Reviewerly/exHarmony huggingface dataset:
A Lifecycle Protection Framework for Creative Artifacts: Blockchain-Enabled Collaborative Evidence Network, Evaluation and Audit

Protecting creative artifact’s value and creator’s contributions is an important and key issue in today’s knowledge-based creative collaboration. Few research focus on the whole lifecycle protection of creative artifacts in collaborative networks. In this paper, we adopt action design research method to guide us to partner with an e-commence association in China to implement a creativity protection platform. We propose a lifecycle protection framework for creative artifacts. The framework includes the ideas and methods of blockchain-enabled evidence network towards collaborative works between multiple stakeholders, as well as the evaluation and audit mechanisms. Our research has been in progress and the preliminary findings and expected outcomes may contribute to creativity support systems, IP/copyright protection, and blockchain literature through collaborative networks perspective, and may have impact on the future applications of blockchain and creativity management.
Semisupervised Subspace Learning With Adaptive Pairwise Graph Embedding

Graph-based semisupervised learning can explore the graph topology information behind the samples, becoming one of the most attractive research areas in machine learning in recent years. Nevertheless, existing graph-based methods also suffer from two shortcomings. On the one hand, the existing methods generate graphs in the original high-dimensional space, which are easily disturbed by noisy and redundancy features, resulting in low-quality constructed graphs that cannot accurately portray the relationships between data. On the other hand, most of the existing models are based on the Gaussian assumption, which cannot capture the local submanifold structure information of the data, thus reducing the discriminativeness of the learned low-dimensional representations. This article proposes a semisupervised subspace learning with adaptive pairwise graph embedding (APGE), which first builds a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k_1$</tex-math> </inline-formula> -nearest neighbor graph on the labeled data to learn local discriminant embeddings for exploring the intrinsic structure of the non-Gaussian labeled data, i.e., the submanifold structure. Then, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k_2$</tex-math> </inline-formula> -nearest neighbor graph is constructed on all samples and mapped to GE learning to adaptively explore the global structure of all samples. Clustering unlabeled data and its corresponding labeled neighbors into the same submanifold, sharing the same label information, improves embedded data’s discriminative ability. And the adaptive neighborhood learning method is used to learn the graph structure in the continuously optimized subspace to ensure that the optimal graph matrix and projection matrix are finally learned, which has strong robustness. Meanwhile, the rank constraint is added to the Laplacian matrix of the similarity matrix of all samples so that the connected components in the obtained similarity matrix are precisely equal to the number of classes in the sample, which makes the structure of the graph clearer and the relationship between the near-neighbor sample points more explicit. Finally, multiple experiments on several synthetic and real-world datasets show that the method performs well in exploring local structure and classification tasks.
Effectiveness of Artificial Intelligence in Building Customer Loyalty: Investigating the Mediating Role of Chatbot in the Tourism Sector of Pakistan

The advent of artificial intelligence (AI) is causing a change in the marketing landscape, and AI may replace human expertise with technology to improve service delivery in service organisations. This study investigates the relationship between artificial intelligence (AI) and customer loyalty in the context of advanced digital marketing, focusing on the mediating function of chatbots. To collect data, a survey was distributed to 104 customers in different hotels and eateries in Pakistan. The hypotheses were tested using confirmatory factor analysis, reliability and validity analysis, and mediation analysis. The findings indicate that AI has a positive effect on customer loyalty. In addition, the results demonstrate that chatbots serve as a mediator between AI and consumer loyalty. This suggests that chatbots, as AI-powered conventional agents, increase customer loyalty by providing personalised and timely support, resolving customer concerns and queries, and delivering a seamless user experience. These findings have significant repercussions for the hospitality industry, particularly regarding how companies can use chatbots and other AI technologies to develop customer loyalty. By incorporating AI-powered chatbots into their digital marketing strategies, businesses can increase consumer engagement, satisfaction, and loyalty. This study contributes to the existing corpus of knowledge by empirically examining the meditation function of chatbots in the AI-customer loyalty relationship within the context of advanced digital marketing. It is a valuable resource for researchers and practitioners who use AI-driven technologies to foster customer loyalty.
Wireless Fall Sensor with GPS location for Monitoring the Elderly

