Reviewerly/exHarmony huggingface dataset:
Towards Human-Level Performance in Solving Double Dummy Bridge Problem

Double Dummy Bridge Problem (DDBP) is a hard classification problem that consists in estimating the number of tricks to be taken by N-S pair during a bridge game. In this paper we propose a new approach to DDBP which utilizes convolutional neural networks (CNNs) and a dedicated matrix representation of the problem, suitable for the CNN application. Following previous studies on the application of neural networks to DDBP, we take a knowledge-free approach, i.e. the CNN models are trained with no use of any expert knowledge or explicitly indicated bridge rules. As a result, two models are derived: a baseline CNN model and its ensemble refinement. The results are compared with two former neural network approaches, showing significant superiority of the CNN-based solution. Depending on the type DDBP deal, i.e. trump or notrump, our approach either outperforms or is slightly inferior to the outcomes of human bridge grandmasters solving DDBP. This state-of-the-art performance is complemented in the paper with an analysis of the internal structure (weight patterns) of the trained CNNs, which partly explains the underlying classification process.
Analysis of Computer Communication Network and Its Security Technology Framework

With the continuous development of computer technology, network security problems are also exposed in the development of information technology. Especially the communication network, as the core part of the communication system, the security of the communication network has great significance for the whole system. At present, in the production and life, the network security problem has become an important hidden factor affecting the development of production and life. In order to strengthen the construction of information security and improve the network security, it is necessary to conduct in-depth analysis of communication network security technology, and on this basis, put forward targeted countermeasures. Based on the current situation of computer communication network, this paper discusses and analyzes the architecture of network security technology.
Adversarial Perturbation Suppression using Adaptive Gaussian Smoothing and Color Reduction

This paper presents a novel context-aware image denoising algorithm that combines an adaptive image smoothing technique and color reduction techniques to remove perturbation from adversarial images. Adaptive image smoothing is achieved using auto-threshold canny edge detection to produce an accurate edge map used to produce a blurred image that preserves more edge features. The proposed algorithm then uses color reduction techniques to reconstruct the image using only a few representative colors. Through this technique, the algorithm can reduce the effects of adversarial perturbations on images. We also discuss experimental data on classification accuracy. Our results showed that the proposed approach reduces adversarial perturbation in adversarial attacks and increases the robustness of the deep convolutional neural network models.
Online Changepoint Detection on a Budget

Changepoints are abrupt variations in the underlying distribution of data. Detecting changes in a data stream is an important problem with many applications. In this paper, we are interested in changepoint detection algorithm which operates in an online setting in the sense that both its storage requirements and worst-case computational complexity per observation are independent of the number of previous observations. We propose an online changepoint detection algorithm for both univariate and multivariate data which compares favorably with offline changepoint detection algorithms while also operating in a strictly more constrained computational model. In addition, we present a simple online hyperparameter auto tuning technique for these algorithms.
Research on Intelligent Operational Assisted Decision-making of Naval Battlefield Based on Deep Reinforcement Learning

research-article Share on Research on Intelligent Operational Assisted Decision-making of Naval Battlefield Based on Deep Reinforcement Learning Authors: Xin-ye Zhao Dalian Naval Academy, China Dalian Naval Academy, ChinaView Profile , Mei Yang Dalian Naval Academy, China Dalian Naval Academy, ChinaView Profile , Cui Peng Dalian Naval Academy, China Dalian Naval Academy, ChinaView Profile , Chao Wang Dalian Naval Academy, China Dalian Naval Academy, ChinaView Profile Authors Info & Claims AISS 2021: 2021 3rd International Conference on Advanced Information Science and System (AISS 2021)November 2021 Article No.: 10Pages 1–7https://doi.org/10.1145/3503047.3503057Online:19 January 2022Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Class imbalance data handling with deep learning–based ubiquitous healthcare monitoring system using wearable devices

Rapid development in the production of wearable devices has taken place for healthcare monitoring system. At the same time, the existence of imbalanced data poses a major influence on the prediction model, and many of the under sampling models require maximum time and decreased performance. In this view, this chapter develops a new class imbalance data handling (CIH) with optimal deep belief network (ODBN) model, named CIH-ODBN for ubiquitous healthcare monitoring system. To handle the class imbalance problem in healthcare data, adaptive synthetic sampling (ADASYN) technique is employed. Besides, the ODBN model is applied to determine the presence of diseases. In addition, rainfall optimization algorithm (ROA) is introduced to tune the hyperparameters of the deep belief network (DBN) model. An extensive implementation analysis was performed to signify the effectual outcome of the CIH-ODBN model. The outcome for the experimental validation verified the effective classification outcome of the CIH-ODBN model with the accuracy of 0.916 and 0.932 on the test diabetes and heart disease dataset.
IDENTIFYING TEACHERS’ LEVEL OF READINESS TO SUPPORT STUDENTS’ SELF-DETERMINATION

