nils-herrmann/clean_scirepeval huggingface dataset:
Online Cost Efficient Customer Recognition System for Retail Analytics

For most physical stores, procuring customer behavior data and client management is di cult yet crucial. For stores, especially luxury stores, who value returning customers the most, identifying customer's identity and past purchase history would significantly improve the sales by personalizing the recommendation. Many chain stores utilize membership card to collect purchase information and change sales strategy accordingly, yet overlooking the vast majority of customers who are not registered members. More detailed statistics including age, gender, customer counts are extremely valuable for adjusting business strategy, which brings the importance of a real-time customer analytic system for retail business. We have developed an accurate, real-time and fully automated system that relies on computer vision and deep learning to identify returning customers and comprehensively filter sales and customer information. To adjust to network bottleneck, limited computation resources, low latency requirement, recognition accuracy and other di culties, we divided the system into two submodules. The system depends on local computation resources to perform customer detection, tracking and feature extraction. Whereas age and gender estimation, and customer recognition are performed on cloud computing resources to compensate for the limited computation resource at local store and to accommodate multiple stores requirements.
3-D Integration of MEMS and CMOS Using Electroless Plated Nickel Through-MEMS-Vias

Three-dimensional integration of microelectromechanical systems (MEMS) and CMOS is able to achieve hetero-integrated microsystems with high performance, small size, low cost, and multiple functions. This paper reports a 3-D integration method using wafer transfer technology and electroless Ni plating with a noncontact induction (ENPNI) technique. Wafer transfer technology based on adhesive bonding and wafer thinning allows stacked integration of MEMS on top of CMOS. To address the challenge in fabricating through-MEMS-vias (TMVs) with small diameters, ENPNI has been developed to fabricate Ni TMVs as both the mechanical supports for suspended MEMS and the electrical connects between MEMS and CMOS. High-density MEMS arrays and CMOS circuits are integrated to demonstrate the feasibility of the 3-D integration method, and thermal cycling and mechanical vibration are performed to evaluate the reliability. Experimental results show that the 3-D integration processes do not exert distinct influences on MEMS arrays and CMOS circuits, and the integrated systems have good yield, uniformity, and reliability. The capability of ENPNI in the fabrication of small TMVs enables 3-D integration of high-density MEMS arrays and CMOS circuits, such as micromirror arrays, infrared focal planes, acoustic sensor arrays, radiation sensor arrays, and so on.
An Ultra-Thin Piezoresistive Stress Sensor for Measurement of Tooth Orthodontic Force in Invisible Aligners

A silicon piezoresistive stress sensor is developed to measure the three components of the orthodontic forces of a tooth in invisible aligners. The stress sensor chip consists of multiple piezoresistor rosettes, and is fixed on the tooth surface to measure the orthodontic force induced stresses. An inverse problem algorithm is established to reconstruct the orthodontic forces of the tooth from the measured stresses. A key thinning process is developed using chemical-mechanical polishing to thin the sensor chip from 650 to 100 , such that it can be accommodated to the limited space between the aligner and the tooth, without much influence on the orthodontic force. The ultra-thin thickness enables the sensor chip to be compliance with tooth deformation, and improves the stress measurement sensitivity as much as 40 times, making it capable of measuring small orthodontic force. The sensor design, fabrication, orthodontic stress measurement, and force reconstruction are given in details, and the experimental results demonstrate the feasibility of using miniaturized and ultra-thin sensors to determine the in vitro orthodontic forces.
Hierarchical joint-guided networks for semantic image segmentation

Semantic image segmentation is now an exciting area of research owing to its various useful applications in daily life. This paper introduces a hierarchical joint-guided network (HJGN) which is mainly composed of proposed hierarchical joint learning convolutional networks (HJLCNs) and proposed joint-guided and making networks (JGMNs). HJLCNs exhibit high robustness in the segmentation of unseen objects that are not contained in training categories. JGMNs are very effective in filling holes and preventing incorrect segmentation predictions. The proposed HJGNs outperform the state-of-the-art methods on the PASCAL VOC 2012 testing set, reaching a mean IU of 80.4%.
A Micromachined Piezoelectric Ultrasonic Transducer Operating in ${\rm d}_{33}$ Mode Using Square Interdigital Electrodes

