Reviewerly/exHarmony huggingface dataset:
Deformation Measurement of Scaling and Rotation Objects Based on Digital Image Correlation Method with Color Information

The digital image correlation (DIC) method is widely used in deformation measurement due to the advantages of being non-contact, high precision, full field measurement, and requiring simple experimental equipment. Traditionally, the grayscale speckle patterns captured by a monochromatic camera are used in the DIC method. With the growing development of consumer color cameras, there is a great potential for developing color information in the DIC method. This paper proposes a scaling- and rotation-invariant DIC (SRI-DIC) method based on the color speckle patterns. For the integer-pixel matching stage, the scaling- and rotation-invariant color histogram feature is used to estimate the initial value of the deformation parameters. For the sub-pixel matching stage, a new error function using the three-channel information of color camera is proposed to avoid the influence of illumination changes. In addition, this paper proposes a reverse retrieve strategy instead of the forward search to reduce the search time. Experiments show that the proposed SRI-DIC algorithm not only has scaling and rotation invariance, robustness, and high efficiency, but also that the average accuracy of the strain result can reach 0.1%.
Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge

Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to. However, existing conversational agents and datasets do not consider such comprehensive information, and thus they have a limitation in generating the utterances where the knowledge and persona are fused properly. To address this issue, we introduce a call For Customized conversation (FoCus) dataset where the customized answers are built with the user's persona and Wikipedia knowledge. To evaluate the abilities to make informative and customized utterances of pre-trained language models, we utilize BART and GPT-2 as well as transformer-based models. We assess their generation abilities with automatic scores and conduct human evaluations for qualitative results. We examine whether the model reflects adequate persona and knowledge with our proposed two sub-tasks, persona grounding (PG) and knowledge grounding (KG). Moreover, we show that the utterances of our data are constructed with the proper knowledge and persona through grounding quality assessment.
FakeGuard: Exploring Haptic Response to Mitigate the Vulnerability in Commercial Fingerprint Anti-Spoofing

How to defend against presentation attacks via artificial fake fingers is a core challenge in fingerprint biometrics.The trade-off among security, usability, and production cost has driven researchers to reach a common standpoint, i.e., integrate the commercial fingerprint technology with anti-spoofing detection (e.g., ridge traits).These anti-spoofing solutions are perceived as sufficiently resilient under the assumption that a fake finger can never closely relate to a live finger due to either composition of spoofing materials or non-automated manufacturing errors.In this paper, we first identify the vulnerability of in-practice antispoofing solutions in commercial fingerprint products.Instead of using expensive 3D fake fingers [1] (above USD $1000), we mimic a more realistic scenario where an attacker fabricates high-precision fake fingerprints using low-cost polyvinylacetate materials [2] (less than USD $50).Building on this, we introduce a practical and secure countermeasure, namely FakeGuard, to overcome the exposed vulnerability.We examine the nature of 3D haptic response effect that arises when a fingertip epidermis interacts with a tactile surface and reflects the disparate anatomy of live and fake fingers.Unlike the previous mitigation strategies, FakeGuard offers both hardware and software compatibility with existing fingerprint scanners.As the first exploration of hapticbased anti-spoofing solution, we demonstrate the capability of FakeGuard to prevent known and unknown fake finger attacks with an average detection error of 1.4%.We also examine and prove FakeGuard resilience against seven different physical attacks, e.g., brute-force through pressure variations or partial fingerprints, haptic response alteration via advanced spoofing materials or side-channel interference, and denial-of-service attacks by manipulating the moisture, lighting, and temperature of the ambient environment.
Development and Simulation of Cyberdisaster Situation Awareness Models

