Reviewerly/exHarmony huggingface dataset:
Chinese Triple Extraction Based on BERT Model

Information extraction (IE) plays a crucial role in natural language processing, which extracts structured facts like entities, attributes, relations and events from unstructured text. The results of information extraction can be applied in many fields including information retrieval, intelligent QA system, to name a few. We define a pair of entities and their relation from a sentence as a triple. Different from most relation extraction tasks, which only extract one relation from a sentence of known entities, we achieved that extracting both relation and entities(a triple, as defined above), from a plain sentence. Until now, there are so many methods proposed to solve information extraction problem and deep learning has made great progress last several years. Among the field of deep learning, the pre-trained model BERT has achieved greatly successful results in a lot of NLP tasks. So we divide our triple extraction task into two sub-tasks, relation classification and entity tagging, and design two models based on BERT for these two sub-tasks, including a CNN-BERT and a Simple BERT. We experimented our models on DuIE Chinese dataset and achieved excellent results.
RoBERTa-IAN for aspect-level sentiment analysis of product reviews

Abstract Accurately mining the emotional information contained in product reviews is of great significance to product sales. Existing research on sentiment analysis of product reviews often ignores the importance of modelling aspect words and context separately. Therefore, this paper proposes a sentiment analysis model for product reviews based on RoBERTa-IAN. Firstly, the context and aspect words of product reviews are transformed into low dimensional vectors through the RoBERTa pre-training model. Then, the low dimensional vector is used as the input of Bi-GRU model to extract semantic features and get the hidden representation. Finally, the attention matrices of context and aspect words are obtained by using the Interactive Attention Networks (IAN), which are used as the input of sentiment classification layer to analyse and classify the sentiment polarity of product reviews. The experimental results on the real commodity Chinese dataset show that the accuracy of RoBERTa-IAN has reached more than 90%.
Activity Recognition for Assisting People with Dementia

Technology can help and provide support to people with dementia to ensure their safety during daily activities. In this paper, we summarize information about activity recognition for people living with dementia. This paper aims to understand the uses and types of applications, the types of sensors/systems, methods, and data used within the scope of human activity recognition to monitor, detect symptoms, or help people with dementia. To this end, 447 abstracts were collected from a Scopus database, which yielded 127 relevant papers and 102 papers that were considered in detail based on the four categories of assessment (application, system/sensors, methods, and data). This paper shows the trend that smart environment technology is most widely used for monitoring people with dementia, wherein machine learning techniques as a method for activity recognition to achieve the results of testing or implementing the system. We conclude that combining sensor devices and the addition of smartphone devices in one system is suitable for implementation because it can be used as an identity such that it distinguishes the object under study with other objects. During the monitoring process, prevention can be achieved simultaneously by adding a warning alarm in the smartphone when people with dementia perform abnormal activities, and the results need to be further analyzed to get the best pattern of activities for people with dementia. Next, the type of application that was initially designed for monitoring can be developed into an assistant for people with dementia.
First‐break automatic picking technology based on semantic segmentation

ABSTRACT With the wide application of the high‐density and high‐productivity acquisition technology in the complex areas of oil fields, the first‐break picking of massive low signal‐to‐noise data is a great challenging job. Conventional first‐break automatic picking methods (Akaike information criterion method, energy ratio method, correlation method and boundary detection method) require a lot of manual adjustments due to their poor anti‐noise performance. A lot of adjustments affect the accuracy and efficiency of picking. First‐break picking takes up about one‐third of the whole processing cycle, which restricts petroleum exploration and development progress severely. In order to overcome the above‐mentioned shortcoming, this paper proposes the first‐break automatic picking technology based on semantic segmentation. Firstly, design the time window for primary wave and pick a certain quantity of first breaks from newly acquired data in different zones of the exploration area using the commonly used Akaike information criterion method and interactive adjustments; and then perform pre‐processing on the data within the time window to extract multiple first‐break attribute features and perform feature enhancement, to obtain multi‐dimensional features data blocks, at the same time, label the first breaks. Secondly, u‐shaped architecture network‐like encoding and decoding network is used to implement end‐to‐end feature learning from primary wave attribute data to first‐break labels. The encoding and decoding process of the encoding and decoding network is used to fuse the extraction and feature positioning of primary wave attribute features. At the same time, normalize each layer and use the rectified linear unit function as a non‐linear factor to improve the generalization and sensitivity of network model to low signal‐to‐noise primary waves. Finally, an optimized deep network model is used to predict the first breaks of the data to improve the accuracy and efficiency of the first‐break picking. This method innovatively fuses the multiple conventional automatic picking methods (Akaike information criterion method, energy ratio method, correlation method and boundary detection method) to extract multiple attribute features of primary wave, and improves the accuracy of the training network model to the first‐break detection using the improved UNet‐like encoding and decoding network. The feasibility of the new method is proved by model data. A comparative test is conducted between the new method and the Akaike information criterion method with the actual data, which verifies that the method in this paper has a higher picking accuracy and stable first‐break processing capability for the data with low signal to noise, our method shows a significant advantage when applied to low signal‐to‐noise seismic records from high‐productivity acquisition and can meet the demands of the accuracy and efficiency for near‐surface model building and static calculation of massive data.
Impoliteness across social media platforms

