Reviewerly/exHarmony huggingface dataset:
Review on Hybrid Recommender System for Mobile Devices

Movie recommendation as a task is complex as there is a large variety in the preference of users across the board, and there are numerous genres of movies. A hybrid approach is more conducive for movie recommendations on mobile devices as it combines the best of the most widely employed methods. This allows the results to be scalable when handling big data tasks as is very common in movie recommendations given the large amount of reviews. In a number of reviews, every user also voices their opinion and analyzing this sentiment is crucial in order to understand the emotional investment a person has in the movie. Therefore, after using a hybrid approach, this study uses sentiment analysis to filter the results further.
Hybrid deep neural network for Bangla automated image descriptor

Automated image to text generation is a computationally challenging computer vision task which requires sufficient comprehension of both syntactic and semantic meaning of an image to generate a meaningful description. Until recent times, it has been studied to a limited scope due to the lack of visual-descriptor dataset and functional models to capture intrinsic complexities involving features of an image. In this study, a novel dataset was constructed by generating Bangla textual descriptor from visual input, called Bangla Natural Language Image to Text (BNLIT), incorporating 100 classes with annotation. A deep neural network-based image captioning model was proposed to generate image description. The model employs Convolutional Neural Network (CNN) to classify the whole dataset, while Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) capture the sequential semantic representation of text-based sentences and generate pertinent description based on the modular complexities of an image. When tested on the new dataset, the model accomplishes significant enhancement of centrality execution for image semantic recovery assignment. For the experiment of that task, we implemented a hybrid image captioning model, which achieved a remarkable result for a new self-made dataset, and that task was new for the Bangladesh perspective. In brief, the model provided benchmark precision in the characteristic Bangla syntax reconstruction and comprehensive numerical analysis of the model execution results on the dataset.
Open Problem: Model Selection for Contextual Bandits

In statistical learning, algorithms for model selection allow the learner to adapt to the complexity of the best hypothesis class in a sequence. We ask whether similar guarantees are possible for contextual bandit learning.
Data Security System for A Bank Based on Two Different Asymmetric Algorithms Cryptography

Recently, a strong security system is very important for a safe banking system. To prevent hacking of important information of bank and client, a secure banking system is a must. This paper deals with a strong security system using a hash function and two different asymmetric algorithms (DSA and RSA) at a time, it will enhance data security. We are using RSA and DSA encryption algorithm to secure our system from unauthorized access. In RSA and DSA, we have to generate two keys called Public and Private Keys. If we use the signer’s private key for encryption, then we have to use the signer’s public key for decryption. The system will verify by confirmation and certificate and the sender will be sent an OTP via mobile phone of the receiver to confirm the authentication. This is the most efficient data security system to save the bank from hacktivism.
NOVICE TEACHERS PEDAGOGICAL CONTENT KNOWLEDGE (PCK) IN TEACHING ENGLISH FOR YOUNG LEARNERS

Novice English teachers in all levels of education were expected to perform well in delivering materials to the students in the classrooms, especially those who were Elementary School teachers. This was because the level of teaching skills would contribute to the success or failure of the teaching process that directly gave impacts to the learners. Therefore, there should be an evaluation to measure their ability, whether most of them were qualified or not. There were two objectives in this research, the first was to figure out the Pedagogical Knowledge (PK) of novice teachers in delivering English material for young learners and the second was to find out the Content Knowledge (CK) of novice teachers in delivering English material for young learners. The study was conducted at SD 140 Seluma, Bengkulu. The research method was a descriptive qualitative, and the technique of data collection used an observation checklist, interview checklist, and documentation. The result showed that the novice English teacher had a good performance in PK and CK with the total percentage of fulfillment to the criteria was a hundred percent for CK and 81 percent for PK, which were considered as good. The researcher concluded that the English novice teacher had acquired the fulfillment of CK and PK indicators in conducting learning and teaching activities.
Intelligent Knowledge Distribution: Constrained-Action POMDPs for Resource-Aware Multiagent Communication

This article addresses a fundamental question of multiagent knowledge distribution: what information should be sent to whom and when with the limited resources available to each agent? Communication requirements for multiagent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multiagent coordination on networked systems, for example, power and bandwidth, this article introduces two concepts for the partially observable Markov decision processes (POMDPs): 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">action-based constraints</i> that yield constrained-action POMDPs (CA-POMDPs) and 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">soft probabilistic constraint satisfaction</i> for the resulting infinite-horizon controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite-state controller (FSC) and optimized with policy iteration. The FSC representation then allows for a combination of the Markov chain Monte Carlo and discrete optimization to improve the probabilistic constraint satisfaction of the controller while minimizing the impact on the value function. Within the CA-POMDP framework, we then propose intelligent knowledge distribution (IKD) which yields per-agent policies for distributing knowledge between agents subject to interaction constraints. Finally, the CA-POMDP and IKD concepts are validated using an asset tracking problem where multiple unmanned aerial vehicles (UAVs) with heterogeneous sensors collaborate to localize a ground asset to assist in avoiding unseen obstacles in a disaster area. The IKD model was able to maintain asset tracking through multiagent communications while only violating soft power and bandwidth constraints 3% of the time, while greedy and naive approaches violated constraints more than 60% of the time.
Decentralized Stock Exchange Implementation using Ethereum

