Miracle-dz/UniFAR-FTUs huggingface dataset:
Is your ad hoc model selection strategy affecting your multimodel inference?

Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected sub-model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex sub-model structures for non-target sub-models. We tested the performance of different model selection strategies using four data sets and three model types. For each data set, we determined the "true" distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of "true" AIC weight we recovered under different model selection strategies. Sequential-by-sub-model strategies often performed poorly. Based on our results, we recommend using a build-up or secondary candidate sets, which were more reliable and carrying all models within 5–10 AIC of the top model forward to subsequent stages. The structure of non-target sub-models was less important. Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.
Video Smoke Detection using Deep Domain Adaptation Enhanced with Synthetic Smoke Images

In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke. Due to the smoke image samples limited in scale and diversity for deep CNN training, we systematically produced adequate synthetic smoke images with a wide variation in the smoke shape, background and lighting conditions. Considering that the appearance gap (dataset bias) between synthetic and real smoke images degrades significantly the performance of the trained model on the test set composed fully of real images, we build deep architectures based on domain adaptation to confuse the distributions of features extracted from synthetic and real smoke images. This approach expands the domain-invariant feature space for smoke image samples. With their approximate feature distribution off nonsmoke images, the recognition rate of the trained model is improved significantly compared to the model trained directly on mixed dataset of synthetic and real images. Experimentally, several deep architectures with different design choices are applied to the smoke detector. The ultimate framework can get a satisfactory result on the test set. We believe that our method own strong robustness and may offer a new way for the video smoke detection.
Comparative Summarization of Temporal Document Collections

A significant fraction of documents these days are accounts of historical events. Biographies or descriptions of entity histories such as histories of geographical places or organizations are examples of such timeline documents. Their content is explicitly or implicitly associated with timestamps indicating occurrence time of described past events. However, the collections of such timeline documents can be quite large and can pose challenge for readers trying to make sense of them. We introduce in this paper a novel research task, Comparative Timeline Summarization (CTS), as an effective strategy to discover important similarities and differences in collections of timeline documents for providing contrastive type of knowledge. We then propose a three-step approach which relies on a dynamic affinity-preserving mutually reinforced random walk for the CTS task and evaluate it on Wikipedia categories. The summaries we generate are in the form of timelines consisting of salient and discriminative sentences ordered chronologically.
Exchange rate and Indian capital market

The paper attempts to investigate whether or not a causal relationship exists between exchange rates and stock market returns in India based on daily data covering January 1, 2006 to April 30, 2009. For analysis purpose, two stock indices (viz., Sensex of Bombay Stock Exchange (BSE), S&P CNX Nifty of National Stock Exchange of India Limited (NSE)), and three exchange rates (viz., Indian Rs./US dollar, Indian Rs./Euro, and Indian Rs./Yen rates) was considered. To examine the issue of causation between stock prices and exchange rates, the standard Granger causality test was performed for the reference period as well as for the calendar years 2006, 2007, 2008 and January – April of 2009. The results suggest that during the reference period as well as in all the calendar years under consideration, the NSE Nifty Granger causes to INR-USD exchange rate, INR-Yen exchange rate and BSE-SENSEX return. However, in the case of NSE Nifty and Euro exchange rate, a stable causal relationship was absent during the reference period as well as during each of the selected calendar year. In the case of EURO exchange rate and BSE Sensex, the analysis reveals significant existence of causal relation from Sensex to EURO with 4 and 5-days lag, during the full reference period, but the relation is absent for the individual calendar years 2006, 2007, and 2008.
Modeling and Simulation of Spread and Effect of Malaria Epidemic

The purpose of this paper is to consider malaria infection (A) and the control of malaria (B) as the two sets of soldiers engage in a war. The principal objectives are to see if it is possible with time to reduce and eradicate malaria in our environment taking reasonable precaution. The methodology approach is to model a mathematical equation using battling method approach to find the time(t) that control malaria in our environment will conquer the malaria infection i.e. when A(t)=0. The number of provided facilities (n) for the protection of malaria is also considered and varied. The result shows that as the number of malaria control increases the control time is decreasing
A Hybrid Architecture for Web-based Expert Systems

