Reviewerly/exHarmony huggingface dataset:
MAUTH: Continuous User Authentication Based on Subtle Intrinsic Muscular Tremors

Continuous authentication is viewed to be increasingly important for mobile devices, which store a wide range of private data and sensitive information of users. Traditional continuous authentication methods need user inputs (e.g. typing, sliding). In this work, we present MAUTH, a zero-effect continuous authentication scheme for mobile devices. With the built-in motion sensors on commercial off-the-shelf (COTS) devices, MAUTH can continuously extract, classify and verify the unique tremor features of users on how their body intrinsically shakes during the normal use of such devices. As a result, it is extremely difficult if not impossible to reproduce the same set of tremors as individuals differ in their muscle development. We implement MAUTH as a software on Android-based smartphones, which demonstrates that MAUTH is light-weight and unobtrusive to its users. We conduct extensive real-world experiments and trace-driven simulations in controlled and uncontrolled environments on 21 volunteers. The results show that MAUTH is difficult to counterfeit and achieves a low average false positive rate (FPR) of 6.73% under real-world spoofing attacks. Moreover, MAUTH is comfortable to use and can achieve a low average false negative rate (FNR) of 2.2% during uncontrolled and continuous usage of devices, leveraging isolation-forest-based classifiers trained with only 40 training samples.
Interdependencies and Integration of Smart Buildings and Smart Cities: A Case of Dubai

The literary meaning of the word ‘smart’ is bright, intelligent, high performing, which, by and large, is also true in the case of ‘smart’ buildings and cities. The present digital age (seems) to be a smart age and smart times, where everything is expected to be smart, including cities and their dwellings or buildings as living and working spaces. This study addresses Dubai, well known for its smart service initiatives, through Dubai Municipality (DM), and Dubai Electricity & Water Authority (DEWA). The Results reveal smart power and water management efficiencies and smart and effective delivery of civic services through the use of ICT. Dubai has plan to turn the sustainable city into power, water, infrastructure, information and communication efficient one by the 2021 Golden Jubilee year of the founding of UAE. The message of it is that the future is of smart dwellings and smart cities, and the concept of ‘smart’ in the case of ultramodern Dubai too should extend to the citizens.
RANCANG BANGUN OBJECT DETECTION PADA ROBOT SOCCER MENGGUNAKAN METODE SINGLE SHOT MULTIBOX DETECTOR (SSD MOBILENETV2)

Artificial intelligence or AI is a technology that emphasizes machine intelligence in respondinglike humans, developed to help support human work. AI has widely applied in various fieldssuch as industry, medical, education, business, and robotics. The development of AI in the fieldof robotics produces autonomous robots, one example is the KRSBI-Beroda soccer robot. Thisresearch discusses the application of AI in the design of object detection systems using thesingle-shot multibox detector (SSD) model on the KRSBI-Beroda soccer robot. This study aimsto produce an AI model in the form of an artificial neural network (ANN) implanted in the soccerrobot to distinguish between ball, goal, robot, and obstacle objects. This object detection systemwas built using a deep learning method assisted by the TensorFlow Object Detection APIframework using the MobileNetV2 SSD model which is run using the python programminglanguage on the NVIDIA Jetson Nano board which is integrated into the C922 Pro webcamcamera. The model was built using a dataset of 977 images consisting of 3064 objects thatwere trained as many as 200,000 steps on Google Colaboratory. The results showed a modelwith an average mAP of 0.80 with an average total loss of 1.5. Validation of the model resultedin a success rate of object prediction with an average accuracy of up to 98.45%.
Full-Field Operating Deflection Shape Measurement of a Structure With a Curved Surface Using a Three-Dimensional Continuously Scanning Laser Doppler Vibrometer System

Abstract This study proposes a novel general-purpose 3D continuously scanning laser Doppler vibrometer (CSLDV) system to measure 3D full-field vibration of a structure with a curved surface in a non-contact and fast way. The proposed 3D CSLDV system consists of three CSLDVs, a profile scanner, and an external controller, and is experimentally validated by measuring 3D full-field vibration of a turbine blade with a curved surface under sinusoidal excitation and identifying its operating deflection shapes (ODSs). A 3D zig-zag scan path is proposed for scanning the curved surface of the blade based on results from the profile scanner, and 6scan angles of mirrors in CSLDVs are adjusted based on relations among their laser beams to focus three laser spots at one location, and direct them to continuously and synchronously scan the proposed 3D scan path. A signal processing method that is referred to as the demodulation method is used to identify 3D ODSs of the blade. The first six ODSs from 3D CSLDV measurement have good agreement with those from a commercial 3D SLDV system with modal assurance criterion values larger than 95%. In the experiment, it took the 3D SLDV system about 900 seconds to scan 85 measurement points, and the 3D CSLDV system 115.5 seconds to scan 132,000 points, indicating that the 3D CSLDV system proposed in this study is much more efficient than the 3D SLDV system for measuring 3D full-field vibration of a structure with a curved surface.
Neural Belief Propagation for Scene Graph Generation

