nils-herrmann/clean_scirepeval huggingface dataset:
Discovering Video Shot Categories by Unsupervised Stochastic Graph Partition

Video shots are often treated as the basic elements for retrieving information from videos. In recent years, video shot categorization has received increasing attention, but most of the methods involve a procedure of supervised learning, i.e., training a multi-class predictor (classifier) on the labeled data. In this paper, we study a general framework to unsupervisedly discover video shot categories. The contributions are three-fold in feature, representation, and inference: (1) A new feature is proposed to capture local information in videos, defined with small video patches (e.g., 11 × 11 × 5 pixels). A dictionary of video words can be thus clustered off-line, characterizing both appearance and motion dynamics. (2) We pose the problem of categorization as an automated graph partition task, in that each graph vertex represents a video shot, and a partitioned sub-graph consisting of connected graph vertices represents a clustered category. The model of each video shot category can be analytically calculated by a projection pursuit type of learning process. (3) An MCMC-based cluster sampling algorithm, namely Swendsen-Wang cuts, is adopted to efficiently solve the graph partition. Unlike traditional graph partition techniques, this algorithm is able to explore the nearly global optimal solution and eliminate the need for good initialization. We apply our method on a wide variety of 1600 video shots collected from Internet as well as a subset of TRECVID 2010 data, and two benchmark metrics, i.e., Purity and Conditional Entropy, are adopted for evaluating performance. The experimental results demonstrate superior performance of our method over other popular state-of-the-art methods.
Charger topology selection for group charging of volume tablet scale devices through wireless power transfer

In group activities, tablet scale devices are convenient in applications, such as for trip group message broadcast/communication. This work is to provide proper charger topologies, and the targeted chargers charge all devices in the same time before given to users. The number of devices is from low tens to high tens. We analyze possible scenarios and propose two charger topologies. One is tube-shape for up to twenty devices, and the other is cubic-shape for more devices up to high tens. Corresponding coil placements are designed, magnetic flux and phases of driving current are discussed.
Data Encryption Using ECG Signals with Chaotic Henon Map

Electrocardiogram (ECG) signals varying from person to person, it could possibly be applied as a tool for biometric recognition. This paper attempts to introduce an individual feature of ECG with a chaotic Henon map for cryptography. The encryption system utilizes a portable instrument (Heart Pal) to collect ECG signals from the encrypted person and applies an intelligent algorithm based on Chaos theory to generate initial keys for chaotic Henon map. High quality randomness of ECG signals results in a widely expanded key space which would be an ideal key generator for data encryption.
Design of an In-vehicle Anti-theft Component

An in-vehicle anti-theft approach, named GIVAC (group identification of in-vehicle anti-theft component), is proposed. The approach proposes that each valuable appliance should be integrated with an GIVAC component which will not enable the functions of the appliances if it should find itself is illegally moved to another car. Each GIVAC-enabled appliance would authenticate itself with the authorized center of the car, upon power-on, to see if it is in the right car (group). The authorized center can also be controlled by the immobilizer. Such an approach would aggregate the anti-theft capability of cars.
Efficient energy managing control for electrical scooters

This study presents a generalised energy recharge control (ERC) scheme for electrical scooters (ESs), which includes a central control unit, a battery recharge-control unit, a voltage boost-control unit, a brake-control unit, a motor and a sealed rechargeable battery. There are three recharge modes provided: (i) direct low-side driving circuit recharge mode, (ii) voltage boost recharge mode and (iii) brake assistant mode. With the proposed ERC mechanism, it is shown that mechanical energy generated by the running wheels can be converted into electrical energy in an efficient way for recharge and the controller would extend the ES's touring range.
Scalable object detection by filter compression with regularized sparse coding

For practical applications, an object detection system requires huge number of classes to meet real world needs. Many successful object detection systems use part-based model which trains several filters (classifiers) for each class to perform multiclass object detection. However, these methods have linear computational complexity in regard to the number of classes and may lead to huge computing time. To solve the problem, some works learn a codebook for the filters and conduct operations only on the codebook to make computational complexity sublinear in regard to the number of classes. But the past studies missed to consider filter characteristics, e.g., filters are weights trained by Support Vector Machine, and rather they applied method such as sparse coding for visual signals' optimization. This misuse results in huge accuracy loss when a large speedup is required. To remedy this shortcoming, we have developed a new method called Regularized Sparse Coding which is designed to reconstruct filter functionality. That is, it reconstructs the ability of filter to produce accurate score for classification. Our method can reconstruct filters by minimizing score map error, while sparse coding reconstructs filters by minimizing appearance error. This different optimization strategy makes our method be able to have small accuracy loss when a large speedup is achieved. On the ILSVRC 2013 dataset, which has 200 classes, this work represents a 16 times speedup using only 1.25% memory on single CPU with 0.04 mAP drop when compared with the original Deformable Part Model. Moreover, parallel computing on GPUs is also applicable for our work to achieve more speedup.
Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm

Learning-to-rank for information retrieval has gained increasing interest in recent years. Inspired by the success of sparse models, we consider the problem of sparse learning-to-rank, where the learned ranking models are constrained to be with only a few nonzero coefficients. We begin by formulating the sparse learning-to-rank problem as a convex optimization problem with a sparse-inducing $(\ell_1)$ constraint. Since the $(\ell_1)$ constraint is nondifferentiable, the critical issue arising here is how to efficiently solve the optimization problem. To address this issue, we propose a learning algorithm from the primal dual perspective. Furthermore, we prove that, after at most $(O({1\over \epsilon } ))$ iterations, the proposed algorithm can guarantee the obtainment of an $(\epsilon)$-accurate solution. This convergence rate is better than that of the popular subgradient descent algorithm. i.e., $(O({1\over \epsilon^2} ))$. Empirical evaluation on several public benchmark data sets demonstrates the effectiveness of the proposed algorithm: 1) Compared to the methods that learn dense models, learning a ranking model with sparsity constraints significantly improves the ranking accuracies. 2) Compared to other methods for sparse learning-to-rank, the proposed algorithm tends to obtain sparser models and has superior performance gain on both ranking accuracies and training time. 3) Compared to several state-of-the-art algorithms, the ranking accuracies of the proposed algorithm are very competitive and stable.
Identification of Flight Vehicle Models Using Fuzzified Eigensystem Realization Algorithm

This paper presents a new approach with a fuzzified eigensystem realization algorithm for identification of flight vehicle models in low-speed wind tunnel (LSWT) and high-speed wind tunnel (HSWT). A variety of variables in model types and testing environment (such as angle-of-attack, sideslip angle, tunnel wind speed) and profile, elevator, and power system (motor and propeller) of mini unmanned aerial vehicle (mini-UAV) model are considered in a power-on mini-UAV testing system in LSWT and an Advisory Group for Aerospace Research and Development (AGARD) standard calibration model in HSWT. The method based on the fuzzy logic inference structure is simple and effective. The results obtained are compared to those obtained by the conventional wind tunnel testing method. To verify the effectiveness of the proposed methodology, simulations are conducted using real-world experimental results that demonstrate that the working performance of the proposed method correlates well as expected.
High-power and high-speed semiconductor switch RSD applied in pulsed power system

Based on the controllable plasma layer conversion principle, the new-type semiconductor pulsed power switch reversely switched dynistor (RSD) turns on uniformly and simultaneously over the whole chip area. It has the advantages of high power and high speed at the same time. The blocking, turn-on and turn-off characteristics of RSD are investigated in this paper systematically. The off-state and on-state characteristics get a trade-off by an optimal design method. The equations during turn-on and turn-off process are obtained by analyzing the carrier motion on different conditions. Then two important parameters, the turn-on voltage and reverse recovery time, are calculated. In the single pulse experiment, peak currents of 21.6 kA and 42.0 kA (pulse widths of 30 mus and 200 mus) are acquired on 20 mm and 40 mm diameter stacks respectively, which have reached the utmost value. In the repetitive frequency experiment, output waveform of 20 Hz is acquired at 4 kV with a resonant triggering way.
Laser Scribing of Sapphire Substrate to Increase Side Light Extraction of GaN-Based Light Emitting Diodes

Generally, the laser scribing was done after GaN-based light emitting diodes (LEDs) growth. This study verified that the utilization of laser scribing leads to an increase in the surface roughness of sapphire substrate sidewalls, which reduces the probability of total internal reflection from light striking the sapphire/air interface. Laser scribing also helps increase side light extraction intensity and output power of GaN-based light emitting diodes. Study results indicated that lasers create a laser scribing layer (LSL) at a depth of approximately 30 μ m after GaN-based LEDs grown in the sapphire substrate undergo laser scribing. Scanning electron microscopy was used to observe the rough surface of the LSL, while near-field optical images verified that rough surface LSL contributes to an increase in side wall light extraction intensity of LEDs. Furthermore, changing the depth of focus of the laser beam (from 0 μm to 36 μm) allows the formation of a large quantity of 3 to 5 μm holes on the LSL. Measurement results indicated that these holes caused the LSL surface to be even rougher, which further strengthened LED side wall light extraction intensity. The results after packaging show that LSL with holes increase output power at 20 mA of GaN-based LEDs by approximately 12.2 %.
Development and Demonstration of a 1 MJ High-Tc SMES

