Reviewerly/exHarmony huggingface dataset:
Multi-factor Authentication for an Administrator's Devices in an IoT Environment

In the information technology era, authentication systems have been developed that use multi-factor authentication to ensure the authorisation of users and administrators. There are many schemes based on factors such as smart cards, biometrics, and token devices. Although these schemes are generally strong, they suffer from several drawbacks such as malicious attacks, factors that may be lost/stolen, and a need for extra hardware/software. In this paper, we propose a strong authentication scheme for an IoT environment to authenticate the owners of devices. Our work supports a negotiation service using an anonymous QR image as a second factor to check the authority of an administrator. The proposed scheme has good security features such as mutual authentication, a secure index file, anonymity of the user’s identity and password, a secure session key, and perfect forward secrecy. Additionally, our work can resist well-known attacks such as the man in the middle, insider, and spoofing attacks, among others. In the real world, we apply our scheme using a mobile phone (Samsung Galaxy S5 model SM-900H) and server (Intel Xeon E3 – 1220LV2 3.5GHZ 4GB RAM). Based on its accuracy and performance standards, we obtain good results in the login and authentication phases. Moreover, the computational cost of our work is comparable to that of related works.
Deep Convolutional Sparse Coding Network for Salient Object Detection in VHR Remote Sensing Images

In order to reduce computational redundancy and increase the speed of image analysis, Saliency Object Detection (SOD) is one of the outstanding methods for Very High Resolution (VHR) remote sensing image analysis. However, Remote sensing images (RSIs) have the characteristics of diverse spatial resolutions and cluttered backgrounds, leading to the direct use of SOD methods for natural scenes generally not achieving satisfactory results. In this paper, combining the advantages of Convolutional Sparse Coding (CSC) and deep neural networks, a deep CSC network model is proposed for SOD of RSIs. First, a CSC Block (SCSB) is constructed by combining the CNN component and the Soft Shrinkage Threshold (SST) function to fully extract the effective information of the image. Then, build a multi-level coding network by stacking multiple CSCBs to enhance the perception of multi-scale and detailed information of salient targets. Finally, multi-level features are integrated in a simple way, and the entire network performs supervised learning in an end-to-end manner. The experimental results on the RSIs data set show that the proposed network model is superior to other methods in both quantitative and qualitative performance comparison.
Needs Assessment QR Code Combination with Google Form for Online Cognitive Assessment

Abstract This study aims 1) to develop online cognitive assessment instruments that meet instrument criteria such as validity, reliability, level of difficulty, different power, 2) develop online cognitive assessment instrument using a combination of qr code and Google forms. The research method refers to the Borg and gall R&amp;D research method. The research subjects were students who were taking theoretical and practice courses on table tennis physical education and recreation education program IKIP Budi Utomo Malang Class of 2017. The results of the initial research of the needs analysis phase given to students and lecturers were 1) cognitive assessment instruments used were still have not met the eligibility criteria, 2) online media has not been used to conduct cognitive assessments, 3) students and lecturers agree if an online cognitive assessment instrument is developed in accordance with the instrument eligibility criteria and the media used a combination of qr code with Google forms.
Model of projects-based internship system for future mathematics teachers

Abstract The article presents a model of a project-oriented system for bachelor’s internships in pedagogical education; one of their teaching profiles is mathematics. The model is built on the basis of a systematic, activity-based and competency-based approach. The project activities play the central role in the internships system. It provides substantively and organizationally the other types of students’ professional activities during the internship and contributes to their formation. The article shows how the requirements for the degree of independence of students’ project activities are consistently changing in the project-oriented system of internships due to the increase in the number of project skills formed during the internship period. The authors reveal the relationship between project skills and universal and general professional competencies of bachelors. The substantive succession of internships is presented that help student to improve in the chosen subject and complete the system of projects by completing the final qualifying thesis. The article considers the relationship of the internship system with the educational environment of the university and the territory. It contributes to the development of methodological support for a project-oriented internship system.
Autonomous Surface Vehicle Control Method Using Deep Reinforcement Learning

Autonomous Surface Vehicle (ASV) provides a new platform for marine exploration and environmental monitoring. It is very important for ASV to have learning ability in the unknown environment. Reinforcement learning is a branch of machine learning. ASV can obtain control behaviors and improve adaptability and autonomy through environmental exploration. This paper carries out dynamic modeling on the four-thruster ASV, especially designs a controller using the DDPG (deep deterministic policy gradient) algorithm. Simulation results show that the DDPG controller can control MIMO (multiple-input multiple-output) nonlinear systems. After training, the ASV can perform fixed-point control, sinusoidal trajectory tracking, and the "reconfigurable" experiments of multiple ASVs in the absence of water flow or water flow interference. The algorithm proposed in this paper has strong robustness and lays a foundation for the research of cooperative control of multiple ASVs.
A nearest-neighbor-based ensemble classifier and its large-sample optimality

