Reviewerly/exHarmony huggingface dataset:
Density based clustering and fuzzy clustering for efficient clustering of big data in hadoop ecosystem

In this paper suggesting to use parallel distributed Hadoop Map Reduce technology.Map Reduce is a parallel technology which can process bigdata by creating multiple instances of thread.Map will take input data and split it into multiple chunks or parts and distribute all parts to different reducers.All reducers will process the data and send result back to mapper.Mapper gather output from all mappers and then generate a single output.Due to multiple parallel processing of Map Reduce technology allow us to process any amount of data.In this paper author describing clustering algorithms such Density Based Clustering and Fuzzy Clustering.Both algorithms are not efficient to group all similar data to single cluster and make some data to compromise by putting little un-similar to different clusters.To implement this project author is using National Climatic Data Center (NCDC) dataset which contains climate information.To find out similar temperature on different dates author is applying Hybrid clustering algorithm.From this dataset author is using date and temperature value and then passing date as key to Mapper and temperature as value to Mapper.Mapper always read data in the form of key value pairs.In dataset date we can find at position 6 to14 and temperature we can find at position 39 to 45. Below are some dataset example values.
A MHABE fine grained access control method for mobile cloud computing

Cloud computing is an Internet-based registering design through which shared assets are given to gadgets on request.It's a rising yet encouraging worldview to coordinating cell phones into distributed computing, and the reconciliation acts in the cloud based various leveled multi-client information shared condition.With incorporating into distributed computing, security issues, for example, information secrecy and client authority may emerge in the versatile distributed computing framework Ciphertext-approach property-based encryption (CP-ABE) has been a favored encryption innovation to take care of the difficult issue of secure information partaking in distributed computing.The common information documents for the most part have the trait of staggered pecking order, especially in the territory of social insurance and the military.Be that as it may, the chain of importance structure of shared records has not been investigated in CP-ABE.Right now, productive document pecking order characteristic-based encryption conspire is proposed in distributed computing.The layered access structures are incorporated into a solitary access structure, and afterward, the progressive records are encoded with the coordinated access structure.The ciphertext segments identified with properties could be shared by the records.Along these lines, both ciphertext stockpiling and time cost of encryption are spared.Additionally, the proposed conspire is end up being secure under the standard supposition.Trial recreation shows that the proposed conspire is exceptionally proficient as far as encryption and decoding.With the quantity of the documents expanding, the benefits of our plan become increasingly prominent.
Intelligent voting via face recognition and detection

A tool for facial recognition is a technology that can recognise or verify a person from a digital image or from a source of video.There are many ways facial recognition systems work, but usually they work by matching selected facial features within a database from a given image with faces.It is also known as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analysing patterns based on the individual's facial textures and shape.In this method, we use three authentication steps for the electors will be used.The first step is the verification of the UID, the second step is the number of the voter card and the third step is the verification, which includes different algorithms for facial recognition.We will offer a comparative analysis between these algorithms in this paper, namely: FisherFace, SURF & Eigenface.
A novel model for dynamic facet ordering for searching the desired product results for user

Faceted perusing is comprehensively utilized in online business destinations.In this we utilize a settled rundown of features.This perusing experiences two principle issues.To begin with, we have to contribute more measure of time to devise a functioning rundown.Second, with a static rundown of aspects, on the off chance that every one of the items will coordinate with the inquiry, it is of no utilization.In this work, we present a motivation for dynamic aspect requesting in web based business.In light of preliminaries for particularity and dissemination of feature esteems, the completely customized process positions those properties and aspects on top that lead to quick bore down for any conceivable target item.In contrast with existing outcomes, the motivation addressees-business definite highlights, for example, the choice of numerous snaps, the mix of aspects by their identical properties, and the a lot of numeric features.In broad generation and client think about, our approach was, as a rule, decidedly contrasted with an aspect list molded by space specialists, an avaricious strategy as beginning stage, and a cutting-edge entropy-based outcome.
A new product recommendation system for recommending products form e-commerce to social media based on user attributes

