Reviewerly/exHarmony huggingface dataset:
How tri-axial sensors influenced the location-based heterogeneous activities recognition rates: an exploratory analysis

Due to the successful enhancement of context-aware applications in health caring, surveillance, security, sports, behavior analysis, and more, the Human Activities Recognition (HAR) has been noted as an emerging research domain. In this paper, we proposed a deep learning-based novel ConvLSTM-HHAR (Convolutional Long short-term Memory-Heterogeneous Human Activities Recognition) model for the recognition of heterogeneous human activities in indoor and outdoor environments. For automatically extracting efficient features from raw sensor data and identifying activities, the proposed model utilizes the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), respectively. We have used the publicly availabel KU-HAR dataset as an experimental dataset which includes a total of eighteen activities (categorized as indoor and outdoor) of 90 subjects collected using a single tri-axial accelerometer and gyroscope sensor. The proposed model has achieved an average accuracy of 99.98%, F1-score of 93.34%, precision of 88.06%, and recall of 100.00% for indoor, outdoor, and indoor + outdoor activities.
Advanced Sequence Analysis

Abstract This chapter discusses more advanced methods for sequence analysis. These include: probabilistic methods using classical planning, Bayesian Networks (BN), Dynamic Bayesian Networks (DBNs), Hidden Markov Models (HMMs), Markov Logic Networks (MLNs), Markov Decision Process (MDP), and Recurrent Neural Networks (RNNs), specifically concentrating on LSTM (Long Short-Term Memory). These techniques are all great but, at this time, are mostly used in academia and less in the industry. Thus, the chapter takes a more academic approach, showing the work and its application to games when possible. The techniques are important as they cultivate future directions of how you can think about modeling, predicting players’ strategies, actions, and churn. We believe these methods can be leveraged in the future as the field advances and will have an impact in the industry. Please note that this chapter was developed in collaboration with several PhD students at Northeastern University, specifically Nathan Partlan, Madkour Abdelrahman Amr, and Sabbir Ahmad, who contributed greatly to this chapter and the case studies discussed.
Research on the Development of Library in the Era of Artificial Intelligence

With the development of science and technology, Artificial Intelligence(AI) has gradually become a global research hotspot. Many countries have also issued plans to upgrade Artificial Intelligence to a national strategy. Artificial Intelligence as a future scientific and technological research hotspot has played a role in promoting all walks of life in society. It will also promote the development of library. Combined with the influence of Artificial Intelligence technology, this paper explores the service transformation, innovative development and technology integration of the library, and then proposes the development of the application of Artificial Intelligence technology in the library in resource construction, information service and librarian construction. The development direction of the library in the era of Artificial Intelligence has laid the foundation for further research on technological innovation and library development in the future.
A Decision Process Model for De-Identification Methods on the Example of Psychometric Data

After years of delay, Germany will offer its citizens with statutory health insurance the voluntary use of an Electronic Health Record (EHR) by law from 2021. Patients are able to upload their medical records into the EHR and from 2023 onward, they will be able to donate their data for research purposes. The donated data will be collected and stored at the Research Data Center (RDC). De-identified datasets can be requested from the RDC, which has to choose a method for de-identification and is responsible for the safety of the published data. Since every dataset is unique, de-identification methods have to be found and chosen individually. Within this work, a decision process model is presented and demonstrated on the example of psychometric data from a digital dementia screening application. As privacy model, the k-anonymity is chosen to demonstrate a desired grade of anonymity. Datafly, Incognito and Bottom-Up Generalization are compared in detail as suitable anonymization algorithms to achieve a dataset that fulfills k- anonymity for a given value of k.
Research on Adaptive Decision Strong Tracking Cubature Kalman Filter in Sensor Networks

An adaptive decision strong tracking cubature Kalman filter (ADSTCKF) was proposed to solve the problem of nonlinear and non-Gaussian noise interference in sensor networks, and the traditional cubature Kalman filter was greatly affected by the noise covariance. Firstly, the suboptimal solution is obtained by introducing the suboptimal fading factor according to the strong tracking cubature Kalman filter (STCKF) algorithm. Secondly, the fading factor is taken as the decision condition, and the initial value is adjusted adaptively to reduce the weight of the measured value and the estimation error. Finally, according to the adjusted suboptimal solution, the measurement update is calculated. Simulation results show that ADSTCKF algorithm can be applied to nonlinear and non-Gaussian noise interference conditions. After applying various algorithms to the constant turn rate motion model (CT), ADSTCKF algorithm can significantly optimize the accuracy and stability of state estimation compared with CKF and STCKF algorithm.
Experimental Analysis of Anomaly Detection Algorithms on Banking data

