Reviewerly/exHarmony huggingface dataset:
Towards Volumetric Video Conferencing

With Social Extended Reality (XR) emerging as a new medium, where users can remotely experience immersive content with others, the vision of a true feeling of ‘being there together’ has become a realistic goal. This keynote will provide an overview about the challenges to achieve such a goal, based on results from practical case studies like the TRANSMIXR and MediaScape XR projects. We will discuss about different technologies, like point clouds, that can be used as the format for representing highly-realistic digital humans, and about metrics and protocols for quantifying the quality of experience. The final intention of the talk is to shed some light on social XR, as a new group of virtual reality experiences based on social photorealistic immersive content. We will discuss about the challenges regarding production and user-centric processes, and discover the new opportunities open by this new medium
Privacy and biometrics for smart healthcare systems: attacks, and techniques

Biometric technology has various applications in smart healthcare systems, including patient authentication, health monitoring, telemedicine, clinical decision support, and personalized care. In addition, medical records contain sensitive and personal information, making them vulnerable to unauthorized access and theft. Because biometric data is distinct and unchangeable, unlike passwords or PINs, using biometric technologies in smart healthcare systems creates privacy problems. This creates privacy concerns as this information is highly sensitive and can be used to identify an individual, making it a valuable target for malicious actors. Subsequently, the storage and use of biometric data in smart healthcare systems must be handled with care to ensure that individuals’ privacy rights are protected. Privacy by design is a concept that emphasizes the importance of incorporating privacy considerations into the design and development of products, services, and systems. In this paper, we presented different forms of biometric factors and technologies and their applications in the smart healthcare system to enhance security and privacy in relation to principles of privacy by design. In addition, the study analyzed a variety of attacks and techniques that can be utilized to compromise biometric technology in a smart healthcare system and presented some open research questions.
A Blockchain-Based Approach for Drug Traceability in Healthcare Supply Chain

Abstract: Healthcare supply chains are complex structures spanning across multiple organizational and geographical boundaries, providing critical backbone to services vital for everyday life. The inherent complexity of such systems can introduce impurities including inaccurate information, lack of transparency and limited data provenance. Counterfeit drugs is one consequence of such limitations within existing supply chains which not only has serious adverse impact on human health but also causes severe economic loss to the healthcare industry. Consequently, existing studies have emphasized the need for a robust, end-to-end track and trace system for pharmaceutical supply chains. Therein, an end-to-end product tracking system across the pharmaceutical supply chain is paramount to ensuring product safety and eliminating counterfeits. Most existing track and trace systems are centralized leading to data privacy, transparency and authenticity issues in healthcare supply chains. In this article, we present an Ethereum blockchain-based approach leveraging smart contracts and decentralized off-chain storage for efficient product traceability in the healthcare supply chain. The smart contract guarantees data provenance, eliminates the need for intermediaries and provides a secure, immutable history of transactions to all stakeholders. We present the system architecture and detailed algorithms that govern the working principles of our proposed solution. We perform testing and validation, and present cost and security analysis of the system to evaluate its effectiveness to enhance traceability within pharmaceutical supply chains.
Service Oriented Fuzzy Decision Model for Health Insurance Claim

Insurance claim is a fascinating issue to study. A potential loss for the insurance company is major coming from this issue. Thus, many studies performed already to answer such an issue. This study takes aim to develop a computational decision model based on service technology. Four fundamental methods operated in constructing the model and its implementation in service technology. The analytical hierarchical process (AHP) and fuzzy logic methods are two methods benefited to construct the model; where the AHP used to prioritize thirteen parameters considered and the fuzzy logic with its inference capability operated to generate the decision. Object oriented is an analysis and design method to analyze and design the model implanting it in service oriented architecture (SOA). Then, SOA conception functioned to deploy the model in the service architecture. Ultimately, the suggested framework comprising three layers of service-oriented architecture (SOA), namely business process, service interface, and application, has been established, alongside the integration of eight essential services that connect these three applications. The model demonstrates simulation outcomes indicating that 31.47% of claims are categorized as low risk and have been approved, 17.64% of claims are considered moderate risk with currently pending decision status (requiring additional investigation), while 50.89% of claims are classified as high risk with also pending decision status.
Data stratification analysis on the propagation of discriminatory effects in binary classification