Various electronic business locales help the arrangement of social login where customers can join the destinations using their informal community characters which fuse their Facebook or Twitter commitments.Customers can moreover introduce their as of late gained stock on microblogs with associations with the online business thing net pages.Starting late, the cutoff points between online business and relational collaboration have wind up being dynamically clouded.Proposed a novel reaction for cross-webpage cold-start thing recommendation, which premiums to incite things from web business destinations to customers at long range casual correspondence locales in "coldstart" conditions the use of measurement attributes, a bother which has barely ever been examined sooner than.A critical endeavor is the best way to deal with use know-how removed from individual to individual correspondence destinations for move-site bloodless-start thing proposal.Proposed to use the associated customers transversely over casual correspondence destinations and web business locales, as an expansion to blueprint's long-range relational correspondence abilities to some other trademark depiction for thing admonishment.Related Work: In our proposal framework for suggesting universities, we chose to adopt an alternate strategy to the issue.Existing methodologies will in general spotlight on client thing lattice procedures and neighborhood approach, and their models mirror this line of reasoning.We despite everything do similitude computations, yet in an alternate path for prescribing universities as settings.There are a few ideas that we use, which are regular to most at present existing proposal schools.our task frameworks depend on data got from the online of clients, for example, sentiments or appraisals, to shape forecasts, or produce proposal of schools.Existing communitarian separating systems include producing a client thing in counterfeit lattice, from which suggestion results could be inferred.
A novel trust evaluation protocol with privacy protection for intercloud using holomorphic encryption

Intercloud looks to encourage asset sharing among mists.To help Intercloud, a trust assessment structure among mists and clients is required.For trust assessment, ordinary conventions are normally founded on an incorporated design concentrating on a single direction relationship.For Intercloud, the earth is exceptionally powerful and disseminated, and connections can be single direction or two-way (i.e., mists offer types of assistance to one another).This paper presents a disseminated trust assessment convention with security assurance for Intercloud.The new commitments and imaginative highlights are abridged beneath.To start with, input is secured by homomorphic encryption with obvious mystery sharing.Second, to take into account the dynamic idea of Intercloud, trust assessment can be led in a disseminated way and is practical in any event, when a portion of the gatherings are disconnected.Third, to encourage modified trust assessment, a creative system is utilized to store criticism, with the end goal that it tends to be handled deftly while securing input protection.The convention has been demonstrated dependent on a conventional security model.Reenactments have been performed to show the adequacy of the convention.The outcomes show that in any event, when half of the mists are pernicious or disconnected, by picking appropriate operational parameters the convention can in any case bolster successful trust assessment with security insurance.
Hand recognition in gaming

Hand motion acknowledgment gives a savvy and characteristic method for human PC collaboration (HCI).It has applications running from customer hardware to control of therapeutic recovery (for example cell phone).So as to recognize hand motions, different sorts of detecting methods are used to get signals for example acknowledgment.Increasing speed base and electromyogram-based strategies are two research branches in the field of hand signal example acknowledgment.Quickening based (ACC-based) motion control is typically contemplated as a valuable connection methodology.It is appropriate to recognize perceptible, bigger scale signals with various hand directions of lower arm developments.With ACC-based strategies some unobtrusive finger or hand development might be overlooked while electromyogram-based (EMG-based) motion acknowledgment systems use multichannel EMG signals which contain rich data about hand motions of different size scales.Because of certain issues natural in the EMG estimations, including the distinguishableness and reproducibility of estimation, the size of discriminable hand signal set is as yet restricted to 4-8 classes.So as to understand a characteristic and powerful signal based HCI framework, the determination of info hand motions that are well discriminable from one another is of essential significance.Thinking about the integral highlights of ACC-and EMG-estimations, we accept that their mix will expand the quantity of discriminable hand, wrist and lower arm signals and the precision of the acknowledgment framework.This paper depicts In IP Gaming we are proposing a framework wherein without utilizing sensors and Devices, we are recognizing the hand and signal with Simple Web camera and playing out the Image Processing system in which utilizing those motion, we can play game on reassure.In Image Process Gaming, the movements are identified through a web camera.These pictures are then passed for the picture handling.The procedures utilized for picture handling are hand motion identification, edge recognition, thresholding, form location.Utilizing OpenCV, which gives a library accumulation of capacities for various picture handling systems, these info pictures can be prepared and relating key strokes will be created.As of late, PC vision improvement has extraordinary headways and our everyday life undertakings are fragmented without utilizing PCs.The real info gadgets like Keyboard and mouse are accustomed to communicating with PCs.Among the different communication systems utilization of hands as an information is an appealing technique for building up regular Human Computer Interaction.By utilizing Hand motions client can convey more data in less Timespan.So for improving the interface among clients and PCs human PCs collaboration (HCI) innovation has incredible use.
Trial-Based Heuristic Tree-Search for Distributed Multi-Agent Planning

