as a single plain-text document, ignoring the multi-field feature of email documents. This paper investigates the multi-field feature, and proposes a multi-field learning (MFL) approach for email spam filtering. The MFL approach divides the complex TC problem of multi-field document into several sub-problems, and conquers each sub-problem separately. At online learning, multi-scorer is learned separately within its text field according to online supervised feedbacks. At online predicting, multi-scorer's output scores are combined to predict the new document's category. The MFL framework is a general frame to combine scorers implemented by any statistical TC algorithms. However, previous TC algorithms often require great training or updating time, which are impractical for large-scale email systems. Considering the space-time spending of email spam filtering, a string-frequency index (SFI) binary TC algorithm is proposed, which is based on the straightforward conditional probability and has low space-time complexity for both online learning and online predicting. The experimental results on TREC spam track show that the performances of online Bayesian and relaxed online SVMs algorithms can be improved by the MFL approach. Especially, the proposed SFI algorithm can achieve the state-of-the-art performance at greatly reduced computational cost within the MFL framework.
Action recognition with multiscale spatio-temporal contexts

The popular bag of words approach for action recognition is based on the classifying quantized local features density. This approach focuses excessively on the local features but discards all information about the interactions among them. Local features themselves may not be discriminative enough, but combined with their contexts, they can be very useful for the recognition of some actions. In this paper, we present a novel representation that captures contextual interactions between interest points, based on the density of all features observed in each interest point's mutliscale spatio-temporal contextual domain. We demonstrate that augmenting local features with our contextual feature significantly improves the recognition performance.
Learning based on kernel discriminant-EM algorithm for image classification

In image classification and other learning-based object recognition tasks, it is often tedious and expensive to label large training data sets. Discriminant-EM (DEM), proposed as a semi-supervised learning framework, takes both labeled and unlabeled data to learn classifiers. The paper extends the linear DEM to a nonlinear kernel algorithm, KDEM, and evaluates KDEM on both benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated.
The organization effects of technical trade measures

With the rapid development of global trade, TBTs have already become the most important barriers to trade suffered by China. Stringent technical requirements drain the exporters' enthusiasm, and impede the technical innovation and market competition. We focus on the TBTs effects at the organization level, building theoretical models to analyze the short-run effects of new issued technical requirements in the prefect competitive market. This paper is intended to provide a new perspective for government authorities and export enterprises to deal with TBTs.
Dynamic Modeling and Simulation of One Kind of Industry Robot by DBEM

A systemic modeling method of an industry robot structure was proposed by mixed fundamental solutions, and the corresponding dynamic characteristics were analyzed. Generally, the robot structure can be modeled in beam, plate, spring and mass elements by a dynamic boundary element method (DBEM). Owing to lots of joints exist in the robot structure, the universal behaviors formulas of stiffness and damp are given in order to reasonable introduced flexible conditions in this paper, and the corresponding relational expressions of flexible conditions are established, too. At last, boundary dynamic equations of the robot structure are deduced based on dynamic and static fundamental solutions. According to the modeling process, flexible joints conditions have been introduced to the equations conveniently by interior items of domain and external force items in the equations. Numerical simulation and experiments show that the established method improves modeling precision and efficiency. With the manufacture industry development fast, all kinds of industry robots are applied to many fields. Owing to the dynamic behaviors of the robot structure can directly affect work efficiency, stability and reliability of robot, it is very important for the robot structure to rapid predict and evaluate the dynamic behaviors at its design stage. So, a mixed modeling method is introduced to analyze one kind of industry robot. A boundary element method is applied to analyze a dynamic problem with respect to time, which is called a dynamic boundary element Method (DBEM). At present, DBEM have received some improvement in theoretical methods and engineering applications (1) . Beskos detailed discussed numerical value solutions and applied improvement for elasticity DBEM problems
Coping Strategy on Non-Structured Problems of Enterprise Crisis Management: Based on Seamless Integration of CMP and Checkland SSM

