Reviewerly/exHarmony huggingface dataset:
IoT enabled Smart Banking System – a Technological Revolution

The main proposal of the Smart Banking System by using IoT is to develop a System that could be easy to use and accessible. IoT solutions make certain Banking &amp; Financial Services (BFS) companies for improved tracking and analysis of client’s behaviors and requirements. In the dominion of interconnected "things", banks are testing better approaches for associating with clients to give them exhortation, and might exhibit money related offers through their cell phone as they stroll past specific stores. They could use a similar way to deal with give direction on sending a notice to "skip Starbucks" as the client overspent on sundries the current month's savings. In the coming future, banks will have an extensive task to perform in managing and control of payments. IoT helps banks in many ways ,ie: facilitating consumers by communicating with right information about different offers especially in banking/finance, and solve different day to day issues of consumers and retain them for longer period. The customer data available through the IoT will identify the financial needs of the client and its value chain that also helps banks provide the value added services and customized financial products to ensure Win-Win situation .This banking system enabled with IoT improves customer loyalty by playing as a powerful facilitator .It transforms the business in the future. Banks must convert IoT data into valuable information that helps in increases their market share and provides better solutions to their customers. As the banking system has become part of a human day to day activity, it efficiently offers many benefits, such as operating a payment system, granting loans, taking deposits and helping with investments etc.
Super-sampling by learning-based super-resolution

In this paper, we present a novel problem of intelligent image processing, which is how to infer a finer image in terms of intensity levels for a given image. We explain the motivation for this effort and present a simple technique that makes it possible to apply the existing learning-based super-resolution methods to this new problem. As a result of the adoption of the intelligent methods, the proposed algorithm needs notably little human assistance. We also verify our algorithm experimentally in the paper.
Using encryption Algorithms to enhance the Data Security in Cloud Computing 

Cloud computing is the concept implemented to decipher the Daily Computing Problems. Cloud computing is basically virtual pool of resources and it provides these resources to users via internet. Cloud computing is the internet based development and used in computer technology. The prevalent problem associated with cloud computing is data privacy, security, anonymity and reliability etc. But the most important between them is security and how cloud provider assures it. In this research paper, the proposed work plan is to eliminate the concerns regarding data privacy using encryption algorithms to enhance the security in cloud as per different perspective of cloud customers.
On Some Methods for Correcting Accelerometer and Gyroscope Data Using Machine Learning Algorithms

This article is devoted to the development of an intelligent system for monitoring the characteristics of the movement of vehicles. In the frame of the article a classifier was developed that allows to correct accelerometer and gyroscope data.
One-Word Answer Correction using Deep Learning Models and OCR

Examinations/Assessments are a way to assess the understanding of a student on a particular subject. Even today many educational organizations prefer to conduct exams by offline mode (pen and paper). And evaluating them is a time-consuming process. There is no effectual model to evaluate Offline descriptive answers automatically. The traditional method involves staff assessing the content manually. In place of this process, a new approach using image captioning by using deep learning algorithms can be implemented. Handwritten Text Recognition (HTR) can be used to evaluate descriptive answers. One-word Answers captured as images are pre-processed to extract the text features using deep learning models and pytesseract. This paper presents a comparison between the CNN-RNN hybrid model and Optical Character Recognition (OCR) to predict a score for one-word answers.
An Improved Technique for Image Deblurring

The predicament in image deblurring, that is, the resurgence of an image against the noise along with the blur is an important issue in task dealing with image processing applications. .Owing to the deblurrng issue, many techniques related to regularization has found a concern in the recent years. In this paper, an efficient regularization method is put forward which utilizes the Lagrangian multiplier for the deblurring process. The major contribution of the proposed scheme is to determine the values of regularization approach towards attaining an better enhanced tradeoff among image resurgence and noise restraint using projection based image deblurring.
Power Grid Data Sharing Technology Based on Communication Data Fusion

Power grid communication is restricted by the communication system. The identity authentication of its nodes and the security of power data generated by the system have been greatly challenged. In this paper, a new raft consensus algorithm based on storage capacity is proposed. The storage proof and spatio-temporal proof of nodes are used as the consensus mechanism to elect all nodes through their comprehensive performance. Map Reduce is introduced to perform parallel operation on the segmented data, which improves the operation ability of the system. Map Reduce is applied to the improved bloom filter algorithm for duplication removal of the primary data. Because the bloom filter algorithm has a certain misjudgment rate, Merkle-DAG technology is used for a second round of data duplication removal. The distributed hash table DHT is used to generate hash index, and the consistent hash algorithm is used to realize the load balancing of storage under the data chain. The experiment shows that the system model proposed in this paper has good security and feasibility through security proof and scheme comparison.
Digital Image Falsification Detection System for Effective Data Communication

