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Background Although advanced non-squamous non-small cell lung cancer (NSCLC) patients have significantly better survival outcomes after pemetrexed based treatment, a subset of patients still show intrinsic resistance and progress rapidly. Therefore we aimed to use a blood-based protein signature (VeriStrat, VS) to analyze whether VS could identify the subset of patients who had poor efficacy on pemetrexed therapy. Methods This study retrospectively analysed 72 advanced lung adenocarcinoma patients who received first-line pemetrexed/platinum or combined with bevacizumab treatment. Results Plasma samples from these patients were analysed using VS and classified into the Good (VS-G) or Poor (VS-P) group. The relationship between efficacy and VS status was further investigated. Of the 72 patients included in this study, 35 (48.6%) were treated with pemetrexed plus platinum and 37 (51.4%) were treated with pemetrexed/platinum combined with bevacizumab. Among all patients, 60 (83.3%) and 12 (16.7%) patients were classified as VS-G and VS-P, respectively. VS-G patients had significantly better median progression-free survival (PFS) (Unreached vs. 4.2 months; P Conclusions Our study indicated that VS might be considered as a novel and valid method to predict the efficacy of pemetrexed-based therapy and identify a subset of advanced lung adenocarcinoma patients who had intrinsic resistance to pemetrexed based regimens. However, larger sample studies are still needed to further confirm this result.

BACKGROUND Taxane-based chemotherapy is widely used in lung cancer. ABCB1 have a role in the prediction of treatment response and toxicity of chemotherapy in solid tumors. In this retrospective study, we investigated ABCB1 polymorphism on response and toxicity in taxane-based chemotherapy in lung cancer patients. METHODS A total of 122 lung cancer patients who received taxane-based chemotherapy were included in this study. Fluorescence in situ hybridization (FISH) was used for ABCB1 polymorphism detection. Turbidimetric inhibition immunoassay was used for pharmacokinetic analysis. Statistical analysis was performed using SPSS 20.0. RESULTS The frequency of the ABCB1 2677 site TT/TG/GG genotype was 32.8%, 43.4% and 23.8%, respectively and the frequency of the 3435 sites the TT/TC/CC genotype was 13.9%, 44.3% and 41.8%, respectively. The occurrence of neurotoxicity was higher in patients who had ABCB1 3435 site mutation (TT 88.2%, TC 22.2%, CC 21.6% P = 0.004). There was no significant difference between ABCB1 genotypes with regard to other chemotherapy-induced toxicity. For non-small cell lung cancer (NSCLC) patients, those harboring ABCB1 2677 and 3435 site wild-type patients had longer median progression-free survival (PFS) in the paclitaxel subgroup (3435 site: TT 3.87 vs. TC 9.50 vs. CC 14.13 months; P

BACKGROUND Small-cell lung cancer (SCLC) is one of the most aggressive types of lung cancer. The prognosis for SCLC patients depends on many factors. The intent of this study was to construct a nomogram model to predict mortality for extensive-stage SCLC. METHODS Original data was collected from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict death probability for extensive-stage SCLC. RESULTS A total of 16 554 extensive-stage SCLC patients from 2004 to 2014 in the SEER database were included in this study. Gender, race, age, TNM staging (including tumor extent, nodal status, and metastasis), and treatment (surgery, chemotherapy, and radiotherapy) were identified as independent predictors for lung cancer-specific death for extensive-stage SCLC patients. A nomogram model was constructed based on multivariate models for lung cancer related death and other cause related death. Performance of the two models was validated by calibration and discrimination, with C-index values of 0.714 and 0.638, respectively. CONCLUSION A prognostic nomogram model was established to predict death probability for extensive-stage SCLC. This validated prognostic model may be beneficial for treatment strategy choice and survival prediction.

