Reviewerly/exHarmony huggingface dataset:
Under Water Image Enhancement Using Gaussian Laplace Transform and CLAHE based Fusion Method

Under water image analysis and it’s quality improvement is a challenging task between researchers. In the last decade there are different researchers work presented in last few years many research work are presented in this area. The motivation of the research article proposed a solution to enhancing brightness and image quality of underwater images. Underwater image arises to issues such poor contrast and low quality. Throughout this investigation, differential evolution algorithm-based contrast enhancement is demonstrated in an undersea image enhancement approach. Using Gaussian and Laplace transforms, the presented methodology demonstrates procedures. In order to proposed method a higher contrast underwater image, a new methodology has been developed that specifies a contrast limit that uses Contrast stretching, based on a Gaussian Laplace Transform, along with other techniques for underwater improvement. The presented method shows good result in terms of visual quality check parameter that is entropy (E) but also mean absolute error (MAE). The proposed method is simulated on MATlab R2016A.
Research on Key Technologies of Large Data Smart Grid Based on Power Grid Operation Simulation

The power system is more reliable and safe, improve the quality of power supply. In order to promote the integration of massive information and energy security in the environment of energy Internet, the core technology of large data smart grid based on grid operation simulation is proposed. In the era of power large data, with the increasing demand for information collection and screening, information communication system has become an important application system of smart grid. The application of large data core technology can help. The development of various fields provides key information to meet the needs of current social development, and promotes the development of power grid planning and construction and service industry.
Blockchain Solutions for Agro-Food Chain Systems

The agro-food chain is a very important area providing consumers with safe, sustainable, affordable, and sufficient food. It is critical that all stakeholders on this chain trust each other and that the food data can be monitored smoothly and securely. Due to food scandals and epidemics frequently encountered in recent years, people’s demand for safe and traceable food increases. Despite the continuing trend of digitalization of the economy, sufficient digitalization in the field of agro-food could not be achieved. Blockchain technology, which has become popular in recent years and plays an important role in the implementation of solutions that make life easier in many areas, also has an important potential in the field of agro-food chain. Blockchain technology enables secure data sharing thanks to its distributed, transparent, unchangeable structure. With this study, the rapidly spreading blockchain and the opportunities it offers will be mentioned, and the studies using this technology in the field of agro-food will be examined. This study will focus on the challenges faced in this industry and the potential of blockchain technology in combination with advanced information and communication technology and the Internet of Things (IoT) devices to overcome these challenges.
Design of an Interactive Device Based on e-Textile Material

Electronic textile (or e-Textile) is the material integrating digital information transmission ability and traditional fabric characteristics, which leads to great tactile interaction. However, the current research of the interaction of e-Textile are still influenced by the interaction mode adopted from electronic devices, such as the capacitive touch of the interactive elements. In this paper, we want to focus on how to interact with e-Textile in more textile characteristic ways, and pay more attention to the tactile semantics of materials in the process of interaction design. In order to apply the unique interactive way and interactive ability of e-Textile, this paper designs a group of interactive lighting devices. The lights combined with e-Textile can be used as output information to give feedback to the interactive behavior. The research for different interaction of e-Textile will expand the extension of fabric interaction mode, and tap the great potential of textile material properties in human-computer interaction.
Multi-level cache management of quantitative trading platform

Abstract With the rapid development of quantitative trading business in the field of investment, quantitative trading platform is becoming an important tool for numerous investing users to participate in quantitative trading. In using the platform, return time of backtesting historical data is a key factor that influences user experience. In the aspect of optimising data access time, cache management is a critical link. Research work on cache management has achieved many referential results. However, quantitative trading platform has its special demands. (1) Data access of users has overlapping characteristics for time-series data. (2) This platform uses a wide variety of caching devices with heterogeneous performance. To address the above problems, a cache management approach adapting quantitative trading platform is proposed. It not only merges the overlapping data in the cache to save space but also places data into multi-level caching devices driven by user experience. Our extensive experiments demonstrate that the proposed approach could improve user experience up to &gt;50% compared with the benchmark algorithms.
SRAUT: Service Resource Availability analysis method with User Tolerance concern

