nils-herrmann/clean_scirepeval huggingface dataset:
A new phylogenetic tree sampling method for maximum parsimony bootstrapping and proof-of-concept implementation

Reconstructing maximum parsimony (MP) trees along with bootstrap for large multiple sequence alignments is a computationally challenging task. In this paper, we present MPBoot, a new method for approximating MP bootstrap based on the idea of a previously introduced method for maximum likelihood bootstrap. MPBoot economizes the computations by only sampling trees on the original alignment and simultaneously calculating exact parsimony scores for all bootstrap alignments. Preliminary experiments on simulated data showed that MPBoot is about 2-3.5 times faster and yields better parsimony scores than a fast bootstrap procedure implemented in TNT. MPBoot also produced less biased support value than TNT for small simulated data sets (<;= 200 sequences). However, for large number of sequences (>=500) MPBoot overestimates the probability that a clade is true, i.e., results in likely high false positive rates. As MPBoot is currently a proof-of-concept implementation, more work is to understand the limitations of the method.
Concatenated coding and hybrid automatic repeat request for wiretap channels

In this study, the authors propose an equivocation scheme for wiretap channels, which is composed of bit-extension mapping, coset coding and hybrid automatic repeat request (HARQ). The inner bit-extension code and outer coset code are used for equivocation of a wiretapper channel, whereas the HARQ scheme is to mitigate noisy errors in a main legitimate channel. These concatenated codes and HARQ are effective and practical for various channel conditions. The average equivocation and the probability of causing imperfect secrecy are analysed for finite codeword lengths. As a function of channel conditions, they investigate the block error rate at the legitimate receiver and the information leakage to the wiretapper. From simulation results, they further determine the minimum requirements of code design for some target values of the `residual' block error rate and information leakage at maximum retransmission.
A vehicle positioning and navigation system design using Mini2440 ARM and Linux

In this paper, we investigate the design and development of a positioning and navigation system using Mini2440 ARM and Linux for mobile vehicles. We comprehensively present the hardware design and software development of GNSS receiver module, map-matching and overall embedded real-time positioning and navigational system. A functional platform shown that the system works well in scenarios of GNSS signals and current 3G data transmission networks in Vietnam. Practical results show that having a limited processing ability, mini2440 ARM fulfils our expectations of fundamental functions from receiving and processing positioning data to display current position, moving history, and navigating vehicle movement on the touchable screen.
Active thermal protection and lifetime extension in 3L-NPC-inverter in the low modulation range

The switches of multilevel inverters in high-power applications are mounted on dedicated heatsinks and there is no heat sharing among the devices. If one semiconductor device is exposed to excessive thermal stress, then the lifespan of the affected device is reduced significantly. This thermal overload may arise due to degradation of the cooling system, failures in firing or in the drivers, inappropriate placing of the semiconductor devices etc. In this paper, a control scheme is devised that can reduce the thermal stress of a particular semiconductor device; the benefit is that the reduction of the lifetime of this device is avoided. Thus, after detecting of the occurrence of a local thermal overload by temperature sensing on the particular heatsink, the control scheme modifies the pulse width modulation in such a way, that the power losses of the stressed device are redistributed among the other semiconductors without limitations of the inverter current. In previous works, this method was already presented for the higher modulation index range; it is now extended to the low modulation range and is validated by experiments.
A comparative evaluation for digital image watermarking techniques in wireless image sensor networks

Wireless sensor networks have emerged as the major criteria that enable the Internet of things evolution. In which, wireless image sensor network have wide application range from special domain to civilian and scientific domain thus it is attracted a lot of research. Since security is of the crucial importance for wireless sensor networks, there are existed several authentication techniques for digital right management, and watermarking is a promising approach for ensuring a part of communication security in these networks due to its popular use and lightweight. To develop a security solution based on this approach, the digital image watermarking techniques need to be considered in different aspects of performance. The paper presents a comparative evaluation on error performance of major digital image processing techniques for JEG/JPEG2000 standard image and its embedded watermark images in a typical wireless image sensor networks. Moreover, a watermark detection probability with varied transform methods is delivered to recognize how attack possibilities. Numerical results will show a best choice of embedded watermark transform method in wireless image sensor networks.
An Enhanced Multi-channel MAC for Vehicular Ad Hoc Networks

