Reviewerly/exHarmony huggingface dataset:
PCNET: Progressive Coupled Network for Real-Time Image Deraining

Image deraining is an effective solution to avoid performance drop of vision-oriented tasks in rainy weather. Most existing image deraining approaches either fail to produce satisfactory restoration results or cost too much computation. In this paper, we propose a low-complexity and high-performance coupled representation module (CRM), designed to learn the joint features of rain-free contents and rain information as well as their blending correlations. To promote the computation efficiency, we employ depth-wise separable convolutions, and construct CRM in an asymmetric U-shaped architecture to reduce model parameters and memory footprint. Our final model–PCNet achieves the progressive separation of rain-free contents and rain streaks using cascaded residual learning. Extensive experiments are conducted to evaluate the efficacy of the proposed PCNet on several synthetic and real-world rain datasets.
Emotion Classification of Text Based on BERT and Broad Learning System

Emotion classification is one of the most important tasks of natural language processing (NLP). It focuses on identifying each kind of emotion expressed in text. However, most of the existing models are based on deep learning methods, which often suffer from long training time, difficulties in convergence and theoretical analysis. To solve the above problems, we propose a method for emotion classification of text based on bidirectional encoder representation from transformers (BERT) and broad learning system (BLS) in this paper. The texts are input into BERT pre-trained model to obtain context-related word embeddings and all word vectors are averaged to obtain sentence embedding. The feature nodes and enhancement nodes of BLS are used to extract the linear and nonlinear features of text, and three cascading structures of BLS are designed to transform input data to improve the ability of text feature extraction. The two groups of features are fused and input into the output layer to obtain the probability distribution of each kind of emotion, so as to achieve emotion classification. Extensive experiments are conducted on datasets from SemEval-2019 Task 3 and SMP2020-EWECT, and the experimental results show that our proposed method can better reduce the training time and improve the classification performance than that of the baseline methods.
An Efficient Color Image Security Technique for IOT using Fast RSA Encryption Technique

implementing the color images encryption is a challenging field of the research for IOT applications. An exponential growth in imaging cameras in IOT uses makes it critical to design the robust image security algorithms. It is also observed that performance of existing encryption methods degrades under the presence of noisy environments. This is the major concern of evaluating the encryption method in this paper. The prime concern of this paper is to design the fast efficient color images encryption algorithm by designing an efficient and robustness RSA encryption algorithm. Method takes the advantage of both preprocessing and the Gaussian pyramid (GP) approach for encryption. To improve the performance it is proposed to use the LAB color space and implement the RSA encryption on luminance (L) component using the GP domain. The median filter and image sharpening is used for preprocessing. The goal is to improve the performance under highly noisy imaging environment. The performance is compared based on the crypto weights and on the basis of visual artifacts and entropy analysis. The decrypted outputs are again converted to color image output. Using the LAB color space is expected to improve the entropy performance of the image. Result of proposed encryption method is evaluated under the different types of the noisy attacks over the color images and also performance is compared with state of art encryption methods. Significant improvement speed of the algorithm is compared in terms of the elapsed time
Cross-Modal Deep Neural Networks based Smartphone Authentication for Intelligent Things System

Nowadays, identity authentication technology, including biometric identification features such as iris and fingerprints, plays an essential role in the safety of intelligent devices. However, it cannot implement real-time and continuous identification of user identity. This paper presents a framework for user authentication from motion signals such as accelerometers and gyroscope signals powered received from smartphones. The proposed innovation scheme including i) a data preprocessing, ii) a novel feature extraction and authentication scheme based on a cross-modal deep neural network by applying a time-distributed Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models. The experimental results of the proposed scheme show the advantage of our approach against methods.
Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework

We present a framework to address a class of sequential decision-making problems. Our framework features learning the optimal control policy with robustness to noisy data, determining the unknown state and action parameters, and performing sensitivity analysis with respect to problem parameters. We consider two broad categories of sequential decision-making problems modeled as infinite horizon Markov decision processes (MDPs) with (and without) an absorbing state. The central idea underlying our framework is to quantify exploration in terms of the Shannon entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory. This resulting policy enhances the quality of exploration early on in the learning process, and consequently allows faster convergence rates and robust solutions even in the presence of noisy data as demonstrated in our comparisons to popular algorithms, such as Q -learning, Double Q -learning, and entropy regularized Soft Q -learning. The framework extends to the class of parameterized MDP and RL problems, where states and actions are parameter dependent, and the objective is to determine the optimal parameters along with the corresponding optimal policy. Here, the associated cost function can possibly be nonconvex with multiple poor local minima. Simulation results applied to a 5G small cell network problem demonstrate the successful determination of communication routes and the small cell locations. We also obtain sensitivity measures to problem parameters and robustness to noisy environment data.
Research on the Satisfaction of Hybrid College English Teaching Based on Artificial Intelligence Rain Classroom