Since environment changes rapidly and becomes more complicated, no enterprise can avoid the threat of crises of different kinds in the age of globalization and network. It is important that enterprise should carry out crisis management (CM) to prevent or alleviate the damage caused by any crises when necessary. However, managers may be at a loss when faced with non-structured problems (NSP). This paper gives definition to NSP in CM process and analyses the reasons NSP exist. After reviewing the contributions crisis management plan (CMP) brings about and its disadvantages, this paper suggests that CMP and Checkland soft system methodology (SSM) should be integrated seamlessly to develop strategy for NSP.
Distribution of Heavy Metal Concentration of Sewage Sludge in China

Heavy metal contained in sewage sludge prevented the safety resource utilization of sludge and need to be disposed. Survey was conducted to find out main heavy metals in sludge generated in wastewater treatment plants in China and approximate content range of heavy metals in sewage sludge. The results showed that in domestic Cu and Zn were the main heavy metals with concentrations exceed the standard highly. And an order of Cr> Zn> Ni> Cu indicated the concentration situation of electroplating sewage sludge. There are a large amount of heavy metal ions in textile and dyeing industry wastewater sewage sluge, and the order of concentration was Zn> Cu> Pb> Ni> Cr> Cd. Cr was the most noteworthy heavy metal hazard heavy metal in tannery sludge.
A proposal for investment recovery of TCSC based on electrical dissecting method

FACTS devices can be utilized to alleviate or reduce the electric network's congestion. Resonable and effective cost recovery mechanism is necessary for the utilization of FACTS. Based on the idea of “sharing the resoponsibility “, a new cost allocation method of FACTS devices is proposed in this paper, by which the cost of FACTS is allocated among all the trading participants according to their own responsibility factors for congestion. Electrical dissection method is used to analize who has caused the congestion and obtain their responsibility factors quantificationally. On the basis of responsibility factors the cost of FACTS devices is allocated. A modified IEEE 30-bus system was used to validate this paper's method and results illustrated it is resonable and effective.
Transcranial cavitation detection in primates during blood-brain barrier opening-a performance assessment study

Focused ultrasound (FUS) has been shown promise in treating the brain locally and noninvasively. Transcranial passive cavitation detection (PCD) provides methodology for monitoring the treatment in real time, but the skull effects remain a major challenge for its translation to the clinic. In this study, we investigated the sensitivity, reliability, and limitations of PCD through primate (macaque and human) skulls in vitro. The results were further correlated with the in vivo macaque studies including the transcranial PCD calibration and real-time monitoring of blood-brain barrier (BBB) opening, with magnetic resonance imaging assessing the opening and safety. The stable cavitation doses using harmonics (SCDh) and ultraharmonics (SCDu), the inertial cavitation dose (ICD), and the cavitation SNR were quantified based on the PCD signals. Results showed that through the macaque skull, the pressure threshold for detecting the SCDh remained the same as without the skull in place, whereas it increased for the SCDu and ICD; through the human skull, it increased for all cavitation doses. The transcranial PCD was found to be reliable both in vitro and in vivo when the transcranial cavitation SNR exceeded the 1-dB detection limit through the in vitro macaque (attenuation: 4.92 dB/mm) and human (attenuation: 7.33 dB/ mm) skull. In addition, using long pulses enabled reliable PCD monitoring and facilitate BBB opening at low pressures. The in vivo results showed that the SCDh became detectable at pressures as low as 100 kPa; the ICD became detectable at 250 kPa, although it could occur at lower pressures; and the SCDu became detectable at 700 kPa and was less reliable at lower pressures. Real-time monitoring of PCD was further implemented during BBB opening, with successful and safe opening achieved at 250 to 600 kPa in both the thalamus and the putamen. In conclusion, this study shows that transcranial PCD in macaques in vitro and in vivo, and in humans in vitro, is reliable by improving the cavitation SNR beyond the 1-dB detection limit.
Analyzing and capturing articulated hand motion in image sequences

