Reviewerly/exHarmony huggingface dataset:
Identity based Encryption with Cloud Revocation Authority

Identity-based coding/encryption (IBE) is a public key encrypted system that take outs the strain of public key infrastructure (PKI) and certified administration in standard crypto public key settings. In this public key system is not used, the downside may be a crucial thing in IBE settings. Many IBE schemes are proposed relating to this issue. Recently, by embedding associate degree computation techniques has been into IBE, Li et al. proposed an Identity-based encryption theme along with a key-update cloud service supplier. Their theme has 2 things one is that the computation overhead and other is communication prices are more than previous IBE schemes. The defect is lack of quantify ability within the sense that the key-update cloud service supplier should keep a secret worth for every user. With this article, we have a tendency to propose a replacement rescindable IBE theme with a cloud revocation authority (CRA) to solve the problems of 2 short things. The work is drastically improved and also the cloud revocation authority holds a secret for all users. For security purpose, we have a tendency to show that the proposed theme is totally secure beneath the additive Diffie-Hellman key Exchange (DBDH) assumption. Finally, we have a tendency to extend the proposed Identity-based encryption theme to gift a CRA cloud revocation authority authentication theme with limited privileges for an oversized range of assorted cloud technique services.
Numerical Analysis of Non Contact Transportation System for Wafer Warping

In this work a comparative analysis of a turbulent jet experiment produced by the injection of dyed water into a shallow tank filled with water of the same density and the results of a numerical modelare presented. Dye was injected with the source fluid as a tracer. The concentration of the dye in the shallow turbulent flow was determined using a video imaging technique.The present laboratory experiments were conducted in a tank of small depth, and it is significantly wide to avoid the effect of the side walls. The space between the parallel walls of the tank can be varied during the experiments. The large-scale turbulent flow in the water sheet between the walls of the tank is confined to essentially two-dimensional motion. The shear on the bottom of the tank is a momentum sink to be considered.A comparison of the numerical resultswith the experimental data showed a very good agreement in terms of the position reached by the jet at different times after injection is initialized. These findings are useful for turbulent modeling of the shallow shear flow and for application to the large scale heat and mass exchange processes in lagoons, lakes, the ocean and the atmosphere.
Ambo University Student’s Case Classification Models using Support Vector Machine

The main objective of ambo university is to provide quality education and improve the overall performance of an students by looking at individual students’ problems cases. One way to analysis students’ cases personally is to identify the problems causes and guide the students to solve the problems. Following this, the department Academic council and Academic Commission is whole authorized people to make the decision manually so this will consume more time and energy. This research focused to learning classification models for predicting students problems cases using support vector classification techniques. Finally, performance of the model evaluated using precision, recall and F-measure evaluation parameters.
User Perspective on the Generation Gap in Using Internet of Things – IoTs: A Conceptual Framework

Rapid changes in technology have caused many changes in business conduct.At present, several technological revolutions are happening such as smart-city, smart-health, smart-traffic, and smart-device.The operations of all of these smart things are based on wireless sensor network connecting them and other machines together.We called them collectively as Internet of Things or IoTs.IoTs technology disrupts businesses and has a huge impact on all generations of humans.New technologies bring with them some useful functionality.However, usually, there is a gap between the full capacity of new technology and its utilization by users.This study explores the conceptual framework of user perspective on generation gap in using IoTs technology, based on Expected Disconfirmation, Educational Technology Gap, and Trust theories.The authors hope that this framework will benefit other researchers in terms of potential factors influencing IoTs technology usage continuance intention in their further research on this issue.
Rules-PPO-QMIX: Multi-Agent Reinforcement Learning with Mixed Rules for Large Scene Tasks

Under the StarCraft multi-agent challenge (SMAC), the large scenes with multiple task objectives are obstacles for directly applying the existing multi-agent reinforcement learning (MARL) algorithms. The main challenge lies in that the recent MARL algorithms have a poor performance in these situations. A novel Rules-PPO-QMIX MARL algorithm is designed to 1) determine the optimal target and paths with tools of manual rules and proximal policy optimization (PPO) and 2) perform decentralised micromanagement near the target with monotonic value function factorisation MARL algorithm (QMIX). In such way, the complete decision-making process is divided into several parts, that is, path planning, target selecting and micromanagement, which is yielded by manual rules, single-agent reinforcement learning and MARL, respectively. The effectiveness of Rules-PPO-QMIX is validated by a testing map of SMAC, with QMIX as baseline. To demonstrate the feasibility on small scenes, a classical 8m SMAC environment is also used to test the proposed algorithm.
Deep learning for graphs

Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs.The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph using message-passing operators.In this paper, we provide an overview of the key concepts in the field, point towards open questions, and frame the contributions of the ESANN 2021 special session into the broader context of deep learning for graphs.
Room for truth: a new ritual for meaning making in a physical digital future

&lt;p&gt;Room for Truth introduces a new physical-digital ritual which strengthens the connection between the self and the other by integrating the body’s sensory potential into social media. From self-trackers to Instagram, people are using technology to facilitate self-observation and connection with community, but mostly media fails to incorporate the body in two major ways. Firstly, the storytelling options available on mainstream platforms are largely restricted to photographic content and text-based commentary. This serves a visual-first design hierarchy which disregards the full sensory capacities of the body as n expressive organ, a natural storytelling interface which is the inheritance of all human beings. Secondly, the built environments in which people engage in genuine self-reflection are often private, tech-free spaces. However, public truth-telling can pull people out of filter bubbles, reduce polarization, instill empathy, and motivate people to take more informed action. The Room for Truth project aims to resolve these challenges by imagining a new social media which uses the body as its generative interface. It reimagines the method, the environment, and the appearance of existing methods of self-reflection to better facilitate collective truth-telling by focusing on uniting technology with the sensory storytelling potential of the human body. Specifically, the Room for Truth prototype introduces a new visual language for truth-telling in three ways: 1) a public-private booth with theming that invites self-reflection; 2) symbolic objects with embedded NFC tags which draw upon memory and sensory attributes to inspire complex storytelling; and 3) a mobile application which captures data and translates it into meaningful abstract animations which are publicly searchable. This paper discusses the Room for Truth project and the implications of a truth-telling process which bridges the physical, digital, and emotional to capture, reflect, contextualize, and transform personal and collective experiences: a new ritual for meaning-making in a physical-digital future.&lt;/p&gt;
Colon Cancer Biopsy Image Analysis using Deep Learning

According to WHO (world health organization) colon cancer is third most commonly identified cancer in male as well as female. It is cancer of colon or rectum, located in inner side of colon wall or rectum wall in large intestine. There are different method used to diagnoses and staging of colon cancer like, colonoscopy, blood testing, biopsy method .Clear analysis of these method used for diagnose the disease. In these method some methods are time consuming if approach is manual analysis and perform on medical images. That’s the reason it required some automated, efficient and accrete machine or technique. To design such machine biopsy image analysis by deep learning play an important role to design and creation of machine that used for biopsy method diagnoses. We work on three model, CNN, VGG16 and InceptionV3 .it provide different accuracy. CNN gives 72%, VGG16 gives 71% and InceptionV3 gives 89% accuracy .Hence proposed method used to significant accuracy improvement automated diagnoses system.
INCREASING STUDENT ACTIVITY IN HISTORY CLASSES AND THE USE OF LOCAL SECOND WORLD WAR HISTORY ISSUES

In the learning process, the main task is to teach history, rely on a national basis, study the traditions of our people, such as enlightenment, tolerance, hospitality, caring, faith, kindness, honor and instill them in our hearts. young people. As in the case of the education system, the growing demand for lessons in modern history, the variety of subjects, the growing attention to historical sources, the daily need for modern pedagogical and information and communication technologies - this is the real picture of today’s education.
Creating stochastic text data to solve privacy issues in social networking

This work addresses one of the privacy concerns regarding testing social media applications, where testing applications such as recommender systems requires input data from real users. The aim of this paper is to show how probability distributions, in particular the Weibull model, can be used to generate large collections of artificial unstructured data to simulate real data for testing recommender systems. In this work we analyzed data from two social media websites: user tweets on Twitter and comments made in discussion forums on Reddit, both of which contain several thousand data instances.
Congratulations! Journal of Robotics and Mechatronics Best Paper Award 2020