Location-based services (LBSs) have recently become a vital part of life. Global Navigation Satellite Systems, e.g., GPS, have been used in a wide range of applications. These systems face a common challenge: their performance can be significantly affected by the presence of obstacles. For instance, when the target object is close to walls, buildings, trees, or located in indoor environments, GPS based services may not work correctly. However, in reality, the presence of obstacles is inevitable in many critical scenarios, such as disaster response and rescue. To address this challenge, we propose an indoor localization system which uses radio frequency identification (RFID) tags to localize moving targets. Our indoor localization algorithm derives the accurate location of a target object using a set of RFID tags. For tag deployment, we have developed a greedy algorithm that uses a novel score function guide the deployment, aiming to guarantee maximum coverage using a limited number of tags. The experimental results show our approach is effective in localizing target objects with high accuracy.

Background Pemetrexed/platinum chemotherapy has been the standard chemotherapy regimen for lung adenocarcinoma patients, but the efficacy varies considerably. Methods To discover new serum biomarkers to predict the efficacy of pemetrexed/platinum chemotherapy, we analyzed 20 serum samples from advanced lung adenocarcinoma patients who received pemetrexed/platinum chemotherapy with the data-independent acquisition (DIA) quantitative mass spectrometry (MS). Results The 20 patients were categorized as "good response" [12 patients achieving partial response (PR)] and "poor response" [8 patients with progressive disease (PD)] groups. Altogether 23 significantly different expressed proteins were identified, which had relative ratios higher than 1.2 or lower than -0.83, with 7 proteins having an area under the curve (AUC) above 0.8. To further validate the DIA results, we used the parallel reaction monitoring (PRM) method to examine 16 candidate serum biomarkers in the study cohort of 20 patients and another cohort of 22 advanced lung adenocarcinoma patients (16 PR and 6 PD). Quantitative validation using PRM correlated well with the DIA results, and 10 promising proteins exhibited a similar up- or downregulation. It is worth noting that glutathione peroxidase 3 (GPX3) exhibits significant upregulation in the poor response group compared with the good response group, which was validated by both DIA and PRM methods. Conclusions Our study confirmed that combined DIA MS and PRM approaches were effective in identifying serum predictive biomarkers for advanced lung adenocarcinoma patients. Further studies are needed to explore the potential biological mechanism underlying these biomarkers.

Today’s online social networks produce a significant amount of data containing rich information. A major challenge is to analyze and make sense of the data, so that useful information can be retrieved efficiently. In many application scenarios, this requires analyzing both the network topology and the textual information contained in the network. However, existing network analysis tools usually focus on one of these aspects, instead of providing end-to-end solutions for this particular research scenario. In this paper, we present NetNet, a social network analysis tool that is specifically designed to simplify the workflow of analyzing social networks. In NetNet, we model social networks as interconnected user nodes with text nodes associated with them, and leverage network analysis and text mining algorithms to perform both tasks seamlessly. We evaluate the performance of NetNet using social network data, and the experimental results show that our design is practically efficient and scalable.

Electronically controllable magnetorheological brakes (MRB) can be used in haptic devices to apply forces/torques to the user in a virtual reality (VR) simulation to improve realism. Precise control of the braking torque is possible with a control system using a Hall sensor which measures the magnetic field. Machine learning models can be used to predict the output torque using the input from the Hall sensor. However, over time the fluid leaks out of the MRB due to failure of rubber seals, which degrades the haptic device performance and presents challenges in torque prediction. In this paper, we present our efforts in developing machine learning based approaches that can capture the dynamic behavior of an MRB and its changing torque output as the fluid leaks out. Extensive experiments have been carried out using data collected from the device, and results show that our 2-Step-RN approach can accurately predict the output torque. Notably, it even outperforms the baseline models which are trained for and operate at a stable fluid level, indicating its great potential for enabling torque control of MRB devices with high fidelity.

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. In this paper, we present our work in designing real-time sensing, and evaluating machine learning algorithms for real-time arrhythmia detection. Most of the existing work applies machine learning algorithms to electrocardiogram (ECG) images to detect abnormal patterns. These approaches are not suitable for real-time processing due to high processing overhead. In our work, we treat data as time series, and evaluate various machine learning algorithms in terms of both learning and computational performance. Our experimental results show that the long short-term memory network (LSTM) has both high accuracy and efficiency, demonstrating great potential for online detection of arrhythmia.