This paper presents the design, fabrication, and characterization of a piezoelectric micromachined ultrasonic transducer (pMUT) operating in d33 mode. A piezoelectric PZT thin film is polarized along the in-plane direction using top square interdigital electrodes (IDEs), and the in-plane polarization and IDEs enable the pMUT to operate in d33 mode, which allows the PZT to convert the ultrasonic pressure-induced in-plane stresses into in-plane direction charges. The square IDEs fully exploit the stresses of the diaphragm to improve sensitivity and increase device capacitance, and the pMUT is optimized by varying the IDE dimensions rather than the thickness of the PZT film. Theoretical and finite-element analyses are performed to optimize the diaphragm and the IDE dimensions in terms of resonance frequency and sensitivity, respectively. The ultrasonic directivity of the pMUTs with different packaging configurations is characterized, showing large ultrasound inlet areas beneficial to ultrasonic applications requiring high directivity.
A Ring-Based Decentralized Collaborative Non-blocking Atomic Commit Protocol

Many asynchronous distributed atomic commit protocols use a coordinator, either an application server or an elected database server, to handle the progress of a transaction. When the coordinator fails or the network becomes partitioned, the transaction may block or may suffer from a split brain syndrome. In this paper we propose a decentralized, collaborative, non-blocking atomic protocol, which piggybacks transaction statuses of all transaction participants onto tokens, and passes the tokens in a logical ring with two distinct directions. Each participant uses the information in the tokens to make a decision of when to go to the next state. Using the state vectors in the tokens, the transaction can progress even if the network is partitioned, and the protocol will ensure uniform agreement on success or failure of the transaction.
Recommend Significant Tags to Travel Photos Based on Web Mining

As the rapid development of smart phones and applications, users are familiar to take photos with smart phones on the trip and share photos to friends on social networks. Those photos are usually tagged and shared with trivial location information, such as attractions derived from mobile locations contributed from Facebook users. However, these attractions are not useful for enhancing the photo semantics. In this paper, we propose the Tag Recommendation System (TRS) that automatically mine significant tags from Flickr's photos so that these tags can be recommended to improve the semantics of photos according to location information. Experiments based on the user experience show that TRS can achieve about 85% satisfaction rates.
Simulation of the transmission line effects based on TLM method

At high frequencies, the trace can suffer various transmission line effects, such as reflections, overshoot, undershoot, and crosstalk, seriously distorting the transmitted signal and damaging signal integrity. In this paper, 3D Transmission-Line Modelling(TLM) method was used to study the effects of transmission line. The characteristic impedance of the stripline and microstrip are obtained using the 3D TLM algorithm. A detailed account of each of transmission line effects is given. The aim is to model and simulate the high speed transmission process, predict the signal integrity issues and extract useful parameters affecting the signal integrity.
Compression of color facial images using feature correction two-stage vector quantization

A Feature Correction Two Stage Vector Quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo ID pictures. This algorithm is extended to color images in this paper. Two options are compared, which apply the FC2VQ algorithm in RGB and YCbCr color spaces, respectively. The RGBFC2VQ algorithm is found to yield better image quality than YCbCr-FC2VQ at similar bitrate. With the RGB-FC2VQ algorithm, a 128/spl times/128 24-bit color ID image (49,152 bytes) can be compressed down to 500 bytes with satisfactory quality.
An improvement to multiple description transform coding

A multiple description transform coding (MDTC) method has been reported previously. The redundancy rate distortion (RRD) performance of this coding scheme for the independent and identically distributed (i.i.d.) two-dimensional (2-D) Gaussian source has been analyzed using mean squared error (MSE) as the distortion measure. At the small redundancy region, the MDTC scheme can achieve excellent RRD performance because a small increase in redundancy can reduce the single description distortion at a rate faster than exponential, but the performance of MDTC becomes increasingly poor at larger redundancies. This paper describes a generalization of the MDTC (GMDTC) scheme, which introduces redundancy both by transform and through correcting the error resulting from a single description. Its RRD performance is closer to the theoretical bound in the entire range of redundancy. Analysis both for a single pair of variables and for multiple variables is presented.
GA2CO: Peak temperature estimation of VLSI circuits

With the continuing increase of chip density and the shrinkage of feature size of transistor in VLSI circuits, high temperature has become a concerned issue. High temperature not only decreases the functionality and reliability of chips, but also causes high package cost in order to cool down the system. For design consideration, one important issue related to temperature is how hot the chip may be. Thus, this paper investigates on the lower bound of peak temperature of a packaged chip and on the patterns that cause such bound. Two algorithms, Genetic Algorithm and Ant Colony Optimization, are applied for finding this lower bound of peak temperature. Experimental results show that the proposed approach obtains an average of 39.03% higher lower bound for ISCAS'85 combinational benchmarks and 6.80% for ISCAS'89 sequential benchmarks as compared to random approach under the TSMC 0.18μm library.
An electric field sensor with double-layer floating structure for measurement of dc synthetic field coupled with ion flow

