Reviewerly/exHarmony huggingface dataset:
Development of an Autonomous Intelligent Mobile Robot Based on AI and SLAM Technology

This paper describes the development of an autonomous intelligent mobile robot. By using a multi-layer sensor fusion, an algorithm which is the combination of the SLAM algorithm and the bubble rebound algorithm is presented. The algorithm allows the robot to perform navigation tasks more efficient. Furthermore, the robot is integrated with artificial intelligence applications including the object detection and the voice processing. All of algorithms and models are so efficient that they can be deployed on a limited-resource embedded computer offering many potential applications.
Improvement of Text-Independent Speaker Verification Using Gender-like Feature

Text-independent speaker verification is a task of verifying a speaker identity from a characteristic of voice. We proposed the combined deep Convolutional Neural Network (CNN) consisting of (i) the first CNN trained to achieve gender classification which is then used to create a gender-like embedding and (ii) the last CNN trained with one additional input, the gender-like feature (embedding) from the first, to classify each speaker. The classification layer of the last CNN is removed to allow the remaining combined deep CNN for one-shot learning and verification of unobserved speaker. Our proposed CNN could obtain better results compared to VGGVox (ResNet-50) by 0.40% of Equal Error Rate (EER) on average. Additionally, we investigated results based on the scenario that the gender is known; the evaluation was performed only on utterance pairs that comply with the scenario. The EER rate of such case that only gender of claimed identity is known is 0.52% lower than that of VGGVox (ResNet-50) on average of two genders. In a more specific situation that the gender of person making a claim is also known, two dedicated networks were retrained for female and male, in addition to our first network which was trained for both. It is interesting that, when compared to the first network, the female network achieved less EER on female-female verification, while the network dedicated for male performed worse. Nevertheless, our two dedicated networks outperformed VGGVox (ResNet-50) by 0.88% of EER.
An Efficient and Secure Hiding Technique for Fingerprint Authentication Based on DNA Sequence

Information security is earning more attention due to the increase in the size of the data being exchanged over the network. One of the suggested solutions to preserve the secrecy is the cryptography of the information by turning them into codes before the data transfer. The stenographic technique is another way to protect the information, and it is based on hiding the information from the advisory. Cryptography and steganography are methods that allow the information to be sent securely. Current studies suggest that the integration of cryptography and steganography techniques will provide more security and confidentiality compared with the application of each technique individually. The DNA steganography uses the DNA sequence as a carrier to enable secure transfer of the critical data. The principle of this technique is basically to encrypt and conceal messages within high capacity of DNA strands to prevent adversaries from reading and deciphering the messages. This research intends to develop a technique that integrates the sciences of steganography and cryptography to protect the secret fingerprint image in the communication channel and to preserve the confidentiality of the information.
VGG-Embedded Adaptive Layer-Normalized Crowd Counting Net with Scale-Shuffling Modules

Crowd counting is widely used in real-time congestion monitoring and public security. Due to the limited data, many methods have little ability to be generalized because the differences between feature domains are not taken into consideration. We propose VGG-embedded adaptive layer normalization (VadaLN) to filter the features that irrelevant to the counting tasks in order that the counting results should not be affected by the image quality, color or illumination. VadaLN is implemented on the pretrained VGG-16 backbone. There is no additional learning parameters required through our method. VadaLN incoporates the proposed scale-shuffling modules (SSM) to relax the distortions in upsampling operations. Besides, nonaligned training methdology for the estimation of density maps is leveraged by an adversarial contextual loss (ACL) to improve the counting performance. Based on the proposed method, we construct an end-to-end trainable baseline model without bells and whistles, namely VadaLNet, which outperforms several recent state-of-the-art methods on commonly used challenging standard benchmarks. The intermediate scale-shuffled results are combined to formulate a scale-complementary strategy as a more powerful network, namely as VadaLNeSt. We implement VadaLNeSt on standard benchmarks, e.g. ShanghaiTech (Part A & Part B), UCF_CC_50, and UCF_QNRF, to show the superiority of our method.
Learnable Higher-Order Representation for Action Recognition

Capturing spatiotemporal dynamics is an essential topic in video recognition. In this paper, we present learnable higher-order operations as a generic family of building blocks for capturing spatiotemporal dynamics from RGB input video space. Similar to higher-order functions, the weights of higher-order operations are themselves derived from the data with learnable parameters. Classical architectures such as residual learning and network-in-network are first-order operations where weights are directly learned from the data. Higher-order operations make it easier to capture context-sensitive patterns, such as motion. Self-attention models are also higher-order operations, but the attention weights are mostly computed from an affine operation or dot product. The learnable higher-order operations can be more generic and flexible. Experimentally, we show that on the task of video recognition, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on Something-Something (V1 and V2), Kinetics and Charades datasets.
Using control synthesis for falsification and corner case generation

