Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Gaussian Process Model Averaging for High-Dimensional Inputs

Many statistical learning methodologies exhibit loss of efficiency and accuracy when applied to large, high-dimensional data-sets. Such loss is exacerbated by noisy data. In this paper, we focus on Gaussian Processes (GPs), a family of non-parametric approaches used in machine learning and Bayesian Optimization. In fact, GPs show difficulty scaling with the input data size and dimensionality. This paper presents, for the first time, the Stochastic GP Model Averaging (SGPMA) algorithm, to tackle both challenges. SGPMA uses a Bayesian approach to weight several predictors, each trained with an independent subset of the initial data-set (solving the large data-sets issue), and defined in a low-dimensional embedding of the original space (solving the high dimensionality). We conduct several experiments with different input size and dimensionality. The results show that our methodology is superior to naive averaging and that the embedding choice is critical to manage the computational cost / prediction accuracy trade-off.
Accelerating Type Confusion Detection with Pointer Analysis

—C++ is widely used in performance critical applications. Due to the lack of type safety, programs written in C++ are vulnerable to memory corruption errors. Type confusion bug is an emerging attack vector. Several type confusion detection tools have been proposed to detect unsafe cast instructions. However, these tools suffer from the problem of high runtime overhead. The perfromance problem has prevented the deployment of type confusion detection tools into production software. This paper proposes a new method to mitigate the performance problem. The novelty of this paper is that we use pointer analysis to identify redundant instrumentations. By removing the identiﬁed redundant instrumentations, we can signiﬁcantly reduce the runtime overhead incurred by type confusion detection tools. We have applied our pointer analysis to Typesan - an-open source type confusion detection tool. Statically, 68.67% of instrumentations for tracing types of objects and 57.84% of instrumentations for verifying downcast instructions can be removed on average. Dynamically, the average (maximum) runtime overhead can be reduced from 40.10% (89.71%) to 12.22% (24.90%).
ALLELOPATHIC POTENTIAL OF ARGEMONE OCHROLEUCA FROM DIFFERENT HABITATS ON SEED GERMINATION OF NATIVE SPECIES AND CULTIVATED CROPS

Allelopathy has been regarded as a mechanism for the successful exotic plant invasion, but this mechanism has not been evaluated for Argemone ochroleuca Sweet; an invasive weed in rangelands and farmlands of the Arabian Peninsula. We investigated whether wild native range plant species (Farsetia aegyptia Turra and Salvia aegyptiaca L.) and forage crops (Hordeum vulgare L. and Medicago sativa L.) respond differently to potential allelopathic effects of aqueous extracts from roots and shoots of A. ochroleuca growing in two habitats; rangelands and farmlands. Almost all the germination indices were sensitive enough to establish the allelopathic potential of aqueous extracts. Inhibition of seed germination of the test species showed species-specific; concentration, organ and habitat dependent response with highest inhibition occurring at 100% concentration of shoot extract from rangeland habitat. Seed germination of F. aegyptia was the most sensitive to different aqueous concentrations extracted from the two habitats, whereas H. vulgare seed germination was the least sensitive. The results suggest different organs of A. ochroleuca exhibit sufficient allelopathic potential in different habitats.
ADSORPTION OF PHARMACEUTICAL CONTAMINANTS FROM AQUEOUS SOLUTIONS BY USING NATURAL PLANTS AS ADSORBENTS ASSISTED BY ULTRASOUND PROCESS

Activated carbon (AC) as a source of natural plants (apricot stones) were using as a good adsorbent for removal of pollutants Ranitidine drug from aqueous solutions by assisted of sonicated. The results of different experiments showed that a (AC) that considered a friendly of the environment have ability to adsorb Ranitidine drug from aqueous solution. The sorption process dependent of the pH and was found the best result at pH 6.5, and the adsorption process has nearly reached equilibrium in 60 min. The experimental data are fitted well to Langmuir isotherm model, and the maximum adsorptive quantity of Ranitidine was 96.071 mg/g according to Langmuir model.
EC DENTAL SCIENCE Systematic Review Oral Health Related Quality of Life (OHRQoL) Following Orthodontic Treatment: An Updated Systematic Review

