Reviewerly/exHarmony huggingface dataset:
HUTNet: An Efficient Convolutional Neural Network for Handwritten Uchen Tibetan Character Recognition

Recognition of handwritten Uchen Tibetan characters input has been considered an efficient way of acquiring mass data in the digital era. However, it still faces considerable challenges due to seriously touching letters and various morphological features of identical characters. Thus, deeper neural networks are required to achieve decent recognition accuracy, making an efficient, lightweight model design important to balance the inevitable trade-off between accuracy and latency. To reduce the learnable parameters of the network as much as possible and maintain acceptable accuracy, we introduce an efficient model named HUTNet based on the internal relationship between floating-point operations per second (FLOPs) and Memory Access Cost. The proposed network achieves a ResNet-18-level accuracy of 96.86%, with only a tenth of the parameters. The subsequent pruning and knowledge distillation strategies were applied to further reduce the inference latency of the model. Experiments on the test set (Handwritten Uchen Tibetan Data set by Wang [HUTDW]) containing 562 classes of 42,068 samples show that the compressed model achieves a 96.83% accuracy while maintaining lower FLOPs and fewer parameters. To verify the effectiveness of HUTNet, we tested it on the Chinese Handwriting Data sets Handwriting Database 1.1 (HWDB1.1), in which HUTNet achieved an accuracy of 97.24%, higher than that of ResNet-18 and ResNet-34. In general, we conduct extensive experiments on resource and accuracy trade-offs and show a stronger performance compared with other famous models on HUTDW and HWDB1.1. It also unlocks the critical bottleneck for handwritten Uchen Tibetan recognition on low-power computing devices.
Consumers and producers: whose personalisation is it?

Organisations need data to personalise. Over time, data have become characterised by the variety, volume and velocity, creating challenges for data management and use. The diversity of sources of data and their location contribute to this problem, which requires mining techniques to extract what is valuable. But data acquisition is a means, not an end in itself, and, in order to personalise, sense has to be made of this information and then applied to consumer groups and individuals that is valued by both the producer and consumer. This chapter summarises the use of data, Big Data and Artificial Intelligence for personalisation and its benefits. More controversial and less explicit are the limitations of data and the implications of data ownership, authenticity, security and privacy to consumers and producers. The possibility of mapping these dimensions and the boundaries of regulation and control in a rapidly evolving field forms the conclusion to this chapter.
Cache-Based Privacy Protection Scheme for Continuous Location Query

Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user's demand, we use a variable-order Markov model to predict the user's future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users' location privacy.
The contemporary phenomenon of personalisation

Personalisation has become a significant aspect of many people's lives in the 21st century, with goods, services and a wide range of relationships between consumers and designs taking on some of the characteristics of traditional ‘bespoke’ items. Yet, despite increasing awareness of personalisation, its manifestations and implications remain uncertain and obscure. This chapter provides an introduction to this rich and complex phenomenon, analyses definitions that take different perspectives on it, and argues that its richness and complexity requires flexibility in investigating it. The recent salience of personalisation is due to developments in technologies that create systems that gather, process, analyse and store data, environments where personalisation takes place and platforms that enable the production and distribution of personalised products. The chapter demonstrates that as commercial use of data and algorithms has advanced, the boundaries between producer-led, consumer-led and co-created personalisation have become more blurred. This leads to a summary of the problems of surveillance, access and privacy that have emerged and the multidisciplinary discussion that has developed round them. The chapter concludes with an outline of the structure and content of the book and underlines the necessity for the end users to be more involved in how personalisation plays out.
Performance Evaluation of a Cloud Datacenter Using CPU Utilization Data

