Reviewerly/exHarmony huggingface dataset:
Closing the convergence gap of SGD without replacement

Stochastic gradient descent without replacement sampling is widely used in practice for model training. However, the vast majority of SGD analyses assumes data is sampled with replacement, and when the function minimized is strongly convex, an $\mathcal{O}\left(\frac{1}{T}\right)$ rate can be established when SGD is run for $T$ iterations. A recent line of breakthrough works on SGD without replacement (SGDo) established an $\mathcal{O}\left(\frac{n}{T^2}\right)$ convergence rate when the function minimized is strongly convex and is a sum of $n$ smooth functions, and an $\mathcal{O}\left(\frac{1}{T^2}+\frac{n^3}{T^3}\right)$ rate for sums of quadratics. On the other hand, the tightest known lower bound postulates an $\Omega\left(\frac{1}{T^2}+\frac{n^2}{T^3}\right)$ rate, leaving open the possibility of better SGDo convergence rates in the general case. In this paper, we close this gap and show that SGD without replacement achieves a rate of $\mathcal{O}\left(\frac{1}{T^2}+\frac{n^2}{T^3}\right)$ when the sum of the functions is a quadratic, and offer a new lower bound of $\Omega\left(\frac{n}{T^2}\right)$ for strongly convex functions that are sums of smooth functions.
One Class Classifier Neural Network for Anomaly Detection in Low Dimensional Feature Spaces

In the last decade, many approaches have been developed to solve one-class classification (OCC) problems for anomaly detection. Many of them rely on estimating the statistical distribution of the data, find hidden patterns, or remap the data in advantageous feature spaces. This kind of techniques usually needs some a priori knowledge of the data distribution (i.e., Gaussian) or the setting of some parameters to achieve good classification performance, making their use less effective when the data distribution is unknown. In this paper, we propose a novel blind anomaly detection for low dimensional feature spaces, that exploits the flexibility of the neural network (NN) structure to find the class boundaries without any information about the shape of the data distribution. To prove the generality of the solution, we tested many different class shapes, and we applied it to a structural health monitoring (SHM) case study. Without requiring the tuning of hyperparameters, the performance of the proposed algorithm overcomes that of some known approaches like principal component analysis (PCA), kernel principal component analysis (KPCA), Gaussian mixture model (GMM), and autoassociative neural network (ANN) in many cases, and performs well in the specific SHM setting.
Dual-fiber point diffraction interferometer to measure the wavefront aberration of an imaging system

A dual-fiber point diffraction interferometer is built to measure the wavefront aberration of an imaging system with high precision. The optical intensity of the test light and reference light are controlled independently, so that high interference contrast can be obtained. The interferometer has several advantages: high precision, a flexible structure, and a quasi-common optical path. System errors of the interferometer that influence the measurement accuracy are considered and calibrated. In the experiments, a projection lens with numerical aperture of 0.3 is measured. The measured wavefront error is 1.5 nm rms, and the measurement repeatability is 35 pm rms.
A window size selection network for stereo dense image matching

Stereo dense image matching normally refers to per-pixel correspondence search between stereo pairs. However, their matching results actually depend on matching window sizes. Large windows usually obtain robust matching results in weak-textured regions, but serious mismatches in depth/disparity (parallax in the epipolar space) inconsistency regions. Small windows compute accurate matching results in depth/disparity inconsistency regions, but it may contain high matching uncertainties in weak-textured regions. To improve matching accuracies, this letter focuses on adaptively selecting appropriate matching windows for each pixel. In general, we propose a window size selection network (WSSN) with the basic assumption that appropriate window sizes are related to image textures and depth/disparity variations. WSSN firstly extracts both image texture features and disparity features by convolutional neural network and then utilizes the fully connected layers to conduct optimal window size selection. Experiments on an aerial image dataset show that our proposed method is capable of selecting appropriate matching windows for each pixel. It achieved the highest matching accuracy when compared with the matching results of a series of fixed matching windows and a state-of-the-art textured based window selection method.
Risley-prism-based visual tracing method for robot guidance

A visual tracing model based on Risley prisms is proposed in this paper. Combined with the boresight adjusting technology and the visual detection technology, a new visual system is established. Placing Risley prisms in front of the camera, the field of view of the camera can be dynamically adjusted so that the imageable area of this camera is expanded greatly. Two real-time visual tracing strategies for dynamic targets are proposed, which effectively avoid the problems of target loss and tracking instability. The deviations between the reference trajectory generated by the manipulator and the actual trajectory detected by our visual system are measured. Experimental results show that the deviations are less than 1.5% in the 250 mm motion space of the manipulator. It is verified that the visual system can be used to guide robots with high precision, which provides a potential method for robot navigation.
An energy-efficient deep convolutional neural networks coprocessor for multi-object detection

