Miracle-dz/UniFAR-FTUs huggingface dataset:
SIS-External Research Statement

My research philosophy revolves around the belief that successful systems research has to demonstrate two things: 1) a novel and interesting approach to an important research problem, and 2) usability. I strongly believe that for a system to be successful, it has to be usable by the target audience. On the other hand, a highly usable system that doesn’t advance the state of the art in any way is not particularly interesting from a research perspective either. My research style is strongly systems-oriented with a large emphasis on hands-on system implementation. A real implementation is crucial, in my opinion, for testing complicated distributed systems as the complexity inherent in these systems is incredibly hard to capture by simulation studies alone. A real implementation is also vital in validating the usability of the system. In addition, the software artifacts from such implementations are important contributions that drive future research and even product development in the area. I am also a strong proponent of collaborative research. Modern systems are so complicated that it is impossible for one person to have all the answers to every problem encountered. As such, I tend to work with about 2-4 other people, each with slightly different skills and backgrounds, to tackle a large systems problem. This multi-disciplinary approach is very apparent in all my research undertakings. I describe some of these undertakings along with avenues of future research in the next few sections. My research revolves around the theme of mobility. Around this theme, I have built clusters of research. In particular, I have research thrusts in the areas of Applications for Mobility, Understanding Mobility in Organizations, and Infrastructure for Mobility.
Database Integrated Analytics Using R: Initial Experiences with SQL-Server + R

Most data scientists use nowadays functional or semi-functional languages like SQL, Scala or R to treat data, obtained directly from databases. Such process requires to fetch data, process it, then store again, and such process tends to be done outside the DB, in often complex data-flows. Recently, database service providers have decided to integrate "R-as-a-Service" in their DB solutions. The analytics engine is called directly from the SQL query tree, and results are returned as part of the same query. Here we show a first taste of such technology by testing the portability of our ALOJA-ML analytics framework, coded in R, to Microsoft SQL-Server 2016, one of the SQL+R solutions released recently. In this work we discuss some data-flow schemes for porting a local DB + analytics engine architecture towards Big Data, focusing specially on the new DB Integrated Analytics approach, and commenting the first experiences in usability and performance obtained from such new services and capabilities.
Saphenous Vein Sparing Superficial Inguinal Dissection in Lower Extremity Melanoma

Aim. The classic inguinal lymph node dissection is the main step for the regional control of the lower extremity melanoma, but this surgical procedure is associated with significant postoperative morbidity. The permanent lymphedema is the most devastating long-term complication leading to a significant decrease in the patient's quality of life. In this study we present our experience with modified, saphenous vein sparing, inguinal lymph node dissections for patients with melanoma of the lower extremity. Methods. Twenty one patients (10 women, 11 men) who underwent saphenous vein sparing superficial inguinal lymph node dissection for the melanoma of lower extremity were included in this study. The effects of saphenous vein sparing on postoperative complications were evaluated. Results. We have observed the decreased rate of long-term lymphedema in patients undergoing inguinal lymphadenectomy for the lower extremity melanoma. Conclusion. The inguinal lymphadenectomy with saphenous vein preservation in lower extremity melanoma patients seems to be an oncologically safe procedure and it may offer reduced long-term morbidity.
Forecasting of currency exchange rates using ANN: a case study

In today's global economy, accuracy in forecasting the foreign exchange rate or at least predicting the trend correctly is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. In this paper, we developed and investigated three artificial neural network (ANN) based forecasting model using standard backpropagation (SBP), scaled conjugate gradient (SCG) and backpropagation with Baysian regularization (BPR) for Australian foreign exchange to predict six different currencies against Australian dollar. Five moving average technical indicators are used to build the models. These models were evaluated on five performance metrics and a comparison was made with traditional ARIMA model. All the ANN based models outperform ARIMA model. It is found that SCG based model performs best when measured on the two most commonly used metrics and shows competitive results when compared with BPR based model on other three metrics. Experimental results demonstrate that ANN based model can closely forecast the forex market.
A system for automated BGA inspection

For the advantages of high I/O count and small size, ball grid array (BGA) package techniques have been developing very fast in recent years. In this paper, a vision-based algorithm was proposed. The BGA grayscale images captured by CCD camera, and some image processing methods were applied to segment and enhance the image of solder ball. Three kinds of feature size were employed here: area, centroid and roundness of solder ball. The defects were detected by matching these features to the template data. The inspection items included missing ball, extra ball, ball bridging, oversize or undersize, offset and deformation. In this research, the speed execution time of inspecting one BGA image is also taken into consideration
Cross-cultural adjustment and expatriation motives among Indian expatriates

