Reviewerly/exHarmony huggingface dataset:
Cognitive Hexagon-Controlled Intelligent Speech Interaction System

Several intelligent speech interaction (ISI) systems have emerged over the past four decades that have served the human community. The research papers show that these systems are very well connected to the cognitive hexagon and the six hybrid approaches. Where this hexagon reveals six distinct cognitive areas, one of the six hybrid perspectives gives rise to the dimensions of speech quality. This survey has been undertaken to reveal the dimensions of speech quality and to discuss the role of cognitive hexagonal regions on these dimensions with hybrid approaches. Here, ISI systems support this discussion and follow them as cognitive machines. An overview of the state of the art related to ISI systems is also described here. Techniques, such as processing [natural language (NL)], speech synthesis [speech-to-text (STT) or text-to-speech (TTS)], computing (voice/mobile), and audio mining are presented in this overview. These are contributing well with technologies, such as the Internet of Things (IoT), Voice over Internet Protocol (VoIP), and cloud-based systems (CBSs). In addition, stochastic components, such as reliability, availability, and failure rate were discussed to analyze whether the Quality of Service (QoS) of these ISI systems is described. Additionally, after the discussion, some aspects of the applications are also discussed along with the essential advantages and significant drawbacks.
Medical Data Analysis Using Feature Extraction and Classification Based on Machine Learning and Metaheuristic Optimization Algorithm

A metaheuristic-based data optimization algorithm with machine learning-based feature extraction and classification architectures is proposed. The medical data collected from hospital database and public health dataset are input to analyze abnormalities through IoT. The data optimization is carried out using metaheuristic-based gravitational search algorithm. When the data is optimized, the loss function during the feature extraction, classification will be minimized for ML architecture. The feature extraction has been carried out for the medical data using Bi-LSTM-based RNN architecture, and the extracted data has been classified using a deep belief network with CNN (DBN-CNN). Collected data have been classified for prediction of abnormal and normal data range. Experimental results show the efficiency of the proposed method when compared to existing techniques, namely accuracy, precision, recall, and F1-score. Confusion matrix shows actual class and predicted class of normal and abnormal data predicted from input data.
The Use of Artificial Intelligence Combined with Wireless Network in Piano Music Teaching

Artificial intelligence (AI) development gives a new development direction to the traditional education model. The education reform in the intelligent environment is urgent. Under this background, the theory, methods, and characteristics of intelligent music education are expounded based on traditional piano music education. AI and Internet-based smart piano education is introduced. The characteristics of smart piano and its methods of assisting music teaching are analyzed. Moreover, literature review and questionnaire are adopted based on intelligent music education and smart piano theory. With two universities A and B in Nanjing as the research object, the questionnaire is designed to investigate and analyze the related problems of smart piano-assisted piano basic teaching in detail. The results show that smart piano positively improves skill training and psychological training in piano teaching. Piano major learners believe that smart piano can obviously help beginners and people with low piano levels. However, smart piano is not as good as traditional piano for high-level learners. In actual teaching, teachers should use different strategies for different levels of learners. Regarding curriculum quality, teachers have not fully played the role of the smart piano, and the curriculum of smart piano-assisted basic piano teaching still needs improvement. Finally, corresponding countermeasures and suggestions for the development direction of smart piano in piano music education are put forward according to the current situation of the smart piano. The results can reflect the current situation of smart piano-assisted basic piano teaching, provide a reference for researchers in related industries, and promote the related development of smart piano.
Farmer’s E-mart : An E-Commerce Store For Crops

Electronic Commerce is process of doing business through computer networks. E-Commerce is now seen as a major tool for many businesses and has become a normal part of almost every other business plan. The immediate benefits, in terms of cost savings, efficiencies and enhanced profitability, it offers, are beneficial at every stage in the supply chain. This paper outlines different aspects of developing an e-commerce website for Indian farmers to get involve in business directly without any need of a mediator in between and the optimum solution to the challenges involved in developing one. It consists of the planning process, which starts with determining the use case, domain modelling and architectural pattern of the web application.The sole purpose of this project is to let farmers gain more income by becoming independent. As our respected Prime Minister often says that farmers are the backbone of a country so It will be our honor to help them with this proposed plan. This e-commerce website will let farmers sell their products directly to end-users at an appropriate cost without any middlemen. Today both the producer and consumer get looted because of the mediators that work in between which eventually led to an increase in the market price that in return affect the overall demand and supply chain. The platform not only helps the producers getting the deserving price for their goods but the customer getting fresh vegetables and fruits at a decent amount.
Projector Compensation Framework Using Differentiable Rendering