In this era, use of credit has increased extensively. People are using credit cards more frequently in their daily lives. Millions of dollars were lost in forgery, however fraud detection methods have been developed to fix such types of problems. However, Nevertheless, we still face such problems through imbalance data. There is a requirement for a centralized claims database to collect views of holistic view of fraudulent characteristic behaviour. Credit card supervised learning are widely used to detect fraud based on the assumption that the pattern of fraud would depend on past transaction. This paper will give succinct understanding to avoid such problems. And will elaborate how we can use unsupervised techniques in forgery or fraud detection. In the beginning all the supervised techniques are given, types of anomalies and classification of anomaly detection techniques are given. In addition to that a project is done with the use of raw bank data and forgery is detected with unsupervised techniques.
Felix Semenovich Voroysky: The 90-th anniversary

The article is dedicated to the 90-th anniversary of Felix S. Voroysky, Candidate of Science in Engineering, Professor, pedagogue, naval officer, retired captain II rank, and an outstanding expert in library and information automation (1931–2008). His life stages, including his service in the navy, jobs in research institutes, information organizations and libraries, participation in library consortia and their projects of corporate e-catalogs and e-libraries, as well as his pedagogical activities, are discussed. The whole spectrum of his academic interests is reflected in 272 publications with many of them still popular after the years gone and are read when training library specialists in structure and technologies of library and information processes. Felix S. Voroysky served on the organizing committees of the largest professional international forums “Crimea” and “LIBCOM” also chairing the section “Automated and corporate library systems and technologies”.
Virtual Reality Percussion Simulator for Medical Student Training

As the ‘inventor’ of percussion as a diagnostic tool, Leopold Auenbrugger can be considered one of the founders of modern medicine [1], [2]. As a technique, it enabled clinicians to identify pathologic changes within a patient in realtime - changes which, until then, had only been identifiable posthumously by way of autopsy. In 1761, having spent seven years working in clinical settings, Auenbrugger published a work entitled “New Invention by Means of Percussing the Human Thorax for Detecting Signs of Obscure Disease of the Interior of the Chest”, in which he described four percussive tones, relating each to specific illnesses. The technique is still relevant today, but the sounds, of which 5 types have now been classified, are nonetheless tricky to identify as they are not so obviously distinct from one another. Training medical staff is therefore key, and in the light of the current pandemic, during which training opportunities with real live patients have been somewhat limited, interest in training with virtual patients has increased. The haptic virtual reality [3] simulator provides a means to this end, enabling excellent training opportunities for medical staff in a safe and economically viable environment.
Long Short Term Memory Networks for Lexical Normalization of Tweets

Lexical normalization is converting a non-standard text into a standard text that is more readable and universal. Data obtained from social media sites and tweets often contain much noise and use non-canonical sentence structures such as non-standard abbreviations, skipping of words, spelling errors, etc. Hence such data needs to be appropriately processed before it can be used. The processing can be done by lexical normalization, which reduces randomness and converts the sentence structure to a predefined standard. Hence lexical normalization can help in improving the performance of systems that use user-generated text as inputs. There are several ways to perform lexical normalization, such as dictionary lookups, most frequent replacements, etc. However, We aim to explore the domain of deep learning to find approaches that can be used to normalize texts lexically.
Grammar efficiency and the One-Meaning–One-Form Principle

The more a linguistic system departs from the One-Meaning–One-Form Principle, the less efficient it is. This notion is used to simplify the previous approach to the evaluation of grammar efficiency. The recently proposed measure of the departure from the Principle is revisited and its simplified version is included in the formula for grammar efficiency. The new and the old grammar-efficiency formulas are compared when applied to parts-of-speech systems as defined by Hengeveld. It is shown that the number of calculations is considerably reduced in the new formula, while the results obtained by the two approaches correlate well.
On a decoding algorithm of <i>m</i>Θ Reed-Muller codes

In this paper, we propose an algorithm for decoding for mΘ Reed-Muller codes. Thus, we present first the notion of mΘ spectrum of Reed-Muller codes as in [2]; and we define secondly the notion of mΘ extension of Reed-Muller codes.
Animated Video Using Duik Bassel Rigging Toolset on Tuberculosis Education

