Reviewerly/exHarmony huggingface dataset:
Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.
ConvELM: Exploiting Extreme Learning Machine on Convolutional Neural Network for Age Estimation

Age estimation is a fascinating field of study because its implementation can be utilized in a variety of contexts. Currently, the Convolutional Neural Network (CNN) technique is frequently utilized to solve the age estimation problem. However, this method has one disadvantage, namely a high computational cost that lengthens the training process. This paper proposes an alternative method for resolving age estimation issues. To overcome the shortcomings of the backpropagation method, the proposed method employs the Extreme Learning Machine (ELM) algorithm as a fully-connected CNN layer. ResNet50 and VGG16 are the CNN architectures employed in this study. Using the UTKFace dataset, the proposed method was trained and evaluated. Using ELM as a fully-connected layer on CNN provided significantly faster training time performance than the fully-connected layer trained with the backpropagation technique. In addition, the proposed combination of CNN and ELM can provide age classification performance that is competitive with state-of-the-art method for age estimation cases.
Noise-cuts-Noise Approach for Mitigating the JPEG Distortions in Deep Learning

Lossy image compression provides an efficient solution to the exchange and storage of large volumes of image data for various applications. The main design principle of a lossy compression algorithm is to discard visually insignificant information as much as possible while keeping the resulted visible artifacts at a minimum. However, these unperceivable defects significantly degrade the performance of a trained deep learning (DL) model. Therefore, to improve the classification performance of the models on noisy images, we propose a noise-based data augmentation technique called noise-cuts-noise approach. The simulation analysis have shown that the proposed method efficiently mitigates the performance gap on highly compressed images for example, the accuracy difference is reduced from 11% to 2% for classification of natural images. For uncompressed images, the model performance is either preserved or improved. In addition, to validate the usefulness of the proposed method, we considered a case study of multi-label classification task in chest X-ray (CXR) images. The model accuracy on highly compressed images with the proposed augmentation method increased 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> on higher resolution images while the accuracy difference reduced from 6% to 1% on smaller resolution images.
Multi-network based MAC Protocol Identification with Decision Fusion

Media access control (MAC) protocol identification is key to obtain the full awareness of wireless environments in both civil as well as military communications. In recent years, deep learning (DL) based MAC protocol identification has attracted great attention due to flourishing of deep neural networks (DNNs). However, existing research on DL based MAC protocol identification mostly exploits only one DNN to complete the identification task, which inevitably suffers from low identification accuracy. To combat the problem, this paper proposes a multi-network based algorithm that utilizes three DNNs, including a convolutional neural network (CNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU), for MAC protocol identification. A decision fusion rule is adopted to fuse the individual results of three DNNs and make the final decision. Experiment results show that the proposed multi-network based algorithm performs better than the DL based methods using the single network.
Automatic Pest Image Acquisition System Hardware Design

The increased growth rate and survival rate of pests due to warm winters induced by climate warming are causing severe damage to crops. However, the detection of pest occurrence in the early stages is difficult because monitoring under the current system monitors is carried out with manual pest traps, thus it is essential to establish a system that can minimize damage to crops through early detection and rapid response through the establishment of a systematic system. This study aims to design the hardware of an automatic pest image collection system with a camera and pest recognition AI algorithm to resolve the problems of the manual pest collection system. Existing systems include pest traps from Green Agrotech and Hanbit Stem but have the disadvantage that pest monitoring is done manually. The system in this study was realized to collect various pests using three cameras and transmit images captured through the cameras to the server. The trap has the advantage that it automatically rotates when pests are collected so the user does not have to manually replace the trap. The automatic collection of pests through this study may reduce direct labor and damage to crops through the early detection of pests.
An End-to-End Motion Planner Using Sensor Fusion for Autonomous Driving

