Reviewerly/exHarmony huggingface dataset:
BizarreVR: Dream-like bizarreness in immersive virtual reality induced changes in conscious experience of reality while leaving spatial presence intact

Differences in conscious experience of reality occur between waking, dreaming, and psychotic states. Between these states, there are systematic differences in the judgment about the reality of the experience when being confronted with bizarre breaks. However, the mechanisms underlying experience of reality in these different states are still unknown. To investigate the effect of bizarre breaks on experience of reality during the wake state, we propose a new paradigm using dream-like bizarreness and immersive virtual reality. Results showed that the realistic non-bizarre virtual environment induced high levels of reality judgment and spatial presence, whereas the confrontation with bizarre breaks induced high levels of experienced bizarreness. Moreover, experienced bizarreness significantly reduced reality judgment in both the bizarre and the realistic condition. Further, there was no effect of bizarre breaks on spatial presence. These results provide proof of concept for the new method to elicit natural bizarre experience within a realistic scenario.
Machine Learning Techniques for Anomaly Detection in Network Traffic

In today's technological era, anomaly detection is a major concern in front of network users. Due to the development of various network techniques, network users are also increased which leads to more traffic on the network, and due to this, it's very difficult to recognize the anomalous patterns. This paper discussed the overview of various ML techniques used to solve the problem of anomaly detection along with their pros and cons and also discussed here the framework/model&#x2019;s accuracy level. In this survey, strategies for identifying and mitigating abnormalities in network traffic are discussed and compared the result in terms of its accuracy and anomaly types. The current research gaps and important research concerns in network traffic anomaly detection are presented in detail. We hope that the analysis, comparisons, and after that, the identification of gaps will point out the researchers in the right direction for doing advanced development in this field.
Phase unwrapping algorithm based on phase edge tracking for dynamic measurement

Phase unwrapping is an essential procedure for fringe projection profilometry (FPP). To improve measurement efficiency and reduce phase unwrapping errors (PUEs) in dynamic measurement, a phase unwrapping algorithm based on phase edge tracking is proposed, which unwraps the current wrapped phase map with the aid of the previously unwrapped one. The phase edges are accurately tracked and their trajectories are used to divide the phase map into several regions, each of which is unwrapped either temporally or spatially according to its properties. It doesn't require extra patterns for phase unwrapping once the initial unwrapped phase map is obtained, thus significantly increasing the frame rate of the 3D result. Meanwhile, it greatly reduces the PUEs caused by noise amplification and motion-induced misalignment of phase edges. Experiments prove that it is capable of retrieving the absolute phase maps of complex dynamic scenes with high unwrapping accuracy and efficiency.
Incorporating Shape-Changing Food Materials Into Everyday Culinary Practices

In recent studies researchers have developed compelling and exciting shape-changing food (SCF) materials. Unfortunately, the adoption of SCFs in culinary practice is still complex. We present a research-through-design exploration of a novel SCF material that can be developed in restaurant kitchens using everyday kitchen appliances with the only addition of a commercial desktop 3D food printer. Our lightweight process involves the preparation of edible paper, which can be folded into origami shapes and is actuated through the addition of an acidic liquid. We facilitated participatory sessions with five chefs in their restaurants and evaluated our material. Our qualitative study contributes design guidelines on how to incorporate SCFs into chefs’ everyday culinary practices. Building on the chefs’ perspectives of our material and its culinary potential we conclude with a speculation of how SCF materials could enable novel multi-sensory and interactive food experiences.
Blockchain-based Platform for Secure Sharing and Validation of Vaccination Certificates

The COVID-19 pandemic has recently emerged as a worldwide health emergency that necessitates coordinated international measures. To contain the virus's spread, governments and health organisations raced to develop vaccines that would lower Covid-19 morbidity, relieve pressure on healthcare systems, and allow economies to open. Following the COVID-19 vaccine, the vaccination certificate has been adopted to help the authorities formulate policies by controlling cross-border travelling. To address serious privacy concerns and eliminate the need for third parties to retain the trust and govern user data, in this paper, we leverage blockchain technologies in developing a secure and verifiable vaccination certificate. Our approach has the advantage of utilising a hybrid approach that implements different advanced technologies, such as the self-sovereignty concept, smart contracts and interPlanetary File System (IPFS). We rely on verifiable credentials paired with smart contracts to make decisions about who can access the system and provide on-chain verification and validation of the user and issuer DIDs. The approach was further analysed, with a focus on performance and security. Our analysis shows that our solution satisfies the security requirements for immunisation certificates.
On the security of 6G use cases: Threat Analysis of 'All-Senses Meeting'

