Reviewerly/exHarmony huggingface dataset:
A Survey of Comparative Learning Methods

Recently, contrast learning has become an important approach in unsupervised learning, which is very good at classifying and predicting unlabeled data, and the best contrast learning models in recent years have been able to achieve the prediction accuracy of supervised learning models. This paper analyzes the algorithmic ideas of contrast learning, discusses the typical contrast learning models in recent years, analyzes their model structures, compares the features and differences among the models, and looks forward to the future directions of contrast learning methods that need to be improved, providing valuable references for subsequent engineering application research.
KTR2DN: Knowledge Transfer with Residual in Residual Dehazing Network

Single-image dehazing is an essential but challenging computer vision problem. Due to the lack of nonhomogeneous haze datasets, most existing image dehazing methods are only applicable to homogeneous rather than nonhomogeneous dehazing tasks. In addition, the results of existing dehazing methods are always blurred in detail. Thus, a novel network structure, Knowledge Transfer with Residual in Residual Dehazing Network, KTR2DN, is proposed, which consists of two parts: a knowledge transfer network and a super-resolution network using the Residual in Residual (R2) block. The former aims to solve the problem of lacking nonhomogeneous haze datasets and it includes a teacher network and a student network. The teacher network restores abundant clear images so that it can lead the student network to extract more free-haze features. The student network reconstructs the dehazing image from the hazy image through the free-haze features with only a minimal dataset. The latter contains a feature fusion block and a R2 block so that it can fine-tune the results of the student network. The feature fusion network receives both high- and low-frequency feature maps and generates fused feature maps. The R2 block then extracts more semantic features from the fused feature maps. Our method achieves better performance on PSNR and SSIM compared with the state-of-the-art methods.
Interval quantile maximum mutual information feature selection algorithm

Feature selection is an essential part of the data pre-processing phase after obtaining key data, and fields such as machine learning, natural language processing and data mining rely on the model's feature selection results and classification accuracy. In the study of feature selection algorithms based on information theory, the problems of feature and label mutual information maximization, minimum redundancy, joint mutual information, and conditional mutual information have been analyzed and verified. However, there is still a weak classification ability of the optimal subset and difficulty in quantifying the degree of feature redundancy. Therefore, a feature selection method based on interval quantile maximum mutual information is proposed in this paper. The method can further enhance the mutual information of selected features and label values, and also quantify the redundancy degree of selected features. To demonstrate the superiority of the method, the method is compared and analyzed with various mutual information filtering methods applied to guided bomb data, and the effectiveness of the method is verified on several public data sets.
Grayscale histogram feature extraction algorithm and its application in medical image analysis

This paper applied grayscale histogram feature extraction to analyse a medical image of tuberculosis lungs. Five different edge detection methods, namely Roberts, Sobel, Prewitt, Canny, and LoG algorithms were used to realise the feature extraction of the medical image and their performace were compared. This study finds that Roberts and Prewitt algorithms were able to extract most information from the medical image, followed by Sobel and Canny algorithms, while LoG algorithm was able to extract the least information from the medical image. The results show that the features extracted by the algorithms can be used for pathological analysis.
Self-supervised Weighted Hashing with Topology Mining

Recently, although significant progress has been made in unsupervised deep hashing, there are still two neglected but crucial problems. On the one hand, most methods force binary codes to obey the similarity relationships between the original image features, but this noisy supervisory signals can confuse the learning of hash functions. On the other hand, retrieval results of a query often share the same Hamming distance, which gives rise to ambiguous ranking. This paper presents a Self-supervised Weighted Hashing (SWH) framework, which fully explores the intrinsic semantics and local topology structures of representation space to tackle the above problems. First, we mine reliable training guidance from two aspects: high-confidence similarity signals and self-supervised geometric signals. Specifically, by studying the feature similarity statistics, a graph-based sharpening strategy is proposed to disentangle noisy signals and strengthen similarity learning confidence. More importantly, angle prediction and consistency learning based on image rotation transformation are leveraged to capture the geometric semantic information and enhance the robustness of binary codes. Second, a self-supervised learning procedure with a pseudo-siamese structure ( i.e., the hash layer and the weight layer) is proposed to learn weighted binary codes to facilitate adaptive fine-grained distance ranking. Comprehensive experiments on three benchmarks for image retrieval show that SWH outperforms existing methods by 3.5% MAP on average.
Graph-Enhanced Question Generation with Question Type Prediction and Statement Recovery

