Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Extraction of Personal Knowledge from Smartphone Push Notifications

Personalized services are in need of a rich and powerful personal knowledge base, i.e. a knowledge base containing information about the user. This paper proposes an approach to extracting personal knowledge from smartphone push notifications, which are used by mobile systems and apps to inform users of a rich range of information. Our solution is based on the insight that most notifications are formatted using templates, while knowledge entities can be usually found within the parameters to the templates. As defining all the notification templates and their semantic rules are impractical due to the huge number of notification templates used by potentially millions of apps, we propose an automated approach for personal knowledge extraction from push notifications. We first discover notification templates through pattern mining, then use machine learning to understand the template semantics. Based on the templates and their semantics, we are able to translate notification text into knowledge facts automatically. Users’ privacy is preserved as we only need to upload the templates to the server for model training, which do not contain any personal information. According to experiments with about 120 million push notifications from 100,000 smartphone users, our system is able to extract personal knowledge accurately and efficiently.
Intrusion Prediction With System-Call Sequence-to-Sequence Model

The advanced development of the Internet facilitates efficient information exchange while also been exploited by adversaries. Intrusion detection system is an important defense component of network security that has always been widely studied in security research. However, the research of intrusion prediction, which is more critical for network security, received less attention. General research methods regarding prediction are analyzing short term of system-calls to predict forthcoming abnormal behaviors. However, those approaches have poor accuracy due to their limited sequence dependency. To solve this problem, in this paper, we take advantage of the remarkable performance of recurrent neural networks in dealing with long sequential problems, introducing the sequence-to-sequence model into our intrusion prediction work. By semantically modeling system-calls, we build a robust system-call sequence-to-sequence prediction model. Our prediction model predicts a sequence of system-calls that will be executed in the future, which will enable the monitoring of system state and the prediction of attack behavior. The experiments show that the prediction method proposed in this paper achieved well prediction performance on ADFA-LD intrusion detection test data set. Moreover, the predicted sequence, combined with the known invoked traces of system call, significantly improves the performance of intrusion detection verified on various classifiers.
Interventions for Cardiovascular Disease Risk Reduction in Korean Americans: A Systematic Review

This systematic review was to identify and synthesize literature that described the cultural appropriateness and effectiveness of interventions aimed at cardiovascular risk reduction in Korean Americans. We searched multiple electronic databases for studies published between January 2000 and August 2017 and identified 14 eligible research reports. All reviewed studies targeted first-generation Korean American adults. Most of the reviewed studies incorporated components of surface structure, and leveraged deep structure in those interventions. Significant changes in cardiovascular health outcomes were reported in most of the reviewed studies; however, the role of cultural factors in the outcomes was rarely evaluated, and few reported long-term effects. Future research needs to consider long-term effects. Deploying cultural factors and evaluating their contributions to the target outcomes will enhance the research on cardiovascular health disparities.
A quantized physical framework for understanding the working mechanism of ion channels

A quantized physical framework, called the five-anchor model, is developed for a general understanding of the working mechanism of ion channels. According to the hypotheses of this model, the following two basic physical principles are assigned to each anchor: the polarity change induced by an electron transition and the mutual repulsion and attraction induced by an electrostatic force. Consequently, many unique phenomena, such as fast and slow inactivation, the stochastic gating pattern and constant conductance of a single ion channel, the difference between electrical and optical stimulation (optogenetics), nerve conduction block and the generation of an action potential, become intrinsic features of this physical model. Moreover, this model also provides a foundation for the probability equation used to calculate the results of electrical stimulation in our previous C-P theory.
Characterization of Dendritic Cell-Derived Extracellular Vesicles During Dengue Virus Infection

