Reviewerly/exHarmony huggingface dataset:
Research on the Application of a High-dimensional Bayesian Network in the Evaluation of Ethnic-Chinese Bilingual Teaching

There is still an imbalance in the quality of basic education in ethnic minority areas, especially in the area of ethnic-Chinese bilingual education resources. Developing ethnic-Chinese bilingual teaching is a task that must be undertaken as a matter of priority. Carrying out feasible evaluation of bilingual teaching is one of the necessary conditions for standardizing and leading the development of bilingual teaching. In assessing the problems in the evaluation of bilingual teaching in the ethnic-Chinese language, there are many indicators, intricate relationships, and regional differences. This paper proposes a performance evaluation model of bilingual teaching based on high-dimensional Bayesian network to find out the causal relationship between the weak indicators of input and output in bilingual teaching. The experiments show that through basic data processing, causality measurement, reduction of clustering, and fusion of hill-climbing algorithms for causal relationship scoring to construct subnets and keyword networking, a high-level Bayesian network bilingual teaching performance evaluation model can accurately analyze the causal relationship between weak indicators to provide decision support for rational allocation of resources and improvement of teaching output.
Essential Features and Critical Issues With Educational Chatbots

Conversational agents, also known as chatbots, are automated systems for engaging in two-way dialogue with human users. These systems have existed in one form or another for at least 60 years but have recently demonstrated significant potential with advances in machine learning and artificial intelligence technologies. The use of conversational agents or chatbots for education can potentially reduce costs and supplement teacher instruction in transformative ways for formal learning. This chapter examines the design and status of chatbots and conversational agents for educational purposes. Common design functions and goals of educational chatbots are described, along with current practical applications of chatbots for educational purposes. Finally, this chapter considers issues about pedagogical commitments, ethics, and equity to suggest future work in the field.
Design and Development of Efficient Cost-Saving Algorithms for Guiding Customer Purchasing Patterns in Modern Consumerism Scenario Using Feed Forward Back Propagation Neural Networks

This research deals with the development of a new architecture. The system deals with self-scanning and self-checkout of the products. Here, we have made an effort to take the system which is a very handy through app. It uses neural networks, fuzzy logic, and genetic algorithms. The database is maintained in the local server, and both mobile and the local host are connected to the same network. As a result, the user can use all the features of this system and send and receive the data which is required by the user. This framework takes care of taxations such as GST. The concept involves designing and implementation of cost-saving algorithms by using feed forward back propagation neural networks for guiding customer purchasing, retrieval, and faster method of accessibility with respect to different patterns.
Anomaly detection for NILM task with Apache Flink

The topic of the 2020 DEBS Grand Challenge is to develop a solution for Non Intrusive Load Monitoring (NILM). Sensors continuously send voltage and current data into a stream processing application that would detect the pattern of power data based on the data characteristics. NILM is important in signal processing especially in those advancing areas such as 5G and IoT products, which generate massive amounts of data from the edge of the network. Our solution focuses on how to divide and parallelize jobs as small as possible while keeping some reasonable Service Level Agreement (SLA) including job sizes and latency so that it would be practical for edge or fog deployment. This paper describes our solution based on Apache Flink, a stream processing framework, and the DBSCAN density based clustering algorithm for anomaly detection through the context of data provided by DEBS Grand Challenge.
Position and Speed Estimation of PMSMs Using Gaussian Processes

In this paper, we present a novel low-cost technique to estimate both the position and the speed of a permanent magnet synchronous motor (PMSM) by sensing its stray magnetic field. At an optimal radial and axial distance, a low-cost magnetoresistive sensor is placed outside at the back of the PMSM. The magnetic field values are recorded for one complete rotor revolution at a resolution of less than a degree for different speeds of operation. Gaussian Processes (GPs) are employed to find a mapping function between the magnetic field values of the permanent magnet and the absolute angular positions. Then, by using the learned GP as a measurement function with an Extended Kalman Filter (EKF), both the angular position and speed of a PMSM can be estimated efficiently. Furthermore, we observe that the magnetic field depends not only on the position but also on the angular speed. To address this, we extend the GP to incorporate multivariate inputs. In order to take the periodicity of the data into account, we employ a periodic kernel for the GP. Additionally, a linear basis function model (LBFM) is introduced to incorporate more training points while maintaining the same computational cost. The GP and LBFM approaches are evaluated with data from a real PMSM experiment setup, and the accuracy of the position and speed state estimation is verified against a high-resolution optical encoder used as ground truth.
An Empirical Study of CERT Capacity in the North Sea