Oral Health Related Quality of Life (OHRQoL) is an essential tool for decreasing the burden of these problems whenever an orthodontic-related procedure is performed. We aim to assess the impact of OHRQoL among adolescents. For that, a systematic electronic database search was conducted for relevant studies published from 2010 and till 18th June 2020 in seven databases. Finally, 12 studies were included in this systematic review. Most of the included studies showed a significant improvement in OHRQoL following orthodontic treatment, at the last follow up point. This improvement includes the domains of physical pain, psychological wellbeing, psychological disability, emotional impact, and social impact. Moreover, there was an initial reduction in OHRQoL scored during the starting phase of orthodontic treatment, due to a combination of physical pain, psychological discomfort and physical disability. On the other hand, a few studies showed a slight (not statistically significant) or no improvement in OHRQoL following orthodontic treatment. In conclusion, improved OHRQoL was significantly associated with orthodontic treatment during adolescence. Although some studies showed fluctuations in the OHRQoL along with the different phases of treatment, the overall improvement was statistically significant at the end of the treatment period. There is a need for large-scale studies with more enhanced inclusion criteria, longer follow-up durations, and using validated OHRQoL assessment measures.
Binary coalescence of air bubbles in viscous liquids in presence of non‐ionic surfactant

Coalescence of air bubbles is important in gas-liquid reactors and food processing operations. Bubbles can be stabilized by using non-ionic surfactants. Binary coalescence of air bubbles in ethylene glycol and aqueous glycerol solutions were studied in this work in presence of Span 80. A novel set-up was developed to study long coalescence times. Coalescence time was observed to follow broad stochastic distributions in all systems. The distributions were fitted with a stochastic model developed earlier. The surface tension of ethylene glycol and glycerol solutions was measured at various concentrations of Span 80. These data were fitted using a surface equation of state derived from the Langmuir isotherm. The effect of surfactant concentration on coalescence time was explained in terms of the surface excess of the surfactant and the repulsive force generated at the air-liquid interface. The results from this work illustrate the stochastic nature of bubble coalescence in viscous liquids. This work also demonstrates how non-ionic surfactants can stabilize bubbles in such liquids.
Multi-Modal Palm Print and Finger Dorsal Biometric Authentication System using C2-based Fusion Algorithm

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this paper, we have proposed a multi-modal personal identification system that fuses palm print and finger dorsal images at feature level. In a company and enterprise world Biometric based authentication system is a most wanted activity. Legitimacy, safety and protection are the most common traits of authentication system. Such that we’ve got proposed C2-based fusion algorithm for combining the two distinctive, intricate, precise specific detailed Palm print and Finger dorsal images for extracting a feature data from the acquired information, and comparing this feature with the template set stored in database. The combined method is carried out for data units for verification and gives better performance.
Recommendation of Branch of Engineering using machine learning

- Student by himself is not mature enough to take right decision in his early life. Selecting wrong courses means mismatch between student aptitude, capability and self interest. Faculty or parents have neither the required knowledge nor experience. Since there is no other reliable source generally available that can guide the student towards the most suitable direction, so this recommender system has been evolved to provide him guidance in selecting a right engineering branch. This system recommend them suitable branch based on their score. In this system, K nearest neighbors is used to recommend branch and collaborative filtering is used to recommend colleges.
A LITERATURE SURVEY ON SINGLE CHANNEL SPEECH ENHANCEMENT TECHNIQUES

: Speech enhancement deals with the handling of noisy speech signals in order to improve people's perception or better system understanding when noise destroys speech information. It is usually difficult to keep speech undistorted while reducing noise and thus limiting the performance of speech enhancement systems— the compromise between distortion of speech and reduction of noise. With noisy speech with medium to high SNR, the goal will be to generate subjectively realistic signal by reducing noise levels, and for those with low SNR, the goal could be to reduced noise level while retaining intelligibility. In this work, discussion on the need for speech enhancement, its applications, and an overview of classification and various approaches available has also been given and done an extensive literature survey on speech enhancement techniques with different platforms.
DIABETES MELLITUS A SHORT REVIEW

Diabetes mellitus is an epidemic and also life threatening disease affecting over millions of people all over the world. Diabetes is caused due to genetic and epigenetic factors. The broad classification of Diabetes includes type I and type II diabetes. Much research on causes, prevention and treatment of Diabetes mellitus is being performed all over the world. This review details about the types of Diabetes mellitus, their symptoms, pathophysiology, drugs and phytocompounds in the treatment of Diabetes mellitus.
An Introduction to Modular Modeling and Simulation with PythonPDEVS and the Building-Block Library PythonPDEVS-BBL