The dengue virus (DENV), transmitted by Aedes spp. mosquitoes, is one of the most important arboviral infections in the world. Dengue begins as a febrile condition, and in certain patients, it can evolve severe clinical outcomes, such as dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). The reasons why certain patients develop DHF or DSS have not been thoroughly elucidated to date, and both patient and viral factors have been implicated. Previous work has shown that a severe immune dysfunction involving dendritic cells and T cells plays a key role in increasing the disease severity, especially in secondary heterologous infections. Extracellular vesicles (EVs) are membranous particles that are secreted by several cell types involved in homeostatic and pathological processes. Secretion of EVs by infected cells can enhance immune responses or favor viral evasion. In this study, we compare the molecular content of EVs that are secreted by human primary dendritic cells under different conditions: uninfected or infected with DENV3 strains isolated from patients with different infection phenotypes (a severe case involving DSS and a mild case). Human monocyte-derived dendritic cells (mdDCs) were infected with the dengue virus strains DENV3 5532 (severe) or DENV3 290 (mild), and the EVs were isolated. The presence of cup-shaped EVs was confirmed by electron microscopy and immunostaining with CD9, CD81, and CD83. The RNA content from the mdDC-infected cells contained several mRNAs and miRNAs related to immune responses compared to the EVs from mock-infected mdDCs. A number of these RNAs were detected exclusively during infection with DENV3 290 or DENV3 5532. This result suggests that the differential immune modulation of mdDCs by dengue strains can be achieved through the EV pathway. Additionally, we observed an association of EVs with DENV-infectious particles that seem to be protected from antibodies targeting the DENV envelope protein. We also showed that EVs derived from cells treated with IFN alpha have a protective effect against DENV infection in other cells. These results suggested that during DENV infection, the EV pathway could be exploited to favor viral viability, although immune mechanisms to counteract viral infection can also involve DC-derived EVs.
Interface Electronic Structures of the L-Cysteine on Noble Metal Surfaces Studied by Ultraviolet Photoelectron Spectroscopy

L-cysteine, one of amino acids, is well known in the bioelectronics field as a linker between proteins and metal electrodes. The interface electronic structures between L-cysteine and metals are therefore very crucial because they dominate the charge carrier injection characteristics into such biological systems. However the interface electronic structures of L-cysteine and metals have been not well understood. In this study, the electronic structures at the interfaces of sequentially deposited L-cysteine layers on the metal surfaces of Au(111), Ag(111), and Cu(111) were investigated by thickness-dependent ultraviolet photoelectron spectroscopy (UPS). At the contact regions, the electronic structures of L-cysteine revealed modification with respect to its bulk phase and significant variation depending on the substrate. The electronic structure at the interfaces including work function, secondary electron cutoff (SECO), highest occupied molecular orbital (HOMO) onset, position of an interface state, charge injection barrier, and ionization energy were estimated for each case. [DOI: 10.1380/ejssnt.2015. 373]
(2015). Adaptive Voltage Scaling with In-Situ Detectors in Commercial FPGAs. IEEE Transactions on Computers, 64(1), 45-53

— This paper investigates the limits of adaptive voltage scaling (AVS) applied to commercial FPGAs which do not specifically support voltage adaptation. An adaptive power architecture based on a modified design flow is created with in-situ detectors and dynamic reconfiguration of clock management resources. AVS is a power-saving technique that enables a device to regulate its own voltage and frequency based on workload, process and operating conditions in a closed-loop configuration. It results in significant improved energy profiles compared with DVFS (Dynamic Voltage Frequency Scaling) in which the device uses a number of pre-calculated valid working points. The results of deploying AVS in FPGAs with in-situ detectors shows power and energy savings exceeding 85% compared with nominal voltage operation at the same frequency. The in-situ detector approach compares favorably with critical path replication based on delay lines since it avoids the need of cumbersome and error-prone delay line calibration.
A Case Study of Economic Load Dispatch for a Thermal Power Plant using Particle Swarm Optimization

This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
An analysis of factors affecting the ethical conduct of microcredit officers

† Ph.D. in Public Sector Economics FGV. Link: World Bank Group/ Washington. Address: 1600 South Eads 127-N, Arlington, VA. E-mail: mauriciojsbm@gmail.com Telephone:(1) 2024031086 Ω Ph.D. in Economics University of Chicago. Link: University of Sao Paulo – USP. Address: Av. Prof. Luciano Gualberto, 908. Predio FEA 2 Sala Cidade Universitaria. Sao Paulo – SP CEP: 05508-010. E-mail: delosso@usp.br Telephone: (11) 3091 6070 Graduated in Business Administration by the FGV. Link: Robert H. Smith School University of Maryland. Address: 1600 South Eads 127-N, Washington DC, USA. E-mail: helena.wagner@gmail.com Telephone: (11)91234275 Ψ PhD in Business Administration by the USP. Link: University Nove de Julho UNINOVE. Address: Rua Trajano Reis 185, bloco 4 apto 12, Jd das Vertentes. Sao Paulo – SP, CEP: 05541 030. E-mail: pmorilha@gmail.com Telephone:(11) 8584 7734 £ PhD in Business Administration by the Universidade de Sao Paulo – USP. Link:University Nove de Julho – UNINOVE. Address: Rua Trajano Reis, 185, bloco 4 apto 12. Jd das Vertentes. Sao Paulo – SP, CEP: 05541 030. E-mail: smuritiba@gmail.com Telephone: (11) 8638 4083
An Association Study on the Glutamate Pathway GRIN2A Gene Polymorphisms with Heroin Dependence

