Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Social Networking Data Using Categorized Dictionary

Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed.  A categorized dictionary is developed for the sentiment classification and further calculation of sentiment accuracy. The concept of categorized dictionary involves the creation of dictionaries for different categories making the comparisons specific. The categorized dictionary includes words defining the positive and negative sentiments related to the particular category. It is used by the mapper reducer algorithm for the classification of sentiments. The data is collected from social networking site and is pre-processed. Since the amount of data is enormous therefore a reliable open-source framework Hadoop is used for the implementation. Hadoop hosts various software utilities to inspect and process any type of big data. The comparative analysis presented in this paper proves the worthiness of the proposed method.
Analysis on the Mechanism of Superconducting Quantum Computer

Abstract Nowadays the quantum computer has been drawing public attention, and how to realize a quantum computer is one of the difficult problems. However, superconducting circuit is a good system for solving the physics of quantum computers, as it has a solid development history and a perfect fit with quantum computers. In this paper, the development history of the superconducting quantum computer will be reviewed. In addition, the Josephson junction is the basis of superconducting quantum computation, based on which as well as in combination with DiVincenzo’s five requirements, superconducting quantum computers have a great potential. For the main part, the advantages and disadvantages of superconducting a quantum computer are analyzed. Some suggestions are also given and the future of superconducting computer is predicted. In conclusion, the superconducting system is found to be a suitable system for quantum computers.
Information Literacy through Library Services: A Key Competency in Knowledge Society

Information Literacy encompasses knowing how to use various tools to retrieve, evaluate, use and communicate information in an ethical way. In a knowledge society, information literacy plays major role in enabling people to independently find information and use it whenever need arises. st In this 21 century, it is not enough to be merely Information Communication Technology (ICT) literate but one will need literacy that helps the use of these applications and tools. This paper is based on the assumption that libraries have a pivotal role to play in knowledge society. It explains the importance of Information Literacy and opportunities for libraries to improve services through ICT as well as the challenges of Information Literacy Instruction, and charged government ministries with responsibility for the planning, budgeting and periodic monitoring of  Information Literacy initiatives, in close consultation with the education, culture and other relevant agencies.Keywords: Library services, Information professionals , Information Literacy,  Knowledge Society.
Developing an Intelligent Personal Tutor to Support EFL Learning

Intelligent personal assistants are critical to facilitate EFL learning efficiency. This study developed an intelligent personal tutor called Blingbang on Microsoft's Bot Framework, aiming to address students' puzzles and evaluate speech and writing from a pedagogical perspective. By following the goal-oriented principle, it was embedded some functional modules and a specific knowledge base to understand user's utterance, detect intent within the EFL domain, decide dialogue policy, and ultimately respond feedback to users. The validity of this system was verified by an empirical study. Results show the favorable performance, especially in answering questions, made participants satisfied. The behaviors users adopted to resolve communication breakdowns include rephrasing questions, changing keywords, or eventually turning questions to the teacher.
Analysis of Online Learning Needs of Islamic Study Materials in The North Zone of Malaysia Polytechnics

This study was conducted with the purpose to identify the level of readiness and needs of the students towards the development of a mobile portal which applied problem-based learning for the Islamic Studies course in polytechnics. This study had implemented the method of survey and quantitative in nature. The number of 228 students who were enrolling the Islamic Studies course from five Malaysia polytechnics in the North Zone were selected randomly as sample. A set of questionnaires was used as research instrument. The data obtained were analysed descriptively using frequency, percentage and mean score in order to identify the level of readiness and needs of the students. The study found that students’ readiness level is very good, with 100% of the respondents owned mobile devices and 93.5% out of total respondents had experience in using the mobile devices for more than 3 years. The level of skill in using the mobile device also was good as all items had reached high level of mean score (3.82 – 4.14). Another criteria of readiness obtained from the data was students’ readiness towards problem-based learning strategy which all items had reached high level of mean score (3.94 – 4.20). Overall, the finding of this research showed that the level of students’ needs was high towards the development of this module as an effort to create an alternative pathway for the pedagogy of Islamic Studies.
Impact of Data Statistics on the Execution Plan of the Oracle Database

Abstract The data in the database is constantly changing. In order to make the statistical information truly reflect the current situation of the database, it is necessary to update the statistical information when needed. This paper uses column statistics as an example to analyze the impact of statistics on the execution plan of a single-table query.
A Study on the Duty Stress Factors of Teachers in Fisheries and Marine High Schools

