Reviewerly/exHarmony huggingface dataset:
Research on focal length measurement of telephoto lens based on Polaris observation

This paper proposes a improved method for measuring the focal length of a telephoto lens. In this paper, a photoelectric theodolite with high accuracy of angle measurement is used. Besides, a CMOS camera is used to take a position image of the center point of the theodolite, and the corresponding azimuth displayed by the theodolite is recorded. Leveling the optoelectronic theodolite on the open field, Polaris is used as a point light source outdoors. Adjusting the point target on the CMOS image, by which the target is placed in the view center of the field (crosshair center). And then, the pitch and azimuth data of the device are recorded. Adding disturbance to the photoelectric theodolite, the CMOS images is recorded. Adapting methods of target feature segmentation, sub-pixel positioning is performed on the point target image which captured by the CMOS camera. And the point target coordinates is obtained. Finally, the result based on the theodolite angle offset, sub-pixel offset and elevation angle data is calculated. The perturbation data is averaged to calculate the focal length of the telephoto lens. Compared with equipment testing result of focal length, experiments have achieved high measurement accuracy. And this method is suitable for detecting the focal length of an optoelectronic device in an outdoor environment.
Improving the efficiency of human action recognition using deep compression

Convolutional neural networks (CNNs) have become the power method for many computer vision applications, including action recognition. However, they are almost computationally and memory intensive, thus are challenging to use and to deploy on systems with limited resources, except for a few recent networks which were specifically designed for mobile and embedded vision applications. In this paper, we propose a novel feature for human action recognition as an input for CNN, named MOMP Image. This idea is simple but quite beneficial since we can directly use the existing CNN models for fine-tuning. We also propose a novel pruning algorithm to decrease computational cost and improve the accuracy of action recognition. The strategy can measure the redundancy of parameters based on their relationship using the covariance and correlation criteria and then prune the less important ones. Our method directly applies to CNNs, both on convolutional and fully connected layers, and requires no specialized software/hardware accelerators. The proposed method is the first time applying network compression for human action recognition. We evaluate our system in the context of action classification on the large-scale action datasets. Our method obtains promising performance as compared to other approaches. The proposed method reduces the model size and decreases over-fitting and therefore increases the overall performance of CNN on the large-scale datasets.
Multi-label Quadruplet Dictionary Learning

The explosion of the label space degrades the performance of the classic multi-class learning models. Label space dimension reduction (LSDR) is developed to reduce the dimension of the label space by learning a latent representation of both the feature space and label space. Almost all existing models adopt a two-step strategy, i.e., first learn the latent space, and then connect the feature space with the label space by the latent space. Additionally, the latent space lacks interpretability for LSDR. In this paper, motivated by cross-modal learning, we propose a novel one-step model, named Quadruplet Dictionary Learning (QDL), for multi-label classification with many labels. QDL models the latent space by the representation coefficients, which own preeminent recoverability, predictability and interpretability. By simultaneously learning two dictionary pairs, the feature space and label space are well bi-directly bridged and recovered by four dictionaries. Experiments on benchmark datasets show that QDL outperforms the state-of-the-art label space dimension reduction algorithms.
Methodology for numerical assessments of the quality of cloud fields images used in time mutability method

The methodology described below supports the time mutability method for estimating cloud drift velocity. A technique for determination numerical values of the contrast and impulse noise level in images of cloud fields is presented. The contrast for the entire image and the level of the impulse noise for the entire image and in addition -in angular directions have been calculated in accordance with the method for drift velocity estimation. The evaluations have been realized by special developed software modules.
Analysis of Visual Distribution Optimization of Power Grid Based on Data Mining

The continuous development of intelligent distribution technology in China has driven the vigorous construction of smart grid. Different data information systems play a vital role in the application of the distribution network, so the data scale of distribution network is expanding, and the complexity of data information is increasing. In order to accurately dig out the hidden laws from the operation and maintenance process of the smart grid, and ensure the stable operation of the smart grid, this study uses data mining related technologies to conduct an in-depth analysis of the visual power distribution technology of the grid. The results show that when the previous item is an uncertain situation such as unplanned power outages or faulty power outages, the data mining results of the latter item include light and no-load distribution transformers, heavy and planned power outages, which are caused by poor maintenance work, so more visual power distribution technology should be used. This study provides a certain reference for the optimization of power grid visualization distribution technology.
An Improved k-Means Methods for Cloud Computing based Coding Big Data Environment