Following the innovations in 5G, with the upcoming 6G, new capabilities and features will enable a plethora of futuristic use-cases, empowering novel ideas and business innovations. Some examples of these use-cases are AI partners and the Internet of Senses (e.g., holograms). It is important to understand the architectural changes foreseen by 6G as well as possible threats never introduced before, starting with ensuring the availability and the privacy of the collected data from the sensors, the cost of which might be directly linked to the correct functioning of vital infrastructures and can even pertain to the safety of human lives. Hence, the understanding of these threats will help to implement the appropriate countermeasures. In this vein, we particularly focus on telepresence, specifically all-senses meeting use case. We first identify the assets involved and the threat agents that could cause harm. This allows us to extract the model related to threat analysis and quantify the threats and the vulnerability criticality. Then, we propose the necessary mitigation mechanisms to remedy the requirements of our threat analysis. Finally, we draw conclusions that could pave the way to future avenues of research.
An Sql Injection Detection Model Using Chi-Square with Classification Techniques

SQL Injection attacks is a common threat to web applications that utilizes poor input validation to implement attack on a target database. It is becoming a very serious problem in web application as successful leads to loss of integrity and confidentiality and this makes it a very sensitive issue of software security. This study gives a review on SQL Injection detection and prevention techniques using machine learning classifiers. Machine Learning approach has been found to be profound for SQLIA mitigation, which is implemented through defensive coding approach. An experimental analysis was performed to evaluate the performance of the learning classification algorithms to choose the best algorithm. It is imperative to note that a good number of the evaluated techniques were able to detect and prevent the SQLIA based on the KDD Test dataset. From the findings, Naive Bayes had the minimum Accuracy 80.01&#x0025;, Sensitivity as well as Specificity while Decision Tree had the highest Accuracy 98.11&#x0025;, Sensitivity and Specificity and therefore was chosen as the best classifier for SQLIA detection and prevention. Therefore, beyond Accuracy, other performance evaluation metrics are critical for optimal algorithm selection for predictive analytics.
A Qualitative Research on the Online Learning Experiences of College Students in Macao during the Pandemic

Studies have been conducted on college students’ experiences to learn online from the perspectives of learning motivation, environment, engagement and effectiveness. However, few have examined how the above factors are relate to each other and the nature of online learning for students across cultural regions. This study used the "categorical-content" analysis method of qualitative research through in-depth interviews with 37 college students. Based on this, it explored the online learning experiences of college students in Macao and constructed a theoretical model. When the mode of learning changed, students faced new challenges and underwent a process of adaptation and transformation. The main research findings include: (a) Whether students have continuous intention to learn online mainly depends on whether students can make reflection-in-action plan in self-regulated learning. (b) The technical environment brings students intuitive experiences, while the potential environment brings students stress-free learning and external interference; (c) Based on the attention on technology and emotion, students have different views on the effectiveness of interaction. (d) Due to cultural differences, local students pay more attention to the emotional experiences, while Chinese mainland students have rational understandings of learning effectiveness. In the interweaving experiences of emotion and rationality, the effectiveness of learning is improved. The recommendations for future online instructional design with a view of improving students’ learning experiences are discussed in this paper.
What to Blame? On the Granularity of Fault Localization for Deep Neural Networks

Validating Deep Neural Networks (DNNs) used for classification is of paramount importance; an approach for this consists in (i) executing the DNN over the test dataset, (ii) collecting information about classifications, and (iii) applying fault localization (FL) techniques to identify the neurons responsible for the misclassifications. DNNs can have multiple misclassification types, and so neurons responsible for one type could be different from those responsible for another type. However, depending on the granularity of the analyzed dataset, FL may not reveal these differences: failure types more frequent in the dataset may mask less frequent ones. We here propose a way to perform FL for DNNs that avoids this masking effect by selecting test data in a granular way. We conduct an empirical study, using a spectrum-based FL approach for DNNs, to assess how FL results change by changing the granularity of the analyzed test data. Namely, we perform FL by using test data with two different granularities: following a state-of-the-art approach that considers all misclassifications for a given class together, and the proposed fine-grained approach. Results show that FL should be done for each misclassification, such that practitioners have a more detailed analysis of the DNN faults and can make a more informed decision on what to fix in the DNN.
Classification of Coarse Aggregate Particle Size Based on Deep Residual Network

