Reviewerly/exHarmony huggingface dataset:
Improve Face Verification Rate Using Image Pre-Processing and FaceNet

Face Verification is mostly used in areas where a false output could end up as a huge mistake. Within the last several years, a lot of improvements have been made, but there is still room for improvement. The quality of the image used to verify are not always the best, with issues such as varying lighting condition, face orientation, etc. In most of the cases where face verification is used, there is usually a limited number of images and people who are not already in the trained database. This research paper presents a method that does not require retraining each time there is a new person not in the database using a pre-trained FaceNet model. Improvement of face verification rate is done in this research paper using image pre-processing on the inputted images, such as using MTCNN to select out the face, face alignment, and brightness adjustment. From testing with Caltech’s Faces 1999 (Front) dataset, our proposed method shows an improvement in accuracy.
HPCache: Memory-Efficient OLAP Through Proportional Caching

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution speedup only when disk accesses are significantly slower than in-memory query processing. On the other hand, fast storage offers loading times that approach or even outperform fully in-memory query execution response times, rendering purely frequency-based statistics incapable of capturing impact of a caching decision on query execution. For example, caching the input of a frequent query that spends most of its time processing joins is less beneficial than caching a page for a slightly less frequent but scan-heavy query. As a result, existing caching policies waste valuable memory space to cache input data that offer little-to-no acceleration for analytics.
Supervised Classification of Healthcare Text Data Based on Context-Defined Categories

Achieving a good success rate in supervised classification analysis of a text dataset, where the relationship between the text and its label can be extracted from the context, but not from isolated words in the text, is still an important challenge facing the fields of statistics and machine learning. For this purpose, we present a novel mathematical framework. We then conduct a comparative study between established classification methods for the case where the relationship between the text and the corresponding label is clearly depicted by specific words in the text. In particular, we use logistic LASSO, artificial neural networks, support vector machines, and decision-tree-like procedures. This methodology is applied to a real case study involving mapping Consolidated Framework for Implementation and Research (CFIR) constructs to health-related text data and achieves a prediction success rate of over 80% when just the first 55% of the text, or more, is used for training and the remaining for testing. The results indicate that the methodology can be useful to accelerate the CFIR coding process.
ChromoFilament: Designing a Thermochromic Filament for Displaying Malleable States

The 3D pen has become a popular crafting tool where hands-on deformations are largely engaged. However, as malleable states are invisible, users might be burnt, or their fabrication might fail. We designed a thermochromic 3D filament, ChromoFilament, that displays the malleable states in three different colors according to the associated temperatures. From color design workshops, we identified proper stages of malleability and design considerations for color combinations, which are applied to ChromoFilament. Next, we depict a way to fabricate ChromoFilament from customizing thermochromic ink to extruding with the coated pellets. Finally, we illustrate the users’ distinctive behaviors with ChromoFilament to imply the effects of visible malleable states. We believe that our material-perspective approach, design process, and a series of findings could not only inspire supporting creativity through thermoforming but also heat-based processing in 3D printing.
High-Accuracy 3D Gaze Estimation with Efficient Recalibration for Head-Mounted Gaze Tracking Systems

The problem of 3D gaze estimation can be viewed as inferring the visual axes from eye images. It remains a challenge especially for the head-mounted gaze tracker (HMGT) with a simple camera setup due to the complexity of the human visual system. Although the mainstream regression-based methods could establish the mapping relationship between eye image features and the gaze point to calculate the visual axes, it may lead to inadequate fitting performance and appreciable extrapolation errors. Moreover, regression-based methods suffer from a degraded user experience because of the increased burden in recalibration procedures when slippage occurs between HMGT and head. To address these issues, a high-accuracy 3D gaze estimation method along with an efficient recalibration approach is proposed with head pose tracking in this paper. The two key parameters, eyeball center and camera optical center, are estimated in head frame with geometry-based method, so that a mapping relationship between two direction features is proposed to calculate the direction of the visual axis. As the direction features are formulated with the accurately estimated parameters, the complexity of mapping relationship could be reduced and a better fitting performance can be achieved. To prevent the noticeable extrapolation errors, direction features with uniform angular intervals for fitting the mapping are retrieved over human's field of view. Additionally, an efficient single-point recalibration method is proposed with an updated eyeball coordinate system, which reduces the burden of calibration procedures significantly. Our experiment results show that the calibration and recalibration methods could improve the gaze estimation accuracy by 35 percent (from a mean error of 2.00 degrees to 1.31 degrees) and 30 percent (from a mean error of 2.00 degrees to 1.41 degrees), respectively, compared with the state-of-the-art methods.
Quantumness of Pure-State Ensembles via Coherence of Gram Matrix Based on Generalized α-z-Relative Rényi Entropy