This talk will describe algorithms that search for "dynamical adversarial examples" or "corner cases" for feedback control systems. This problem is related to the falsification problem, where the goal is to find initial conditions, disturbance profiles, and environment behaviors that force the system to violate its specifications. As opposed to the commonly adopted falsification approaches that treat the system under test as a black-box, we propose a synthesis-guided approach, which leverages the knowledge of a plant model if it exists and treats only the controller and perception mechanism as black-box. Our algorithm uses the plant model and backward reachable set computations to guide the search for falsifying trajectories. We will demonstrate the approach with examples from autonomous systems, including those using perception-based neural network controllers.
A Joint Super-Resolution and Deformable Registration Network for 3D Brain Images

In this paper, we propose a joint network for 3D brain images registration with super-resolution to reduce deformable image registration errors caused by low resolution. Basically, the task of deformable image registration is to find the displacement field between the reference image and the moving image. Many research works have been done for deformable image registration, under the assumption that the image resolution is high enough. However, due to the limited level of current acquisition instruments, the resolution of images is not high enough usually. As low resolution images might cause large registration errors, this paper presents a new approach that performs joint super-resolution and deformable image registration. Experiments with 3D brain images show that the joint network does help to reduce the registration errors significantly.
Blockchains for Use in Construction and Engineering Projects

This chapter describes blockchains and illustrates this explanation using the results of a prototype project for an industrial application for a construction project. The chapter describes the application and how modular software components can be used to assemble a blockchain solution. The chapter concludes with a design of the system architecture. The background to blockchain technology includes a description of the evolving nature due to communal, open software consortia and an accelerated prototyping of systems. Four recommendations are made in the chapter. These include the need to form consortia for prototyping applications, encouraging government involvement, the need for engagement with the open software development community, and the suggestion that systems should be designed to support Lean production. A final section offers a range of discussion topics on the current state of the technology and where to expect area of increased interest. These are summarized in three areas: Lean management, Industry 4.0 and smart cities, and topics around privacy and security.
A Hybrid Algorithm For Robot Path Planning

In this paper, first an improved repulsive potential field function is proposed to make the path smoother and to solve the global minimum problem. Then a hybrid algorithm is proposed which improves the defects of A* algorithm and the artificial potential field method and has high successful probability in experiments.
Effectiveness of physical education classes for university students in a remote format

Implementation in practice of distance learning technologies in the discipline culture with University students requires an analysis of the impact of independent training on the body of students, which is why the research is relevant. The aim of the study was to determine the effectiveness of physical education in the context of distance learning. The research used the following methods: to identify the attitude of female students to distance learning, a questionnaire was conducted; to determine the indicator of the functional capabilities of the cardiovascular system, girls were asked to conduct a Harvard step test at home; methods of mathematical statistics were used as data processing. The research was conducted with a group of second-year female students of the faculty of Humanities of Orenburg state University. The research involved 32 female students, according to the results of the survey, who have experience in sports at the school curriculum level.
1920년대 조선인 지식인과 여학생의 여성 중등교육 인식

The purpose of this study is to shed light on the dominant social discourses concerning educated women in the 1920s by analyzing what korean intellectuals and school girls thought the importance of secondary woman education as. Korean intellectuals and school girls'' perceptions on the importance of secondary education for women manifested in broadly three different ways. First, these women were expected to acquire a self-identity as an independent human being. Second, educated women could enhance their social status through enactment of their newly acquired knowledge. Third, educated women were still expected assume a traditional role of a wise mother and good wife. But educated school girls resisted the traditional social discourses on women''s role and sought to utilize the knowledge they learned at school to manage and be in charge of their household. Korean intellectuals and school girls believed that women, like men, should grow into independent agents of their lives and that education was to play a vital role in the process. Such conceptions on the importance of women''s education were shaped by the broader social changes of the time, but at the same time, were results of active women''s movement.
Increasing the Interest in Learning the History of Indonesian National Heroes through an Augmented Reality Book