Cloud computing and its associated virtualization have already been the most vital architectures in the current computer system design. Due to the popularity and progress of cloud computing in different organizations, performance evaluation of cloud computing is particularly significant, which helps computer designers make plans for the system’s capacity. This paper aims to evaluate the performance of a cloud datacenter Bitbrains, using a queueing model only from CPU utilization data. More precisely, a simple but non-trivial queueing model is used to represent the task processing of each virtual machine (VM) in the cloud, where the input stream is supposed to follow a non-homogeneous Poisson process (NHPP). Then, the parameters of arrival streams for each VM in the cloud are estimated. Furthermore, the superposition of estimated arrivals is applied to represent the CPU behavior of an integrated virtual platform. Finally, the performance of the integrated virtual platform is evaluated based on the superposition of the estimations.
KNN and Webgis Classification to Recommend Mountain Location According to Hiker Abilities

The increasing number of climbers has an impact on the need for a system that can recommend mountains for climbingaccording to the ability of climbers. This study aims to create a system that can help climbers determine the mountainaccording to their abilities. Researchers use one of the methods in data mining, namely classification, using the K =Nearest Neighbor (K-NN) algorithm.This research has produced a web-based system where this system can classify and provide recommendationsaccording to the ability of climbers. This system is equipped with a hiking trail map which is expected to help make iteasier for climbers to choose the mountain they will climb.
A Multi-Level Consistency Network for High-Fidelity Virtual Try-On

The 2D virtual try-on task aims to transfer a target clothing image to the corresponding region of a person image. Although an extensive amount of research has been conducted due to its immense applications, this task still remains a great challenge to handle some complicated issues (e.g., non-rigid shapes, large occlusions and arbitrary poses). To this end, we propose a novel network with structural and textural consistency-preserving mechanism for producing high-fidelity try-on images. Specifically, we first generate the semantic layout of a clothing-agnostic person to obtain the segmentation map, which is used as the transforming conditions of the target clothes. Based on a recurrent network structure, the transform lookup is performed to iteratively update a dense flow. Then, we adopt a thin-plate-spline-based warping method to estimate the coarse offset flow for all key-point positions. Guided by this sparse flow, a multi-scale deformable convolution module is designed to further iteratively predict the fine offsets for densely sampled positions, by which the clothing item and person shape can be accurately aligned. Finally, we develop a refinement module to effectively fuse the global and local features, which can render accurate geometric structures of the body parts and maintain texture sharpness of the clothes. Extensive experiments on benchmark datasets demonstrate that our method outperforms other state-of-the-art methods in terms of quantitative and qualitative try-on results. The code is available on: https://github.com/TJU-WEIHAO/MLCN .
Persons repairing: reficio ergo sum

‘Repairing things repairs people’ is the principle that underlies this chapter, which connects it to the subject of the book – repair has implications for personhood. Noting the current interest in repair in popular culture and academia, as well as the broad scope of the topic, we approach it from the perspective of individual material engagement. This leads to a discussion of the place skill has in repair practices that connects it to a post-cognitivist approach to human knowledge. The chapter moves on to think about the social location of repair practices, reviewing several ethnographic studies of repair and analysing how individual skilled people and organisations can stimulate repair in the context of concern for the environmental impact of over-consumption. Via a brief excursion that takes in Heidegger's zuhanden/vorhanden concept, to pinpoint the moment when the need to repair an item becomes clear, the chapter notes the benefits that accrue to individuals from the calming, mindful actions that repairing often involves. It finishes by briefly noting that repair aligns with the ethics of technology that Hans Jonas's developed, based on the principle of responsible actions.
Intra-person multi-task learning method for chronic-disease prediction

In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imputation for time series (BRITS) and the least absolute shrinkage and selection operator (LASSO); (2) a convolutional neural network and long short-term memory (CNN-LSTM) for single-task models; and (3) a novel intra-person multi-task learning CNN-LSTM framework developed to predict multiple chronic diseases simultaneously. Our multi-task learning method between correlated chronic diseases produced a more stable and accurate system than single-task models and other baseline recurrent networks. Furthermore, the proposed model was tested using different time steps to illustrate its flexibility and generalization across multiple time steps.
Community Capacity and Resilience in Facing The Threat of Fire Disaster (Case Study: Barrang Lompo Island, Makassar City)