Projection mapping is a widely adopted technique in various applications.A typically projector-camera system consists of a projector and camera pair where a projector emits an application-specific input image onto a surface, and the camera captures the projected output image.Given an ideal projection surface with white-colored planar geometries, the camera (or a viewer) can observe the projected output image without any visual distortion.The surface, however, can have arbitrary shapes and textured colors in practice, and it often introduces visually distracting artifacts to the output image.It results in lowering the viewing experience drastically.We propose a projector compensation framework that adjusts the projector input image so that the camera can see the projected output image with a much-reduced distortion.Our key contribution is to model the real projection mapping process with a virtual but controllable light simulation and optimize the projector input using differentiable rendering.We demonstrate that our new framework produces a more accurate output than state-of-the-art methods given complex projection surfaces.
Quality of Data in Machine Learning

A common assumption exists according to which machine learning models improve their performance when they have more data to learn from. In this study, the authors wished to clarify the dilemma by performing an empirical experiment utilizing novel vocational student data. The experiment compared different machine learning algorithms while varying the number of data and feature combinations available for training and testing the models. The experiment revealed that the increase of data records or their sample frequency does not immediately lead to significant increases in the model accuracies or performance, however the variance of accuracies does diminish in the case of ensemble models. Similar phenomenon was witnessed while increasing the number of input features for the models. The study refutes the starting assumption and continues to state that in this case the significance in data lies in the quality of the data instead of the quantity of the data.
Fall Guardian: An Intelligent Fall Detection and Monitoring System for Elderly

With the boom in the elderly population worldwide, the number of fall detection systems have greatly increased. With intelligent detection and monitoring, timely medical help can be ensured. Throughout the last decade, various systems were developed to detect falls accurately and notify caregivers timely. But most systems were developed based on sensor data of specific quality of sensors. As a result, those systems worked poorly on devices with different qualities of the same sensors. In this paper, we propose a system of fall detection and fall monitoring, Fall Guardian, which can be implemented in android devices with various sensor qualities. In this system, we sent collected sensor data to a cloud server with a Random Forest classifier and several thresholds for preprocessing, feature extraction, and prediction. Then a post-fall movement detection algorithm checked the severity of fall injury and alerted caregiver of elderly user with the elderly’s GPS location. Then we tested this application on multiple devices. The proposed system reached the highest accuracy of 98.32% and 96.64% with and without gyroscope sensor data. Environmental tests performed on twelve test subjects using four different smartphone models yielded an overall accuracy of 95.83%, a sensitivity of 93.33%, and a specificity of 98.33%.
Bi-directional semi-quantum secure direct communication protocol based on high-dimensional single-particle states

Semi-quantum secure direct communication allows the quantum party and the classical party to transmit secure messages directly, but does not need sharing a secret key in advance. To increase the information transmission efficiency and practicability of semi-quantum secure direct communication, a bidirectional semi-quantum secure direct communication protocol with high-dimensional single-particle states is designed. The proposed protocol involves quantum party Alice and classical party Bob. Each participant can receive a secret message while sending a secret message. Unlike most of existing quantum secure direct communication protocols, it is not necessary for the classical party Bob in the proposed protocol to possess the capability of measuring quantum states, which greatly enhances the feasibility of the protocol. The protocol allows the classical party Bob to implement the unitary operations on particles and reorder the quantum sequence. Furthermore, the quantum party Alice and the classical party Bob can verify the correctness of the received secret message with the Hash function. Security analysis indicates that without being discovered by the legitimate participants, Eve cannot obtain the secret message with common attack, such as intercept-resend attack, measure-resend attack, tampering attack and entanglement-measure attack. Compared with the typical semi-quantum secure direct communication protocols, the proposed protocol has a high qubit efficiency of about 28.6%. In addition, the transmission efficiency of secret message is greatly enhanced, since the proposed protocol utilizes the high-dimensional single-particle states as the carrier of secret message.
Personalized Recommendation of Location-Based Services Using Spatio-Temporal-Aware Long and Short Term Neural Network