A superconducting magnetic energy storage system (SMES), with stored energy of 1 MJ and compensation power of 0.5 MVA, has been developed successfully, and now is operating at the world's first superconducting power substation at Baiyin National High-Tech Industrial Development Zone, Gansu Province, China. The SMES employs a high Tc superconducting magnet, which consists of 44 pancakes, operates at 4.2 K in liquid helium, and is cooled down by 4 G-M cryo-coolers. The SMES connects to a 10.5 kV power grid by the use of a power conversion system. Since 16 February, 2011, the SMES has been operating reliably, and providing good-quality power for three companies.
An MFCC-Based Speaker Identification System

Nowadays, many speech recognition applications have been used by people in the world. Typical examples are the SIRI of iPhone, Google speech recognition system, and mobile phones operated by voice, etc. On the contrary, speaker identification in its current stage is relatively immature. Therefore, in this paper, we study a speaker identification technique which first takes the original voice signals of a person, e.g., Bob, and then normalizes the audio energies of the signals. After that, the audio signals is converted from time domain to frequency domain by employing Fourier transformation approach. Next, a MFCC-based human auditory filtering model is utilized to identify the energy levels of different frequencies as the quantified characteristics of Bob's voice. Further, the probability density function of Gaussian mixture model is utilized to indicate the distribution of the quantified characteristics as Bob's specific acoustic model. When receiving an unknown person, e.g., x's voice, the system processes the voice with the same procedure, and compares the processing result, which is x's acoustic model, with known-people's acoustic models collected in an acoustic-model database beforehand to identify who the most possible speaker is.
On the sensitivity of realizations of real coefficient digital filters using complex arithmetic

Previously, new structures using complex arithmetic were proposed for the implementation of real coefficient IIR digital filters. The quantization of the real-equivalent of a complex coefficient in these filters violates an assumption usually adopted in the analysis of coefficient sensitivity, i.e., quantization errors are uncorrelated to each other. In the present paper, a simple expression is derived to measure the L/sub 2/ sensitivity, of structures using complex arithmetic. Then, an explicit expression of the L/sub 2/ sensitivity is obtained for second-order digital filters with minimum norm.<<ETX>>
A DC-11.5 GHz Low-Power, Wideband Amplifier Using Splitting-Load Inductive Peaking Technique

A DC-11.5 GHz low-power amplifier is developed in commercial 0.13 mum, CMOS technology. This amplifier design is based on a three-stage shunt-feedback inverter-configuration with splitting load inductive peaking technique. The peaking inductor is placed at the gate of the nMOS to compensate gain roll-off of the inverter stage and extend its operating bandwidth. This amplifier achieves a gain flatness of 13.21 dB from dc to 11.5 GHz with I/O return losses better than 17 dB at a power consumption of 9.1 mW. The measured noise figure is less than 5.6 dB between 1-11 GHz. The output P1 dB is 8 dBm and input third-order intercept point is 10 dBm. The total chip size is 0.34 mm2 including all testing pads, with a core area of only 0.08 mm2.
Innovation and information technology in virtual organizations: An empirical study

Research concerning innovation management by definition involves multilevel analysis. Focusing on the innovation at individual and organizational levels, this study examines the intensity of the relationship between individual creativity and organizational innovation to identify the construct of innovation composition process across these two levels. Due to a shift in organizational change management, which exceeds the convenience of advanced information technologies (IT), this study identifies and analyzes the effect of virtual organizational (VO) structure on the interaction between individual creativity and organizational innovation. Through the regression analysis of 128 information and electronic companies in Taiwan, the results show that the adoption of VO positively influences organizational innovation. Furthermore, it positively moderates the relationship between individual creativity and organizational innovation.
Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing

Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a new benchmark Look into Person (LIP) that makes a significant advance in terms of scalability, diversity and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels, which are captured from a wider range of viewpoints, occlusions and background complexity. Given these rich annotations we perform detailed analysis of the leading human parsing approaches, gaining insights into the success and failures of these methods. Furthermore, in contrast to the existing efforts on improving the feature discriminative capability, we solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision (i.e., no need for specifically labeling human joints in model training). Our self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results. Extensive evaluations on our LIP and the public PASCAL-Person-Part dataset demonstrate the superiority of our method.
Optimal Charging Control of Energy Storage and Electric Vehicle of an Individual in the Internet of Energy With Energy Trading