With the popularity of cloud computing and big data techniques, the coding environment has been integrated into a big data environment based on cloud computing. In order to better handle the coding data in the cloud computing based big data environment, there are two main problems should be solved. One problem is that the tasks' quantities cannot be met by the traditional one-dimensional search algorithm. The other problem is that different tasks have different requirements. Then, in this paper, a new clustering algorithm in the coding big data environment is proposed. Firstly, the clustering is added to the big data environment and use the clustering to deal with the code nodes in the big data environment. Secondly, different weights are assigned to different tasks to achieve efficient scheduling of the big data environment.
Ship Image Denoising Algorithm Research Based on Deep Learning

During the voyage, the ship is vulnerable to the influence of the external environment, and the image quality of the obtained ship needs to be improved, which affects the ship identification to a certain extent. Based on the deep learning DnCNN algorithm, an improved denoising algorithm is proposed. In order to improve the training range of neurons, Leakly ReLU function is used instead of ReLU activation function in network training to extract and learn the features of the input image, so as to improve the fitting ability of the network. The training network of DnCNN algorithm is simplified and the residual network is added to make the network training faster and more stable. The experimental results show that the improved algorithm has a better denoising effect on ship image.
Custom Dataset Creation with Tensorflow Framework and Image Processing for Google T-Rex

This paper describes a methodology of creating custom dataset and then using convolution neural network (CNN) model for performing image processing operations on Google's t-rex game using tensorflow and keras framework. Machine learning professionals depend on online available datasets, specifically for computer vision based algorithms, since deep neural networks require specific input shape, size and channels before starting training process. Unfortunately, google's t-rex game does not have any directly available dataset unlike various image processing datasets. Therefore in this paper step by step process is provided for creating, training, and testing customized dataset with help of CNN model coded in python programming language for both GPU and CPU architectures.
Human Versus Machine and Human-Machine Teaming on Masked Language Modeling Tasks

The objective of AI-based masked language modeling (MLM) is to mask one or more words in a sentence and have the Natural Language Processing (NLP) model identify the masked words given the other words (representing context) in a sentence. In this study, using real examples collected from an online translation study group, we identify multiple human strategies to perform masked language modeling tasks: looking up definitions; comparing/contrasting; relying on common sense and knowledge; relying on statistical properties of past experiences; building an augmented context (using a list of keywords); and using it on a web search engine. In terms of human versus machine performance, the MLM algorithm’s performance is equal to the level of an average human expert, but it still cannot compete with the best human performance. The human experts’ strengths are the awareness of global knowledge, deep understanding of concepts, events, public opinion, etc. The human experts’ usual weaknesses, on the other hand, are the lack of domain knowledge and human biases. The machine’s strength is its comprehensive and encompassing coverage gleaned by learning from a large corpus, so that it can sometimes fill human experts’ knowledge gaps and correct human bias. But the machine could suffer from lack of true understanding and machine bias due to misleading statistical patterns. One common trait shared by human experts and the MLM algorithm is that they both can make decisions based on statistical observations. Therefore, it stands to reason that a human and machine can form a team to achieve better overall performance. In most cases, humans are not aware of their knowledge limitation or bias, so AI algorithm should take a proactive role in making suggestions, not a reactive role to be activated when human feels the need. In addition, it would be beneficial if the AI algorithm lists definition and sample usages of the word they suggest because humans need to be educated. The important skills demonstrated by human experts seem to be their ability to manipulate context for sensitivity analyses and/or the ability to gauge context-word interactions to understand the context. To improve human-machine teaming, it would be beneficial to incorporate human creativity into interface and interaction designs—humans can quickly input different context manipulation and word-context combinations, and the machine can provide quick feedback based on its extensive knowledge based from a large corpus. This teaming arrangement helps facilitate the joining of forces between human creativity and machine intelligence.
Calculations in a System

The calculations in a system are variables whose value is obtained through a function over other system variables. Depending on that function, we will have auxiliary calculations, logical calculations, or upper/low bound calculations. The chapter will also analyze their identification in the description of a system.
Fine – Grained Image Classification Using Transfer Learning and Context Encoding

Fine - grained classification is a class of problems whose aim is to distinguish between classes of objects that are similar in a great manner. However, this is not an easy task, and difficult tasks require a plethora of tools and creative solutions. In this paper, we present a solution to one fine - grained classification problem, combining deep learning and transfer learning for best end results by using featurization layers of a pretrained neural network as a feature generator, and then feeding those features to our custom neural classifier. Our best results are over 92% overall accuracy.
Evaluating Microsoft Face API in the context of student classroom attendance

