nils-herrmann/clean_scirepeval huggingface dataset:
Image Based Facial Micro-Expression Recognition Using Deep Learning on Small Datasets

Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted features, for example, Local Binary Pattern-Three Orthogonal Planes (LBP-TOP), Gabor filter and optical flow. Recently, Deep Convolutional neural systems have demonstrated a high degree effectiveness for difficult face recognition tasks. This paper explores the possible use of deep learning for micro-expression recognition. To develop a reliable deep neural network extensive training sets are required with a huge number of labeled image samples. However, micro-expression recognition is a challenging task due to the repressed facial appearance and short duration, which results in the lack of training data. In this paper, we propose to generate extensive training datasets of synthetic images using data augmentation on CASME and CASME II databases. Then, these datasets are combined to tune a satisfactory CNN-based micro-expression recognizer. Experimental results demonstrate the effectiveness of the proposed CNN approach in image based micro- expression recognition and present comparable results with the best-related works.
A meta-synthesis of evaluating complex systems based on fuzzy set and rough set

The world is becoming more and more complex. In order to evaluate complex systems, we develop a new evaluating method, which is the meta-synthesis of improved Delphi approach, analytic hierarchy process, gray interconnect degree and fuzzy comprehensive evaluation. According to the theory of fuzzy set and methodology from qualitative to quantitative, we propose the steps of the methods and discuss the concordance of evaluation, redundant verify etc. Using the method, the accuracy of evaluating method was estimated, and a practical example for accuracy of the method was given. It is shown in our research that the method can be used to evaluate complex system projects.
Metasurface for polarization and phase manipulation of the electromagnetic wave simultaneously

Metamaterials has inspired many novel physical mechanism and applications for the past decades. In these cases, the thickness of metamaterial is a must to achieve the unique electromagnetic response, which will definitely result in bulky structures and the accompanying loss, especially in microwave region. In this paper, our efforts in planar metasurface are reviewed with emphasis on manipulation of the phase and polarization state of electromagnetic wave. The latest results show that by introducing magnetic resonance between the layers of the metasurface, the polarization state and the phase of the incident wave can be fully controlled by the metasurface with a relatively high efficiency.
Discussion on stability coefficient of double-leg and thin-wall pier during the cantilever construction stage

To study the stability of double-leg and thin-wall pier during the cantilever construction stage, a simple analysis model is established. Considering pier self-weight and unequal space of longitudinal stiffness brace, the stability coefficient calculation formula of each cantilever construction stage is deduced based on the principle of stationary potential energy. It is also pointed out the essential of stability analysis on such structure. The comparisions of the stability coefficient calculation formula between this paper and literature[8] are presented. The reason for the different points between them are also given.
Application of Extended Belief Network Model for Scientific Document Retrieval

This work presents an extended belief network model to deal with the problem of searching for scientific document. The original belief network model is extended by combine reference evidence. An algorithm is introduced to compute the two metrics: worth and concern which are used to measure the important degree of scientific document based on the reference evidence. We discuss a strategy of the extended belief network model, and experiment with it using a Chinese document collection from the CNKI. The results show that this combination can improve the retrieval performance without requiring any extra information from the user query. In our experiment, the improvements reach up to 11% in terms of average precision.
Opportunities and Challenges Towards Cognitive IT Service Management in Real World

More and more industries are experiencing digital disruption triggered by new technologies for example cloud, mobile, Internet-of-Things, Big Data, and Artificial Intelligence. Majority of applications are predicted to provide cognitive capabilities to amplify human skills and expertise in coming two years. Information Technology (IT) services industry is also shifting from people-led and technology-assisted model into a people-assisted and technology-led model. However, the ever-changing IT technologies, increasingly complicated IT environments, and ever-shortening IT delivery cycles in real world pose great challenges to existing IT Service Management (ITSM) technologies. This paper aims to discuss the trends, opportunities, and challenges in transformation of real-world ITSM in cognitive era and to trigger more practical research work in this exploited area. It firstly reviews the evolution of ITSM and discusses key technologies behind the evolution. Then, it summarizes opportunities and challenges in transforming ITSM with cognitive capabilities in real word. Further, we discuss key enabling technologies to drive the evolution of ITSM towards Cognitive one. Finally, we conclude the paper and envision real-world best practices in this area.
Research on the millimeter wave gain equalizer

Gain equalizer is often used to adjust the output gain of traveling wave tubes(TWT) or high power solid state amplifiers. Traditionally, millimeter wave gain equalizer is composd of waveguide with electromagnetic wave absorber in resonance circuit. However, this structure is large, heavy, complex, not convenient to integrate. In this paper, a millimeter wave gain equalizer is fuifilled by microstrip resonant branch with thin film resistance integrated. The simulated results show that the equalization curve meet requirements well. Finally, on the basis of technology precision, we researched the error analysis of thin film resistance, the results proved this structure is practical and the design procedure is effective.
Relative entropy normalized Gaussian supervector for speech emotion recognition using kernel extreme learning machine

