Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Site Evaluation of the Colistin Broth Disk Elution Test To Determine Colistin <italic toggle='yes'>In Vitro</italic> Activity against Gram-Negative Bacilli

Limited methods for colistin MIC determination are available to clinical microbiology laboratories. The purpose of this study was to evaluate the accuracy of the colistin broth disk elution (CBDE) test compared to that of broth microdilution (BMD) for identifying colistin MICs. CBDE was compared to colistin BMD using a collection of Gram-negative bacilli tested at two U.S. microbiology laboratories. The isolates tested included 121 retrospective clinical isolates, 45 prospective clinical isolates, and 6 mcr-1-positive Escherichia coli isolates. CBDE was performed with four 10-ml cation-adjusted Mueller-Hinton broth tubes per isolate, to which 0, 1, 2, and 4 colistin 10g disks were added, generating final concentrations in the tubes of 0 (growth control), 1, 2, and 4 g/ml, respectively. MICs were evaluated visually and interpreted using Clinical and Laboratory Standards Institute breakpoints. Site 2 also compared CBDE to the reference broth macrodilution (BMAD) method (n 110 isolates). Overall, CBDE yielded a categorical agreement (CA) and essential agreement (EA) of 98% and 99%, respectively, compared to the results of colistin BMD. Very major errors occurred for mcr-1-producing strains, with MICs fluctuating from 2 to 4 g/ml on repeat testing. The results for all other isolates were in CA with those of BMD. CBDE versus BMAD had an EA of 100% and a CA of 100%. Compared to currently used techniques, CBDE is an easy and practical method to perform colistin MIC testing. Some mcr-1-producing isolates yielded MICs of 2 g/ml by CBDE and 4 g/ml by BMD. As such, the results for isolates with colistin MICs of 2 g/ml by CBDE should be confirmed by the reference BMD method, and isolates with MICs of 2 g/ml should be evaluated for the presence of mcr genes.
DO WE REALLY KNOW THE PREDICTORS OF COMPETENCE-CREA TING R&D SUBSIDIARIES? UNCOVERING THE MEDIATION OF DUAL NETWORK EMBEDDEDNESS RUNNING TITLE: DO WE REALLY KNOW THE PREDICTORS OF COMPETENCE-CREATING R&D SUBSIDIARIES

Many changes have been recorded in the R&D role pla yed by the foreign subsidiaries of multinational corporations (MNCs), to the extent that today many re recognised as key players in the development of firm innovation-related competences. Given this trend, w e examine the predictors of a subsidiary’s R&D role as a competence-creating contributor to the MNC’s long-t erm success. Traditionally, the predictors of subsi diaries’ R&D roles have been sought in the specific features of the internal corporate and external host-countr y environments. However, we find that favourable corp orateand country-level conditions may not necessa rily lead to the enhancement of a subsidiary’s R&D role unless dual embeddedness – that is, the subsidiary’ s embeddedness in the knowledge networks of the MNC ( internal embeddedness) and in those of the host cou ntry (external embeddedness) – is well established. The main contribution of this paper is the development of a multiple mediation model that disentangles the way in which corporate and host-country environments interrelate with a subsidiary’s dual embeddedness i n the expected configuration of its competence-crea ting R&D role. In developing the model, we use the PLS-SEM m ethod to estimate the relationship between these el ements and, eventually, to forecast the subsidiary’s compe tence-creating R&D role. The proposed model should help managers shape the fate of the subsidiary’s R&D str ategic role.
Vet: identifying and avoiding UI exploration tarpits

Despite over a decade of research, it is still challenging for mobile UI testing tools to achieve satisfactory effectiveness, especially on industrial apps with rich features and large code bases. Our experiences suggest that existing mobile UI testing tools are prone to exploration tarpits, where the tools get stuck with a small fraction of app functionalities for an extensive amount of time. For example, a tool logs out an app at early stages without being able to log back in, and since then the tool gets stuck with exploring the app’s pre-login functionalities (i.e., exploration tarpits) instead of its main functionalities. While tool vendors/users can manually hardcode rules for the tools to avoid specific exploration tarpits, these rules can hardly generalize, being fragile in face of diverted testing environments, fast app iterations, and the demand of batch testing product lines. To identify and resolve exploration tarpits, we propose VET, a general approach including a supporting system for the given specific Android UI testing tool on the given specific app under test (AUT). VET runs the tool on the AUT for some time and records UI traces, based on which VET identifies exploration tarpits by recognizing their patterns in the UI traces. VET then pinpoints the actions (e.g., clicking logout) or the screens that lead to or exhibit exploration tarpits. In subsequent test runs, VET guides the testing tool to prevent or recover from exploration tarpits. From our evaluation with state-of-the-art Android UI testing tools on popular industrial apps, VET identifies exploration tarpits that cost up to 98.6% testing time budget. These exploration tarpits reveal not only limitations in UI exploration strategies but also defects in tool implementations. VET automatically addresses the identified exploration tarpits, enabling each evaluated tool to achieve higher code coverage and improve crash-triggering capabilities.
Deep Learning for Mobile Multimedia

Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments.
Simulation of spiking activities neuron models using the Euler method

Simulation of neuron spiking activity models has been carried out using the Euler method. This study aims to simulate spiking activity in a neuron model. The neuron model used is the Hodgkin-Huxley neuron model, Integrate and Fire neuron model, Wilson neuron model, and Izhikevich neuron model. The research was conducted by implementing the mathematical equations of each neuron model used and then recording the membrane potential changes from time to time using the Euler method in MATLAB. The different forms of spiking activity were done by varying the variable’s value in each mathematical equation of a neuron model that describes the processing of action potentials (spikes) influenced by ion channel activity. The results showed that the Integrate and Fire neuron models produce regular spiking (RS), Hodgkin-Huxley neuron models have regular spiking (RS) forms, Wilson neuron models produce regular spiking (RS), fast-spiking (FS), and intrinsic bursting (IB), Izhikevich neuron model produces regular spiking (RS), fast-spiking (FS), intrinsic bursting (IB), chattering neurons (CH), and low threshold spiking (LTS). The complexity of the variables used and the spiking activity generated by each neuron model can provide an overview of computational efficiency and proximity to actual biological neurons.
Genetic variation of seven years old Araucaria cunninghamii progeny test in Bondowoso East Java

Araucaria cunninghamii is conifer trees with great commercial value for tropical and sub-tropical region. The genetic variation in growth i.e: height, diameter, volume was examined on 7 years old of A. cunninghamii progeny test at the area of Forest for Special Purpose (KHDTK) in Bondowoso, East Java. The progeny trial included 80 families from six provenances, with four replications and four trees in each row plots. The analysis showed that there were great variations of genetic growth within provenances and between provenances. It was discovered that A. cunninghamii from Fak-fak Papua had the best height growth (mean height= 10.65 m with volume mean annual increments =8.39 m 3 ha-1 year-1), while A. cunninghamii from Serui Papua had the best diameter growth (mean diameter =15.84 cm). The individual heritability for height and diameter were classified as moderate (h 2i = 0.16; 0.30). The genetic correlation between the height and diameter was quite strong (r g = 0.79). As a result of the findings, the future genetic program strategy in the research will focus on increasing genetic biodiversity for the species in order to increase Mean Annual Increment (MAI) for supplying Indonesia’s wood demand. The genetic diversity will be achieved through some steps in the strategy i.e: best genetic growth selection within provenances; combining of genetic of the best growth between provenances; and establishment of the progeny trial for next generations.
Earthquake Magnitude Estimation Based on Machine Learning: Application to Earthquake Early Warning System

Indonesia has high level of seismic activity, so determining magnitude of an earthquake is important in the Earthquake Early Warning System. In the Earthquake Early Warning System, the parameter magnitude must be estimated earlier, so that warnings can be disseminated before the S and surface waves arrive. In previous studies machine learning technology can be used to recognized earthquake events and extract hidden information with massive datasets. This study was a preliminary, proposed the alternative methods to calculate the earthquake magnitude as fast as possible, the data was 1s before and 3 seconds after the P wave from the 3-component single station raw seismogram historical data and developed with a classification deep neural network (DNN) model, classical machine learning random forest (RF) algorithm and the regression deep neural network (DNN). Results from the statistical analysis show that the waveform can be modelled by deep neural network (DNN) models. Classification DNN Model that we constructed reaches good pattern which final loss of 0.63. If it benchmarked to another model such as Random forest (RF), Classification DNN was a better model than RF which is determined by final loss of RF. Our recommendation related to estimate the magnitude from seismic raw modelling are better using Classification DNN with larger dataset. In our study, with relatively small dataset, modelling using RF algorithm can be another option. Another suggestion related this work was utilizing the Regression DNN, that resulting best alternative related to estimation of magnitude.
Systematic variation of design aspects for a significant increase in thermal fracture resistance of alumina microthrusters