This paper proposed an energy-efficient deep convolution neural networks coprocessor (DCNNs-CP) architecture for multi-object detection applications based on deep learning algorithms. The DCNNs-CP can support both convolutional layers and fully connected layers to accelerate various mobile deep learning algorithms. It also supports maximum and mean pooling operations through a separate pooling module structure. Besides, a reconfigurable activation function module supporting four nonlinear functions is also realized in this coprocessor. The DCNNs-CP chip was implemented in 55 nm CMOS process technology and occupied the 4 mm2 die area. The DCNNs-CP supports 8-bit and 16-bit fixed-point data precision and achieves a peak performance of 3.4 Tops/W at 1.2 V supply voltage and a maximum frequency of 500 MHz, represent 2.13x improvements over reported hardware accelerators. Besides, the chip achieves 0.85 Tops/W · mm2 energy efficiency per area and 34.0 Tops/W · MB energy efficiency per memory (on-chip memory), making it suitable to be integrated with the mobile devices.
Research and Practice of Reform on College English Teaching under the Environment of Information Technology

The rapid development of information technology has provided a new opportunity for the development of college English teaching in China. Under the background of informationization, the reform of college English teaching can meet the students' demand for individualized and differentiated learning. This paper analyzes the current situation of college English teaching and English teaching mode of information technology, and puts forward the strategy of English teaching reform under the background of informationization, in order to improve students’ English proficiency in all aspects and promote the rapid development of English education and teaching in colleges and universities.
Quadratic Video Interpolation for VTSR Challenge

Video interpolation is an important problem in image manipulation, which has drawn increased interests from the vision and graphics communities. In this work, we apply the quadratic video interpolation algorithm to the VTSR challenge of Advances in Image Manipulation (AIM) 2019, and introduce a joint finetuning scheme to exploit more training data. We provide a concise description of the quadratic model and present a detailed analysis of the results in the VTSR challenge. Extensive experiments demonstrate that our network generates high-quality interpolation results and outperforms the state-of-the-arts by a large margin.
Polarization-maintaining design for satellite-based quantum communication terminals

We report the polarization preservation of quantum light in the optical systems of the terminals of the world’s first quantum communication satellite, Micius. The main scientific experimental goals and polarization requirements of the satellite and ground station are introduced firstly. Three optical terminals are equipped on Micius to achieve our scientific goals, including a quantum entanglement source (QES), quantum entanglement transmitter (QET) and quantum key distribution transmitter (QKDT), and the function of each optical terminal is described briefly. A polarization-maintaining design for the terminals on Micius is critical for quantum communication, and the optical structure of the QKDT and QET is determined by using three polarization-maintaining methods. The optical configurations of the QKDT and QET are introduced, and the polarization-maintaining methods are described in detail. The final polarization extinction ratios of the QKDT and QET at wavelengths of 850 nm and 810 nm are better than 500:1, which provides critical technical support for realizing the scientific goals of Micius.
Forecasting the Performance of a Photovoltaic Solar System Installed in other Locations using Artificial Neural Networks

Photovoltaic solar energy has been spread all over the world, and in Brazil this energy source has been getting considerable space in the last years, being driven mainly by the energy crises. However, when installed in regions with low incidence of solar irradiation, this technology presents a loss of efficiency in the generation of energy. As an alternative to this consideration, a power prediction study could be conducted prior to its installation, based on local climate information that directly influences power generation, verifying the feasibility of system implementation and avoiding unrewarded investment. Therefore, the objective of this work is to predict the viability of the installation of a photovoltaic system of 3kWp in different places, with the assist of an Artificial Neural Network. Thus, the feedforward network was used for the training, being trained and validated with the support of Matlab®, and inserting samples of temperature and solar irradiation as input variables. Through the performance methods, the results are favorable for this application, presenting validations with RMSE% in the range of 13-20% and R of not less than 0.93. The predictions presented RMSE% around 19-25% and average powers close to the real values generated by the PV system.
An Encrypted Image Retrieval Method Based on SimHash in Cloud Computing

