Reviewerly/exHarmony huggingface dataset:
An Efficient and Reliable Chaos-Based IoT Security Core for UDP/IP Wireless Communication

The ultimate focus of this paper is to provide a hyperchaos-based reconfigurable platform for the real-time securing of communicating embedded systems interconnected in networks according to the Internet of Things (IoT) standards. The proposed platform’s Register Transfer Level (RTL) architecture is entirely developed and designed from scratch using the VHSIC Hardware Description Language (VHDL). The original idea consists of exploiting the nonlinearity of a discretized and optimized 4D Lorenz hyperchaotic system as an encryption keystream generator in a symmetric cryptosystem to secure wireless communicating embedded systems and adapted to the UDP/IP protocol. It was necessary to go through three essential steps to achieve this goal. First, a lightweight and energy-efficient hyperchaos-based encryption IP core is designed, implemented on an FPGA circuit and dedicated to IoT device security, denoted Hyperchaotic-based IoT Device Security Core (HC-IoT-DSC). The designed encryption IP core combines three subsystems: a multiple key size hyperchaotic key generator (HC-KG), a hyperchaotic synchronization by dynamic feedback modulation technique (HCS-DFM), and an online FIPS 140-2-based built-in self-security test (BISST) module. Second, a secure UDP/IP stack is totally implemented using the VHDL language. Third, the proposed architecture was integrated into real-world and real-time secure wireless communication at a distance of 2 km between two delocalized network nodes employing the Xilinx ML605 FPGA platform and the ZigBee E800-DTU module. A panoply of online/offline investigations and experiments were carried out intensely, deeply, and thoroughly to analyze, evaluate and validate the robustness and security aspects of the proposed scheme regarding all the aspects related to embedded system security. Notably, the evaluations were conducted in two phases for all the platform components before and after integrating the proposed security core in real-time wireless communication. The investigations and implementation findings validate that the proposed architecture can attain good performances, and confirm the feasibility of the adopted approach for IoT applications. Furthermore, the timing and power efficiency results present an excellent trade-off between design performance and high-security achievement.
Digital Value Stream Mapping: Application of UWB Real Time Location Systems

Value stream mapping (VSM) is a valuable practice employed by industry experts to identify inefficiencies in the value chain due to its visual representation capability and general ease of use. VSM supports the visualization of product and material flows as a snapshot and enhances understanding of production behavior across multiple domains; however, it can lead to oversimplification, tedious/inaccurate data collection, and can have a static nature. Through improved digital integration this paper presents a semiautomated VSM solution that leverages dynamic Real Time Location System (RTLS) data to track motion throughout a production floor. This solution incorporates a high precision UWB RTLS, that allows for the real-time analysis and formulation of different state maps to track processes, equipment, people, and materials on the manufacturing floor. This multi-state mapping outcome has demonstrated the ability to allow for the rapid comparisons of various state maps and supports more effective and robust decision making. Based on initial results the system was able to continuously map accurate and reliable shop floor data and update new maps with limited human intervention, demonstrating the ability for RTLS to enhance VSM.
Application of Active Learning Strategies in Metaverse to Improve Student Engagement: An Immersive Blended Pedagogy Bridging Patient Care and Scientific Inquiry in Pandemic

Problem: The pandemic requires the expanded use of technology and the implementation of innovative strategies to improve online learning experience. We aim to explore an online pedagogy that exposes learners to a realistic clinical scenario and stimulates active learning to improve student engagement and bridge patient care and scientific inquiry.Approach: We converted sessions of continuing medical education with emphasis on clinical problem case study and skill training in 2022 to a new blended online pedagogy based on metaverse where we applied modified TBL utilizing the strengths of PBL and mechanism map for clinical problem solving, and the PAR cycle for clinical training. Then we used an online survey tool to compare learners’ satisfaction between traditional online TBL model and the blended teaching model in metaverse and evaluate its efficacy in improving learners’ confidence in self-directed learning, communication skills, collaborative learning, problem analyzing and solving, innovation, critical reflection and proficiency in clinical skills.Outcomes: Student engagement was improved. Learners were more satisfied with the blended teaching model in metaverse in terms of accessibility of the programme, stable and immersive experience of the sessions, straightforwardness of the sessions, challenges in the sessions, interaction in the sessions, innovation of the sessions, opportunities for participation and variety in forms. The new pedagogy had some positive impact on learners’ confidence in clinical reflection, creativity, collaborative learning, and communication skills. Next Steps: Blended teaching model in metaverse will bring positive impact to online active learning. The exposure to patient-driven inquiry in metaverse may increase learners’ interest in sustaining the bridge between patient care and scientific inquiry. Next steps will be including more participants and analytical items such as learners’ performance for analysis, and discussion on detrimental effect that medical education in metaverse may bring to teachers and learners.
Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text

