Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification

In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%

Repairable queue with non-exponential service time and variable breakdown rates

Consider a single server queue in which the service station may breakdown according to a Poisson process with rates γ in busy time and γ’ in idle time respectively. After a breakdown, the service station will be repaired immediately and the repair time is assumed to have an exponential distribution with rate δ. Suppose the arrival time has an exponential distribution with rate λ, and the probability density function g(t) and the cumulative distribution function G(t) of the service time are such that the rate g(t)/[1 – G(t)] tends to a constant as t tends to infinity. When the queue is in a stationary state, we derive a set of equations for the probabilities of the queue length and the states of the arrival and service processes. Solving the equations, we obtain approximate results for the stationary probabilities which can be used to obtain the stationary queue length distribution of the syste

New approach for finding basic performance measures of single server queue

Consider the single server queue in which the system capacity is infinite and the customers are served on a first come, first served basis. Suppose the probability density functio

Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising

The advancement of the communication technology and the popularity of the smart phones foster the booming of video ads. Baidu, as one of the leading search engine companies in the world, receives billions of search queries per day. How to pair the video ads with the user search is the core task of Baidu video advertising. Due to the modality gap, the query-to-video retrieval is much more challenging than traditional query-to-document retrieval and image-to-image search. Traditionally, the query-to-video retrieval is tackled by the query-to-title retrieval, which is not reliable when the quality of tiles are not high. With the rapid progress achieved in computer vision and natural language processing in recent years, content-based search methods becomes promising for the query-to-video retrieval. Benefited from pretraining on large-scale datasets, some visionBERT methods based on cross-modal attention have achieved excellent performance in many vision-language tasks not only in academia but also in industry. Nevertheless, the expensive computation cost of cross-modal attention makes it impractical for large-scale search in industrial applications. In this work, we present a tree-based combo-attention network (TCAN) which has been recently launched in Baidu's dynamic video advertising platform. It provides a practical solution to deploy the heavy cross-modal attention for the large-scale query-to-video search. After launching tree-based combo-attention network, click-through rate gets improved by 2.29\% and conversion rate get improved by 2.63\%.Comment: This revision is based on a manuscript submitted in October 2020, to ICDE 2021. We thank the Program Committee for their valuable comment

Human activity recognition with self-attention

In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models

News Reliability Evaluation using Latent Semantic Analysis

The rapid rise and widespread of ‘Fake News’ has severe implications in the society today. Much efforts have been directed towards the development of methods to verify news reliability on the Internet in recent years. In this paper, an automated news reliability evaluation system was proposed. The system utilizes term several Natural Language Processing (NLP) techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), Phrase Detection and Cosine Similarity in tandem with Latent Semantic Analysis (LSA). A collection of 9203 labelled articles from both reliable and unreliable sources were collected. This dataset was then applied random test-train split to create the training dataset and testing dataset. The final results obtained shows 81.87% for precision and 86.95% for recall with the accuracy being 73.33%

Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification

In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%

SymmeGuess: Fun beyond Symmetry Learning

Computer application can be used for artistic design and as a means to facilitate the understanding of mathematical concepts. This paper presents a game-based application as a visual learning tool for mathematics and art subjects. The objectives of this application are to enable learners to build their knowledge on symmetry and to explore the beauty of symmetrical patterns with confidence and enjoyment

Causal analysis between altered levels of interleukins and obstructive sleep apnea

BackgroundInflammation proteins including interleukins (ILs) have been reported to be related to obstructive sleep apnea (OSA). The aims of this study were to estimate the levels for several key interleukins in OSA and the causal effects between them.MethodWeighted mean difference (WMD) was used to compare the expression differences of interleukins between OSA and control, and the changed levels during OSA treatments in the meta-analysis section. A two-sample Mendelian randomization (MR) was used to estimate the causal directions and effect sizes between OSA risks and interleukins. The inverse-variance weighting (IVW) was used as the primary method followed by several other MR methods including MR Egger, Weighted median, and MR-Robust Adjusted Profile Score as sensitivity analysis.ResultsNine different interleukins—IL-1β, IL-2, IL-4, IL-6, IL-8, IL-12, IL-17, IL-18, and IL-23—were elevated in OSA compared with control to varying degrees, ranging from 0.82 to 100.14 pg/ml, and one interleukin, IL-10, was decreased by 0.77 pg/ml. Increased IL-1β, IL-6, and IL-8 rather than IL-10 can be reduced in OSA by effective treatments. Further, the MR analysis of the IVW method showed that there was no significant evidence to support the causal relationships between OSA and the nine interleukins—IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-17, and IL-18. Among them, the causal effect of OSA on IL-5 was almost significant [estimate: 0.267 (−0.030, 0.564), p = 0.078]. These results were consistent in the sensitivity analysis.ConclusionsAlthough IL-1β, IL-2, IL-4, IL-6, IL-8, IL-12, IL-17, IL-18, and IL-23 were increasing and IL-10 was reducing in OSA, no significant causal relationships were observed between them by MR analysis. Further research is needed to test the causality of OSA risk on elevated IL-5 level

Liquid chromatography coupled with time-of-flight and ion trap mass spectrometry for qualitative analysis of herbal medicines

AbstractWith the expansion of herbal medicine (HM) market, the issue on how to apply up-to-date analytical tools on qualitative analysis of HMs to assure their quality, safety and efficacy has been arousing great attention. Due to its inherent characteristics of accurate mass measurements and multiple stages analysis, the integrated strategy of liquid chromatography (LC) coupled with time-of-flight mass spectrometry (TOF-MS) and ion trap mass spectrometry (IT-MS) is well-suited to be performed as qualitative analysis tool in this field. The purpose of this review is to provide an overview on the potential of this integrated strategy, including the review of general features of LC-IT-MS and LC-TOF-MS, the advantages of their combination, the common procedures for structure elucidation, the potential of LC-hybrid-IT-TOF/MS and also the summary and discussion of the applications of the integrated strategy for HM qualitative analysis (2006–2011). The advantages and future developments of LC coupled with IT and TOF-MS are highlighted