Fall is the most consequential cause of injury in elderly that may lead to death sometimes, most of the deaths occurs due to the late response after the fall transpires. Elder people living in rural areas face this astringent issue and as the medical centres are very infrequent at those places, doctors cannot attend on them immediately, so this elderly fall detection system provides functionalities to notify the fall information to the server automatically which reduces the burden of caretakers who need to avail them perpetually. The amelioration of low performance utilizing a accelerometer at wrists and the inconvenience of sensor attached to a waist was concentrated on by integrating the efficacious motion sensing technology involved IMU like MPU-6050 which gives digital output from which Acceleration, Velocity and Quaternion data are observed and compared and additionally changing the sensor location from a waist to wrists. The fall detection system server is the web application server; it allows the doctor at medical centre to monitor the real- time fall in the webpage. Once the fall information is received, the server updates the fall information on the page automatically. By this way the doctor who is attending on, can know the health status of his patient immediately by the notification on the webpage whenever there is an emergency.
Minimal codewords in Norm-Trace codes

Abstract In this paper, we consider the affine variety codes obtained evaluating the polynomials $$by=a_kx^k+\ldots +a_1x+a_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>b</mml:mi><mml:mi>y</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:msup><mml:mi>x</mml:mi><mml:mi>k</mml:mi></mml:msup><mml:mo>+</mml:mo><mml:mo>…</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mi>x</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:math> , $$b,a_i\in {\mathbb {F}}_{q^r}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>b</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:msup><mml:mi>q</mml:mi><mml:mi>r</mml:mi></mml:msup></mml:msub></mml:mrow></mml:math> , at the affine $${{\mathbb {F}}}_{q^r}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>F</mml:mi><mml:msup><mml:mi>q</mml:mi><mml:mi>r</mml:mi></mml:msup></mml:msub></mml:math> -rational points of the Norm-Trace curve. In particular, we investigate the weight distribution and the set of minimal codewords. Our approach, which uses tools of algebraic geometry, is based on the study of the absolute irreducibility of certain algebraic varieties.
Like I Said: archive of tweets

Hopefully I will soon break out of this funk of posting about either Mastodon or Twitter. But not yet! This post is to say that: &lt;strong&gt;I made a static archive of tweets for @quantixed and for @clathrin.&lt;/strong&gt; There, you can read all my posts, which ended in 2022. &lt;strong&gt;How did I do it and why?&lt;/strong&gt; I made the archive with this excellent tool written by Darius Kazemi.
Digitalization of Genetic Information: Prospects and Challenges

Abstract —The process of the digitalization of biological information is currently in its early stages of development and is characterized by the accelerated exponential growth of databases, which offers great opportunities for the development of personalized medicine, biodiversity conservation, the development of biotechnology and agriculture, but, at the same time, provides additional opportunities for the development of dual-use technologies and biological terrorism. The purpose of this work is to analyze the current situation in the field of digital sequence information and the global challenges facing humanity as the volume of genetic information continues to increase. It is urgent to develop mechanisms of national and international control in this area, as well as to find the optimal balance between the requirements of confidentiality of personal data of the population, compliance with legal and ethical standards, the need for scientific research, the development of personalized medicine technologies, and the development of business models and organizational and legal forms able to accompany further growth of digital sequence information.
Securing Higher Education Institutions in the Fourth Industrial Revolution: Developing a Cybersecurity Risk Management Framework in Malaysia

In the context of the fourth industrial revolution, higher education institutions offer a complex, compelling, and exciting opportunity to positively transform society. However, the use of cyber-attacks on various aspects of universities' operating systems has become a cause for concern. This is attributed to the emergence of new approaches, modified frameworks, and increasingly sophisticated information technology (IT) infrastructure, which may pose unknown risks.
Application of Immersive Virtual Reality in the Non-Renewable Energy Industry Area

The Immersive Virtual Reality (VR) has the potential to significantly enhance the non-renewable energy (NRE) industry by improving safety and efficiency in operational and training processes. In order to explore applications of immersive technologies aimed at enhancing processes within the NRE energy sectors, a systematic review of the literature published in the last decade was conducted. Utilizing the PRISMA framework with snowballing, a collection of 53 articles was acquired, and subsequent examination based on indicators such as the energy industry sector, VR application use cases, and employed VR technologies (both hardware and software) revealed that only a limited number of studies (16 out of 53) met the eligibility criteria. The results revealed that in 94% of the publications, immersive VR technologies are utilized for training purposes. This research provides valuable insights and knowledge to stakeholders in the NRE industry, including companies, policymakers, and researchers, who can utilize the findings to implement immersive virtual reality technologies for complex simulations and remote operations.
Selecting cardiac magnetic resonance images suitable for annotation of pulmonary arteries using an active-learning based deep learning model

