Miracle-dz/UniFAR-FTUs huggingface dataset:
A Result evaluation of frequent Pattern Mining Algorithm in Cloud Computing Environments using Modified Apriori Algorithm

: Cloud computing has demonstrated that processing very large datasets over commodity clusters can be done by giving the right programming model. Cloud can be meant as an infrastructure that provides resources and/or service over the internet. A cloud can be a storage cloud that provides block or file based storage service or it can be a compute cloud that provides computational services. Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Mining association rules is one of the most important aspects in data mining. Association rules are dependency rules which predict occurrence of an item based on occurrences of other items. Apriori is the best-known algorithm to mine association rules. The Apriori algorithm had a major problem of multiple scans through the entire data. It required a lot of space and time. The main issue with data mining techniques is that the space required for the item set and there operations are very huge. If we combine data mining techniques with cloud computing environment, then we can rent the space from the cloud providers on demand. This solution can solve the problem of huge space with better application usage in low cost. We can apply data mining techniques without taking any consideration of space. The modification in our paper suggests that we do not scan the whole database to count
RECENT STUDIES GIVE UNIFIED PICTURE OF TIRE NOISE

THE NOISE FROM TRUCK TIRES INCREASES WITH RISING SPEED, LOAD, AND WEAR. RECAPS OF AN OLDER DESIGN ARE NOISIER THAN ORIGINAL CROSS LUG TIRES WHICH, IN TURN, ARE NOISIER THAN RIB DESIGNS. IT IS POSSIBLE TO COMPARE FOUR SEPARATE STUDIES (BY GM, NBS, RMA, AND AN SAE COMMITTEE) BECAUSE EACH USED BASICALLY THE SAME PROCEDURE (THE SAE DRIVE-BY TECHNIQUE WITH A MICROPHONE 50 FT TO THE SIDE) TO MEASURE SOUND PRESSURE LEVEL (SPL). TRUCKS WERE COASTED PAST THE SOUND METER AT DIFFERENT SPEEDS, AND EACH INVESTIGATOR USED 50 OR 55 MPH FOR AT LEAST ONE RUN. /AUTHOR/
A REVIEW OF SWARMINTELLIGENCECHARACTERISTICS

Swarm robotics technology is another approach to manage the coordination of multi-robot systems which include of huge quantities of generally basic robots which takes its motivation from social bugs. The most wonderful normal for swarm robots are the capacity to work helpfully to accomplish a shared objective. This paper is make very effective analysis about the major characteristics of swarm intelligences.
Planning German Army Helicopter Maintenance and Mission Assignment

Abstract : German Army light helicopter transportation regiments operate 45 Bell UH-1D helicopters to support demanding missions throughout Europe. Maintenance period scheduling, major exercise and regular mission assignment decisions directly influence the readiness of the helicopter fleet. Currently, all planning is done manually, which is unstructured and time consuming. This thesis describes a decision support system designed to assist with maintenance planning and mission assignment. The yearly maintenance and event scheduling problem and the short term mission assignment tasks are formulated and solved as elastic mixed integer linear programs. Resulting yearly schedules and short term sortie plans are both generated in a fraction of the time previously required with solution quality superior to their manual counterparts. Helicopter, Maintenance planning, Mission assignment.
Extracellular Vesicles Physiological Role and the Particular Case of Disease-Spreading Mechanisms in Polyglutamine Diseases

Recent research demonstrated pathological spreading of the disease-causing proteins from one focal point across other brain regions for some neurodegenerative diseases, such as Parkinson’s and Alzheimer’s disease. Spreading mediated by extracellular vesicles is one of the proposed disease-spreading mechanisms. Extracellular vesicles are cell membrane-derived vesicles, used by cells for cell-to-cell communication and excretion of toxic components. Importantly, extracellular vesicles carrying pathological molecules, when internalized by “healthy” cells, may trigger pathological pathways and, consequently, promote disease spreading to neighboring cells. Polyglutamine diseases are a group of genetic neurodegenerative disorders characterized by the accumulation of mutant misfolded proteins carrying an expanded tract of glutamines, including Huntington’s and Machado–Joseph disease. The pathological spread of the misfolded proteins or the corresponding mutant mRNA has been explored. The understanding of the disease-spreading mechanism that plays a key role in the pathology progression of these diseases can result in the development of effective therapeutic approaches to stop disease progression, arresting the spread of the toxic components and disease aggravation. Therefore, the present review’s main focus is the disease-spreading mechanisms with emphasis on polyglutamine diseases and the putative role played by extracellular vesicles in this process.
Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification

Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance.
Notice of RetractionThe Photocatalytic Degradation of Methylene Blue in Wastewater by Nano-Structured Cr-Doped TiO2 under Low Power Visible-Light Irradiation

Through high-energy ball-milling technique, the nano structured TiO2 photocatalysts with Cr-doped series (0.1 wt%, 1 wt%, 1.5 wt%, 5 wt%) were synthesized. XRD results show the materials are poly-crystal with average grain sizes 20 nm and mixed phase TiO2 (the phase mole ratio of 80 % anatase to 20% rutile). Diffusion reflectance spectra show that Cr-doped to TiO2 produces some red shift of energy band for adsorbing some visible light. 1 wt% Cr-doped TiOι is the largest red shift of energy band in the experiment samples. The photocatalytic degradation of MB (methylene blue) results also show that 1 wt% Cr-doped TiO2 has the best properties under low power 40 watts office fluorescent light irradiation in the experiment samples due to the superiority condition of adsorbing the visible light. Hence, the nano-structured Cr-doped TiO2 photocatalysts for visible light irradiation will be applied to the organic degradation in wastewater treatment.
Interactive Decision and Objective Space Exploration for Search Based Refactoring

—Due to the conﬂicting nature of quality measures, there are always multiple refactoring options to ﬁx quality issues. Thus, interaction with developers is critical to inject their preferences. While several interactive techniques have been proposed, developers still need to examine large numbers of possible refactorings, which makes the interaction time-consuming. Furthermore, existing interactive tools are limited to the ”objective space” to show developers the impacts of refactorings on quality attributes. However, the “decision space” is also important since developers may want to focus on speciﬁc code locations. In this paper, we propose an interactive approach that enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Developers may be interested in looking at refactoring strategies that can improve a speciﬁc quality attribute, such as extendibility (objective space), but such strategies may be related to different code locations (decision space). A plethora of solutions is generated at ﬁrst using multi-objective search that tries to ﬁnd the possible trade-offs between quality objectives. Then, an unsupervised learning algorithm clusters the trade-off solutions based on their quality metrics, and another clustering algorithm is applied within each cluster of the objective space to identify solutions related to different code locations. The objective and decision spaces can now be explored more efﬁciently by the developer, who can give feedback on a smaller number of solutions. This feedback is then used to generate constraints for the optimization process, to focus on the developer’s regions of interest in both the decision and objective spaces. A manual validation of selected refactoring solutions by developers conﬁrms that our approach outperforms state of the art refactoring techniques.
Construction Safety in Malaysia: A Review of Industry Performance and Outlook for the Future

In 1994, the Occupational Safety and Health Act (OSHA) was gazetted in Malaysia. In tandem with existing safety legislation, OSHA 1994 is designed to reduce industrial accident rates. This paper addresses the subsequent construction safety performance. The construction industry has been plagued by a poor safety record due to a confluence of factors. The paper suggests that the presence of a significant number of foreign workers contribute to high construction casualty figures. The paper highlights the confusing signals sent out to the industry by the Department of Occupational Safety and Health (DOSH) and the Construction Industry Development Board (CIDB) arising from conflicting instructions and initiatives. The paper draws upon some of the data compiled during a national study conducted in 1996–1997 that examined foreign and local site operatives in Malaysia. By referring to developments that have happened since the study the paper is made current.
Cultural Interactivity in the Digital Age By

-The essay addresses mediated communication in a context of technological convergence that has reorganized the logic of production and transmission of symbolic content worldwide. To do this, it focuses on the cultural dimension of participation that audiences have today, particularly on their ability to interact and the implications this has for the media industry and our society. This is particularly sensitive in the midst of a global pandemic, where long-term personal relationships that are important to our well-being are lost or modified. It describes some conceptual approaches that deserve some attention due to their relevance to the debate and their daily manifestation in social life. Finally, it warns of certain risks, particularly associated with some principles that may be threatened in this process of rearranging the media ecosystem.
Modeling and Simulation of Double Diode Solar Module using MATLAB/Simulink

Abstract : : This article deals with the double diode MATLAB modelling of a photovoltaic module. In this paper, double diode model was developed using the fundamental equations. To validate the simulink/ MATLAB model. , developed model results are compared with the experimental data sheet. The developed double diode model is operated at the different operating conditions i.e. at the irradiances 1000W/m and 800 W/m; the temperatures 25 C and 20 C. The required resistances are extracted by using the newton-rapshon method. The simulated results are perfectly coinciding with the experimental values.
J an 2 01 9 A MODEL FOR RANDOM CHAIN COMPLEXES