Face recognition technology is having a tremendous impact on the vast majority of today's human activities - mostly for leisure and security purposes. In this paper, we evaluate Microsoft Face API technology in the context of estimating classroom student attendance. A short overview of face recognition technology is presented, alongside common algorithms used in today's state-of-the-art face recognition systems. Finally, Microsoft Face API is evaluated with respect to image quality, blur, occlusion, and in-the-wild with an attempt to evaluate the efficiency of the recognition system in the context of student classroom attendance.
Perception of Synthetic Voices in Human-Agent Interaction

We present a preliminary study on auditory categorization of synthetic voices by naïve human listeners. Different re-synthesized speech stimuli are rated in a perception test. These include linguistic variations which are not commonly employed in spoken dialog systems. The results of the conducted experiments are important for further research and development on human-agent interaction which aims to offer a more natural verbal interaction.
Orpheus: A New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference

Optimising deep learning inference across edge devices and optimisation targets such as inference time, memory footprint and power consumption is a key challenge due to the ubiquity of neural networks. Today, production deep learning frameworks provide useful abstractions to aid machine learning engineers and systems researchers. However, in exchange they can suffer from compatibility challenges (especially on constrained platforms), inaccessible code complexity, or design choices that otherwise limit research from a systems perspective. This paper presents Orpheus, a new deep learning framework for easy prototyping, deployment and evaluation of inference optimisations. Orpheus features a small codebase, minimal dependencies, and a simple process for integrating other third party systems. We present some preliminary evaluation results.
Creation of a Character Control System Using Fuzzy Japanese Speech Input

In this study, we developed a character control interface for a virtual character using fuzzy voice input in Japanese. Previous voice input interfaces have used pre-registered words in the system to control characters. The system uses the Japanese WordNet, a Japanese semantic dictionary, to associate words including synonyms with the character's commands so that even fuzzy voice input can be easily controlled. This system is considered to be effective as a voice input interface for systems that require instantaneous operations, such as action games.
DEVELOPMENT OF VISUALIZATION SYSTEM FOR SOUND ENVIRONMENT SIMULATION OF DISASTER RADIO USING MIXED REALITY

The importance of the administrative authorities communicates information about disaster prevention is increased. When a natural disaster occurs in Japan, administrative authorities communicate this information using disaster administrative radio system. Ministry of Internal Affairs and Communications recommends distribution individual house receivers to all family, however, many municipalities use not individual house receivers but outdoor loudspeakers due to financial matters and limited cost-effectiveness.
An Improved Method of Evidence Theory Based on Membership Function

Abstract Aimed at the two major problems of the basic probability assignment and conflict management in classical evidence theory, a basic probability assignment(BPA) based on membership function is proposed to alleviate the dilemma caused by the above two problems, and an example is given to show the effectiveness of this method.
Tensor total variation regularised low‐rank approximation framework for video deraining

Outdoor monitoring systems are known to exhibit better performance under normal weather conditions, while it lacks effectiveness under inclement conditions. Often video footage captured by the camera under rainy conditions comprises several visual distortions. It eventually leads to flaws when handled with succeeding computer vision algorithms, namely the object identification and tracking. Additionally, eliminating such unpleasant rainy effects is essential prior to the processing of video footage by suitable algorithms. The present work attempts to formulate a new low-rank tensor recovery based deraining algorithm that enables to remove the rain streaks from video footage. The proposed method detects the rain streaks by adopting optical flow estimation along with the brightness features inherent with the rain streaks. A unified framework comprised of tensor singular value decomposition (t-SVD) based weighted nuclear norm minimisation and tensor total variation (TTV) regularisation effectively removes rain streaks and recovers the original rain-free data from the available rainy data. The use of t-SVD enforces the concept of low rankness and also exploits the temporal redundancy among the video frames. Furthermore, TTV regularisation facilitates to promote the temporal continuity for discriminating most of the natural image contents from sparse rain streaks by preserving piece-wise smoothness of video frames. Comprehensive experimental findings based on real and synthetic data with dynamic background show that the rain streaks are more efficaciously eliminated by adopting the proposed method without much loss in the information.
Visual Pencil: Design of Portable Human-Computer Interaction Based on 2D Visual Tracking