A nonparametric approach is proposed to combine several individual classifiers in order to construct an asymptotically more accurate classification rule in the sense that its misclassification error rate is, asymptotically, at least as low as that of the best individual classifier. The proposed method uses a nearest neighbour type approach to estimate the conditional expectation of the class associated with a new observation (conditional on the vector of individual predictions). Both mechanics and the theoretical validity of the proposed approach are discussed. As an interesting byproduct of our results, it is shown that the proposed method can also be applied to any single classifier in which case the resulting new classifier will be at least as good as the original one. Several numerical examples, involving both real and simulated data, are also given. These numerical studies further confirm the superiority of the proposed classifier.
Security Provision in The Cloud Agent Architecture for Efficient User Authentication for Cloud Communication

Agent?based Inter Cloud monetary model for reinforcing customer- to-cloud course of action and cloud to cloud trade was first raised, this work offers a response for this new and testing issue by detailing 1) novel shows that demonstrate the guidelines of relationship between purchaser subject matter experts and Cloud administrators, and among Cloud administrators, and 2) novel frameworks that oversee client administrators and Cloud experts in making the best decisions. First the customer needs to move their records into cloud. On that time cloud expert community as of now buy a space from cloud, by then the CSP(Cloud Service Provider) gives the customer name and secret key to buyer. Every Entity is independently checked by the verification. After the effective confirmation of client, the Cloud Agent set up a Secure Communication Protocol between the Client and Provider. The Data is shipped off the cloud specialist for check and approval. When the Cloud Agent, Authorizes the information it is shipped off the cloud for capacity. The Cloud Agent is likewise allotted with proficient checking of dynamic assets. Each customer can introduce its organization sales to various fogs and each cloud can recognize requests from various clients, Many-to-various game plan model for trading cloud resources. It contains purchaser specialists and cloud provider administrators following up to support customers and fogs independently. In a cloud market, PAs deal with CAs to develop organization level Agreement for satisfying organization necessities from consumers.
ADMM-Based Sparse Distributed Learning for Stochastic Configuration Networks

Stochastic configuration networks (SCNs) are a new model of random neural networks (RNNs), which have demonstrated excellent capabilities in large-scale data analysis. For the processing of increasing data, distributed algorithms have become increasingly important. In this paper, the data sets are stored in multiple agents, and the centralized SCNs are reconstructed into equivalent sub-problems. The input weights and deviations are randomly generated under the constraints of supervised inequalities, ensuring the general approximation of the model, and proposed Based on the Alternating Direction Method of Multipliers (ADMM) distributed algorithm, L1 regularization is applied, sparse solutions are found, output weights are obtained, and the stochastic neural network model is improved. This method proves to be effective and greatly reduces the amount of information that needs to be exchanged between computing agents.
Data Pre-processing for Efficient Design of Machine Learning-Based Models to be Applied in the Steel Sector

Data pre-processing plays a significant role in data mining in ensuring good quality data especially when dealing with industrial datasets. Dimensionality reduction and outliers detection are significant tasks in machine learning and data mining applications. Many industrial real-world datasets are affected by several errors and present a huge number of process variables. This circumstance can cause problems when the data are exploited to develop efficient machine learning-based models. In this paper some automatic pre-processing procedures are proposed considering both detection of anomalous data and informative variables selection to be used before models training in order to improve their performance. The paper proposes three industrial case studies coming from the context of the steelmaking industry and all the obtained results prove the efficiency of a correct and appropriate pre-processing stage.
A Website Group Data Security Analysis Model Based on Word Embedding Expansion

To solve the problem of analysing website group data security, an analysis model based on word embedding expansion is proposed. Firstly, the word embedding method is used to expand the sensitive word dictionary. Then the crawled content is preprocessed and feeded into the filtering algorithm to filter the sensitive words. Finally, an analysis algorithm is designed to handle the final data and give the result. Experiments show the model proposed in this paper has a good security analysis performance.
Res2-Unet: An Enhanced Network for Generalized Nuclear Segmentation in Pathological Images