The Discrete Event System Specification (DEVS) is a popular formalism devised by Bernard Zeigler in the late 1970s for modeling complex dynamical systems using a discrete event abstraction. At this abstraction level, a timed sequence of pertinent "events" input to a system (or internal timeouts) causes instantaneous changes to the state of the system. Main advantages of DEVS are its precise, implementation independent specification, and its support for modular composition. This tutorial introduces the Classic DEVS formalism in a bottom-up fashion, using a simple traffic light example. The syntax and operational semantics of Atomic (i.e., non-hierarchical) models are introduced first. Coupled (i.e., hierarchical) models are introduced to structure and couple Atomic models. We continue to actual applications of DEVS, with an example in performance analysis of queueing systems. This uses generator, queue, etc. components from our PythonPDEVS Building Block Library. All examples in the paper are presented using the language PythonPDEVS and its simulator, though this introduction is equally applicable to other DEVS implementations. We conclude with further reading on DEVS theory, variants, and tools.
Dynamics and Free Energy of Polymers Partitioning into a Nanoscale Pore

Membrane-bound proteinaceous nanoscale pores allow us to simultaneously observe the thermodynamic and kinetic properties of differently sized polymers within their confines. We determine the dynamic partitioning of poly(ethylene glycol) (PEG) into the pore formed by Staphylococcus aureus α-toxin and evaluate the free energy of polymer confinement by measuring polymer-induced changes to the pore's ionic conductance. The free energy deduced from the partition coefficient has a sharper dependence on polymer length (or weight) than scaling theory predicts. Moreover, the polymer-induced conductance fluctuations show a striking nonmonotonic dependence on the polymer molecular weight. The movement of polymer inside the pore is characterized by a diffusion coefficient that is orders of magnitude smaller than that for polymer in the bulk aqueous solution, which suggests that PEG has an attractive interaction with the pore. Using an ad-hoc approach, we show that a simple molecular weight-dependent modification of t.
Microwave Convective Drying and Storage of Soy-Fortified Paneer

Drying of acid-coagulated protein-rich food products by conventional methods produces insurmountable problems like formation of a moisture-retaining crust, longer drying time, poor rehydration characteristics and oiling off during drying. These problems can be minimized by the use of electromagnetic energy for drying because of its rapid and uniform dissipation. The use of microwaves for drying of soy-fortified paneer (SFP) has been explored in the present study. The effect of microwave convective drying variables such as hot air temperature and microwave power levels on drying rate and diffusivity were analysed. Both the drying rate and diffusivity increased with increase in hot air temperature and microwave power level. A hot air temperature of 53:5°C and microwave power of 111:5W gave good-quality dried SFP cubes of uniform texture and surface, unblemished and with clear color. The dried SFP cubes rehydrated well and had a shelf life of 118 days under accelerated storage conditions (38 ± 2°C, 90% relative humidity).
Interfacial reaction during metallization of cured polyimide: An XPS study

To characterize the intrinsic adhesion properties of various metal–polyimide systems we have used x‐ray photoelectron spectroscopy (XPS) to monitor interfacial reactions during metallization of cured polyimide at room temperature. In situ measurements were made in a UHV system when monolayers and submonolayers of Cr, Ni, Cu, or Ag film were successively deposited on a freshly cured PI substrate. Examination of the changes in various core electron spectra associated with the substrate and overlayer and their comparison with expected escape length effect indicate that while Cr and Ni react with the pendent oxygen in the substrate, Cu and Ag do not. A simple thermodynamic calculation shows that limiting values can be derived for the effective partial molar free energy of the pendent oxygen in PI, which may be used to predict whether a given metal will form chemical bonds with PI during metallization at room temperature.
Semi-Automated System For Filtering Objectionable Content Using Video Metadata

: The most popular and widely accepted video sharing websites on Internet are YouTube, Vimeo, Metacafe, Veoh, The Internet Archive, Crackle, etc. Ample numbers of videos on these platforms have unwanted content, so mining of video metadata can be employed to identify such videos. Upon downloading several videos as dataset and manually explaining the dataset will help in studying and categorizing training dataset. The detection of obnoxious videos and frame in the dataset can be classified using ―One class classifier approach‖. Image thumbnails are used instead of video thumbnails for efficient bandwidth consumption. Offline mode is enhanced by providing full-download option to the users, without having to re-synchronization periodically. Integration with YouTube is a feature included to play the selected video content.
Matrix Reconstruction: Skeleton Decomposition versus Singular Value Decomposition

—In this work, Skeleton Decomposition (SD) and Singular Value Decomposition (SVD) are compared and evaluated for reconstruction of data matrices whose columns come from a union of subspaces. Speciﬁcally, an original data matrix is reconstructed from noise-contaminated version of it. First, matrix reconstruction using SD iteratively is introduced and alternative methods for forming SD-based reconstruction are discussed. Then, through exhaustive simulations, effects of process parameters such as noise level, data size, number of subspaces and their dimensions are evaluated for reconstruction performance. It is also shown that SD-based reconstruction is more effective when data is drawn from a union of low dimensional subspaces compared to a single space of the same dimension.
DEPLOYING DISCRETE-EVENT SIMULATION AND CONTINUOUS IMPROVEMENT TO INCREASE PRODUCTION RATE IN A MODULAR CONSTRUCTION FACILITY