Autonomous driving vehicles and advanced driver-assistance systems are gaining tremendous attention with the hope of providing a new transportation mode that is more convenient and ensures road safety. Different types of sensors are deployed together with the aid of deep learning (DL) techniques to help the vehicle perceive the surrounding environment and navigate toward the destination. In this paper, we implemented a deep learning-based motion planner using sensor fusion from LiDAR point clouds and camera RGB images to predict future waypoints. The model is trained in an end-to-end manner in which input are the multimodal sensor data, and output is the predicted future waypoints. A transformer module with a self-attention mechanism is used to integrate the representation of the two sensor modalities. During training, auxiliary tasks including depth estimation and bird-eye-view semantic segmentation are carried out to provide an intermediate representation of the perception process as well as to enhance the performance of the motion planning task. Experimental results obtained from different model configurations on the Longest6 benchmark have shown that our proposed model achieves competitive performance compared to baselines.
Analysis of Teaching and Learning Environment for Data Science and AI Education (Focused on 2022 Revised Curriculum)

As the importance of big data and artificial intelligence technology grows, data science and AI education are being emphasized for students who will live in the future society. Reflecting the trend of the times, the Republic of Korea is striving to develop students' future capabilities through the subjects of ‘Data Science’ and ‘Artificial Intelligence Basics’ in the 2022 revised curriculum. As a result of the analysis focusing on the teaching and learning environment of ‘Artificial Intelligence Basic’ textbooks, programming tools mainly helped achieve the Achievement Standards of the curriculum and used tools suitable for the students level. In addition, artificial intelligence algorithms of various supervised and unsupervised learning were handled as educational content and programming practice using datasets was guided. Finally, the subject of the dataset has a problem that is not related to real life of students, and research related to the teaching and learning environment needs to be continuously conducted along with the improvement of learning materials.
Neural Network based Unsupervised Face and Mask Detection in Surveillance Networks

In the post-pandemic world, surveillance cameras play a key aspect when it comes to detecting various kinds of security risks. These can range from burglars entering a premises to an individual wearing or not wearing a mask where convention dictates one way versus the other. We are proposing a system that would allow autonomously detecting these security risks with minimal human intervention. We propose using Multi-task Cascaded Convolutional Neural Networks as the face detector, a choice of a complete range of classic image feature extractors, and the Kernel-based Online Anomaly Detection algorithm to identify the potential risk in real time. We tested our proposed framework on three different datasets including real-world settings. Our proposed framework yielded high detection rates with low false alarm rates, in addition to being adaptive, portable, and requiring minimal infrastructure.
Contextual Dependency-aware Graph Convolutional Network for Extracting Entity Relations

Dependency trees contain structural information about long-distance relations between words, which is proven to be useful for relation extraction. With the continuous development of deep learning, graph neural network can best adapt to the inherent patterns of graph data, such as dependency trees. However, existing dependency-based models suffer from the noise in dependency trees generated by heuristic dependency parsing, which distorts the modeling of contextual information, especially when dependency types are introduced. Furthermore, many pruning strategies lose the complete pattern of language itself. The above two aspects may cause the model to produce semantic confusion which is bad for relation extraction. In this paper, we propose a Contextual Dependency-aware Graph Convolutional Network(C-DAGCN), which works on the complete dependency trees and perceives the importance of word dependencies from multiple angles without worrying about the noise in heuristic dependency information. Specifically, we propose the global relative position knowledge associated with the current word(CW-GRPK) which implies the associations between words, to globally enhance the text structure modeling. After that, our model weighs multiple structure knowledge to capture fine-grained word-level interactions, and encodes the knowledge-enhanced word graph without losing global linguistic patterns to constructs high-level linguistic representation for relation prediction. Experimental results show that our method outperforms nine strong baseline models on two benchmark datasets.
Deep Learning-based Human Vehicle Interface for Smart Golf Cart

This paper proposes a system in which a golf cart recognizes and tracks a user using a deep learning algorithm. Existing tracking golf carts use image processing algorithms or wearable sensors. However, image processing algorithms have low user recognition and tracking capabilities. In addition, the recognition and tracking system using a wearable sensor has a problem that requires an additional wearable sensor. We propose a non-attached smart golf cart using a deep learning algorithm to solve this problem. Deep learning object detection and classification algorithms are used to detect people and hands and recognize gestures in the detected hands. The golf cart performs user recognition, tracking, and human vehicle interface(HVI) by using the box of people and hands and gesture information. This paper verifies the algorithm on the golf cart.
3D Human Pose Estimation Using Blazepose and Direct Linear Transform (DLT) for Joint Angle Measurement

