Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement

H.265/HEVC achieves an average bitrate reduction of 50% for fixed video quality compared with the H.264/AVC standard, while computation complexity is significantly increased. The purpose of this work is to improve coding efficiency for the next-generation video-coding standards. Therefore, by developing a novel spatial neighborhood subset, efficient spatial correlation-based motion vector prediction (MVP) with the coding-unit (CU) depth-prediction algorithm is proposed to improve coding efficiency. Firstly, by exploiting the reliability of neighboring candidate motion vectors (MVs), the spatial-candidate MVs are used to determine the optimized MVP for motion-data coding. Secondly, the spatial correlation-based coding-unit depth-prediction is presented to achieve a better trade-off between coding efficiency and computation complexity for interprediction. This approach can satisfy an extreme requirement of high coding efficiency with not-high requirements for real-time processing. The simulation results demonstrate that overall bitrates can be reduced, on average, by 5.35%, up to 9.89% compared with H.265/HEVC reference software in terms of the Bjontegaard Metric

Domain Adaptation through Photorealistic Enhanced Images for Semantic Segmentation

In this paper, three types of domain adaptation which are defined as image-level domain adaptation, interdomain adaptation, and intradomain adaptation are efficiently combined to construct a high efficiency framework for semantic segmentation. The proposed domain adaptation platform can achieve a high reduction of time-consuming to generate exhausted supervised data in the real world using photorealistic images. The proposed framework achieved a mean Intersection-over-Union (mIoU) of 45.0%. Furthermore, by combining the proposed method with intradomain adaptation, the improvement of 1.2% mIoU is achieved compared to previous work

Design of an Unsupervised Machine Learning-Based Movie Recommender System

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We~propose methods optimizing K so that each cluster may not significantly increase variance. We~are limited to using groupings based on Genre and Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and~Betweenness Centrality. We also used average similarity, computational time, association rule with Apriori algorithm, and clustering performance evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and~Davies--Bouldin Index

DRMS for Patient-Level Lymph Node Status Classification

Generally, automatic diagnosis of the presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Detection and classification of breast cancer metastases have high clinical relevance, especially in whole-slide images of histological lymph node sections. Fast early detection leads to huge improvement of patient’s survival rate. However, currently pathologists mainly detect the metastases with microscopic assessments. This diagnosis procedure is extremely laborious and prone to inevitable missed diagnoses. Therefore, automated, accurate patient-level classification would hold great promise to reduce the pathologist’s workload while also reduce the subjectivity of diagnosis. In this paper, we provide a novel deep regional metastases segmentation (DRMS) framework for the patient-level lymph node status classification. First, a deep segmentation network (DSNet) is proposed to detect the regional metastases in patch-level. Then, we adopt the density-based spatial clustering of applications with noise (DBSCAN) to predict the whole metastases from individual slides. Finally, we determine patient-level pN-stages by aggregating each individual slide-level prediction. In combination with the above techniques, the framework can make better use of the multi-grained information in histological lymph node section of whole-slice images. Experiments on large-scale clinical datasets (e.g., CAMELYON17) demonstrate that our method delivers advanced performance and provides consistent and accurate metastasis detection in clinical trials

BOB-RED queue management for IEEE 802.15.4 wireless sensor networks

This study is aimed at exploring why many economists propose a transfer scheme and debt mutualisation for the Eurozone. This would equip the Eurozone with better tools to deal with an economic shock, like the 2010-2012 sovereign debt crisis, thus making it more financially stable. After the theoretical presentation, the study presents a unique institutional design with an EU Treasury that manages debt mutualisation and a transfer scheme as well as other competences that address other present economic challenges. Crucial to the study are the issues of moral hazard and adverse selection that arise when thinking of European economic integration.L’objectiu del treball és explorar la raó per la qual molts economistes proposen un sistema de transferències fiscals i la mutualització del deute a l’Eurozona. Així se la dotaria amb eines més efectives per pal·liar un xoc econòmic, com la crisi del deute sobirà del 2010-2012. A continuació, es presenta un disseny institucional únic d’un Tresor de l’Euro que gestionaria les competències esmentades (i d’altres) per combatre alguns dels reptes econòmics actuals. El risc moral i de selecció adversa, qüestions que sorgeixen en pensar la drecera que ha de prendre la integració econòmica Europea, són cabdals per aquest estudi