Abstract With the rapid development of Artificial Intelligence (hereinafter referred to as AI), TRC has become a popular teaching tool. Through the rain classroom (hereinafter referred to as TRC) mixed teaching, we can strengthen the quality and feasibility of College English teaching (hereinafter referred to as CET). TRC is a lightweight intelligent teaching tool, which can build a synchronous dual channel between teachers and students (hereinafter referred to as TAS). Through the rain class, we can analyze the asynchronous teaching of CET, which will achieve high-quality English teaching before, during and after class. Through the construction of hybrid CET mode based on TRC, this paper realizes the English teaching before, during, after class and evaluation. Compared with traditional teaching, this paper analyzes the students’ satisfaction with rain class. Finally, this paper puts forward some suggestions, which can better improve students’ enthusiasm, autonomy and satisfaction.
Joint Entity and Relation Extraction for Long Text

Extracting relation triplets from unstructured text has been well studied in recent years. However, previous works focus on solving the relation overlapping problem, and few of them deal with the long text relation extraction. In this work, we introduce a novel end-to-end joint entity and relation extraction model, namely, LTRel, which is capable of extracting relation triplets from long text based on a cross-sentence relation classification algorithm. On the other hand, due to the importance of entity recognition to the entire end-to-end model, we refine the entity tagging scheme and the feature representation of TPLinker, which save the memory space and computation, and also improve the accuracy. We evaluate our model on two public datasets: the English dataset NYT and the Chinese dataset DuIE2.0 proposed by Baidu, both of which are better than state-of-the-art on F1 score, especially significant on the Chinese dataset with a higher proportion of long text samples.
DISSIMILAR: Towards fake news detection using information hiding, signal processing and machine learning

Digital media have changed the classical model of mass media that considers the transmitter of a message and a passive receiver, to a model where users of the digital media can appropriate the contents, recreate, and circulate them. In this context, online social media are a suitable circuit for the distribution of fake news and the spread of disinformation. Particularly, photo and video editing tools and recent advances in artificial intelligence allow non-professionals to easily counterfeit multimedia documents and create deep fakes. To avoid the spread of disinformation, some online social media deploy methods to filter fake content. Although this can be an effective method, its centralized approach gives an enormous power to the manager of these services. Considering the above, this paper outlines the main principles and research approach of the ongoing DISSIMILAR project, which is focused on the detection of fake news on social media platforms using information hiding techniques, in particular, digital watermarking, combined with machine learning approaches.
Understanding Scholarly Neural Network System Diagrams Through Application of VisDNA

We utilise VisDNA as a tool for understanding neural network system architecture diagrams. Through examples from scholarly proceedings, we find that the application of the framework to this ecological and complex domain is effective for reflecting on these diagrams. We argue for additional vocabulary to describe semiotic variability and internal inconsistency or misuse of visual encoding principles in diagrams. Further, for application to system diagrams, we propose the addition of “Grouping by Object” as a new visual encoding principle, and “Emphasising” as a new visual encoding type.
Improved Wolf Pack Algorithm for Solving a Class of Multi-Objective Route Optimization Problem

Abstract This paper considers a class of Multi-objective path planning and solving algorithms. The choice of step size is a very important factor that affects the iterative algorithm. This paper improves the classical Wolf Pack Algorithm (WPA) by designing an adaptive step size. This new improved wolf pack algorithm (IWPA) improves the classical wolf colony algorithm by designing an adaptive step size. It effectively solves the problem that the WPA may fall into a endless loop. The simulation results by MATLAB show that the IWPA has a good effectiveness than the WPA to solve multi-objective route optimization.
Refining The Bounding Volumes for Lossless Compression of Voxelized Point Clouds Geometry