Abstract—Question Generation (QG) is an essential task in natural language processing, which aims to generate grammatical questions for given sentences or paragraphs. This task focuses on understanding the structures and semantics of sentences. This paper proposes a graph-enhanced question generation model based on Variational Autoencoders (VAE). We construct a semantic graph by dependency parsing and then encode the graph by iteration of Gated Graph Neural Networks (GGNN). Then we get the graph-enhanced representation by fusing the sentence-level and graph-level representation to perform pre-training of statement recovery and joint training of question type prediction and generation. Experiments show promising results of our proposed model on the most extensively used Question Answering (QA) dataset SQuAD.
New advances in permutation decoding of first-order Reed-Muller codes

In this paper we describe a variation of the classical permutation decoding algorithm that can be applied to any affine-invariant code with respect to certain type of information sets. In particular, we can apply it to the family of first-order Reed-Muller codes with respect to the information sets introduced in [2]. Using this algorithm we improve considerably the number of errors we can correct in comparison with the known results in this topic.
Virtual view synthesis using joint information from multi-view

Immersive media has attracted widespread attention with the development of virtual reality. Three Degree of Freedom Plus media greatly enhances the user experience by allowing users’ head motion and viewpoint switching within a certain range. Due to the limitation of panoramic video acquisition and transmission, it is impossible to obtain videos from any viewpoint directly. Virtual view synthesis is the general solution to this problem. However, existing algorithms do not adequately consider the pixel correlation between multiple views. Thus, we propose a virtual view synthesis algorithm using joint information from multi-view panoramic videos to further explore the pixel correlation. Specifically, sub-pixels from different reference views in the virtual view are obtained by performing multi-view three-dimensional image warping. Dedicated area division and interpolation methods are then designed to improve the synthesized quality. Experimental results show that the proposed algorithm outperforms the state-of-the-art virtual view synthesis algorithms in performance and efficiency.
Mobile Based Mosquito Larvae Recognition from Photo Image Using Convolutional Neural Network

The high number of dengue fever in Indonesia is a severe problem that affects the health of the Indonesian people. Coupled with the pandemic conditions that limit all movements of various groups due to social distancing, starting from health volunteers who must serve the community related to Covid-19. Routine inspection of mosquito larvae in every household must still be conducted to avoid the breeding of these mosquito larvae. Thus, an automatic and independent mosquito larva recognition system is needed from images taken via mobile devices that make it easier for each family head to identify areas of the home environment whether there are mosquito larvae or not. This paper proposes a new approach to larva recognition using convolutional neural network based on the TensorFlow library. The TensorFlow Serving and TensorFlow Lite were evaluated to get the best model with limited memory so that it can be used on mobile devices. The system classifies photo images into 2 labels, namely larvae or not. The result of this system is the presence or absence of mosquito larvae from the image. The TensorFlow Serving model produces an average testing accuracy of 96.1%, which is better than TensorFlow Lite of 90.1% in testing various conditions. The developed convolution neural network model produces a better model than the previous method.
Graph representation learning via redundancy reduction