Currently, the aggravated contradiction between the society need for the readiness of students to self-determination and the level of teachers’ training to support students’ self-determination causes new techniques to measure teachers’ readiness to be developed. The purpose of the article is to describe a technique to determine the level of teachers’ readiness to support students’ selfdetermination. The technique will help to realize teachers’ attitudes to the importance of supporting self-determination as a professional function and their interest in its implementation. The research had several stages, at each stage the relevant theoretical and empirical methods were used. An integrated approach to the problem made it possible to synthesize theory, to develop the technique aimed to determine the level of teachers' readiness to support students’ self-determination, and to measure the level of teachers’ readiness. The methodology was applied in secondary schools of the Krasnoarmeyskiy district of the Chelyabinsk region, where the average level of teachers’ readiness to support students' self-determination was revealed. The presented technique can be used to prepare teachers to support students’ self-determination and to increase their level of readiness to perform this professional function.
Application of Big Data in Chinese College English Teaching Reform Based on CBI

The arrival of new era of big data has exerted influence on Chinese college English teaching and will change its current situation of “teaching language for language’s sake” producing learners only mastering common English without professional ability in specialized field. Chinese college English teaching needs thorough reform to change the current situation which is lagging behind the requirements of international compound talents. Application of big data has made the reform possible and feasible. By analyzing the current situation and developing tendency of Chinese English teaching, this paper argues that by applying big data, it is feasible to implement general-subject English project based on CBI in Chinese universities, and some strategies have been proposed to solve the bottleneck problem in Chinese English teaching reform by using big data.
A Sample Weighting and Score Aggregation Method for Multi-query Object Matching

In this paper, we propose a simple and effective method to properly assign weights to the query samples and compute aggregated matching scores using these weights in multi-query object matching. Multi-query object matching commonly exists in many real-life problems such as finding suspicious objects in surveillance videos. In this problem, a query object is represented by multiple samples and the matching candidates in a database are ranked according to their similarities to these query samples. In this context, query samples are not equally effective to find the target object in the database, thus one of the key challenges is how to measure the effectiveness of each query to find the correct matching object. So far, however, very little attention has been paid to address this issue. Therefore, we propose a simple but effective way, Inverse Model Frequency (IMF), to measure of matching effectiveness of query samples. Furthermore, we introduce a new score aggregation method to boost the object matching performance given multiple queries. We tested the proposed method for vehicle re-identification and image retrieval tasks. Our proposed approach achieves state-of-the-art matching accuracy on two vehicle re-identification datasets (VehicleID/VeRi-776) and two image retrieval datasets (the original & revisited Oxford/Paris). The proposed approach can seamlessly plug into many existing multi-query object matching approaches to further boost their performance with minimal effort.
Using DAWs as modelling tools for learning design sound-based applications in education

This article discusses a proposed design and sound tool teaching and learning approach, with interesting solution-based challenges not immediately associated with traditional DAW instruction. By stepping outside of the usual boundaries of DAW use, music production teaching is presented with a number of novel learning challenges. There is potential for DAWs, especially in educational settings, to be used to enhance the discipline, encourage experimentation and stimulate design-based ideas that promote DAW use beyond the mixing and engineering type contexts. By shifting DAWs into areas of sound-based music, as proposed by Landy, this innovative approach, facilitates deeper, experiential learning where sound is treated as the basic musical unit, therefore allowing for a potentially greater range of designed outputs.
A Study on Neural-Network-Based Text-to-Speech Adaptation Techniques for Vietnamese