This paper describes a novel lossless compression method for point cloud geometry, building on a recent lossy compression method that aimed at reconstructing only the bounding volume of a point cloud. The proposed scheme starts by partially reconstructing the geometry from the two depthmaps associated to a single projection direction. The partial reconstruction obtained from the depthmaps is completed to a full reconstruction of the point cloud by sweeping section by section along one direction and encoding the points which were not contained in the two depthmaps. The main ingredient is a list-based encoding of the inner points (situated inside the feasible regions) by a novel arithmetic three dimensional context coding procedure that efficiently utilizes rotational invariances present in the input data. State-of-the-art bits-per-voxel results are obtained on benchmark datasets.
A Simple, Effective Way To Improve Neural Net Classification: Ensembling Unit Activations With A Sparse Oblique Decision Tree

We propose a new type of ensemble method that is specially designed for neural nets, and which produces surprising improvements in accuracy at a very small cost, without requiring to train a new neural net. The idea is to concatenate the output activations of internal layers of the neural net into an “ensemble feature vector”, and train on this a decision tree to predict the class labels while also doing feature selection. For this to succeed we rely on a recently proposed algorithm to train decision trees - Tree Alternating Optimization. This simple procedure consistently improves over simply ensembling the nets in the traditional way, achieving relative error decreases of well over 10% of the original nets on the well known image classification benchmarks. As a subproduct, we also can obtain an architecture consisting of a neural net feature extraction followed by a tree classifier that is faster and more compact than the original net.
A Light-weight Ship Detection and Recognition Method Based on YOLOv4

Ship detection and recognition based on deep learning often needs high standard hardware support while achieving high precision, which is difficult to adapt to offshore resource-limited platforms. Trying to solve this problem, this paper adopts the one-step target detection model YOLOv4 as the framework and applies a comprehensive network simplifying method. Firstly, this method applies different lightweight backbone networks in the framework to obtain the ideal Mobilenetv2-YOLOv4 network, and then conducts sparse training based on the scale factor of the batch normalization layer. Finally, it selects an appropriate threshold to prune unessential channel, which obtains a light-weight ship detection neural network for ship detection and recognition. The average accuracy of the network for detecting and identifying targets of 8 types of ships reaches 92.8% on average, the real-time detection speed is 37 frames per second, and the detection efficiency is 70% higher than that of the original network, which is capable of real-time detection under the condition of limited resources. The results also show that under simple tasks, appropriate methods can effectively compress the network parameters and computations while maintaining accuracy.
Automated generation of realistic test inputs for web APIs

Testing web APIs automatically requires generating input data values such as addressess, coordinates or country codes. Generating meaningful values for these types of parameters randomly is rarely feasible, which means a major obstacle for current test case generation approaches. In this paper, we present ARTE, the first semantic-based approach for the Automated generation of Realistic TEst inputs for web APIs. Specifically, ARTE leverages the specification of the API under test to search for meaningful test inputs for the API parameters in knowledge bases like DBpedia. Our approach has been integrated into RESTest, a state-of-the-art tool for API testing, achieving an unprecedented level of automation which allows to generate up to 100% more valid API calls than existing fuzzing techniques, 30% on average. Evaluation results on a set of 26 real-world APIs show that ARTE can generate realistic inputs for 7 out of every 10 parameters, outperforming related approaches.
Comparing the Effect of Product-Based Metrics on the Translation Process

Characteristics of the translation product are often used in translation process research as predictors for cognitive load, and by extension translation difficulty. In the last decade, user-activity information such as eye-tracking data has been increasingly employed as an experimental tool for that purpose. In this paper, we take a similar approach. We look for significant effects that different predictors may have on three different eye-tracking measures: First Fixation Duration (duration of first fixation on a token), Eye-Key Span (duration between first fixation on a token and the first keystroke contributing to its translation), and Total Reading Time on source tokens (sum of fixations on a token). As predictors we make use of a set of established metrics involving (lexico)semantics and word order, while also investigating the effect of more recent ones concerning syntax, semantics or both. Our results show a, particularly late, positive effect of many of the proposed predictors, suggesting that both fine-grained metrics of syntactic phenomena (such as word reordering) as well as coarse-grained ones (encapsulating both syntactic and semantic information) contribute to translation difficulties. The effect on especially late measures may indicate that the linguistic phenomena that our metrics capture (e.g., word reordering) are resolved in later stages during cognitive processing such as problem-solving and revision.
DFPN: Deformable Frame Prediction Network

