Reviewerly/exHarmony huggingface dataset:
Image Compression with Laplacian Guided Scale Space Inpainting

We present an image compression algorithm that preserves high-frequency details and information of rare occurrences. Our approach can be thought of as image inpainting in the frequency scale space. Given an image, we construct a Laplacian image pyramid, and store only the finest and coarsest levels, thereby removing the middle-frequency of the image. Using a network backbone borrowed from an image super-resolution algorithm, we train our network to hallucinate the missing middle-level Laplacian image. We introduce a novel training paradigm where we train our algorithm using only a face dataset where the faces are aligned and scaled correctly. We demonstrate that image compression learned on this restricted dataset leads to better GAN network [1] convergence and generalization to completely different image domains. We also show that Lapacian inpainting could be simplified further with a few selective pixels as seeds.
Neural Discriminant Analysis For Fine-Grained Classification

Feature learning is an essential process in image and object classification, even more so for fine-grained classification where objects have a similar global structure and subtle differences in local parts. Inspired by Linear Discriminant Analysis (LDA), we propose a two-phase optimization to transform deep features from their original space to a lower dimension space using neural networks with two primary goals: minimizing variances within each class and maximizing pairwise distances between features from different classes. The approach produces more discriminative features that lead to improvements in classification performance. We evaluate our method on four well-known fine-grained classification datasets. Our optimization leads to significantly better classification accuracy (up to 6.4% increase from the baseline). The standard deviations of the accuracy over multiple training runs show that our approach yields more consistent and reliable results than transfer learning.
Study on the Influence of Touch Screen Button Size on Operation Performance

Touch devices are widely used in our lives. The size of the touch screen interface buttons will affect the user’s interaction efficiency and accuracy. At present, research on the size of touch screen button is mainly focused on handheld mobile devices and desktop touch devices. Research is still relatively scarce. In order to improve the user’s operating efficiency and user's experience, according to the actual needs of the user, this study selects nine types of touch interface buttons sizes for a desktop touch device with a screen tilt angle of 60°, and conducts comparative experimental research, mainly using task performance indicators method of measurement. The experiment results show that the minimum recommended button size for the touch screen interface is 6.25 mm, and the larger the button size, the higher the performance. The research conclusions can be used to support touch screen interface design.
Cultivation of Social Teaching Model of College Physical Education Based on Improved Apriori Algorithm

In view of the existing defects, based on the analysis of the ideas and methods improved by predecessors, and combining with the characteristics of mobile platform teaching evaluation, the original classic Apriori algorithm is improved. The improved database only needs to scan the original database once, store the transactions belonging to each frequent itemset, and store these collections in the newly added database. Therefore, when counting the candidate item set generated by self-join, only the intersection of the transaction set stored in the subset of the candidate item set is required to obtain the number of supported items in the candidate item set. The improved algorithm can reduce the scanning times of the original database and compress the scanning range, thus improving the efficiency of the algorithm. The teaching evaluation system based on mobile platform meets the needs of all the teachers and students and provides great convenience. Therefore, it can greatly improve the participation rate and make the data more comprehensive and complete.
Relational Database Watermarking Based on Chinese Word Segmentation and Word Embedding

With the development of big data, relational databases are playing an important role in enterprises, military affairs, medical, etc. Moreover, they are vulnerable to piracy, forgery, and tampering. Consequently, the copyright protection of relational databases has become an issue with increasing concern. The use of digital watermarking technology can solve this problem effectively. In this paper, by using Chinese word segmentation and word embedding, the non-numerical attributes of the relational database with Chinese natural language are chosen to embed watermark. The method of the virtual splitting of the attribute column and the principle of modification minimum are proposed to guarantee the watermark capacity and reduce the data modification rate. The simulations are carried out to prove the algorithm has strong robustness to defense malicious attacks.
Video Key Object Detection Network via Reinforcement Learning