How embarrassing it is to give up the excellent service recommended by the system just because it is not available. This is indeed common when people use service recommendation system in practice. In traditional recommendation system research, the main factors are User profile, QoS, SLA, etc. However, in reality, if the recommended resources are not immediately available, users will not hesitate to ignore them. Long waiting time or waiting queue is not very friendly to users. In the long run, if such case always happens, it will affect users’ perception of the recommendation system, even the recommended services. Therefore, this paper proposes a service resource availability analysis method considering user tolerance (SRAUT). The service resource availability will be calculated by comprehensively utilizing the waiting time of the service and the different tolerance of different people to the waiting time. In other words, it varies from person to person. In order to achieve this, we conducted a controlled experiment on a representative population. This experiment helps to obtain the user tolerance representation of different groups by using disordered multinomial classification logistic regression. Finally, the traditional queuing model is improved by using this representation to form a more personalized service resource availability analysis method. The analysis results can be used to improve the traditional recommendation algorithm. In order to prove the effectiveness of this modification, this paper carried out verification experiments in the same controlled experimental population. The experimental results show that the SRAUT can significantly reduce the user loss rate and the invalid waiting time compared with the traditional queuing model without considering the user tolerance.
Research on the Precision of Grassroots Public Cultural Services Based on User Persona

As a product of the era of big data, User Persona meets the needs of users for personalized services. This article outlines the concept of User Persona and related application research. At the same time, it discusses the feasibility and necessity of applying User Persona to public cultural services, constructs individual and group User Persona models of community residents, and proposes the implementation process. From the perspective of practice, this paper tests the feasibility of the research. From the perspective of users, it provides a reference for the realization of precision of public cultural services.
Automatic Fetus Head Segmentation In Ultrasound Images By Attention Based Encoder Decoder Network

Fetus Head Circumference is an important biometric parameter for detecting growth abnormalities and estimating gestational age. For prenatal screening, 2D ultrasound is preferred. Because of its disadvantages like missing boundaries and low signal to noise ratio, measurement of head circumference is time consuming and challenging. So, precise automatic segmentation is needed to help sonographers in efficient head circumference measurement. We propose an encoder decoder network having dense blocks that strengthen feature propagation and channel attention in bottleneck to enhance the important features. The network performs better than UNet. It has lesser inference time, takes 7 times lesser training time, 5 times lesser parameters and storage size than UNet. It can be used in real time because of the average inference time of 0.0037 seconds per batch of 4. The proposed network achieved an average difference of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${0.77\pm 2.91{\mathrm{m}}\mathrm{m}}$</tex> and an average absolute difference of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2.16}{\pm}$</tex> 2.09 mm on the HC18 challenge test dataset.
Performance assessment of solar power plant: Case Study in Kemaman Terengganu

This research aims to assess the performance of PV solar farm power generation over the 24 months of 2019 and 2020. A case study was based on PV solar farm owned by Epic Solar Sdn Bhd located in Kemaman, Terengganu. It will also compare the estimated power generated with the actual performance. The assessment involves the monthly output power yield for 2019 and 2020. The impact of solar irradiance, rainfall and dust were also highlighted in terms of power production. Lastly, the suggestion for better improvement of the power production was outlined. This baseline is useful as a guideline and reference to improve the PV system performance.
Beta-divergence for Nonnegative Matrix Factorization

The beta-divergences has been largely used in the machine learning literature. In this paper, we will go into detail about what they are, where they come from, their relation with Bregman divergence, and why they are so useful in many machine learning algorithms. In particular, Nonnegative Matrix Factorization (NMF), witch we presented as an example using Majorization-Minimization approach.
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach.

In the context of neural machine translation, data augmentation (DA) techniques may be used for generating additional training samples when the available parallel data are scarce. Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences. In this paper, we propose to follow a completely different approach and present a multi-task DA approach in which we generate new sentence pairs with transformations, such as reversing the order of the target sentence, which produce unfluent target sentences. During training, these augmented sentences are used as auxiliary tasks in a multi-task framework with the aim of providing new contexts where the target prefix is not informative enough to predict the next word. This strengthens the encoder and forces the decoder to pay more attention to the source representations of the encoder. Experiments carried out on six low-resource translation tasks show consistent improvements over the baseline and over DA methods aiming at extending the support of the empirical data distribution. The systems trained with our approach rely more on the source tokens, are more robust against domain shift and suffer less hallucinations.
Performance Evaluation of Speaker Identification in Language and Emotion Mismatch Conditions on Eastern and North Eastern Low Resource Languages of India