Aiming at continuous improvement, a modular construction company attained favorable results by implementing recommendations that were based on value stream mapping analysis. Yet, there is still a need to assess the production lines in a unified and integrated manner. As such, this study employed simulation to model five major production lines in the factory to evaluate their performance concurrently and suggest improvements. Bottlenecks were identified by tracking the waiting times at different stations, and an iterative and sequential approach was adopted. After eight suggested improvements and tested scenarios, results showed a 17.8% reduction in the unit cycle time and 22% increase in the weekly production rate. The study's major takeaway is the importance of studying improvements in an integrated manner to avoid shifting bottlenecks, achieving local improvements that do not guarantee global improvements, and underestimating the effect of minor changes on the overall process. Simulation modelling helped target these issues.
Self Supervision for Attention Networks

In recent years, the attention mechanism has become a fairly popular concept and has proven to be successful in many machine learning applications. However, deep learning models do not employ supervision for these attention mechanisms which can improve the model’s performance significantly. Therefore, in this paper, we tackle this limitation and propose a novel method to improve the attention mechanism by inducing "self-supervision". We devise a technique to generate desirable attention maps for any model that utilizes an attention module. This is achieved by examining the model’s output for different regions sampled from the input and obtaining the attention probability distributions that enhance the proficiency of the model. The attention distributions thus obtained are used for supervision. We rely on the fact, that attenuation of the unimportant parts, allows a model to attend to more salient regions, thus strengthening the prediction accuracy. The quantitative and qualitative results published in this paper show that this method successfully improves the attention mechanism as well as the model’s accuracy. In addition to the task of Visual Question Answering(VQA), we also show results on the task of Image classification and Text classification to prove that our method can be generalized to any vision and language model that uses an attention module.
Edge-cloud IoT Data Analytics: Intelligence at the Edge with Deep Learning

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for ML tasks. Consequently, this thesis aims to combine edge and cloud computing for IoT data analytics by taking advantage of edge nodes to reduce data transfer. Feature learning is performed on the edge with deep learning: the encoder part of the trained autoencoder is placed on the edge to extract relevant features. These features are sent to the cloud where the final ML task is performed directly, or the original features are first restored using the decoder part of the autoencoder. In the single-node model, all data are considered together and processed on a single edge node. In the multi-node model, sensors are grouped according to the IoT device locations or according to similarities between sensors, and different groups are processed on different edge nodes. The evaluation was performed on the task of human activity recognition from sensor data. Results show that data can be reduced on the edge up to 80% without significant loss in accuracy.
STUDY OF MATERIAL CHARACTERISTICS FOR EFFICIENT DESIGN OF SPRAY PAINTING ROBOT

1Service Engineer, FANUC India Private Limited, Pune, Maharashtra410501 2 Resources and Content Consultant, Australian Institute of Engineering, Ahmedabad, Gujarat380009 ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract This paper is proposed to study material characteristics and material selection for industrial robots typically used for spray painting application. In this paper we studied material properties required for the design of a spraypainting robot, so that it can work efficiently for a long time. Initially this paper introduced spray painting robots and tried to describe why robots are required in spray painting applications. Then why material selection is required in spray painting robots and what are the basic properties of material required for the same. Then based on the required properties of material, we try to define some materials suitable for spray painting robots. This paper includes merits and demerits of all materials that we listed out for spray painting robot application. And lastly, we try to conclude the best suitable material for spray painting robots. For reference of spraypainting robot structure and design parameters we used data from reference [2].
INTELLIGENT DYNAMIC ENQUIRY METHODOLOGY FOR INNOVATION TECHNOLOGY

The explicit objective of this study is to develop a methodological approach based on identification of online community members(cid:146) needs towards the specification phase in Agent-Oriented Software Engineering. Intelligent Dynamic Enquiry (IDE) methodology is based on the Dynamic Enquiry. The implicit objective is to design a tailored Agent-Oriented System based on members(cid:146) proposals to promote participation in online dialogue. The suggested frameworks bridge a theoretical gap regarding a methodology for patterns recognition as well as online communities. Active Readers On the Web (AROW) definition for users and a division between e nformatic, pseudo-e nergetic and e nergetic communities, as well as blended and online communities seemed to be needed. (cid:145) Spiral Identification of Information Locus in Online Discussion (cid:146) (SILOD) portrays the raised stages and structure properties revealed by IDE. The tailored system would enhance creativity, online dialogue and save significant time for newcomers(cid:146) introduction and engagement in virtual communities.