Abstract This research is based on the high intensity of fires in dense residential areas of Makassar City, which is caused by high vulnerability to fire and low community capacity and resilience. Barrang Lompo Island is an area with a fairly high population and density of buildings. So that the potential of fires is also likely to occur on Barrang Lompo. This study aimed to identify the level of community capacity and resilience using a community capital framework approach. The capacity stage analysis is carried out by converting qualitative data into a capacity assessment scale. Then, using expert opinion to assess the value of each variable using AHP method. Furthermore, classifying the level of Community capacity is classified using the scoring method. The results show that community capacity are classified as moderate to high, with a score of 1.89-2.31. In general, the capacity and resilience of fire, including individual capabilities, social capital, local culture, natural and physical capital, are moderate to high. Meanwhile, policy and financial aspects are quite low. In conclusion, to improve community resilience, it is recommended to improve individual preparedness through education, create a small island fire management policy, provide emergency transport and improve the local economy.
Semantic Similarity-Based Mobile Application Isomorphic Graphical User Interface Identification

Applying robots to mobile application testing is an emerging approach to automated black-box testing. The key to supporting automated robot testing is the efficient modeling of GUI elements. Since the application under testing often contains a large number of similar GUIs, the GUI model obtained often contains many redundant nodes. This causes the state space explosion of GUI models which has a serious effect on the efficiency of GUI testing. Hence, how to accurately identify isomorphic GUIs and construct quasi-concise GUI models are key challenges faced today. We thus propose a semantic similarity-based approach to identifying isomorphic GUIs for mobile applications. Using this approach, the information of GUI elements is first identified by deep learning network models, then, the GUI structure model feature vector and the semantic model feature vector are extracted and finally merged to generate a GUI embedding vector with semantic information. Finally, the isomorphic GUIs are identified by cosine similarity. Then, three experiments are conducted to verify the generalizability and effectiveness of the method. The experiments demonstrate that the proposed method can accurately identify isomorphic GUIs and shows high compatibility in terms of cross-platform and cross-device applications.
The framing of time-dependent machine learning models improves risk estimation among young individuals with acute coronary syndromes

Acute coronary syndrome (ACS) is a common cause of death in individuals older than 55 years. Although younger individuals are less frequently seen with ACS, this clinical event has increasing incidence trends, shows high recurrence rates and triggers considerable economic burden. Young individuals with ACS (yACS) are usually underrepresented and show idiosyncratic epidemiologic features compared to older subjects. These differences may justify why available risk prediction models usually penalize yACS with higher false positive rates compared to older subjects. We hypothesized that exploring temporal framing structures such as prediction time, observation windows and subgroup-specific prediction, could improve time-dependent prediction metrics. Among individuals who have experienced ACS (nglobal_cohort = 6341 and nyACS = 2242), the predictive accuracy for adverse clinical events was optimized by using specific rules for yACS and splitting short-term and long-term prediction windows, leading to the detection of 80% of events, compared to 69% by using a rule designed for the global cohort.
Bottom-up and Top-down Object Inference Networks for Image Captioning

A bottom-up and top-down attention mechanism has led to the revolutionizing of image captioning techniques, which enables object-level attention for multi-step reasoning over all the detected objects. However, when humans describe an image, they often apply their own subjective experience to focus on only a few salient objects that are worthy of mention, rather than all objects in this image. The focused objects are further allocated in linguistic order, yielding the “object sequence of interest” to compose an enriched description. In this work, we present the Bottom-up and Top-down Object inference Network (BTO-Net), which novelly exploits the object sequence of interest as top-down signals to guide image captioning. Technically, conditioned on the bottom-up signals (all detected objects), an LSTM-based object inference module is first learned to produce the object sequence of interest, which acts as the top-down prior to mimic the subjective experience of humans. Next, both of the bottom-up and top-down signals are dynamically integrated via an attention mechanism for sentence generation. Furthermore, to prevent the cacophony of intermixed cross-modal signals, a contrastive learning-based objective is involved to restrict the interaction between bottom-up and top-down signals, and thus leads to reliable and explainable cross-modal reasoning. Our BTO-Net obtains competitive performances on the COCO benchmark, in particular, 134.1% CIDEr on the COCO Karpathy test split. Source code is available at https://github.com/YehLi/BTO-Net .
Calculation of Theoretical Travel Time and Automatic Picking of Actual Travel Time in Seismic Data