This paper describes the impact of spoken language and emotional variation in a multilingual speaker identification (SID) system. The development of speech technology applications in low resource languages (LRL) is challenging due to the unavailability of proper speech corpus. This paper illustrates performance analysis of SID in six Eastern and North Eastern (E&NE) Indian languages and an emotional corpus of six basic emotions. For this purpose, six experimentations are carried out using the collected LRL of E&NE data to build speaker identification models. Speaker-specific acoustic characteristics are extracted from the speech segments in terms of short-term spectral features, i.e., shifted delta cepstral (SDC) and partial correlation (PARCOR) coefficients. Gaussian mixture model (GMM) and support vector machine (SVM)-based models are developed to represent the speaker-specific information captured through the spectral features. Apart from that, to build the modern SID i-vectors, time delay neural networks (TDNN) and recurrent neural network with long short-term memory (LSTM-RNN) have been considered. For the evaluation, equal error rate (EER) has been used as a performance matrix of the SID system. Performances of the developed systems are analyzed with different emotional native and non-native language corpus in terms of speaker identification (SID) accuracy in six different experiments.
Motion Control for an Autonomous Personal Mobility Based on the Potential Field Considering the Motion Characteristics of Obstacles

In recent years, Personal Mobility (PM), which is a small, low-speed electric vehicle, has been attracting attention as a new means of transportation to replace walking and driving, and research and development of autonomous driving technology for PM, especially to support the disabled and elderly, are underway. Since PM has a high affinity with pedestrians, it is expected to be effectively used in “shared space,” which is a road space where pedestrians, bicycles, automobiles, and streetcars coexist. In order for PM to run autonomously in the “shared space”, it is important to have a motion control method that can safely avoid obstacles of various shapes, such as pedestrians and streetcars, while considering the comfort of the occupants. Conventionally proposed methods allow PM to smoothly avoid various obstacles based on the prediction of their motion, but if the prediction deviates significantly, it becomes difficult to avoid the obstacle. Therefore, in this paper, we propose a new motion control method for PM that takes into account the motion characteristics of obstacles. Specifically, we define a new potential field that reflects the kinematic characteristics such as the magnitude and frequency of directional and velocity changes caused by obstacles, and control the motion of the PM based on this field. This is an extension of the conventional potential field method as a path planning method. Various simulation results have shown the effectiveness of the proposed method.
Detection of Normal and Abnormal Conditions for Boundary Surveillance using Unmanned Aerial Vehicle

There are several advanced methods to detect objects, humans, etc., which requires large dataset for training. In this paper, a difference of difference (DoD) technique has been proposed to detect normal and abnormal conditions with minimal dataset during surveillance. This dataset has been used for setting a threshold value to detect condition without training. Captured images have been compared with created dataset toget net difference value and this value has been compared with the threshold value to detect normal and abnormal conditions. This technique can be used at no-man zone areas, boundaries and security borders, and also it can be used as a front-liner along with any advanced technique for surveillance application. This method has been simulated on real images using MATLAB and conducted practical experiment using quad rotor, RPi and ROS.
Experimental Analysis of ADFGVX Cipher

In recent decades, with the development of computer and Internet, the cryptography has been greatly developed. Classical cryptography has been gradually replaced by modern cryptography. However, it does not mean that classical ciphers are useless. To tackle this issue, this paper proposes a method to analyze the ADFGVX cipher. The key part of this paper is about how the ADFGVX cipher works, how to decrypt this cipher and how to break it. The analysis of ADFGVX cipher shows that this classical cipher is unbreakable without the aid of a computer and reveals how to break the cipher through computer.
Clustering with label constrained Dirichlet process mixture model

In this paper, we present a constrained Dirichlet process mixture model with labels as side information. Specifically, the labeled information is incorporated through the use of a product partition prior to give clusters of instances with similar labels a higher prior preference. The proposed formulation is further extended to handle multiple side information. The empirical results on several benchmark datasets show that our method can consistently improve its clustering performance as more labeled data become available. Even in the presence of noisy labels, the proposed method rarely performs worse than its unsupervised counterpart. The effectiveness of the proposed method is also demonstrated through an application of magnetic resonance imaging for identifying major brain tissues.
Eye movement and Frontal face of human body to Interact with Computer: A Recognition System