In video understanding, a core task is to detect the objects in frames and recently state-of-art methods are proposed to exhaustively detect the possible objects. Few studies are discussing exact these objects by the importance of object roles, which refer to ones that are relatively concerned and attract more attention of the audience in a short video clip. This paper intends to represent the audience's attention transfer mechanism by attractive objects (key object) and proposes a key object detection network based on temporal reinforcement learning (RL). Temporal RL means our model structure using LSTM to output RL reward values dynamically. In a sense, it avoids formal reward setting may cause the RL result does not achieve expectation. In order to mark the object that attracts the audience's attention in successive switching video clips, our method keeps an eye of the attractive object by iterating the value of temporal RL strategy. The proposed model can detect multiple key objects simultaneously with the help of a temporal RL strategy that analyses the attention transfer. Specifically, the spatial features and temporal features, extracted by the ensemble convolution networks, are sent into the RL model to represent the objects, corresponding motions and less relative backgrounds. Google AVA [24] dataset, annotate the position and action of the main characters among many existing objects, are selected as the experiment dataset. Compared with other models, the results show that the proposed method can continuously focus on the attractive objects in the sequenced fragments. Different from the general structure of the deep reinforcement model based on DQN network, our temporal RL parameters are mainly generated and calculated in the KOD-attention block. Therefore, in the process of simulating the attention transfer mechanism, our method uses lightweight computation to achieve satisfactory detection precision and speed in the performance of key object detection.
Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach

Deep Neural Networks (DNNs) have gained significant popularity in various Natural Language Processing tasks. However, the lack of interpretability of DNNs induces challenges to evaluate the robustness of DNNs. In this paper, we particularly focus on DNNs on sentiment analysis and conduct an empirical investigation on the sensitivity of DNNs. Specifically, we apply a scoring function to rank words importance without depending on the parameters or structure of the deep neural model. Then, we scan characteristics of these words to identify the model's weakness and perturb words to craft targeted attacks that exploit this weakness. We conduct extensive experiments on different neural network models across several real-world datasets. We report four intriguing findings: i) modern deep learning models for sentiment analysis ignore important sentiment terms such as opinion adjectives (i.e., amazing or terrible), ii) adjective words contribute to fooling sentiment analysis models more than other Parts-of-Speech (POS) categories, iii) changing or removing up to 10 adjectives words in a review text only decreases the accuracy up to 2%, and iv) modern models are unable to recognize the difference between an objective and a subjective review text <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Simplified Tabu Search with Random-Based Searches for Bound Constrained Global Optimization

This paper proposes a simplified version of the tabu search algorithm that solely uses randomly generated direction vectors in the exploration and intensification search procedures, in order to define a set of trial points while searching in the neighborhood of a given point. In the diversification procedure, points that are inside any already visited region with a relative small visited frequency may be accepted, apart from those that are outside the visited regions.The produced numerical results show the robustness of the proposed method. Its efficiency when compared to other known metaheuristics available in the literature is encouraging.
MDT: Unsupervised Multi-Domain Image-To-Image Translator Based On Generative Adversarial Networks

In recent years, many methods have been proposed to tackle unsupervised image-to-image translation. However, they mainly focus on two-domain scenarios. To address issues like large cost of training time and resources in translation across multiple (more than two) number of domains, we propose an unsupervised method based on generative adversarial networks. Our method has only one encoder for the consideration of efficiency, together with several domain-specified decoders to transform an image into multiple domains without needing an input domain label. In addition, we propose to employ two constraints namely reconstruction loss and identity loss to further improve the generation. We conduct experiments on two datasets. The results demonstrate the effectiveness and efficiency of our proposed method against state-of-the-art methods.
Automatic Detection of Bike Riders Without Helmet

As per Indian scenario, motorcyclists break traffic rules very frequently by not wearing helmet and Indian traffic police is not strict to such riders because of their tedious work. Automatic Detection of Bike Riders without Helmet system is an application of computer vision and image processing technology that takes video of vehicles and take the vehicle frame as input image. This paper presents the technique by which motorcyclists without helmet are detected. In this technique moving vehicles are detected by thresholding and then classified into motorcyclist &amp; non-motorcyclist by area and aspect ratio. To detect helmet first ROI is determined and by cascade classifier without helmet are detected. Finally we get the details of the particular bike rider from the database.
Four Models of Hopfield-Type Octonion Neural Networks and Their Existing Conditions of Energy Functions