A recent technique is to represent the knowledge base of an expert system in XML format. XML parsers are then used to convert XML data into expert system language code. The code is executed or interpreted when providing responses to user queries. Web-based expert system (WBES) architectures may be characterized according to where the application knowledge base resides. Applications of both client and server-sided WBES architectures appear in the literature. A hybrid architecture is proposed where servers provide responses to complex queries using server-based processing of code, and clients handle simple queries using data from the XML knowledge base file. That is, both client and server have access to the knowledge base and share the processing of handling user queries. These concepts are illustrated by a comprehensive description of a small passenger information system, deployed in the hybrid web architecture. A server hosts an XML file describing the passenger network, services, stations and connections. This version of the knowledge base is imported by the client generic web page and used to provide a custom-built user interface consisting of entities derived straight from the knowledge base. At the server, the XML format is converted to Prolog code for handling of complex queries.
Face Images Database Indexing for Person Identification Problem

Face biometric data are with high dimensional features and hence, traditional searching techniques are not applicable to retrieve them. As a consequence, it is an issue to identify a person with face data from a large pool of face database in real-time. This paper addresses this issue and proposes an indexing technique to narrow down the search space. We create a two level index space based on the SURF key points and divide the index space into a number of cells. We define a set of hash functions to store the SURF descriptors of a face image into the cell. The SURF descriptors within an index cell are stored into kd-tree. A candidate set is retrieved from the index space by applying the same hash functions on the query key points and kd-tree based nearest neighbor searching. Finally, we rank the retrieved candidates according to their occurrences. We have done our experiment with three popular face databases namely, FERET, FRGC and CalTech face databases and achieved 95.57%, 97.00% and 92.31% hit rate with 7.90%, 12.55% and 23.72% penetration rate for FERET, FRGC and CalTech databases, respectively. The hit rate increases to 97.78%, 99.36% and 100% for FERET, FRGC and CalTech databases, respectively when we consider top fifty ranks. Further, in our proposed approach, it is possible to retrieve a set of face templates similar with query template in the order of milliseconds. From the experimental results we can substantiate that application of indexing using hash function on SURF key points is effective for fast and accurate face image retrieval.
Ensemble of Cubist models for soy yield prediction using soil features and remote sensing variables

The goal of this work is to develop a predictive model for selecting elite soy variants for commercial production. Current breeding practices for new soy variants require rigorous evaluation over three stages of field tests, corresponding to three successive growing seasons. We propose to leverage machine learning methods for identifying high yielding variants using remote sensing and soil features. To support this proposition, we trained an ensemble of fifteen decision tree models, one for each relative maturity band. Collectively, our models identified fifteen elite varieties from 21 predictive variables to forecast soybean yields in 2015 at 58 test locations. This method can boost commercial soy yields by about 5% and shorten the time for commercial variant development. CCS Concepts •CCS → Applied computing → Computers in other domains → Agriculture
Plasma Exosome-derived MicroRNAs as Novel Biomarkers of Traumatic Brain Injury in Rats

Traumatic brain injury (TBI) is a widespread central nervous system (CNS) condition and a leading cause of death, disability, and long-term disability including seizures and emotional and behavioral issues. To date, applicable diagnostic biomarkers have not been elucidated. MicroRNAs (miRNAs) are enriched and stable in exosomes in plasma. Therefore, we speculated that miRNAs in plasma exosomes might serve as novel biomarkers for TBI diagnosis and are also involved in the pathogenesis of TBI. In this study, we first isolated exosomes from peripheral blood plasma in rats with TBI and then investigated the alterations in miRNA expression in exosomes by high-throughput RNA sequencing. As a result, we identified 50 significantly differentially expressed miRNAs, including 31 upregulated and 19 downregulated miRNAs. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the most highly correlated pathways that were identified were the MAPK signaling pathway, regulation of actin cytoskeleton, Rap1 signaling pathway and Ras signaling pathway. This study provides novel perspectives on miRNAs in peripheral blood plasma exosomes, which not only could be used as biomarkers of TBI diagnosis but could also be manipulated as therapeutic targets of TBI.
Proximity-constrained reinforcement learning

Reinforcement learning research is difficult to reproduce (Henderson et al., 2017): the algorithms can be brittle and sensitive to hyperparameters. Our goal is to develop a generic, efficient method to make reinforcement learning algorithms more robust by constraining gradient updates of policy parameters. Inspired by proximal methods in policy optimization (Schulman et al., 2015, 2017) and variational inference (Altosaar et al., 2017), we develop a framework for constraining parameter updates in policy optimization. We carry out an empirical study and demonstrate that our method with an entropy proximity statistic leads to more stable learning and increased exploration using proximal policy optimization (Schulman et al., 2017) in a continuous control task.
Getting the Most Out of the Protein Data Bank

