Reviewerly/exHarmony huggingface dataset:
SEED: A Cross-Layer Semantic Enhanced SLU Model With Role Context Differentiated Fusion

The mainstream SLU models, such as SDEN, take the joint training way of slot filling and intent detection because of their correlation and add contextual information to improve the model performance by the contextual vector. Although these models have proved effective, it also brings challenges for slot filling. The slot filling decoder is fed with the deep-layer semantic encoding without alignment information, which will affect the performance of slot filling. The alignment information of the history utterances is attenuated in the context vector because of the repeated fusion process, which is not conducive to the performance improvement of slot filling. In order to solve the above problems, we proposed a novel cross layer semantic enhanced SLU model with role context differentiated fusion, which contains two important improvements: 1) the word embedding information of the current utterance is introduced into the slot filling decoder to strengthen the alignment information based on the mutual attention mechanism; 2) the utterances of different roles are fused in different ways to reserve the alignment information of history utterances in the contextual vector. A large number of experiments were carried out on the standard dataset from SDEN, named KVRET*, and the results verify the effectiveness of our new model. Our model can increase the F1 score of slot filling by more than 7.5% than the existing models.
Energy Forecasting of Grid Connected Roof Mounted Solar PV Using PV*SOL

Solar energy is renewable energy source which can be easily converted into electrical energy. This paper presents the selection of proper PV system, battery and inverter for a particular application for any location based on its climatic condition before implementing in real time. So before implementing the experimental set up, the entire system is simulated using any PV*SOL simulation software and the obtained results used to decide and modify the design of planned system. In this paper, a Grid coupled PV system along with electrical battery, electrical vehicle and consumption load is analyzed. Four PV module is used and each PV module uses different tracking systems. Production forecast with consumption, usage of PV energy, coverage of consumption of electric vehicle, battery, grid and other electrical appliances are analyzed by using PV*Sol.
Finding most informative common ancestor in cross-ontological semantic similarity assessment: An intrinsic information content-based approach

Semantic Similarity (SS) has become a long-standing research domain in artificial intelligence and cognitive science for measuring the strength of the semantic relationship between entities (e.g., words, documents). Several ontology-based SS measures have been proposed in the recent time due to their ability of mimicking the cognitive process of humans. Among them, intrinsic information content (IC) based approaches have shown a significant correlation with human assessment. The design principle of the existing intrinsic IC-based SS measures constrain themselves to be applicable in a single ontology. However, such SS measures can be leveraged within two ontologies with the help of identifying the most informative common ancestors (MICA) across the ontologies. Existing IC-based MICA identification algorithms follow string matching of the labels of the concepts. In this paper, we propose a novel intrinsic IC-based MICA finding algorithm that exploits two domain-ontologies for finding SS without using string matching of the labels. The proposed approach has been evaluated using a widely used benchmark dataset of medical terms. The experimental results show that the proposed IC-based approach can be a stepping stone to a new direction in the process of finding MICA over two ontologies.
Basic Refinery Training Courseware in Enhancing Technical Employees’ Visualization Skills

Various research has been conducted to enhance the employees’ visualization skills in different areas and the importance and the role of visualization skills towards the employee’s achievement, especially in the industry such as manufacturing, medical, oil and gas field, and many more. To improve visualization skills, the use of effective training methods and tools should be applied to facilitate and enhance the understanding and performance in general, in a way to help them solve problems at the workplace. Thus, the aim of this paper focuses to the attention on the practical use of augmented reality (AR) with the ability of digital mobile application in the crude oil refinery process within the oil and gas industry environment, describing the potential and the effectiveness of the technology to enhance the visualization skill among the technical employees. This study used Basic refinery courseware that can be accessed via digital network to give an interactive training experience that can enhance technical employees' visualization skills. This courseware shows the positive effect and impact on technical employees’ visualization skills and performance based on the results. The results have also shown an increased motivation, making them more cooperative and developing visualization skills more quickly. Considering these results, it can be concluded that Basic Refinery training courseware based on augmented reality application is an effective and beneficial tool to be applied in training programs among the workplace's technical employees.
Pain Expression-based Visual Feedback Method for Care Training Assistant Robot with Musculoskeletal Symptoms