Recently, models of neural networks in the real domain have been extended into the high dimensional domain such as the complex number and quaternion domain, and several high-dimensional models have been proposed. These extensions are generalized by introducing Clifford algebra (geometric algebra). In this paper we extend conventional real-valued Hopfield-type neural networks into the octonion domain and discuss their dynamics. The octonions represent a particular extension of the quaternions which also represent a particular extension of the complex numbers and have 7 imaginary parts. They are non-commutative and non-associative on multiplication and do not belong to Clifford algebra due to the latter fact. With this in mind we propose four models of octonion Hopfield-type neural networks. We derive existence conditions of an energy function and construct energy function for each model.
Content Extraction Method of Educational Resources Based on DOM

In order to improve the communication efficiency of fashion education resources, it is necessary to classify the original digital resources and convert them into documents that meet the communication format based on new media technologies (such as WeChat public number platform). This paper analyzes the storage structure of clothing art education resources, and proposes a method based on dom technology to automatically extract the information of subject-oriented resource library, and designs the corresponding program to realize the application of new media technology WeChat public platform to promote educational resources to the society. It has greatly improved the efficiency of resource dissemination.
Knowledge-Based Recommendation System for Online Business Using Web Usage Mining

Internet is the easiest and cheapest way to do business nowadays. Due to this every organization moves toward it. This increases a variety of problems like information overload, irrelevant information that creates confusion overhead to the customer as well as to the enterprise. Recommendation systems in online business play a vital role in assisting the customers to find the best and relevant products that meet their requirements. It is widely used in the online business environment because of its powerful personalization and efficiency features, hence variety of its design techniques are the attention of the researchers. This paper discusses various recommendation models based on users’ reviews and ratings of the products. It proposes knowledge-based recommendation system that uses heterogeneous information source in the form of triplet relation between user and the product. The proposed system compares with the baselines of the other recommendation systems also.
Q-learning with exploration driven by internal dynamics in chaotic neural network

This paper shows chaos-based reinforcement learning (RL) using a chaotic neural network (NN) functions not only with Actor-Critic, but also with Q-learning. In chaos-based RL that we have proposed, exploration is performed based on internal dynamics in a chaotic NN and the dynamics is expected to grow rational through learning. Q-learning is a very popular RL method and widely used in several researches. We focused on whether Q-learning can be adopted to chaos-based RL. Then we demonstrated the agent can learn a goal task in a grid world environment with chaos-based RL using Q-learning. It was also shown that, as learning progresses, irregularity in the network outputs originated from the internal chaotic dynamics decreases and the agent can automatically switch from exploration mode to exploitation mode. Moreover, it was confirmed that the agent can adapt to changes in the environment and automatically resume exploration.
Influencing web customers. Deep learning practical application

With the global expansion of internet businesses, companies look for possibilities to increase their turnovers in virtual media. In these circumstances, it is significant for companies to employ online user behavior data in order to understand user intents and, to some extent, influence user online activity. This research examines the practical solution of the stated goal through an application of deep learning models. The contribution of the current study is an investigation of the success of real-time influence on web portal users to conduct specific activities leading to the achievement of predefined business goals. The study investigates several top-rated over-sampling techniques while solving class imbalance problem to evaluate their impact on the deep learning model performance. The study was conducted on the Lithuanian online educational platform. The obtained results indicate the feasibility of the suggested method.
Human Activity Recognition Based on Ensemble Classifier Model

In this work, we address the problem of Human Activity Recognition (HAR), applied to service robot. In addition, a real time HRI system able to understand some common interactive human activities is designed. To classify activities into different classes, a combination of three supervised machine-learning algorithms: Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Network (ANN) is used, based on the idea that a set of classifiers improve machine learning results. Our approach uses as input a view invariant 3D data of skeleton joints, which are rich body movement information recorded from a single Microsoft Kinect camera to create specific dataset of six interactive activities. The algorithm was able to successfully classify and recognize activities being performed in front of the camera. The system framework is realized on the Robot Operating System (ROS), and real-life activity interaction between our service robot and the user was conducted to demonstrate the effectiveness of the developed HRI system.
OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area

The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the consideration of effective mapping area. A feature extraction network is used to obtain high level feature, and the true bounding box of the object in the original image is mapped to the grid of feature map, in order to perform category prediction and bounding box regression. The proposed algorithm is evaluated with the Pascal Voc dataset, and the experiments indicate that it has a better result.
Selective Feature Network for Object Detection