Human pose estimation is a field of computer vision science that studies the determination of joint points in the human body based on images or videos. One of the applications of human pose estimation is to evaluate human movement and performance. In this study, 3D markerless human pose estimation was carried out using the direct linear transform and deep learning Blazepose methods. System testing was carried out on the push-up movement by comparing the data results of the markerless system with the marker-based motion capture system. Push-ups are excellent exercises for developing upper body strength or endurance, such as the arms and shoulders. Push-ups are widely used in rehabilitation or recovery after surgical procedures. The American College of Sports Medicine (ACSM) has established a standard for assessing a person's physical endurance based on the number of successful push-ups. Quantitatively, of all the mean absolute errors calculated, 70.9% were below 30 mm, and for measuring joint angles (elbows, hips, and knees) 43% were below 5 degrees. An error value below 30 mm indicates that the system can be used for human movement analysis.
An Integrated Approach to Near-duplicate Image Detection

Near-duplicate image detection is a task to find clusters of images that are considered to be the same pictures in human view. This is important in image recommendation systems, because when the systems recommend candidate images, redundancies of retrieved candidate images need to be avoided. In addition, in the era of big-data where image data is overflowing, its importance in terms of saving storage resources further increases. In this paper, we propose a robust model for detecting various types of near-duplicate images by integrating four different detection modules, where we use multiple image feature extractors such as Gabor filter and deep networks. The four modules are then integrated to conduct the multivariate log-likelihood ratio test for detecting duplication. Through computational experiments, we confirmed that our method reaches state-of-the-art performance.
Parallel Corpus Curation for Filipino Text-to-SQL Semantic Parsing

Text-to-SQL models were developed over the years to allow non-technical users to interact with relational databases. Deep learning approaches require large amounts of labeled data, but the majority of the available datasets used today for natural language processing task are in English. These make text-to-SQL semantic parsing in Filipino a promising yet challenging endeavor. This research presents the iTanong corpus-a hand-labeled parallel semantic parsing corpus for Filipino Text-to-SQL tasks. The frequent code-switching or the practice of alternating between two or more languages or varieties of language in conversation and written text poses another challenge in semantic parsing for the Filipino language. The iTanong corpus contains 16,113 Filipino question and SQL pairs from two institutional databases sourced from students and employees and curated by the research team. The researchers employed Part-of-Speech tagging to guide the annotation process and analyze the various structure of the natural language queries. The usability of the corpus is tested with GPT-3 with 1,150 question-SQL pairs and achieved an execution and exact-match accuracy of 87.4% and 89.8%, respectively.
Crossover Methods Comparison in Flood Evacuation Route Optimization

This study aims to implement the genetic algorithm by testing the appropriate crossover methods in order to obtain optimal disaster evacuation routes based three main indicators, namely travel time, possible transportation mode, and affected road conditions. The research phase begins with establishing a flood-affected area scenario consisting of the victim's initial location, evacuation location, routing areas, affected road conditions, distance, as well as travel time. The genetic algorithm is applied by representing the genes and chromosomes based on the available data, generating the initial population and calculating the fitness value. At the stage of determining the parent in forming a new individual, roulette wheel selection is used. For the crossover method to produce new individuals, there are 3 methods tested namely single-point, two-point and uniform crossover. The new formed individuals are then mutated with a probability level of 0.1. The last stage is to form a new population by sorting individuals with the highest fitness value. These processes took place with an iteration limit of 1000. Based on the results of the implementation and tests conducted, the uniform crossover method has the most optimal results with accuracy 90% and highest fitness value of 0.896. Meanwhile, the two others methods two-point and single-point have extremely lower accuracy which are 70% and 60% respectively. This result confirmed the statement of previous research which convinced that the uniform crossover is the most effective crossover method.
Hierarchical Bayesian Attractor Model for Dynamic Task Allocation in Edge-Cloud Computing