Electric field measurements have a wide range of applications in many fields. But for ion flow close to high-voltage DC (HVDC) transmission lines, the current measurements are invalid. This paper for the first time presents a novel electric field sensor (EFS) with double-layer floating structure to measure the target electric fields (DC synthetic fields) that are strongly coupled with ion flow. The theoretical analysis, fabrication process and test results, are presented respectively. The general electric field-Ein consists of three parts: E0 is the DC synthetic field, E1 stands for the field generated by charges attached on the top metal and E2 represents the electric field induced by the redistribution of ion flow. For given two distances to the top metal, the results show that E0 remains unchanged and E1 will increase linearly with the increasing of surface charge density on the top metal. Using the proposed analytic equation, there exists a relative ratio-λ keeping constant with the increasing of surface charge density on the top metal. The novel EFS can address the challenge in measuring the DC electric field coupled with ion flow close to HVDC transmission lines.
Joint video scene segmentation and classification based on hidden Markov model

Video classification and segmentation are fundamental steps for efficient accessing, retrieval and browsing of large amounts of video data. We have developed a scene classification scheme using a hidden Markov model (HMM) based classifier. By utilizing the temporal behaviors of different scene classes, the HMM classifier can effectively classify video segments into one of the pre-defined scene classes. In this paper, we describe two approaches for joint video classification and segmentation based on a HMM, which works by searching for the most likely class transition path utilizing the dynamic programming technique.
Regularization methods for image texture classification

Texture plays an important role for the analysis of multimedia data. In this paper we present several regularization methods for image texture classification. The motivation is that we observe that the commonly used method LSE is unstable in practical applications. This motivates us to develop much more stable method. Regularization is such a technique which successfully suppress the instability due to noise or truncation error when computing. We present several regularization methods include standard regularization (SR), penalized regularization (PR) and total variation based regularization (TVR) for reducing instability in texture analysis, and apply which to image texture classification. Optimization technique is employed for computation. Experiment results demonstrate our new algorithms are superior to LSE and seems promising in practical applications.
An unsupervised multi-resolution object extraction algorithm using video-cube

We propose a fast video object segmentation method that detects object boundaries accurately, and does not require any user assistance. Video streams are considered as 3D data, called video-cubes, to take advantage of 3D signal processing techniques. After a video sequence is filtered, marker nodes are selected from the color gradient. A volume around each marker is grown by using color/texture distance criteria. Then volumes that have similar characteristics are merged. Self-descriptors for each volume, mutual descriptors for each pair of volumes are computed. These descriptors capture motion and spatial information of volumes. In the clustering stage, volumes are classified into objects in a fine-to-coarse hierarchy. While applying and relaxing descriptor based adaptive, similarity scores are estimated for each possible pair-wise combination of volumes. The pair that gives the maximum score is clustered iteratively. Finally, an object-based multi-resolution representation tree is assembled.
Structure design of through silicon via interconnects and growth of carbon nanotubes

In this paper, we design a new program to achieve carbon nanotubes through silicon vias (TSV) interconnects compatible with CMOS technology using an auxiliary TSV structures and laser local heating method. Auxiliary TSV structures are made by two wafers. One wafer is etched into ups and downs structures and then deposited SiNx and SiO2 film. The other one is made into TSV structures by deep silicon etching, depositing thin film and wafer thinning and polish. Two wafers are bonded together by patterned BCB (Benzocyclobutene). For the entire device structures, we use FDTD solutions software to simulate infrared optical properties of SiNx. SiNx film layer can absorb 10.6 μm infrared laser strongly. Thermal simulation of geometrical model by COMSOL Multiphysics software shows well temperature compatibility. It turns out the program is compatible with current CMOS processes. Using a laser local heating method, we have studied the local growth of carbon nanotubes. Carbon nanotubes grown in TSVs are sparse, intertwined and have a poor morphology; the reason for this is that TSVs' geometric sizes limit gases flows. Carbon nanotubes interconnect still need further study.
Analytical Model to Evaluate the Role of Deep Trap State in the Reliability of NAND Flash Memory and Its Process Dependence

An elementary step of trap and detrap processes of electron in the tunnel oxide during program/erase in NAND Flash memory is precisely studied. Owing to the high electric field during program and erase (P/E), the electron trapping and detrapping occur at the same time. Consequently, the detrapping process only leaves electrons in the deeper trap energy state (Etrap) than 3.5 eV. In addition, as-grown trap density (Ne) and capture cross section (σ) in the deep trap state can be specified to explain the measured data including the tunneling current modulation with cycling and the VT shift by oxide trap. The P/E endurance characteristics using dry and plasma oxidation processes are analyzed and compared. In both processes, σ has the same value (~4 × 10-17 cm2). However, Ne depends on the oxidation process. In dry oxidation, Ne is ~1.88 × 1019 cm-3. On the other hand, in plasma oxidation, 30% reduction (~1.25 × 1019 cm-3) can be found.
Analysis and improvement of multiple description motion compensation video coding for lossy packet networks