Traditional stock exchange systems are centralized and have high transaction fees, third party brokerage and less secure environment vulnerable to attacks. Blockchain decentralizes the exchange and creates a fair trading environment for every individual. This paper aims to implement a decentralized stock exchange based on blockchain. The proposed system aims to bring down the transaction costs and reduce the time taken to settle orders and eliminate the need of third parties for executing trades. Three different contracts one for buyer, seller and exchange are used to separate stock exchange functionality and optimize transaction costs. The system is implemented using Ethereum. The system is evaluated for scalability of number of transactions and scalability of number of nodes using sampled trading data from NASDAQ. Transaction fees for the miner at 16.5 Gwei is 99.98% lesser than the brokerage fees of traditional traders for the same transactions. Experimental results of the proposed method show that it is possible to eliminate third party brokerages, reduce transaction fees to almost 0 and create a peer-to-peer trading environment that does not require a central exchange.
Extraction of a Knowledge Graph from French Cultural Heritage Documents

Cultural heritage in Quebec is often represented as collections of French documents that contain a lot of valuable, yet unstructured, data. One of the current aims of the Quebec Ministry of Culture and Communications (MCCQ) is to learn a knowledge graph from unstructured documents to offer an integrated semantic portal on Quebec’s cultural heritage. In the context of this project, we describe a machine learning and open information extraction approach that leverages named entity extraction and open relation extraction in English to extract a knowledge graph from French documents. We also enhance the generic entities that can be recognized in texts with domain-related types. Our results show that our method leads to a substantial enrichment of the knowledge graph based on the initial corpus provided by the MCCQ.
A study on the effect of proportional logit model on satisfaction of uncontacted lectures

2020년은 코로나 바이러스(COVID -19)로 인한 전 세계의 산업, 기술, 교육, 환경 등 모든 분야에서 패러다임이 급변하였으며 특히 교육계도 향후 도래될 미래의 교육환경 변화에 초긴장을 하고 있는 상황이다. 특히 원격강의(비접촉강의, Uncontacted lecture)의 확산과 이에 따른 대책 마련 등이 병행되어야 하는 시급성을 안고 있는 상황이다. 이에 경기북부에 위치한 D대학은 실습과목이 집중되어 있는 공과대 학생들을 대상으로 원격강의에 대한 온라인 설문조사를 실시하고 원격강의에 대한 교육만족도와 원격강의에 영향을 주는 주요 요인들에 대해 분석하고자 한다. 만족도에 영향을 미칠 것으로 고려된 요인들은 학습자의 학습태도 영역, 교수의 열정 등 교육자관련영역, 교육교재 및 내용구성 등의 교육 컨텐츠 영역, 이러닝 교육시스템 환경, 이론수업 영역, 실습수업영역, 과제 및 질의 응답관련, 학습 성취도, 교육만족도 등 총 60여 문항으로 구성되었다. 여러 요인간의 시각적 관계 파악을 위해 다중대응분석의 차원축약 플롯을 제시하고, 전반적인 교육 만족도에 미치는 요인분석을 위해 비례오즈 로짓모형을 실시하고자 한다. 이를 통해 원격강의의 문제점, 원격강의의 니즈, 개선사항 등을 분석함으로써 향후 온라인 기반 원격강의의 확대 시 무엇을 준비하고 대책을 마련해야 하는지에 대한 방향점을 모색하고자 한다.In 2020, the paradigm is rapidly changing in all fields of industry, technology, education, and the environment caused by the corona virus (COVID -19). In particular, the education area is paying attention to changes in the future educational environment. There is an urgent need to spread remote(uncontacted) lectures and prepare countermeasures. Accordingly, D University intends to conduct an online survey on remote lectures for engineering students with practical subjects and to analyze the major factors affecting educational satisfaction for remote lectures. Factors considered to affect satisfaction include the teacher s learning attitude area, the educator-related area such as the passion of professors, the educational content area, e-learning education system environment, the theoretical instruction, and practicality, assignment, and the survey consists of a total of 60 questions. In order to understand the visual relationship between various factors, a multiple correspondence analysis plot is presented, and a proportional odds logit model is used to analyze the impact on overall educational satisfaction. Through this study, by analyzing the needs of remote lectures and improvements, I would like to find a direction for what to prepare for the expansion of remote lectures in the future.
TEACHER TRAINING AND EDUCATION PROGRAMS IN LATVIA: ARE E-COMPETENCES INCLUDED?