Speech emotion recognition is a challenging and significant task. On the one hand, the emotion features need to be robust enough to capture the emotion information, and while on the other, machine learning algorithms need to be insensitive to model the utterance. In this paper, we presented a novel framework of speech emotion recognition to address the two above-mentioned challenges. Relative Entropy based Normalization (REN) was proposed to normalize the supervectors of Gaussian Mixture Model-Universal Background Model (GMM-UBM) as the features to emotions. The Kernel Extreme Learning Machine (KELM) was adopted as the classifier to identify the emotion represented by the normalized supervectors. Experimental results on the EMR_1309 corpus showed the proposed framework outperformed the state-of-the-art i-vector based systems.
Measuring and modeling the effects of substrate noise on the LNA for a CMOS GPS receiver

The influence of substrate noise coupling on the performance of a low-noise amplifier (LNA) for a CMOS GPS receiver has been investigated both analytically and experimentally. A frequency-domain approach has been used to model both noise injection into the substrate from digital circuitry integrated on the same chip and the mechanisms by which that noise can affect analog circuit behavior. The results of this study reveal that substrate noise can modulate the LNA input signal as well as couple directly to the amplifier's output.
An improved Ka-band four-way broadband power divider

This paper introduces a four-way power divider based on the improved radial waveguide with inductive wall and stepped-waveguide at the Ka-band. This structure exhibits broadband (with a stepped-impedance waveguide transformer) and a good agreement in amplitude and phase (with a symmetrical four output port). The EM simulation shows the achievable bandwidth is approximately 20% and the insertion loss of each output port is less than 0.2dB
Deep networks with stochastic depth for acoustic modelling

Training very deep neural networks is very difficult because of gradient degradation. However, the incomparable expressiveness of the many deep layers is highly desirable at testing time and usually leads to better performance. Recently, training techniques such as residual networks that enable us to train very deep networks have proved to be a great success. In this paper, we studied the application of the recently proposed deep networks with stochastic depth (DNSD) to train deeper acoustic models for speech recognition. By randomly dropping a subset of layers during training, the studied stochastic depth training method helps reduce the training time substantially, yet the networks trained are much deeper since all the layers are kept during testing. We investigated this approach on the TIMIT data set. Our preliminary experimental results show that when training data are limited, deep networks with stochastic depth helps very little. However, when more training data are available, DNSD significantly improves the recognition accuracy, compared with a conventional deep neural networks.
Mobile Landmark Search with 3D Models

Landmark search is crucial to improve the quality of travel experience. Smart phones make it possible to search landmarks anytime and anywhere. Most of the existing work computes image features on smart phones locally after taking a landmark image. Compared with sending original image to the remote server, sending computed features saves network bandwidth and consequently makes sending process fast. However, this scheme would be restricted by the limitations of phone battery power and computational ability. In this paper, we propose to send compressed (low resolution) images to remote server instead of computing image features locally for landmark recognition and search. To this end, a robust 3D model based method is proposed to recognize query images with corresponding landmarks. Using the proposed method, images with low resolution can be recognized accurately, even though images only contain a small part of the landmark or are taken under various conditions of lighting, zoom, occlusions and different viewpoints. In order to provide an attractive landmark search result, a 3D texture model is generated to respond to a landmark query. The proposed search approach, which opens up a new direction, starts from a 2D compressed image query input and ends with a 3D model search result.
Learning cross-lingual knowledge with multilingual BLSTM for emphasis detection with limited training data

Bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) has achieved state-of-the-art performance in many sequence processing problems given its capability in capturing contextual information. However, for languages with limited amount of training data, it is still difficult to obtain a high quality BLSTM model for emphasis detection, the aim of which is to recognize the emphasized speech segments from natural speech. To address this problem, in this paper, we propose a multilingual BLSTM (MTL-BLSTM) model where the hidden layers are shared across different languages while the softmax output layer is language-dependent. The MTL-BLSTM can learn cross-lingual knowledge and transfer this knowledge to both languages to improve the emphasis detection performance. Experimental results demonstrate our method can outperform the comparison methods over 2–15.6% and 2.9–15.4% on the English corpus and Mandarin corpus in terms of relative F1-measure, respectively.
Behaviors of Plasma Bullet Propagation and Effects of Gas Flow Rate