Abstract This paper responds to the call for more comparative research across online social media platforms ( Graham and Hardaker 2017 , 808) and examines discourse across two such platforms, YouTube and Reddit. More specifically, it attempts to investigate whether the affordances of these platforms have an impact on the amount and form of impoliteness employed by their users. Data on a highly contested topic (the July 2018 wildfires in Attica, Greece) is studied both qualitatively and quantitatively. First, small but representative samples are qualitatively analyzed on the basis of the two major impoliteness types: implicational and conventionalized and their subcategories. In addition, swearword keywords extracted by means of corpus analysis tools are analyzed. It is shown that YouTube involves a great deal of (conventionalized) impoliteness ( Culpeper 2011a ) which could be due to factors such as the total absence of moderation, of post length limit and of detailed personal profiles as well as the presence of videos as stimuli for interaction. Considerably less impoliteness appears on Reddit, whose forum-like nature makes it a place that mostly invites civil interaction. Implicationally impolite ( Culpeper 2011a ) posts outnumber conventionally impolite ones, perhaps owing to the heavy moderation, the existence of public profiles and the size and coherence of the user community. It is concluded that, due to their characteristics, platforms seem to attract a certain userbase with its own motives and mindset, which in turn shape the impoliteness found within them.
Opinion Classification of Product Reviews Using Naïve Bayes, Logistic Regression and Sentiwordnet: Challenges and Survey

Abstract In recent years, automated opinion classification has evolved as one of the most demanding area in natural language processing. Many such systems have been implemented and developed for the summarization and classification of text and reviews of online products. There are many data sources and domains which sells the online products, such as Amazon, Flipkart, Snapdeal etc. In the same direction, this paper is intended to present a detailed review and comparative analysis of various existing sentiment analysis algorithms especially for the Amazon products, which have worked upon the supervised learning techniques called Naïve Bayes, logistic regression and SentiWordNet. Various key parameters and aspects of such a comparative tour are the use of feature reduction method, sentiment polarity, dataset domain and sources, product name, data set size and classifier. Further this paper includes the discussion on their accuracy results; additional results including important findings; and needs, challenges and limitations. Lastly, the performance of these algorithms is evaluated by comparing the % usage of the key parameters.
Phase-Based Calibration Method for a Binocular Vision Sensor

In this paper, a phase based method to calibrate a binocular vision sensor is presented.In this method, only a surface plate is utilized.A series of phase shifting patterns are projected onto the surface plate.In this case, fundamental matrix can be calculated out from point correspondences on two image planes.As intrinsic parameters of each camera have been obtained according to related calibration method, essential matrix can be worked out.Then, rotation matrix and translation matrix with a coefficient are deduced based on singular value decomposition of the essential matrix.As size of the pattern can be confirmed from a look-up table, the coefficient is determined.So far, the binocular vision sensor is calibrated.In our proposed method, only one planar target without any pattern is necessary, which is with high precision and easy machined.As the calibration method is based on related phase algorithm, the calibration process is not affected by the ambient light which makes our calibration method robust.Experiment results show the precision of our proposed method.When we utilized a laser displacement sensor with a precision of 0.01 mm to determine size of the pattern, root mean square error of our calibration method is 0.103 mm with regard to the measurement area of about 100 × 80 mm.Moreover, as planar features are widely existing, our proposed method is very suitable for on-site calibration and auto-calibration.
What went wrong? Identification of everyday object manipulation anomalies