TB is an infectious and deadly disease that attacks the lungs and is caused by Mycobacterium tuberculosis. Indonesia occupies the third position as a country with the highest number of TB patients in the world according to the World Health Organization (WHO). In addition to the high number of TB patients, there are still many TB patients who do not complete their treatment. Patients who do not complete their treatment can harm others and themselves. As long as a patient has not been declared cured, there is still the potential to be a source of transmission for others. In addition, patients who do not complete their treatment can potentially become MDR - TB (Multi Drug Resistant TB) because the TB bacteria mutate so that they are resistant to drugs given by doctors. Tuberculosis is still a stigma in society, causing some patients to tend to close themselves off and be reluctant to have their health checked. These problems raise new problems in increasing the transmission of TB. It is known that this problem occurs because of the low knowledge and awareness of the community regarding TB. Therefore, an animated explainer video was made that aims to educate the public about TB and how to treat it. This video will be created using Duik Bassel, a plugin that helps in the character rigging process. Rigging is the process of giving a character structure so that it can be moved in the animation process. The use of Duik Bassel can make the animation process easier, and character movements can be more dynamic.
Using Verbatims as a Basis for Building a Customer Journey Map: A Case Study

Customer Journey Map is currently a very used canvas in the UX (User Experience) practice and design processes. Although it is widely discussed, both in the academic and industrial domains; practitioners still present questions about how to model this diagram. Customer Journey Maps are typically generated from the Personas technique, which is generally created by interviews or observations. On the other hand, many organizations employ a tool called NPS (Net Promoter Score). This tool generates both quantitative and qualitative data. The tool obtains expressions from the customer about the service or product called “Verbatim” about the qualitative data. These verbatims capture faithfully the event that took place when customers interacted with the financial products, services, systems, or channels. In that sense, we present a case study where a different approach is employed to build a Customer Journey Map about customers and their User Experience interacting with ATMs in a financial institution in collaborative sessions. In this sense, by applying this approach, we could map the touchpoints by analyzing verbatims. This way, verbatims could constitute a better source of information over interviews. The integration of the verbatim analysis in the CJM process could effortlessly scale as the data gathered from customers grows, promotes the sharing of knowledge inside the organization, and the culture of data-driven decision-making. In the end, we could obtain crucial insights and pain points that could generate new opportunities, requirements, and even new projects for the channel development backlog. We shared the results with a multidisciplinary audience with positive feedback, and they suggested that for new initiatives and analysis, to apply this new approach.
On The Authentication Method Based On Visual Cryptography

In this work, we build, implement and research a new authentication approach. Our method is based on the use of visual cryptography. In the authentication system, the user needs to combine the image stored in the smartphone's memory (the first shadow) with the image stored in the memory of the stationary computer (the second shadow), find the password and enter it in the input field. It is well known that it is theoretically impossible to get information about the secret by one shadow. This is the basis of the resistance of our scheme to the loss/theft of one shadow. In our work, experiments were carried out in order to study the performance and practical applicability of this scheme. Based on the results of the experiments we determine a set of hard-to-recognize characters and, as a result, a set of admissible (easily recognizable) characters. Also, the results of the experiments made it possible to identify the characteristics of smartphones that affect the success of recognition of a secret image. We determine the ways of further improvement of the system to reduce the probability of errors.
Knowledge distillation based on decision boundary instances generated by DBI-GAN

In this paper, we propose a new model-free knowledge distillation scheme. We attempt on distilling the knowledge about the decision-making process and design a novel generative adversarial network, named as DBI-GAN, to generate instances located on the decision boundary of the teacher network. By depicting the decision-making process of the teacher network with the decision boundary instances, it is possible for us to transfer such knowledge to a student network. Based on the decision boundary instances, a new knowledge distillation scheme is proposed. In our scheme, we focus on the student network’s “weak-points region”, where wrong decisions are made. We locate these weak-points regions by evaluate the student network using confusion matrix. Then, a specific dataset, which consists of the original train data and synthesized DBIs that located in the weak-points regions, is constructed for knowledge transferring. With the constructed sample set, the student network is then optimized with a new KD loss function. We evaluate the effectiveness of proposed scheme on banchmark datasets and state-of-the-art performance is achieved.
RETRACTED CHAPTER: An Efficient Channel Attention CNN for Facial Expression Recognition