This paper documents the results of an empirical study of cyber incident response readiness in the Norwegian petroleum industry. The study addressed the Computer Emergency Response Team (CERT) capacity among various actors in the industry in handling critical cybersecurity incidents in industrial control and safety systems, with a focus on Operational Technology (OT) systems. The paper presents results from interviews with personnel in petroleum companies as well as interviews with national and international CERT actors. The informants in the petroleum industry are relatively satisfied with their own CERT capacity today, but it is acknowledged that one can always improve. Oil and gas companies and drilling companies share information and experience in various (virtual) meeting places and forums organized by external actors, but there is little focus, especially among the smaller companies, on systematic sharing of information and experiences of cyber incidents. There is a strong need for coordinating and harmonizing cybersecurity in IT and OT systems, as there are significant differences in terminology, maturity of technical solutions and culture today. CERT actors pointed out a need for better communication and contact between CERT actors and key persons within the companies, something that could be accomplished with the establishment of a petroleum sector Information Sharing and Analysis Centre (ISAC).
Multi-Variant eXecution: State-of-the-Art and Research Challenges

As Internet security issues are becoming increasingly serious, many scholars and experts are exploring new defense strategies. The availability of multi-core computers and cloud computing resources has brought back the original costly defense methods to people's vision. Among them, the Multi-Variant eXecution (MVX) defense has received widespread attention and is considered a security technology that may change the rules of cyberspace games. In this paper, we analyze and summarize the current MVX defense technology, focusing on the evolution of the architecture and the key technologies. Finally, we point out the challenges to MVX and discuss future research, which is of great significance for the next work.
Efficient Pore Network Extraction Method Based on the Distance Transform

Digital twins of materials allow to achieve accurate predictions that help creating novel and tailor-made materials with higher standards. In this paper, we are interested in the characterization of porous media. Our attention is drawn to develop a method to describe accurately the pore network microstructure of porous materials as presented in [7]. This work proposes an efficient algorithm based on the distance transform method [12] which is a widely used method in image processing. The followed approach suggests that a distance transform map, obtained from a microstructure image, passes through different steps. Starting from local maxima extraction and filtering operation, to end up with another distance transform with source propagation. We illustrate our algorithm with the well-known Pore Network Model of the literature [13], which supposes that the pore structure is either a network of connected cylinders or cylinders and spheres. Our approach is also applied on multi-scale Boolean random models modelling complex porous media microstructures [11]. The porous media morphological characteristics extracted could be used to simulate complex phenomena as the physisorption isotherms or other experimental techniques.
Improvement of Final Exponentiation for Pairings on BLS Curves with Embedding Degree 15

To be suitable in practice, pairings are typically carried out by two steps, which consist of the Miller loop and final exponentiation. To improve the final exponentiation step of a pairing on the BLS family of pairing-friendly elliptic curves with embedding degree 15, the authors provide a new representation of the exponent. The proposal can achieve a more reduction of the calculation cost of the final exponentiation than the previous method by Fouotsa et al.
Technology Stewardship Training for Agricultural Communities of Practice: Establishing a Participatory Action Research Program in Sri Lanka

This paper reports on a technology stewardship training program to promote ICT leadership development with agricultural extension practitioners in Sri Lanka. Technology stewardship is an approach adapted from the communities of practice literature that recognizes the importance, practically and ethically, of guiding change from within a community. The technology steward's role in development is not to impose ICT solutions on a community of practice but instead to empower members as part of a "change through choice" strategy, with the end goal of improving the informational capabilities of the community. Researchers assessed the training program using a multimethod approach with a single embedded case study. Data were collected using a pre-course survey, formal course evaluation, classroom observation, and semi-structured interviews with participants. Findings from this study show a positive response to technology stewardship training among agricultural extension practitioners in the course, that learning objectives of the course are achievable when offered as an in-service training program, that self-confidence with ICT is improved, and that some participants applied their learning in a post-course activity. This study contributes to a better understanding of the role of social learning to foster change in ICT practices among communities of practice in agricultural extension services, and in contributing to effective use of ICT for development more broadly.
Security threats identification and definition in early design phases of automotive IT services