One of the main goals of text-to-speech adaptation techniques is to produce a model that can generate good quality audio given a small amount of training data. In fact, TTS systems for rich-resource languages have good quality because of a large amount of data, but training models with small datasets (or low-resources) is not an easy task, which often produces low-quality sounds. One of the approaches to overcome the data limitation is fine-tuning. However, we still need a pre-trained model which learns from large amount of data in advance. The paper presents two contributions: (1) a study on the amounts of data needed for a traditional fine-tuning method for Vietnamese, where we change the data and run the training for a few more iterations; (2) we present a new fine-tuning pipeline which allows us to borrow a pre-trained model from English and adapt it to any Vietnamese voices with a very small amount of data while still maintaining a good speech synthetic sound. Our experiments show that with only 4 minutes of data, we can synthesize a new voice with a good similarity score, and with 16 minutes of data, the model can generate audio with a 3.8 MOS score.
Blind Descent: A Prequel to Gradient Descent

We describe an alternative learning method for neural networks, which we call Blind Descent. By design, Blind Descent does not face problems like exploding or vanishing gradients. In Blind Descent, gradients are not used to guide the learning process. In this paper, we present Blind Descent as a more fundamental learning process compared to gradient descent. We also show that gradient descent can be seen as a specific case of the Blind Descent algorithm. We also train two neural network architectures, a multilayer perceptron and a convolutional neural network, using the most general Blind Descent algorithm to demonstrate a proof of concept.
Accurate Polar Harmonic Transform-Based Watermarking Using Blind Statistical Detector

Digital image watermarking is an effective image copyright protection technology, which embeds the copyright information into the image to be protected, therefore achieving the purpose of image copyright protection. The recently proposed Polar harmonic transforms (PHTs) have provided a set of powerful tools for image representation. However, the accuracy of PHTs suffers from various errors, such as the geometric and numerical integration errors. In this paper, we propose an accurate computational framework of PHTs based on wavelet integration approach and present a novel accurate PHT-based multiplicative watermarking algorithm. We embed watermark data into selected blocks of the host image by modifying the PHT magnitudes due to strong robustness against various attacks. At the receiver, the distribution of watermarked noisy PHT magnitudes is analytically calculated; closed form expressions are obtained for extracting the watermark bits. Compared with other decoders, the proposed decoder has better performance in terms of watermark robustness. In addition, the proposed watermarking algorithm can effectively resist geometrical attacks and common image processing attacks.
Scene Graph Generation Using Depth, Spatial, and Visual Cues in 2D Images

To understand an image or a scene properly, it is necessary to identify objects participating in the scene, their relationships, and various attributes that describe their properties.A scene graph is a high-level representation that confines all these features in a structured manner.Scene graph generation includes multiple challenges like the semantics of relationships considered and the availability of a wellbalanced dataset with sufficient training examples.We tried to mitigate these problems by extracting two subsets, VG-R10 and VG-A16, from the popular Visual Genome dataset.Also, a framework (S2G) is proposed for generating scene graphs directly from images using depth and spatial information of object pairs.Evaluations on the scene graph generation model reveal that the proposed framework achieves better results on our data than the state-of-the-art.
Image Colorization Using Deep Convolution Autoencoder

Authors present a deep convolution autoencoder based image colorization system that colorize the black and white images without any direct human interaction. It takes input of grayscale images and return the colored images of inputted image. By using these kind of deep learning model and artificial intelligence, the human effort and mistakes can be reduced on a large-scale works. For implementing this approach, various kind of deep neural network architecture have been explored like VGG16, ImageNet and capsule network. Different suitable Image data have been chosen to train the image colorization model. The concept of autoencoder is used in this approach, because it was very efficient in learning the abstract representation of any king of data, and the decoder can built the approximately same representation of data by using the data encoded by the encoder. CIELAB color space is used to get L (The lightness of an image which is similar to the grayscale image) and ab channels (a varies from red to green and b varies from blue to yellow) of image. Mean squared error (MSE) loss function is used to calculate the loss which is calculated by comparing actual ab channel and outputted ab channel and gradient descent is used to reduce the loss. The whole model is trained on the Flickr dataset and some custom-made dataset. Trained model is tested on various sized images and results are very satisfying. In this paper, results have been compared with the ground truth of the image.
Redactable Blockchain Technology Based on Distributed Key Management and Trusted Execution Environment