The IPIN 2019 Indoor Localisation Competition—Description and Results

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks

Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement

H.265/HEVC achieves an average bitrate reduction of 50% for fixed video quality compared with the H.264/AVC standard, while computation complexity is significantly increased. The purpose of this work is to improve coding efficiency for the next-generation video-coding standards. Therefore, by developing a novel spatial neighborhood subset, efficient spatial correlation-based motion vector prediction (MVP) with the coding-unit (CU) depth-prediction algorithm is proposed to improve coding efficiency. Firstly, by exploiting the reliability of neighboring candidate motion vectors (MVs), the spatial-candidate MVs are used to determine the optimized MVP for motion-data coding. Secondly, the spatial correlation-based coding-unit depth-prediction is presented to achieve a better trade-off between coding efficiency and computation complexity for interprediction. This approach can satisfy an extreme requirement of high coding efficiency with not-high requirements for real-time processing. The simulation results demonstrate that overall bitrates can be reduced, on average, by 5.35%, up to 9.89% compared with H.265/HEVC reference software in terms of the Bjontegaard Metric

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset

Feature Extraction for Cocoa Bean Digital Image Classification Prediction for Smart Farming Application

The implementation of Industry 4.0 emphasizes the capability and competitiveness in agriculture application, which is the essential framework of a country’s economy that procures raw materials and resources. Human workers currently employ the traditional assessment method and classification of cocoa beans, which requires a significant amount of time. Advanced agricultural development and procedural operations differ significantly from those of several decades earlier, principally because of technological developments, including sensors, devices, appliances, and information technology. Artificial intelligence, as one of the foremost techniques that revitalized the implementation of Industry 4.0, has extraordinary potential and prospective applications. This study demonstrated a methodology for textural feature analysis on digital images of cocoa beans. The co-occurrence matrix features of the gray level co-occurrence matrix (GLCM) were compared with the convolutional neural network (CNN) method for the feature extraction method. In addition, we applied several classifiers for conclusive assessment and classification to obtain an accuracy performance analysis. Our results showed that using the GLCM texture feature extraction can contribute more reliable results than using CNN feature extraction from the final classification. Our method was implemented through on-site preprocessing within a low-performance computational device. It also helped to foster the use of modern Internet of Things (IoT) technologies among farmers and to increase the security of the food supply chain as a whole

A Human-Detection Method Based on YOLOv5 and Transfer Learning Using Thermal Image Data from UAV Perspective for Surveillance System

At this time, many illegal activities are being been carried out, such as illegal mining, hunting, logging, and forest burning. These things can have a substantial negative impact on the environment. These illegal activities are increasingly rampant because of the limited number of officers and the high cost required to monitor them. One possible solution is to create a surveillance system that utilizes artificial intelligence to monitor the area. Unmanned aerial vehicles (UAV) and NVIDIA Jetson modules (general-purpose GPUs) can be inexpensive and efficient because they use few resources. The problem from the object-detection field utilizing the drone’s perspective is that the objects are relatively small compared to the observation space, and there are also illumination and environmental challenges. In this study, we will demonstrate the use of the state-of-the-art object-detection method you only look once (YOLO) v5 using a dataset of visual images taken from a UAV (RGB-image), along with thermal infrared information (TIR), to find poachers. There are seven scenario training methods that we have employed in this research with RGB and thermal infrared data to find the best model that we will deploy on the Jetson Nano module later. The experimental result shows that a new model with pre-trained model transfer learning from the MS COCO dataset can improve YOLOv5 to detect the human–object in the RGBT image dataset