Traditional aggregate particle size detection mainly relies on manual batch sieving, which is time-consuming and inefficiency. To achieve rapid automatic detection of aggregate particle sizes, a mechanical symmetric classification model of coarse aggregate particle size, based on a deep residual network, is proposed in this paper. First, aggregate images are collected by the optical vertical projection acquisition platform. The collected aggregate images are corrected, and their geometric parameters are extracted. Second, various digital image processing methods, such as size correction and morphological processing, are used to improve the image quality and enlarge the image dataset of different aggregate particle sizes. Then, the deep residual network model (ResNet50) is built to train the aggregate image dataset to achieve accurate classification of aggregate sizes. Finally, compared with the traditional single geometric particle size classification model, the comparative results show that the accuracy of the coarse aggregate classification model proposed in this paper is nearly 20% higher than that of the traditional method, reaching 0.833. The proposed model realizes the automatic classification of coarse aggregate particle size, which can significantly improve the efficiency of aggregate automatic detection.
Medium is a powerful message: Pictures signal less power than words

This research shows people are perceived as less powerful when they use pictures versus words. This effect was found across picture types (company logos, emojis, and photographs) and use contexts (clothing prints, written messages, and Zoom profiles). Mediation analysis and a mediation-by-moderation design show this happens because picture-use signals a greater desire for social proximity (versus distance) than word-use, and a desire for social proximity is associated with lower power. Finally, we find that people strategically use words (pictures) when aiming to signal more (less) power. We refute alternative explanations including differences in the content of pictures and words, the medium's perceived appropriateness, the context's formality, and the target's age and gender. Our research shows pictures and words are not interchangeable means of representation. Rather, they signal distinct social values with reputational consequences.
Prototype of a Transition Metal Visualization App for the Learning of Stereochemistry in a General Chemistry Course: Initial Findings and Reflections

As part of COVID-19 preparedness, a student-developed, Android-based app was used as a pre-laboratory learning aid for a molecular modeling laboratory in a first-year general chemistry course. A worksheet activity with trigger codes and questions related to spatial features of transition metal complexes was designed. Using the Transition Metal Visualization (TMVis) app, learners activated augmented reality models on their mobile phones. Actual models were then constructed in class. Mean stereochemistry post-laboratory grades increased with stronger worksheet grades, though differences between worksheet grades for app and non-app users were not significant. Feedback revealed that approximately 60% of respondents preferred the physical modeling kit, citing ease of visualization and hands-on interactivity as its key strengths. Improvements for future work were proposed in light of the results and limitations.
Wearable Surgical Optical Tracking System Based on Multi-Modular Sensor Fusion

Optical tracking system (OTS) is an important part of surgical navigation and has been commonly employed in clinical practice due to high accuracy and precision. However, current OTS is bulky, which reduces the flexibility for clinical procedures. To address this drawback, we propose a wearable surgical instrument tracking system based on a multi-modular sensor fusion approach. The proposed tracking system consists of a multispectrum red–green–blue-depth (RGB-D) sensor and the accompanying tracking target. It significantly increases the agility of the tracking device while obtaining high tracking accuracy. We propose a plane-based refinement algorithm (PRA) to estimate marker centers, which ensures the accuracy and precision for the short baseline of the wearable stereo camera. A 2-D marker-based instrument identification algorithm (MIIA) has also been proposed to identify different instruments. Both simulations and real experiments have been carried out to validate the feasibility and performance of the proposed system. These accurate and robust experimental results have verified the feasibility of the proposed wearable surgical instrument tracking system.
Glaze Epochs: Understanding Lifelong Material Relationships within Ceramics Studios

The "material turn" in HCI has placed a renewed focus on informing design from the relationships found in material-based interactions. While several ethnographic works provide insight into how practitioners converse with materials, it is less understood how these conversations transform into a skilled practitioner's relationship with a material. We examine the material practice of glazing that gives ceramics its decorative and functional characteristics and involves fusing mixtures of silica, alumina, and flux onto a clay body through kiln firing. This practice evolves over decades developing from multiple trajectories including theoretical foundations, systematic experimentation, and happy accidents. This work describes virtual site visits with six expert ceramicists and documents how material knowledge is externalized in practice, teaching, and the studio environment. We synthesize our findings into a framework to inform the design of material interactions that moves beyond discrete and momentary material encounters towards lifelong material epochs.
Screening dementia and predicting high dementia risk groups using machine learning