A human patient simulator (HPS) can achieve effective visual-, auditory-, text-, and alarm-based feedback methods in care or nursing education. Among these, the method of visual feedback is important to design an HPS that can express emotions or feelings of pain like an actual human does because this method allows an immediate reaction between robots and humans. This study aims to develop an avatar-based visual feedback method for a care training assistant robot that can express pain states in joint care education. First, this study introduces its own pain facial expression database from Ritsumeikan University (RU-PITENS) for an avatar with pain expression. The RU-PITENS database contains pain images of 41 Japanese people in their 20s, 30s, 40s, and 60s, and an experiment of pain stimulus is conducted based on transcutaneous electrical nerve stimulation, which is low-cost and easy to use in daily life. Based on the pain images in the RU-PITENS database, we generated an avatar with pain expression to achieve the goal of our study. Since the RUPITENS database does not contain the quantitative pain level, the Siamese network was used to calculate the pain intensity. In addition, the care training assistant robot (CaTARo) developed in our previous study reproduces symptoms of musculoskeletal diseases, and the pain of CaTARo was measured using fuzzy logic theory. As a result, a visual feedback system was constructed to express five types of pain (no pain at all, very faint, weak, moderate, and strong pain) with avatars according to the intensity of the pain output of CaTARo in care training environments.
Using CNN to Optimize Traffic Classification for Smart Homes in 5G Era

With the rapid development and progress of the Internet of Things and artificial intelligence, more and more businesses have combined housing with emerging technologies to create smart homes to improve residents' quality of life. Many services similar to the three major application scenarios of 5G will be applied to different smart devices in future smart homes. Therefore, the overall network traffic of smart homes will inevitably increase substantially, making network traffic management in smart homes an issue worthy of in-depth discussion. However, due to the widespread use of network encryption, it is not easy to obtain information from most network application services by decrypting the traffic. It is also difficult to classify various service flows through traditional network traffic classification methods into distinct application categories for management. This research assumes that Internet Service Providers (ISPs) have to manage tens of thousands of smart homes equipped with various kinds of IoT devices. We used software-defined networking (SDN) technology to simulate a multi-tenant smart home environment, simulate different types of smart home service traffic, and use convolutional neural networks (CNN) to classify network traffic. ISP operators can thus set the bandwidth ratio according to the classified service category to effectively improve QoS and user QoE. The experimental results show that the traffic classification accuracy of the CNN model for smart homes can reach 86.5%, which is higher than the general neural network model by 6.5%.
Toward a Vulnerability Mitigation Model

Abstract This chapter demonstrates how the elements of a cybersecurity incident can be analysed systematically, and suggests an alternative way to mitigate the causes and consequences of such incidents. Cybersecurity incidents can be explained in terms of a sequence of elements linking the attacking agents to their objectives: the attacking agent uses tools to exploit vulnerabilities, causing actions on a specific target to obtain unauthorized results, achieving their objectives. Cyber security can be improved by stopping the flow of the attack by mitigating one or more elements that make up the process. Unfortunately, most of these elements have characteristics that limit the opportunities for mitigation. The least difficult element to mitigate is vulnerability. The current model of vulnerability mitigation has behaved for the corporate environment, which can pay for specialized tools and consulting. This is an excellent business model but inaccessible to the public. A new model is necessary to prevent cybersecurity incidents on a broader, more inclusive level. The main proposal for vulnerability mitigation is multisector cooperation to create an independent, trustworthy, and secure vulnerability database, based on a new vulnerability report protocol developed in accordance with researchers, companies, governments, and society. However, this proposal creates some social, political, and technical challenges.
K-Cosine-Means Clustering Algorithm

K-means algorithm is a clustering algorithm that is one of the most widely used unsupervised techniques in data mining. This paper presents an extension of K-means algorithms named K-cosine-means algorithm. While the K-means algorithm initializes the centroids randomly and uses the Euclidean distance measure to assign data points to clusters, our proposed algorithm inherits a systematic approach from K-means++ to initialize the centroids and utilizes Cosine similarity to assign data points to clusters. We have performed experiments on both homogeneous datasets (Iris and Seeds datasets) and heterogeneous dataset (Hepatitis dataset). From experimental results, we have observed better clustering accuracy on homogeneous datasets compared to other variants of the K-means algorithm, namely, K-means, IK-means, K-means++, WK-means, MWK-means, iWK-means, and iMWK-means. However, for heterogeneous dataset, we have observed better clustering accuracy compared to standard K-means, K-means++, and iK-means algorithms.
Add Deputy Leader-an Improved Stake-based Consensus Protocol

