Reviewerly/exHarmony huggingface dataset:
Design of a simulator of communication buses in a car for the needs of external teaching

The new coronavirus (SARS-CoV-2) pandemic has required a dramatic shift in the way our students are educated. Distance or online learning has become the norm and we have had to make a lot of effort to ensure that further education takes place during this difficult period. Distance or online education is not a new concept, but for many of us it is a shift away from our traditional model of classroom teaching for students [1]. In this article, we present a teaching model that we designed for students of the Autotronics study program.
Android-based fashion terms digital dictionary application development

There is no digital dictionary application in the fashion sector. There are books for fashion terms that cannot be carried since they are printed. An Android-based digital dictionary application can be carried easily. Hence, this research developed a digital dictionary for fashion terms based on Android. This research was conducted using the ADDIE (Analysis, Development, Design, Implementation, and Evaluation) development model. The validity test from material experts, media experts, and trials results were declared feasible. Thus, the Android-based digital dictionary application for fashion terms was feasible and can be used for teaching purposes in the fashion area.
Identification and Analysis of Tool Wear State Based on Deep Learning

Tool wear monitoring plays an important role in the safe production of machine tools and the improvement of production efficiency. Traditional tool wear detection mostly requires manual recognition. Aiming at the problem of low manual recognition efficiency, a neural network model based on the combination of 1-Dimensional Convolutional Neural Network (1D CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) is proposed. Normalization and data cleaning methods are used to process the data. The discrete vibration signal points are transformed into a continuous time series of 48 vibration signals. At the same time, the Dropout layer is introduced to reduce the phenomenon of data overfitting and realize the rapid identification and classification of tool wear. Experiments show that the recognition accuracy of the model in this paper reaches 99.53%. Compared with a single network model, it has the advantages of higher recognition rate, faster speed and better generalization performance.
A Pigeon Group Decomposition Based Algorithm for Logistics Service Transaction Matching Under Blockchain

The transmission security problem in the industrial Internet of Things is the focus of industrial manufacturing development. Blockchain technology can well solve this problem. The massive data generated in the Industrial Internet of Things will be transmitted to the blockchain system through the gateway, the throughput and deployment costs need to be considered. This paper proposes a pigeon flock algorithm based on decomposition to solve this problem. It uses the excellent navigation ability of carrier pigeons to improve search efficiency. At the same time, two independent archives are used to balance convergence and diversity. After the solution set in the convergence archives is classified, the solution set is evaluated by the Tchebycheff function, and some better solutions are retained. The diversity archive chooses some solutions that are quite different from other solutions to maintain diversity. This article uses this algorithm to obtain the solutions with low throughput and high deployment when the blockchain is implemented in the Industrial Internet of Things. Finally, 7 questions in the DTLZ test question are selected to prove the validity of the proposed algorithm. These questions have 5, 10, and 15 goals. In the experimental results, it can be seen that the proposed algorithm is better than NSGAIII, MOEADD and NSGAII on most of the test problems.
Experimental verification of trade-off relation for coherence and disturbance

Abstract When a quantum system is sent through a noisy channel, it is usually disturbed. At the same time, the system undergoes decoherence and tends to lose some delicate quantum features. For a particular basis, the coherence of the state changes. Otherwise, if the system is not disturbed, its state might retain all of coherence. As quantum noisy channels lead to both disturbance and decoherence, it is natural to ask about the relation between disturbance and decoherence. Recently, a trade-off relation for coherence and disturbance has been presented by Sharma and Pati (2018 Phys. Rev. A 97 062308). In this paper, with entangled photon pairs and linear optics, we experimentally verify this trade-off relation for a single-qubit system undergoing various noisy channels. Our experimental results agree with the theoretical predictions and provide a quantitative understanding of the relation between quantum channels and resources.
Accurate Detection of Dementia from Speech Transcripts Using RoBERTa Model

Dementia is a serious disease that is very common in the elderly population. Automatic detection of dementia is a difficult task that may involve the analysis of acoustic features of speech, linguistic features of transcripts, and mental state exams. In this work, we explore the limits of using speech transcripts from doctor-patient conversations to detect dementia. The dataset is prepared from Pitt corpus, which is a part of DementiaBank, a shared database of multimedia interactions for studying communication in dementia. We use a sophisticated natural language processing approach, namely RoBERTa, which addresses the problem by using transformers and self-attention mechanism. We compare RoBERTa with a baseline BERT model. We show that dementia detection using well-prepared speech transcripts alone can lead to detection rates above 90% for RoBERTa model in a near-balanced dataset, outperforming the baseline model.
P‐121: CNN Based Edge Preserve Segmentation for FIB, TEM Image Analysis