The IEEE 1609.4  is a MAC extension of IEEE 802.11p  to support multi-channel operations. However, the IEEE 1609.4 does not allow nodes to exchange non-safety messages during the CCH interval. This paper proposes a Vehicular Enhanced Multi-channel MAC protocol (VEMMAC) for Vehicular Ad hoc Networks (VANETs). The VEMMAC adopts the IEEE 1609.4 with alternating sequences of the Control Channel (CCH) interval and the Service Channel (SCH) interval. Different from the IEEE 1609.4, the VEMMAC allows nodes to transmit non-safety messages during CCH interval and broadcast safety messages twice with each in the CCH and SCH interval. Our proposal can utilize the channel resources more efficiently than the IEEE 1609.4. The simulation results show that the proposed VEMMAC protocol achieves higher throughput for service data and is more reliable for safety messages broadcast than other protocols.
Contourlets: a new directional multiresolution image representation

We propose a new scheme, named contourlet, that provides a flexible multiresolution, local and directional image expansion. The contourlet transform is realized efficiently via a double iterated filter bank structure. Furthermore, it can be designed to satisfy the anisotropy scaling relation for curves, and thus offers a fast and structured curvelet-like decomposition. As a result, the contourlet transform provides a sparse representation for two-dimensional piecewise smooth signals resembling images. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing tasks.
InP Bipolar ICs: Scaling Roadmaps, Frequency Limits, Manufacturable Technologies

Indium phosphide heterojunction bipolar transistors (HBTs) find applications in very wide-band digital and mixed-signal integrated circuits (ICs). Devices fabricated in high-yield process flows at 500 nm feature size obtain 450 GHz cutoff frequencies and 5 V breakdown and enable high yield fabrication of integrated circuits having more than 3000 transistors. Laboratory devices at 250 nm feature size obtain 755 GHz . We describe device and circuit bandwidth limits associated with HBTs, develop scaling roadmaps for HBTs having lithographic minimum feature sizes between 512 and 64 nm, and identify key technological challenges in realizing 480-GHz digital ICs and 1000-GHz amplifiers. Key features of manufacturable self-aligned dielectric sidewall processes are described in detail.
Design and Implementation of Sensor Network on the NGN/IMS

The sensor networks had been used for various application areas and showed effectiveness under the prior application planning and right circumstances that includes cost, data handling and other resources. In order to generalize, it is required to reduce initial cost and use data effectively. This paper proposes connecting a sensor network with the IMS that is the service platform of the NGN to solve these requirements. This paper shows prototype implementation of proposed system for collecting electric power consumption.
ℋ∞ observer-based control for discrete-time one-sided Lipschitz systems with unknown inputs

This paper investigates the problem of robust observer-based stabilization for a class of one-sided nonlinear discrete-time systems subjected to unknown inputs. We propose a simple simultaneous state and input estimator. A nonlinear controller is then proposed to compensate the effects of unknown inputs and to ensure asymptotic stability in closed loop. Several mathematical artifacts are used to deduce stability conditions expressed in terms of LMIs. To show high performances of the proposed technique, a relevant example is provided with comparisons to recent results.
Automatic license plate recognition using mobile device

The rapid development of intelligent transport systems (ITS) brings us a safer and more convenient life. In this field, automatic license plate recognition (ALPR) plays an important role in many applications which have been deployed in reality such as stolen car detection, parking system management, and automatic transport charging system. The traditional ALPR methods usually need a high resolution camera to capture good quality images and a powerful computer to process the complex algorithms. However, with many great scientific breakthroughs in mobile device technology, we can easily buy smartphone/tablet which equipped strong central processing unit (CPU) and excellent camera to meet the requirements of ALPR. In this paper, we develop an Android program which processes the image captured by built-in camera of mobile device to have license plate number and save it into database for further applications. Open-source OpenCV libraries are imported into project for some image processing steps with purpose of programming time saving. We apply Tesseract engine and neural network which are two optical character recognition (OCR) methods to convert license plate image to machine-encoded text.
A Comparison Between Single-Phase Quasi-  $Z$-Source and Quasi-Switched Boost Inverters

The properties of a single-phase quasi Z-source inverter (qZSI) and a single-phase quasi-switched boost inverter (qSBI), both of which are single-stage buck-boost inverters, are investigated and compared. For the same operating conditions, qSBI has the following advantages over qZSI: 1) Three capacitors are saved; 2) the current rating on both of its switches and diodes is lower; 3) its boost factor is higher with an equivalent parasitic effect; and 4) its efficiency is higher. However, qSBI has one more active switch and one more diode than Z-source/ qZSIs. In addition, the capacitor voltage stress of qSBI is higher than that of qZSI. The dc and ac component circuit analysis, impedance design with low-frequency and high-frequency ripples, component stresses, and power loss calculation are presented. A prototype based on a TMS320F28335 DSP is built in order to compare the operating principle of qSBI and qZSI.
An incremental recognition method for online handwritten mathematical expressions