We introduce a model for random chain complexes over a finite field. The randomness in our complex comes from choosing the entries in the matrices that represent the boundary maps uniformly over Fq, conditioned on ensuring that the composition of consecutive boundary maps is the zero map. We then investigate the combinatorial and homological properties of this random chain complex.
HawkEye: a robust reputation system for community-based counter-misinformation

Twitter's Birdwatch is a new community-driven misinformation detection platform where users provide notes to label tweet accuracy, and rate the 'helpfulness' of other users' notes. This work investigates the robustness of Birdwatch against adversaries injecting fake ratings and shows that the current Birdwatch system is vulnerable to adversarial attacks. To overcome this vulnerability, we develop HawkEye, a cold-start-aware graph-based recursive algorithm and show that HawkEye is more robust against adversarial manipulation and outperforms Birdwatch in identifying accurate and misleading tweets. Code and data are available at https://github.com/srijankr/hawkeye.
A Literature Study of Various Classification Techniques for Rough ascription of Missing Values

: The classification of incomplete patterns is an uncommonly troublesome task in light of the way that the challenge (incomplete case) with different possible estimations of missing qualities may yield specific classification happens. The precariousness (unclearness) of classification is generally realized by the nonattendance of information of the missing data. Another model based credal classification (PCC) methodology is proposed to oversee incomplete patterns due to the conviction work structure used generally as a piece of evidential intuition approach. The class models procured through planning tests are independently used to gage the missing qualities. Routinely, in a c-class issue, one needs to oversee c models, which yield c estimations of the missing qualities. The various changed patterns, in light of all conceivable possible estimation have been gathered by a standard classifier and we can get at most c unmistakable classification comes to fruition for an incomplete illustration. In this paper, we examine about different incomplete example classification and evidential thinking systems utilized as a part of the territory of information mining
Bridging Radiotherapy to Immunotherapy: The IFN–JAK–STAT Axis

The unprecedented successes of immunotherapies (IOs) including immune checkpoint blockers (ICBs) and adoptive T-cell therapy (ACT) in patients with late-stage cancer provide proof-of-principle evidence that harnessing the immune system, in particular T cells, can be an effective approach to eradicate cancer. This instills strong interests in understanding the immunomodulatory effects of radiotherapy (RT), an area that was actually investigated more than a century ago but had been largely ignored for many decades. With the “newly” discovered immunogenic responses from RT, numerous endeavors have been undertaken to combine RT with IOs, in order to bolster anti-tumor immunity. However, the underlying mechanisms are not well defined, which is a subject of much investigation. We therefore conducted a systematic literature search on the molecular underpinnings of RT-induced immunomodulation and IOs, which identified the IFN–JAK–STAT pathway as a major regulator. Our further analysis of relevant studies revealed that the signaling strength and duration of this pathway in response to RT and IOs may determine eventual immunological outcomes. We propose that strategic targeting of this axis can boost the immunostimulatory effects of RT and radiosensitizing effects of IOs, thereby promoting the efficacy of combination therapy of RT and IOs.
An Analysis of Structured Optimal Policies for Hypertension Treatment Planning: The Tradeoff Between Optimality and Interpretability

Planning: The Tradeoff Between Optimality and Interpretability Gian-Gabriel P. Garcia, Lauren N. Steimle, Wesley J. Marrero, and Jeremy B. Sussman H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI Problem definition: In medical decision-making, Markov decision processes (MDPs) are useful for deriving optimal treatment policies when a patient’s health evolves stochastically over time. Yet, optimal policies may lack structure that is interpretable to human decision-makers. When interpretability is valued by practitioners, suboptimal yet interpretable policies may be preferred over uninterpretable optimal policies. Interpretability is especially critical in hypertension treatment, where complicated clinical guidelines have drawn substantial controversy from practitioners. In this research, we design and analyze a class of interpretable policies for MDPs which leverage the natural interpretability of monotonicity, i.e., the intensity of the prescribed action increases with the severity of the state. Methodology/results: We present mixed integer programming formulations to obtain the optimal monotone policy and class-ordered monotone policy (CMP). The novel CMP generalizes monotone policies by imposing monotonicity over classes of states and actions rather than states and actions themselves. We show that the optimal monotone policy can be found in polynomial time, but the degree is non-trivial. We then analyze the performance gap of monotone policies and CMPs relative to the optimal policy using the price of interpretability (PI). Under mild conditions, we prove that the CMP achieves a PI no greater than the optimal monotone policy. Finally, we demonstrate the practicality of these methods for hypertension treatment and derive patient-level and population-level insights. Overall, the CMP’s flexibility allows it to outperform the monotone policy, achieving the greatest benefit for patients with stage 2 hypertension. Nevertheless, both interpretable policies retain low PIs and save over 3,200 quality-adjusted life years and prevent nearly 300 cardiovascular events over clinical guidelines while retaining a clinically intuitive structure. Managerial implications: CMPs retain the interpretability of monotone policies while achieving superior performance. In practical applications, CMPs can provide more instinctive strategies than the optimal policy with minimal losses in performance.
A REVIEW ON SUSTAINED RELEASE DRUG DELIVERY SYSTEM