User behavioral data are critical for predicting the next item in a recommendation system, which can be acquired by location-based services.However, existing approaches directly use latitude and longitude information, rather than fully exploitation of the location information.In order to better utilize location information and user behavior data, considering the shortcomings of existing models, a spatial-temporal long and short-term neural network(SLSTNN) is proposed in this paper for location-based personalized service recommendation.SLSTNN is the first attempt to comprehensively characterize the long and short term sequences of users, which is achieved by a double-layer attention mechanism and integrated with a deep neural network to improve the representation of spatio-temporal data.In addition, the explicit feature cross-network is employed to characterize user profiles and context features.Experimental results demonstrate that the proposed SLSTNN framework outperforms the state-of-the-art methods with an improvement of online conversion rate by 2.14%.SLSTNN addresses the insufficient feature crossing problem in the simple sequence model and can be potentially used in many recommendation systems.
Motion Planning of Manipulator by Points-Guided Sampling Network

This paper proposes a network called points-guided sampling net (PGSN) to guide the sampling process in sampling-based motion planner by utilizing the geometric information of obstacles. The geometric information is extracted from the point cloud of obstacles. By analyzing the properties of the point cloud, we propose a VAE feature extraction net that incorporates the variational autoencoder (VAE) framework with unique architectures designed for point clouds. Furthermore, we design a multi-modal sampling net to model the probability distribution of the states based on training trajectories taken from different environments. Based on PGSN, we propose a sampling-based motion planning algorithm called the point-guided rapidly-exploring random tree (PG-RRT). Three experiments are conducted to verify the proposed PGSN: Exp I shows the proposed VAE feature extraction net can successfully extract geometric features from the inputted point cloud; Exp II verifies the multi-modal sampling net successfully chooses corresponding mode with respect to extracted features; Exp III demonstrates the efficacy of our PG-RRT algorithm by showing PG-RRT outperforms other algorithms. Moreover, we provide theoretical analysis and insights towards understanding our model. Note to Practitioners—Obstacles cause lots of the sampling space invalid, thus the traditional sampling-based motion planning (SBMP) algorithm is usually unable to generate a trajectory within a reasonable short period of time. To improve the success rate and efficiency of SBMP, this paper proposes a novel deep neural network called points-guided sampling net (PGSN). PGSN is designed to exploit: (1) environmental point clouds and (2) training trajectories from multiple environments with different obstacles. In the first step, the point clouds include important geometric information. To utilize this information, we adopt a variational autoencoder approach which combines an encoder and a decoder together to extract geometric features more accurately from point clouds. In the second step, trajectories from multiple environments have a multi-modal property which can be represented by a truncated multivariate Gaussian mixture model. We propose a multi-modal sampling net to learn optimal parameters of this model from the training trajectories, and to select corresponding mode based on the extracted features. Experiments demonstrate that the proposed algorithm is feasible and can achieve higher success rate than the state-of-the-art methods. Our method uses a single frame of point cloud to improve efficiency, therefore multiple point clouds from different perspective maybe needed when objects occlude with each other.
An Efficient Video Inpainting Approach Using Deep Belief Network

The video inpainting process helps in several video editing and restoration processes like unwanted object removal, scratch or damage rebuilding, and retargeting.It intends to fill spatio-temporal holes with reasonable content in the video.Inspite of the recent advancements of deep learning for image inpainting, it is challenging to outspread the techniques into the videos owing to the extra time dimensions.In this view, this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network (VIA-BASDBN).The proposed VIA-BASDBN technique initially converts the videos into a set of frames and they are again split into a region of 5*5 blocks.In addition, the VIA-BASDBN technique involves the design of optimal DBN model, which receives input features from Local Binary Patterns (LBP) to categorize the blocks into smooth or structured regions.Furthermore, the weight vectors of the DBN model are optimally chosen by the use of BAS technique.Finally, the inpainting of the smooth and structured regions takes place using the mean and patch matching approaches respectively.The patch matching process depends upon the minimal Euclidean distance among the extracted SIFT features of the actual and references patches.In order to examine the effective outcome of the VIA-BASDBN technique, a series of simulations take place and the results denoted the promising performance.
Automated System for Face-Mask Detection Using Convolutional Neural Network

Coronavirus Disease 2019 (COVID-19) pandemic is affecting the health of the global population severely. It is one of the deadliest diseases in history and has severely affected all the countries. The only way to prevent the spread of corona is to cover faces and follow social distancing norms until a vaccine is developed. The face mask is effective in blocking the droplets that contain the COVID-19 virus. Hence, it is necessary to wear a face mask as a precautionary measure against it. In the proposed work, the face mask detection model is generated using an optimized neural network architecture for performing the classification task (mask or no mask). For training and model assessment, a dataset of 8695 images has been taken from four different sources. The model achieves a validation accuracy of 99.52%.
YOLOv5 Based Pedestrian Safety Detection in Underground Coal Mines