Purpose - Globalization has led to individuals working and living outside their native country. This study examined relationship between motives for expatriation and cross-cultural adjustment in Indian expatriates working in the USA IT (Information Technology) industry. Additionally, the moderating effects of self-initiated expatriates (SIE) and organizational expatriates (OE) on the relationship between motives for expatriation and cross-cultural adjustment were studied. Also, existing measures in this area of research were analyzed to improve validity and utility for future studies. Participants responded to questions via an internet survey. Design/methodology/approach - Data were gathered from 336 Indian information technology professionals working in America. We evaluated the psychometric quality of reasons for expatriation and cross-cultural adjustment measures by considering various fit statistics, modification indices, and rational judgment based on item content. We used a maximum likelihood extraction method with an oblique rotation (Geomin) for these factor analyses, given the theoretically and empirically supported relationship between the subdimensions of both measures. Using these purified measures, a hierarchical regression analyses procedure was used to test the hypothesized relationships. A computational tool called PROCESS was used to test the hypothesized moderating effect of expatriate type on the relationship between motives for expatriation and cross-cultural adjustment. Findings - Preliminary factor analytic work suggested modifications to the reasons for expatriation measure used to quantify participants’ motives for expatriation. Using this revised measure, those with stronger financial (mercenary) motives for expatriation also reported less positive cultural adjustment, while those with stronger exploratory motives for expatriation reported more positive cultural adjustment. Some evidence was also observed for a weak, but notable moderating effect of expatriate type (SIE vs. OE) on the relationship between expatriation motives and cultural adjustment. Originality/value - This study presents a revised measure of expatriation motives, as well as expanded theoretical and empirical evidence base to help future researchers working with expatriates. The findings also may be helpful to organizations and consultants who work to prepare expatriates for their assignments, especially when these expatriates are Indian professionals preparing to work in the USA.
Dynamic Asset Allocation with Predictable Returns and Transaction Costs

We propose a simple approach to dynamic multi-period portfolio choice with transaction costs that is tractable in settings with a large number of securities, realistic return dynamics with multiple risk factors, many predictor variables, and stochastic volatility. We obtain a closed-form solution for an optimal trading rule when the problem is restricted to a broad class of strategies we define as 'linearity generating strategies' (LGS). When restricted to this class, the non-linear dynamic optimization problem reduces to a deterministic linear-quadratic optimization problem in the parameters of the trading strategies. We show that the LGS approach dominates several alternatives in realistic settings, and in particular when the covariance structure and transaction costs are stochastic.
Adaptive Coordinated Control in the Multi-Agent 3D Dynamic Battlefield: Adaptive Coordinated ControL of Intelligent Multi-Agent TEams (ACCLIMATE)

ions of Hybrid Systems for Architecture Design and Validation The overall command and control framework has a hierarchy of abstractions: high-level planning, team organization and trajectory planning. A key to this hierarchical decomposition is the ability for higher levels to have consistent abstractions of the lower layers. Our previous work [17] has been on the development of hierarchically consistent abstractions of continuous control systems and the composition of a special class of controllers with guarantees [41, 42]. We also have studied the topology of the space to define control laws, their domains and the arbitration scheme among the control laws [1]. We propose to extend this research in two key directions: 1. Hierarchical abstractions of hybrid systems. Consistent abstractions of hybrid systems are useful since for decision making at the high levels we need discrete state approximations of continuous state trajectory plans. However, the granularity of the discrete abstraction should not be so coarse as to destroy features of the plan to be implemented at the lower levels such as asking for conflicting or infeasible trajectories for UAVs. Thus, while there is an infinity of abstractions of a continuous system, the abstraction driven by specific requirements on the functionality needs to be developed.
Obtaining formal models from Ladder diagrams

Programmable Logic Controllers (PLC) have been used in industrial environment to control several different tasks. Such devices can be programmed using one of the languages specified in the IEC 61131–3 standard, such as Ladder or Functional Logic Diagrams. PLCs are widely used in a number of critical environment, such as in the extraction processes of oil and gas. For this reason, systems running on PLCs requires a high level of reliability. In general, it is necessary to perform formal verification to achieve such a level of reliability. Formal behavioral models of the system are built to simulate and verify desired properties. Among many available formalisms, Petri Nets (PN) have been used successfully although the task of modeling demands extra time in the development process. Aiming to amortize time invested in formal verification, this paper presents a method to automatically obtain formal models, described with a class of PN called Coloured Petri Nets, directly from Ladder diagrams. With these models in hand, it is not only possible to simulate the system but it is also possible to apply automatic verification techniques.
MODEL ARTIFICIAL NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI LAJU INFLASI