An Ar dielectric-barrier-discharge atmospheric-pressure plasma jet (APPJ) driven by a 34-kHz sinusoidal power source is employed to investigate plasma bullet propagation in positive and negative half cycles by using an intensified charge-coupled device camera. The effects of different Ar gas flow rates on plasma bullet behavior are also presented in this paper. It is found that the duration and velocity of bullets in a positive discharge current pulse are significantly shorter and faster than those in a negative discharge current pulse and APPJ seems easier to form the plasma bullet phenomena during the positive discharge current pulse. The differences between positive and negative discharge current pulses can be explained by using the cathode-directed streamer and negative corona theories, respectively. It is considered that, due to the possible turbulence flow, the propagation length of plasma bullets is decreased with the increment of gas flow rates.
The Study of Mammary Disease CAD System

An X-ray image CAD (computer aided diagnosis) system aiming at oriental female's dense mammary gland is proposed. The system acquires assistant diagnosis result through the synthetic analysis of both direct and indirect symptoms of the mammary disease. In the detection and decision of the direct symptom, the system adds features of micro-calcification shape description and mass margin roughness rate to achieve preferable result. Besides, the self-study faction in the detection and decision model based on the machine learning arithmetic is well actualized by building up the large digital mammary disease photo & text database. The system is capable to provide clinic assistant diagnosis for the mammary doctor as a second X-ray image reader in order to improve the diagnostic accuracy and convenience and supply reliable data source for research purpose.
Compact radio frequency model of graphene resonant channel transistor

Radio frequency nano-electromechanical resonators have great potential in the application of bio-sensor, quantum physics studies, and high frequency communications. The theory and model of radio frequency graphene resonant channel transistor is presented in this paper. A compact electrical equivalent circuit model has been given out based on the analysis of electromechanical model of doubly clamped beam and electronic field effect transistor model. And the experimental results show that the simulation result fits the measurement results excellently. The results of this paper can impetus the development handset high-sensitivities sensors.
Research on red wave and green wave coordinated control model in arterial road for different traffic demands

By taking into consideration the road traffic congestion on urban center and a bottleneck junction, based on the flow-sharing and traffic management strategy, a mixed-integer linear programming model is established by using the method of time-space analysis, which can carry out green wave control and red wave control for different directions in arterial road. The various traffic control needs can be satisfied to adapt to different traffic conditions by adjusting the red wave weighting factor in the objective function. The optimal solution of the model can be achieved by use of LINGO software and programming language. Finally, the example effectively demonstrates that the model can maximize the bandwidths of green wave and red wave for different directions and obtain precise timing schemes with a strong scientific rationality.
A 2 GHz high isolation DPDT switch MMIC

A double-pole double-throw (DPDT) switch monolithic microwave integrated circuit (MMIC) has been designed to operate from dc to 2 GHz using a commercial 0.18-/spl mu/m GaAs pseudomorphic high electron-mobility transistor (pHEMT) process. The DPDT switch employs four shunt FET blocks to achieve high isolation. The ON gate bias is set to 0.6 V to reduce the insertion loss. The simulated results of the DPDT switch chip show an insertion loss of less than 0.61 dB and isolation of more than 50 dB up to 2 GHz.
Design and development of a leg-wheel hybrid robot “HyTRo-I”

This paper proposes a novel and mechanically decoupled leg and wheel hybrid transformable robot called HyTRo-I that combines two mobility concepts. For example, while wheeled vehicles shares higher speed than legged and tracked machines on a flat ground, they have relatively lower degree of flexibility than the other two on irregular terrain. The HyTRo-I robot evolves three motion modes: wheeled rolling, quadrupedal walking and leg-wheel hybrid mode. Despite the over-whelming complexity of obstacles, only several typical obstacles are selected for the study, which are stairs, large protrusions and ditches. Firstly, the transition locomotion mechanism between wheeled rolling mode and quadrupedal walking mode should be studied in detail. In the course of reciprocal transition locomotion, the static and reversible transformation gait not only guarantees the shifting stability and a small number of transition steps, but also the relatively balanced torque of joints. Secondly, after HyTRo-I converting to effective locomotion mode, the adaptive gait control strategies are proposed to traverse three types of obstacles. Finally, a serial of experiments were performed to verify the validity of the proposed transformation gait and adaptive step-up gaits.
Atomic-layer-deposited LaAlO3/SrTiO3 all oxide field-effect transistors