An increasing and aging patient population poses a growing burden on healthcare professionals. Automation of medical imaging diagnostics holds promise for enhancing patient care and reducing manpower required to accommodate an increasing patient-population. Deep learning, a subset of machine learning, has the potential to facilitate automated diagnostics, but commonly requires large-scaled labeled datasets. In medical domains, data is often abundant but labeling is a laborious and costly task. Active learning provides a method to optimize the selection of unlabeled samples that are most suitable for improvement of the model and incorporate them into the model training process. This approach proves beneficial when only a small number of labeled samples are available. Various selection methods currently exist, but most of them employ fixed querying schedules. There is limited research on how the timing of a query can impact performance in relation to the number of queried samples. This paper proposes a novel approach called dynamic querying, which aims to optimize the timing of queries to enhance model development while utilizing as few labeled images as possible. The performance of the proposed model is compared to a model trained utilizing a fully-supervised training method, and its effectiveness is assessed based on dataset size requirements and loss rates. Dynamic querying demonstrates a considerably faster learning curve in relation to the number of labeled samples used, achieving an accuracy of 70% using only 24 samples, compared to 82% for a fully-supervised model trained on the complete training dataset of 1017 images.
ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation

Recently, recommendation systems, especially interpretable ones, have become increasingly popular. The recommendation system provides personalized recommendations to users based on their previous behaviour data. Existing approaches often transfer methods in disentangle learning, especially Variational Auto-Encoder (VAE) framework, into the recommendation system. However, VAE is proved sub-optimal due to the independence factor assumption. Unfortunately, few research focuses on the VAE framework without an independent assumption for recommendation system. To escape from sub-optimality from independence assumption and provide more interpretability, in this paper, we propose a new method for top-n recommendation tasks called Item Concept Causal Variational Auto-Encoder (ICCVAE), enabling to build up causal structure for factors, i.e., item concepts in recommendation system. We conduct experiments to prove superiority for ICCVAE. Our framework can reach up to 5.3%, 3.4%, and 6.1% definite improvement over baselines in terms of Recall@20, Recall@50 and NDCG@100.
Adversarial Defense with Local Robust Principal Component Analysis and Wavelet Denoising

In this work, a local robust principal component analysis (RPCA)-based adversarial defense methodology is presented. The proposed defense is inspired by recent works of input reconstruction paradigm. The proposed method uses localised processing to apply RPCA towards removal of adversarial perturbations. The reconstructed image is guided by applying wavelet denoising to remove residual perturbation which improves the defense more efficiently. Experimental results are presented using state-of-the-art C&W attack and Square attack to show the effectiveness of the proposed adversarial defense on a pre-trained classifier model.
Localized Knowledge Distillation Helps IoT Devices Provide High-performance Visual Services

Deploying high-performance convolutional neural networks (CNNs) on ubiquitous Internet of Things (IoT) devices to provide convenient services has attracted increasing attention. However, most existing studies have two limitations, (i) low performance due to lack of effective learning strategies; (ii) difficulty to measure resource consumption due to lack of real deployment. To this end, this paper proposes a novel localized knowledge distillation (LKD) method to train the resource-efficient CNN and implements a cloud-assisted system to evaluate the on-device performance. The proposed LKD follows the layer-wise heterogeneous information distribution in the CNN and distills the knowledge from features that contains the most crucial knowledge to the resource-efficient CNN. Thus, the knowledge guided to learn by limited parameters is reduced to the crucial part, which is more reliable for the resource-efficient CNN. The cloud-assisted system consists cloud server and IoT devices, which allows the training and deployment of the resource-efficient CNN, and the measurement of the corresponding resource consumption. Experimental results show that the proposed LKD could improve the performance on standard benchmarks close to the counterpart CNN, and the robustness on corrupted data by approximately 11.3% for the resource-efficient CNN. The measurements on the cloud-assisted system also demonstrate the resource efficiency for transmission and on-device running.
Identifying illicit transactions in Bitcoin Tumbler services using Supervised Machine Learning Algorithms