With the large-scale application of the Internet of things in society, the problem of security and privacy has become more and more serious. Since the invention of bitcoin in 2008, the exquisite design of bitcoin has caused a certain degree of absolute security performance, which has inspired a large number of researchers to apply these ideas to other fields, such as the security protection of the Internet of things. Consensus mechanism needs a huge amount of computation, which cannot be directly applied to other fields, so the research and discussion of lightweight blockchain technology is meaningful. This paper proposes a cooperative consensus protocol of leader and deputy leader based on PoS and PBFT, the deputy node repackages the transaction when the leader node proves to be wrong and use a random value to determine who is responsible for updating the final block. The simple simulation experiment proves the effectiveness of our protocol.
Reverse-Engineering the Physical Configuration of Smart Homes

A major concern with cyber-security for Smart Homes are attacks against the privacy of their residents. Unfortunately, as has been shown in recent years, traditional cyber-security techniques, such a authentication, firewalls, and encrypted communication, are not sufficient to protect against privacy-invasion attacks. Researchers have shown that an attacker can detect the activities of the occupants by passively monitoring the encrypted wireless network traffic of the smart devices.In this paper, we focus on the question how the smart devices in a Smart Home are physically arranged. More specifically, we want to identify devices that are located in the same room or at least in close proximity. Similar to other works, we will only rely on information obtained through passive network measurements. We present techniques to determine whether a smart lamp and an IP camera are located in the same room. We then extend those techniques to cameras that have an automatic infrared mode switch. Finally, we show how the relative position and orientation of multiple cameras can be estimated.
Towards scalable Photonic Neural Networks with (3+1)D integrated optics

Integrated photonic architectures have the potential to revolutionize neural network computing. However, conventional 2D lithography strongly limits the size of integrated photonic neural networks due to fundamental scaling laws. We want to overcome this problem by integrating neural networks using 3D printed photonic waveguides. For that we demonstrate complex 3D multimode waveguide networks based on polymer waveguides surrounded by air. Furthermore, we recently developed a (3+1)D direct laser writing technique where we dynamically and locally control the writing power in order to realize single mode step or graded index waveguides.
Research and Event Control on Risk Factors of Auto Insurance Claim

With the formal implementation of the “Guiding Opinions on Implementing the Comprehensive Reform of Auto Insurance”, the average reduction in premiums paid by vehicles across the country is about 27%. Under the condition of stable compensation level, the decrease of earned premium leads to the increase of comprehensive compensation ratio, which leads to the further increase of comprehensive cost ratio of auto insurance business and the increasing difficulty of making profit of auto insurance business. Therefore, in order to reduce the overall cost rate, auto insurance companies need to strengthen the risk management and control of auto insurance claims expenditures, and reduce economic losses due to fraud cases or human causes. This article first uses the fusion of the AHP-DEMATEL method and the entropy method to study, analyze and evaluate the risk factors of auto insurance claims, and establish the linkage correspondence between risk factors and risk management; then based on the improved compound event processing model, The risk events that trigger risk factors are captured to achieve real-time management and control of risk events; finally, combined with the weight score of the risk factor, the risk score evaluation of the claim case and the risk control score of the auto insurance business under the action of the risk factor can be formed.
Demonstration of the Improvement of Golf Swing Practice Effectiveness Using Sonification Feedback

The purpose of this study was to improve the competitive performance of golfers. We measured body movements during the golf swing using two different methods: accelerometer and OpenPose coordinate estimation. Furthermore, discussions were held on the use of Sonification as a means of feeding back the measured data. At this time, we compared the sounds of beginners and advanced players to see if there was any difference. The results of the experiment showed that the advanced players were able to produce similar sounds consistently. The novice had a different sound each time, and the distance of the ball was shorter. The feedback using Sonification suggested that if we can track the sound so that it is the same as that of the advanced player, our performance may improve.
A Detailed Survey on Bit Plane Complexity Segmentation (BPCS) and RSA Algorithm for Secured Medical Data Transfer

For computer users, data security is a major concern. In the medical field the data must be present in a hidden form rather than original. Each of us has essential medical data that we want to keep safe from others. Bit Plane Complexity Segmentation (BPCS) is described in this article as a method for hiding information within images in various planes. The algorithm can be used with the RSA public key encryption scheme to enhance security in data transmission. It is important to determine a threshold (approximate) value so that the stego and original images are the same, and so that the hiding capacity is greater with less computing cost. This process imposes a number of restrictions. This paper presents two modules encryption and decryption while transferring medical text in a hidden mode. Error rate is calculating with PSNR, BER and MER with three bit planes Red, Blue and Green.
Neural Collaborative Filtering Recommendation Algorithm Based on Popularity Feature

