Reviewerly/exHarmony huggingface dataset:
Path Planning of the Turning Back of an Autonomous Large-Scale Six-Wheeled Dump Truck for Loading/Leaving Sediment Based on Backhoe Work

There is an urgent need to automate earthmoving activities for large-scale six-wheeled dump trucks using retrofitted sensors and driving robots. A dump truck stops at a loading position with its bed facing the backhoe to fill the bed with sediment quickly. It also stops with its bed facing the leaving position. Therefore, it is necessary to plan ahead to safely and accurately enter and exit those working positions. However, existing path planners did not generate a path with a turning point that is suitable for large-scale six-wheeled dump truck loading sediment. In this paper, we propose an online pathplanning method with the following main features. First, a path is planned to move the dump truck forward and/or backward to the working position to load and/or leave sediment. Second, when a new working position is designated, a path from the turning position to the new goal position is automatically established. We verified the proposed method using a simulator of the dump truck (3.5 m &#x00D7; 11 m), showing that it is possible to properly account for the turning radius of the dump truck. The result reveals that it is possible to replan with a working position shift of 4.8 m or 33&#x00B0; from a 25 m distance and that the time required for replanning can be shortened to &#x2264; 7.56 s. We also confirmed that the large-scale autonomous dump truck can automatically stop at the loading position in cooperation with a humanoperated backhoe 10 out of 10 times with a stopping accuracy of 0.57 m and 5.29&#x00B0;.
Image inpainting by reducing edge blur and error accumulation

Small scale damaged image inpainting is divided into iterative inpainting and interpolation-based image inpainting algorithms. In previous studies, it is found that there are some phenomena such as sawtooth effect, edge blur and so on. The sawtooth effect exists because the weight calculation of neighborhood pixels only considers the spatial information, ignores the image feature information. In addition, there are many factors leading to edge blur including the error accumulation of inpainted pixels, the low weight of gradient direction, and the inpainting priority of pixel. In this paper, we propose an image inpainting method based on Fast Marching Method (FMM). Firstly we use gradient direction and isophote direction to maintain the edge consistency, so as to improve the algorithm effect. Secondly we explore the confidence matrix to reduce the error accumulation. Finally we use the low rank of image to preprocess the image region to reduce the algorithm time complexity. The results of the experiment show that our improved method get well performance in inpainting procedure than the-state-of-the-art method.
Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition

Recurrent neural networks (RNNs) have been widely used to solve sequence problems due to their capability of modeling temporal dependency. Despite the rich varieties of RNN models proposed in the literature, the problem of different sampling rates or performing speeds in sequence tasks has not been explicitly considered in the network and the corresponding training and testing processes. This paper addresses the problem of different sampling rates or performing speeds in the skeleton-based action recognition with RNNs. Specifically, the recently proposed independently recurrent neural network (IndRNN) is used as the RNN network due to its well-behaved and easily regulated gradient backpropagation through time. Samples are extracted with variable sampling rates and thus of different lengths, then processed by IndRNN with different time steps. In order to accommodate the differences in terms of gradients introduced by the backpropagation through time under variable time steps, a learning rate adjustment method is further proposed in the paper. Different learning rate adjustment factors are obtained for different layers by analyzing the gradient behavior under IndRNN. Experiments on skeleton-based action recognition are conducted to verify its effectiveness, and the results show that the proposed variable rate IndRNN network can significantly improve the performance over the RNN models under the conventional training strategies.
Open domain Conversational Model using transfer learning

Conversational modeling is a complex task, comprising of natural language understanding and generating text as a response to the input given. In recent years, there has been the rise of a new set of conversational AI systems that are based on deep-learning neural networks architectures. With the advent of bigger and larger Transformer based Language Models trained on gigabytes of textual data and billions of parameters, the language generators are now able to generate answers, write articles and summarize texts with human-like fluency. In this paper, an open domain conversational agent that leverages transfer-learning based on OpenAI&#x2019;s GPT-2 transformer language model has been implemented and then fine-tunes it on the Reddit dataset to generate relevant and in-context responses. Experimental results and live talking with the system show that the sentence formation for the responses are quite natural and efficiently uses the history of chat to select the best response.
Nudging Occupants for Energy-Saving through Voice-Based Proactive Virtual Assistants