In this proposed system a digital imagefalsification can be identified using the combination of both adaptive over block based segmentation, feature keypointbased feature extraction algorithms(Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF)) and forgery region extraction algorithm. The proposed falsification detection algorithm comprises both block based falsification detection algorithm (adaptive over block based segmentation and block feature matching algorithm) and the keypoint based falsification detection algorithm(forgery region extraction algorithm). Adaptive over block based Segmentation algorithm adaptively segments the input digital image into separate(non overlapped) blocks in irregular manner. Scale Invariant Feature Transform (SIFT) algorithm and Speeded Up Robust Features (SURF) algorithms are used to draw out features from the segmentedblocks as a block features. Then the extracted features are matched with the feature points of other segmented block. If the feature key points are matched with any other feature point presents in the segmented blocks, then the matched feature points are marked as Labeled key Points (LKP), which can be doubted as a forged regions. Finally, the Forgery Region Extraction algorithm can be used to detect the forged region from the input digital image based on the extracted labeled feature points. The experimental outcomesdisplay that the novelfalsification detection system can accomplished the requirements compared with the existing digital imagefalsification detection methods.
Empirical and statistical comparison of intermediate steps of AES-128 and RSA in terms of time consumption

Cryptographic algorithms are composed of many complex mathematical functions. When analyzing the complexity of these algorithms, one fixes priory the overall complexity of the algorithm as the complexity of the most dominating operations for a group of operations. Generally, it is the count of this operation which determines the complexity of the algorithm in case of compounding operations. We have instead used the weight factor to determine the complexity of an algorithm with many operating functions working simultaneously and have taken time of the operation as a measure of the weight factor. We statistically analyze the two most used operations in RSA, namely “power” and “mod,” through a method of revised difference to compare whether these are statistically similar or dissimilar. We have also calculated the empirical computational complexity of these two operations through the fundamental theorem of finite differences to verify whether these operations are statistically dissimilar and if so then which of the two is dominant. We have also analyzed empirically the complexity of each of the four sub-steps involved in the encryption and decryption of AES-128, to determine which operation dominates the most and consumes most of the time in an overall run time of AES-128.
Graduation Mentoring Recommender - Hybrid Recommendation System for Customizing the Undergraduate Student’s Formative Path

The search for a personalized education for students has been the subject of study for many years. As a contribution to a personalized learning process, this paper proposes a recommendation system model to suggest complementary activities according to their professional and personal goals. As part of the construction of the model, two experiments were conducted to better understand the scenario of the recommendations and obtain information. The first experiment used Collaborative Filtering (CF) techniques, where the objective was to generate a list of recommendations to the student based on the activities already taken. The second expeliment, besides using CF techniques, was based on the content of the activities, composing a hybrid approach to recommendation. The main contributions of this work are to evaluate the benefits that recommendation systems can offer to the student’s formative path and to propose a model of recommendation system for the expansion of the formative path of the undergladuate student through complementary activities according to their professional preferences.
Indirect adaptive fuzzy control of non-linear systems using fuzzy supervisory term

This paper focuses on the indirect adaptive fuzzy control of Single Input Single Output (SISO) non-linear systems with unknown linearities. The proposed adaptive fuzzy controller is based on feedback linearisation. Its parameters are updated online according to some adaptive laws such as tracking error-based method, composite tracking and modelling error-based approach as well as filtered composite tracking and modelling error-based approach. However, the approximation error introduced into feedback loop increases the difficulty to guarantee the stability of the closed-loop control system. To solve this problem a supervisory term should be added to the control law. In this paper, a fuzzy supervisory control is proposed to overcome the problem of chattering phenomena introduced by the classical supervisory term. Theoretical and simulation results prove the effectiveness of the proposed approaches. Faster and improved tracking is obtained by using second and third aforementioned adaptive laws combined with fuzzy supervisory control.
Automatic regrouping of trajectories based on classification and regression tree