Learning history of national heroes is pivotal as a form of respecting people who had scarified their life for the nation and country. However, mostly national heroes history books have not attracted high interest from the community, due to its presentation with a lot of information in text form. With the advancement in new media technology, this study has proposed to utilize augmented reality (AR) book as a media for learning history of Indonesian national heroes. This study is a preliminary study that also tried to analyze its impact on audience's interest and understanding as well the effectiveness of the AR book as media for transferring information or knowledge. This study was conducted by doing interview and low fidelity prototype demonstration of Rasuna Said on 15 participants from different walk of life. The result revealed that participants showed more interests on the history, as well it seems to help them in understanding the presented history better. Furthermore, the participants believed that AR book is an effective media for knowledge transfer. This is a promising result that could be used as supporting information for further study in developing AR book to fulfill the needs of learning history.
A Travel Recommendation Method Based on User Personalized Characteristics with Collaborative Fusion Matrix

With the improvement of people's living standards, people's spiritual needs continue to increase, which makes personalized travel recommendation more and more popular. However, the complicated and overloaded information in the internet have caused troubles for people to choose travel scenic spots. Although collaborative filtering is widely used for recommendation algorithms, neither user-based nor item-based collaborative filtering take the users' personalized characteristics into account. In order to recommend better travel scenic spots to users, we propose a personalized collaborative fusion algorithm combining user-based original collaborative filtering matrix with users' personalized preference matrix to make the recommendation more accurate and attractive. We build our experiments on the data of Qunaer, the results show that our proposed algorithms can not only recommend similar users' travel scenic spots, but also consider users' own personalized characteristics.
Tiny Encryption Algorithm on Discrete Cosine Transform Watermarking

Watermarking is a method embeds a secret imformation into a host such that can not be detected by the human senses. Hiding the secret information such as legal ownership is a part of information security. In this research, Tiny Encryption Algorithm (TEA) is implemented on watermarking using Discrete Cosine Transform (DCT) to add strenghten information security. We analyze the security of TEA using Avalanche Effect and the effect when implemented on DCT watermarking in terms of imperceptibility. Secret text is encrypted then embedded into image using the DCT. The results of the embedding are then extracted to obtain the hidden text, then proves the image belongs to the copyright holder by decrypting the extracted results. Testing results using Avalanche effect obtained value of 49.25% which is strong and good for encryption. The effect of TEA on DCT watermarking shows that the encryption does not destroy the host image.
Using models of cortical development based on sparse coding to discriminate between real and synthetically-generated faces

We compare the robustness of image classifiers based on state-of-the-art Deep Neural Networks (DNNs) with classifiers based on a model of cortical development using a single layer of sparse coding. The comparison is based on the ability of the two distinct types of classifiers to distinguish between faces of celebrities from the CelebA dataset and synthetic faces created by the ProGAN multi-scale GAN, trained on the same CelebA images. We examine the robustness of DNNs compared to classifiers based on sparse coding after the addition of universal adversarial perturbations (UAPs), which fool most or all of the DNN classifiers we examined. Our results show that simple classifiers based on sparse coding are robust to UAPs that substantially degrade performance across a wide range of DNN architectures. We hypothesize that sparse latent representations, which correspond to fixed points of a dynamical attractor-or Hopfield network-are naturally denoising and remove small adversarial perturbations. We observe that analogous but reduced robustness is conferred by deep denoising autoencoders. Our results suggest that DNN-based classifiers may be designed to rely on more robust features, and thus may be less susceptible to adversarial attacks, if preceded by a denoising pre-processing layer.
Design of a smart bracelet system for the elderly based on MTK6580

Nowadays, there are various types of smart bracelets in the market, and their functions are becoming more and more complex. However, there are not many bracelets facing the elderly and many of the physiological behaviors and living conditions of the elderly are different from those of the young. For example, the elderly are facing the risk of losing and falling. For these reasons, based on the Android system, this article has studied a smart bracelet system that not only collects heart rate and blood pressure, but also addresses the problems of falling and losing of the elderly. The bracelet in this article is a functional design for a specific group, which has important meaning and practical value.
Key Management Using Particle Swarm Optimization in MANET

With the accretion in the demand for robustness in the cryptographic algorithms, we are compelled to find a practical answer to this desire of reliable and dynamic mode of communication. Though many of the cryptographic devices possess mobility and hence are engaged within a mobile ad hoc network (MANET), yet the security goals still remain the same, viz. availability, integrity, confidentiality, non-repudiation, and authenticity. This work is an attempt to devise a secured and authenticated procedure for key management employing asymmetric cryptography. The following procedure would be employed in MANETs. This project is an amalgamation of the two most researched topics in the current cyber world. The approach presented here is an attempt where we exploit the randomness associated with the crossover and mutation process of the genetic algorithm; moreover, to make it unbreachable, a scheduled random factor is included which is derived using the particle swarm optimization. At last, the scheme is implemented in Python 3 and implemented for encrypting a plaintext document into ciphertext and decrypting the ciphertext into the respective plaintext. Furthermore, the algorithm devised here is common and can be further implemented for any secured transmission of data.
Research on Semantic Segmentation of Thangka Images