Navigation Based on Metric Route Information in Places Where the Mobile Robot Visits for the First Time We are pleased to announce that the 13th Journal of Robotics and Mechatronics Best Paper Award (JRM Best Paper Award 2020) has been decided by the JRM editorial committee. The following paper won the JRM Best Paper Award 2020, severely selected from among all 77 papers published in Vol.31 (2019). The Best Paper Award ceremony was held in Gakushi-Kaikan, Tokyo, Japan, on December 23, 2020 (both on-site and online), attended by the authors and JRM editorial committee members who took part in the selection process. The award winner will also be announced on the JRM website and was given a certificate and a nearly US$1,000 honorarium. We congratulate the winners and sincerely wish them success in the future. Asahi Handa, Azumi Suzuki, Hisashi Date, Ryohsuke Mitsudome, Takashi Tsubouchi, and Akihisa Ohya J. Robot. Mechatron., Vol.31, No.2, pp. 180-193, April 2019 doi: 10.20965/jrm.2019.p0180
Signature Authentication using Deep Learning

This paper depicts the strategy to confirm marks utilizing profound learning. The paper covers the different modules and the design required to accomplish the reason. Convolutional neural systems are actualized to parse marks and feed forward neural systems are executed to investigate the attributes of the mark. Angle plummet is utilized to address mistakes with a technique got back to spread. This paper discusses how the mark on the check is contrasted with the mark in the database and how a last verification score is given to the client
An Efficient E2C2 Visual Cryptographic Technique to Secure Medical Images in Cloud Environment

Secure storage and easy retrieval of data is major concern for patients when medical data is stored over cloud platform. The unique features of cloud environment are high performance, data availability and security among which security of data stored in cloud environment is sensitive it can be easily attacked by intruders. Concerning medical images, while storing data, proper secure authentication method should be applied in order to avoid misuse of medical data. When this medical image is retrieved, the clarity of retrieved image should be high in order to determine the exact cause of sickness. This paper focuses on proposing a secure storage and retrieval of medical images stored in cloud with visual cryptographic technique using AES algorithm. The experimental results show that the proposed system is able to store images securely and retrieve medical images with high clarity.
Secure MEReq: A Tool Support to Check for Completeness of Security Requirements

Quality security requirements help secure software development to succeed. While considerable research can be discovered in the field of demands elicitation, less attention has been paid to the writing of full security specifications. The demands engineers (REs) are still challenged and tedious in implementing and reporting full safety needs derived from Natural language. This is due to their tendency to misunderstand the real needs and the security terms used by inexperienced REs leading to incomplete security requirements. Motivated from these problems, we have developed a prototype tool, called SecureMEReq to improve the writing of complete security requirements. This tool provides four important key-features, which are (1) extraction of template-based components from client-stakeholders; (2) analysis of template-based density from SRCLib; (3) analysis of requirements syntax density from SecLib; and (4) analysis of completeness prioritization. To do this, we used our pattern libraries: SecLib and SRCLib to support the automation process of elicitation, especially in writing the security requirements. Our evaluation results show that our prototype tool is capable to facilitate the writing of complete security requirements and useful in assisting the REs to elicit the security requirements.
Computer System for Perception of Lung Cancer

Detection of cancer in its previous stages to increases survival rate of patient. CAD system is efficient because it take minimum time to detect weather the patient has cancer or not. It is very difficult for detection of lung cancer its earlier stage as it takes many tests. There are many of the CAD system which is designed for earlier detection of tumors. Many CAD systems have been designed in past for early detection of lung tumor. For segmentation purpose Thresholding is used and detection of area in which suspected tumor part growing algorithms is used. There are various factor is calculated using GLCM. Multilayer feed forward BPNN approaches for classify the feature set. Performance is calculated in form of mean square error (MSE) using BPNN. The CAD (computer aided diagnosis) model gives 90% true count. For implementation purpose MATLAB is used.
FUSION OF INFRARED SENSORS AND CAMERA FOR MOBILE ROBOT NAVIGATION SYSTEM - SIMULATION SCENARIO