Because of thermal stresses occurring upon rapid heating or cooling, microcomponents made from high-temperature co-fired ceramics (HTCC) often fail at temperatures far below what the materials can withstand per se. This work investigates how resistance to thermal fracture in HTCC microcomponents can be increased by improving the component design, aiming at increasing the thermal performance of a microthruster with integrated heaters. The effect of four design parameters: component and cavity geometries (circular or square), heater location (central or peripheral), and addition of embedded platinum layers, on thermal fracture resistance was investigated through a full factorial designed experiment. Components of different designs were manufactured, and their thermal fracture resistance tested by rapid heating until failure. Peripheral heater location and presence of embedded platinum layers were seen to improve resistance to thermal fracture, whereas the shape of the component and the cavity did not significantly affect thermal performance. The most favourable design was then used for a cold gas microthruster that was fabricated and evaluated with respect to thermal fracture resistance. The microthruster survived rapid heating up to 1460 °C and was operated as a cold gas thruster at temperatures up to 772 °C, which is more than twice the maximum temperatures previously reported for alumina microthrusters.
Study of Electronic and Magnetic Properties of Single Layered Graphene with Vacancy and (-OH) Adsorption by Density Functional Theory Calculation

Graphene is one of the most popular two-dimensional materials. However, a zero bandgap character of graphene restricts some nanoelectronics uses. Thus a defect or functional group is generally introduced to create magnetism in graphene Here, we study electronic and magnetic properties of single-layered graphene having a vacancy and hydroxide (-OH) adsorption. The calculation is performed by employing the spin-polarized density functional theory (DFT) method using the Quantum Espresso package. Modeled systems used in the calculation are the 4x4x1 graphene supercell (G), single vacancy graphene (SVG), and SVG with OH adsorption (G-OH). The band gaps calculated from SVG obtained 1.2 eV (spin-up) and 0.7 eV (spin-down), and G-OH obtained 0.8 eV (spin-up) and 1.2 eV (spin-down) after optimized structure. Moreover, the magnetic moment is estimated to be 0.69 μ B per cell and 1.00 μ B per cell for SVG and G-OH, respectively. The result shows that the defect influences electronic and magnetic properties on graphene. The results of this analysis can be used for future research of graphene applications.
Ecological status based on the diversity of macrozoobenthic in Cengkok coastal waters, Banten Bay, Indonesia

Cengkok Coastal waters is one of the estuaries in Banten Bay. This coastal area has been subjected to various anthropogenic stresses in recent years, which can affect the structure of the macrozoobenthic communities. This study aims to estimate the ecological status of the waters using macrozoobenthic diversity in the bay. The study was conducted from May to September 2019 at five stations. Macrozoobenthic were sampled using a Van Veen grab sampler. Analysis was carried out to reveal genera composition, density, diversity, evenness, dominance indices in the bay. The results showed that macrozoobenthic obtained consisted of Bivalvia, Gastropod, Polychaeta and Malacostraca. Based on time, the density of macrozoobenthic was higher in August 2019 and lower in September. The diversity index range from 0.872 to 1.443. The evenness index range from 0.368 to 0.698, and the dominance index from 0.487 to 0.711. Based on location, the highest density of macrozoobenthic was at St. 5 and the lowest at St. 2. The diversity index range from 0.547 to 1.875. The evenness index from 0.254 to 0.737 and dominance index from 0.374 to 0.813. Macrozoobenthic in this area was strongly influenced by the salinity fluctuation of the coastal waters. Based on the values of the diversity, evenness and dominance indices, this coastal waters has diversity ranged from low to moderate, with dominant macrozoobenthic at certain times and locations.
Deep Learning: A Predictive Iot Data Analytics Method

IoT provides a platform for sensor devices enabling information sharing across the platforms. IoT allows these devices to communicate without any interruption within a smart environment in convenient manner. In this digital era, correlation of data and Internet of Things (IoT) is red-hot area for data analysts. Amalgamation of Big data analytics with machine learning concepts has engendered interest of many researchers. Any IoT framework produces gigantic amount of data. This data is of no use if not analyzed and reviewed properly. This study is focusing on predictive analytics techniques that can be used in an IoT enabled environment. Paper sheds light on various data analytical methodologies. Brief introduction to architecture of deep learning is given. There are different intelligent machine learning algorithms elucidated in this study and deep learning is also one of them. Deep Learning is considered as the advanced artificial intelligence technology, which can be used in analytics and learning process of IoT data. It expounds the reasons of using deep learning for predictive IoT data analytics. Besides, introducing readers to different algorithms of deep learning which can be helpful in predictive analytics, it also elaborates their working in detail with the help of their architectures.
Consistency analysis of the extended observability matrix of output-error closed-loop subspace model identification ⋆