Human Computer Interface systems are extending their boundaries in our daily life and become an important subject in every field. The field of face recognition has gain a lot of attention for researchers. Many researchers have contributed their work in this field by using various methods. Real time face detection is still a tedious task and achieves a good performance of the system. In this paper we will consider all the techniques of face recognition and also Human Computer Interaction. Detecting and normalizing human faces from live video streams is the first crucial step in a face verification/recognition system. The contributing disciplines include computer science, cognitive science, human factors, software engineering, management science, psychology, sociology, and anthropology. Early research and development in human-computer interaction focused on issues directly related to the user interface.
Ship Recognition Technology of High Precision Remote Sensing Images Based on YOLO

Due to the vast sea area of China, it is not easy for ships to identify and dispatch at sea, so the target recognition of ships has always been the focus of the development of the target recognition and detection technology. However, the traditional methods of ship recognition are slow and not practical. The target recognition technology of remote sensing images based on deep learning makes up for this shortcoming, and the recognition is fast and accurate. In this paper, YOLO V3 deep learning network is used to create a high-precision remote sensing image training and testing data set. YOLO V3 network is trained and tested, and then TX2 module is used as the carrier to design a concise user interface to test the effect of the computer running in the embedded system.
Cognitive Advisory Agent

Machine learning workflows involve extracting data from a dataset, training the model, predicting future behavior, and finally saving the results. At times, merging features from similar datasets or appending additional features to existing dataset may help improve trained model performance and accuracy. However, merging data is a complex operation, requiring deep analysis on features to be merged. While the state-of-the-art methods work to resolve the merging of datasets, there is a need for a cognitive system to performs this task efficiently.In this paper, we propose a novel cognitive advisory agent, that dynamically provides actionable insights while merging datasets to improve the performance of the model. The agent uses reward function while performing reinforcement learning in order to analyze appending features from different datasets. We demonstrate the efficacy of our method on a real-world use case and share the encouraging results.
INVITED: kCC-Net for Compression of Biomedical Image Segmentation Networks

Convolutional neural networks (CNNs) for biomedical image segmentation are often of very large size, resulting in high memory costs and high latency of operations. To ensure CNNs’ accommodation of key computing resource constraints in specific applications, network compression is commonly used. However, time-consuming training/validation experiments are often involved when searching for a compressed CNN for a specific imaging application, in order to achieve a desired compromise between the network size and network accuracy. Recognizing that biomedical images tend to have relatively uniform target objects, we present kCC-Net, a framework to reduce the cost of compressing CNNs for biomedical image segmentation. kCC-Net first uses training data complexity and target network architecture to estimate the network accuracy degradation caused by compression and compute a layer-wise multiplier for generating a compressed network, referred to as CC-Net. To enhance kCC-Net’s ability to extract rich hierarchical features, we incorporate a multi-scale approach by utilizing multiple submodules of CC-Net to generate a new network which is capable of extracting finer features. Verified using three public biomedical image segmentation datasets, our proposed kCC-Net framework is shown to be effective, retaining up to $\sim 95$% of the full-sized networks’ segmentation accuracy, while utilizing $\sim 51 x$ fewer network trainable weights (average reduction) of the full-sized networks.
IVCGAN:An Improved GAN for Voice Conversion

In the task of voice conversion (VC), the voice of the source speaker is converted into the voice of the target speaker, but the voice content does not change in this task. There has been some research in voice conversion and some progress and results have been achieved. In this paper, we propose a new voice conversion network based on GAN network, which is a voice conversion technique that relies on non-parallel data and is capable of converting samples of arbitrary duration. This network is called IVCGAN. The network consists of a discriminator and a generator, in which the function of discriminator is to distinguish the real speech from the converted speech, and to classify the source speakers corresponding to the speech, while the function of generator is to deceive the discriminator. We tested the proposed model on a clear and clean voice data set, and the experimental results show that this method can effectively achieve voice conversion, and its performance is better than that of baseline.