In recent years, self-supervised learning methods based on mutual information maximization have achieved remarkable success on graph data tasks. However, most of them heavily rely on a large number of negative samples, which is computationally expensive. These methods also fail to extract the semantic cluster information of the data. To overcome these problems, we propose a novel self-supervised approach called Graph Representation Learing via Redundancy Reduction (GRRR) to learn node representations based on the redundancy-reduction principle. The proposed GRRR preserves as much topological information of the graph as possible, and minimizes the redundancy of representation in terms of node instance and semantic cluster information. Specifically, we first design three graph data augmentation strategies to construct two augmented views. Then, to filter the redundant information in each augmented view, we propose the self-redundancy reduction module which implements the structural reconstruction. Finally, we propose the joint redundancy reduction module to further filter undesirable information via a cross-view approach. It preserves the most essential instance features and semantic cluster information by maximizing the agreement across different views not only on node instance features but also on cluster assignments. Results on several benchmark datasets show that GRRR outperforms state-of-the-art methods in downstream node classification, link prediction, and node clustering tasks.
PartitionTuner: An operator scheduler for deep‐learning compilers supporting multiple heterogeneous processing units

Abstract Recently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep‐learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep‐learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep‐learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator‐scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type‐based operator‐scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling‐based operator‐scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.
Transfer subspace learning via label release and contribution degree distinction

For sample face recognition issues, the transfer subspace learning approach works well. However, the majority of transfer subspace learning techniques now in use cannot decrease intra-class differences while increasing inter-class differences. In addition, when reconstructing samples, the contribution of each reconstructed sample is not considered. In this paper, we propose a Label Release and Contribution Degree Distinction (LRCDD)-based transfer subspace learning strategy to enhance recognition performance to solve these problems. Specifically, LRCDD reduces the intra-class differences and expands the inter-class differences by introducing the label release model into subspace learning. Assigning an unknown weight coefficient to the representation coefficient of each sample and performing weight learning makes the reconstructed sample in the subspace learning process more accurate, and thus a better transformation matrix or subspace is learned. We introduce a constraint of null diagonal to prevent the data sample from being represented by itself. This avoids multiple representations for each data point. Experimental result demonstrates that the average recognition rate of LRCDD on the EYB, AR, IJB-C, MegaFace, RFW, CPLFW, Flickr-Faces-HQ and Tufts-Face databases are 62.51%, 70.58%, 81.69%, 83.66%, 82.26%, 73.22%, 84.97% and 90.91%, respectively, which are higher than those of the state-of-the-art methods.
Task offloading under deterministic demand for vehicular edge computing

Abstract In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0‐1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.
Engineering superpositions of N00N states using an asymmetric non-linear Mach–Zehnder interferometer

We revisit a method for mapping arbitrary single-mode pure states into superpositions of N00N states using an asymmetric non-linear Mach–Zehnder interferometer (ANLMZI). This method would allow one to tailor-make superpositions of N00N states where each axis of the two-mode joint-photon number distribution is weighted by the statistics of any single-mode pure state. The non-linearity of the ANLMZI comes in the form of a χ(3) self-Kerr interaction occurring on one of the intermediary modes of the interferometer. Motivated by the non-classical interference effects that occur at a beam splitter, we introduce inverse-engineering techniques aimed toward extrapolating optimal transformations for generating N00N state superpositions. These techniques are general enough so as to be employed to probe the means of generating states of any desired quantum properties.
Mathematical aspects of division property

This work surveys mathematical aspects of division property, which is a state-of-the-art technique in cryptanalysis of symmetric-key algorithms, such as authenticated encryption, block ciphers and stream ciphers. It aims to find integral distinguishers and cube attacks, which exploit weaknesses in the algebraic normal forms of the output coordinates of the involved vectorial Boolean functions. Division property can also be used to provide arguments for security of primitives against these attacks. The focus of this work is a formal presentation of the theory behind the division property, including rigorous proofs, which were often omitted in the existing literature. This survey covers the two major variants of division property, namely conventional and perfect division property. In addition, we explore relationships of the technique with classic degree bounds.
What caused what? The Israeli Shekel and cryptocurrencies