The purpose of this study was to comprehensively identify the job stress of teachers specializing in fisheries and maritime affairs according to social demographic factors such as school, gender, age, homeroom, and position, and analyze the one-dimensional causes through correlation analysis with relevant variables to enhance the improvement measures to reduce the job stress of teachers specialized in fisheries and marine high schools in the future and provide the necessary basic data. The research subjects were surveyed by 150 teachers of fisheries and marine transport high schools and conducted the research with the results were conducted with the results. In addition, the specific empirical analysis methods used in the recovered data are as follows. First, the reliability test of the questions was conducted to measure the reliability of the questions. Second, frequency analysis was conducted to identify the general characteristics of the subjects. Third, the Correlation Analysis method was used to examine the relationship between the variables related to organizational fairness and organizational immersion. All the empirical analyses of this study are significant levels p.It was verified in 05, and statistical processing was analyzed using the SPSSWIN 24.0 program. The duty stress of teachers by school was found to be generally high in b, high school. And for duty stress by gender, female teachers were the highest for administrative work and professional growth, and teachers who worked for more than 21 years had the highest stress. Stress caused by homeroom teacher was high, and there was no difference in position.
Functional encryption based approaches for practical privacy-preserving machine learning

Machine learning (ML) is increasingly being used in a wide variety of application domains. However, deploying ML solutions poses a significant challenge because of increasing privacy concerns, and requirements imposed by privacy-related regulations. To tackle serious privacy concerns in ML-based applications, significant recent research efforts have focused on developing privacy-preserving ML (PPML) approaches by integrating into ML pipeline existing anonymization mechanisms or emerging privacy protection approaches such as differential privacy, secure computation, and other architectural frameworks. While promising, existing secure computation based approaches, however, have significant computational efficiency issues and hence, are not practical.In this dissertation, we address several challenges related to PPML and propose practical secure computation based approaches to solve them. We consider both two-tier cloud-based and three-tier hybrid cloud-edge based PPML architectures and address both emerging deep learning models and federated learning approaches. The proposed approaches enable us to outsource data or update a locally trained model in a privacy-preserving manner by employing computation over encrypted datasets or local models. Our proposed secure computation solutions are based on functional encryption (FE) techniques. Evaluation of the proposed approaches shows that they are efficient and more practical than existing approaches, and provide strong privacy guarantees. We also address issues related to the trustworthiness of various entities within the proposed PPML infrastructures. This includes a third-party authority (TPA) which plays a critical role in the proposed FE-based PPML solutions, and cloud service providers. To ensure that such entities can be trusted, we propose a transparency and accountability framework using blockchain. We show that the proposed transparency framework is effective and guarantees security properties. Experimental evaluation shows that the proposed framework is efficient.
Preparation of Household Water Filter

ABSTRACTThere is an urgent need to identify cheaper and efficient methods of removing contaminants as the demand for clean water rises.  In this study, the household water filters were prepared from low-cost materials based on kaolin powder and combustible materials as palm fronds powder which acts as pore creating the agent. The filter samples with different content (0, 10, 20, 30) wt% of palm fronds powder were fabricated using a dry pressing method and fired at 1250oC. Thereafter, the porous ceramic filter samples were carbonized at 950oC for 6 hrs. The ceramic filters were characterized by X-ray diffraction and SEM, physical properties (linear shrinkage (L.Sh.), apparent porosity (A.P.), water absorption (W. A.), apparent solid density (A.S.D.), permeability and diametrical compression strength) before and after carbonization. The total dissolved solids (TDS), turbidity (Tur.), pH, conductivity (Ec), free Cl, and salt percentage (sal %) of water from both household water filters with and without activated carbon was also examined before and after filtration. The presence of heavy metals from the water was analyzed before and after filtration.
A Survey and Taxonomy of Intent-Based Code Search