Background The five-year cumulative incidence rate in patients diagnosed with stage I small-cell lung cancer (SCLC) who were instructed to undergo surgery was from 40 to 60%.The death competition influence the accuracy of the classical survival analyses. The aim of the study is to investigate the mortality of stage I small-cell lung cancer (SCLC) patients in the presence of competing risks according to a proportional hazards model, and to establish a competing risk nomogram to predict probabilities of both cause-specific death and death resulting from other causes. Methods The study subjects were patients diagnosed with stage I SCLC according to ICD-O-3. First, the cumulative incidence functions (CIFs) of cause-specific death, as well as of death resulting from other causes, were calculated. Then, a proportional hazards model for the sub-distribution of competing risks and a monogram were constructed to evaluate the probability of mortality in stage I SCLC patients. Results 1811 patients were included in this study. The five-year probabilities of death due to specific causes and other causes were 61.5 and 13.6%, respectively. Tumor size, extent of tumor, surgery, and radiotherapy were identified as the predictors of death resulting from specific causes in stage I SCLC. The results showed that surgery could effectively reduce the cancer-specific death, and the one-year cumulative incidence dropped from 34.5 to 11.2%. Like surgery, chemotherapy and radiotherapy improved the one-year survival rate. Conclusions We constructed a predictive model for stage I SCLC using the data from the SEER database. The proportional sub-distribution models of competing risks revealed the predictors of death resulting from both specific causes and other causes. The competing risk nomogram that we built to predict the prognosis showed good reliability and could provide beneficial and individualized predictive information for stage I SCLC patients.

Over the last five years, Apache Spark has become a major software platform for in-memory data analysis. Acknowledging its widespread use, we present a comprehensive study of system characteristics of Spark targeting scientific data analytics performing large-scale matrix operations. We compare its performance to SciDB, a disk-based platform for array data analysis. A benchmark, ArrayBench, is developed to evaluate the performance of four analytics processing gene expression matrices using basic data operators of Spark and SciDB. It is applied to data from a real biological workflow whose data inputs are in matrix form. Herein, we report the findings, which shed light on the improvement of Spark and SciDB and the future development of data-intensive scientific data analytics using the in-memory computing frameworks.

Concurrency bugs are difficult to diagnose and fix, due to the nature of the bugs and how they manifest themselves during execution. Traditional approaches for diagnosing concurrency bugs attempt to reproduce the exact execution schedule which reveals the bug, resulting in high runtime overhead. In this paper, we present our work in identifying concurrency bugs using resource consumption footprints. This is based on the observation that resource access and consumption patterns are critical indications of the run-time behavior of concurrent software, and can be used as a powerful mechanism to guide the software debugging process. We demonstrate that monitoring resource footprints at runtime can effectively help detect software bugs. Specifically, for MPI programs, a simple SVM classifier can detect deadlocks with high accuracy using only the CPU usage patterns.

Resource shortage and environmental degradation have long been the major problems that the current generation is facing, and these problems are even more severe in developing countries, such as China. To address these challenges, being able to utilize existing resources more efficiently is a key. The first step towards efficient use of resources is to design and develop an effective and scalable approach to measure and evaluate resource efficiency. This has been a challenging problem due to a lack of widely accepted standards, as well as the complexity of the data collected by different communities. In this paper, we address this problem using an improved data envelopment analysis (DEA) model of Bootstrap. We leverage machine learning techniques to enhance the scalability, and explore the potentials of predicting future efficiency trend using historical data. Using this approach, we measure the coal resource efficiency of 30 provinces in China from 2000 to 2015. Our results show that the proposed approach can significantly improve the accuracy of the measurement of a traditional Data Envelopment Analysis(DEA) model. In addition, when applying machine learning techniques to the data, the proposed model demonstrates better potentials for scalable and accurate prediction on the coal resource efficiency. Our approach can be easily adopted by various organizations to evaluate the resource efficiency over time, and develop more efficient strategies for resource use based on the measurement data.