Unfair decision-making supported by machine learning, which harms or benefits a specific group of people, is frequent. In many cases, the models only reproduce the biases in the data, which does not absolve its responsibility for these decisions. Thus, with the increase in the automation of activities through machine learning models, it is mandatory to prospect solutions that add fairness factors to the models and clarity about the supported decisions. One option to mitigate model discrimination is quantifying the ratio of instances belonging to each target class to build data sets that approximate the actual data distribution. This alternative aims to reduce the responsibility of data on discriminatory effects and direct the function of treating them to the models. In this sense, we propose to analyze different types of data stratification, including stratification by sociodemographic groups that are historically unprivileged, and associate these stratification types to the fairer or unfairer models. According to our results, stratification by class and group of people helps to develop fairer models, reducing the discriminatory effects in binary classification.
Model and Algorithm-Agnostic Clustering Interpretability

Data clustering through unsupervised algorithms is an important technique in several applications, both in research and industrial projects, allowing similar elements to be associated with each other for knowledge extraction. After grouping, the interpretation and understanding of the created clusters is a crucial step so that they can be used in decision-making. However, this is not a trivial task, since it requires manual and repetitive analyses, which consume time and resources of those involved. In the present work, a solution for the interpretability of clusters generated by unsupervised learning is proposed. Unlike existing solutions in the literature, the proposed approach is independent of the model and algorithm used for clustering, and generates easy-to-understand descriptions for end users, facilitating their use by teams from different areas of the companies. The results showed that the solution was able to provide a friendly description to interpret the 13 clusters created to segment 263,684 customers of a company.
Proposal of a Method for Identifying Unfairness in Machine Learning Models based on Counterfactual Explanations

As machine learning models continue impacting diverse areas of society, the need to ensure fairness in decision-making becomes increasingly vital. Unfair outcomes resulting from biased data can have profound societal implications. This work proposes a method for identifying unfairness and mitigating biases in machine learning models based on counterfactual explanations. By analyzing the model’s equity implications after training, we provide insight into the potential of the method proposed to address equity issues. The findings of this study contribute to advancing the understanding of fairness assessment techniques, emphasizing the importance of post-training counterfactual approaches in ensuring fair decision-making processes in machine learning models.
Hyperparameter Tuning of Semi-Supervised Learning for Indonesian Text Annotation

A crucial issue in sentiment analysis primarily relies on the annotation task involving data labeling. This critical step is typically performed by linguists, as the nuanced meaning of text significantly influences its contextual interpretation. If there is a large volume of data, annotation is time-consuming and financially burdensome. Addressing these challenges, a semi-supervised learning annotation (SSL) that integrates human annotator and artificial intelligence algorithms emerges as a potent solution. Building accurate SSL needs to explore the best architecture, including a combination of machine learning and mechanism. This research aims to construct semi-supervised model annotation text by tuning the parameter of the machine learning algorithm to gain the most accurate model. This study employed a Support Vector Machine and a Random Forest algorithm to build semi-supervised annotation. Grid-Search and Random-Search were employed to tune the Random Forest and Support Vector Machine parameters. The semi-supervised annotation model was applied to annotate Indonesian texts. The outcomes signify that hyperparameter-tuning enhances SSL performance, surpassing the performance achieved using default parameters. The experiment also shows that the SSL annotation using a Support Vector Machine tuned by Grid Search and Random Search is more robust than the Random Forest algorithm. Hyperparameter tuning is also robust to training data that contains many manual labeling errors by experts.
SMART EXAM AUTOMATION WITH QUESTION PAPER GENERATOR AND ANSWER CHECKER SYSTEM USING ARTIFICIAL INTELLIGENCE

An automatic answer checker application that checks answers similar to a human being. This application is built to check answers in an examination and appears the answer is correct or wrong. The system requires you to store the original answer for the system. This facility is provided to the admin. The admin may insert questions and respective subjective answers in the system. These answers are stored as Exel files. When a user takes the test he is provided with questions and area to type his answers. Once the user enters his/her answers the system then compares this answer to original answer written in database and gives result.
Feature Selection through Biclustering to Identify Specific Language Impairment

Failure to express yourself verbally is a condition that affects nearly 7% of children worldwide, known as specific language impairment. The diagnosis is complex, involving specialists such as speech therapists and pediatricians. The dataset used in this work has many attributes and imbalanced data, which can harm knowledge discovery. We used biclustering to identify clusters that characterize children with speech problems and those with typical development. We propose selecting attributes through the significance analysis of biclusters, which enhanced the F-score and accuracy in models generated by using 90% of instances from the training dataset, compared to results from the original data.
Improving Knowledge Base Updates with CAIA: A Method Utilizing Capsule Network and Attentive Intratriplet Association Features