Scene graph generation aims to interpret an input image by explicitly modelling the objects contained therein and their relationships. In existing methods the problem is predominantly solved by message passing neural network models. Unfortunately, in such models, the variational distributions generally ignore the structural dependencies among the output variables, and most of the scoring functions only consider pairwise dependencies. This can lead to inconsistent interpretations. In this article, we propose a novel neural belief propagation method seeking to replace the traditional mean field approximation with a structural Bethe approximation. To find a better bias-variance trade-off, higher-order dependencies among three or more output variables are also incorporated into the relevant scoring function. The proposed method achieves the state-of-the-art performance on various popular scene graph generation benchmarks.
Products of Variables in Structural Equation Models

A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Products of Variables Model (PoV). Some useful and practical features of PoV models include the estimation of interactions between latent variables, latent variable moderators, manifest moderators with missing values, and manifest or latent squared terms. Expected means and covariances are analytically derived for a simple product of two variables and it is shown that the method reproduces previously published results for this special case. It is shown algebraically that using centered multiplicands results in an unidentified model, but if the multiplicands have non-zero means, the result is identified. The method has been implemented in OpenMx and Ωnyx and is applied in five extensive simulations.
A Study on the Operation of Extracurricular Programs: Focusing on the University of Education

Purpose: The purpose of this study was to propose an effective operational plan for organizing thecontent of extracurricular programs in order to nurture university students into becoming excellentteachers.&#x0D; Methods: By reviewing previous studies on extracurricular programs, the concept of extracurricularprograms was organized and the necessity of introducing extracurricular programs in universities ofEducation was extracted. Based on the characteristics and reality of the University of Education, thecriteria for selecting the contents of the extracurricular programs were set, and the contents of theprograms were selected accordingly.&#x0D; Results: The focus of the extracurricular programs is to cultivate moral character, and for this, itis necessary to cultivate thinking ability, improve moral sensitivity, and practice. As for the detailscorresponding to each of these items, reading and listening to lectures were assigned to foster thinkingability, movie appreciation and art experience were assigned to enhance moral sensitivity, and sharingactivities, various experience activities, and participation in school events were assigned to enhance theability to practice. The core of the actual operation plan is to inspire students' willingness to participateand increase effectiveness. In order to enhance the willingness to participate, we proposed a plan tolink excellent student awards and publicity, and to increase effectiveness, to link the programs withschool projects and classes.&#x0D; Conclusion: Considering the current situation of nurturing elementary school teachers, it is urgent tointroduce extracurricular programs. In this situation, this study is meaningful in that it suggests thecontents and operation method of extracurricular programs suitable for universities of Education.
Redefining Public Services in Academic Libraries

AbstractAbstractUser services are often seen as the public-facing work of a library. Yet, all aspects of a library are to provide service to users, including the back-of-the-house work of technical services or collections departments. When done with a user focus this behind-the-scenes work, while often imperceptible, should be considered a public service. Doing so allows back-of-the-house library staff to view their work more holistically and enables deeper collaboration across teams. By creating a service model where all aspects of the library serve the user, libraries can provide a strong user experience.Keywords: Academic librariescollectionspublic servicesservice modelsystemsuser services Notes1 Online Dictionary for Library and Information Science by Joan M. Reitz, accessed online at: https://products.abc-clio.com/ODLIS/odlis_about2 SaD has the following functional areas: electronic resource management, acquisitions, cataloging, systems support and maintenance, and website support. CaDS has the following functional areas: collection development and management, special collections and university archives, and scholarly communications.3 The service support priorities are articulated in the GVSU Libraries’ Digital Strategic Framework. https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=1023&context=library_reports4 Webpage about the library service platform (LSP) migration: https://www.gvsu.edu/library/library-services-platform-migration-100.htm5 GVSU Libraries collection development policy: https://www.gvsu.edu/library/collection-development-policy-106.htm6 PDA and DDA programs are one and the same, varying in name based on the provider’s identification of their particular program.
An Overview of Blockchain Technology for Intellectual Property Management