Enhancing the abilities of service robots is important for expanding what they can achieve in everyday manipulation tasks. In addition, it is also essential to ensure that they can determine what they cannot achieve. Such necessity may arise due to anomalies during task execution. These situations should be detected and identified to overcome and recover from them. Identification necessitates a deeper time series analysis of onboard sensor readings to keep track of and relate anomaly indicators since some indicators may be perceived long before the detection of an anomaly. These sensor readings are usually taken asynchronously and need to be fused effectively for correct interpretations. In this paper, we propose a multimodal long short-term memory-based (LSTM-based) anomaly identification approach that takes into account real-time observations by fusing visual, auditory and proprioceptive sensory modalities during everyday object manipulation tasks. The symptoms of anomalies are first trained and then are classified based on the learned models in real time. We first provide a comparative analysis of our method with hidden Markov models (HMMs), conditional random fields (CRFs) and gated recurring units (GRUs) on a Baxter robot executing everyday object manipulation scenarios. Then, we analyze the impact of each modality and various feature extraction techniques on the performance of the identification problem. We show that our method has the ability to identify anomalies by capturing long-term dependencies between the anomaly indicators. The results indicate that the LSTM-based anomaly identification method outperforms the closest baseline with a 2% improvement of f-score (0.92) in classifying anomalies that occur during run-time.
Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic classification, the characteristics of cancer patches in the X-ray image are of critical importance. Due to the slight variation between the textures, using just one feature or using a few features contributes to inaccurate classification outcomes. The present study focuses on five different algorithms for extracting features that can extract further different features. The algorithms are GLCM, LBGLCM, LBP, GLRLM, and SFTA from 8 image groups, and then, the extracted feature spaces are combined. The dataset used for classification is most probably imbalanced. Additionally, another focal point is to eradicate the unbalanced data problem by creating more samples using the ADASYN algorithm so that the error rate is minimized and the accuracy is increased. By using the ReliefF algorithm, it skips less contributing features that relieve the burden on the process. Finally, the feedforward neural network is used for the classification of data. The proposed method showed 99.5% micro, 99.5% macro, 0.5% misclassification, 99.5% recall rats, specificity 99.4%, precision 99.5%, and accuracy 99.5%, showing its robustness in these results. To assess the feasibility of the new system, the INbreast database was used.
Determining the Logical Relation between Two Sentences by Using the Masked Language Model of BERT

In this paper, we evaluated the suitability of BERT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">,</sup> s Masked Language Model to determine the relation between two sentences, s1 and s2. A mask word was placed immediately before s2, creating a BERT input sequence of the form "[CLS] s1 [SEP] [MASK] s2 [SEP]." The relation between the sentences was determined from the most likely conjunctives for the [MASK] token, as predicted by BERT. In this study, we used a list of most common conjunctives and a set of sentence pairs containing those conjunctives to test whether the conjunction could be predicted when it is masked. We compared our results with the case of solving the sentence relation problem by using two different supervised learning methods.
A Curious Case of Cryptokick

Crypto-kick is a patented technology by Nike to primarily combat counterfeiting of footwear using block-chain, which got granted within 196 days from being filed. This case study starts by reviewing the counterfeit trade market of consumer goods. Then the concept of non fungible token has been discussed and how it forms the core concept of the footwearâ€™s patent that aims in blurring the realms between physical and digital assets. Finally, the fast track patent process Track One has been explored which are available for such rapidly evolving technology space.
Synthesizing researches on Knowledge Management and Agile Software Development using the Meta-ethnography method

Software development processes are considered as knowledge intensive and therefore Knowledge Management (KM) can be applied to efficiently manage the knowledge generated. Agile practices can benefit software organizations in terms of KM. Some studies have already presented evidence about this relationship. However, the intersection of these two areas still requires further clarification. This study synthesizes research on KM and Agile Software Development (ASD) using the meta-ethnography method considering Scrum and XP frameworks. In order to achieve the proposed goal, first, a diagnostic was conducted in different project domains with agile and traditional software development in order to identify the performance of KM activities. Second, the phases of the meta-ethnography analysis method were applied in a set of studies selected from a tertiary review on KM and ASD, as well as classic guides and area references. Finally, the relationships that were identified among the areas investigated were analyzed from interviews with agile development methodology experts. The most common activity investigated between KM and ASD is knowledge sharing. However, in the practical view of software development companies, the attention is on how to use the generated knowledge. The clarification of how KM is present in each agile value, practices, and artifacts allows a reflection on how much knowledge was created, shared, and applied during ASD. Besides, such results presented in this study enable organizations to know each other better and to explore more each KM activity, thus contributing to delivering more value to the customer.
AI K–12 Education Service