As part of security and privacy threats modelling process, the systematic identification, definition and analysis of threats is important to accurately highlight the resources that would ultimately play a critical role in achieving the organization's business objectives. We show how we proceeded to identify and formalize the security threats based on the automotive emergency service use-case scenario. Furthermore, we propose a technique to scale for scenarios with many stakeholders. Even though the related information system was in its concept phase (i.e. only coarse risk assessment was possible), we focused on the rigorous dichotomy of the business assets and critical resources and the mapping in-between via the security goals set definition, a technique that facilitates the definition of the attacks' goals, as well as a more precise evaluation of the impacts on organization business assets.
Deep Contextual of Document Using Deep LSTM Meet Matrix Factorization to Handle Sparse Data: Proposed Model

Abstract Recommender system is important tool in big data era. It has responsible to make suggestion about product or service automatically for web application or mobile. In everyday utility, we cannot escape for information about food, travelling, social network, ticketing, news and etc. What the best choice for customer necessary is recommender system task to provide relevant information. Collaborative filtering is most useful recommender system technique in which considering user behaviour in the past to calculate recommendation. The first generation of collaborative filtering exploit statistical approach to calculate product recommendation. However, traditional collaborative filtering facing serious problem in scalability, accuracy and shortcoming in large data. Model based in the second generation of collaborative filtering to produce product recommendation where this model rely on matrix factorization to produce recommendation. Model based proven better performance over memory based. However, model-based performance degrades significantly when met with sparse data due the number of rating are very small. This problem popular called sparse data problem. Several methods proposed by researchers to handle sparse data problem. Mostly of them exploit text document to increase recommendation performance. However, majority of model fail to gain text document understanding. This study proceeds ongoing process with several stage. First, develop model to interpreted text document using LSTM aims to capture contextual understanding of document. Second, integrated LSTM with matrix factorization. This step aims to produce rating prediction considering text document of the product. The first step completely finished. According to experiment report, this model success to capture contextual of the document then transform into 2D space text document representation. For the further research, we are going to integrated with matrix factorization and evaluation result of rating prediction using RMSE metric evaluation.
Abnormality Detection Approach in Smart Homes using Case-based Reasoning

Today, the population of elderly people is dramatically increasing. To help with the problem, smart homes provide technologies and services that can help elderly people to live independently and comfortably in their own homes. One such service in smart homes is the detection of abnormal situations based on individuals' daily routine. This is important as some situations can lead to serious health issues if they have not been detected in the early stage. This paper presents a conceptual model for abnormality detection using case-based reasoning. It utilizes previous cases, which are built from a publicly available smart home dataset. To evaluate the performance, the cases are divided into two case-based sizes which contain seven and fourteen days of monitoring task. To avoid bias, the performance is also measured against two voluntary individuals who have no knowledge of the dataset. The results show that the system is able to detect abnormal situations with the best accuracy of 81.3%.
Real-time temporal audio watermarking for mobile communication

The world around us changes and digital is entering in all aspects of our lives. One aspect that is on the verge of changing is the `analog' signed paper contracts. A lot of contracts are made over telephone nowadays and the audio recording of the discussion about the agreement it can be seen as a `digital' contract. One problem arises, the authenticity of the recording. One side of the contract may rule later that the recording has been tampered and a `no' or a `yes' has been deleted or replaced. In this paper a watermarking algorithm is proposed for obtaining audio recordings which can be 100% proven as authentic or fake. This way no side of an over phone agreement can argue that the recording is altered. A simple check will prove 100% the authenticity of the recording. This way, cut and paste edits of more than 5 ms are easily detected.
Making Space for Uncertainty

Designing appropriate, safe and beneficial products and services for people with complex diseases presents a challenge for designers who have limited knowledge of the disease. This chapter explores some of these challenges and addresses how to support designers during the design process by exploring uncertainty. It is underpinned by research undertaken as part of a project to explore design processes and designs of hand-held playful objects for people living with advanced dementia. Specifically, this chapter reports on a workshop with designers that enabled them to draw on their experience and training in design. It provided creative opportunities to reflect on their personal values and challenges their preconceptions about designing for people with the disease.
The Research on Enhancing the Super-Resolving Effect of Noisy Images through Structural Information and Denoising Preprocessing