We have demonstrated well-behaved accumulation-mode all oxide NMOSFETs with amorphous atomic-layer-deposited (ALD) LaAlO<inf>3</inf> gate dielectric stacks on crystalline SrTiO<inf>3</inf> substrates. A maximum drain current exceeding 10 mA/mm has been obtained on a 3.75µm-gate-length device, proving a very conductive channel can be formed at the oxide-oxide interface. Four different gate dielectric stacks, which are Lafirst cycle LaAlO<inf>3</inf>, Al-first cycle LaAlO<inf>3</inf>, LaAlO<inf>3</inf> with 1.5 nm La<inf>2</inf>O<inf>3</inf> interfacial layer, and LaAlO<inf>3</inf> with 1.8 nm Al<inf>2</inf>O<inf>3</inf> interfacial layer, have been deposited on SrTiO<inf>3</inf> substrates to systematically study their effects on the conductivity at the different oxide-oxide interfaces. The experimental results show that a La-initiated interfacial layer is preferable to form a more conducting channel at the LaAlO<inf>3</inf>/SrTiO<inf>3</inf> interface. Low temperature characteristics have also been utilized to provide an in-depth understanding of the channel formation at the oxide-oxide interface. The availability of the MBE technology to epitaxially grow SrTiO<inf>3</inf> on Si substrate provides the pathway to integrate ALD LaAlO<inf>3</inf>/SrTiO<inf>3</inf> devices on Si platform.
Low-power dividerless frequency synthesis using aperture phase detection

A phase-locked-loop (PLL)-based frequency synthesizer incorporating a phase detector that operates on a windowing technique eliminates the need for a frequency divider. This new loop architecture is applied to generate the 1.573-GHz local oscillator (LO) for a Global Positioning System receiver. The LO circuits in the locked mode consume only 36 mW of the total 115-mW receiver power, as a result of the power saved by eliminating the divider. The PLL's loop bandwidth is measured to he 6 MHz, with a reference spurious level of -47 dBc. The front-end receiver, including the synthesizer, is fabricated in a 0.5-/spl mu/m, triple-metal, single-poly CMOS process and operates on a 2.5-V supply.
Cohort-Based Speaker Model Synthesis for Channel Robust Speaker Recognition

Speaker recognition over a public telephone network involves various types of transmission channels and handsets, which leads to mismatched channels (between the enrolled models and the test utterances), and hence to a significant decline in the speaker recognition performance. In this paper a cohort-based speaker model synthesis algorithm, which aims at synthesizing speaker models for channels where no enrollment data is available is proposed. This algorithm applies a priori knowledge of channels extracted from speaker-specific cohort sets to synthesize speaker models. Results for the China Criminal Police College (CCPC) speaker recognition corpus, which contains utterances from both a landline and a mobile channel, show significant improvements over the HT-norm and UBM-based speaker model synthesis algorithms
LTCC RF property investigation for stripline application

This paper explores the RF property of new low temperature co-fired ceramic (LTCC). The permittivity of the fired tape is confirmed by stripline ring resonator method. The transmission performance of stripline is characterized by multi-line calibration procedures. Measured scatter parameters of two transmission lines with different lengths are used to confirm the propagation constant. The permittivity is experimentally verified on DuPont 9k7 material up to 67GHz, and the propagation constant of 4-layer stripline is extracted from 1GHz to 11GHz.
A new wavelet-based cone-beam local reconstruction algorithm for off-centered circular trajectory

Cone-beam computed tomography for circular trajectory has been widely used. Sometimes off-centered circular trajectory has to be used because of some mechanical restrictions. In this paper, we develop an algorithm to reconstruct ROI from local cone-beam projections for off-centered circular trajectory. The proposed method is derived from combination of α-FDK and wavelet-based algorithm, and their advantages are maintained. Neither severe truncation artifacts nor details blurring occurs in the reconstructed image.
Consistency of signal coordinate control models under different traffic conditions

Considering the different time that platoon reaches downstream intersection, this paper proposes corresponding stop and delay coordinated control models for under-saturated and over-saturated traffic conditions. With the cumulative arrival-departure diagrams, uniform stop and delay models for under-saturated and over-saturated traffic conditions are proved to be consistent and accurate by formula derivation. It provides a basis on deducing the stop and delay general formulae under signal coordinate control mode theoretically.
High-performance surface channel In-rich In0.75Ga0.25As MOSFETs with ALD high-k as gate dielectric

High-performance inversion-type enhancement-mode n-channel MOSFETs on In-rich In<sub>0.75</sub>Ga<sub>0.25</sub>As using ALD Al<sub>2</sub>O<sub>3</sub> as high-k gate dielectrics are demonstrated. The maximum drain current, peak transconductance, and the effective electron velocity of 1.0 A/mm, 0.43 S/mm and 1.0times10<sup>7</sup> cm/s at drain voltage of 2.0 V are achieved at 0.75-mum gate length devices. The device performance of In-rich InGaAs NMOSFETs with different indium contents, In<sub>0.53</sub>Ga<sub>0.47</sub>As, In<sub>0.65</sub>Ga<sub>0.35</sub>As and In<sub>0.75</sub>Ga<sub>0.25</sub>As, are systematically studied.