With the massive growth of images data and the rise of cloud computing that can provide cheap storage space and convenient access, more and more users store data in cloud server. However, how to quickly query the expected data with privacy-preserving is still a challenging in the encryption image data retrieval. Towards this goal, this paper proposes a ciphertext image retrieval method based on SimHash in cloud computing. Firstly, we extract local feature of images, and then cluster the features by K-means. Based on it, the visual word codebook is introduced to represent feature information of images, which hashes the codebook to the corresponding fingerprint. Finally, the image feature vector is generated by SimHash searchable encryption feature algorithm for similarity retrieval. Extensive experiments on two public datasets validate the effectiveness of our method. Besides, the proposed method outperforms one popular searchable encryption, and the results are competitive to the state-of-the-art.
E-Wallet Transactional Framework for Digital Economy: A Perspective from Islamic Financial Engineering

The development of electronic wallet (e-wallet) aims to encourage customers and small business owners to use digital payments for a safer, cashless, and efficient transaction. This study developed a procedure to discover the transactional framework for the e-wallet payment system in the digital economy. This study explained the procedure of the transaction process, starting from registration of user and service provider, payment composition, and profit generation for the business model of e-wallet provider. This study provides a precise and reliable analysis to increase public awareness about the e-wallet transaction. At the same time, the framework in this study functions to help small and medium industries grow in the digital economy segments.
Critical Review on Course Recommendation System with Various Similarities

A user-based Course Recommendation System is developed in this paper. The recommender system is constructed as an online website/application which is capable of producing a personalized list of courses which a user can take. Modern versions of traditional recommender system, such as collaborative filtering are considered to be efficient in this domain of user-based recommendation. On the basis of collaborative filtering principle, the recommendation process is divided into three steps, representation of student data, generation of neighbor user (student) and the generation of course recommendations. In order to compute the likenesses amid each user, the Cosine method is adopted during the process of the generation of neighbors. Then the course recommendations are generated according to the ratings of the other students and his personal ratings.
Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Ant Colony Optimization

Path Planning
Robust Human Action Recognition System via Image Processing

Human actions detection is very much investigated in utilization of artificial intelligence and computer vision. Numerous effective action recognition strategies have demonstrated and the action information are successfully gained from motion videos and still pictures. In order to get the equivalent actions, the proper activity information gained from various kinds of media like videos or pictures might be connected. The majority of the existing video activity action identification strategies experience the ill effects of inadequate marked recordings. In that cases, over-fitting should be a potential issue and the execution of activity acknowledgment is controlled. In this paper, image processing techniques are used in order to recognize the different hand poster of the human body, also the over-fitting can be eased and the execution of activity acknowledgment is improved. Initially, the human action video including hand waving, walking, jogging, clapping, boxing is converted into image of 2D frames and then it is preprocessed followed by feature extraction using LST and classification by KNN classifier has been done individually. The kernel principal component analysis (KPCA) technique is used in the proposed system for finding the image features and joined features.The extracted features from the frames are compared with trained quantized dataset in order to identify the actions. The advantage of quantized dataset is that it occupies very less space. Thus, the result shows which action is present in the examined data. Trials on open benchmark data sets and genuine world data sets demonstrate that our technique outflanks a few other cutting edge activity acknowledgment strategies.
Content-Adaptive U-Net Architecture for Medical Image Segmentation

In this paper, we introduce a modification of the popular U-Net neural network architecture for medical image segmentation. Our proposed architecture replaces the concatenation operations in the traditional U-Net's skip connections with content-adaptive convolution, thereby significantly reducing the number of parameters of the network. Our experiments on two segmentation tasks - cell nuclei segmentation, and pneumo-thorax segmentation - demonstrated that the modified architecture achieves higher segmentation accuracy compared to the original U-net.
A Steganography Method Using Neural Networks

This work proposes a method of steganography without embedding that uses an artificial neural network (ANN) for subliminally communicating some sensitive information using digital images. The proposed method uses the scaled conjugate gradient (SCG) learning to configure an ANN and replicate a secret message from a cover image. The configuration values are sent to destination alongside the cover image, and the secret message is easily recovered. The method was proved using four ANN architectures changing the number of neurons and by using different cover images. This work presents a steganographic method that allows the exchange of sensitive information between two entities. This method has no capacity limitations, it does not produce perceptibility features of the sensitive information in the cover image and it includes an implicit mechanism of integrity/authentication of the cover image. To estimate the performance of the proposed method, the mean square error (MSE) and the peak signal to noise ratio (PSNR) between the recovered secret image and the original secret image, as well as the computation time in the training stage, for artificial neural network architectures with different number of neurons are calculated. Finally, it is shown that the proposed method has better performance when using digital images with large changes in hue. In these cases, the recovered secret image was equal to the original secret image. This opens the possibility of testing the proposed steganographic method to communicate data and not just digital images.
Interferometric two-photon-absorption spectroscopy with three entangled photons