As consumers are increasingly exposed to numerous products equipped with displays in everyday life, they are expecting more various types of displays. In order to meet this demands, it is required to improve rapid product development and secure stable production capacity, which causes analytic work on product to be intensely increasing. However, measurement of the product for Analysis Engineering is to be done by human power, which is costly and time consuming. With this kind of measuring process, measurement values may vary depending on the individual or by human error, which makes consistency of measurement difficult. So as to eliminate these simple repetitive tasks and to improve consistency of measurement, an automatic measuring system for structure analysis is required. In this paper, we propose the automatic measuring system and the model for the system.
Novel Image Caption System Using Deep Convolutional Neural Networks (VGG16)

With advances in artificial intelligence field and computer vision, image captioning (IC) tool has progressively attracted researchers' attention. IC automatically generates natural text descriptions according to the image content. IC combines the knowledge of computer vision and natural language processing. In this article, a Novel Image Captioning system was developed. The final system was validated on Flicker8K dataset. The novel designed system consists of Long Short Time Memory (LSTM) and VGG16 with Convolution Neural Network (CNN). The main improvements of this system are in structure of designed system by adapting batch size. Also, studying deep learning parameters such as Regularization terms that can be added to loss function, CNN Optimizers and Dropout layer. The results showed the effectiveness of the designed system. Finally, this article highlighted some open challenges in the describing images task.
65‐3: <i>Invited Paper:</i> Deep Learning‐Based Image Enhancement for HDR Imaging

High dynamic range (HDR) techniques have received significant attention in generating realistic, high‐quality images and videos and improving visual quality in new display systems. We have witnessed remarkable advances in HDR reconstruction using deep learning technologies in recent years. This review examines recent developments in HDR reconstruction using a deep learning approach, which takes a single low dynamic range (LDR) image as an input and aims to restore an HDR image featuring higher color gamut and a higher detail retention than the LDR image. We aim to provide a comprehensive survey in this field. Since there are numerous HDR algorithms, it is necessary to evaluate and organize theirperformance, therefore, we evaluate them using two objective evaluation metrics.
Simulation of English Word Order Sorting Based on Semionline Model and Artificial Intelligence

To improve the word order ranking effect of English language retrieval, based on machine learning algorithms, this paper combines a semionline model to construct an artificial intelligence ranking model for English word order based on a semionline model and establishes a semisupervised ELM regression model. Moreover, this paper derives the mathematical model of semisupervised ELM in detail and uses FCM clustering to screen credible samples, ELM collaborative training to mark each other's samples, and the marked samples to calculate the output weights of semisupervised ELM regression. In addition, based on continuous learning of OSELMR, this paper uses confidence evaluation to screen out credible unlabeled samples, OSELM collaborative training to mark the credible samples with each other, and credible unlabeled samples to calculate the output weight of SSOSELMR. Finally, this paper designs a control experiment to analyze the model algorithm, compares and counts the parameters, and draws a statistical graph. The research results show that the model constructed in this paper is effective.
Ensure Appropriate Asset Retention

An asset security domain, throughout its life cycle, focuses on handling, collecting, and protecting information. This domain, as a primary step, classifies information based on its value to the organization. Depending on the classification, all follow-on actions vary. For example, unclassified data uses fewer security controls, whereas highly classified data requires stringent security controls.
The Ethics of Going Deep: Challenges in Machine Learning for Sensitive Security Domains

Sometimes, machine learning models can determine the trajectory of human life, and a series of cascading ethical failures could be irreversible. Ethical concerns are nevertheless set to increase, in particular when the injection of algorithmic forms of decision-making occurs in highly sensitive security contexts. In cybercrime, there have been cases of algorithms that have not identified racist and hateful speeches, as well as missing the identification of Image Based Sexual Abuse cases. Hence, this paper intends to add a voice of caution on the vulnerabilities pervading the different stages of a machine learning development pipeline and the ethical challenges that these potentially nurture and perpetuate. To highlight both the issues and potential fixes in an adversarial environment, we use Child Sexual Exploitation and its implications on the Internet as a case study, being 2021 its worst year according to the Internet Watch Foundation.
A Study of Bidirectional Encoder Representations from Transformers for Sequential Recommendations