Developing green energy to be applied in green cities has received much attention. The Internet of energy (IoE) effectively improves networking of distributed green energies through extending smart grids with bidirectional transmission of energy and distributed renewable energy facilities. Previous works on the IoE focused on decisions of IoE operators or optimization of the whole system. However, few considered optimal decisions of a single end user in the IoE. Therefore, this work creates a mixed-integer linear programming (MILP) model for a single end user that considers green energy generation, an energy storage, an electric vehicle, and an IoE-based energy trading platform to reduce energy waste. This model considers a complete system of charging control of multiple facilities of a single end user in the IoE, and allows the end user to purchase energy and sell green energy through the IoE, in which the energy prices of the electrical grid and the IoE platform are set by the power company and the energy market, respectively. Because MILP is NP complete and the proposed model involves a large number of variables and constraints, this paper further proposes a genetic algorithm for this problem, in which a repairing scheme is proposed to handle solution infeasibility of all constraints. By simulation, the proposed algorithm is verified to effectively reduce energy waste.
Fabrication of InGaP/Al/sub 0.98/Ga/sub 0.02/As/GaAs oxide-confined collector-up heterojunction bipolar transistors

A partially oxidized Al/sub 0.98/Ga/sub 0.02/As layer was introduced between the emitter and base of collector-up heterojunction bipolar transistors (C-up HBTs) to suppress the leakage current and improve the current gain. Dependence of device current gain and leakage current on oxidation temperature was investigated. At lower oxidation temperature, the current gain can be effectively improved. Current gain and base sheet resistance were 79 and 203 ohm/sq. for the C-up HBT oxidized at 400/spl deg/C.
Realization of Anti-Lock Braking Strategy for Electric Scooters

This research studies a novel method of realizing a nonmechanical antilock braking system (ABS) controller for electric scooters (ESs) based on regenerative, kinetic, and short-circuit braking mechanisms. In which, a boundary layer speed control is proposed for a guarantee of the optimal slip ratio between tires and road surface. The antilock braking controller, combined with this controller, drives a low-side driving circuit to induce either an open-circuit or a short-circuit loop on the motor stator's coil to a load; it thus produces braking actions analogous to those in the conventional ABS control. The proposed ABS controller is practically realized. Improvement of the braking performance for the ABS action is further addressed via real-world experiments.
Tactile and shear stress sensing array using capacitive mechanisms with floating electrodes

This work presents the development of a capacitive tactile and shear-stress sensing array which can be easily and reliably fabricated by using MEMS techniques and flexible printed circuit board (FPCB) technologies. The sensing array consists of two micromachined PDMS structures and an FPCB with sensing electrodes. Each shear sensing element comprises four capacitive sensing element arranged in 2×2 array, and each capacitive sensing element has two sensing electrodes and a common floating electrode. The proposed design can effectively reduce the complexity of the capacitor structure without compromise in sensitivity.
Enhancement of Light Output Power of GaN-Based Light-Emitting Diodes by a Reflective Current Blocking Layer

In this study, a GaN-based light-emitting diode (LED) with a distributed Bragg reflector (DBR) current blocking layer (CBL) beneath the p-pad electrode is demonstrated. A high reflectivity DBR structure is composed of five periods SiO2 and TiO2 layers. At a driving current of 20 mA, the light output power of the LEDs with DBR CBL was 16.8% and 11.3% higher than the LEDs without and with SiO2 CBL. The improvement is contributed to the DBR CBL can deflect the current away from the p-electrode pad and also prevent the light absorption by the opaque metal electrode. It was found that LEDs with DBR CBL shows better current spreading and broader far-field pattern.
Structural and Magnetic Properties of  $\text{L}1_{\textrm {0}}$ -FePt-Based Exchange Coupled Composite Films With FeRu Underlayers

The (001) textured L10-FePt thin films have been prepared on FeRu underlayers by dc magnetron sputtering. By taking the FePt layer as the top layer, the hard layer of exchange coupled composite (ECC) films is close to the magnetic head, which consequently makes the larger head field benefit the magnetic reversal of the composite. In this paper, the dependence of structural and magnetic properties of the FePt-based ECC films on the thickness of the FeRu layer is investigated.
Development of an industrial forecasting tool in wind power

The rapid growth of wind generation is introducing additional variability and uncertainty into power system operations and planning. These inherent characteristics of wind power have both technical and commercial implications for efficient planning and operation of power systems. As the penetration of wind power increases, the importance of accurate forecasting of this variable generation source over a number of time frames becomes more important. In this paper, the state-of-the-art wind power forecast technologies and developed commercial forecasting software have been investigated. Furthermore, the contribution of this paper is to design an industrial tool for wind power forecasting, which can be utilized to train forecasting models, predict wind power, extract proper historical data, compute forecasting residuals, accelerate the forecasting speed with the modified Levenberg-Marquardt algorithm, and executing combination forecasts with Genetic Algorithm method.