Decomposing complex tasks into simple sub-trajectories can greatly reduce the difficulty of modelling and generalisation. Using dynamic movement primitive (DMP) to generalise these sub-trajectories and combining the generalised sub-trajectories in a different order can generalise the original task to a new task, which greatly improves the generalisation ability of DMP. In previous work, we manually determined the recombination order of trajectories, but this method was inefficient and time-consuming. Here, we automate the procedure with the decision tree approach. First, we use some known decision results as prior information to generate decision trees. Then, we input the starting and ending coordinates of each sub-trajectory of the new task into the decision tree. The decision tree will make decisions based on the coordinate information and choose which kind of trajectory to generalise to realise the new task. Simulation results are used to verify the effectiveness of the proposed method.
Uncovering Communication Messages for Health Promotion: The Case of Arthritis

We introduce the science of Mind Genomics to the study of large sets of messages, specifically those found on the Internet and dealing with arthritis.Through experimental design, we create small sets of 25 test combinations, comprising 20 messages in different combinations of 2-5 elements.We use the experimental design to estimate the contribution of each message to interest.We then extend the estimated contributions to elements that were untested.This approach enables us to assess how 170 different messages about arthritis appeal to the general public, and reveals four new emergent mind-sets, responding to different types of messages.We then introduce a tool, the Person Viewpoint Identifier (PVI), allowing a medical professional to assign a new person to the appropriate mind-set, setting up a knowledge base for more effective messaging for health-promotion, and more personcompatible treatment.
LWE-based multi-authority attribute-based encryption scheme with hidden policies

Most attribute-based encryption schemes are based on bilinear maps, which have large computational and storage overhead and cannot resist quantum attacks. In view of the above problems, a LWE-based multi-authority attribute-based encryption scheme with hidden policies is proposed. Firstly, the scheme uses the lattice theory to replace the previous bilinear maps, and supports multi-authority to manage different attribute sets. Secondly, the Shamir secret sharing mechanism is introduced to construct the access tree structure, which improves the flexibility of the system. Also, the access policies can be completely hidden so as to protect the privacy of users. Finally, the scheme is proved to be secure against the chosen plaintext attack under the standard model. Compared with the existing related schemes, the size of public parameters, master secret key, user's private key and ciphertext are all optimised in some degree. Therefore it is more suitable for data encryption in cloud environment.
Big data automatic analysis system and its applications in rockburst experiment

In 2006, State Key Laboratory for GeoMechanics and Deep Underground Engineering, GDLab for short, successfully reproduced the rockburst procedure indoors. Since then, a series of valuable research results has been gained in the area of rockburst mechanism. At the same time, there are some dilemmas, such as data storage dilemma, data analysis dilemma and prediction accuracy dilemma. GDLab has accumulated more than 500 TB data of rockburst experiment. But so far, the amount of analysed data is less than 5%. The primary cause of these dilemmas is the large amount of experimental data in the procedure of study of rockburst. In this paper, a novel big data automatic analysis system for rockburst experiment is proposed. Various modules and algorithms are designed and realised. Theoretical analysis and experimental research show that big data automatic analysis system for rockburst experiment can improve the existing research mechanism of rockburst. It also can make many impossible things become possible. The work of this paper lays a theoretical foundation for rockburst mechanism research.
Geospatial and Artificial Neural Network Applications for Prioritization of Watershed Prediction

Characteristics of the river basin formed by natural factors and non-natural factors that makes up an ecosystem. One of the characters that create the river basin of the physical element is morphometry. Morphometry has three parameters such as linear, relief, and areal. The linear aspect consist of stream order (U), stream length (Lu), bifurcation ratio (Rb), stream length ratio (Rl), bifurcation ratio (Rb). Relief aspect consists of basin relief (Bh), relief ratio (Rh), ruggedness number (Rn). Areal elements comprise drainage density (Dd), stream frequency (Fs), texture ratio (T), form factor (Rf), circularity ratio (Rc), elongation ratio (Re), length of overland flow (Lg), constant channel maintenance (C). This study aims to analyze the characteristics of watershed morphometry and implement Geographical Information System (GIS) and Artificial Neural Network (ANN) to get watershed priority predictions. After analyze, the prioritization based on morphometry that is six sub-watershed with very high priority, two sub-watershed with high priority, four sub-watershed with medium priority and five sub-watershed have low priorities. From the test results by measuring method using a neural network based, it is known that neural network algorithms yield accuracy values 90.00%, and class precision 90.82 %. The model produced satisfactory results and showed a very good agreement between the predicted and observed data.
Quantum information processing with frequency-bin qubits: progress, status, and challenges