Sequential recommender systems seek to capture information about user affinities and behaviors considering their sequential series of interactions. While former models based on Markov Chains and Recurrent Neural Networks were used to model future interactions with the last interaction, sequential recommendation aspires to express more than that. In this paper, we detail BERT4Rec, a sequential recommendation approach, based on bidirectional encoder of self-attention based Transformer mechanisms. Such model, inspired by recent advances in NLP, learns to predict a masked item in a user’s sequence using the Cloze objective. The model unmasks the randomly masked items by capturing attention states from both left and right sides and short-term and long-term contexts. Such a sequential model has been applied in what follows to new datasets from Amazon reviews of different characteristics. Thus, our main contribution consists of testing this approach and its limits on several recommendation datasets.
Study on the influence of haze on radiation

In recent years, haze weather has a certain impact on the power generation efficiency of photovoltaic power plants in China. In this paper, the influence of relative humidity and mass concentration of haze on irradiation is studied by mathematical modeling. The simulation results show that: (relative humidity) increases from 5% to 95%, and irradiation decreases by 53.9%. At 30%, 50%, 60%, 75%, 80%, 90%, the haze mass concentration increased from 0ug/m3 to 500ug/m3, and the irradiation decreased by 17.1%, 28.8%, 39.9%, 73.9%, 80.5%, 78.9%, respectively. Haze mass concentration in 100ug/m3,200ug/m3,300ug/m3,400ug/m3,500ug/m3 increased from30% to 90% irradiation decreased by 59.5%, 67.1%, 75.1%, 79.8%, 83.4%; and the corresponding empirical formula is obtained. This conclusion can provide a theoretical basis for photovoltaic power station to predict power generation under haze weather.
Model Based Anomaly Detection in High Dimensional DATA

Anomaly detection is a growing research issue in several application domains. This paper attempts to provide a structured overview of anomaly detection research. A state-of-the-art of anomaly detection techniques is then presented. A classification of methods based both on the type of datasets (Big DATA, data flow, graphs, time series, etc.), application domains (fraud detection, intrusion detection, medical anomaly detection, etc.) and the approach considered (Deep Learning, statistical, classification, clustering based, etc.) is proposed. We propose a multi-vision approach of case base representation for anomaly detection in a high- dimensional data using Case-Based Reasoning.
Robustness of vehicle identification via trajectory dynamics to noisy measurements and malicious attacks

The possibility of using infrastructure-based sensors to identify individual vehicles, and then actuate transportation control infrastructure in response to their individual dynamics will enable to next generation of traffic infrastructure control. However, any such system to identify individual vehicles in the flow will be prone to faulty data or worse, cyberattacks where a malicious actor intentionally injects faulty data. With this context in mind, we investigate the resilience of a recently proposed deep learning based approach to identify individual vehicles in the traffic flow. We conduct numerical experiments where increasing amounts of noise is injected into time series trajectory data and conclude that while the proposed classification method is accurate at identifying individual vehicles when there is no noise, classification accuracy deteriorates quickly when noise is injected.
Towards Comparison of Table Type Taxonomies

Tables are ubiquitously used to share relational data in various media and formats. Particularly, an enormous number of HTML tables are contained in web pages. They are a valuable data source for applications of web mining, question-answering, and knowledge base construction. However, not all HTML tables are genuine (i.e. containing relational data). Most of them are utilised as means of layout and navigation. In turn, the genuine tables can have different features of layouts, formatting, and content. One of the prevalent problems in web table extraction is to determine main functional and layout types and properties of tables wide-spread on the Web. Currently, a wide range of taxonomies of table types and properties is available to researchers and practitioners. All of these taxonomies could be utilised for table type classification in order to choose further type-specific treatment of tabular data. The existing taxonomies provide similar table types but use the confusing terminology. This paper is an attempt to overview the existing taxonomies of table types by matching their terminology and comparing them qualitatively.
Smooth Stitching Method of VR Panoramic Image Based on Improved SIFT Algorithm

Aiming at the shortcomings of the traditional Harris algorithm, a smooth mosaic square of VR panoramic image based on the Improved SIFT algorithm is proposed. Firstly, the SIFT feature points are extracted by the SIFT detection algorithm, and then the Harris corners of VR panoramic image are detected in the neighborhood of the SIFT feature points to enhance the smoothness of the feature points of the panoramic image. At the same time, with the help of the invariance of SIFT features to rotation and scale, as well as the good smoothness to noise interference and brightness change, the problem that VR panoramic image mosaic is sensitive to scale change is solved, which makes the smooth mosaic of VR panoramic image possible. The experimental results show that the smooth stitching method of VR panoramic image based on the Improved SIFT algorithm has good smoothness, and the matching speed is also significantly improved.
Trinity AI Co-Designer for Hierarchical Oracle-guided Design of Cyber-Physical Systems