Brands and consumers alike have become creators and distributors of digital words, thus generating increasing interest in insights to be gained from text-based content. This work develops an algorithm to identify textual paralanguage, defined as nonverbal parts of speech expressed in online communication. The authors develop and validate a paralanguage classifier (called PARA) using social media data from Twitter, YouTube, and Instagram (N = 1,241,489 posts). Using auditory, tactile, and visual properties of text, PARA detects nonverbal communication cues, aspects of text often neglected by other word-based sentiment lexica. This work is the first to reveal the importance of textual paralanguage as a critical indicator of sentiment valence and intensity. The authors further demonstrate that automatically detected textual paralanguage can predict consumer engagement above and beyond existing text analytics tools. The algorithm is designed for researchers, scholars, and practitioners seeking to optimize marketing communications and offers a methodological advancement to quantify the importance of not only what is said verbally but how it is said nonverbally.
Bidirectional Spatio-Temporal Feature Learning With Multiscale Evaluation for Video Anomaly Detection

Video anomaly detection aims to detect the segments containing abnormal events from video sequence, which is a current research hotspot due to the importance in maintaining social security. Recent detection methods tend to build frame reconstruction or frame prediction model based on deep learning to learn features of events. The reconstruction-based methods reproduce the input frame one-to-one, inevitably losing some temporal features. The prediction-based methods predict frames according to the natural time order, but ignore the reverse time information, causing the deviation in information learning. Besides, anomaly evaluation methods based on patch-level error neglect the diversity of object sizes in complex scenes, and it is difficult to determine the optimal size of the error patch accurately. For these issues, we propose a bidirectional spatio-temporal feature learning framework with multi-scale anomaly evaluation strategy. A video sequence is input to a double-encoder double-decoder network, and bidirectional spatio-temporal features are obtained for bidirectional prediction by fusing forward and backward features extracted from the two encoders. The multi-scale anomaly evaluation method is implemented based on error pyramid and mean pooling, which effectively detects target objects with different sizes. Experiments on several publicly video datasets show that our method outperforms most of existing methods.
SmartDagger: a bytecode-based static analysis approach for detecting cross-contract vulnerability

With the increasing popularity of blockchain, automatically detecting vulnerabilities in smart contracts is becoming a significant problem. Prior research mainly identifies smart contract vulnerabilities without considering the interactions between multiple contracts. Due to the lack of analyzing the fine-grained contextual information during cross-contract invocations, existing approaches often produced a large number of false positives and false negatives. This paper proposes SmartDagger, a new framework for detecting cross-contract vulnerability through static analysis at the bytecode level. SmartDagger integrates a set of novel mechanisms to ensure its effectiveness and efficiency for cross-contract vulnerability detection. Particularly, SmartDagger effectively recovers the contract attribute information from the smart contract bytecode, which is critical for accurately identifying cross-contract vulnerabilities. Besides, instead of performing the typical whole-program analysis which is heavy-weight and time-consuming, SmartDagger selectively analyzes a subset of functions and reuses the data-flow results, which helps to improve its efficiency. Our further evaluation over a manually labelled dataset showed that SmartDagger significantly outperforms other state-of-the-art tools (i.e., Oyente, Slither, Osiris, and Mythril) for detecting cross-contract vulnerabilities. In addition, running SmartDagger over a randomly selected dataset of 250 smart contracts in the real-world, SmartDagger detects 11 cross-contract vulnerabilities, all of which are missed by prior tools.
Curriculum-Style Local-to-Global Adaptation for Cross-Domain Remote Sensing Image Segmentation