Frequency-bin encoding has emerged as a promising approach for fiber- and chip-compatible quantum information processing. We overview the basic theory and experiments, as well as offer perspectives on opportunities for continued advances.
Cluster-based optimal VM placement using crow search algorithm for cloud data centres

The extensive use of computational power from data centres causes huge energy consumption. A good virtual machine (VM) placement strategy would make better consolidation of VMs in a data centre (DC) that reduces energy consumption. However, it is hard to balance hosts in DCs due to the workload fluctuation by application and scaling of VMs. The optimal decision on VM placement and consolidation is an NP-hard problem and many researchers have proposed solutions to tackle this problem but they lack efficient exploitation of the mechanisms. Therefore, this paper proposes a hierarchical cluster-based approach with a meta-heuristic crow search algorithm (CSA) for the optimal selection of hosts to place the VMs and consolidate the maximum number of VMs on a minimum number of hosts. The work is simulated in CloudSim using real workload traces. Experimental results show that proposed work reduces energy consumption, SLA violations and VM migrations while ensuring better resource utilisation.
Learning Performance Assessment using Mobile-Based Augmented Reality Application for Preschool Environment

In the past ten years, technology and research in augmented reality (AR) have rapidly evolved in various fields, which led to a significant advancement in AR technology and application development. However, most of the recent development in AR are mainly focusing on how to overcome hardware and software issues. It is undeniable that overcoming technical problems are crucial in AR development and rapid development for technology advancement; however, it neglects the end user. This study presents learning performance assessment using mobile-based augmented reality application for Malaysian preschool in rural areas. This study used non probability sampling and a paired t-test to compare the data collected from the sample test. The students showed a significant interest in learning using augmented reality technology
Investigating the Impact of Visuohaptic Simulations for Conceptual Understanding in Electricity and Magnetism

Magana's research interests are centered on the integration of cyberinfrastructure, computation, and computational tools and methods to: (a) leverage the understanding of complex phenomena in science and engineering and (b) support scientific inquiry learning and innovation.Specific efforts focus on studying cyberinfrastructure affordances and identifying how to incorporate advances from the learning sciences into authoring curriculum, assessment, and learning materials to appropriately support learning processes.
Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

Stuttering or Stammering is a speech defect within which sounds, syllables, or words are rehashed or delayed, disrupting the traditional flow of speech. Stuttering can make it hard to speak with other individuals, which regularly have an effect on an individual's quality of life. Automatic Speech Recognition (ASR) system is a technology that converts audio speech signal into corresponding text. Presently ASR systems play a major role in controlling or providing inputs to the various applications. Such an ASR system and Machine Translation Application suffers a lot due to stuttering (speech dysfluency). Dysfluencies will affect the phrase consciousness accuracy of an ASR, with the aid of increasing word addition, substitution and dismissal rates. In this work we focused on detecting and removing the prolongation, silent pauses and repetition to generate proper text sequence for the given stuttered speech signal. The stuttered speech recognition consists of two stages namely classification using LSTM and testing in ASR. The major phases of classification system are Re-sampling, Segmentation, Pre-Emphasis, Epoch Extraction and Classification. The current work is carried out in UCLASS Stuttering dataset using MATLAB with 4% to 6% increase in accuracy when compare with ANN and SVM.
Fuzzy Tsukamoto for Determinen Linen Priority

With problems at the hospital Dr. Cipto Mangunkusumo, namely borrowing linen when nurses make loans to the logistics division, so that the logistics division has difficulty in determining which linen is used more frequently or lent by nurses, because the type of linen that is very much causes difficulties in the division in determining which linen is obliged to return it quickly to the logistics division. In order for linen to be available again in the logistics division, in addition to a very large loan every month, the logistics division had difficulty determining the average loss of linen in one year. That way the problem that occurs to solve this problem is to use a method namely Fuzzy Tsukamoto to determine the average amount of loss of linen and to determine priority linen that will be replaced earlier when the linen change schedule is in progress or the linen return is speeded up by returning the type of linen that is very often used for. After calculating using the Fuzzy Tsukamoto method, priority linen can be produced, including a Bolster, Blanket, Mattress Pad, and Bed Cover. Based on the calculation of Fuzzy Tsukamoto by doing several stages, namely Fuzzyfication, Fuzzy Rule, Fuzzy Inference System, and Defuzzyfication resulting in an average amount of linen loss in one year is 222.