This paper presents a simulation scenario of a mobile robot navigation system.Navigation system is based on data collecting from three infrared sensors and camera, on which basis the fusion process of detection and avoidance of obstacles is realized.Information from camera was used to detect edges of the obstacles in the environment, while infrared sensors were used to measure the distance from the obstacles.Multilayer perceptron network, trained with backpropagation algorithm, was used for classification of detected obstacles.Experiment was realized through simulation in the simulation environment "Robotino SIM".The control algorithm was implemented in MATLAB.Sensor fusion has proven to be a much better solution than using infrared sensors or cameras separately.This experiment showed that the developed algorithm gives very good results (accuracy: 89.61%), and the navigation system itself performs required tasks of detecting and avoiding most of the obstacles on which it was tested.
Controlling SMPs using Authenticated Configurations

Hierarchical databases must work. In fact, few electrical engineers would disagree with the exploration of object-oriented language. In our research we introduce an analysis of operating systems (Owler), proving that the World Wide Web [5] can be made signed, reliable, and classical
A Wireless Sensor System for Identifying Bed Exits for Hospitalized Older People

Falls of older people in hospitals are common, so there should me techniques in order to reduce these falls. Pressure sensor systems were used for the detection of these falls, which resulted in a larger number of false alarms. Wireless sensors became more advanced in current scenario and using these wireless sensors falls can be prevented mostly. Unlike pressure sensors usage which needs a constant maintainance of the system wireless sensor system is more better in many ways. The type of design which can monitor health condition and analyze the movement and give an alert if patient is moving out of his bed going wrong and we can transmit data wirelessly anywhere by using GSM technology.
Schematizing Insured Healthcare Dossiers in Cloud using Blockading Maneuver Sequence Amplification (BMSA)

Blockade technology in healthcare industry captures the focus in the newfangled years. As a major development it has resulted in the inclusion even in marketing commerce. This progression has enriched the user preoccupied with pharmaceutical sector consequently endorsing possible manipulations and potentials. This conduct in blockading maneuver is done by calculating total file numerals through tokenization which subsequently processed by encryption. Whereas Cloud-Based Manufacturing delegates on-demand ingress to manufacturing stratagems, a reliable emissary is required for transactions between the users who aspire to suffice manufacturing services. Therefore it results in counting to the lines that are availed in an indicted block by the usage of that exacting value derived by division. The health records enclosed in a block is encrypted by blockading maneuver accordingly. In the previous works only permission is accessed to the user and encrypts the whole file. This concept of blockage advances in displaying contents in attendance and also permits to read the intact block. The files are stocked up in a block to encrypt them each. This method in endowing two strategies upshots in reducing decryption time. Ensuring data availability and uploading data by the user in encrypted form is scrutinized as a chief component in this line of attack.
Mobile Farming: Level Of Use By Small Scale Farmers In Zimbabwe

While the mobile phone has revolutionized some sectors of the economy such as banking, its impact in agriculture in Zimbabwe is yet to be established.For farmers to adapt to the fastchanging global environment and remain competitive and relevant, they must integrate ICTs in their farming activities.The study was carried out on farmers in Mushandike Irrigation Scheme in Masvingo district in Zimbabwe.Because of the farmers' long stay in commercial farming activities, they were clearly aware of their information requirements in farming.Questionnaires were distributed to 54 farmers and interviews were conducted with 10 farmers from the Irrigation Scheme.The level of use of mobile phone applications was found to be very poor.The extension worker was seen as the main source of information when it comes to dealing with traditional issues such as pests and crop diseases.Small scale farmers were not benefitting from mobile applications.The mobile phones were mainly used for voice communications and texting messages, Whatsup, Internet surfing and online banking.Although this kind of utilization is positive for the smallscale farmer, there is still a lot that farmers can benefit from mobile phones.Farmers were not utilizing dedicated or specialized applications.Thorough awareness campaigns should be carried out by government and other stakeholders to enlighten farmers on the benefits of using mobile applications.Index Terms-Mobile farming, use of ICTs in farming, small scale farmers in Zimbabwe, Information needs of small scale farmers in Zimbabwe
Self-Adaptive particle swarm optimization for large-scale feature selection in classification

© 2019 Association for Computing Machinery. Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to fnd a suitable EC method and corresponding suitable parameter values for a given largescale feature selection problem if we want to solve it effectively and efciently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for largescale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classifcation accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems. © Xue 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'ACM Transactions on Knowledge Discovery from Data', https://dx.doi.org/10.1145/3340848.