The design of complex cyber-physical systems entails satisfying several competing performance objectives. In practice, some design requirements are often implicit in the intuition and knowledge of designers who have many years of experience working with similar designs. Designers use this experience to sample a few promising candidates in the design space and evaluate or simulate them using detailed, typically slow scientific models. The goal in design is usually to generate a diverse set of high-performing design configurations that allow trade-offs across different objectives and avoid early concretization. In this paper, we present a demonstration of Trinity AI co-designer that implements a machine learning approach to automate some aspects of system design. Trinity implements an extension of oracle-guided inductive synthesis, where the learning approaches interact with a hierarchy of oracles that range from detailed slow-to-evaluate scientific models to fast but low fidelity deep neural network surrogate models and symbolic rules. The goal is to enable fast design iterations for earlier phases of design. Trinity uses deep generative models to learn a manifold of the valid design space, followed by a joint exploration and optimization of designs over the learned manifold, producing a diverse set of optimal designs with respect to given design objectives. In our demonstration, we will use several case studies including the design of propellers, a ground vehicle, air vehicle and underwater vehicle. Across these case studies, we successfully show how our method generates high-performing and diverse designs.
COVID-19 CONTACT TRACING AND TRACKING

The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems. COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic. Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions. In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals. However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability. The use of Covid-19 contact tracing applications is close to be unheard of in Nigeria. We are far behind in leveraging on mobile app technology to combat the continued spread of this deadly virus. This project proposes a mobile app solution that provides adequate information to the contacted victims and as well warns the non-contacted of the dangers of leaving home without their kits. KEYWORDS: Covid-19, covid-19 contact tracing, contact tracing apps, coronavirus, mobile app, covid-19 alert.
Machine Learning Algorithms and Its Applications: A Survey

Abstract: In todays present world lots of microelectronic statistics is created in apiece and every field. The data that we obtain contains valuable info to forecast the future. Owing to the enormous in magnitude, the physical forecasting gives a intricate chore to humans. To overwhelm this problem, the data model is made in such a way so that it can predict the future by the situation with the aid of training data and test datasets. To Pullman the machine or the data model, numerous types of machine learning algorithms (ML) and tools are available. This paper will emphasis on the review of the few machine learning algorithms(ML) and methods used in numerous applications and domains in a detailed manner
Civil Servant Position Recommendation Algorithm Based on Model Fusion

Recommending positions for civil servants efficiently, scientifically, and accurately has always been an urgent need for government departments in personnel management. However, most of the existing position recommendation algorithms do not use the sequence information in the historical working experience of civil servants; and only use collaborative filtering or features for the recommendation. Our motivation is to solve the shortcomings of existing position recommendation algorithms and realize efficient and accurate recommendations. Therefore, we propose a model fusion recommendation algorithm based on sequence, effectively fusing sequence and feature information. We use SR-GNN (Session-based Recommendation with Graph Neural Networks) as our base learner to learn sequence information, and we also use logistic regression, Xgboost, LightGBM as our base learners to learn feature information. Finally, Xgboost is used as our meta learner to fuse the output of our base learner. Experiments on the open civil servant resume data set show that our method has achieved better results than the original sub learner.
P‐95: Comparative Effectiveness Study on Foldable Display Module Application of Thin Metal Sheet

A patterned metal plate fabricated by etching process is generally employed on the bottom of a foldable display to meet mechanical properties such as rigidity and foldability. Although polyimide film has been largely applied on the top of the patterned metal plate, there have been several limitations in terms of crease and pattern protrusion as well as pattern borderline perception, impact resistance, and finger touch heterogeneity. The purpose of this study is to substitute polyimide film with a thin metal plate and assess comparative effectiveness. The result shows that it is effective in reducing crease and pattern protrusion in particular.
Human Daily Activities: From Detection to Prediction

While the number of connected objects in the world of Internet of Thing (IoT) is increasing, an efficient and intelligent solution to exploit the huge amount of generated data from home appliances does not exist. Smart homes powered by IoT devices are able to automate and monitor the every day activities of home owners, and improve the life quality especially for elderly and disabled people. In this paper, we take advantage of deep learning and machine learning techniques and propose a comprehensive solution to tackle this problem. Our proposed approach is able to analyze raw and unlabeled IoT data from the connected devices in a smart home and provide data-driven services. After analysing and processing raw data, and then human activity detection and recognition in real time, we predict the upcoming activity and its occurring time using LSTM (Long Short-Term Memory). Our simulation results show that the developed models can predict a wide range of human activities from unlabeled data with the accuracy of 85%.