Edge computing responsive applications have been gaining attention in recent years, especially in AI technology. AI distillation techniques allow compact models to be placed at the edge or terminal, where computational power is limited, with lower latency than can be processed by AI in the cloud. However, AI with smaller models is generally less accurate, so the tradeoff between accuracy and latency, as well as power consumption, must be taken into account when determining processing assignments. Conventionally, such task allocation problems have required heuristic release due to computational difficulties. On the other hand, the heuristic release method has a problem of deviation from the optimal solution when the environment is quasi-static. Our research group takes the approach of continuously searching for the optimal solution in a quasi-static environment while immediately determining the quasi-optimal solution for dynamic environmental changes based on similarities with the past quasi-static environment. In particular, the Bayesian attractor model (BAM), which models brain decision making, is used to select a quasi-optimal solution based on similarity. However, BAM has the problem that appropriate selection becomes difficult when the number of alternatives increases. In this paper, we extend the BAM to a hierarchical model, inspired by the fact that in human decision making, related concepts and operations are grouped into chunks and organized hierarchically. We show that this allows us to maintain a high rate of correct responses even when the number of choices increases. We also investigate the composition of appropriate conceptual hierarchies and updating methods in the temporal hierarchy.
Blockchain-based Data Quality Assessment to Improve Distributed Machine Learning

Data quality assessment is critical for distributed machine learning (DML). Data collected from heterogeneous Internet of things (IoT) devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a blockchain-based approach to assess the quality of data that are not independent and identically distributed (non-IID). A blockchain running atop mobile edge computing (MEC) is helpful to protect privacy, security, and integrity of healthcare data when IoT devices are connected to MEC servers. Therefore, it is critical to integrate data quality assessment module on blockchain when building a blockchain-enabled DML system. In this paper, we jointly consider information loss and marginal utility of non-IID data samples. Specifically, we use Kullback-Leibler (KL) divergence to evaluate the information loss between IID and non-IID data samples and apply the reciprocal of data quantity to model the marginal utility of data samples. Human activities and handwritten digit recognition data sets are used for performance evaluations. Experiments show that our proposed scheme outperforms benchmarks regarding model test accuracy on various non-IID data samples.
Philippine National Elections 2022: Voter Preferences and Topics of Discussion on Twitter

Studies have shown how social networking sites have been used in the political landscape as a tool to disseminate information, influence people in their political views and voting decisions, and even predict election results. This study analyzes voter preferences and identifies the topics of discussion on 2022 election-related tweets using sentiment analysis and topic modelling. Naive Bayes and Support Vector Machine are used for the sentiment analysis classifier models and Biterm Topic Modeling for identifying the most discussed topics. The results of sentiment analysis show that the Naive Bayes classifier gained a higher accuracy score of 73% than Support Vector Machine with 69%. By focusing on the leading presidential candidates, the sentiment classification revealed that Leni Robredo obtained higher positive sentiment rating than Bongbong Marcos, and is the most tweeted candidate. Significant issues regarding the candidates and the elections are determined from the topic models.
22.7 DL-VOPU: An Energy-Efficient Domain-Specific Deep-Learning-Based Visual Object Processing Unit Supporting Multi-Scale Semantic Feature Extraction for Mobile Object Detection/Tracking Applications

In the recent years, deep learning-based visual object detection/tracking (VODT) has been widely used in intelligent applications such as autonomous driving, UAV, smart robot and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{VR}/\text{AR}$</tex> . As general Al hardware platforms, GPUs and general Al processors are often used for accelerating VODT. However, without a domain-specific architecture, it is difficult for these processors to achieve high energy efficiency, making them unsuitable for mobile VODT applications. Recently, some dedicated VODT processors have been proposed with improved energy efficiency [1]–[3]. As shown in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Fig}. 22.7.1$</tex> , these designs have several issues: 1) they only support a single task (either detection or tracking), 2) they lack full support for multi-scale semantic feature extraction <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{MSFE}){-}$</tex> based state-of-the-art VODT frameworks [4], and 3) they do not sufficiently exploit domain-specific features for energy efficiency optimization. To address these issues, in this work, a deep learning-based visual object processor (named DL-VOPU) is proposed for mobile VODT applications. It exploits diverse domain-specific features to achieve record-high energy efficiency for VODT, while supporting MSFE-based VODT frameworks with a programmable backbone network. The DL-VOPU features: 1) an energy-efficient MSFE-aware Al architecture, 2) an object-oriented adaptive computing technique for energy-efficient object tracking, 3) a parallel frame-difference computing technique for energy-efficient neural network (NN) computation on video streams, and 4) a unified data compression & computing technique to address data redundancy in VODT processing.