Purpose – the purpose of this research is to review the contents of Latvian teacher training and education programs in order to identify if the development of students’ e-competences is included into these programs as an important objective and value. The importance of the development of these competences has been stressed in various EU documents and scientific literature. The scientific importance of the paper lies in gathering evidence for the inclusion of e-competences into teacher training and education curricula and demonstrating that e-competences have not yet acquired a value status in teacher training and education programs. Research methodology – the methodology included the keyword in context and concordance analysis of self-assessment reports and program descriptions, which were run in the software AntConc. Findings – out of 190,000 word tokens, the KWIC analysis identified only 75 entries related to e-competences, most of which included basic skills of information and communication technologies. Other more advanced concepts, such as virtual reality, artificial intelligence, adaptive spaces, e-competences, e-education and e-learning, were hardly mentioned. Research limitations – one limitation of this research is the focus on Latvian teacher training and education programs without their comparison to similar European programs, which would allow for determining the competitiveness of such Latvian programs in Europe. Practical implications – the obtained results suggest that the development of e-competences has not been perceived as value in teacher training and education programs in Latvia, and in order to bridge this gap, programming and artificial intelligence courses should be introduced into the curricula of such programs. Originality/Value – the research has demonstrated that the EU aim of boosting the competitiveness of the European education through the development of e-competences is yet to be implemented into teacher training and education programs in Latvia.
Accurate Pixel-Wise Skin Segmentation Using Shallow Fully Convolutional Neural Network

Skin segmentation plays an important role in human activity recognition, video surveillance, hand gesture identification, face detection, human tracking and robotic surgery. The accurate segmentation of the skin is necessary to recognize the human activity. Segmentation of skin is easy to realize in ideal situations because of similar backgrounds. But it becomes complicated because of presence of skin-like pixels, background illuminations, and certain changes in environment. These problems are addressed by incorporating preprocessing stages in current studies, but this raises the total cost of the system. However, there are some limitations associated with these methods in terms of accuracy and processing speed. In this work, we propose a skin semantic segmentation network (SSS-Net) that is able to capture the multi-scale contextual information and refines the segmentation results especially along object boundaries. Moreover our network helps to reduce the cost of the preprocessing as well. We have performed experiments on the five open datasets of human activity recognition for the segmentation of skin. Experimental results show SSS-Net outperforms the state-of-the-art methods in skin segmentation in terms of accuracies.
Construction of Industrial Internet of Things Based on MQTT and OPC UA Protocols

At present, the Internet of Things has become a hot area of global concern and is considered to be another major scientific and technological innovation after the Internet. The Internet of Things can be analyzed from two aspects, one is to access the Internet, and the other is the connection between things. The Industrial Internet of Things, as a new type of industrial ecosystem, collects resources and data during the industrial production process by using miniature low-cost controllers and high-bandwidth wireless networks to achieve flexible configuration of manufacturing raw materials and improve resource utilization. The Industrial Internet of Things not only includes traditional software elements, but also requires hardware controllers and sensors, as well as cloud service platforms, to ultimately achieve intelligent manufacturing. The Industrial Internet of Things based on MQTT adopts the method of upper computer (WeChat applet) + server (Alibaba Cloud Server) + lower computer (WiFi module ESP8266 NodeMcu) to realize data collection and control of industrial production process. Based on OPC UA protocol, The Industrial Internet of Things, by designing an intelligent node device based on embedded Linux system, uses Qt to build the OPC UA protocol. OPC UA is a C / S architecture real-time database framework. By building an OPC UA Client on a handheld terminal device, An OPC UA Serve is set up on the smart terminal device, and the information collected by the handheld terminal is sent to the smart terminal. At the same time, multiple smart terminals are connected through the OPC UA protocol to perform data interaction and information transfer. At the same time, an edge computing algorithm is embedded in the smart terminal, and a large amount of data collected is processed for analysis, screening, and calculation. At the same time, the processed information is transmitted or processed. Reduce data interactions between data sources and data centers, and reduce the amount of information transmitted and stored.
A Genetic Algorithm Based Medical Image Watermarking for Improving Robustness and Fidelity in Wavelet Domain

In modern medical diagnosis, protecting the medical images from vulnerability attacks gains more importance. The proposed work focuses on providing better robustness, when the image has undergone various geometric attacks combined with balancing the trade-off between imperceptibility and robustness, by introducing genetic algorithm based watermarking method combined with Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). False positive errors due to singular values are balanced by calculating Key Component (KC). Authentication of watermarks for patient verification is done at the watermark extraction phase by decrypting the watermark through logistic map permutation method. The Peak Signal-to-Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC) values calculate multi-objective fitness values of the chromosome. The proposed method shows the better robustness and security to the medical images along with authentication to the watermark.
Tackling the DMN Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