The morphology of nuclei in a pathological image plays an essential role in deriving high-quality diagnosis to pathologists. Recently, deep learning techniques have pushed forward this field significantly in the generalization ability, i.e., segmenting nuclei from different patients and organs by using the same CNN model. However, it remains challenging to design an effective network that segments nuclei accurately, due to their diverse color and morphological appearances, nuclei touching or overlapping, etc. In this paper, we propose a novel network named Res2-Unet to relief this problem. Res2-Unet inherits the contracting-expansive structure of U-Net. It is featured by employing advanced network modules such as the residual and squeeze-and-excitation (SE) to enhance the segmentation capability. The residual module is utilized in both contracting and expansive paths for comprehensive feature extraction and fusion, respectively. While the SE module enables selective feature propagation between the two paths. We evaluate Res2-Unet on two public nuclei segmentation benchmarks. The experiments show that by equipping the modules individually and jointly, performance gains are consistently observed compared to the baseline and several existing methods.
Development of Interdisciplinary Integrated Teaching Model on Sensor Technology and Applications for Industrial Education

The objective of this research is to develop and evaluate the efficiency of an interdisciplinary integrated teaching model on sensor technology and applications for industrial education. The research starts with the development of an interdisciplinary integrated teaching model based on STEAM education approach which consists of six steps including preparation, pre-learning, information, activity, discussion, and assessment. The developed instructional package for sensor technology and applications include a lesson plan, information sheets, worksheets, an achievement test, a simulation program, PowerPoint presentations, and an experimental set. The quality of teaching model was evaluated by five experts. The results revealed a high level of appropriateness. Then, the teaching model was applied to 21 bachelor students at the Faculty of Industrial Technology, Chitralada Technology Institute. The results illustrated that 1) The results show that the quality of the developed interdisciplinary integrated teaching model was more appropriate. 2) the developed instructional package was efficient with 78.33/79.76; 3) the score of the post-test was significantly higher than that of the pre-test when comparing the learning progress (.05); and 4) the students was highly satisfied.
Attribute-Based Encryption in Multi-owner Setting

Measure structure framework attempt to apply based directory listing allows search questions and supports encoded information at a decent pace in the fine-grained cloud location. In any event, earlier CP-ABKS plans were used to support in reality, unexpressed tri-owner frameworks cannot be legally related to the tri-owner setting), in the absence of acknowledging higher computing cost and point of confinement value. What is more, because of security weights on find a workable pace, existing plans are vulnerable against isolated keyword guessing ambushes if the keyword range is of larger space. Likewise, it will be hard to see malevolent clients that releases riddle keys when many information client has a relative subset of chrematistics. At this moment, present a security saving CP-ABKS framework with camouflaged access approach in shared multi-owner setting (essential ABKS-SM structure), and show how it is improved to help hurtful client following (adjusted ABKS-SM framework). We can show that by ABKS-SM structures accomplish explicit certainty, negate isolated keyword hypothesizing snare in the standard bilinear party model. We moreover assess their introduction utilizing authentic world datasets.
Exploring Document-Level Neural Machine Translation for English-Vietnamese

In Neural Machine Translation, the Transformer model has proven to be the state-of-the-art in multiple translation tasks. However, as a Seq2seq model, it can not abstract the contextual information when translating a document from one to another language. In the translation process, there are cases where, without the surrounding contextual information from consecutive sentences, an individual sentence causes ambiguity translations. The document-level approach makes the translation much more coherent and fluent by conserving the connectivity between sentences in the whole document to improve the quality of translation and human readability. Recent works show that models that are able to encapsulate these contextual information gain better results and evaluation than conventional sentence-level models. This paper conducts experiments and analyzes various context-aware models specifically in English-Vietnamese translation tasks.
Qualitative Analysis for Visual Attention of Students by Using the Technology of ICT

ICT tools and machine learning tools are used to analyze the visual attention of the student. The student's attention score is calculated for the analysis of the visual attention of the student. For this purpose, the authors have developed a software package (i.e., Visual Attention Tool [VAT]) based on the ICT that extracts the frames from a video stream that is taken through the webcam attached to the student's laptop. Each image is converted into a grayscale image, enhanced by image processing, then face detection is performed by following eye detection. This real-time processing of video produces a dataset by tracking the faces and eyes. It measures the attention level of the student with the timestamp. A manual observer also calculates the student's attention score focusing face and eye contact and produces a dataset manually. Then a comparative analysis of both datasets is performed in statistical and machine learning tools.
Multimedia Contents Retrieval based on 12-Mood Vector

The preferences of Web information purchasers are changing. Cost-effectiveness is becoming less regarded than costsatisfaction, which emphasizes the purchaser's psychological satisfaction. In applications of SNS(Social Network Services) based on folksonomy, a method to improve a user's cost-satisfaction in multimedia content retrieval is to use the mood inherent in multimedia items but applications of SNS encounter problems due to synonyms. In our previous study, some problems of synonyms could be solved by using internal tags consisted of arousal and valence (AV) in Thayer's Two-dimensional Model. However, in recall level 0.1, the retrieval performance of the previous study was less than a keyword-based method. In this paper, for improving the retrieval performance of recall level 0.1, a new method using 12 moods vector is proposed, and the proposed method shows good retrieval performance than the previous method and the keyword-based method.
Research and Design of Intelligent Solar Tracking Power Generation and Rainwater Collection System