Abstract This article provides an overview of relevant scientific venues, journals, projects and resources in the context of AI K-12 education.
A Smartphone Application for Fall Detection Using Accelerometer and ConvLSTM Network

A fall is defined as an unexpected change in the disposition of the human body, causing it hit brutally the ground. Falls often occur because of external factors that escapes the person's attention. A fall can happen to anyone at any time, however the elderly are particularly affected by these incidents. It can cause simple damages as well as more serious ones, and can even lead to death. Thus, requiring emergency interventions to provide medical assistance. This paper aims to study the problem of automatic fall detection using a phone accelerometer sensor and deep neural networks. We propose a new ConvLSTM neural network architecture for the classification of activities as fall and non-fall. We evaluate the proposed network on two public activities databases and compare with a state of the art network bases on LSTM layers. Moreover, we design and implement a mobile fall detection application.
Implementation of professionally oriented ICT in the process of managers training

Abstract The article touches upon the problem of introducing professionally oriented software products based on information and communication technologies into the educational process of training managers of the hotel, restaurant and tourism business. The purpose of the study is to prove the effectiveness of the implementation of professionally oriented software products based on ICT in the training of managers. The research is based on the analysis of recommendations of employers and managers of the specialties “Tourism” and “Hotel business”, the content of educational programs for training bachelors in management, the content of general professional and special competencies, the structural and logical scheme of disciplines. The most effective information and communication technologies using computer software products that contribute to the formation of a set of professional competencies in future managers have been identified: MS Project, Teamwork, TeamLab, Open Workbench, GanttProject, dotProject, Outlook, OneNote, EverNote, Nirvana, Wunderlist, Toggl, MS Office, Office 365, Document.Online, AllFusion Process Modeler 7, MS Visio, MS PowerPoint, MS Sway, Libre Office.Impress, FreeMind, Mind42, ViSta, MacANOVA, Matrixer. The effectiveness of these technologies has been experimentally tested in the course of practical training in the format of full-time distance learning and practical training of student managers. The research describes the technology of using professional software products in the educational process. The effectiveness of these technologies has been experimentally tested in the course of practical training in the format of full-time distance learning and practical training of student managers. The research describes the technology of using professional software products in the educational process. The results of the pedagogical experiment confirmed that the introduction of information and communication technologies contributes to the formation of professional competencies in the field of ICT among students of this specialty. The conducted research proves the need for changes in the working curricula of disciplines “Information systems and technologies”, “Practices of information and communication technologies”, “Statistics”, “Management and administration”, “Finance, money and credit”, “Accounting and audit”. “Business foreign language”, “Professional rhetoric”, “Foreign professional language”, “PR and advertising technologies”, “Business accounting”, “International business management”, “Electronic business information technologies”.
Online Phase Measurement Profilometry for a Fast-Moving Object

When the measured object is fast moving online, the captured deformed pattern may appear as motion blur, and some phase information will be lost. Therefore, the frame rate has to be improved by adjusting the image acquisition mode of the camera to adapt to a fast-moving object, but the resolution of the captured deformed pattern will be sacrificed. So a super-resolution image reconstruction method based on maximum a posteriori (MAP) estimation is adopted to obtain high-resolution deformed patterns, and in this way, the reconstructed high-resolution deformed patterns also have a good effect on noise suppression. Finally, all the reconstructed high-resolution equivalent phase shifting deformed patterns are used for online three-dimensional (3D) reconstruction. Experimental results prove the effectiveness of the proposed method. The proposed method has a good application prospect in high-precision and fast online 3D measurement.
Smart University and Artificial Intelligence

The concept of smart university and Artificial Intelligence (AI) is gaining enormous attention, specifically during the covid19 impact phase, where the education institutions are searching digital platforms and Internet of Things (IoT) for academic continuities. The application AI is changing the operating models of several academic institutions, extending numerous possibilities for learning and development. Though AI provide several opportunities to academic institutions, it has it disadvantages also. Exploring the structure, system, process, strategy, infrastructure, and skills towards a smart university, this chapter has explored the concepts, tools, opportunities, advantages, and disadvantages of AI, with qualitative content analysis and narratives. The output of the chapter facilitates expansion of body of knowledge integrating contents of smart university, smart infrastructure, smart pedagogue, smart classrooms, smart teachers, smart learners, artificial intelligence, online education, artificial intelligence applications, role of AI in education, and AI applications for education, facilitating theoretical implications. Modern universities with integrated AI applications provide new vistas to students learning experience, teacher’s instructional improvement and facilitating effortless academic administration.