Although domain adaptation has been extensively studied in natural image-based segmentation tasks, the research on cross-domain segmentation for very-high-resolution (VHR) remote sensing images (RSIs) still remains underexplored. The VHR RSI-based cross-domain segmentation mainly faces two critical challenges: 1) large area land covers with many diverse object categories bring severe local patch-level data distribution deviations, thus yielding different adaptation difficulties for different local patches and 2) different VHR sensor types or dynamically changing modes cause the VHR images to go through intensive data distribution differences even for the same geographical location, resulting in different global feature-level domain gaps. To address these challenges, we propose a curriculum-style local-to-global cross-domain adaptation framework for the segmentation of VHR RSIs. The proposed curriculum-style adaptation performs the adaptation process in an easy-to-hard way according to the adaptation difficulties that can be obtained using an entropy-based score for each patch of the target domain and, thus, well aligns the local patches in a domain image. The proposed local-to-global adaptation performs the feature alignment process from the locally semantic to globally structural feature discrepancies and consists of a semantic-level domain classifier and an entropy-level domain classifier that can reduce the above cross-domain feature discrepancies. Extensive experiments have been conducted in various cross-domain scenarios, including geographic location variations and imaging mode variations, and the experimental results demonstrate that the proposed method can significantly boost the domain adaptability of segmentation networks for VHR RSIs.
Gradient Leakage Attack Resilient Deep Learning

Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromising model training quality, and yet secretly reconstruct sensitive training data using leaked gradients with high attack success rate. Although deep learning with differential privacy is a defacto standard for publishing deep learning models with differential privacy guarantee, we show that differentially private algorithms with fixed privacy parameters are vulnerable against gradient leakage attacks. This paper investigates alternative approaches to gradient leakage resilient deep learning with differential privacy (DP). <i>First</i>, we analyze existing implementation of deep learning with differential privacy, which use fixed noise variance to injects constant noise to the gradients in all layers using fixed privacy parameters. Despite the DP guarantee provided, the method suffers from low accuracy and is vulnerable to gradient leakage attacks. <i>Second</i>, we present a gradient leakage resilient deep learning approach with differential privacy guarantee by using dynamic privacy parameters. Unlike fixed-parameter strategies that result in constant noise variance, different dynamic parameter strategies present alternative techniques to introduce adaptive noise variance and adaptive noise injection which are closely aligned to the trend of gradient updates during differentially private model training. <i>Finally</i>, we describe four complementary metrics to evaluate and compare alternative approaches. Extensive experiments on six benchmark datasets show that differentially private deep learning with dynamic privacy parameters outperforms the deep learning using fixed DP parameters, and existing adaptive clipping approaches in all aspects: compelling accuracy performance, strong differential privacy guarantee, and high attack resilience.
Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al., Public Health Nutr 12(4):444-454, 2009), anemia affects 1.62 billion people constituting 24.8% of the population and is considered the world's second leading cause of illness. The Peripheral Blood Smear (PBS) examination plays an important role in evaluating hematological disorders. Anemia is diagnosed using PBS. Being the most powerful analytical tool, manual analysis approach is still in use even though it is tedious, prone to errors, time-consuming and requires qualified laboratorians. It is evident that there is a need for an inexpensive, automatic and robust technique to detect RBC disorders from PBS. Automation of PBS analysis is very active field of research that motivated many research groups to develop methods using image processing. In this paper, we present a review of the methods used to analyze the characteristics of RBC from PBS images using image processing techniques. We have categorized these methods into three groups based on approaches such as RBC segmentation, RBC classification and detection of anemia, and classification of anemia. The outcome of this review has been presented as a list of observations.
Online Mathematics Education during the COVID-19 Pandemic: Didactic Strategies, Educational Resources, and Educational Contexts

One of the impacts of the COVID-19 pandemic has been restrictions on mobility and thus the closure of schools. This has had consequences on the teaching strategies of primary mathematics educators who were not familiar with online education. Most schools in Chile have adopted virtual and hybrid classes to continue educational processes. From a quantitative approach with a sample of n = 105 primary school educators and through an online survey, we analyzed how educators implemented the mathematics curriculum during the pandemic using various didactic strategies and educational resources, as well as their respective contexts. The results show that there is a relationship between the level of technical knowledge of teachers, the years of experience, and the types of teaching strategies they use. Likewise, differences were found between educators in rural and urban sectors according to the use of teaching strategies and the types of educational resources used. Regarding the didactic strategies, it is shown that the emerging strategies most used are metaphorical and analogical, whereas in traditional strategies the automation of procedures is imposed. The implications for practice include suggestions and guidelines for improving the training and professional development of mathematics teachers including increasing and strengthening the number and quality of teachers’ didactic strategies and online pedagogical management skills and promoting metacognition through virtual forums. Finally, we discuss the context of the use of didactic strategies in mathematics during the pandemic, analyzing its challenges and opportunities.
Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model