Capturing the human hand motion from video involves the estimation of the rigid global hand pose as well as the nonrigid finger articulation. The complexity induced by the high degrees of freedom of the articulated hand challenges many visual tracking techniques. For example, the particle filtering technique is plagued by the demanding requirement of a huge number of particles and the phenomenon of particle degeneracy. This paper presents a novel approach to tracking the articulated hand in video by learning and integrating natural hand motion priors. To cope with the finger articulation, this paper proposes a powerful sequential Monte Carlo tracking algorithm based on importance sampling techniques, where the importance function is based on an initial manifold model of the articulation configuration space learned from motion-captured data. In addition, this paper presents a divide-and-conquer strategy that decouples the hand poses and finger articulations and integrates them in an iterative framework to reduce the complexity of the problem. Our experiments show that this approach is effective and efficient for tracking the articulated hand. This approach can be extended to track other articulated targets.
Multi-objective Flexible Job Shop Scheduling Problem Based on Monte-Carlo Tree Search

Flexible job-shop scheduling problem (FJSP) is very important in both fields of production management and combinatorial optimization. This paper focuses on the multiobjective flexible job shop scheduling problem (MO-FJSP) with three objectives which minimizing make span, total workload and maximal workload, respectively, with Pareto manner. In addition, Monte-Carlo Tree Search (MCTS) is successful in computer Go and many other games. Hence, solving FJSP by MCTS is a new attempt. In this paper, we propose an MCTS algorithm for FJSP, by incorporating Variable Neighborhood Descent Algorithm and other techniques like Rapid Action Value Estimates Heuristic and Transposition Table. Our algorithm finds Pareto solutions of the benchmark problems proposed by Kacem et al. within 116 seconds: 4 solutions in 4×5, 3 in 10×7, 4 in 8×8, 4 in 10×10 and 2 in 15×10. These solutions are the same as the best found to date. Although one article claimed to have an extra 8×8 solution, that article did not find some of the above solutions.
Improved modulation classification of MPSK signals based on high order cumulants

An improved modulation classification algorithm of MPSK signals based on high order cumulants is proposed and analyzed. Under the condition of unknow carrier frequency, first the constant amplitude and delayed complex conjugated multiplication is used to preprocess the original signal, then four order cumulants are exploited to construct the classified character. The unchangeness of classified character is proved in detail, and the robustness of the algorithm is analyzed aiming at short wave channels. Theoretical analysis and practical signal testing justify the effectiveness of the new algorithm.
Power supply system for a compact 1.2 GEV booster synchrotron

Low cost power supply system for compact full energy booster synchrotron was designed, developed and successfully commissioned at Duke University. 500 kW second hand thyristor controlled power supply has been completely rebuilt to provide high accuracy ramping of current in the range between 150 A and 700 A in a 1.3 sec repetition cycle. Reproducibility of current at injection and extraction energy of better than 0.2 % was achieved. Conflict of requirements of a fast ramp operation and a magnet protection in the case of emergency shutdown was resolved by means of additional thyristor switches. All trim power supplies involved in ramp have been matched with main power supply for the time response and voltage range. Vertical injection to and extraction from the booster requires a strong Y-bump. Combination of low voltage DC power supply and pulse boosting circuit has eliminated the need of expensive power supply for peak power about 4 kW. Challenges of design, main parameters of the booster power supply system and discussion of operation experience are presented in this paper.
X-ray inspection image enhencement using shearlet transform

In automatic X-ray inspection, to denoise and enhance characteristics is a very important issue for application, especially the images used to inspect the wires in chips. Because the methods used to enhance or denoise may degrade the characteristic of the wires which can be seen as curves such that the efficiency is not enough. Shearlet has been approved of its perfect performance on curve description. In this paper, the method is applied and the method of coefficient modification is used. As a result, it shows that the wires are effectively enhanced while the noise is also attenuated.
A Modified PSO Method Based on Cultural Algorithm and Quantum Computing

A novel Cultural Quantum-behaved Particle Swarm Optimization algorithm (CQPSO) is proposed in this paper to improve the performance of the Quantum-behaved PSO (QPSO). The cultural framework is embedded in our QPSO, and the knowledge stored in the belief space can guide the evolution of the QPSO. A total of 15 high-dimensional and multi-modal functions as well as an optimal pressure vessel design problem are employed to investigate the proposed algorithm. Numerical simulations results demonstrate that the CQPSO can indeed outperform the QPSO.
Distributed Video Coding With Feedback Channel Constraints