In this paper, the authors introduce the novel concept of intent-based code search that categorizes code search goals into a hierarchy. They will explore state-of-the-art techniques in source code search covering various tools, techniques, and algorithms related to source code search. They will survey the code search field through the core use cases of code search such as code reusability, code understanding, and code repair. They propose a user intent-based taxonomy based on the code search goals. The code search goal taxonomy is derived based on deep analysis of literature survey of code search, and the taxonomy is validated based on their exclusive developer survey conducted as part of this paper. The code search goal taxonomy is based on logical categorization of code search goals and shared characteristics (query type, expected response, and such) for each of the categories in the taxonomy. The paper also details the latest trends and surveys the code search tools and the implications on tool design.
Array Password Authentication Using Inhibitor Arc Generating Array Language and Colored Petri Net Generating Square Arrays of Side 2k

To secure the information provided, Web sites ask the users to create a password. We have seen in many instances such passwords are hacked and information gets leaked. Security of login-password system is the foremost requirement in the field of information technology. In this paper a new password authentication system is proposed. Authentication using Array Password procedure is proposed, Inhibitor Arc generating Array language and Colored Petri net generating square arrays of side 2k are discussed. These Petri net model generates Arrays, Authentication and implementation process of the these Arrays into Array Password methodology is described with illustrations.
Distributed Sparse SGD with Majority Voting.

Distributed learning, particularly variants of distributed stochastic gradient descent (DSGD), are widely employed to speed up training by leveraging computational resources of several workers. However, in practise, communication delay becomes a bottleneck due to the significant amount of information that needs to be exchanged between the workers and the parameter server. One of the most efficient strategies to mitigate the communication bottleneck is top-K sparsification. However, top-K sparsification requires additional communication load to represent the sparsity pattern, and the mismatch between the sparsity patterns of the workers prevents exploitation of efficient communication protocols. To address these issues, we introduce a novel majority voting based sparse communication strategy, in which the workers first seek a consensus on the structure of the sparse representation. This strategy provides a significant reduction in the communication load and allows using the same sparsity level in both communication directions. Through extensive simulations on the CIFAR-10 dataset, we show that it is possible to achieve up to x4000 compression without any loss in the test accuracy.
Comparative of Techniques: Activation by Sequence, Morph Target Animation and CG/HLSL Programming in Surgery Incision Simulation for Virtual Reality

In this decade, the way of simulating medical scenarios has evolved considerably, for some years now, this area and virtual reality came together giving life to a much more immersive form of simulation. The great challenge in medical simulation is to achieve a considerable level of realism and performance, as it is limited by the complexity of the scenario and other factors, the hardware factor being the main limiting. Giving the user the possibility to choose between greater or less realism requires that it be defined with which technique it would be achieved, therefore, this research compares three forms of surgical incision simulation for virtual reality: Activation by Sequence, Morph Target Animation and CG/HLSL Programming, evaluating factors such as: frames per-second (fps), CPU and GPU usage, which helped to obtain the level of realism of each technique; resulting in that CG/HLSL Programming uses fewer resources, with 27% CPU usage, 5% integrated GPU, 45% dedicated GPU, 60 fps and 44.3% realism, continuing, with an intermediate level use of resources Activation by Sequence with 11% CPU usage, 18% integrated GPU, 57% dedicated GPU, 60 fps, providing 46.5% realism, finally, the technique that used the most resources and obtained the highest level of realism was Morph Target Animation with 23% CPU usage, 22% integrated GPU, 77% dedicated GPU, 53 fps and 51.3% realism; these techniques can be used depending on the objective of the project where more or less realism is required, considering the use of hardware resources.
Performance Analysis and Optimization of Serialization Techniques for Deep Neural Networks

Deep Neural Networks (DNN) are being increasingly used to solve complex problems. While exciting research efforts are ongoing to improve the inference performance, other concerns such as the model loading time and memory usage of DNN models have taken a backseat. Use of an efficient data serialization library is necessary for tackling these concerns, especially for heavy DNNs like VGG, which requires around 550 MB just for storage. This paper analyses the effects of prominent DNN framework serialization libraries like FlatBuffers and Protocol Buffers on model loading time and memory consumption. Our comparative analysis demonstrates that using FlatBuffers in Caffe framework results up to 75% improvement in model loading time and up to 48% peak memory usage reduction (dependent on the size of model used) as compared to default Protocol Buffers. Further, we propose memory optimized and embedded device oriented layout for Protocol Buffers specifically for neural network use-cases. This layout significantly reduces the model loading time and memory consumption of Protocol Buffers and achieve similar results as produced by the state-of-the-art FlatBuffers. We achieve around 67% improvement in model loading time and 47.6% peak memory usage reduction with the proposed Protocol Buffer layout. We show that the superiority of Proposed Protocol Buffers in data mutation i.e. serialization plus deserialization performance for on-device training use-cases gives it an edge over existing FlatBuffers.
Novel efficient lattice-based IBE schemes with CPK for fog computing

