NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series

As more connected devices are implemented in a cyber-physical world and data is expected to be collected and processed in real time, the ability to handle time series data has become increasingly significant. To help analyze time series in data mining applications, many time series representation approaches have been proposed to convert a raw time series into another series for representing the original time series. However, existing approaches are not designed for open-ended time series (which is a sequence of data points being continuously collected at a fixed interval without any length limit) because these approaches need to know the total length of the target time series in advance and pre-process the entire time series using normalization methods. Furthermore, many representation approaches require users to configure and tune some parameters beforehand in order to achieve satisfactory representation results. In this paper, we propose NP-Free, a real-time Normalization-free and Parameter-tuning-free representation approach for open-ended time series. Without needing to use any normalization method or tune any parameter, NP-Free can generate a representation for a raw time series on the fly by converting each data point of the time series into a root-mean-square error (RMSE) value based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward strategy. To demonstrate the capability of NP-Free in representing time series, we conducted several experiments based on real-world open-source time series datasets. We also evaluated the time consumption of NP-Free in generating representations.Comment: 9 pages, 12 figures, 9 tables, and this paper was accepted by 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC 2023

Hybrid Job-driven Scheduling for Virtual MapReduce Clusters

It is cost-efficient for a tenant with a limited budget to establish a virtual MapReduce cluster by renting multiple virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant's perspective. JoSS provides not only job level scheduling, but also map-task level scheduling and reduce-task level scheduling. JoSS classifies MapReduce jobs based on job scale and job type and designs an appropriate scheduling policy to schedule each class of jobs. The goal is to improve data locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations of JoSS are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations with current scheduling algorithms supported by Hadoop. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-data locality, and network overhead without incurring significant overhead. In addition, the two variations are separately suitable for different MapReduce-workload scenarios and provide the best job performance among all tested algorithms.Comment: 13 pages and 17 figure

How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?

Anomaly detection is the process of identifying unexpected events or ab-normalities in data, and it has been applied in many different areas such as system monitoring, fraud detection, healthcare, intrusion detection, etc. Providing real-time, lightweight, and proactive anomaly detection for time series with neither human intervention nor domain knowledge could be highly valuable since it reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous event occurs. To our knowledge, RePAD (Real-time Proactive Anomaly Detection algorithm) is a generic approach with all above-mentioned features. To achieve real-time and lightweight detection, RePAD utilizes Long Short-Term Memory (LSTM) to detect whether or not each upcoming data point is anomalous based on short-term historical data points. However, it is unclear that how different amounts of historical data points affect the performance of RePAD. Therefore, in this paper, we investigate the impact of different amounts of historical data on RePAD by introducing a set of performance metrics that cover novel detection accuracy measures, time efficiency, readiness, and resource consumption, etc. Empirical experiments based on real-world time series datasets are conducted to evaluate RePAD in different scenarios, and the experimental results are presented and discussed.Comment: 12 pages, 5 figures, and 9 tables, Proceedings of the 35th International Conference on Advanced Information Network-ing and Applications (AINA 2021

ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series

Anomaly detection is an active research topic in many different fields such as intrusion detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many existing anomaly detection approaches require either human intervention or domain knowledge, and may suffer from high computation complexity, consequently hindering their applicability in real-world scenarios. Therefore, a lightweight and ready-to-go approach that is able to detect anomalies in real-time is highly sought-after. Such an approach could be easily and immediately applied to perform time series anomaly detection on any commodity machine. The approach could provide timely anomaly alerts and by that enable appropriate countermeasures to be undertaken as early as possible. With these goals in mind, this paper introduces ReRe, which is a Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to predict and jointly determine whether or not an upcoming data point is anomalous based on short-term historical data points and two long-term self-adaptive thresholds. Experiments based on real-world time-series datasets demonstrate the good performance of ReRe in real-time anomaly detection without requiring human intervention or domain knowledge.Comment: 10 pages, 9 figures, COMPSAC 202

Carthamus tinctorius Enhances the Antitumor Activity of Dendritic Cell Vaccines via Polarization toward Th1 Cytokines and Increase of Cytotoxic T Lymphocytes