New technologies such as artificial intelligence, the internet of things, big data, and cloud computing have changed the overall society and economy, and the medical field particularly has tried to combine traditional examination methods and new technologies. The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence. This review introduces: (1) the definition, main concepts, and classification of machine learning and overall distinction of it from traditional statistical analysis models; and (2) the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry. As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia, various machine learning algorithms such as boosting model, artificial neural network, and random forest were used for predicting dementia. The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.
From wave-particle duality to wave-particle-mixedness triality: an uncertainty approach

Abstract The wave-particle duality, as a manifestation of Bohr’s complementarity, is usually quantified in terms of path predictability and interference visibility. Various characterizations of the wave-particle duality have been proposed from an operational perspective, most of them are in forms of inequalities, and some of them are expressed in forms of equalities by incorporating entanglement or coherence. In this work, we shed different insights into the nature of the wave-particle duality by casting it into a form of information conservation in a multi-path interferometer, with uncertainty as a unified theme. More specifically, by employing the simple yet fundamental concept of variance, we establish a resolution of unity, which can be interpreted as a complementarity relation among wave feature, particle feature, and mixedness of a quantum state. This refines or reinterprets some conventional approaches to wave-particle duality, and highlights informational aspects of the issue. The key idea of our approach lies in that a quantum state, as a Hermitian operator, can also be naturally regarded as an observable, with measurement uncertainty (in a state) and state uncertainty (in a measurement) being exploited to quantify particle feature and wave feature of a quantum state, respectively. These two kinds of uncertainties, although both are defined via variance, have fundamentally different properties and capture different features of a state. Together with the mixedness, which is a kind of uncertainty intrinsic to a quantum state, they add up to unity, and thus lead to a characterization of the wave-particle-mixedness complementarity. This triality relation is further illustrated by examples and compared with some popular wave-particle duality or triality relations.
A Novel Secure Root Key Updating Scheme for LoRaWANs Based on <i>CTR_AES</i> DRBG 128

A long-range wide area network (LoRaWAN) has a weakness in terms of key management: its root key is static, meaning that it never changes. Since all cryptographic keys are derived from the root key, such a weakness endangers LoRaWAN security. This paper proposes a novel secure root key updating scheme for LoRaWAN that involves periodically changing the root key value based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CTR_AES DRBG 128</i> algorithm. The scheme consists of two sequential phases: the initialization process that occurs at the end device and the root key update process that occurs at the join server. To validate the proposed scheme, we conduct randomness and communication protocol tests. The results indicate that the proposed scheme has a high degree of randomness, passes all 15 statistical tests in the NIST suite, and has secure communication protocols. The analyses verify that the new scheme has a mechanism to resist replay attacks and protects data integrity. The main advantage of the scheme is that it has a perfect forward secrecy feature that enhances the root key updating scheme with a lightweight computational load for the end device; additionally, root key updating can be performed automatically from a remote distance within the LoRaWAN coverage network. The proposed scheme also supports simultaneous updates for implementation in a large area with many devices. Finally, the overall results demonstrate how our proposed scheme may compensate for the weakness of LoRaWANs in key management and improve their security performance.
Deep Learning and Image Processing Combined Organization of Shirakawa’s Hand-Notated Documents on OBI Research

The purpose of this work is to organize Professor Shirakawa&#x2019;s newly discovered hand-notated documents on his Oracle Bone Inscription (OBI) research. During the second half of the 20th-century, Professor Shirakawa was a prominent researcher on Chinese culture, especially in the field of OBIs, and he left behind many research documents he had notated by hand. However, some of these documents have not been properly organized yet. The reorganization of OBIs is not only helpful for better understanding his research but also for further studying about OBIs in general and their importance in ancient Chinese history. Part of Professor Shirakawa&#x2019;s hand-notated research documents on OBIs is introduced to the world for the first time in this work. For organizing these documents, Firstly, a morphology-based segmentation method is applied to segment the characters in the documents and then the paper proposes a slight neural network for removing the noise from the mis- segmented characters. Finally, a dynamic K-means method is applied for classifying the segmented characters. Specifically, the histogram of oriented gradients (HOG) descriptors are extracted as features, and the class number of K is dynamically decided by using the silhouette coefficient. The results of this evaluation showed that the accuracy of noise and character classification after segmentation achieves 96.50%, and the accuracy of character classification achieves 74.91%. The results demonstrate the effectiveness of the proposed method.