We propose an interferometric pump-probe technique that employs three entangled photons generated by cascaded spontaneous parametric downconversion. A Mach–Zehnder interferometer made of two three-port waveguide arrays is inserted in the optical path. Two independent phases introduced to manipulate the entangled photon state serve as control parameters and can selectively excite matter pathways. Compared to two-photon-absorption of an entangled photon-pair, the three-photon signals are significantly enhanced by frequency-dispersed photon-counting detection.
Generative Multi-Task Learning for Text Classification

Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. In this paper, a generative multi-task learning (MTL) approach for text classification and categorization is proposed, which is composed of a shared encoder, a multi-label classification decoder and a hierarchical categorization decoder. In the two decoders, a label-order-independent multi-label classification loss function and a hierarchical structure mask matrix are introduced. Experiments conducted on the real-world public security dataset show that the proposed approach has obvious advantages over the baseline approaches and can enhance the semantic association between the results of the classification and categorization tasks.
On the Performance of Oversampling Techniques for Class Imbalance Problems

Although over 90 oversampling approaches have been developed in the imbalance learning domain, most of the empirical study and application work are still based on the "classical" resampling techniques. In this paper, several experiments on 19 benchmark datasets are set up to study the efficiency of six powerful oversampling approaches, including both "classical" and new ones. According to our experimental results, oversampling techniques that consider the minority class distribution (new ones) perform better in most cases and RACOG gives the best performance among the six reviewed approaches. We further validate our conclusion on our real-world inspired vehicle datasets and also find applying oversampling techniques can improve the performance by around 10%. In addition, seven data complexity measures are considered for the initial purpose of investigating the relationship between data complexity measures and the choice of resampling techniques. Although no obvious relationship can be abstracted in our experiments, we find F1v value, a measure for evaluating the overlap which most researchers ignore, has a strong negative correlation with the potential AUC value (after resampling).
On the operational meaning and practical aspects of using the security parameter in quantum key distribution

Abstract We discuss the operational meaning of a commonly accepted security parameter in quantum key distribution, which is based on the trace distance. We separately consider the cases of using a key in a one-time pad and in a computationally secure cipher. Some practical aspects of using the security parameter are also elucidated, which are usually not paid enough attention in theoretical studies and which therefore may cause difficulties for experimentalists and engineers. It is shown that a one-time pad requires not only a higher key generation rate than computationally secure ciphers, but also a significantly stronger condition on the key security parameter.
Impact of AR Display Context Switching and Focal Distance Switching on Human Performance: Replication on an AR Haploscope

In augmented reality (AR) environments, information is often distributed between real and virtual contexts, and often appears at different distances from the user. Therefore, to integrate the information, users must repeatedly switch context and refocus the eyes. Previously, Gabbard, Mehra, and Swan (2018) examined these issues, using a text-based visual search task and a monocular optical see-through AR display. In this work, the authors report a replication of this earlier experiment, using a custom-built AR haploscope. The successful replication, on a very different display, is consistent with the hypothesis that the findings are a general property of AR.
Reconstruction of Hyperspectral Images From Spectral Compressed Sensing Based on a Multitype Mixing Model

Hyperspectral compressed sensing (HCS) based on spectral unmixing technique has shown great reconstruction performance. In particular, the linear mixed model (LMM) has been widely used in HCS reconstruction. However, due to the complexity of environmental conditions, instrumental configurations, and material nonlinear mixing effects, LMM cannot accurately represent the hyperspectral images, which limits the improvement of reconstruction quality. In this article, first, by introducing spectral variability, nonlinear mixing, and residuals, a multitype mixed model (MMM) is proposed to establish a more accurate hyperspectral image model. Then, a novel MMM-based HCS is proposed, which performs spectral compressed sampling at the sampling stage only, and at the reconstruction stage, by using spectral unmixing, an MMM-based HCS super-resolution reconstruction algorithm from spectral compressed sensing data is developed, and the alternating direction multiplier method is employed to estimate each component of the MMM, furthermore, reasonable prior knowledge of each component is introduced to improve the estimation accuracy. Experimental results on hyperspectral datasets demonstrate that the proposed model outperforms those state-of-the-art methods based on the LMM in terms of HCS reconstruction quality.