This paper presents an incremental recognition method for online handwritten mathematical expressions (MEs), which is used for busy recognition interface (recognition while writing) without large waiting time. We employ local processing strategy and focus on recent strokes. For the latest stroke, we perform segmentation, recognition and update of Cocke-Younger-Kasami (CYK) table. We also reuse the segmentation and recognition candidates in the previous processes. Moreover, using multi-thread reduces the waiting time. Experiments on our data set show the effectiveness of the incremental method not only in small waiting time but also keeping almost the same recognition rate of the batch recognition method without significant decrease. We also propose the combination of the two methods which succeeds the advantages of the both.
Power allocation for OFDM system in a high-speed train environment

This paper considers power allocation between data and pilot symbols for an orthogonal frequency division multiplexing (OFDM) system in a high-speed train (HST) environment. The channel gains are very quickly time-varying within an OFDM symbol so both their estimation and symbol detection must be simultaneously implemented with unavoidable inter-carrier interferences. The average channel complex gains are estimated and are used to calculate the basis expansion model (BEM) coefficients of the HST channel in data detection. We choose the effective signal-to-interference and noise ratio (SINR) in symbol detection as the cost function and propose the effective algorithm to maximize it. The simulation results confirm the viability of our proposed algorithm.
A Hidden Topic-Based Framework toward Building Applications with Short Web Documents

This paper introduces a hidden topic-based framework for processing short and sparse documents (e.g., search result snippets, product descriptions, book/movie summaries, and advertising messages) on the Web. The framework focuses on solving two main challenges posed by these kinds of documents: 1) data sparseness and 2) synonyms/homonyms. The former leads to the lack of shared words and contexts among documents while the latter are big linguistic obstacles in natural language processing (NLP) and information retrieval (IR). The underlying idea of the framework is that common hidden topics discovered from large external data sets (universal data sets), when included, can make short documents less sparse and more topic-oriented. Furthermore, hidden topics from universal data sets help handle unseen data better. The proposed framework can also be applied for different natural languages and data domains. We carefully evaluated the framework by carrying out two experiments for two important online applications (Web search result classification and matching/ranking for contextual advertising) with large-scale universal data sets and we achieved significant results.
Binary Space Partitioning (BSP) for urban terrain

Urban terrain is complex and they present a very challenging and difficult environment for simulating virtual forces as well as for rendering. The objective of this work is to research on Binary Space Partition technique (BSP) for modeling urban terrain environments. BSP is a method for recursively subdividing a space into convex sets by hyper-planes. This subdivision gives rise to a representation of the scene by means of a tree data structure known as a BSP tree. Originally, this approach was proposed in 3D computer graphics to increase the rendering efficiency. Some other applications include performing geometrical operations with shapes (constructive solid geometry) in CAD, collision detection in robotics and 3D computer games, and other computer applications that involve handling of complex spatial scenes.
Collaborative filtering by multi-task learning

Collaborative filtering is a technique to predict userspsila interests for items by exploiting the behavior patterns of a group of users with similar preferences. This technique has been widely used for recommender systems and has a number of successful applications in E-commerce. In practice, a major challenge when applying collaborative filtering is that a typical user provides ratings for just a small number of items, thus the amount of training data is sparse with respect to the size of the domain. In this paper, we present a method to address this problem. Our method formulates the collaborative filtering problem in a multi-task learning framework by treating each user rating prediction as a classification problem and solving multiple classification problems together. By doing this, the method allows sharing information among different classifiers and thus reduces the effect of data sparsity.
Deep learning and sub-tree mining for document level sentiment classification

Recently, with the development of the online social network, sentiment classification (SC) which determines opinions of people is a significant task in natural language processing. In this research, we propose a model which combines deep learning and sub-tree mining to resolve sentiment classification problem. Stanford Parser is used to extract the relation from the beginning to the end of the sentences and each sentence is represented as a tree. Afterwards, FindBestSub-tree algorithm with sub-tree mining technique eliminates outliers in the dataset. Then, the order of the words in a sentence changes according to DFS (Depth First Search) from a tree after outlier removal phase. Finally, the association between all words in a sentence and all sentences in a document is captured by LSTM and GRNN, respectively. Document sentiment classification experiment is conducted on multi-domain sentiment dataset. The elimination of outliers leads to higher performance in this model1. In our experiment, the proposed method achieves improvements in term of accuracy in a range of 0.14% - 6.93% over LSTM + GRNN model.
An Advanced Closed-Loop Control to Improve the Performance of Hybrid Stepper Motors