Learned frame prediction is a current problem of interest in computer vision and video processing/compression. Although several deep network architectures have been proposed for learned frame prediction, to the best of our knowledge, there is no work based on using deformable convolutions for frame prediction. To this effect, we propose a deformable frame prediction network (DFPN) for task-oriented implicit motion modeling and next frame prediction. Experimental results demonstrate that the proposed DFPN model achieves state of the art results in next frame prediction in sequences with global motion. Our models and results are available at https://github.com/makinyilmaz/DFPN.
Distributed Unit Security for 5G Base-Stations using Blockchain

5G in the United States has been rapidly growing this past year as the New Radio (NR) standards have been finalized. The top three US cellular carriers cover most major cities in 2019, and the plans are to cover most of the nation by the close of 2020. Field testing shows that 5G is meeting the promise of gigabit speeds and single digit latency over millimeter wave. 5G Security is ever so more critical with the expectation of massive IoT, M2M, VANET, and High-Speed Fixed Wireless. More devices will rely on commercially available wireless internet, it should be expected that 5G Security will be thoroughly tested by unscrupulous individuals. To enhance security, this paper will cover utilizing Blockchain for identity management of the next generation NodeB (gNB) for the user equipment (UE) would make Rogue Cellsite, man-in-the-middle-attacks, or Stingray much harder to perform. This would prevent the UE from blindly connecting to any gNB it sees and sharing sensitive information because of the connection requested from an untrusted source. While there are other potential security flaws in 5G, implementing Blockchain in a commercial network would enhance attach and handover security for all devices that use 5G.
A statistical framework for evaluating convolutional neural networks. Application to colon cancer

"Purpose: Explore the efficiency of two convolutional neural networks in helping physicians in establishing colon cancer diagnosis from histopathological image scans. Methods: The dataset used in this study contains 357 histopathological image slides that ranged from benign cases to colon cancer grade three. The slides were collected by doctors at the Emergency Hospital of Craiova, Romania. The study proposes a statistical framework that studies the performances of two convolutional neural networks AlexNet and GoogleNet. Results: AlexNet has revealed a competitive accuracy in comparison with GoogleNet. To prove the robustness of the AlexNet in fair terms, we have performed a thorough statistical analysis of its performance. Conclusions: On this particular dataset which contains histopathological image scans regarding colon cancer, the convolutional neural network AlexNet proved to be superior to GoogleNet. "
Learning VAE with Categorical Labels for Generating Conditional Handwritten Characters

The variational autoencoder (VAE) has succeeded in learning disentangled latent representations from data without supervision. Well disentangled representations can express interpretable semantic value, which is useful for various tasks, including image generation. However, the conventional VAE model is not suitable for data generation with specific category labels because it is challenging to acquire categorical information as latent variables. Therefore, we propose a framework for learning label representations in a VAE by using supervised categorical labels associated with data. Through experiments, we show that this framework is useful for generating data belonging to a specific category. Furthermore, we found that our framework successfully disentangled latent factors from similar data of different classes.
Neuropsychological Support of Education and Creative Activity of Primary School Age Children with Special Educational Needs

It was analyzed that theoretical bases of the realization process of the neuropsychological approach in the correction of the children's development who have difficulties in training based on needed complex diagnostics in the conditions of constant monitoring of a condition in their development, constant improvement of methods and receptions of inclusive training of primary school age pupils. It is determined that the application of corrective neuropsychological influence on the damaged development of personality is one of the important areas of correctional pedagogy, which uses the compensatory capabilities of the child's brain. The article describes the content and features of neuropsychological features of developmental correction of primary school age children who have learning difficulties (psychomotor skills, speech, cognitive processes, visual-objective perception, emotional disorders, etc.). Means of neuropsychological correction are presented, which are represented by two blocks: formation and development of a sensorimotor component of higher mental functions and development and correction of cognitive functions and components that are part of them. The organizational and pedagogical conditions for the development of junior schoolchildren's creativity with special educational needs have been developed and experimentally tested, in particular: the creation of a creative development environment through the introduction of game teaching methods; intensification of subject to subject interaction of participants of the educational process in the conditions of inclusive education based on partnership pedagogy; maximum enrichment of subjects with creative content to increase the experience of creative self-expression of students in lessons and extracurricular activities successfully tested during the formative stage of the experiment.