Scale variation is one of the important challenges in object detection. Many state-of-the-art objectors tackle this problem by utilizing the feature pyramids. However, the current methods of producing feature pyramids are still inefficient to integrate the semantic information from other layers. In this work, our motivation is to build a feature pyramid efficiently with the selected contextual feature by integrating the informative features and suppressing the useless ones. To achieve this goal, we propose a novel single-stage detection network termed Selective Feature Network(SFNet) which consists of a semantic-enhanced module and a selective feature module. The semantic-enhanced module improves the semantics of basic pyramids via a lightweight architecture. In conjunction with that, a selective feature module is employed to combine features across different channels and scales by attention mechanism. The resulting contextual feature is then injected into the pyramidal features. Comprehensive experiments are performed on PASCAL VOC and MS COCO datasets. Results demonstrate that, with a VGG16 based SFNet, our approach obtains significant improvements over the competitors without losing real-time processing speed.
Embedding Weighted Feature Aggregation Network with Domain Knowledge Integration for Breast Ultrasound Image Segmentation

Breast cancer is the most common cancer in women, and ultrasound imaging is one of the most widely used approaches for diagnosis due to its non-radioactive process, ease of operation and low cost. Moreover, image segmentation plays a great role in medical image analysis, since it affects the accuracy of computer aided diagnosis (CAD) results. However, the malignant mass of breast in ultrasound images often appears irregular boundary and indistinct margin which is difficult to distinguish from other surrounding tissues. Therefore, breast ultrasound images segmentation is significant for diagnosis, and it has attracted the attention of researchers for many years. In this paper, we propose a weighted feature aggregation network with fusing domain knowledge for two-dimensional breast ultrasound images segmentation. (I) We modify the U-Net by adding a classification branch, in which BI-RADS category information is applied as the classification label. (II) In order to deal with the artifacts in ultrasound, such as posterior shadowing, we conduct Squeeze-and-Excitation (SE) block and aggregation mechanism to compose the up-sampling part in U-Net. (III) We employ the conditional random field (CRF) to optimize segmentation to make the boundaries more continuous and integral after getting the output of U-Net. The experiment conducted on a challenging and representative dataset includes more than three thousand two-dimensional breast ultrasound images. Our method achieves Jaccard Index of 84.9%, Matthew correlation coefficient of 90.9%, and Dice Coefficient of 90.8% in testing which demonstrates the potential clinical value of our work.
A Fast Method For Shape Template Generation

Disease diagnosis often requires segmentation of structures from a given image followed by shape analysis. Shape analysis entails quantifying the variability in a shape by constructing a template for a given population. We propose an orientation-invariant representation using varifolds for the shape elements in a given shape population and present a novel diffeomorphic Log-demons based template creation pipeline. The proposed method generates a good quality template at a significantly less computation time compared to state of the art method.
Clustering by Unified Principal Component Analysis and Fuzzy C-Means with Sparsity Constraint

For clustering high-dimensional data, most of the state-of-the-art algorithms often extract principal component beforehand, and then conduct a concrete clustering method. However, the two-stage strategy may deviate from assignments by directly optimizing the unified objective function. Different from the traditional methods, we propose a novel method referred to as clustering by unified principal component analysis and fuzzy c-means (UPF) for clustering high-dimensional data. Our model can explore underlying clustering structure in low-dimensional space and finish clustering simultaneously. In particular, we impose a L0-norm constraint on the membership matrix to make the matrix more sparse. To solve the model, we propose an effective iterative optimization algorithm. Extensive experiments on several benchmark data sets in comparison with two-stage algorithms are conducted to validate effectiveness of the proposed method. The experiments results demonstrate that the performance of our proposed method is superiority.
Differential Privacy Information Publishing Algorithm based on Cluster Anonymity

With the development of Internet technology, the attacker gets more and more complex background knowledge, which makes the anonymous model susceptible to background attack. Although the differential privacy model can resist the background attack, it reduces the versatility of the data. In this paper, this paper proposes a differential privacy information publishing algorithm based on clustering anonymity. The algorithm uses the cluster anonymous algorithm based on KD tree to cluster the original data sets and gets anonymous tables by anonymous operation. Finally, the algorithm adds noise to the anonymous table to satisfy the definition of differential privacy. The algorithm is compared with the DCMDP (Density-Based Clustering Mechanism with Differential Privacy, DCMDP) algorithm under different privacy budgets. The experiments show that as the privacy budget increases, the algorithm reduces the information loss by about 80% of the published data.