This paper presents an effective closed-loop control approach for a stepper motor. The proposed approach consists of motor parameter identification, closed-loop current control, closed-loop position control, and damping control. The presented implementation achieves accurate position, high torque, and smooth speed. By using an improved proportional-integral current control algorithm, this approach not only guarantees the current tracking performance but also increases the bandwidth during the speed response. The advanced position controller achieves both high torque and the synchronous condition of the motor after a loss of synchronism occurs. In addition, this method applies an effective speed damping method to remove resonance at low speed and reduce vibration at high speed. All the proposed algorithms were implemented on an industrial test-bench. The experimental results demonstrate the validity of the proposed approach for practical applications.
Comparison of Watermarking Schemes Using Linear and Nonlinear Feature Matching

Recently, the feature point matching based water-marking techniques have been paying attention for resisting the geometric attacks. We present a performance analysis of robust watermarking using linear and nonlinear feature. In particular, we consider the geometric attacks and the signal processing attacks for image watermarking. In order to analyze the efficiency of linear and nonlinear feature, we employ the linear and the nonlinear feature matching technique in the image watermarking. The extracted feature points can survive against several attacks, therefore, those can be used as reference points for restoration before the extraction of the watermark information. For blindness and robustness, we embed the watermark into the low-band of the discrete cosine transform (DCT) domain. Experimental results show that our performance analysis of watermarking methods using linear and nonlinear feature matching against the geometric attacks and the signal processing attacks. These include the JPEG compression, the filtering attacks, and so on.
Application of the lowest-ID algorithm in cluster-based TDMA system for VANETs

To gather data of a larger area and improve the capability in the system, vehicles in Vehicular Ad-hoc NETworks (VANETs) are organized into clusters. Moreover, the effectiveness of application in VANET depends on performance of Medium Access Control (MAC) protocol. In addition, cluster-based MAC protocols can avoid or limit channel contention, provide fairness to channel access, increase radio network capacity management by reusing the spatial network and effectively controlling the topology of the network. This paper proposes a cluster-based TDMA system using the lowest-ID algorithm for Vehicular Ad hoc NETworks (VANETs). Using the lowest-ID algorithm, the analytical and simulated results show that not only the average number of time slots for electing a vehicle head but also total number of time slots before data can be successfully transmitted is less than the existing cluster-based TDMA system and IEEE 802.11p.
A Method to Aid Recovery and Maintenance of the Input Error Correction Features

In an information system, inputs are submitted to the system from its external environment. However, many input errors cannot be detected automatically and therefore result in errors in the effects raised by the system. Hence, the provision of input error correction features to correct these erroneous effects is critical. The recovery and maintenance of these features are complex and tedious. We have discovered some interesting control flow graph properties with regard to input errors and the implementation of their correction features. This paper proposes a method for the automated recovery of after-effect input error correction features from these properties. Based on the recovered information, we further propose a method to aid the maintenance of these features using decomposition slicing. All the empirical properties have been validated statistically. The approach has also been evaluated through some case studies
An X-band high-efficiency MMIC power amplifier with 20-dB return losses

Describes the design principles and measured performance of an X-band high-efficiency monolithic-microwave-integrated-circuit (MMIC) power amplifier and discuss pertinent factors of the ion-implantation process. Also presented is a worst-case power prediction of the chip performance and a large-signal design using small-signal simulation. This balanced amplifier is fully monolithic with input and output return losses of better than 20 dB provided by Lange couplers. These return losses make it very convenient to cascade with other components. For high-efficiency operation, the drain voltage is 6 V. Across the 40% bandwidth from 8 to 12 GHz, the amplifier produces 1.6 to 2.1 W of output power at 33 to 40% power-added efficiency. For high-power operation, the drain voltage is 8.5 V. The amplifier can produce 2.4 to 2.8 W of output power at 26 to 29% power-added efficiency across the same 40% bandwidth.
Boosting Speed and Accuracy in Deformable Part Models for Face Image in the Wild

Face detection using part based model becomes a new trend in Computer Vision. Following this trend, we propose an extension of Deformable Part Models to detect faces which increases not only precision but also speed compared with current versions of DPM. First, to reduce computation cost, we create a lookup table instead of repeatedly calculating scores in each processing step by approximating inner product between HOG features and weight vectors. Furthermore, early cascading method is also introduced to boost up speed. Second, we propose new integrated model for face representation and its score of detection. Besides, the intuitive non-maximum suppression is also proposed to get more accuracy in detecting result. We evaluate the merit of our method on the public dataset Face Detection Data Set and Benchmark (FDDB). Experimental results shows that our proposed method can significantly boost 5.5 times in speed of DPM method for face detection while achieve up to 94.64% the accuracy of the state-of-the-art technique. This leads to a promising way to combine DPM with other techniques to solve difficulties of face detection in the wild.