This paper describes an extension to the Decision Model and Notation (DMN) standard, called cDMN. DMN is a user-friendly, table-based notation for decision logic. cDMN aims to enlarge the expressivity of DMN in order to solve more complex problems, while retaining DMN’s goal of being readable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive, both in readability and compactness. Moreover, cDMN is able to solve more challenges than any other approach.
AEC-Net: Attention and Edge Constraint Network for Medical Image Segmentation

Semantic segmentation is a fundamental and challenging problem in medical image analysis. At present, deep convolutional neural network plays a dominant role in medical image segmentation. The existing problems of this field are making less use of image information and learning few edge features, which may lead to the ambiguous boundary and inhomogeneous intensity distribution of the result. Since the characteristics of different stages are highly inconsistent, these two cannot be directly combined. In this paper, we proposed the Attention and Edge Constraint Network (AEC-Net) to optimize features by introducing attention mechanisms in the lower-level features, so that it can be better combined with higher-level features. Meanwhile, an edge branch is added to the network which can learn edge and texture features simultaneously. We evaluated this model on three datasets, including skin cancer segmentation, vessel segmentation, and lung segmentation. Results demonstrate that the proposed model has achieved state-of-the-art performance on all datasets.
A Model to Detect Phishing Websites using Support Vector Classifier and a Deep Neural Network Algorithm

Phishing refers is the process whereby an attacker pretends to be a legitimate one for the purpose of getting vital information such as personal information, credit card details and confidential passwords from user.Phishing are usually done through websites Urls, emails, text messages and phone calls.Once they successfully acquire user's vital information, they used it in gaining access to the user's account which can to financial theft and loss.This paper presents a model in detecting phishing websites using support vector classifier and a deep neural network algorithm.We used a urlset dataset which comprises of 48,009 legitimate website Urls and 48,009 phishing Urls making a total of 98,019 websites Urls.The dataset was pre-processed by removing all Nan and finite values therefore making it clean and fit for training.After processing, we used feature extraction in deducting the dataset dimension and some unwanted feature columns thereby reducing the dataset from 16 feature columns to 2 feature columns; with the domain feature column (this holds the domain name/website Urls) and the label feature column (this holds the binary values 0 and 1, where 0 represent a legitimate website Url and 1 represent a phishing website).We also used CountVectorizer in converting text documents (domain column) to a vector of term/token counts.CountVectorizer also enables the pre-processing of text data prior to generating the vector representation.After training, support vector classifier showed that the result of accuracy was 97.21% while our deep learning algorithm was 98.33% of the total 98,018 url dataset studied.Thereafter we saved and deployed both models to web using flask
Nonmonotonic Inferences with Qualitative Conditionals Based on Preferred Structures on Worlds

A conditional knowledge base $$\mathcal {R}$$ is a set of conditionals of the form “If A then usually B”. Using structural information derived from the conditionals in $$\mathcal {R}$$ , we introduce the preferred structure relation on worlds. The preferred structure relation is the core ingredient of a new inference relation called system W inference that inductively completes the knowledge given explicitly in $$\mathcal {R}$$ . We show that system W exhibits desirable inference properties like satisfying system P and avoiding, in contrast to, e.g., system Z, the drowning problem. It fully captures and strictly extends both system Z and skeptical c-inference. In contrast to skeptical c-inference, it does not require to solve a complex constraint satisfaction problem, but is as tractable as system Z.
ARM-Embedded Implementation of a Novel Color Image Encryption and Transmission System Based on Optical Chaos

Cloud servers are very suitable for sharing, and storing image data, and meanwhile it is confronted with many security, and privacy problems.Optical chaos, and hardware implementation of image encryption possess distinct superiorities in secret image sharing.Hence, combining the advantages of advanced reduced instruction set computer machine (ARM)-embedded platform Exynos4412 model, together with vertical-cavity surface emitting laser (VCSEL) subject to positive optoelectronic feedback (POEF), a novel color image encryption, and transmission system is experimentally realized, and appraised.We investigate the dynamic behaviors of the VCSEL, and obtain the chaotic output.An analog to digital conversion (ADC) module is utilized for optical chaos data acquisition.The improved gravity model, and the double sine map are exploited to encrypt the image, and the encrypted image is securely shared by cloud services.The user can download the encrypted image, and decrypt it by the hardware board.The experiment results reveal that the system has an excellent resistance to common attacks.Compared to the numerical simulation, our hardware implementation for such a color image encryption, and transmission system is formidable to actualize, but we successfully implemented, and tested in a real-world environment.By comparing experimental, and simulation results, the consistent results are found.Therefore, in the case of similar encryption effect, the user's information privacy protection in the actual industrial or commercial systems can be securely guaranteed on our hardware platform.