Background: Heroin dependence (HD) is a complex disorder characterized by disruption in particular circuits of the brain and influenced by environmental and genetic factors. Glutamate pathway plays a role in normal brain functions including learning, memory, and cognition. Disturbances in Glutamate pathways are implicated in many psychiatric disorders, including heroin dependence, and polymorphisms present in these pathway genes are reported to increase the risk of developing heroin dependence. Aim: To identify association of single nucleotide polymorphisms (SNPs) of Glutamate pathway genes with heroin dependence and correlate with heroin use parameters. Method: A total of 103 HD patients were recruited as per DSM IV criteria from the Department of Psychiatry, and 100 healthy volunteers from the general population. Genomic DNA from peripheral blood samples was processed for PCR followed by restriction digestion to screen for presence of GRIN2A polymorphisms in the glutamate pathway. GRIN2A SNPs i.e. rs11866328, rs1071502, rs1375067, rs1530669, rs12325652, rs16966381, rs1104068, rs16966448, rs9927871 and rs1366076 were selected based on the Hap Map project and Tagger program (r2 ≥ 0.8). Genotype and allele frequencies were estimated and the difference between patient and control groups were assessed by chi-square test of significance and the results correlated with duration, age at onset of heroin use, the quantity of heroin consumed and WHO ASSIST score. Statistical analysis was done using Haploview v4.1 and SPSSv21.0. Results: Haplotype analyses revealed three SNPs (rs1071502-rs1366076-rs1104068) with alleles A-T-A to confer risk while the haplotypes A-T-G had a protective effect on HD. Another haplotype (rs1530669-rs9927871) was also found significantly associated with heroin dependence (p=0.039). Conclusion: The study reports for the first time, a possible association of GRIN2A SNPs with age at onset of heroin use, duration and quantity of use, and also suggests an important role in severity of heroin dependence.
Environmental Uncertainty as a Contingent Factor of Business Strategy Decisions: Introducing an Alternative Measure of Uncertainty

This study aims to examine a contingent factor of business strategy decisions, namely environmental uncertainty. The study applies secondary data as an alternative method to analyze technological uncertainty: a component of environmental uncertainty. To examine environmental uncertainty, this study develops an Environmental Uncertainty Index (EUI). Utilizing a sample of manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the period from 2009 to 2012 and a multinomial logistic regression, this study finds that the probability of a company pursuing a prospector strategy is greater than an analyzer strategy. Notwithstanding, the study fails to prove that the probability of a company opting for a defender strategy is greater than an analyzer approach. The findings suggest that the new measure of technological uncertainty is more applicable than the other existing measures. Furthermore, EUI measures the environmental uncertainty objectively, therefore, this new measure could be applied to future research. In general, this study broadens understanding concerning the relationship between business strategy and its contingent factors, namely environmental uncertainty.
Effectiveness of ERules in Generating Non Redundant Rule Sets in Pharmacy Database

‘Erules’ [3] is an integrated algorithm that is used to mine any data warehouse to extract useful and reliable rule sets effectively. It is used to generate positive &negative; conjunctive & disjunctive rules with the help of genetic algorithm and modified FP growth & Apriori Algorithms accordingly. It is an integrated algorithm for useful and effective association rule mining to capture even useful rare items; Lift Factor is also used to analyze the strength of derived rules. However redundant rules were one of the major challenges which were not addressed. This paper concisely deals the elimination of rule sets with the appropriate modification with the existing algorithm so that it can generate positive and negative rule sets for the non redundant rules with less cost. Besides a voluminous Pharmacy data set has been taken and the effectiveness /performance of ‘Erules’ got measured on it.
Malignant Melanocytic Matricoma and Criteria for Malignancy

Melanocytic matricoma is a very rare adnexal tumor that recapitulates the anagen hair follicle bulb. Few cases of malignant melanocytic matricoma have been reported, thus criteria for malignant potential have not been previously reviewed. We report a case of an 81 year old male that was originally diagnosed as atypical melanocytic matricoma. The site was re-excised with a small focus of residual tumor, but all margins were negative. The re-excision site healed without complication. However, two years after the original biopsy, a new 8 mm firm nodule appeared at the re-excision site, which was subsequently diagnosed as malignant melanocytic matricoma (MMM). In the following paper we will report a new case of MMM and review potential criteria that could be used in the future to establish malignant potential.
Optimal Placement of Pressure Gauges for Water Distribution Networks Using Entropy Theory Based on Pressure Dependent Hydraulic Simulation