Multiple description motion compensation (MDMC) is a multiple description coding scheme which has shown good error resilience performance. This paper investigates the behavior of an MDMC video codec in a packet loss environment. A model for the decoder distortion induced by transmission errors is developed and their accuracy is validated by simulation studies. The model shows the necessity of coding mismatch signals in an MDMC codec. Also, a new mismatch signal coding method and the corresponding decoding strategy are presented which improves upon the original MDMC scheme in a packet lossy environment.
Major Cast Detection in Video Using Both Speaker and Face Information

Major casts, for example, the anchor persons or reporters in news broadcast programs and the principle characters in movies, play an important role in video, and their occurrences provide meaningful indices for organizing and presenting video content. This paper describes a new approach for automatically generating a list of major casts in a video sequence based on multiple modalities, specifically, speaker information in audio track and face information in video track. The core algorithm is composed of three steps. First, speaker boundaries are detected and speaker segments are clustered in audio stream. Second, face appearances are tracked and face tracks are clustered in video stream. Finally, correspondences between speakers and faces are determined based on their temporal co-occurrence. A list of major casts is constructed and ranked in an order that reflects each cast's importance, which is determined by the accumulative temporal and spatial presence of the cast. The proposed algorithm has been integrated in a major cast based video browsing system, which presents the face icon and marks the speech locations in time stream for each detected major cast. The system provides a semantically meaningful summary of the video content, which helps the user to effectively digest the theme of the video
Asymmetrie Kernel Convolutional Neural Network for acoustic scenes classification

In this work, we propose an Asymmetric Kernel Convolutional Neural Network (AKCNN) for Acoustic Scenes Classification (ASC). Its kernel shape is not the traditional square but asymmetric in width and height. It also uses Weight Normalization (WN) to accelerate the training process because it can early converge the training loss and accuracy. The best of all, WN can help increase the accuracy of ASC. TUT Acoustic Scenes 2016 Dataset  is used for evaluation. The result shows that AKCNN achieves accuracy 86.7%. If we rank the score in DCASE2016 ASC Challenge, it shows that the system would have a higher score than the 5th place.
Ba/sub 0.5/Sr/sub 0.5/TiO/sub 3/ ferroelectric thick films with uniform thickness and its applications to RF MEMS devices

Ba/sub 0.5/Sr/sub 0.5/TiO/sub 3/ (BST) thick films for RF MEMS applications were prepared by a modified sol-gel method and characterized by microwave measurements. In order to obtain thick films and low loss tangent, poly vinyl-pyrrolidone (PVP) and Mg were doped into the solution to avoid crack formations and reduce loss tangent. Interdigital capacitors and CPW microstrip lines for low and high frequency measurements, respectively, were patterned on BST films. Their theoretical models with three-layer structures were established with conformal mapping techniques to extract dielectric properties of BST at frequencies from 50 M to 26 GHz. Experiments show that PVP and Mg are effective in improving the performance of thick films. Applications of BST thick films to RF MEMS devices were discussed.
Signal Space CoSaMP for sparse recovery with partially known support

In this work, we extend the study of compressive sensing on Signal Space CoSaMP with redundant dictionaries by utilizing the partially known information. Under the assumption that the signal of interest, with some known locations of the nonzero coefficients, has a sparse representation under some redundant dictionaries, we modify the Signal Space CoSaMP algorithm by incorporating the partially known support into the recovery process. We then provide theoretical recovery guarantees of this proposed algorithm by using the well-known D-RIP condition. Numerical experiments are conducted to verify the improved performance, i.e., fewer measurements are needed to yield a good approximate reconstruction of the original signal.
Simultaneous Stabilization and Tracking of Nonholonomic Mobile Robots: A Lyapunov-Based Approach

A smooth time-varying controller is proposed to simultaneously address the stabilization and tracking problems of nonholonomic mobile robots for most admissible reference trajectories without switching. The controller is developed with the aid of a delicately designed time-varying signal and Lyapunov method. Computational simplification and asymptotic convergence of regulation or tracking errors are achieved by the proposed controller. Our approach provides an interesting way to unify the existing results on point stabilization and trajectory tracking of mobile robots. The simulation and experimental results on a wheeled mobile robot are presented to demonstrate the effectiveness of the proposed controller.
A Review on Trust and Reputation for Web Service Selection

A trust and reputation mechanism is a mechanism using consumers' feedbacks to identify good services from bad ones. Compared with other approaches, it has more advantages in solving the selection problem for web services. The paper proposes a typology to classify trust and reputation systems using the three criteria, centralized or decentralized, person or resource, global or personalized. Inspired by the criteria, some potential research directions for web service selection are pointed out.