The Gram matrix of a set of quantum pure states plays key roles in quantum information theory. It has been highlighted that the Gram matrix of a pure-state ensemble can be viewed as a quantum state, and the quantumness of a pure-state ensemble can thus be quantified by the coherence of the Gram matrix [Europhys. Lett. \textbf{134} 30003]. Instead of the $l_1$-norm of coherence and the relative entropy of coherence, we utilize the generalized $α$-$z$-relative Rényi entropy of coherence of the Gram matrix to quantify the quantumness of a pure-state ensemble and explore its properties. We show the usefulness of this quantifier by calculating the quantumness of six important pure-state ensembles. Furthermore, we compare our quantumness with other existing ones and show their features as well as orderings.
Adaptive Unsupervised-Shadow-Detection Approach for Remote-Sensing Image Based on Multichannel Features

Shadow detection is an essential research topic in the remote-sensing domain, as the presence of shadow causes the loss of ground-object information in real areas. It is hard to define specific threshold values for the identification of shadow areas with the existing unsupervised approaches due to the complexity of remote-sensing scenes. In this study, an adaptive unsupervised-shadow-detection method based on multichannel features is proposed, which can adaptively distinguish shadow in different scenes. First, new multichannel features were designed in the hue, saturation, and intensity color space, and the shadow properties of high hue, high saturation, and low intensity were considered to solve the insufficient feature-extraction problem of shadows. Then, a dynamic local adaptive particle swarm optimization was proposed to calculate the segmentation thresholds for shadows in an adaptive manner. Finally, experiments performed on the Aerial Imagery dataset for Shadow Detection (AISD) demonstrated the superior performance of the proposed approach in comparison with traditional unsupervised shadow-detection and state-of-the-art deep-learning methods. The experimental results show that the proposed approach can detect the shadow areas in remote-sensing images more accurately and efficiently, with the F index being 82.70% on the testing images. Thus, the proposed approach has better application potential in scenarios without a large number of labeled samples.
Programmable Broad Learning System to Detect Concealed and Imbalanced Baggage Threats

Manual screening of baggage at airports, shopping malls, and shipments to identify potentially dangerous items is a time-consuming process that requires the unwavering efforts of a human observer. Numerous researchers have addressed this issue by developing autonomous threat detection systems. However, the performance of these systems is still vulnerable to high occlusion and unbalanced contraband data. In this paper, we present a novel programmable CNN-driven broad learning system (BLS) that automatically adapts its design specifications to effectively recognize the concealed and imbalanced contraband data depicted within the baggage X-ray scans. First, the input scan is passed to the CNN backbone to extract distinct latent features. These features are then passed to the BLS model, which determines whether the scan contains potentially dangerous items or not. Additionally, the BLS’s architecture (within the proposed framework) is programmed in such a way that no human effort is required to optimize it for producing the best threat detection performance. This novel design adaptation is performed via heuristics and greedy searches that quantify the importance of each edge fusing the adjacent node pairs to optimize the network’s overall performance. The proposed system is thoroughly tested on three datasets, namely GDXray, SIXray, and COMPASS-XP, on which it leads the state-of-the-art by 2.94%, 19.33%, and 13.38%, respectively, in terms of F1 score.
Addressing Hiccups in Conversations with Recommender Systems

Conversational Agents (CAs) employing voice as their main interaction mode produce natural language utterances with the aim of mimicking human conversations. To unveil hiccups in conversations with recommender systems, we observed users interacting with CAs. Our findings suggest that those occur as users struggle to start the session, as CAs do not appear exploratory, and as CAs remained silent after offering recommendation(s) or after reporting errors. Users enacted mental models derived from years of experience with Graphical User Interfaces, but also expected human-like characteristics such as explanations and proactivity. Anchoring on these, we designed a dialogue model for a multimodal Conversational Recommender System (CRS) mimicking humans and GUIs. We probed the state of hiccups further with a Wizard-of-Oz prototype implementing this dialogue model. Our findings suggest that participants rapidly adopted GUI mimicries, cooperated for error resolution, appreciated explainable recommendations, and provided insights to improve persisting hiccups in proactivity and navigation. Based on these, we provide implications for design to address hiccups in CRS.
Convolutional Neural Networks- Bidirectional Long Short Term Memory Based Fake Review Classification