This study proposed a pressure driven entropy method (PDEM) that determines a priority order of pressure gauge locations, which enables the impact of abnormal condition (e.g., pipe failures) to be quantitatively identified in water distribution networks (WDNs). The method developed utilizes the entropy method from information theory and pressure driven analysis (PDA), which is the latest hydraulic analysis method. The conventional hydraulic approach has problems in determining the locations of pressure gauges, attributable to unrealistic results under abnormal conditions (e.g., negative pressure). The proposed method was applied to two benchmark pipe networks and one real pipe network. The priority order for optimal locations was produced, and the result was compared to existing approach. The results of the conventional method show that the pressure reduction difference of each node became so excessive, which resulted in a distorted distribution. However, with the method developed, which considers the connectivity of a system and the influence among nodes based on PDA and entropy method results, pressure gauges can be more realistically and reasonably located.
Analog gradient beamformer for a wireless ultrasound scanner

This paper presents a novel beamformer architecture for a low-cost receiver front-end, and investigates if the image quality can be maintained. The system is oriented to the development of a hand-held wireless ultrasound probe based on Synthetic Aperture Sequential Beamforming, and has the advantage of effectively reducing circuit complexity and power dissipation. The array of transducers is divided into sub-apertures, in which the signals from the single channels are aligned through a network of cascaded gradient delays, and summed in the analog domain before A/D conversion. The delay values are quantized to simplify the shifting unit, and a single A/D converter is needed for each sub-aperture yielding a compact, low-power architecture that can be integrated in a single chip. A simulation study was performed using a 3:75MHz convex array, and the point spread function (PSF) for different configurations was evaluated in terms of lateral full-width-at-half-maximum (FWHM) and −20 dB cystic resolution (CR). Several setups were simulated varying the sub-aperture size N and the quantization step, and design constraints were obtained comparing the PSF to that of an ideal non-quantized system. The PSF is shown for N = 32 with a quantization step of 12 ns. For this configuration, the FWHM is degraded by 0.25% and the CR is 8.70% lower compared to the ideal situation. The results demonstrate that the gradient beamformer provides an adequate image quality, and open the way to a fully-integrated chip for a compact, low-cost, wireless ultrasound probe.
Feasibility of a Motion-Activated Videogame for Substance Use Disorder Relapse Prevention in Youth: A Pilot Randomized Trial Authors:

Background: Body motion-activated video games are a promising strategy for promoting addiction treatment engagement and adherence among youth. Objective: This pilot randomized trial (N=80) investigated the feasibility of a body-motion activated video game prototype, Recovery Warrior 2.0, targeting relapse prevention in the context of a community inpatient care program for youth. Materials and Methods: Participants ages 15-25 were recruited from an inpatient drug treatment program and randomized to treatment as usual (TAU) or to game play + TAU. Assessments were conducted at baseline, prior to discharge, and at 4-and 8-weeks post discharge. Results: The provision of the game play intervention was found to be feasible in the inpatient setting. On average, participants in the intervention group played for 36.6 minutes and played on 3.6 different days. Participants in the intervention group mostly agreed that they would use the refusal skills taught by the game. Those in the intervention group reported attending more outpatient counseling sessions than the control group (10.8 versus 4.8, respectively), but differences were not significant. The game had no effect on drug use at 4-weeks or 8-weeks post discharge, with the exception of a benefit reported at 4-week follow up to participants in treatment for marijuana addiction (p<.05). Conclusions: Preliminary evidence indicates that a motion activated video game for addiction recovery appears to be feasible and acceptable for youth within the context of inpatient treatment. With further development, such games hold promise as a tool for youth substance use
Study of Optimization in Fragmented Item-sets for Business Intelligence

Association Rule is one of the techniques in the process data mining problems and it might be the most researched one. Some of them have developed fragment rule mining which is based on associations among large data set. Discovering item sets is the key point in fragment rule mining. Major challenge in developing fragment rule mining algorithms is the extremely large number of rules generated which makes the algorithms inefficient and makes it difficult for the end users to cope up with the generated rules. In this research, we concentrate on optimization of fragmented items sets generated from fragment rule mining. We proposed an innovative approach to find optimized association rules within inter-transaction of fragment mining. Design of this method represented in this paper which gives idea of fragmented item sets generated from fragment rule mining on which optimization is performed. This deals mainly with reducing the time and space complexity required for processing the data using fragment mining & generate strong rules using genetic algorithm. This also reduces the width of sliding window for large data as compared to FITI because of fragmented attributes. Genetic algorithm heuristic is mainly used to generate useful solutions to optimization and search problems. In previous research many have proposed genetic approach for mining interesting association rules from large dataset. In this paper we propose knowledge based method which provides the major advancement, integrating a genetic algorithm in fragment rule mining to obtain effective rules that potentially be used for business intelligent applications.