At present, most of the small-scale solar power generation systems are fixed, which generally have low power generation efficiency and single system function. In order to solve this problem, this paper designs a set of solar power generation system with light tracing to achieve higher power generation efficiency. At the same time, it has a rainwater collection system, so that the system can collect rainwater resources in rainy days, which can be applied to urban and road lighting and greening and other occasions. Through theoretical analysis, the scale model is built and tested. The experimental results show that the power generation under sunny and cloudy test can be improved by 20.229% and 19.096% respectively. It show that the system can track the sun's path intelligently by sensors, increase the efficiency of power generation, and effectively solve the problem that the popular power generation system can only play a role in sunny days.
An Improved Boolean Load Matrix-Based Frequent Pattern Mining

Frequent Pattern Mining (FPM) has been playing an essential role in data mining research. In literature, many algorithms have proposed to discover interesting association patterns. However, frequent pattern mining in a large-scale dataset is a complex task because of a prohibitively large number of the smaller pattern need to be generated first to identify the extended pattern. The complexity in terms of computational time is a big concern. In this paper, we have proposed an updated novel FPM algorithm that uses a boolean matrix and then decomposed the matrix vertically. Here we have reduced the computational time by reducing a few steps and added itemset if and only if satisfy the condition instead of pruning after added into the candidate itemset. Our proposed algorithm has reduced the computational time at least two times on the generation of the boolean matrix (i.e. Q), frequent-1 matrix (i.e., F1) and frequent-1 itemset (i.e., L1), which has improved the efficiency of the algorithm.
Comparing Machine Translation Accuracy of Attention Models

Machine translation models using encoder and decoder architecture do not give accuracy as high as expectation. One reason for this ineffectiveness is due to lack of attention mechanism during training phase. Attention-based models overcome drawbacks of previous ones and obtain noteworthy improvement in terms of accuracy. In the paper, we experiment three attention models and evaluate their BLEU scores on small data sets. Bahdanau model achieves high accuracy, Transformer model obtains good accuracy while Luong model only gets acceptable accuracy.
Metaphysical principles of causality and normativity in philosophy of education

The article is devoted to the metaphysical foundations of the philosophy of education, which are considered as sources of modeling the image of the future person, as well as the commission of certain actions. It investigates the evolution of these principles during the human civilization development. The principle of causality, considered as a fundamental ontological characteristic of being, suggests that a person can realize his desire for freedom only by subordinating his life to a universal objective law. Every phenomenon is seen in a causal perspective as a consequence of some cause and at the same time as the cause of some other effect.
Mapping DCNN to a Three Layer Modular Architecture: A Systematic Way for Obtaining Wider and More Effective Network

We propose a modular Deep Convolutional Neural Network (DCNN) architecture which has the property of block-like design and re-usage of parameters by certain blocks. We leverage networks from the ResNet family as the backbone for our proposed architecture. The proposed network architecture is composed of three primary blocks, i) common block ii) hidden blocks and iii) output block. The common block mainly focuses on learning low-level features which are afterward fed to ρ hidden blocks at a time, where ρ is the branching factor. The hidden blocks are placed in a row-wise manner and replicated ρ times depending on the computational availability. The output block takes features as an input from the common block and all the hidden blocks during both training and testing phases. In addition, each of the hidden blocks together with the common block is a stand-alone neural network, which implies that we have ρ neural networks coupled in a single framework. During the training phase, in every single epoch, each of the stand-alone neural networks (i.e. common block + hidden block) is trained in Round Robin fashion followed by fine-tuning the whole network graph. Inference can be performed using either the whole network graph or just any sub-graph of it. To substantiate the effectiveness of our proposed neural architecture we performed experiment on CIFAR-10 and CIFAR-100 datasets.
Privacy Preserving Statistical Detection of Adversarial Instances

Adversarial instances are malicious input designed by attackers to cause a classification model to make a false prediction, e.g. in Spam detection. Effective solutions have been proposed to detect and block adversarial instances in real time. Still, the proposed approaches fail to detect adversarial instances over private input (required by many on-line platforms analyzing sensitive personal data). In this work, we propose a novel framework that applies a statistical test to detect adversarial instances when data under analysis are in private format. The practical feasibility of our approach in terms of computation cost is shown through an experimental evaluation.