When it comes to deciding whether or not to purchase a product, online reviews have recently surpassed all other sources of information. Fake product reviews have become a lucrative business in recent years because they can either promote or degrade a product depending on how they are written. For many business owners, detecting fake reviews has been a hot topic of discussion. This study uses data preprocessing, feature extraction, and classification to detect fabricated product reviews, and the results are published. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are two common deep learning models that can discover complicated patterns in textual data. Long Short-Term Memory (LSTM) is a recurrent neural network with a tree topology that analyses variable-length sequential data. The bi-directional LSTM allows you to examine certain sequences from front to back as well as back to front. After extracting preprocessing information, a convolutional neural network with Bidirectional Long Short-Term Memory (CNN-BiLSTM) technology is utilized to identify and authenticate the review. At the end of the process, the effectiveness of the proposed strategy is assessed. The data demonstrate that the proposed strategy is effective regardless of the used goods or emotions.
Big data and ML: Interaction &amp; Challenges

A broad variety of applications are making use of machine learning (ML). In recent years, it's been pushed to the forefront because of the advertising of big data. In the face of massive data, ml algorithms haven't been this promising, and this is due to massive breaches in databases. However, although big data allows ML algorithms to identify finer patterns as well as generate more precise and timely recommendations than ever before, it poses significant obstacles to ML, such as model adaptability and edge systems. A framework for ML on large data (MLBiD) is presented in this study to lead the consideration of its advantages and disadvantages. Pre-processing, learning, and assessment are all part of the ML framework. Big data, users, domains, and systems are all included in the framework as well. Issues and challenges related to protecting database, gathered reports, program resources, which are faced by the system has been pointed out. Objective lies in presenting solutions for the same using ML.
Exploration of COVID 19 Tweets Data for the Prediction of Negative Ontologies through Deep Learning Techniques

COVID-19 is an infectious disease, which was first appeared in December 2019 in Wuhan, China. This virus has spread all over the world. So in a situation like this, Twitter is helping people by giving the latest information and to connect with others. As the WHO giving health information, this paper work is an implementation of automation for extracting details of Covid-19 from the latest Tweets of Twitter Social media. Most of the people started with Negative tweets about covid19, but with increasing time people shifted towards positive and neutral comments. At some time most of the comments are about winning against coronavirus. To understand the people’s opinion towards this pandemic through their tweets, we have tried to come up with an algorithm that will try to analyze the tweets using the modern computational power and some of the advanced algorithms and finally concluded at a point. Sentiment analysis using LSTM (Long Short Term Memory) which is a type of Recurrent Neural Networks, has been applied to tweets having covid19 Hash tags to see people’s reactions to the pandemic. The tweets are classified and labeled as positive, negative, and neutral then visualized the result. Tweets are categorized into three classes and derive some useful patterns from them and trying to come up with some generalized algorithms so that it cannot only be applied for Covid19 or some health-related, rather apply all kind of tweets or some other social media platform such as Instagram or LinkedIn.
An augmented reality study for public participation in urban planning

Ongoing urbanisation processes invoke immense construction activities, for which citizens often participate in planning. Yet, imagining planned buildings based on visual representations is a highly demanding task. While traditional methods, such as construction spans, 2D, or 3D visualisation often fail to offer a complete picture, we propose Augmented Reality (AR) as a more adequate tool. We first present an evaluation of the suitability of AR compared to construction spans for a future building and assess which degree of abstraction of AR is most effective, as well as difficulty of interpreting them correctly. In a between-subjects field study we compare construction spans and a prototype AR application including three levels of detail (LOD) of the same building project. Participants solve two estimation tasks using the construction spans and six estimation tasks using the AR application, before answering a questionnaire on the different visualisation methods. We find participants are confident about the potential of AR, but no significant differences between the different LOD groups in subjective assessment. Results suggest that previous knowledge (e.g. in GIS) may have a positive impact on dimension estimation performance. Also, details, such as façade elements or windows, could facilitate estimation tasks because they allow inferences about a building's size.
Adaptive 2D skeleton deformation based on view agnostic network for action recognition