Prediction of inflation is needed by policy makers, investors and companies to plan economic strategies in anticipation of the inflation rate and financial planning in the future. Accurate prediction of the inflation rate will contribute to making the right decision. Many modeling used by researchers to obtain the best prediction accuracy is the most common and econometric models (eg AR, MA, ARIMA, etc.) but in the development, models of artificial neural network (ANN) from widely used because it proved that ANN models have better accuracy econometric models, especially compared to the predictions of inflation. ANN reliability further developed by several researchers through integration with other models, one of which integration between the ANN is optimized by particle swarm optimization (PSO). This integration is used to overcome weaknesses and improve the mutual advantages to each model in order to obtain a better measurement results. Testing capabilities in research, testing the ability of artificial neural network models (ANN), integrated with particle swarm optimization (PSO) based on the weight attribute or attribute weighting, hereinafter referred to awPSO-ANN. Test results show that the prediction rate of general inflation awPSO-ANN gives better RMSE value (0.157) compared ANN before the optimization (0.181)
BF-Classifier: Background/Foreground Classification and Segmentation of Soundscape Recordings

Segmentation and classification is an important but time consuming part of the process of using soundscape recordings in sound design and research. Background and foreground are general classes referring to a signal's perceptual attributes, and used as a criteria by sound designers when segmenting sound files. We establish the background / foreground classification task within a musicological and production-related context, and present a method for automatic segmentation of soundscape recordings based on this task. We created a soundscape corpus with ground truth data obtained from a human perception study. An analysis of the corpus showed an average agreement of each class - background 92.5%, foreground 80.8%, and background with foreground 75.3%. We then used the corpus to train a machine learning technique using a Support Vector Machines classifier. An analysis of the classifier demonstrated similar results to the average human performance (background 96.7%, foreground 80%, and background with foreground 86.7%). We then report an experiment evaluating the classifier with different analysis windows sizes, which demonstrates how smaller window sizes result in a diminishing performance of the classifier.
Research Challenges of Business-Driven IT Management

Business-driven IT management (BDIM) aims at managing enterprise IT infrastructure and services efficiently and at improving business results at the same time. BDIM is based on mappings between IT technical performance metrics and business relevant metrics and exploit the linkage to provide decision support to IT management so as to maximize business value and IT-Business alignment. As an example, the number of successfully executed IT infrastructure changes can be mapped to financial loss due to the service disruption experienced by customers when the changes take place. Up to now, there has been some research effort in proposing and applying BDIM solutions to IT management with potential gains for the business. However, much remains to be researched, prototyped and validated before this new IT management discipline can become widely applicable. This paper presents a research agenda for BDIM. After reviewing BDIM concepts and proposing a framework to assist in defining and describing BDIM usage domains, the paper discusses ongoing work and outlines some research challenges.
An Iterative Thresholding Method for Epiphysis ROI Segmentation for Radiographic Images

Radiographic images play a vital role in identifying the Bone age of a live or dead human. Bone age is estimated using the epiphysis and carpal bones of hand x-ray. To achieve age estimation correctly the separation of bone pixels and soft tissue pixels in the ROI image is mandatory. The existing approaches like sobel, canny, clustering, region growing, watershed can be applied, but they need proper pre processing and correct initial seed point estimation to identify the edges uniquely. Therefore this paper proposes a Bone Pixel Enhancement (BPE) method, first to enhance the bone pixels by applying normal distribution to distinguishing the bone, soft tissue and background pixels. Thereafter an iterative threshold method is applied for suppressing the soft tissue pixels and background pixels. The proposed approach doesn't need any initial seed point to be chosen. Performance is compared with sobel, canny, adaptive means, region growing and watershed by evaluating the metrics Precision, Recall, Specificity, F-measure, Error rate and Accuracy. Proposed algorithm had success rate same as clustering with right center estimate. Here 97% accuracy in average is achieved by considering 50 images.
A standardized MRI stroke protocol: comparison with CT in hyperacute intracerebral hemorrhage.

BACKGROUND AND PURPOSE Diagnostic imaging in hyperacute ischemic stroke has been revolutionized by the introduction of diffusion- and perfusion-weighted MRI (DWI and PWI). CT, however, is still needed to exclude intracerebral hemorrhage (ICH). The purpose of our study was to determine the diagnostic accuracy of a standardized, multimodal MRI (mMRI) stroke protocol in the qualitative and quantitative assessment of hyperacute ICH (<6 hours). METHODS We investigated 9 patients with hyperacute ICH with CT followed immediately by a standardized mMRI stroke protocol (DWI, PWI [T2*-WI], FLAIR, T2-WI, and MRA). The time interval between MRI and symptom onset ranged from 3 hours to 5 hours 45 minutes. We analyzed and compared the size of the hematoma on CT and all mMRI images by semiautomatic volumetry. RESULTS ICH was unambiguously identified on the basis of all mMRI sequences. With increasing susceptibility effect (T2*-WI), the ICH, appearing as an area of hyperintensity with central signal loss, became qualitatively most evident. Regarding quantitation, T2*-WI overestimated (median and mean difference, 18.9%/17.8%; SD final sigma=24.4%) and DWI correlated best (median and mean difference, 3.97%/-4.36%; SD final sigma=37. 42%) with hematoma size on CT. CONCLUSIONS Multimodal stroke MRI is as reliable as CT in the assessment of hyperacute ICH. Therefore, additional CT is no longer necessary to rule out ICH in hyperacute stroke. The use of mMRI alone in the diagnostic workup of a hyperacute stroke patient saves time and costs while rendering all the critical information needed to initiate an optimal treatment.
Birth Weight, Breast Cancer and the Potential Mediating Hormonal Environment