In the recommendation system, compared with the explicit feedback, implicit feedback has the characteristics of large quantity, easier tracking and easier access. Even in many practical application scenarios of the recommendation system, there is only implicit feedback without explicit feedback Based on implicit feedback to recommend there has been a problem, that is usually implicit feedback provides only reflect the noise signal of user preferences, because although the observed interactions at least reflect the user’s interest in the item, but not observed the interaction may be missing data, and is not necessarily negative samples, so it’s natural lack of negative feedback In this work, this paper improves the neural colla-borative filtering algorithm (NCF) by extracting the popularity characteristics as the basis for extracting negative samples from implicit feedback data. To a certain extent, it solves the problem that negative sampling introduces a lot of noise in previous work. At the same time, we found that the gradient vanishing in the training process when using the traditional loss function Binary Cross Entropy (BCE). In order to solve this problem, a new loss function, BCE-Max, was proposed in this paper. Based on the above improvements, we proposed a neural collaborative filtering recommendation algorithm (PNCF) based on popularity characteristics. By comparing the performance of PNCF, NCF, traditional collaborative filtering and some matrix factorization recommendation algorithms in the evaluation indexes such as HR@10, NDCG@10 and avg_popularity@10, we find that PNCF has certain advantages over these recommendation algorithms. Meanwhile, We found that compared with NCF, the experimental results of PNCF improved by about 1.2% and 2% onHR@10 and NDCG@10 respectively, and even improved by about 2% on HR@1 and NDCG@I.
A Preliminary Study of the Behavioural Intention to Use Pervasive Tools in Malaysia Higher Education

Pervasive tools for instance mobile devices, wearable computers, RFID tags and sensors have been proven in the past studies in bringing advantages in education. Pervasive tools have been employed in the educational purposes by a number of researchers and proved that the utilization of pervasive tools is reported as pervasive, persistent, allow students to access to education in flexible way. Student are able to access to relevant educational materials and are enhanced by context-aware applications. However, previous studies of literature have placed little emphasis on students' acceptance of pervasive tools in Malaysia higher education. Understanding the factors played a huge part in determining the factors of technology adoption. The advancements in technology itself cannot make sure that pervasive tools can be widely accepted by students. As a result, it is essential to investigate the reasons for acceptance or rejection of technology. This goal of this study was to combine TAM and TTF and included external factors in examining the behavioral intention of students to use pervasive tools in Malaysia private university. The preliminary analysis of this study has adopted quantitative research methods and showed the reliability of the proposed constructs of TAM and TTF of the proposed model. Pilot test results implies students' positive affects where students tend to adopt pervasive tools in the learning process. Actual data collection will be implemented to further validate the findings from the pilot test.
Facial Expression Recognition Using Sparse Complex Matrix Factorization with Ridge Term Regularization

This work proposes a novel method of matrix factorization on the complex domain to obtain both extracted features and coefficient matrix with high recognition results in facial expression recognition. The real data matrix is transformed into a complex number based on the Euler representation of complex numbers. Sparse regularization in dimensionality reduction using ridge term (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -norm) is applied into this study. Basic complex matrix factorization (CMF) is modified into sparse complex matrix factorization using ridge term (SCMF-L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) which adding sparse L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -norm constraint in the coefficient. The gradient descent method is used to solve optimization problems. Experiments on facial expression recognition scenario reveal that the proposed methods provide better recognition results that prevalent NMF and CMF methods.
Unsupervised Fall Detection Approach Using Human Skeletons

Fall detection in the elderly persons is crucial to prevent serious medical hazards. Human intervention can overcome such accidents but require extreme vigilance on part of the medical staff. The proposed approach aims to detect human fall behaviour automatically through visual data and alerts the respective caretakers and authorities. Immediate necessary actions can then be taken to rectify the situation in case of injuries and rescue operation. We detect human fall in an unsupervised way by using normal visual data only. Based on human skeletons, a recurrent encoder decoder model learns the normal human behaviour. We analyse the results in light of varying thresholds and input data. We have evaluated the proposed approach on Le2i fall detection dataset. The results on Le2i fall detection dataset indicate an accuracy of 77.52% which is comparable with the state-of-the art approaches.
OBJECTIVITY OF TEACHERS' PEDAGOGICAL ACTIVITY IN THE LEARNING PROCESS

The aim of the research is to focus on the objectivity of teachers' pedagogical activity in the learning process at universities and to show the connection of this problem with philosophical teaching. The methodological basis of the study is the basic principles of philosophy on dialectical relationships, the interdependence of phenomena and processes of socio-pedagogical activities, the role of the individuality in the development of its material and spiritual foundations. The research uses a systematic approach to identify the scale, complexity, integrity and dynamics of the study of the objectivity of pedagogical activities of teachers in the learning process.