Carthamus tinctorius (CT), also named safflower, is a traditional Chinese medicine widely used to improve blood circulation. CT also has been studied for its antitumor activity in certain cancers. To investigate the effects of CT on the dendritic cell (DC)-based vaccine in cancer treatment, cytokine secretion of mouse splenic T lymphocytes and the maturation of DCs in response to CT were analyzed. To assess the antitumor activity of CT extract on mouse CD117+ (c-kit)-derived DCs pulsed with JC mammal tumor antigens, the JC tumor was challenged by the CT-treated DC vaccine in vivo. CT stimulated IFN-γ and IL-10 secretion of splenic T lymphocytes and enhanced the maturation of DCs by enhancing immunological molecule expression. When DC vaccine was pulsed with tumor antigens along with CT extract, the levels of TNF-α and IL-1β were dramatically increased with a dose-dependent response and more immunologic and co-stimulatory molecules were expressed on the DC surface. In addition, CT-treated tumor lysate-pulsed DC vaccine reduced the tumor weight in tumor-bearing mice by 15.3% more than tumor lysate-pulsed DC vaccine without CT treatment. CT polarized cytokine secretion toward the Th1 pathway and also increased the population of cytotoxic T lymphocytes ex vivo. In conclusion, CT activates DCs might promote the recognition of antigens and facilitate antigen presentation to Th1 immune responses

Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee

<p>Abstract</p> <p>Background</p> <p>Transmembrane proteins (TMPs) constitute about 20~30% of all protein coding genes. The relative lack of experimental structure has so far made it hard to develop specific alignment methods and the current state of the art (PRALINE™) only manages to recapitulate 50% of the positions in the reference alignments available from the BAliBASE2-ref7.</p> <p>Methods</p> <p>We show how homology extension can be adapted and combined with a consistency based approach in order to significantly improve the multiple sequence alignment of alpha-helical TMPs. TM-Coffee is a special mode of PSI-Coffee able to efficiently align TMPs, while using a reduced reference database for homology extension.</p> <p>Results</p> <p>Our benchmarking on BAliBASE2-ref7 alpha-helical TMPs shows a significant improvement over the most accurate methods such as MSAProbs, Kalign, PROMALS, MAFFT, ProbCons and PRALINE™. We also estimated the influence of the database used for homology extension and show that highly non-redundant UniRef databases can be used to obtain similar results at a significantly reduced computational cost over full protein databases. TM-Coffee is part of the T-Coffee package, a web server is also available from <url>http://tcoffee.crg.cat/tmcoffee</url> and a freeware open source code can be downloaded from <url>http://www.tcoffee.org/Packages/Stable/Latest</url>.</p

Bowel Preparation of Outpatients for Intravenous Urography: Efficacy of Castor Oil Versus Bisacodyl

The purpose of this study was to compare the efficacy of two laxatives, castor oil and bisacodyl, in the routine bowel preparation of outpatients for intravenous urography (IVU). We used castor oil in patients undergoing IVU for 1 month, and then used bisacodyl in patients undergoing IVU for another month. Two uroradiologists, unaware of the method of bowel preparation, reviewed the standard radiographs and graded the residue in the large bowel and the clearness of the opacified urinary collecting system. In total, 71 consecutive outpatients received castor oil, and 84 received bisacodyl. For the castor oil group, grades from the two uroradiologists did not differ in terms of fecal residue on plain abdominal images (p = 0.54), and visualization of the urinary system on the left (p = 0.36) and right sides (p = 0.63). Findings were similar for bisacodyl recipients (p = 0.11, 0.59, and 0.32, respectively). When the laxative effect of the two agents was compared, we found no difference in the grading of fecal residue on plain abdominal images (p = 0.14), or in visualization of the urinary system on the left (p = 0.31) and right sides (p = 0.98). In conclusion, we observed no difference in laxative efficacy between castor oil and bisacodyl; thus, bisacodyl may be a useful alternative for bowel preparation before IVU

Modeling and Simulation of Spark Streaming

As more and more devices connect to Internet of Things, unbounded streams of data will be generated, which have to be processed "on the fly" in order to trigger automated actions and deliver real-time services. Spark Streaming is a popular realtime stream processing framework. To make efficient use of Spark Streaming and achieve stable stream processing, it requires a careful interplay between different parameter configurations. Mistakes may lead to significant resource overprovisioning and bad performance. To alleviate such issues, this paper develops an executable and configurable model named SSP (stands for Spark Streaming Processing) to model and simulate Spark Streaming. SSP is written in ABS, which is a formal, executable, and object-oriented language for modeling distributed systems by means of concurrent object groups. SSP allows users to rapidly evaluate and compare different parameter configurations without deploying their applications on a cluster/cloud. The simulation results show that SSP is able to mimic Spark Streaming in different scenarios.Comment: 7 pages and 13 figures. This paper is published in IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA 2018