Background Malignant pleural mesothelioma (MPM) is a rare but aggressive tumor that originates from the pleura and has a poor prognosis. Eligible patients can benefit from surgery, but their survival is affected by many factors. Therefore, we created a graphic model that could predict the prognosis of surgically treated patients. Methods We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results database from 2004 to 2014 to identify the key factors affecting the prognosis of surgically treated MPM patients. On this basis we built a nomogram to predict survival. We then evaluated the performance of the nomogram in a validation cohort. Results In a training cohort of 828 cases, independent prognostic factors, including age, gender, histological type, differentiation, N stage, chemotherapy, type of surgery, and lymph node dissection, were identified. We then developed a nomogram to evaluate individual patient survival. In Kaplan-Meier analysis, a higher score in the nomogram was associated with a worse prognosis. We also used a validation cohort consisting of 312 patients to evaluate the performance of the nomogram, which was well calibrated and had good discrimination ability, with concordance indices of 0.715 and 0.656 for the training and validation cohorts, respectively. Conclusion This study has improved our understanding of resected MPM and shown that key factors, including age and histological type, are associated with overall survival. The nomogram is a reliable tool that can help clinicians turn individualized prediction into reality and maximize patient benefit by identifying the most beneficial treatment approach.

Background Although programmed cell death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint inhibitors have shown prominent efficacy for treatment of advanced lung cancer, the outcomes of metastatic lung cancer remain poor throughout the world. Although progression-free survival (PFS) and overall survival (OS) have improved in the first- and second-line therapy settings for advanced lung cancer, the response rates to PD-1/PD-L1 inhibition range from 20% to 40%. Furthermore, patients may be at risk for immune-related adverse events (irAEs); hence, appropriate patient selection is crucial. This study aimed to identify a panel of plasma cytokines representing prognostic and predictive biomarkers of the response to anti-PD-1/PD-L1 treatment. Methods We prospectively studied 32 lung cancer patients who received anti-PD-1/PD-L1 antibody immunotherapy. Plasma cytokines in peripheral blood samples were evaluated and analyzed using flow cytometry at the time of diagnosis and at 2 months after the initiation of PD-1/PD-L1 inhibition. Results The baseline plasma concentrations of interleukin-18 (IL-18) and C-X-C motif chemokine ligand 10 (CXCL10) were correlated with the degree of tumor response. Moreover, the magnitude of plasma IL-18 and CXCL10 level fluctuations were correlated significantly with the objective tumor response to anti-PD-1/PD-L1 immunotherapy, and patients with high CXCL10 expression had significantly shorter PFS than those with low CXCL10 expression. A strong positive correlation between the fluctuation of IL-18 and interleukin-8 (IL-8) levels was detected, as was a negative correlation between the fluctuation of IL-18 and CXCL10 levels. The level of plasma C-C motif chemokine ligand 5 (CCL5) was significantly higher in patients with irAEs than in those without irAEs. Conclusions Plasma cytokines are related to the clinical efficacy of PD-1/PD-L1 inhibitors. IL-18 and CXCL10 are potential predictive markers for anti-PD-1/PD-L1 therapy in lung cancer patients and may play an important role in selecting patients who would benefit from PD-1/PD-L1 inhibitors.

Background Primary tumors located in the right and left side have distinctive prognoses, but the details have not been fully identified in non-small cell lung cancer (NSCLC). This study investigated the impact of primary tumor side on long-term survival in NSCLC patients. Methods Data of 90 407 patients from the Surveillance, Epidemiology, and End Results (SEER) Program were analyzed. To avoid bias between groups, we used innovative propensity score matching (PSM) analysis. Results There was no significant distinction in overall survival (OS) between right (n = 53 496) and left (n = 36 911) side tumors (hazard ratio [HR] 0.993, 95% confidence interval [CI] 0.9756-1.011; P = 0.432). Left side was associated with superior five-year cancer-specific survival (CSS) compared to right side NSCLC (HR 0.977, 95% CI 0.9574-0.9969; P = 0.024). No significant difference was observed in OS (P = 0.689) or CSS (P = 0.288) after PSM analysis. In the 51 319 patients who underwent surgery, left side (n = 21 245) was associated with poor OS compared to right side (n = 30 074) NSCLC (HR 1.039, 95% CI 1.011-1.067; P = 0.006), while CSS was similar (HR 1.031, 95% CI 0.997-1.065; P = 0.069). In patients who underwent surgery, there was also no significant difference in OS (P = 0.986) or CSS (P = 0.979) after PSM analysis. Conclusion The prognosis between right and left side NSCLC in stage I-IIIA was similar regardless of whether patients underwent surgery. Primary tumor side cannot be considered a prognostic factor when choosing appropriate treatment.