Blockchain is a distributed digital ledger of cryptographically signed transactions grouped into a chain of blocks. The emergence of Blockchain has changed how information can be stored and processed securely. The research interest in Blockchain has significantly evolved as the technology has found applications beyond the finance industry. Blockchain has gone beyond the simple financial transaction application to include being used to securely share patient records, smart contracts, digital identity and assets management. As the technology evolves more and more industrialists, researchers are starting to explore its potential use in managing intellectual property. This paper explores the feasibility of adopting Blockchain for IP management, highlighting potential challenges and possible solutions.
NIX-TTS: Lightweight and End-to-End Text-to-Speech Via Module-Wise Distillation

Several solutions for lightweight TTS have shown promising results. Still, they either rely on a hand-crafted design that reaches non-optimum size or use a neural architecture search but often suffer training costs. We present Nix- TTS, a lightweight TTS achieved via knowledge distillation to a high-quality yet large-sized, non-autoregressive, and end-to-end (vocoder-free) TTS teacher model. Specifically, we offer module-wise distillation, enabling flexible and independent distillation to the encoder and decoder module. The resulting Nix - TTS inherited the advantageous properties of being non-autoregressive and end-to-end from the teacher, yet significantly smaller in size, with only 5.23M parameters or up to 89.34% reduction of the teacher model; it also achieves over 3.04x and 8.36x inference speedup on Intel-i7 CPU and Raspberry Pi 3B respectively and still retains a fair voice naturalness and intelligibility compared to the teacher model. We provide pretrained models and audio samples of Nix-TTS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/rendchevi/nix-tts.
Can differentiated and personalized mixed-reality simulations transform teacher learning?

Mixed-reality simulations (MRSs) create new opportunities for responsive teacher learning.This experimental study examined, starting from baseline Simulation 0 (Sim0) across three MRSs, the effect of differentiated coaching treatment in Simulation 1 (Sim1), and personalized practice in Simulation 2 (Sim2) on teachers' provision of high-information feedback to avatar students as measured by the mean of feedback frequency weighted by quality.Motivational-supportive factors of MRS, including teacher perceptions of cognitive load, realism, and task-value were also examined.Participants were teachers at different career points (i.e., preservice: n = 69; early career: n = 66; and in-service: n = 68).Block randomization by grade level taught (elementary and secondary) and assessment of current feedback practices achieved pretreatment equivalence.Quantitative analyses revealed coaching significantly increased teachers' use of high-information feedback.Teacher-selected personalized practice also impacted feedback.For example, the Sim2 decision to continue where the Sim1 action stopped was significantly related to decreases in clarify and value feedback and increases in pushing student thinking.Overall, differentiated and personalized professional development (PD) through MRS was valued by educators across the career trajectory and led to a complex evolution in teacher practices.However, the effects of the MRS were moderated by teaching experience.Teachers reported the MRS felt realistic and valuable; however, early career teachers' perceptions were significantly lower than their preservice and in-service peers, especially under the coaching condition.In-service teachers reported greater cognitive load than less experienced teachers.Our discussion explores transforming teacher education across a career trajectory through assessing teaching expertise, differentiating PD, and personalizing practice.
Keypoint Aware Robust Representation for Transformer-Based Re-Identification of Occluded Person

Occluded person Re-identification is a challenging task which aims to find or distinguish a specific person when the human body is occluded by obstacles, other persons or oneself. Some recent state-of-the-art works adopting a transformer and/or pose-guided methods have improved the feature representation and performances, but are still in trouble with both weak representation and heavy structure. In this paper, we suggest the novel methods of transformer-based Re-identification for the occluded person as follows. First, in data augmentation, instead of deleting an arbitrary area, only a part containing the keypoint features of a person is deleted for effective learning in occlusion. Second, we suggest a unique hierarchical patch and feature based cross attention combining the reliability-enhanced heatmaps and the output of the transformer intermediate layer, which can more effectively pay attention to the non-occluded human region. Third, we propose an entire-partial loss function with non-similarity grouping for more robust feature representation. We compare our approach and various existing methods for the mAP and Rank-1 performances on the Occluded-Duke, Occluded-ReID, Market-1501 and DukeMTMC datasets. Experimental results demonstrate that our proposed model not only outperforms the existing methods, but also has the smallest scale among state-of-the-art methods.
Dynamic Mosaic algorithm for data augmentation