Many of the distributed video coding (DVC) systems described in the literature make use of a feedback channel from the decoder to the encoder to determine the rate. However, the number of requests through the feedback channel is often high, and as a result the overall delay of the system could be unacceptable in practical applications. As a solution, feedback-free DVC systems have been proposed, but the problem with these solutions is that they incorporate a difficult trade-off between encoder complexity and compression performance. Recognizing that a limited form of feedback may be supported in many video-streaming scenarios, in this paper we propose a method for constraining the number of feedback requests to a fixed maximum number of N requests for an entire Wyner-Ziv (WZ) frame. The proposed technique estimates the WZ rate at the decoder using information obtained from previously decoded WZ frames and defines the N requests by minimizing the expected rate overhead. Tests on eight sequences show that the rate penalty is less than 5% when only five requests are allowed per WZ frame (for a group of pictures of size four). Furthermore, due to improvements from previous work, the system is able to perform better than or similar to DISCOVER even when up to two requests per WZ frame are allowed. The practical usefulness of the proposed approach is studied by estimating end-to-end delay and encoder buffer requirements, indicating that DVC with constrained feedback can be an important solution in the context of video-streaming scenarios.
Analysis, design and implementation of secure LoRaWAN sensor networks

LoRaWAN is a LPWAN (Low Power Wide Area Network) technology used in a large variety of Internet of Things (IoT) applications. The paper addresses the security concerns of data protection and data privacy in sensor networks that make use of the LoRaWAN (Long-Range Wide Area Network) protocol specification. In this context, this paper performs an in-depth analysis of the security aspects in LoRaWAN sensor networks. Additionally, a novel secure LoRaWAN network architecture is proposed. The architecture provides protected data transmission and prevents unauthorized access and data loss. Based on experimental results and security tests, recommendations are made concerning the best practices to be followed in order to provide secure data transmission and data privacy in applications built using the LoRaWAN protocol specification.
Unitary Transforms Using Time-Frequency Warping for Digital Holograms of Deep Scenes

With the advent of ultrahigh-resolution holographic displays, viewing macroscopic deep scenes with large viewing angles becomes a possibility. These deep holograms possess different signal properties in contrast with common applications where the scene content is assumed to lie around a planar slice. Therefore, the conventional approach of refocusing at a fixed depth is ineffective. There is a need for an efficient invertible transformation that is able to account for the wide depth range of macroscopic three-dimensional scenes. To this end, we derive necessary invertibility conditions for the diffraction from nonplanar surfaces for symmetric light propagation kernels, such as Fresnel diffraction. We construct a unitary transform for modeling deep holographic scenes using a generalization of linear canonical transforms. From the symplectic properties of the time–frequency domain, we obtain invertibility conditions of the transforms depending on surface shape, hologram bandwidth, and wavelength. These transforms can be subsequently combined with other sparsifying transforms for compression. Experiments demonstrate one application in lossy coding of holograms by implementing a computationally efficient subset of the transforms for piecewise depth profiles that is combined with the JPEG 2000 codec. Results show improved reconstruction quality. A significant visual gain is observed as the depth information is well preserved under identical encoding rates in contrast to using Fresnel propagation at a fixed depth. This paper shows that it is possible to effectively represent holograms of variable-depth scenes and our local adaptive transform leads to a practical holographic compression framework.
Correlation channel estimation in pixel-domain distributed video coding

The paper addresses an essential problem in distributed video coding (DVC) which is modeling and estimation of the correlation channel. Current works assume the distribution of the correlation noise to be independent from the realization of the side-information. The paper demonstrates that this assumption is inaccurate and proposes a novel model which depends on the realization of the side-information. The proposed model is experimentally validated showing remarkable accuracy over conventional models. Driven by the side-information-dependency of the proposed model, a novel technique for estimating the correlation channel in video at the decoder is introduced. The proposed technique is incorporated into a unidirectional spatial-domain DVC system achieving similar performance compared to the state-of-the-art transform-domain DVC at a much lower encoding complexity.