This article examines the relationships between 21 fiat currencies to Bitcoin price movements using daily data from the beginning of 2012 to the end of March 2021. We use a two-stage analysis. The first stage excludes currencies, which are uncorrelated with the daily returns of the Bitcoin. The second stage is to run a Granger-causality test on the remaining six currencies to examine whether lagged excess returns on the Bitcoin Granger-cause the excess returns on the currencies or vice versa. Results support the conclusion that the return on Bitcoin Granger-Cause the return on the Israeli Shekel (ILS), but not vice versa. A possible explanation of these outcomes is the susceptibility of financial investors in Bitcoin and the ILS to the Israeli economy.
User-defined mid-air gestures for multiscale GIS interface interaction

Multisize geographic information system (GIS) has widely used in social life, and the introduction of natural interactive methods such as gesture interaction further broadens the applications of GIS. Gestures, as a natural means of expression, bring a convenient experience to the use of GIS. The user-elicitation method can determine the user’s original operational intention and take advantage of the natural and convenient gesture interaction. However, current research on elicitation methods mainly focuses on defining the function factors of operation commands and rarely considers the influence of environmental factors such as spatial sizes. This paper takes GIS as the object, which is closely related to multisize space. This paper reports a research project on user-defined gestures for interacting with two-dimensional maps in a virtual reality environment, including three types of GIS operation commands and six sizes of maps. The results showed that the user-defined gestures for the same GIS operation commands varied across map sizes. The increase in map size increased the amount of gestural movement. Drawing on our research, we developed a set of gestures for multisize GIS interface interaction. We hope that this research can provide a guiding foundation for mid-air gesture design for all multisize interfaces.
CFNet: Head detection network based on multi‐layer feature fusion and attention mechanism

Abstract Recently, head detection has been widely used in target detection, which has a great application value for improving security prevention and control in public places, as well as enhancing target tracking and identification in national defense, criminal investigation, and other fields. However, detecting small targets accurately at long distances is very difficult, and current methods often lack optimization of multi‐resolution features. Therefore, the authors propose a one‐stage detection network CFNet (cross‐layer feature fusion and fusion weight attention network), in which a fusion weight attention mechanism module (FWAM) is proposed to give different weights to the fused features in order to distinguish the importance of different features. The module increases the weights of features that contain strong information so that the fused features are focused on feature points that are beneficial for optimal head detection. Meanwhile, a cross‐layer feature fusion module is proposed to fuse information from different resolution feature maps to compensate for the decrease in detection accuracy caused by the omission of information features at low resolution, and a connection network for contextual information fusion is constructed, while weight parameter value settings are introduced to optimize the detection effect after fusion of different resolution features. In order to better reflect the effectiveness of the network, the experiments are performed on the SCUT‐HEAD PartA dataset and the Brainwash dataset; the results show that the network the authors proposed is better than the existing comparison methods, which proves the robustness and effectiveness of the network.
Low‐light image enhancement via span correction function and discrete mapping model

Abstract This paper proposes a new low‐light image enhancement method, which we call the Local Discrete Mapping Method. The new method limits the processing range to small areas with a high information relevance, which can better coordinate the enhancement quality of each area. First, the discrete mapping relationship of pixels (called discrete mapping points) globally occupying a small part of the critical gray value was extracted and designated to keep the enhancement amplitude of each local area consistent. Then, other free mapping points were adjusted according to the local features to achieve the best visual effect in each local area. In addition, this paper also proposes a span correction function that takes the gray span between local pixels as the adjustment object. The function can preserve the gray difference between freely mapped pixels to the maximum and significantly reduce detailed damage in the local area. Finally, we used 1500 test images and eleven objective evaluation indicators in the public dataset to comprehensively test the seven methods. The experimental results showed that the proposed method has an excellent dark area quality enhancement, brightness detail protection, overall noise suppression, and processing speed. It is significantly better than similar methods in terms of visual quality and quantitative testing.