Sentiment analysis of netizens' comments can accurately grasp the psychology of netizens and reduce the risks brought by online public opinion. However, there is currently no effective method to solve the problems of short text, open word range, and sometimes reversed word order in comments. To better solve the above problems, this article proposes a hybrid model of sentiment classification, which is based on bidirectional encoder representations from transformers (BERT), bidirectional long short-term memory (BiLSTM) and a text convolution neural network (TextCNN) (BERT-BiLSTM-TextCNN). The experimental results show that (1) the hybrid model proposed in this article can better combine the advantages of BiLSTM and TextCNN; it not only captures local correlation while retaining context information but also has high accuracy and stability. (2) The BERT-BiLSTM-TextCNN model can extract important emotional information more flexibly in text and achieve multiclass classification tasks of emotions more accurately. The innovations of this study are as follows: (1) the use of BERT to generate word vectors has the advantages of more prior information and a full combination of contextual semantics; (2) the BiLSTM model, as a bidirectional context mechanism model, can obtain contextual information well; and (3) the TextCNN model can obtain important features well in the problem of text classification, and the combined effect of the three modules can significantly improve the accuracy of emotional multilabel classification.
Customer comfort during service robot interactions

Abstract Customer comfort during service interactions is essential for creating enjoyable customer experiences. However, although service robots are already being used in a number of service industries, it is currently not clear how customer comfort can be ensured during these novel types of service interactions. Based on a 2 × 2 online between-subjects design including 161 respondents using pictorial and text-based scenario descriptions, we empirically demonstrate that human-like (vs machine-like) service robots make customers feel more comfortable because they facilitate rapport building. Social presence does not underlie this relationship. Importantly, we find that these positive effects diminish in the presence of service failures.
Aspect2Labels: A novelistic decision support system for higher educational institutions by using multi-layer topic modelling approach

Aspect-based sentiment analysis (ABSA) has gained a rising concentration recently. It aims to provide a set of aspect terms and sentiments from a piece of text. Educational Data Mining (EDM) is now an essential tool for analysing pedagogical data. In academic institutions, student feedback is an influential gauge to measure the quality of the teaching–learning process. It helps higher education institutions to reconsider and improve their policies for student recruitment and retention. This paper proposed a situation awareness multi-layer topic modelling and enhanced hybrid machine learning approach for evaluating students’ textual feedback data in academic institutions. The proposed Aspect2Labels (A2L) approach is divided our system into three layers. To preserve semantic information, we extracted general aspects terms in the first layer known as high-level aspects. We pulled low-level aspects terms associated with high-level aspect terms in the second layer and the third layer used for sentiment orientation. We used zero-shot learning, LDA, and different variants of LDA for the aspect extraction process. We performed annotation on unlabelled students’ comments using our proposed A2L approach, and we obtained 91.3% accuracy in this process. We developed and tested novel algorithms for aspect terms mapping to label each aspect term to corresponding feedback. Different machine learning algorithms have been used to classify sentiments according to extracted aspects. We have also proposed and used Variable Global Feature Selection Scheme (VGFSS) and Variable Stopwords Filtering (VSF) to improve the performance of classifiers. We have managed to get 97% and 93% accuracy on the test dataset using Support Vector Machine (SVM) and Artificial Neural Networks (ANN), respectively. We highly suggest that our novel approach of aspect-oriented sentiment analysis could provide adequate understanding to analyse students’ feedback.
Convolutional Neural Network Based Partial Face Detection

Due to the massive explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In the world, there are a lot of scenarios where a simple crime can be prevented before it may even happen or find the person responsible for it. A face is one distinctive feature that we have and can differentiate easily among many other species. But not just different species, it also plays a significant role in determining someone from the same species as us, humans. Regarding this critical feature, a single problem occurs most often nowadays. When the camera is pointed, it cannot detect a person’s face, and it becomes a poor image. On the other hand, where there was a robbery and a security camera installed, the robber’s identity is almost indistinguishable due to the low-quality camera. But just making an excellent algorithm to work and detecting a face reduces the cost of hardware, and it doesn’t cost that much to focus on that area. Facial recognition, widget control, and such can be done by detecting the face correctly. This study aims to create and enhance a machine learning model that correctly recognizes faces. Total 627 Data have been collected from different Bangladeshi people's faces on four angels. In this work, CNN, Harr Cascade, Cascaded CNN, Deep CNN & MTCNN are these five machine learning approaches implemented to get the best accuracy of our dataset. After creating and running the model, Multi-Task Convolutional Neural Network (MTCNN) achieved 96.2% best model accuracy with training data rather than other machine learning models.