With the advancement of the Internet of Things (IoT) technologies, smart homes have promoted human-building interaction and sustainability in occupants’ daily life. The rise of voice-based AI-powered virtual assistants has brought new potentials to provide occupants with a convenient and intuitive interface to interact with smart homes. Aiming at enhancing the human-building bi-directional communication, voice-based proactive virtual assistants integrated with smart home ecosystems—that is, Smart Home Assistants (SHAs)—were investigated in this study. A comprehensive data collection was conducted through an online experiment, in which 307 valid questionnaire responses with participants’ demographic background information and their feedback to proactive SHAs were collected. Occupants’ perception of the proactive SHAs was evaluated among different groups of users with various demographic backgrounds. Five occupants’ socio-demographic background features were identified to have a significant impact on their acceptance level to the energy-saving suggestions by proactive SHAs, including gender, age, education level, employment status, and the number of occupants in a residence. By utilizing these demographic features, ensemble learning models can predict occupants’ perception of proactive SHAs with good performance (accuracy in the range of 0.69–0.75). Findings in this study will provide a valuable reference for academic researchers and industry practitioners in the development of personalized proactive smart home systems for human-building interaction.
Innovative Development of Intangible Culture of Arts and Crafts in Artificial Intelligence Decision Support System

The Chinese nation has accumulated a lot of precious, rich, and wonderful material and intangible culture in its historical evolution, but these cultures are facing the problem of inheritance difficulties in the long term, and it is becoming more and more difficult to innovate. In order to solve these problems, this paper puts forward the innovative development of the intangible culture of arts and crafts based on artificial intelligence decision support system, with the purpose of studying cultural inheritance, regional brand hematopoiesis promotion, and organizational goal’s support for intangible culture. The method of this paper is to study the basic principles of artificial bee colony algorithms and decision support systems, and then evaluate the value of intangible culture. The purpose of this paper is to analyze the development of local intangible culture and innovation models based on local knowledge systems and confirm the influence of technological innovation on intangible culture so as to promote the sustainable development of intangible culture. This paper describes the present situation of intangible cultural innovation and then explores the innovative ways of intangible culture based on the experiment of cultural development of artificial intelligence. The experimental results show that after using the decision support method mentioned in this paper, the technical progress index of Jingdezhen porcelain culture has increased by 2.4%.
Topic Based Partitioning for Selective Search Using Sharding Technique

In today&#x0027;s technological revolution many IT industries, government and private organisations are generating huge volume of data every day. To process this data distributed architecture systems are used. The major challenges in processing data in distributed systems are scalability, reliability, availability and portability. The traditional search techniques used for searching and processing data in distributed systems have high latency and less throughput. As a case study Elasticsearch is used to store and process data. Elastic search uses distributed architecture where Master node is connected to multiple slave nodes or data nodes. The main goal of this paper is to efficiently store and process data in elasticsearch using sharding technique. Sharding is a horizontal data partitioning technique which partitions the data into multiple data nodes. In Existing search operation all the data nodes were searched to retrieve relevant data matching to the user query. Using sharding technique we will search only selected shards to retrieve relevant data this reduces the latency and enhances the throughput, scalability and performance of distributed processing system. Results interpret that using sharding techniques the throughput and performance has enhanced. A comparative study analysis of different shard selection technique is carried out considering precision and similarity score metrics and results infer that ReDDe shard selection algorithm is efficient compared to exhaustive and CORI techniques.
A study on factors that affect error rates in handwriting examinations of Korean characters by forensic document examiners and non-experts