Background Previous studies have shown that woman’s risk of breast cancer in later life is associated with her infants birth weights. The objective of this study was to determine if this association is independent of breast cancer risk factors, mother’s own birth weight and to evaluate association between infants birth weight and hormonal environment during pregnancy. Independent association would have implications for understanding the mechanism, but also for prediction and prevention of breast cancer. Methods and Findings Risk of breast cancer in relation to a first infant’s birth weight, mother’s own birth weight and breast cancer risk factors were evaluated in a prospective cohort of 410 women in the Framingham Study. Serum concentrations of estriol (E3), anti-estrogen alpha-fetoprotein (AFP), and pregnancy-associated plasma protein-A (PAPP-A) were measured in 23,824 pregnant women from a separate prospective cohort, the FASTER trial. During follow-up (median, 14 years) 31 women (7.6 %) were diagnosed with breast cancer. Women with large birth weight infants (in the top quintile) had a higher breast cancer risk compared to other women (hazard ratio (HR), 2.5; 95% confidence interval (CI), 1.2–5.2; P = 0.012). The finding was not affected by adjustment for birth weight of the mother and traditional breast cancer risk factors (adjusted HR, 2.5; 95% CI, 1.2–5.6; P = 0.021). An infant’s birth weight had a strong positive relationship with the mother’s serum E3/AFP ratio and PAPP-A concentration during pregnancy. Adjustment for breast cancer risk factors did not have a material effect on these relationships. Conclusions Giving birth to an infant with high birth weight was associated with increased breast cancer risk in later life, independently of mother’s own birth weight and breast cancer risk factors and was also associated with a hormonal environment during pregnancy favoring future breast cancer development and progression.
Multi-agents designed for Web-based cooperative tutoring

Cooperative instruction has always been one of important research focuses in the field of Web-based intelligent tutoring system (ITS). With the rapid advance of distance learning and networking technology, cooperative ITSs have been a rapidly developing area of R&D in AI. Based on multi-agent system (MAS) and client-server model, this paper designed multi-agents for Web-based cooperative ITS with autonomous, learning and cooperative abilities, creating a distributed collaborative and interactive e-learning environment.
Digital Gene Expression Analysis Based on Integrated De Novo Transcriptome Assembly of Sweet Potato [Ipomoea batatas (L.) Lam.]

Background Sweet potato (Ipomoea batatas L. [Lam.]) ranks among the top six most important food crops in the world. It is widely grown throughout the world with high and stable yield, strong adaptability, rich nutrient content, and multiple uses. However, little is known about the molecular biology of this important non-model organism due to lack of genomic resources. Hence, studies based on high-throughput sequencing technologies are needed to get a comprehensive and integrated genomic resource and better understanding of gene expression patterns in different tissues and at various developmental stages. Methodology/Principal Findings Illumina paired-end (PE) RNA-Sequencing was performed, and generated 48.7 million of 75 bp PE reads. These reads were de novo assembled into 128,052 transcripts (≥100 bp), which correspond to 41.1 million base pairs, by using a combined assembly strategy. Transcripts were annotated by Blast2GO and 51,763 transcripts got BLASTX hits, in which 39,677 transcripts have GO terms and 14,117 have ECs that are associated with 147 KEGG pathways. Furthermore, transcriptome differences of seven tissues were analyzed by using Illumina digital gene expression (DGE) tag profiling and numerous differentially and specifically expressed transcripts were identified. Moreover, the expression characteristics of genes involved in viral genomes, starch metabolism and potential stress tolerance and insect resistance were also identified. Conclusions/Significance The combined de novo transcriptome assembly strategy can be applied to other organisms whose reference genomes are not available. The data provided here represent the most comprehensive and integrated genomic resources for cloning and identifying genes of interest in sweet potato. Characterization of sweet potato transcriptome provides an effective tool for better understanding the molecular mechanisms of cellular processes including development of leaves and storage roots, tissue-specific gene expression, potential biotic and abiotic stress response in sweet potato.