Safety detection is important for preventing accidents occurence in underground coal mines (UCM). However, the safety detection in UCM could be seriously interfered by complex environmental factors, i.e., dim light and dense dust. In this paper, we construct a simulated dataset for pedestrian safety detection in UCM (SDUCM-dataset), which can not only simulate real coal mine scenes, but also satisfy common safety detection tasks requirement, so as to solve the problem of lack of public coal mine environment dataset. Moreover, a novel YOLOv5 (You Only Look Once version 5) based detector named YOLo-UCM (YOLOv5 based safety detector in UCM) is well designed to detect whether there are potential safety hazards which can achieve higher efficiency than most one-stage object detectors and maintain comparable accuracy compared to the two-stage object detectors. Vision Transformer, Merge Non-Maximum Suppression and Meta-AconC (Activate or not version C) are further introduced to improve the model performance with higher detection accuracy but without significant loss of the detection speed. Experiments are performed to verify the efficiency of the proposed method for pedestrian safety detection in UCM, which demonstrate that the SDUCM-dataset can support the training of the YOLo-UCM, achieving delicate balance between the accuracy and speed in UCM environments.
DeepFake Detection

Abstract One particular disconcerting form of disinformation are the impersonating audios/videos backed by advanced AI technologies, in particular, deep neural networks (DNNs). These media forgeries are commonly known as the DeepFakes. The AI-based tools are making it easier and faster than ever to create compelling fakes that are challenging to spot. While there are interesting and creative applications of this technology, it can be weaponized to cause negative consequences. In this chapter, we survey the state-of-the-art DeepFake detection methods.
Semantic-Sparse Colorization Network for Deep Exemplar-Based Colorization

Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to establish an accurate correspondence between these two images. Previous approaches have attempted to construct such a correspondence but are faced with two obstacles. First, using luminance channel for the calculation of correspondence is inaccurate. Second, the dense correspondence they built introduces wrong matching results and increases the computation burden. To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner. Our network can perfectly balance the global and local colors while alleviating the ambiguous matching problem. Experiments show that our method outperforms existing methods in both quantitative and qualitative evaluation and achieves state-of-the-art performance.
In-Network Processing for Low-Latency Industrial Anomaly Detection in Softwarized Networks

Modern manufacturers are currently undertaking the integration of novel digital technologies - such as 5G-based wireless networks, the Internet of Things (IoT), and cloud computing - to elevate their production process to a brand new level, the level of smart factories. In the setting of a modern smart factory, time-critical applications are increasingly important to facilitate efficient and safe production. However, these applications suffer from delays in data transmission and processing due to the high density of wireless sensors and the large volumes of data that they generate. As the advent of next-generation networks has made network nodes intelligent and capable of handling multiple network functions, the increased computational power of the nodes makes it possible to offload some of the computational overhead. In this paper, we show for the first time our IA-Net-Lite industrial anomaly detection system with the novel capability of in-network data processing. IA-Net-Lite utilizes intelligent network devices to combine data transmission and processing, as well as to progressively filter redundant data in order to optimize service latency. By testing in a practical network emulator, we showed that the proposed approach can reduce the service latency by up to 40%. Moreover, the benefits of our approach could potentially be exploited in other large-volume and artificial intelligence applications.
LD-Net: A Lightweight Network for Real-Time Self-Supervised Monocular Depth Estimation

Self-supervised monocular depth estimation from video sequences is promising for 3D environments perception. However, most existing methods use complicated depth networks to realize monocular depth estimation, which are often difficultly applied to resource-constrained devices. To solve this problem, in this letter, we propose a novel encoder-decoder-based lightweight depth network (LD-Net). Briefly speaking, the encoder is composed of six efficient downsampling units and the Atrous Spatial Pyramid Pooling (ASPP) module. The decoder consists of some novel upsampling units that adopt the sub-pixel convolutional layer (SP). Experiments tested on the KITTI dataset show that the proposed LD-Net can reach nearly 150 frames per second (FPS) on GPU, and remarkably decreases the model parameters while maintaining competitive accuracy compared with other state-of-the-art self-supervised monocular depth estimation methods.