The Protein Data Bank archive (PDB) is the primary repository for atomic structures of biologically important molecules. These structures are available for use free of charge, and thus provide an invaluable resource to illustrators and educators in the biological sciences. The structures, however, pose many challenges for the non-expert user. Several of the major challenges are presented here along with ideas for how approach them.
Evaluation of the relationships between aerobic fitness and O2 uptake to heart beat during an incremental exercise tests

Maximal exercise capacity (Wmax) and anaerobic threshold (AT) are important tools used non-invasively to evaluate aerobic fitness levels and clinically possible risk. It has been shown that O2 uptake to heartbeat ratio (O2 pulse) at the maximal exercise test is an important parameter used to evaluate fitness levels of the subjects. However, O2 pulse at the anaerobic threshold and fitness status of the subjects has not been described yet. In this study, we comparatively evaluated the fitness levels and O2 pulse at AT and at Wmax in healthy young male subjects during an incremental exercise test. A total of 20 healthy male performed an incremental exercise test (15 W/min) to exhaustion on a cycle ergometer. Ventilatory and pulmonary gas exchange parameters were measured breath-by-breath using the metabolic gas analyser and turbine volume-meter. AT has been estimated using the V-slope method. The work rate at AT and Wmax was found to be 135 ± 4 W and 225 ± 4 W, respectively. O2 pulse was found to be 13.49 ± 0.3 ml/beat at AT and 16.53 ± 0.31 ml/beat at Wmax. There was a significant positive correlation between Wmax and O2 pulse R = 0.75184 (P < 0.05). In addition, there was a significant correlation between work production capacity for each kg of body weight and O2 pulse R = 0.68811 (p < 0.0001). We have shown that O2 pulse at AT can also be used to evaluate the fitness status. Thus, considering effective fitness measurement at the AT, investigators should be avoiding difficulty in maximal exercise performance.
Corporate Governance And Voluntary Disclosure Level; Evidence From Manufacturing Companies In Sri Lanka

: This study investigate the relationship between Relationship between Corporate Governance Attributes and Voluntary Disclosure Level of Sri Lankan manufacturing companies listed in Colombo Stock Exchange (CSE). The corporate governance attributes include ownership Concentration (OC), Board Independence (BI),CEO Duality (CD) and Number of Female Directors (FD). In here research use relative disclosure index for measuring voluntary disclosure level. Further researcher chose the 31 companies as sample of the study. The data collection methodology of the study is content analysis of annual reports of the manufacturing firms listed CSE for the period of 2015-2018. In order to analyse the outcomes, researcher occupied STATA Data Analysis Software. The results indicate that ownership concentration is negatively associated with the voluntary disclosure level. Board independence, CEO Duality and were found to have insignificant to determine the voluntary disclosure level. But female directors in the board have positive significant relationship with voluntary disclosure level. This study provide empirical background for Sri Lankan regulatory bodies and policy makers to enhance the corporate governance mechanism and voluntary disclosure level of the listed firms. The findings also contribute to understand the weak disclosure practices of the manufacturing firms.
A >100 Gbps Inline AES-GCM Hardware Engine and Protected DMA Transfers between SGX Enclave and FPGA Accelerator Device

This paper proposes a method to protect DMA data transfer that can be used to offload computation to an accelerator. The proposal minimizes changes in the hardware platform and to the application and SW stack. The paper describes the end-to-end scheme to protect communication between an application running inside a SGX enclave and a FPGA accelerator optimized for bandwidth and latency and details the implementation of AES-GCM hardware engines with high bandwidth and low latency.
A Review on Motor Imagery Signal Classification for BCI

Brain computer interface (BCI) is an evolving technology from past few years. Scalp recorded electroencephalogram (EEG) based BCI technologies are widely used because of safety, low cost and portability. Millions of people are suffering from stroke worldwide and become disabled. They may lose communication control and fall into the locked in state (LIS) or completely locked in state (CLIS). Motor imagery brain computer interface (MI-BCI) can provide non-muscular channel for communication to those who are suffering from neuronal disorders, only by imagination of different motor tasks e.g. left-right hand and foot movement imagination. EEG signals are time varying, non-stationary random signals which are changes in person to person and occurs at different frequencies. For real time application of such a system efficient classification of motor tasks is required. The biggest challenge in MI-BCI system design is extraction of robust, informative and discriminative features which can be converted into device commands. The main application of MI-BCI is neurorehabilitation and control of wheelchair or robotic limbs. The objective of this paper is to give brief information about different stages of EEG based MI-BCI system. It also includes the review on motor imagery signal classification.
SYSTEMATIC SYNTHESIS FOR SECOND GENERATION CURRENT CONTROL CONVEYOR-BASED ADMITTANCE CONVERTERS