Sustained release matrix tablets facilitate prolonged and continuous drug release and improve the bioavailability of drugs while avoiding unwanted side effects. The goal in designing sustained or controlled delivery systems is to reduce frequency of dosing or to increase the effectiveness of the drug by localization at the site of action, reducing the dose required, providing uniform drug delivery. Sustained release drug administration means not only prolongation of duration of drug delivery, but the term also implies the predictability and reproducibility of drug release kinetics. The controlled release of drug substances and their effective transport to sites of action can be exploited to maximize the beneficial clinical response and to minimize the incidence of unbeneficial adverse reactions and side effects. Oral ingestion has long been the most convenient and commonly employed route of drug delivery. Indeed, for sustained release systems, oral route of administration has received most of the attention with respect to research on physiological and drug constraints as well as design and testing of products.
Children’s Generalization of Novel Relational Nouns in Comparison Contexts: An Eye Tracking Analysis

Comparison settings (i.e. several stimuli introduced simultaneously) favor novel word learning and generalization. This study investigates the temporal dynamics of 6-year-olds solving strategies in a relational noun (e.g. “x is the dax for y”) comparison and generalization task with eye tracking data. We manipulated conceptual distance between the task’s items and recorded children’s performances and eye tracking data. We analyze and interpret solving strategies following the predictions made by two hypotheses, the Projection-First and Alignment-first. Eye tracking data clearly revealed that children, first, extract the relation from comparisons of items within a pair and search for a match for the extracted relation, which confirms the predictions of the projection-first hypothesis. Further analyses on error and correct trials suggest that errors occurred in the late, choice, phase of a trial.
PRIVACY PROTECTION IN SMARTPHONE’S BY USING MOBILE CLOUD FRAMEWORK

— we are living in the modern era where we enjoy the benefit brought by various kinds of IoT devices, our private data are leaked. Smartphone, as the typical hub of IoT devices, use various kinds of applications, which collect our private data. In fact, private data leakage, as a possible danger, is caused by the current design trend in industry, which is increasing day-by-day. However, few people notice the side-effect, but they never stop using “smart” devices. In this paper, we have introduced the behaviors of data collection, clarified the motivations and motive behind them. Cloud computing is the perfect way out with sufficient resources and admirable services. This paper introduces a mobile–cloud framework to provide fine-grained permission authorization service for IoT devices. To protect confidentiality of the sensitive data while supporting data is encrypted by the proposed convergent encryption technique before out sourcing, with using AES algorithm.
The Simulation of the Traction Drive with Middle-Frequency Transformer

This paper presents research motivated by industrial demand for special traction drive topology devoted to minimization of traction transformer weight against topology with classical 50Hz traction transformer. The special traction drive topology for AC power systems consists of input high voltage trolley converter (single phase matrix converter) –middle frequency transformer - output converter - traction motor has been described. The main attention has been given tothe control algorithm of the traction topology (inserting of NULL vector of matrix converter and Two-value control ofsecondary active rectifier).
THE EFFECT OF NON-INTEREST INCOME ON BANK PERFORMANCE AND RISK: EVIDENCE FROM INDIA

In recent times, the banks had largely diversified its income sources from traditional activities to other non-traditional activities such as brokerage and fee based services, sale of third party products, securities and forex transactions, etc., This study attempts to investigate the impact of non-interest income on bank performance and risk. The sample of 57 banks (23 public sector banks, 11 old generation banks, 7 new generation banks, 16 foreign banks) is used. The data is collected for the period of 13 years i.e., from 2005 to 2017. The effect of different components of non-interest income on the banks performance and risk are analyzed using statistics and correlation matrix. The findings of this study show that the non-interest income increases both the profitability and risk. It also recommends public and private sector banks to focus on diversified income and aggressive marketing of ancillary services to increase their income.