Research in skeleton-based human action recognition has generated great interest from the computer vision community, owing to the feasibility and popularity of skeleton data. Some broadly used 2D pose estimation methods, which can be used to generate high-quality skeletons in real time using only low-power equipment, improve 2D skeleton-based action recognition. However, view variations in action representations influence their performance. In this paper, a view agnostic network for 2D skeleton-based action recognition, which narrows the representation differences in view variations by applying an adaptive frame-level skeleton deformation before feature extraction, is proposed. Specifically, the deformation is realized by a body-level affine transformation and a joint-level offset compensation, which aims to approximatively determine a new observation viewpoint for better intragroup consistency in a learning-based data-driven manner. Meanwhile, an adaptive spatial–temporal graph convolutional LSTM (ASTGCN-LSTM), which can model the co-occurrence relationship in the spatial–temporal domain for skeleton sequences more effectively, is introduced. Experiments are conducted on Kinetics-Skeleton and NTU120 RGB+D action recognition datasets, which are used to demonstrate the effectiveness of the proposed method.
Student assistants in Future Classroom Labs moving between figured worlds and becoming a resource for developing professional digital competence in teacher education

This article examines student teachers employed as student assistants in initial teacher education (ITE) in Future Classroom Labs (FCL) in Norway, and aims to explore their participation in different figured worlds as students and student assistants respectively, but also what ITE could potentially gain from employing such student assistants.The data gathered are 15 qualitative interviews with student assistants and FCL managers in ITE.The study takes an activity-theoretical and cultural psychological approach to their situated learning, and their navigation through and between the different figured worlds in which they take part.The study shows that student assistants, understandably, enter the job with a student perspective, but that they, over the course of their employment in the FCL, develop a new perspective (a development perspective), giving them a unique, dual perspective as both student and teacher educator.The study suggests that the student assistants can contribute to both overcoming the student-educator dichotomy and bridging elements of the theory-practice gap.The study also highlights how the third space of digital technology and the FCL could potentially blur the same dichotomy.Meanwhile, it shows some potential conflicts related to role confusion, if the responsibilities and expectations of students and educators are not clearly mapped and communicated.Yet overall, the study suggests potential with having student assistants in FCLs in ITE.
Simulation Performance Comparison of A*, GLS, RRT and PRM Path Planning Algorithms

Path planning is among the essential qualities of an autonomous robot. The ability to build a collision-free pathway from a pre-defined point to another is known as path planning. There are a variety of approaches offered, all of which vary depending on the search pattern and the map representation method. In this study, four robust path planning algorithms, namely: Probabilistic Roadmaps (PRMs), A-star, the Rapidly Exploring Random Trees (RRTs), and Generalized Laser Simulator (GLS), were simulated and their performance was measured and compared according to the total path distance covered, search time and path smoothness. The result obtained reveals that all the four algorithms could navigate and generate a feasible through the 2D map successfully. The GLS algorithm performs better in all the measured parameters followed by the PRM, RRT, and then the A* algorithm.
Chaladohn: Website for Avoiding of Online Shopping Scams in Thailand

This paper presents the website called Chaladohn (cha-lad-ohn). This website was designed and developed to aid Thai victims from online scammers; a collaborative project by the Faculty of Engineering, Rajamangala University of Technology Phra Nakhon and the Investigation Division, Metropolitan Police Division 8. The major key features provided by this website are checking and alerting users about scammers from names, surnames, telephone numbers, bank accounts, and/or e-wallet accounts. Therefore, online shoppers can avoid scams if the checked results show evidence. Furthermore, users can add new lists of scammers into the database of the system when they identify themselves and provide reliable evidence. After the research and development, the website went through beta testing and received an average voting score of 4.24±0.95 out of 5 from 122 subjects, which is considered excellent. Furthermore, in real practice, police officers utilized the information from the system for arresting scammers associated with several cases within a few weeks after launching Chaladohn.
Securing Clinical Information Through Multimedia Watermarking Techniques

Today, in this technological world, the information available can be easily copied, manipulated, as well as broadcasted among different channels, which should be protected. In the case of the medical field, the reliability and authenticity of e-medical data is one of the serious concerns. The patient's data without the knowledge of user might be utilized by the intruders. Medical data is highly valuable for the purpose of diagnosis and treatment. Due to increase in the usage of internet, copyright protection, content authentication, and identity theft are considered to be the bottleneck issues for content proprietors. To resolve those issues, the authors focused on the study of watermarking technology, which is used to improve the security of data and protect information from unauthorized access. Multimedia watermarking plays a predominant role in preserving the information from unauthorized access. This chapter aims to provide the detailed review of various watermarking techniques, their applications for security purposes, and the e-verification of medical information.