The electrical faults of generators on an electrical grid, i.e., generator events (GE), must be detected efficiently when they occur, as these events can propagate to the rest of the grid in a cascading manner, leading to outages and wide-area blackouts. Many reasons exist that give rise to these faults, but at its most fundamental, they constitute an inability of a generator to match the grid usage requirements. In this paper, we present an efficient algorithm to accurately identify the occurrence of generator events within an electrical grid, using the monitoring data obtained from phasor measurement units (PMUs). Specifically, we have developed a machine learning algorithm that takes as input PMU data, and subsequently flags instances where a GE had taken place. The detection is performed in near real-time with the help of a standard off-the-shelf processing unit. Furthermore, we set out to create electrical fault maps that demarcate the progression of the event as it takes place. Experimental results show that our algorithm can accurately and efficiently identify the occurrence of a GE. In addition, we are also able to report a fault network map, which provides a powerful tool for troubleshooting.

Serverless computing has recently emerged as a new execution model for cloud computing, in which service providers offer compute runtimes, also known as Function-as-a-Service (FaaS) platforms, allowing users to develop, execute and manage application functionalities. Following the rapid adoption of FaaS technologies and the introduction of numerous self hosted FaaS systems, the need for real time monitoring and scheduling of functions in an ecosystem of providers has become critical. In this paper, we present MPSC, a framework for supporting Multi-Provider Serverless Computing. MPSC monitors the performance of serverless providers in real time, and schedules applications across these providers. In addition, MPSC also provides APIs for users to define their own scheduling algorithms. When compared to scheduling on a single cloud resource MPSC provides a 4X speedup across multiple providers in a volatile edge computing environment.

The Actor model of concurrency provides a convenient way to build large-scale distributed systems, harnessing its inherent parallelism and the message-driven nature. As a result, the Actor model has been widely used in a variety of distributed applications, especially cloud-based applications. However, software bugs in actor systems are not well studied. In this paper, we present a comprehensive analysis on known bugs in actor-based systems. The contributions of this paper are two-fold. First, we have created an actor bug taxonomy, and carefully examined and categorized bugs reported in a number of real-world actor systems. Second, we have compared the characteristics actor bugs with bugs reported in cloud systems in general. Our results shed lights on understanding the benefits and unique challenges for utilizing the Actor model for system development, as well as deployment in open systems like clouds.

The integration of monitoring and control networks at different voltage levels and across utility boundaries has made it harder to maintain and asses the resilience of power systems due to increasing cyber attacks. On the software side, a variety of research efforts pursue cyber protection algorithms, such as spoof detection techniques. On the hardware and firmware side, research has demonstrated the feasibility of adversarial attacks by providing an entry point at the device level. This work proposes and evaluates two detection performance metrics for a variety of cyber spoofing attacks introduced in a realistic Phasor Measurement Unit (PMU) network for a hybrid transmission and distribution power system. This research finds that both proposed metrics show promise in aiding a spoof detection algorithm in consistently detecting spoofs in power system measurements.

The Actor model provides inherent parallelism, along with other convenient features to build large-scale distributed systems. In this paper, we present ActorEdge, an Actor based distributed framework for edge computing. ActorEdge provides straitforward integration with existing technologies, while enabling application developers to dynamically utilize computational resources on the edge of the clouds. ActorEdge has proven to outperform cloud computing options by providing superior quality of service, measuring a 10x lower latency, 30% less jitter, and greater bandwidth. Using this framework, programmers can easily develop and deploy their applications on a heterogeneous system, including cloud servers/data centers, edge servers, and mobile devices.