The data security of fog computing is a key problem for the Internet of things. Identity-based encryption (IBE) from lattices is extremely suitable for fog computing. It is able to not only simplify certificate management, but also resist quantum attacks. In this paper, firstly, we construct a novel efficient lattice-based IBE scheme with Combined Public Key (CPK) technique by keeping from consumptive trapdoor generation algorithm and preimage sampling algorithm, which is required by the existing lattice-based IBE schemes based on learning with errors (LWE). In addition, its key storage cost is lower and it is IND-ID-CPA secure in the random oracle model. Furthermore, based on this, an enhanced lattice-based IBE scheme with IND-ID-CCA security is developed by employing strong one-time signature. Our schemes only need <i>O</i>(<i>n</i><sup>3</sup>/log <i>n</i>) additions of vectors, while the existing schemes need at least <i>O</i>(<i>n</i><sup>3</sup>) of additions and multiplications in Setup and Extract phase.
Automatic QoS-aware web services composition based on set-cover problem

By definition, web-services composition works on developing merely optimum coordination among a number of available web-services to provide a new composed web-service intended to satisfy some users requirements for which a single web service is not (good) enough. In this article, the formulation of the automatic web-services composition is proposed as several set-cover problems and an approximation algorithm has been exploited to solve them. In proposed method, the web-service composition has been carried out within two main phases, the top-down expansion of the composition tree, and the production of composed service by bottom-up traversal of composition tree. In the first phase, the production of a composition tree (similar to the production of tree in problemsolving by searching) is proposed by starting from the output or post-conditions of the requested service towards its input or pre-conditions. Each node or state of the tree is a set of inputs and/or outputs or conditions, and services as tree edges illustrate the transition from one node to another. In the second phase, finding the path from the leaves of the produced composition tree to the root is considered equal to reaching the output of requested service, and this path specifies the involved services and the composition plan. The requested service input set determines the available leaves of the composition tree. To achieve each non-leaf node of the tree, a set-cover problem is produced and solved using a greedy approximation algorithm. If the production and solving of the set-cover problems continues hierarchically until it reaches the root node, the composition plan and cost of the required composition service will be specified. The main focus of this research is the joint sequential and parallel composition with the aim of producing near-optimal and QoS-aware composed services.
Exploring the Aesthetic Potential of AMB’s MOC and AAM

The Microfluidic Oracle Chip (MOC) & Autopoiesis Answering Machine (AAM) by Agnes Meyer-Brandis (AMB) is the result of an artistic investigation of processes and phenomena invisible to the unaided ...
Concept of Virtualization Linked to Energy Storage and Green Computing: A Case Study

In broad sense, the concept of virtualization is termed as platform consisting of numerous of virtualized and interconnected systems. In such case, the system hardware in large scale with strong Internet connectivity is linked to virtual machine-based servers with adequate operating systems and deployment of relevant software. In many cases, the platform as a service is having a provision to execute the application in the environment with suitable programming-level platforms. Accordingly, the database in virtual platform is interlinked with the communication network initiating the nodal activities. In this case, each node is termed as single database with individual local database management system. The database management system in this case controls the creation, maintenance, and use of a database. Also it gives permission to conveniently develop databases for other numerous applications. Citing the concept of virtual databases, it may be noted that Windows Azure, Amazon EC2, etc., typically run in virtual platform. Manytimes, it has been observed that the organizations become data-centric and continuously produce large amounts of data along with other commercial information. In such case, the mechanism data stored in the database requires to be managed in proper way. In many cases, the diversities are observed linked to energy systems. So, during accumulation, it is essential to maintain the sequence of storage allocation in energy systems. The concept of green computing links to utilization of resources including computer peripherals in the eco-friendly environment and proper disposal of computing devices. Also implementation of green data centers gives positive support toward designing, coding, and yielding proper utilization of power with minimal consumption of energy. In this work, it is aimed to analyze the mechanisms as well as efficiency of cloud databases in virtual machines and examining feasible solutions. Also it is aimed to exploit the knowledge of database management systems along with mechanisms of virtual storage system to enhance the deployment of databases in the cloud.