Proponents view the blockchain technology as transformative because its emergence has solved many security problems in the open environment. However, it is necessary to study redactable blockchain in specific circumstances to remove the limitations brought by its immutability. This paper presents a secure redactable blockchain using distributed key management and Trusted Execution Environment (TEE). We first realizes hash collision by replacing the hash function in the blockchain with the distributed chameleon hash. The distributed key management is then adopted to ensure the security of the trapdoor, which is the core of chameleon hash. And the security of the trapdoor-related calculation is strengthened through TEE, which can ensure confidentiality and integrity of data and computations by putting code and data in a secure region. Moreover, we analyze the security of the scheme in several attack scenarios and implement a redactable blockchain based on EOS testnet. Our results demonstrate that the average time cost of distributed chameleon hash we used is more than that of the standard chameleon hash but within 15%. Nevertheless, the harm is negligible for the hash calculation time on the order of milliseconds while the security is greatly improved.
Voice Commerce: Anthropomorphism Using Voice Assistants

Voice assistants comprehend and use complex natural language and have become more pervasive in people's homes. Many effects of these technologies on consumer behaviour and their implications for manufacturers and businesses are still unexplored in literature. This paper aims to understand the impact of anthropomorphism on consumer attitudes and behavioural intentions. More specifically, the paper intends to investigate 1) whether and 2) how anthropomorphism in voice assistants influences the manner consumers evaluate products and establish purchase intentions when shopping with voice assistants. Hence, the paper concludes that anthropomorphism plays a notable role in changing the consumer's attitude and behavioural intentions. However, its impact could be easily manipulated by other technical constraints such as interaction fluency, intent classification, lack of visual display etc. Multiple interpretation attempts are presented and possible implications for manufacturers and e-retailers discussed.
Scale-insensitive property of the temporal phase unwrapping in fringe profilometry applications

Multi-frequency fringe projection profilometers that rely on temporal phase unwrapping require frequency selection that provides minor unwrapping errors with the smaller number of fringe patterns projected. The frequency selection presents a scale-insensitive property for 3D profilometers that is analysed and investigated by analytical, simulation and practical means in this work. This property has been tested in an experimental profilometer with a parallel axis optical configuration. The scale-insensitive performance of the temporal phase unwrapping is analysed over a range of Field of Views (FOVs). It is shown that after typical calibration procedures, the scaling constants of the temporal phase unwrapping remain constant for different scales of FOVs. Additionally, a theoretical relation among fringes period in emitters of Digital Light Processors (DLPs) and camera detectors for the fringe profilometer used is presented and confirmed by experimental results.
Two-layer Federated Learning for Scene Text Detection

Incident scene text detection, as the most crucial step of an incident scene text recognition system, has received increasing research attention. In this paper, a two-layer mobile federated learning model (TMFL) is proposed to protect data privacy and improve training efficiency. Particularly, a fast scene text detector is proposed to detect the multi-directional and multi-scale text by using an asymmetric convolution based feature pyramid network (AC-FPN). Compared with the traditional feature pyramid, asymmetric convolutions can effectively extract rotation-invariant features to improve the model's robustness to directed text. Moreover, in order to achieve a balance between the detection accuracy and efficiency, we modify the lightweight backbone of mobilenetv3, and integrate it with the asymmetric convolution based feature pyramid. In addition, we evaluate the performance of our detector on three benchmark datasets, where the results show that both the accuracy and the speed can be improved. Our detector can achieve an F-measure of 87.8 on the ICDAR2013, 80.5 on the MSRA-TD500 and 84.1 on the ICDAR2015 dataset, running at 32.5 FPS.
T2FRF Filter: An Effective Algorithm for the Restoration of Fingerprint Images

Images captured in dim light are hardly satisfactory and increasing the International Organization for Standardization (ISO) for a short duration of exposure makes them noisy. The image restoration methods have a wide range of applications in the field of medical imaging, computer vision, remote sensing, and graphic design. Although the use of flash improves the lighting, it changed the image tone besides developing unnecessary highlight and shadow. Thus, these drawbacks are overcome using the image restoration methods that recovered the image with high quality from the degraded observation. The main challenge in the image restoration approach is recovering the degraded image contaminated with the noise. In this research, an effective algorithm, named T2FRF filter, is developed for the restoration of the image. The noisy pixel is identified from the input fingerprint image using Deep Convolutional Neural Network (Deep CNN), which is trained using the neighboring pixels. The Rider Optimization Algorithm (ROA) is used for the removal of the noisy pixel in the image. The enhancement of the pixel is performed using the type II fuzzy system. The developed T2FRF filter is measured using the metrics, such as correlation coefficient and Peak Signal to Noise Ratio (PSNR) for evaluating the performance. When compared with the existing image restoration method, the developed method obtained a maximum correlation coefficient of 0.7504 and a maximum PSNR of 28.2467dB, respectively.