The ongoing and effective application of the knowledge base hinges on the dynamic updating of the knowledge base, which, in turn, depends on the accurate verification of triplets. Current methods struggle to manage unknown new entities and relations or have issues with relation characterization. This paper presents a novel approach to enhance the precision and efficiency of triplet verification during the knowledge base update process, utilizing a combination of a capsule network and an innovative feature known as the capsule network and attentive intratriplet association (CAIA). The method initially draws comprehensive triplet features from both structural and textual data. Following this, association attention values between entities and relations are derived and included with the triplet features as association features. A two-layer capsule network is then used to score the triplets. Experimental results demonstrate that the CAIA method outperforms established baseline methods in three key metrics—H@1, H@3, and mean reciprocal rank (MRR) on FB15k-237-OWE+ and DBPedia50k+ datasets—showing its superior accuracy for entities, and the average increase was about 2.8%. It also excels in relation accuracy, showing the best results for the three metrics of mean rank, H@1, and MRR on both datasets, and the average improvement was about 9.1%. Additionally, more optimal parameter values in the current experimental environment are identified by comparing the effects of the different neuron numbers and capsule numbers. Based on the above research, this paper fills the research gap of deep learning in knowledge base updating.
Accompaniment in making learning media to increase interest in learning at SDN Balongmojo Mojokerto

School is a place for students to study. Often we meet in-class there are students who are less enthusiastic in studying and are less responsive to what is conveyed by the teacher. Students sometimes feel bored with lecture-based learning models, so educators need to conduct self-evaluations and improve the quality of learning in class. In this case, improving the quality of learning in the classroom can be done in various ways and teachers have a very important role in increasing students' interest in learning so that students feel comfortable and the knowledge they gain will be easy for them to accept. This study aims to analyze the use of instructional media in increasing students' interest in learning. The purpose of this service activity is to provide knowledge to SDN Balongmojo Mojokerto teachers about the use of plastic and paper waste to make innovative media. The target of this service is to provide information and assistance to teachers at SDN Balongmojo Mojokerto regarding the use of plastic waste and cardboard for making innovative learning media. The method used in this service is interactive training and mentoring. As a result of this community service activity, teachers at SDN Balongmojo Mojokerto are able to design, create, and practice making innovative media using plastic waste and cardboard
Cryptocurrency: Historical Development, Operations and Accountability

The functioning of financial markets is impacted by the global cryptocurrency market, which also has significant effects on entire economies. Cryptocurrencies, which are still a relatively new idea and are constantly evolving and changing the financial landscape of the world, have started to take hold in a variety of industries, from financial markets to day-to-day commercial life. A new financial era has allegedly dawned with the advent of cryptocurrency. Cryptocurrencies are projected to become more prevalent and a part of daily life in the future as their popularity grows every day. Virtual money and Bitcoin have been discovered to have high levels of transaction volumes in the majority of nations. By looking over the existing literature and previous studies, the study used an exploratory methodology. Many businesses already pay their employees with bitcoins. The purpose of the study is to provide details on virtual currencies, including information about how each cryptocurrency, including Bitcoin, functions and its characteristics, as well as to make assessments regarding the accounting of their business dealings using Bitcoin in accordance with international accounting standards. According to the findings, the held cryptocurrency should be revalued at the end of the term, and any disparities resulting from gains or losses should be taxed. The importance of conducting studies for this is also underlined, as is the control over the cryptocurrency owned by businesses and the transactions done with it. Although the intangible asset accounting method is now thought to be the most appropriate way, future advances will inevitably lead to the creation of a new account class or group for cryptocurrencies.
A Family of Binary Locally Repairable Codes for Coded Distributed Computing

One of the main bottlenecks in distributed computing systems is the stragglers’ problem. Error correction codes have been proposed to alleviate this problem at the cost of coding complexity for the master node. In this work, we aim to reduce this coding complexity and propose a novel family of binary locally repairable codes (BLRC) to encode the distributed tasks in a linear matrix-vector multiplication problem. In comparison to the widely used maximum distance separable (MDS) codes, our proposed codes (i) eliminate the costly multiplication operations from the encoding and decoding processes, (ii) allow for low-complexity recovery within the local groups. We analyze the complexity of our proposed codes and through simulations show that compared to MDS codes, our codes reduce the overall encoding plus computation plus decoding time by more than 35% in many practical scenarios.
Story Shaping: Teaching Agents Human-Like Behavior with Stories