Thangka is one of the important artistic works in Chinese culture. In order to better promote the understanding and research of digital thangka images, this article explores the semantic segmentation of the headdress of the main statue of thangka. In this article, we use FCN as the benchmark model, and choose PSPNet, Deeplab V3+, Mask R-CNN, DANet, Cascade Mask R-CNN and other five mainstream semantic segmentation models based on deep learning. Then carry out experimental research separately. We explore a more suitable deep subdivision research model for Thangka image semantics. The construction of Thangka image semantic segmentation model provides experimental and theoretical basis for the next step. In this paper, using the same parameters and not using the pre-training model, the conventional training experiments and comparative training experiments under different backbone networks were carried out. Experiments show that using FCN as the baseline model, cascaded Mask R-CNN has the best segmentation effect in headwear segmentation. Its mIoU is 62.59%. In different backbone networks, PSPNet segmentation effect is significantly different.
Efficient Nodes Representation Learning with Residual Feature Propagation

Graph Convolutional Networks (GCN) and their variants have achieved brilliant results in graph representation learning. However, most existing methods cannot be utilized for deep architectures and can only capture the low order proximity in networks. In this paper, we have proposed a Residual Simple Graph Convolutional Network (RSGCN), which can aggregate information from distant neighbor node features without over-smoothing and vanishing gradients. Given that node features of the same class have certain similarity, a weighted feature propagation is considered to ensure effective information aggregation by giving higher weights to similar neighbor nodes. Experimental results on several datasets of node classification demonstrate the proposed methods outperform the state-of-the-art methods in terms of effectiveness and efficiency.
MSIGEN: Multi-Scale Illumination-Guided Low-Light Image Enhancement Network

Abstract Images lack of light often show the characteristics of low visibility, low contrast and high noise, due to limited imaging equipment. To address the problem, inspired by Retinex theory, we propose a two-stage method called Multi-Scale Illumination-Guided Low-Light Image Enhancement Network (denoted as MSIGEN). In the first stage, we employ an enhancement module to achieve low-light image enhancement, including a Decom-Net for decomposing into illumination and reflectance, an Adjust-Net for illumination adjustment, a Restore-Net for the illumination fusion map guided reflectance restoration. Secondly, the enhancement module is introduced to refine the initial images to effectively remove visual defects amplified and ensure the naturalness of the image. In addition, extensive experiment results demonstrate the advantages of our method and the effect of this method has reached the state-of-the-art.
υ-Nonparallel parametric margin fuzzy support vector machine

Classification problem is an important research direction in machine learning. υ-nonparallel support vector machine (υ-NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. However, when solving classification problems, υ-NPSVM will encounter the problem of sample noises and heteroscedastic noise structure, which will affect its performance. In this paper, two improvements are made on the υ-NPSVM model, and a υ-nonparallel parametric margin fuzzy support vector machine (par-υ-FNPSVM) is established. On the one hand, for the noises that may exist in the data set, the neighbor information is used to add fuzzy membership to the samples, so that the contribution of each sample to the classification is treated differently. On the other hand, in order to reduce the effect of heteroscedastic structure, an insensitive loss function is introduced. The advantages of the new model are verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of it.
Recognizing User Activity Using a Smartphone's Accelerometer and Deep Neural Network Classifier

Along with today's fast-growing technology, machines/devices especially mobile devices have been developed using many sensors to simplify the user's activities. One of the most known and frequently used sensors is called accelerometer, daily used as a step counter, image stabilization, and user interfaces control. However, activity recognition is considered a difficult task due to the reality that each activity has its unique features and there is no clear analytical way to analyze sensor data into specific forms of action in general. This study examines the potential and exciting ability of the accelerometer to recognize user activity by making simple prototype to support the implementation of this user activity recognition. After data acquisition, deep learning classifier will be used to differentiate activities. This research will show the efficiency and utilization of using accelerometer combined with deep learning in recognizing user activity, which can be associated with many applications for advance study such as falling detection, abnormality detection, and prediction of human behavior.