Cyberdisasters require an organization’s disaster team to be prepared. Disaster events are difficult to predict, but the impact of this risk on an organization is large. However, organizations sometimes struggle in being prepared for disaster situations. Here, awareness of disaster situations when analysing priority disasters (e.g., earthquakes and pandemics) and how to mitigate them can help an organization’s preparedness. Mitigation scenarios need to be determined and simulated so that a disaster team is ready to face disaster. Using Endsley’s situational awareness model and a tabletop exercise, this study aimed to help a disaster team determine cyberdisaster risk priority and assess a team’s preparedness for dealing with a cyberdisaster. The situation awareness model was divided into two stages: awareness of cyberdisaster situations and tabletop evaluations. Awareness of a disaster situation was carried out by determining the highest priority for disaster risk using the fuzzy failure modes and effects analysis (FMEA) method. The results of the first study show that the high-risk category contains ransomware attacks during pandemics and earthquakes. The second study performed a tabletop simulation questionnaire survey of earthquakes and ransomware attacks during a pandemic for several disaster teams with 152 respondents. The results of the survey evaluation of the earthquakes and ransomware attacks simulation survey show that the effect factors of cyberdisaster simulation decisions are 95% system capability (p &lt; 0.05), 90% knowledge (p &lt; 0.05), and 90% awareness of a disaster situation (p &lt; 0.05); these factors show the effect of a disaster team’s decision during a tabletop simulation. The novelty of this research lies in building a model for how an organizational process determines the priority of a cyberdisaster tabletop simulation and the factors that contribute to increasing a disaster team’s awareness in dealing with cyberattacks.
A Privacy Protection Scheme for Cross-Chain Transactions Based on Group Signature and Relay Chain

Recently, with the rapid development of blockchain technology, the information interaction and value transfer problems between different blockchains have become the focus of research. The cross-chain technology is to solve the cross-chain operation problems of assets and data between different chains. However, the existing cross-chain technology has the problem of identity privacy leakage. Therefore, this article proposes a cross-chain privacy protection scheme for consortium blockchains based on group signature, certificate authority and relay chain. The scheme is divided into three cross-chain service layers, called the management layer, the transaction layer, and the group layer. The management layer is responsible for the forwarding of cross-chain transactions, the transaction layer includes the blockchains that actually participate in cross-chain transactions, and the group layer is responsible for group signature related work. Through this scheme, the identity privacy of both parties to the transaction can be protected during the cross-chain transaction process.
Visual Commonsense in Pretrained Unimodal and Multimodal Models

Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple.Text and image corpora, being subject to reporting bias, represent this worldknowledge to varying degrees of faithfulness.In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes.To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects.We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021).We then use our dataset to evaluate pretrained unimodal models and multimodal models.Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias.Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data.
Design of the Floating Hologram Method with a Reverse Pyramid Type for CT and MR Diagnosis in Clinical Room

In the field of medical diagnosis, big data and three-dimensional (3D) imaging diagnosis technology are being applied due to the development of these technologies. Using radiology diagnosis methods, medical staff are increasing their understanding and ability to explain symptoms to patients, but they are experiencing difficulties due to communication problems. Therefore, if the medical staff shows the lesion by providing the patient with a 3D image, the understanding of the patient can be increased. This paper proposes the design of a system to produce an inverted pyramid-shaped floating holographic image to increase the patient's understanding. The hologram system consists of an optical source generator and a beam mirror and utilizes a technology to plot an image using a 45° refraction angle of the beam of the optical source. Selected objects for observation were liver, colon, and lung, and to observe these tissues, a Computed Tomography (CT) image was input to the hologram system through the picture archiving and communication system (PACS), and the image was displayed. Tissues observed through the mirror can be observed from the left, right, front, and back with a 360° anterior view. Therefore, it is possible to observe at the desired position by the medical staff and the patient in the treatment room, and the image is large and clear, so it is very satisfying to observe. As a holographic imaging diagnostic system, it is expected that this study can be used in clinics, medical education rooms, and operating rooms in the future.
Development of a method for detecting GPS spoofing attacks on unmanned aerial vehicles

As in other vehicles, unmanned aerial vehicles (UAV) mainly use GPS (Global Positioning System) for the provision of navigation. Non-execution of necessary measures on UAV, availability of the devices used in the process of attack may cause GPS spoofing attack on UAV. The quick detection of the attack plays an important role in obtaining safety precautions. The use of artificial neural networks in the detection of such attacks is very convenient. Therefore, in the article new approach based on convolutional neural network (CNN) method is proposed in order to detect GPS spoofing attack. The new approach has been developed for two different types of UAVs. As a result of conducted experiments, high-accuracy detection of GPS spoofing attack has been provided.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction

More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability is underexplored.In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models.We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints.Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet.
Research on Data Visualization Technology Based on Python