Convenient and efficient human-computer interaction has always been the goal pursued by researchers. In this paper, we present a vision-based handwriting interaction device for non-touch screens called Visual Pencil. Unlike the conventional interactive method that captures the user's gestures using a motionless visual sensor, Visual Pencil directly mounts the visual sensor in the device. More specifically, the visual data are processed by the feature detection algorithm, and the movement of the pen tip in a 2D plane is calculated, so as to control the cursor and achieve the purpose of interaction. We further define performance indicators for evaluating the design and select more reasonable parameters through specific experiments to achieve the best interaction performance. The experiments verify that the proposed design is able to fulfill the desired operations. The significance of this design lies in that it integrates the functions of the mouse and smart pen, thus removing the requirement of more auxiliary equipment. This work sheds light on the interaction with non-touch screen computers.
Active Learning Embedded in Incremental Decision Trees

As technology evolves and electronic devices become widespread, the amount of data produced in the form of stream increases in enormous proportions. Data streams are an online source of data, meaning that it keeps producing data continuously. This creates the need for fast and reliable methods to analyse and extract information from these sources. Stream mining algorithms exist for this purpose, but the use of supervised machine learning is extremely limited in the stream domain since it is unfeasible to label every data instance requested to be processed. Tackling this problem, our paper proposes the use of active learning techniques for stream mining algorithms, specifically incremental Hoeffding trees-based. It is important to mention that the active learning techniques were implemented to match the stream mining constraints regarding low computational cost. We took advantage of the incremental tree original structure to avoid overburdening the original computational cost when selecting a label. In other words, the statistical strategy to grow each incremental tree has supported the execution of active learning. Using techniques of uncertainty sampling, we were able to drastically reduce the number of labels required at the cost of a very small reduction in accuracy. Particularly with Budget Entropy there was an average negative impact of accuracy about \(4\%\) using only \(14\%\) of samples labelled.
Targeted Learning for the Dynamic Selection of Channel Estimation Methodology

The explosive expansion of collected data - in terms of dimensionality, diversity, volume - increases more rapidly than we can analyze to draw useful conclusions, make informed decisions, and provide specific recommendations. Various fields such as medical, healthcare, aviation, telecommunication require new tools to process the data which they collect to process effectively and economically and benefit from the estimated quantities that were learned from the data itself. In particular, there are different methodologies proposed and used in telecommunications to estimate the channel coefficients of different types of channels. All these methodologies are grounded based on the assumption of the statistical property of the channel. However, a flexible solution that can dynamically deploy different methods based on the received signal yields higher performance and maintained over time. In this paper, we propose to apply targeted learning and explore the suitable parameters for a communication system. The initial results demonstrate it is possible to distinguish and identify the best methodology to fit the current channel conditions.
A Partially Observable Markov-Decision-Process-Based Blackboard Architecture for Cognitive Agents in Partially Observable Environments

Partial observability, or the inability of an agent to fully observe the state of its environment, exists in many real-world problem domains. However, most cognitive architectures do not have a theoretical foundation that allows for a systematic approach to handling partial observability. To address this issue, in this article, we propose a novel cognitive architecture based on the theory of partially observable Markov decision processes (POMDPs). We focus on one of the most fundamental cognitive architectures, the blackboard architecture, and reformulate it using the POMDP framework so that the agent can automatically decide how to use its information processing modules in partially observable environments. We propose a two-layer POMDP model for the blackboard architecture in which one layer models the dynamics of the environment and the other layer models the dynamics of the blackboard and knowledge sources within the agent. We also provide a planning method to find the optimal actions for the two-layer POMDP model based on the partially observable upper confidence bounds applied to trees (PO-UCT) algorithm. We validate the proposed architecture in both numerical and real-robot experiments.
Evaluation of edge detection operators on the water area discovery task

Image edges are considered to be a type of important information that can be extracted by applying detectors with different methodologies. Edge detection is a major and important topic in computer vision and image processing. The technique of detecting edges in an image serves as a step for pre-processing many image processing algorithms, such as image enhancement, image segmentation, image tracking and encoding [10]. Edge detection is at the heart of all stages in image processing and object recognition. It is a process of identifying and locating abrupt breaks in the image, where the breaks represent changes in the intensity of the pixels that make up the boundaries of the objects in the image. There are a number of edge detection techniques, each of which is designed to be sensitive to certain types of edges. This article presents some of them, then analyzes the errors that may occur. The implementation is made on the software product MATLAB.