This paper studies statistical properties of a closed-loop subspace model identification method for a system described with the output-error state-space representation. For details, the limit value of the matrix to be singular-value-decomposed to estimate the extended observability matrix is investigated. The contribution is to ensure that the estimate of the extended observability matrix is consistent up to a similarity transform.
A Survey on Bridge Health Monitoring Systems

1-4Computer Department of AISSMS COE PUNE 5Prof. Manasi Phadatare, Computer Department of AISSMS COE PUNE -----------------------------------------------------------------------------***---------------------------------------------------------------------------Abstract Internet of Things has always taken up a step forward towards providing solutions with great ease. One such system in which the Internet of Things is emerging as a good solution to some extent is the bridge health monitoring (BHM). Bridges are such Clementine materials which are day-night exposed to extreme climatic conditions causing their wear and tear in various aspects. These factors can be: cracks on the bridge surface, overload on the bridge, flood conditions near the bridges etc.This BHM system would use various sensors (water level sensor, IR sensor, etc.) to collect the data, an algorithm (Rapid screening algorithm, image processing) or technique can be used to solve the cracks detection problem in tough or hard to reach areas of bridge. The data can be analyzed and computed to check whether the bridge has any cracks, or has any flood like conditions near it. If at all there is something wrong with the bridge condition regarding the large number of cracks or rising water level, an alert message can be sent to the concerned person. The result of this system would be used to analyze the real time health of the bridge and to take necessary actions towards safety if there is any negative output.
Backpropagating Smoothly Improves Transferability of Adversarial Examples

Probably the most popular yet controversial explanation for adversarial examples is the hypothesis on the linear nature of modern DNNs. Initially supported by the FGSMattack this has been challenged by prior works from various perspectives. Further aligning with the linearity hypothesis, a recent work shows that backpropagating linearly (LinBP) improves the transferability of adversarial examples. One widely recognized issue of the commonly used ReLU activation function is that its derivative is non-continuous. We conjecture that the reason LinBP improves the transferability is mainly due to a continuous approximation for the ReLU in the backward pass. In other words, backpropagating continuously might be sufficient for improving transferability. To this end, we propose ConBP that adopts a smooth yet non-linear gradient approximation. Our ConBP consistently achieves equivalent or superior performance than the recently proposed LinBP, suggesting the core source of improved transferability lies in the approximation derivative being smooth, regardless of being linear or not. Our work highlights that any new evidence for either supporting or refuting the linearity hypothesis deserves a closer look. As a byproduct, our investigation also results in a new variant backpropagation method for improving the transferability of adversarial examples.
Data Attribute Security for Distributed Database

Privacy-preserving publishing of micro data plays important in recent year because it is important to secure the sensitive information from data. We have used several micro data anonymization techniques such as Generalization, Bucketization and Slicing. A Slicing is a technique which divides data into two partitions such as horizontal and vertical. Slicing provides better data effectiveness than generalization and it also protect from membership disclosure. Data Provider aware algorithm is used for anonymization of the dataset which result into the reduction of the waiting time. Problem of attacks are removed with SQL Injection Prevention Technique. Experiments confirms that our approach achieve better utility and efficiency satisfying the security of the database.
Legal Judgement Prediction System

With the development and innovation of machine learning technology, more and more fields try to apply artificial intelligence to practical scenarios. We try to use a machine learning model to assist the judgment of the preliminary case results. In this paper, we analyze the basic description of the case, and apply a machine learning model to predict the possible IPC Section that will be applicable based on the fact of the case. This will also provide information such as penalty, accusation and legal provisions etc. On the one hand, the forecasting results can help the judges and lawyers to make decisions, on the other hand, it can also help the nonlegal professionals to have a basic understanding and judgment of the case
A Novel Fast Switching Photonic Phase Shifter for Ka-Band

Photonic phase shifters for Ka-band have been used in satellite communication and in many other communication systems. In these applications speed, bandwidth, power consumption and size have become more significant. In communication systems beamforming with fast switching phase shifters has special importance for Ka band. Integrated microwave photonics allow the implementation of phase shifters. We propose a novel architecture for photonic phase shifter with switching time of about 30 to 52 fs . as compared to existing architecture. The phase shifter architecture has been designed with DC tunable ring resonator, S-bend waveguide switch and nanowire DC tunable phase shifter. The designed nanowire waveguide for DC tunable ring resonator, S-bend waveguide switch and DC tunable phase shifter has been simulated using OptiFDTD as well as with MATLAB.