This study aims to research on data visualization technology using Python language. Python is an open-source programming language developed with the community-based model. In this paper, different visual presentation types are introduced, such as visualization of text data, network visualization, and visualization of spatial information. Additionally, common data visualization techniques are presented in this paper. Finally, some data visualization tools (Smartbi, D3, Google Chart API, Processing, and Rapidminer) are described in this paper.
Smart Toys, Smart Tangibles, Robots and other Smart Things for Children

Smart toys are regarded as able to offer possibilities to develop social, cognitive, and behavioural skills (among others); however, while appealing, such claims are not yet substantiated by rigorous and sufficient scientific evidence. The first edition of the workshop on Smart Toys, Smart Tangibles, Robots and other Smart Things for Children took place at the 19th Interaction Design and Children Conference (IDC’20), bringing together experts from different fields working on smart technologies for children. Following the workshop, participants were invited to submit their work to this Special Issue in the International Journal of Child Computer Interaction. Together, these contributions address relevant issues in the emerging areas of smart toys and interactive technologies for children, offering different approaches and perspectives, such us as guidelines for the prototyping of innovative Toy Interfaces; guidelines to support the design of inclusive technologies for children with special needs, design considerations about the social aspects of the experience with the technology, concerns of data protection and children’s understanding of data. This editorial introduction draws attention to the great potential and need for furthering research on the field to unleash the potential that the new generation of smart toys and related devices may bring.
Learning to characterize performance regression introducing code changes

For many software projects, performance is a critical requirement. Such projects often create performance tests alongside their other tests which specifically check for performance issues. Detecting code changes responsible for performance regression is challenging due to the increasing number of daily committed changes. And so, it is hard to individually monitor these changes while being committed to the main branch. Existing studies try to solve this issue by predicting whether or not a commit will cause a performance regression to occur. The aim is that by creating a system that can successfully evaluate the need to run the performance tests on a change, we can reduce the number of times we run the tests, and thus save cost. In this paper, we propose an approach that directs testing to commits causing performance regression by building classifiers. The approach is addressing the severity of class imbalance any project could have due to the fact that code changes introducing regression are rare but costly. We found that Decision Forest with Random Over Sampling provides better detection for code changes introducing regression without hindering testing team by conducting unnecessary tests. With the aid of resampling techniques, the detection rate of code changes introducing regression improved by about 50% from traditional classifiers.
Enhancing English-Speaking Skills for Food and Beverage Services for Tourism Students Through A Collaborative Learning

The collaborative learning approach has been widely used to improve the quality of learning in a variety of courses. This present study used the collaborative approach in English learning for the specific purpose of restaurant services. The research used a mixed-method. The research data were obtained from the result of pre-and post-performance tests, participant observation notes, and unstructured interview notes. There were 35 students involved as the sample and informants. The quantitative data obtained were calculated by using a t-test and the qualitative data obtained were analyzed using content analysis. The significance value (2-tailed) of the implementation of the pre-test and post-test was 0.000 (p &lt; 0.05). Thus, the results of the pre-and post-test experienced a significant change. The students showed positive feedback toward the collaborative approach applied. Based on the findings, collaborative learning could enhance both students’ English-speaking skills and job-related skills.
Teacher digital competence development in higher education: Overview of systematic reviews

The scope of digital technology integration in university teaching has changed our understanding of teacher readiness and teacher competence. Recently, faced with the digitalisation of higher education (HE), the construct of teacher digital competence (TDC) has emerged. Although there are many recent systematic reviews on digital competence from a range of perspectives and geographic settings, such reviews often show a limited view of a larger digital competence landscape in HE. The current study on TDC development in HE aims to synthesise knowledge to provide an integrated and global assessment of existing evidence. We carried out a systematic overview, especially suited for identifying, synthesising and critically appraising published reviews on a given topic amidst an abundance of research. We identified three clear settings by synthesising 740 studies across 13 systematic reviews. Results reveal a significant interest in TDC in Spain, conducted by researchers in the field of educational technology concerned with teacher training and teacher professional development. We make recommendations to reorient the field by understanding TDC development through an integrated, transversal and holistic perspective; moving away from basic forms of research; and conducting and reporting research in line with methodological guidelines to ensure the highest possible standards. Implications for practice or policy: Stakeholders interested in better fostering TDC could complement training and evaluation with an integrated and systems-based approach, including sustaining an institutional culture that strategically supports TDC development. Researchers could move away from basic forms of research design in order to advance the field beyond self-assessment and evaluation studies. Systematic review research can be improved by following rigorous methodological guidelines, including critical appraisal and transparent methods to synthesise studies, to ensure the highest academic integrity.
How Can I Know What You Think?:Assessing Representational Similarity in Neural Systems