Both noise and structure matter in single image super-resolution (SISR). Recent researches have benefited from a generative adversarial network (GAN) that promotes the development of SISR by recovering photo-realistic images. However, noise and structural distortion are detrimental to SISR. In this paper, we focus on eliminating noise and geometric distortion during super-resolving noisy images. It includes a denoising preprocessing module and a structure-keeping branch. At the same time, the advantages of GAN are still used to generate satisfying details. Especially, on the basis of the original SISR, the gradient branch is developed, and the denoising preprocessing module is designed before the SR branch. Denoising preprocessing eliminates noise by learning the noise distribution and utilizing residual-skip. By restoring the high-resolution(HR) gradient maps and combining gradient loss with space loss to guide the parameter optimization, the gradient branch brings additional structural constraints. Experimental results show that we have obtained better Perceptual Index (PI) and Learned Perceptual Image Patch Similarity (LPIPS) performance on the noisy images, and Peak Signal to Noise Ratio(PSNR) and Structure Similarity (SSIM) are equivalent compared with the most reported SR method combined with DNCNN. Taking the Urban100 dataset with noise intensity in 25 as an example, four indexes of the proposed method are respectively 3.6976(PI), 0.1124(LPIPS), 24.652(PSNR) and 0.9481(SSIM). Combined with the performance under different noise intensity and different datasets reflected in box-and-whiskers plots, the values of PI and LPIPS are the best among all comparison methods, and PSNR and SSIM also achieve equivalent effects. Also, the visual results show that the proposed method of enhancing the super-resolving effect of noisy images through structural information and denoising preprocessing(SNS) is not affected by the noise while preserving the geometric structure in SR processing.
A Design Exploration Framework for Secure IoT-Systems

Cybersecurity is vital for embedded systems, especially for Internet of Things (IoT) systems. IoT systems have become essential in our daily lives, as they are usable for various application areas. They are usually small, connected with other systems, and perform a wide range of tasks. They are subject to multiple constraints in terms of performance, power consumption, chip area, etc. Attackers often target such devices as they are in constant interaction with each other or connected to the internet during private data processing. Cybersecurity, thus, plays a vital role in the design of IoT systems. Hence, designing secure IoT systems is a complex task, particularly for designers with limited security know-how. Security measures increase both computation time and power consumption, creating a conflict between these constraints, which must be solved by the designers. Balancing these constraints is a highly complex task. In this paper, we propose a new approach for considering security constraints in design space exploration, including possible security attacks on embedded systems. The method simplifies the consideration of security requirements at the start of the system design. We introduce a security attack based design space exploration framework, capable of finding the optimal design for an IoT system, based on its architectural, behavioral, and security attack description. The paper shows the feasibility and benefits of the framework, employing a secure sensor use case.
Learning Low-Rank Sparse Representations With Robust Relationship Inference for Image Memorability Prediction

Image memorability prediction aims to estimate the degree to which an image will be remembered by observers. Generally, the core problem in image memorability prediction is how to obtain effective representations to characterize the visual content of an image. In contrast to existing methods, which focus more on exploring the factors that make images memorable, in this paper, we first propose a general framework for learning joint low-rank and sparse principal feature representations, called the LSPFR framework, to obtain the lowest-rank intrinsic representation for image memorability prediction. By considering the joint optimization of the nuclear and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norms, the global low-rank structure and the local patterns embedded in data can be exploited to make the learned features more robust and informative. To improve our framework based on the exploitation of sample relationship structure information, we present an extended version of LSPFR, named E-LSPFR, in which the underlying relationship structure matrix is inferred through a negative log-likelihood term with a sparsity constraint. The results of experiments conducted on four publicly available datasets confirm the superior performance of our proposed approaches.
The influence of plenum geometry toward engine performance at BM student formula car

Engine performance is a major factor in an automotive competition, including Student Formula SAE (FSAE). One aspect that influence the improving engine performance is the intake system. One of the FSAE regulation atates that all vehicle must use 20 mm resistor o their intake system. With this regulation, the supply of air that enter the engine is very limited. To improve the engine performance, the use of plenum is the intake system can be one of it’s solution. The author analyzes the effect of plenum geometry towards the powertrain performance of the seventh generation of Bimasakti car (BM7). BM7 use KTM 450 SX-F, a single-cylinder, 450 cc engine as their prime mover. There are 4 design of plenum that became the independent variable on this research. One of those is the actual design of plenum that has been installed in BM7, the other 3 is a new design that shaped up base on the objective of the research. All those design will be simulated using Ricardo WAVE software and flow simulations using ANSYS software were also conducted to study the flow forms in plenum. The result of this research shows that the used of author’s plenum design can increase the maximum power by 7.26% and maximum torque by 20.96% compared to the BM7’s actual plenum. From fluid flow simulation, the author know that in some designs, there is amount of air that still move inside the plenum that rub the other air and causes pressure loss.