We present a novel search scheme for privacy-preserving multi-agent planning. Inspired by UCT search, the scheme is based on growing an asynchronous search tree by running repeated trials through the tree. We describe key differences to classical multi-agent forward search, discuss theoretical properties of the presented approach, and evaluate it based on benchmarks from the CoDMAP competition. As a secondary contribution, we describe a technique that extends the regular search approach by small explorative trials which are performed subsequent to each node expansion. We show that this technique significantly increases the number of problems solved for all algorithms considered, including MAFS.
SemEval-2014 Task 9: Sentiment Analysis in Twitter

We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year's task that ran successfully as part of SemEval-2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1-score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.
Specification of User Interface Interactions and Screens for Mobile App Development

&lt;p&gt;The size and complexity of mobile apps has been increasing in recent years resulting in a rising need for formal specification similar to traditional software projects with languages like UML. However, mobile apps are driven more strongly by user interface needs and user interface design plays a more prominent role in the software engineering process in comparison to traditional software projects. User Interface design on the other hand, often lacks techniques for formal specification. In this paper, UML statecharts are proposed for the preceding process of user interface specification as part of the app interaction design. In addition, a technique for specifying single screens of a mobile application is presented.&lt;/p&gt;
DEVELOPMENT OF TEACHING MATERIAL IN NARRATIVE TEXT BASED ON SPARKOL VIDEOSCRIBE AT MA RAUDHTUL HASANAH, MEDAN

The purpose of this development teaching the material research was to increase the students’ interest in X grade MAS PP Raudhatul Hasanah Medan. The development of teaching the material narrative text based on Sarkol VideoScribe adapts of the curriculum 2013 on third competency. The advantages of this development based on Sparkol VideoScribe such as: (1) Enhance teaching the material to be the most attractive and effective. (2) Can help to improve the interesting of students in reading and being active in the classroom. (3) Stimulate students’ attending of the lesson. (4) Increase students’ achievement in learning process. (5) Make the students are more exited. (6) The teachers enjoy and serve in learning atmosphere. This research and development approach will result in a finished product. The five-step ADDIE development paradigm is the one that was used. Analysis, design, development, implementation, and evaluation are the development measures. Before looking for the t-test result, the investigation establishes the results of normality and homogeneity. T-count is 16,2 and T-table is 1,6892 as a consequence of the calculation. Because t-count &gt; t-table and there is a notable difference between learning outcomes prior to and following the use of teaching materials based on Sparkol VideoScribe's subtheme "the smartest animal" in X grade students at MAS PP Raudhatul Hasanah Medan, it means that Ho was rejected and Ha was accepted.
A Compact and Flexible FPGA Accelerator for Regular and Octave Convolutional Neural Networks

&lt;p&gt;Convolutional Neural Networks has (CNN) have been widely used in various Internet-of-Things (IoT) applications to offer smart solutions in our daily lives. Many hardware accelerators were proposed to speed up CNN, so that it can be used in the IoT sensor nodes. Recently, octave convolution was proposed to remove spatial redundancy from input feature maps, thus reducing the CNN computation and memory cost. Although octave CNN is an attractive candidate for IoT applications, it cannot replace regular CNN, which is already widely used in many applications. Hence, it is desirable to have a hardware accelerator that can flexibly support both regular and octave convolution. In this work, we present the first compact and flexible CNN accelerator on an Field-Programmable-Gate-Array (FPGA), which supports normal, pointwise and depthwise convolutions for state-of-the-art octave convolution, as well as the existing regular convolution. To ensure efficient accelerator utilization, a novel adaptive scheduling scheme is presented to schedule the number of computation tasks based on varying feature map dimensions across CNN layers. A novel integration technique is presented to execute the varying computational patterns in octave convolution due to multiple branching in a single computation unit. An efficient memory scheme and execution scheduling for octave convolution are also proposed to overlap the long data fetch time with the on-going octave computation for better overall computation performance. Comparing to other state-of-the-art work, our proposed accelerator achieved 1.98x higher computation density for MobileNetV2 implementation on XC7ZU9EG, as well as about 83.9% lesser power for ResNet-50 implementation on VU9P. &lt;/p&gt;
Asynchronous Distributed Protocol for Service Provisioning in the Edge-Cloud Continuum