How do my mental states compare to yours? We suggest that, while we may not be able to compare experiences, we can compare neural representations, and that the correct way to compare neural representations is through analysis of the distances between them. In this paper, we present a technique for measuring the similarities between representations at various layers of neural networks. We then use the measure to demonstrate empirically that different artificial neural networks trained by backpropagation on the same categorization task, even with different representational encodings of the input patterns and different numbers of hidden units, reach states in which representations at the hidden units are similar.
The Confirmatory Factor Analysis (CFA) of E-Procurement Adoption Model in Malaysian Construction Industry

Structural equation modelling (SEM) is the first generation path modelling that is widely used by researchers and practitioners nowadays to analyse the interrelationship among variables in a model.In this study, the questionnaire was designed based on nine constructs of exogenous, mediating and endogenous used.Performance expectancy, effort expectancy, social influence and facilitating condition are as exogenous constructs.The mediating constructs consists of usage, user satisfaction, user anxiety and user resistance.Performance impact becomes one single endogenous construct.The questionnaires were distributed to 300 contractors from G4 and G5 classifications in Malaysia Construction Industry during the workshops organized by CIDB.The total of 250 questionnaires were returned to the researcher for the validity and reliability test in this study.The ultimate objective of this article is to acquire the best fit of a research instrument for the effective study using structural equation model (SEM) that enables the study to take into account the unreliable factors (items) between exogenous and endogenous constructs.The items of the constructs undergo the confirmatory factor analysis (CFA) procedure involved in the uni dimensionality test, convergent validity, construct validity and discriminant validity.The result revealed the constructs of the research model achieved the validity and reliability for other further analysis in acquiring high accuracy on the prediction outcomes.
Improving Span Representation for Domain-adapted Coreference Resolution

Recent work has shown fine-tuning neural coreference models can produce strong performance when adapting to different domains.However, at the same time, this can require a large amount of annotated target examples.In this work, we focus on supervised domain adaptation for clinical notes, proposing the use of concept knowledge to more efficiently adapt coreference models to a new domain.We develop methods to improve the span representations via (1) a retrofitting loss to incentivize span representations to satisfy a knowledgebased distance function and (2) a scaffolding loss to guide the recovery of knowledge from the span representation.By integrating these losses, our model is able to improve our baseline precision and F-1 score.In particular, we show that incorporating knowledge with endto-end coreference models results in better performance on the most challenging, domainspecific spans 1 .
Density Based Traffic Controller with Emergency Override using CCTV Camera

The aim of this project is to provide alternative solution for the traffic signal system in clearing high density traffic jam. Now a day’s more number of vehicles is coming on to the road creating more traffic congestion at any junction. The traffic congestion is a severe problem when there arises high density at a particular junction. Especially when there is an emergency like ambulance, fire brigade stuck in the traffic they require priority to go first. In such cases it is necessary to override the normal signal timings automatically. To overcome this problem, this project uses CCTV cameras on each side of junction. It assigns longer green light with the help of the micro controller whenever it sensing the heavy density and whenever it finds emergency vehicles stuck in traffic like ambulance, fire brigade etc they require priority to go first. In this manner it overrides the standard signal timings there by it saves the waiting time of the vehicular. This project uses micro controller interfacing with CCTV aligned in the sight configuration across the load for detecting the density. Once the image is captured from the CCTV footage, it is converted into greyscale image. The greyscale image is passed through the median filter in order to reduce the noise present in it. Further Canny edge detection finds the intensity gradients of the images by suppressing all the other edges that weak and not connected to the strong edges. Then based on the canny image the density is calculated and turns the green light on at the heavy density road side of junction