We used the ray tracing technique based on the IASP91 Earth model to calculate the travel times in order to identify the phases. This technique can calculate the travel times for the seismic phases in the conventional travel time tables. The waveform data received from the stations in the Guangxi area are selected for analysis and discussion. The outcomes of the numerical modeling and its use demonstrate that there is good agreement in terms of the absolute differences between the calculated and theoretical travel times from the ISAP91 tables. The relative residuals are determined directly from the actual arrival times picking during the correlation analysis, and the validity of the travel time method for picking seismic phases by correlation analysis can be demonstrated.
Enhanced water cycle algorithm using Hookes and Jeeves method for clustering large gas data

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Hasnanizan Taib, Ardeshir Bahreininejad; Enhanced water cycle algorithm using Hookes and Jeeves method for clustering large gas data. AIP Conference Proceedings 10 January 2023; 2643 (1): 050056. https://doi.org/10.1063/5.0110484 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Universiti Teknologi Brunei air handling unit conversion

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Haji Muhammad Adam Haji Rosli, Mohamed Hairol Haji Mohamed Ali; Universiti Teknologi Brunei air handling unit conversion. AIP Conference Proceedings 10 January 2023; 2643 (1): 050054. https://doi.org/10.1063/5.0113639 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Accruement of nonlinear dynamical system and its dynamics: electronics and cryptographic engineering

In this chapter, we present a new chaotic structure, which is accruement of two nonlinear dynamical systems by using the proportional fractional derivative. A new chaotic structure is arrangement of two dynamical systems where one is a line equilibrium and the other is a rigid-body dynamical system. Chaotic dynamic systems are very sensitive to equation parameters and initial values, whereas rigid-body systems have zero deformation under the influence of applied external force and torque. The engendered chaotic structure qualitative and dynamical properties are studied by stability, phase portraits, and Lyapunov exponents. The phase trajectories of a chaotic system are presented by a Poincaré map, and the system behaves in chaotic manner. The new chaotic structure generates random numbers, which extensively proceed through NIST-800-22 testing to make sure that the data generated from the new chaotic structure are random. This randomness is used in cryptographic applications such as image, voice, and PDF file encryption. Security analysis is incorporated to make sure that the cryptographic applications are secure from potential third-party attacks. Electronic circuits are designed with passive components to ensure that the application generated from this new chaotic structure is of potential use.
Education 4.0: Case Study on Selection of Digital Learning Platform and Communication Tools for Future Education 4.0 in India

This Industrial Revolution 4.0 is in boom across the globe; which is an indication for Universities and Colleges to adopt the future i.e. Education 4.0. Since the Last Decade, there has been a drastic change in the field of education. It has changed from physical learning to digital learning due to Covid-19. As digital learning came into the picture many developments have happened in using industrial revolution 4.0. This transformation has taught all of us how to use ICT effectively for conducting the lectures for the students remotely and smoothly. ICT has enabled new ways of student learning with the challenge of the availability of good learning platforms and tools. Industrial Revolution 4.0 and Education 4.0 work hand in hand for bringing new ways of learning. Industry 4.0 is majorly working on Robotic Process Automation, Industrial Internet of Things, Artificial Intelligence, and Smart Technologies. All the Technologies which are associated with Industry 4.0 have a massive impact on individuals' everyday lifestyles. To improve education, learning skills, and personal skills in students, universities and colleges need to adopt Education 4.0 which is an evolution in the Education System. Teaching students digital technologies in their current syllabus will change the learning habits of the students which will in turn improve universities and colleges. Future of Education will be completely based on Education 4.0 which will restructure the current education system. This case study will be focusing on the selection of Digital Learning Platform and Communication Tools for Future Education 4.0 in India on the basis of different parameters.