&lt;p&gt; In the edge-cloud continuum, datacenters provide microservices (MSs) to mobile users, with each MS having specific latency constraints and computational requirements. Deploying such a variety of MSs matching their requirements with the available computing resources is challenging. In addition, time-critical MSs may have to be migrated as the users move, to keep meeting their latency constraints. Unlike previous work relying on a central orchestrator with an always-updated global view of the available resources and of the users’ locations, this work envisions a distributed solution to the above issues. In particular, we propose a distributed asynchronous protocol for MS deployment in the cloud-edge continuum that (i) dramatically reduces the system overhead compared to a centralized approach, and (ii) increases the system stability by avoiding having a single point of failure as in the case of a central orchestrator. Our solution ensures cost-efficient feasible placement of MSs, while using negligible bandwidth. &lt;/p&gt;
Multi-objective Scientific Workflows Scheduling in An Energy-aware Cloud Environment

Abstract The cloud has become an essential element for businesses globally, providing constant data availability while improving data center energy efficiency and reducing carbon footprints. However, data centers' energy consumption remains high, and energy reduction has become a crucial objective. In this paper, we aim to address this concern by proposing a multi-objective scientific workflow scheduling scheme on heterogeneous computing systems using a hybrid genetic algorithm approach. Our algorithm incorporates Hill Climbing to generate an initial population of chromosomes, which we further optimize using a genetic algorithm to assign each task to the most suitable virtual machine. The proposed fitness function is carefully designed to evaluate each chromosome's fitness to solve the scheduling problem. The algorithm's parameters and operators significantly affect the solution's performance, and our results show that the proposed algorithm outperforms other scheduling methods in terms of quality, reducing energy consumption, processing time, and cost. We believe that our proposed approach can play a crucial role in reducing the energy consumption and carbon footprint of cloud data centers and provide a green solution for scientific workflow scheduling.
Towards a Multi-Agent approach for Workflow Scheduling on Hybrid Clouds

Abstract Since scientific research and industrial applications are becoming ever-increasingly demanding in computing resources, Cloud Computing infrastructures are attracting a widespread interest thanks to their promising hallmarks such as scalability, high-performance, reliability and the pay-per-use strategy. The execution of these high-performant applications on such kind of computing environments in respect of optimizing many conflicting objectives brings us to a challenging issue commonly known as the Multi-objective workflows scheduling on large scale distributed systems. Bearing this in mind, we outline in the present paper our proposed approach called Genetically-modified Multi-objective Particle Swarm Optimization (GMPSO) for scheduling application workflows on hybrid Clouds in the context of high-performance computing in attempt to optimize Makespan and Cost. To achieve this, we have designed our proper encoding as well as a set of particular operators. Conducted simulations lead to significant results compared with a set of well-performed algorithms such NSGA-II, OMOPSO and SMPSO, especially, for the most-demanding workload of workflows.
An Adaptive Node Network Based on Multitask Deep Learning

Abstract Multitask learning (MTL) improves the performance achieved on each task by exploiting the relevant information between tasks. At present, most of the mainstream deep MTL models are based on hard parameter sharing mechanisms, which can reduce the risk of model overfitting. However, negative knowledge transfer may occur, which hinders the performance improvement achieved for each task. In this paper, for situations when multiple tasks are jointly trained, we propose a deep MTL method for adaptive nodes. On the basis of the hard parameter sharing network architecture, the number of nodes in the network is dynamically updated by setting a continuous gradient difference-based sign threshold and a warm-up training iteration threshold through the relationships between the parameters and the loss function. After each task fully utilizes the shared information, adaptive nodes are used to further optimize each task, reducing the impact of negative migration. By using simulation and case studies, we demonstrate theoretical proof that the performance of the proposed method is better than that of the competing approach. MSC: 68T07; 68W99
Volunteering and Loneliness: The Differential Effects of Volunteer Types

As the sense of loneliness has emerged as a serious social issue, several research has appeared recently. While previous studies has confirmed the positive implications of volunteering to perceived loneliness, they tended to focus on the old who has retired or widowed. But the literature indicates that people suffer from loneliness all over the life courses and so the study that cover those who are young as well as old is necessary. Using the data from a nationwide sample of 5,000 adults, this research examines the relationship between volunteering and loneliness in Korea. We find that volunteering is negatively associated with sense of loneliness for Koreans. In particular, participations in preservation of environment and supports of the social weak has strong positive implications. These findings recommend volunteering as a good cure of loneliness.