&lt;abstract&gt;&lt;p&gt;Convolutional Neural Networks (CNNs) have achieved remarkable results in the computer vision field. However, the newly proposed network architecture has deeper network layers and more parameters, which is more prone to overfitting, resulting in reduced recognition accuracy of the CNNs. To improve the recognition accuracy of the model of image recognition used in CNNs and overcome the problem of overfitting, this paper proposes an improved data augmentation approach based on mosaic algorithm, named Dynamic Mosaic algorithm, to solve the problem of the information waste caused by the gray background in mosaic images. This algorithm improves the original mosaic algorithm by adding a dynamic adjustment step that reduces the proportion of gray background in the mosaic image by dynamically increasing the number of spliced images. Moreover, to relieve the problem of network overfitting, also a Multi-Type Data Augmentation (MTDA) strategy, based on the Dynamic Mosaic algorithm, is introduced. The strategy divides the training samples into four parts, and each part uses different data augmentation operations to improve the information variance between the training samples, thereby preventing the network from overfitting. To evaluate the effectiveness of the Dynamic Mosaic algorithm and the MTDA strategy, we conducted a series of experiments on the Pascal VOC dataset and compared it with other state-of-the-art algorithms. The experimental results show that the Dynamic Mosaic algorithm and MTDA strategy can effectively improve the recognition accuracy of the model, and the recognition accuracy is better than other advanced algorithms.&lt;/p&gt; &lt;/abstract&gt;
Self-supervised Video-centralised Transformer for Video Face Clustering

This article presents a novel method for face clustering in videos using a video-centralised transformer. Previous works often employed contrastive learning to learn frame-level representation and used average pooling to aggregate the features along the temporal dimension. This approach may not fully capture the complicated video dynamics. In addition, despite the recent progress in video-based contrastive learning, few have attempted to learn a self-supervised clustering-friendly face representation that benefits the video face clustering task. To overcome these limitations, our method employs a transformer to directly learn video-level representations that can better reflect the temporally-varying property of faces in videos, while we also propose a video-centralised self-supervised framework to train the transformer model. We also investigate face clustering in egocentric videos, a fast-emerging field that has not been studied yet in works related to face clustering. To this end, we present and release the first large-scale egocentric video face clustering dataset named EasyCom-Clustering. We evaluate our proposed method on both the widely used Big Bang Theory (BBT) dataset and the new EasyCom-Clustering dataset. Results show the performance of our video-centralised transformer has surpassed all previous state-of-the-art methods on both benchmarks, exhibiting a self-attentive understanding of face videos.
Effective Document Image Enhancement Using tokens-to-token Transformer Network

Document image enhancement is a fundamental and important stage for attaining the best performance in any document analysis assignment because there are many degradation situations that could harm document images, making it more difficult to recognize and analyze them. In this paper, we propose to employ a Tokens-to-Token Transformer net- work for document image enhancement, a novel encoder-decoder architecture based on a tokens-to-token vision transformer. The proposed architecture uses a tokens-to-token architecture in the encoder section. Each image is divided into a set of tokens with a de- fined length using the ViT model, which is then applied several times to model the global relationship between the tokens. However, the conventional tokenization of input data does not adequately reflect the crucial local structure between adjacent pixels of the input image, which results in low efficiency. Instead of using a simple ViT and hard splitting of images for the document image enhancement task, we employed a progressive tokeniza- tion technique to capture this local information from an image for achieving more ef- fective results. Experiments on various DIBCO and H-DIBCO benchmarks demonstrate that the proposed model outperforms the existing CNN and ViT-based state-of-the-art methods. In this research, the primary area of examination is the application of the pro- posed architecture to the task of document binarization. The source code will be made available at https://github.com/RisabBiswas/T2T-BinFormer.
Do Content Management Systems Impact the Security of Free Content Websites?

This paper investigates the potential causes of the vulnerabilities of free content websites to address risks and maliciousness. Assembling more than 1,500 websites with free and premium content, we identify their content management system (CMS) and malicious attributes. We use frequency analysis at both the aggregate and per category of content (books, games, movies, music, and software), utilizing the unpatched vulnerabilities, total vulnerabilities, malicious count, and percentiles to uncover trends and affinities of usage and maliciousness of CMS’s and their contribution to those websites. Moreover, we find that, despite the significant number of custom code websites, the use of CMS’s is pervasive, with varying trends across types and categories. Finally, we find that even a small number of unpatched vulnerabilities in popular CMS’s could be a potential cause for significant maliciousness.
A Group Clustering Recommendation Approach Based on Energy Distance

Recommendation system focus on the individual recommendation and using relationships between users and users or between items and items by distance or similarity measures. In reality, there are many situations when recommending to individuals is not as important as recommending to groups. Researches on the relationship between a group and a group have not yet been interested in the recommendation. This paper mainly focus on applying the energy distance for group recommendation system. The proposed recommendation model is evaluated on the Jester5k and the MovieLens datasets. The experiment result shows the feasibility of applying the potential energy for the group recommendation problems.