Digital currencies have emerged as a new platform for money laundering as the first blockchain platform to support smart contracts, and the number of transaction records for Bitcoin has expanded substantially in recent years. Due to privacy services such as tumblers, which conceal the identities of payers and payees, pseudonymous digital currencies are frequently referred to as anonymous address and untraceable. A large proportion of accounts participating in Bitcoin transactions are engaging in illegal activity. According to the digital ledger, a transaction was transferred from one of many potential payers to one of many possible payees. As a consequence, it is vital to have a dependable system for classifying accounts and keeping track of the transactions linked with each account. We utilized machine learning techniques to apply multi-class classification methods to a Bitcoin transaction record obtained from kaggle.com in order to improve the model’s accuracy. Experiment results show that ML algorithms like logistic regression, random forests, and multilayer perceptron’s with the macro-average F1 score for bitcoin tumblers outperform state-of-the-art approaches for identifying unknown digital currency accounts, with the RF model outperforming LR and MLP by 97% accuracy.
A Programming Language Learning Service by Linking Stack Overflow with Textbooks

Before software developers actually start coding, an essential requirement is to be proficient in a programming language. However, as an important learning resource, programming language textbooks usually contain much about syntax and semantics of a programming language, but little about pragmatics, which is highly relevant with practical skills in solving real-world problems using programming languages. This makes it difficult for learners to transfer knowledge from textbooks to practice.To solve this problem, this paper proposes a programming language learning service, utilizing abundant development knowledge in Stack Overflow (SO) Q&A posts as the pragmatics knowledge of programming languages to make up for the shortage of pragmatics knowledge in textbooks. To link SO Q&A posts with textbooks, this paper first proposes a deep learning classification model to recognize SO posts containing development knowledge. Second, to align filtered SO posts with programming language learning processes, this paper proposes a weakly supervised link approach to matching SO posts with chapters and sections of textbooks. The feature of weak supervision ensures that the approach can be easily extended to other textbooks. Based on these modules above, this paper implements the programming language learning service in the form of a Chrome plugin. We conduct experiments and user study. Their results demonstrate the effectiveness, scalability and stability of the service.
Improvement of Position Measurement by TDOA Technique using K-Means Clustering

Time difference of arrival(TDOA) is one of the well-defined techniques for passive detection and localization. TDOA required four receivers to determine the position measurement of the aerial target. Single position measurement does not fulfill all requirements for aerial target tracking, such as reliability and verifiability of the position measurement. In order to increase reliability, more target position measurements need to be made. In traditional ways, eight receivers are required for two target position measurements. A tracking system’s receiver is one of the expansive components. Instead of taking a new set of four receivers to get a positional measurement, combinations of receivers can yield more position measurements about the target with the same number of receivers. In data fusion, multiple data are merged together to produce more accurate, reliable, and usable information than can be obtained from any single source alone. In this paper, Clustering is used to fuse the data to obtain more accurate position measurements than those provided by only a single source. Since the TDOA system is a real-time system, the time complexity is one of the most crucial parameters of the proposed system. The K-means clustering algorithm is the fastest out of all the clustering algorithms that can be used since it is more computationally efficient than other clustering algorithms. The proposed method provides more accurate measurements than the highest accuracy measurement possible with TDOA using one of the combinations of receivers.
Design and Development an e-Lapor Application to Support Public Complaint Services in Tunjungtirto Village

The process of public complaints is carried out in a structured manner starting from the central government level to the village government level. In practice, the problem in the public complaint process is when the community as the service user is dissatisfied with the services provided because the complaints submitted are not properly managed or responded to by the complaint officer. The business process for public complaint services in Tunjungtirto village is still carried out conventionally, where people who wish to submit complaints must go to the Village Consultative Body (BPD) office and fill in complaints or aspirations in the community aspirations data book. This is prone to conditions where public complaints are not followed up because they are not properly recorded. This study aims to build a website-based information system with features for complaints, aspirations, and requests for information. The system development method used in this study is the waterfall method where the method is sequential-based software development. The results of application development will be tested at the Tunjungtirto Village BPD office. Based on black box testing, the test results are obtained with a maximum score of 2 for each feature. This shows that in terms of functionality, the application is running according to its purpose. The existence of main features such as displaying complaint data and automatic notifications on the application can assist village officials in managing community complaints and responding to and following up on complaints to the service or escalating complaints to the local government so that community complaints can be managed properly.