Reward design for reinforcement learning agents can be difficult in situations where one not only wants the agent to achieve some effect in the world but where one also cares about how that effect is achieved. For example, we might wish for an agent to adhere to a tacit understanding of commonsense, align itself to a preference for how to behave for purposes of safety, or taking on a particular role in an interactive game. Storytelling is a mode for communicating tacit procedural knowledge. We introduce a technique, Story Shaping, in which a reinforcement learning agent infers tacit knowledge from an exemplar story of how to accomplish a task and intrinsically rewards itself for performing actions that make its current environment adhere to that of the inferred story world. Specifically, Story Shaping infers a knowledge graph representation of the world state from observations, and also infers a knowledge graph from the exemplar story. An intrinsic reward is generated based on the similarity between the agent's inferred world state graph and the inferred story world graph. We conducted experiments in text-based games requiring commonsense reasoning and shaping the behaviors of agents as virtual game characters.
Are You Talking to Me? A Case Study in Emotional Human-Machine Interaction

We present Stanley, a digital sculpture designed to engage audiences with the spontaneous and captivating emotional expressions of an artificial human. A 3D-printed face is brought to life through video projection mapping and a set of machine learning libraries and APIs, enabling real-time, embodied interactions with our virtual character. Stanley’s personality is shaped by traditional acting methods applied to a large language model. By creating human-machine encounters in emotionally salient scenarios, we explore how insights from acting and directing for the stage and the screen can enhance the development of compelling virtual agents. By interacting with Stanley, the audience experiences an entertaining yet unsettling encounter with AI technology, fostering a deeper understanding of machine learning techniques and enabling their critical reflection.
Learning-Based Model for Phishing Attack Detection

The growing amount of phishing outbreaks is one of the main concern for security scientists nowadays. Signature-based approaches are used in the traditional methods for categorizing phishing websites. Such tools and techniques fail with advanced and complex phishing webpage. Consequently, learning-based algorithms are widely adopted in many industries. Such algorithms can be applied to analyze phishing websites. The detection accuracy can be increased with a large dataset and complex features. This chapter addresses the problem of analyzing such phishing activities on web pages. Explicitly, we propose a machine learning-based framework with various supervised learning algorithms such as Random Forest, Support Vector Machine, and Decision Trees. We perform the hyperparameter optimization of these algorithms using sci-kit-learn machine learning frameworks. In the end, we discussed the applied implementation of classifying the phishing attack on the real-world dataset available on Kaggle.
Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog

Data-driven approaches to identify geophysical signals have proven beneficial in high dimensional environments where model-driven methods fall short. GNSS offers a source of unsaturated ground motion observations that are the data currency of ground motion forecasting and rapid seismic hazard assessment and alerting. However, these GNSS-sourced signals are superposed onto hardware-, location- and time-dependent noise signatures influenced by the Earth’s atmosphere, low-cost or spaceborne oscillators, and complex radio frequency environments. Eschewing heuristic or physics based models for a data-driven approach in this context is a step forward in autonomous signal discrimination. However, the performance of a data-driven approach depends upon substantial representative samples with accurate classifications, and more complex algorithm architectures for deeper scientific insights compound this need. The existing catalogs of high-rate (≥1Hz) GNSS ground motions are relatively limited. In this work, we model and evaluate the probabilistic noise of GNSS velocity measurements over a hemispheric network. We generate stochastic noise time series to augment transferred low-noise strong motion signals from within 70 kilometers of strong events (≥ MW 5.0) from an existing inertial catalog. We leverage known signal and noise information to assess feature extraction strategies and quantify augmentation benefits. We find a classifier model trained on this expanded pseudo-synthetic catalog improves generalization compared to a model trained solely on a real-GNSS velocity catalog, and offers a framework for future enhanced data driven approaches.
Marketing data security and privacy protection based on federated gamma in cloud computing environment

This study aims to address the existing data security and user privacy vulnerabilities in the “cloud” environment, which is essential for ensuring the safety and integrity of data in the era of big data and cloud computing. To achieve this purpose, we propose a novel approach that combines the logit link function with a longitudinal joint learning framework for the gamma regression model. This approach enhances the application of the model and the loss function, providing a robust solution for data security and user privacy in cloud-based systems. While cloud computing technology has greatly improved the convenience of work and life, it has also introduced significant challenges related to data security and user privacy. This study leverages semantic web technology and blockchain technology to establish a distributed and credit-guaranteed product quality and safety traceability application. By designing a concept verification system and ensuring data integrity at each stage of the product supply chain, this approach addresses these challenges effectively. The distributed network architecture employed in our technical design ensures overall system stability, reliability, and sustainability, with no single point of failure.