Modern high performance computing and cloud computing infrastructures often leverage Graphic Processing Units (GPUs) to provide accelerated, massively parallel computational power. This performance gain, however, may also introduce higher energy consumption. The energy challenge has become more and more pronounced when the system scales. To address this challenge, we propose Archon, a framework for supporting energy-efficient computing on CPU-GPU heterogeneous architectures. Specifically, Archon takes user's programs as input, automatically distribute the workload between CPU and GPU, and dynamically tunes the distribution ratio at runtime for an energy-efficient execution. Experiments have been carried out to evaluate the effectiveness of Archon, and the results show that it can achieve considerable energy savings at runtime, without significant efforts from the programmers.

Graphical abstract, Tylvalosin, a new broad-spectrum, third-generation macrolides, may exert a variety of pharmacological activities. Here, we report on its anti-oxidative and anti-inflammatory activity in RAW 264.7 macrophages and mouse treated with lipopolysaccharide (LPS) as well as piglet challenged with porcine reproductive and respiratory syndrome virus (PRRSV). Tylvalosin treatment markedly decreased IL-8, IL-6, IL-1β, PGE2, TNF-α and NO levels in vitro and in vivo. LPS and PRRSV-induced reactive oxygen species (ROS) production, and the lipid peroxidation in mice lung tissues reduced after tylvalosin treatments. In mouse acute lung injury model induced by LPS, tylvalosin administration significantly attenuated tissues injury, and reduced the inflammatory cells recruitment and activation. The evaluated phospholipase A2 (PLA2) activity and the increased expressions of cPLA2-IVA, p-cPLA2-IVA and sPLA2-IVE were lowered by tylvalosin. Consistent with the mouse results, tylvalosin pretreatment attenuated piglet lung scores with improved growth performance and normal rectal temperature in piglet model induced by PRRSV. Furthermore, tylvalosin attenuated the IκBα phosphorylation and degradation, and blocked the NF-κB p65 translocation. These results indicate that in addition to its direct antimicrobial effect, tylvalosin exhibits anti-inflammatory property and attenuates acute lung injury through suppression of NF-κB activation.

Over the last five years, Apache Spark has become a major software platform for in-memory data analysis. Acknowledging its widespread use, we present a comprehensive study of system characteristics of Spark with a focus on scientific data analytics performing large-scale matrix operations. We compare its performance to SciDB, a disk-based platform for array data analysis. A benchmark, ArrayBench, is developed to evaluate the performance of matrix processing for scientific data analytics. ArrayBench is applied to data from a real biological workflow whose data inputs are in matrix form. Herein, we report the findings, which shed light on the improvement of Spark and SciDB and future development of large-scale scientific data analytics.

A supervised statistical model for detecting the activities of daily living ADL from sensor data streams is proposed in this paper. This method works in two stages aiming at capturing temporal intra-and inter-activity relationships. In the first stage each activity is modeled separately by a Markov model where sensors correspond to states. By modeling each sensor as a state we capture the absolute and relational temporal features within the activities. A novel data segmentation approach is proposed for accurate inferencing at the first stage. To boost the accuracy, a second stage consisting of a Hidden Markov Model is added that serves two purposes. Firstly, it acts as a corrective stage, as it learns the probability of each activity being incorrectly inferred by the first stage, so that they can be corrected at the second stage. Secondly, it introduces inter-activity transition information to capture possible time-dependent relationships between two contiguous activities. We applied our method to three smart house datasets. Comparison of the results to other traditional approaches for ADL identification shows competitive or better performance. The paper also proposes a deployment of our methodology using an agent-based architecture.

Edema toxin (ET), which is composed of a potent adenylate cyclase (AC), edema factor (EF), and protective antigen (PA), is one of the major toxicity factors of Bacillus anthracis. In this study, we introduced mutations in full-length EF to generate alanine EF(H351A) and arginine EF(H351R) variants. In vitro activity analysis displayed that the adenylyl cyclase activity of both the mutants was significantly diminished compared with the wild-type EF. When the native and mutant toxins were administered subcutaneously in a mouse footpad edema model, severe acute swelling was evoked by wild-type ET, while the symptoms induced by mutant toxins were very minor. Systemic administration of these EF variants caused non-lethal hepatotoxicity. In addition, EF(H351R) exhibited slightly higher activity in causing more severe edema than EF(H351A). Our findings demonstrate that the toxicity of ET is not abolished by substitution of EF residue His351 by alanine or arginine. These results also indicate the potential of the mouse footpad edema model as a sensitive method for evaluating both ET toxicity and the efficacy of candidate therapeutic agents.

Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of “if” but a matter of “when” in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.

In the past decade, with the world-wide initiative of upgrading the electrical grid to smart grid, a significant amount of data have been generated by the grid on a daily basis. Therefore, there has been an increasing need in handling and processing these data efficiently. In this paper, we present our experience in applying unsupervised clustering on PMU data for event characterization on the smart grid. We show that although the PMU data are time series in nature, it is more efficient and robust to apply clustering methods on carefully selected features from the data collected at certain instantaneous moments in time. These features are more representative at the moments when the events have the most impact on the grid. Experiments have been carried out on real PMU data collected by Bonneville Power Administration in their wide-area monitoring system in the pacific northwest, and the results show that our instantaneous clustering method achieves high homogeneity, which provides great potentials for identifying unknown events in the grid without substantial training data.

Cloud computing has been widely used by computational scientists and engineers as a means to run large-scale simulations while circumventing capital investment of hardware. However, a challenging problem is to accurately estimate how much cloud resources that a specific computation requires, in order to execute computations in a cost-effective way. In this paper, we use a real-world molecular dynamics (MD) simulation as a motivating scenario and present our work in modeling parallel execution of such a computation on the cloud. Our model estimates the workload of an MD simulation at fine-grained detail, and based on that estimate, calculates the time required to run the simulation. The accuracy of the model has been evaluated using various types of MD simulations on different scales.

The second international workshop on Software Engineering for Parallel Systems (SEPS) will be held in Pittsburgh, PA, USA on October 27, 2015 and co-located with the ACM SIGPLAN conference on Systems, Programming, Languages and Applications: Software for Humanity (SPLASH 2015). The purpose of this workshop is to provide a stable forum for researchers and practi-tioners dealing with compelling challenges of the software development life cycle on modern parallel platforms. The increased complexity of parallel applications on modern parallel platforms (e.g. multicore, manycore, distributed or hybrid) requires more insight into development processes, and necessitates the use of advanced methods and techniques supporting developers in creating parallel applications or parallelizing and reengineering sequential legacy applications. We aim to advance the state of the art in different phases of parallel software development, covering software engineering aspects such as requirements engineering and software specification; design and implementation; program analysis, profiling and tuning; testing and debugging.

Security and privacy have long been the primary concerns of cloud computing platforms. Hybrid clouds provide potentials for handling data separately based on their sensitivity, harnessing the heterogeneous architecture. In this paper, we design and implement a privacy-aware framework to address data privacy challenges by supporting sensitive data segregation on hybrid clouds. Specifically, we model data sensitivity in a comprehensive and dynamic manner using a set of tagging mechanisms, which include a coarse-grained file level tagging, a fine-grained line level tagging, temporal and spatial tagging. The framework can also process data dynamically generated on-the-fly. We demonstrate the effectiveness of this framework using a big data application, and the experimental results show that the privacy-aware framework successfully enables data sensitivity protection while providing good performance.

Phasor measurement units (PMUs) are widely used in power transmission systems to provide synchronized measurements for the purpose of fault detection. However, how to efficiently deploy those devices across a power grid — so that a comprehensive coverage can be provided at a relatively low cost — remains a challenge. In this paper, we present a sensitivity study of a PMU-based fault detection method using three different distance metrics. This study can serve as a guideline for efficient PMU deployment. To illustrate the effectiveness of this approach, we have derived an alternative PMU placement plan for a power grid. Experimental results show that our PMU placement reduces the required PMU deployment by more than 80% as compared to the original placement, yet still provides similar level of accuracy in fault detection.