In order to overcome the difficulty of extracting facial expression features by neural network model, and the problems of complicated training process and parameter redundancy caused by stacking deep network structure, this paper proposes a CNN model that introduces an attention mechanism. In this paper, facial expression images are used as the object, and the research of cfacial expression recognition based on convolutional neural network is carried out. Based on the construction of natural expression feature views in the natural environment, the automatic data enhancement technology for deep convolutional neural networks is introduced, and the attention mechanism is combined to adjust the weights of different channels, and the facial expression recognition with texture information extraction as the traction is established deep learning model and facial expression recognition mechanism. At the same time, we propose a combined loss function. The effectiveness of this method is verified on the FER2013, FERplus, CK+, SFEW and RAF-DB data sets, and good results have been achieved.
Orientation and Line Thickness Determination in Binary Images

This paper addresses the problems of orientation determination of lines in binary images and the determination of line thickness. The orientation problem utilizes the Radon transform, while the line thickness problem determines the thickness of lines at selected angles by considering the pattern of the pixels of those lines. The Radon transform maps a line at a given angle to a point in feature space (also known as the sinogram). The sinogram is typically generated for a wide range of angles (from 0 to 179 degrees in this case). Consequently, lines at particular angles will map to points whose sinogram value is greater than that of other points in the sinogram, thereby generating local peaks in the sinogram.
Dynamic Systems and Control

Dynamic systems are discussed in this chapter. This chapter is different in style in comparison to the other chapters of the book. The idea is to describe and expand on the origins of the Kalman filter and to provide some insights by bringing together scientists of different disciplines working on similar methods. The chapter first defines dynamic systems and then focuses on linear systems. The state space representation of a system is discussed and consideration is given to both discrete-time and continuous-time systems. Stability of linear and non-linear systems is discussed and in the latter case the direct and indirect Lyapunov methods are described. The celebrated Kalman-Bucy and extended Kalman-Bucy filter in continuous-time are discussed. Feedback control is reviewed and we present an application of vibration control for a twin rotor static rig, which can be used for air-vehicle testing. Throughout the chapter we use a number of relatively simple examples for illustration.
A Meta-Review of Learning Research in Immersive Virtual Reality

Research and development in virtual reality (VR) continues to influence all sectors of society. This has been particularly the case in the application of VR for learning and training. Due to the affordability of VR, it increasingly is providing a safe and cost-effective technology for studying learning and training. In this paper, we summarize findings from recent compilations of research in virtual reality that examined VR and learning. From this, we identify a set of recommendations distilled from these reviews in order to help the training research community structure their research based upon the extent empirical base. Our goal is to help the training community more effectively explore VR as a technology for learning.
Augmented Reality Android App To Shoot Prefab on Scanned Surface

In today world we surrounded by technology. AR technology is now being used in a lot of field like education, health care, entertainment, gaming, military simulation, modular design and military training simulation. Big tech giants like google, apple, Facebook are in race to create the best and immersive Augmented Reality. They provide us the platform like ARCore by google, ARKit by apple and AR Studio by Facebook. Unity integrated with ARCore with inbuild feature like motion tracking, lighting, environmental awareness we can easily created our own app.
Impact of Digitalization of the Healthcare Industry and Covid 19 Management: Case of the UAE

Technology has changed the face of almost every sphere of life including the healthcare system which was erstwhile considered a subject of pure clinical evaluation. The onset of the Covid 19 pandemic underscored adaptation to technology as the singular solution to address the banes of the huge number of people afflicted with it globally with limited healthcare workers and resources. The opening of a small healthcare centre in the Al Ras region marked the beginning of healthcare in Dubai. The healthcare system of the UAE has grown exponentially over the years, and it now offers a variety of specialized services that sets it apart from the others. The UAE healthcare industry is increasingly improving to meet the changing needs of its people as well as the country's goal to become a regional medical tourism hub. The government is implementing several long-term projects to achieve balanced growth and to incorporate sustainable growth in the industry while meeting the immediate needs of the population. Comprehensive health-care reforms have been enacted over the last ten years. This paper examines the development and results of Health-Care reforms in the UAE using primary data collected by conducting a survey in a prominent health organization in the UAE. The main aim of the survey was to study the impact of digitalization on the healthcare sector in the UAE. The case in point was the influence of digitalized measures including Big Data Analytics and Artificial Intelligence (AI) in handling the Covid-19 situation from the point of view of the healthcare sector employees.
OntoAugment

Ontology matching enables harmonizing heterogeneous data models. Existing ontology matching approaches include machine learning. In particular, recent works leverage weak supervision (WS) through programmatic labeling to avoid the intensive hand-labeling for large ontologies. Programmatic labeling relies on heuristics and rules, called Labeling Functions (LFs), that generate noisy and incomplete labels. However, to cover a reasonable portion of the dataset, programmatic labeling might require a significant number of LFs that might be time expensive and not always straightforward to program.