To synthesize admittance converter circuits employing the second generation current control conveyor (CCCII), based on the nodal admittance matrix (NAM) of the admittance converter, 16 pathological models of the positive and negative admittance converters are respectively synthesized by means of NAM expansion. The synthesized positive and negative admittance converters, using CCCIIs, have 16 different forms, respectively. These converters employ canonic number of components and are easy for the integrated circuit realization. Their parameters can be electronically tuned through tuning bias currents of CCCIIs. Finally, in order to verify the validity of the derived converters, the paper-and-pencil analyses are introduced. Moreover, a negative resistance oscillator is designed by employing a negative admittance converter and a positive admittance converter. The Multisim simulation results are in good agreement with the synthesis approach.
Adversarial Attacks and Defenses in Intrusion Detection Systems: A Survey

The world is becoming more digitized and inter-connected by the day and securing our digital infrastructure is not a topic we can take lightly anymore. Intrusion detection systems (IDSs) have been an integral part of the cybersecurity stack ever since their introduction in the 1980s. Traditionally such systems have relied on signatures and heuristics, however, recently growing demand for scalability, advances in computational power, and increasing dataset availability, have paved the way for machine learning approaches. The challenge is that even though machine learning can do a better job at detecting intrusions in normal conditions – it itself is left vulnerable to adaptive adversaries who understand how these systems work and “think”. In this survey we review the different kinds of attacks such an adversary can mount on IDSs, and perhaps more importantly, the various defenses available for making IDSs more robust. We start by proving some historic context on the matter and introducing the basic taxonomy of adversarial machine learning, before diving into the methods, attacks and defenses in the second part of the write-up.
ingle cell oil production integrated to a sugarcane-mill : Conceptual esign , process specifications and economic analysis using molasses s raw material

This study aimed to assess the single-cell oil production integrated to a sugarcane-mill and molasses as main raw material. Rhodotorula glutinis lipid production with low-cost raw sugarcane molasses was accessed. The envisage process relies in an optimized sugarcane molasses culture media in a fed-batch strategy to achieve a high-cell density high-lipid cell content. A Process Flow Design (PFD) integrated to a typical Brazilian sugar mill was proposed according to fermentation experiments. Performed economic analysis provided data for discussion on strategic equipment that can facilitate the viability to the process. Typical results of established protocol were total biomass concentration 62.25 g l−1, lipid productivity 0.42 g l−1 h−1, sugar conversion yield to lipids 0.21 g of lipid g−1 of total reducing sugar. Microbial olasses rocess design conomic analysis lipid production and defatted biomass plant production was proposed at a nominal capacity of respectively 16,720 ton and 21,600 ton per year, running 8300 h per year. The projected selling prices of these products were US$ 1,300.00/ton of lipid and US$ 500.00/ton of defatted yeast. It was demonstrated that the industrial plant was potentially attractive when capital investment cost was decreased with the use of low cost epoxy-lined carbon steel stirred bioreactor. In this alternative, the internal rate of return (IRR) will be 24.61% leading the NPV (at 7% interest rate) of US$ 67,797,000/year (before taxes).
Framework for A Personalized Intelligent Assistant to Elderly People for Activities of Daily Living

The increasing population of elderly people is associated with the need to meet their increasing requirements and to provide solutions that can improve their quality of life in a smart home. In addition to fear and anxiety towards interfacing with systems; cognitive disabilities, weakened memory, disorganized behavior and even physical limitations are some of the problems that elderly people tend to face with increasing age. The essence of providing technology-based solutions to address these needs of elderly people and to create smart and assisted living spaces for the elderly; lies in developing systems that can adapt by addressing their diversity and can augment their performances in the context of their day to day goals. Therefore, this work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living (ADLs) in a smart and connected Internet of Things (IoT) based environment. This Personalized Intelligent Assistant can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience. To uphold the efficacy of this proposed framework, it has been tested on a couple of datasets for modelling an average user and a specific user respectively. The results presented show that the model achieves a performance accuracy of 73.12% when modelling a specific user, which is considerably higher than its performance while modelling an average user, this upholds the relevance for development and implementation of this proposed framework.