The Wide-Area Measurement System (WAMS) technology utilizes Phasor Measurement Unit (PMU) to monitor real-time power system data, which can provide unique insights into the global operation of the power grid. A PMU based wide-area fault detection technique for single-line-to-ground fault is proposed in this paper. The proposed technique is verified to be accurate by conducting case studies in a large power grid.

In this paper we propose a two-stage, supervised statistical model for detecting the activities of daily living (ADL) from sensor data streams. In the first stage each activity is modeled separately by a Markov model where sensors correspond to states. By modeling each sensor as a state we capture the absolute and relational temporal features of the atomic activities. A novel data segmentation approach is proposed for accurate inferencing at the first stage. To boost the accuracy, a second stage consisting of a Hidden Markov Model is added that serves two purposes. Firstly, it acts as a corrective stage, as it learns the probability of each activity being incorrectly inferred by the first stage, so that they can be corrected at the second stage. Secondly, it introduces inter-activity transition information to capture possible time-dependent relationships between two contiguous activities. We applied our method to three ADL datasets to show its suitability to this domain.

The growing popularity of grid and cloud computing has led to a renewed interest in resource control and coordination. The Actor model, which encapsulates objects along with threads of execution, offers a convenient way for scheduling computations' access to resources by way of scheduling of the actor threads. However, efficient Actor implementations do not use a thread for each actor, making implementation of fine-grained resource scheduling decisions difficult. This paper presents our work on integrating mechanisms for deadline assurance into an optimized implementation of Actors. We achieve this by using deadline-driven adaptive scheduling, which prioritizes individual message deliveries and method executions involved in a distributed computation, based on the calculated deadlines by which each must be completed. These deadlines can be efficiently calculated at run-time for an important class of computations which use pipeline interaction style. Additionally, a tuner dynamically balances -- manually or automatically -- the overhead of the control mechanisms against the extent of control exercised. Experimental evaluation shows that the approach offers effective support for timeliness requirements (for multimedia QoS, for example) at the cost of a relatively modest and adjustable overhead.

There is growing interest in the energy consumed by computer systems, for both individual (battery life) and environmental (global warming) reasons. Multicore architectures offer a potential opportunity for energy conservation by allowing cores to operate at lower frequencies. Previous work on analyzing power consumption of multicores assumes that all cores must run at the same frequency. However, new technologies, such as fast voltage scaling and Turbo Boost, allow cores to operate at different frequencies. In this paper, we present an energy-aware resource management model, ROT-MCP, which provides a flexible way to analyze energy consumption of multicores operating at non-uniform frequencies. This information can then be used to generate a energy-efficient schedule for execution of the computations - as well as a schedule of frequency changes on a per-core basis - while satisfying performance requirements of computations. Experimental results show that the energy savings achieved using this approach far outweigh the energy consumed in the reasoning required for generating the schedules.

In an open distributed system, computational resources are peer-owned, and distributed over time and space. The fact that these resources can dynamically join or leave the system (or can be discovered at runtime) leads to opportunities to carry out computations without statically owned resources, harnessing the collective compute power of the resources connected by the Internet. However, realizing this potential requires efficient and scalable resource discovery, coordination and control, not to mention that the dynamicity that makes opportunities visible at runtime also leads to uncertainty about continuous availability of the needed resources. In this paper, we present our experimental work using a resource-oriented temporal logic, ROTA, to reason about accommodating mobile distributed computations in an open system. Our results show that the approach leads to better accommodation of computations and scales well by avoiding unnecessary migrations.

In an open distributed system, computations can be carried out without statically owned resources, harnessing the collective compute power of the resources connected by the Internet. However, realizing this potential requires efficient and scalable resource discovery, coordination and control, which present challenges in a dynamic, open environment. We present ROTA, a resource-oriented temporal logic, which addresses these challenges by enabling computations to reason about future availability of resources. In ROTA, computational resources are defined over time and space, and represented using resource terms, which specify key attributes of resources. Syntax and semantics of ROTA are described. Theorems are derived to illustrate how the logic can be used to express resource properties of the system in time and space, track resource utilization, and calculate future availability of resources. Particularly, at any time, given a computation, it is possible to evaluate whether its deadline constraint can be assured by the available resources.