Forensic handwriting examinations are often considered important in trials, and especially those related to property issues. There is also increasing demand by courts for the quantification of handwriting examination results. Notably, in forensic science, error rates in forensic handwriting examinations should be carefully quantified in order to ensure their reliability as evidence. The present study examined error rates in forensic handwriting examinations conducted by both forensic document examiners (FDEs) and non-experts. To this end, four FDEs affiliated with forensic science institutes and a group of 20 laypersons were asked to perform a forensic handwriting examination of Korean characters in various forms (long text, short text, and signature samples) in blind test settings; i.e., specific information about the test was not given. The test was composed of 180 questions. The results showed that the incorrect answers are much higher in the non-expert group than the expert group though; however, the percentage of "inconclusive opinion" answers was higher in the expert group. It was also found that, in the expert group, error rates could be lowered by allowing a joint examination (or a peer review) to be performed. Meanwhile, the expert group showed relatively high error rates when dealing with handwriting samples simulated or disguised, rather than those that contained a small number of characters, such as signature samples. In the non-expert group, financial reward did not contribute to increasing the correct answers (%) of handwriting examination but rather encouraged examiners not to choose "inconclusive opinion" answers. This result indicates that a financial reward may unintentionally affect the examination results. The major findings of the present study are of significance in that this study is the first attempt to specifically examine error rates in forensic handwriting examinations of Korean characters conducted by both expert and non-expert groups. This study also demonstrated that the exchange of opinions among FDEs affected the resultant error rates and overall examination results. It was also found that financial reward may affect handwriting examination results as well.
Formalism-Driven Development: Concepts, Taxonomy, and Practice

Formal methods are crucial in program specification and verification. Instead of building cases to test functionalities, formal methods specify functionalities as properties and mathematically prove them. Nevertheless, the applicability of formal methods is limited in most development processes due to the requirement of mathematical knowledge for developers. To promote the application of formal methods, we formulate formalism-driven development (FDD), which is an iterative and incremental development process that guides developers to adopt proper formal methods throughout the whole development lifespan. In FDD, system graphs, a variant of transition systems optimized for usability, are designed to model system structures and behaviors with representative properties. System graphs are built iteratively and incrementally via refinement. Properties of system graphs are specified in propositional and temporal logics and verified by model-checking techniques with interpretation over transition system. In addition, skeleton programs are generated based on system graphs and expose implementable interfaces for executing external algorithms and emitting observable effects. Furthermore, we present Seniz, a framework that practicalizes and automates FDD. In this paper, we explicate the concepts and taxonomy of FDD and discuss its practice.
Shouted and whispered speech compensation for speaker verification systems

Nowadays, speaker verification systems begin to perform very well under normal speech conditions due to the plethora of neutrally-phonated speech data available, which are used to train such systems. Nevertheless, the use of vocal effort modes other than normal severely degrades performance because of vocal effort mismatch. In this paper, in which we consider whispered, normal and shouted speech production modes, we first study how vocal effort mismatch negatively affects speaker verification performance. Then, in order to mitigate this issue, we describe a series of techniques for score calibration and speaker embedding compensation relying on logistic regression-based vocal effort mode detection. To test the validity of all of these methodologies, speaker verification experiments using a modern x-vector-based speaker verification system are carried out. Experimental results show that we can achieve, when combining score calibration and embedding compensation relying upon vocal effort mode detection, up to 19% and 52% equal error rate (EER) relative improvements under the shouted-normal and whispered-normal scenarios, respectively, in comparison with a system applying neither calibration nor compensation. Compared to our previous work [1], we obtain a 7.3% relative improvement in terms of EER when adding score calibration in shouted-normal All vs. All condition.
Predictive maintenance using AI for Motor health prediction system

Predictive maintenance using AI for Motor health prediction system (PdMA AI System) is a system developed to predict damage and lifespan of motors, which motors are responsible for converting electricity into mechanical energy for machines in the production process. PdMA AI System allows operators and maintenance personnel to be informed about damage to the motor in advance. The system's process working with a motor that has a malfunction is detected. The system will automatically notify operators and maintainers through the website. It is able to plan maintenance effectively in advance. As a result, it helps to reduce downtime of the machines that math the production process to stop, and support prolong the service life of the motor. The researchers compared test the k-nearest neighbor, logistic regression, naïve bayes and support vector machine algorithms, which found that logistic regression is suitable for all test data groups. Logistic regression algorithm was used to develop a model for predicting motor abnormalities with an accuracy of 98% of motor health forecasts.
Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer

Most CycleGAN domain transfer architectures require a large amount of data belonging to domains on which the domain transfer task is to be applied. Nevertheless, in many real-world applications one of the domains is reduced, i.e., scarce. This means that it has much less training data available in comparison to the other domain, which is fully observable. In order to tackle the problem of using CycleGAN framework in such unfavorable application scenarios, we propose and invoke a novel Bootstrapped SSL CycleGAN architecture (BTS-SSL), where the mentioned problem is overcome using two strategies. Firstly, by using a relatively small percentage of available labelled training data from the reduced or scarce domain and a Semi-Supervised Learning (SSL) approach, we prevent overfitting of the discriminator belonging to the reduced domain, which would otherwise occur during initial training iterations due to the small amount of available training data in the scarce domain. Secondly, after initial learning guided by the described SSL strategy, additional bootstrapping (BTS) of the reduced data domain is performed by inserting artifically generated training examples into the training poll of the data discriminator belonging to the scarce domain. Bootstrapped samples are generated by the already trained neural network that performs transferring from the fully observable to the scarce domain. The described procedure is periodically repeated during the training process several times and results in significantly improved performance of the final model in comparison to the original unsupervised CycleGAN approach. The same also holds in comparison to the solutions that are exclusively based either on the described SSL, or on the bootstrapping strategy, i.e., when these are applied separately. Moreover, in the considered scarce scenarios it also shows competitive results in comparison to the fully supervised solution based on the pix2pix method. In that sense, it is directly applicable to many domain transfer tasks that are relying on the CycleGAN architecture.
Photo2Video: Semantic-Aware Deep Learning-Based Video Generation from Still Content

Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.
Image Steganography using Improved Exploiting Modification Direction

The present approach is based on the Improved Exploiting Modification Direction (IEMD) method of image steganography. This approach increases security and ensure that the message can&#x2019;t recover without knowing the embedding equation and secret key. Now as technology is improving the significance of data secrecy and clandestine communication is increased, to fulfill this requirement we have introduced Vigenerer cipher to encrypt our secret text, further to increase the secrecy of original data we have to convert 2D image into 1D data stream by using zigzag traversing, and by choosing value n i.e. number of pixels required, to encrypt $(M=2^{\ast}n+1)$-ary number and number of digits D in M - ary number that increase external attacker difficulties and improve data security. This IEMD method can hide an $(M=2^{\ast}n+1)$-ary number just modify a bit from n pixel values that can achieve embedding capacity 1 bpp without disturbing the image quality. However considering minimum value of n i.e. n=2 gives min. PSNR is 57.14 dB. Further increasing n value PSNR is also increases up to 59.67dB with maintaining embedding capacity is approximately 1 bpp. attacker on stegographic image.
Adaptive Mixed-Attribute Data Clustering Method Based on Density Peaks

The clustering of mixed-attribute data is a vital and challenging issue. The density peaks clustering algorithm brings us a simple and efficient solution, but it mainly focuses on numerical attribute data clustering and cannot be adaptive. In this paper, we studied the adaptive improvement method of such an algorithm and proposed an adaptive mixed-attribute data clustering method based on density peaks called AMDPC. In this algorithm, we used the unified distance metric of mixed-attribute data to construct the distance matrix, calculated the local density based on K-nearest neighbors, and proposed the automatic determination method of cluster centers based on three inflection points. Experimental results on real University of California-Irvine (UCI) datasets showed that the proposed AMDPC algorithm could realize adaptive clustering of mixed-attribute data, can automatically obtain the correct number of clusters, and improved the clustering accuracy of all datasets by more than 22.58%, by 24.25%, by 28.03%, by 22.5%, and by 10.12% for the Heart, Cleveland, Credit, Acute, and Adult datasets compared to that of the traditional K-prototype algorithm, respectively. It also outperformed a modified density peaks clustering algorithm for mixed-attribute data (DPC_M) algorithms.
Highly Reliable Vehicle Detection through CNN with Attention Mechanism

This paper presents a novel approach to provide reliable vehicle detection by using CNN-based approach and lane information. We firstly propose an adaptive RoI strategy that utilizes road lane information to give focus on frontal area for vehicle detection. Then we introduce a novel attention mechanism that automatically learns an attention map to refine the features for detection. Experimental results show a large improvement (+73 &#x0025; on recall rate) for long-range (30 to 60m) vehicle detection which is extremely useful for users of driving assistant systems.