Reviewerly/exHarmony huggingface dataset:
Performance Analysis of the Distributed Support Vector Machine Algorithm Using Spark for Predicting Flight Delays

In big data analysis requires powerful machine learning frameworks, strategies, and environments to analyze data at scale. Therefore, Apache Spark is used as a cluster computing framework to process big data in parallel and can run on multiple clusters. In this study, the Support Vector Machine (SVM) algorithm is used as a classification method to predict whether a flight will experience a delayed arrival. This study also aims to analyze the performance of the distributed SVM algorithm using the Apache Spark framework in classifying delayed flight arrivals. Running time evaluation is important in proving how fast the data processing is done using Apache Spark. In addition, there is a test to prove the effect of using the SVM algorithm with a distributed system on the results of the classification accuracy of delayed flight arrivals. Distributed SVM performance testing is carried out using variations in data size and the number of worker nodes in the built cluster. From the test results, it was found that the most effective number of worker nodes used in the flight delay classification process was 4 worker nodes with the lowest running time results from the experiment of 4 variations in the number of worker nodes. In terms of accuracy, adding the number of worker nodes does not affect the accuracy of the program. The difference in the accuracy of results is caused by the random oversampling process on the data performed on each system test. Using the SVM algorithm with Spark is sufficient to provide good performance in the classification process with the highest accuracy result in the test being 93.98 %.
A Cost-Effective and Energy-Efficient Approach to Workload Distribution in an Integrated Data Center

The paper aims at low-cost and energy-efficient workload distribution for each data center by utilizing green energy while reducing the consumption of brown energy from non-renewable sources like coal, gas, and oil. To this end, the proposed algorithm takes into account that the local green and brown energy is never going off in non-peak time in the data center, extra renewable energy is saved and used later at peak time. We propose a way to use the concept of green workload and green service against brown workload and brown service depicting green and economic factors equally. This displays the differences between green energy maximization and total cost minimization resulting in green and brown workload allocation with savings calculated for each time slot and per day. Hence, this workload distribution algorithm is designed, and the business process modelings, graphs, algorithm flowchart, and result analysis are demonstrated in the paper such that the energy consumption and cost are reduced.
Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic

The COVID-19 pandemic has an impact on the economy of Padang City. To revive the economy, especially in the tourism sector, the government is trying to improve services to visitors so that the number of tourist visits increases again. As one of the efforts is to find out the opinion of tourists on the beach tourism area visited. This research aims to assist the Padang City government in knowing the positive or negative responses of tourists through the sentiment analysis process to the beach tourism they visit so that The Government of Padang City can determine the policies to be taken in connection with the reviews given by beach tourism visitors. By using reviews on Google Maps on the attractions of Air Manis Beach, Padang Beach, Pasir Jambak Beach, Nirwana Beach, and Pasir Putih Beach, clustering is carried out with the Naive Bayes classification algorithm. Based on the results of the analysis that has been done, 2 of the 5 beaches get negative reviews, namely Pasir Jambak Beach and Pasir Putih Beach which get negative values of 0.550 and 0.650.
Short Concurrent Covert Authenticated Key Exchange (Short cAKE)

Von Ahn, Hopper and Langford introduced the notion of steganographic a.k.a. covert computation, to capture distributed computation where the attackers must not be able to distinguish honest parties from entities emitting random bitstrings. This indistinguishability should hold for the duration of the computation except for what is revealed by the intended outputs of the computed functionality. An important case of covert computation is mutually authenticated key exchange, a.k.a. mutual authentication. Mutual authentication is a fundamental primitive often preceding more complex secure protocols used for distributed computation. However, standard authentication implementations are not covert, which allows a network adversary to target or block parties who engage in authentication. Therefore, mutual authentication is one of the premier use cases of covert computation and has numerous real-world applications, e.g., for enabling authentication over steganographic channels in a network controlled by a discriminatory entity. We improve on the state of the art in covert authentication by presenting a protocol that retains covertness and security under concurrent composition, has minimal message complexity, and reduces protocol bandwidth by an order of magnitude compared to previous constructions. To model the security of our scheme we develop a UC model which captures standard features of secure mutual authentication but extends them to covertness. We prove our construction secure in this UC model. We also provide a proof-of-concept implementation of our scheme.
Digital and Communicative Affordances in the Translingual Online Space: Insights from Brazilian Food Discourses in the UK

This article will delve into the potential of social media spaces as arenas for processes of identification and community construction, using multilingual data from Brazilian food discourses collected between October 2017 and February 2020 as part of a larger computer-mediated communication (CMC) study.While the study retains its analytical focus on Facebook, and specifically the public company Pages used by Brazilian food outlets in the UK for promotional purposes, it also attends to the interwoven state of online communication.Within this context, digital affordances and actions are part of the accomplishment of communicative goals and thus relevant in everyday meaning-making processes and as a means of getting things done.This article presents a discourse-oriented analysis of CMC from Pages, examining communicative strategies emergent therein.The analysis points to how language use and platform features and structure relate to the production of meaning entwined with processes of identification and the creation of rapid senses of connectivity by communicators in the translingual online space.Drawing from online observation, discursive insights from the dataset and the perspective of interviewed entrepreneurs, this article explores digital and communicative affordances encapsulated in Facebook Pages' posts and "reactions" in relation to everyday online communication practices and actions in the promotional context.
Sigma Protocols from Verifiable Secret Sharing and Their Applications

Sigma protocols are one of the most common and efficient zero-knowledge proofs (ZKPs). Over the decades, a large number of Sigma protocols are proposed, yet few works pay attention to the common design principal. In this work, we propose a generic framework of Sigma protocols for algebraic statements from verifiable secret sharing (VSS) schemes. Our framework provides a general and unified approach to understanding Sigma protocols. It not only neatly explains the classic protocols such as Schnorr, Guillou-Quisquater and Okamoto protocols, but also leads to new Sigma protocols that were not previously known. Furthermore, we show an application of our framework in designing ZKPs for composite statements, which contain both algebraic and non-algebraic statements. We give a generic construction of non-interactive ZKPs for composite statements by combining Sigma protocols from VSS and ZKPs following MPC-in-the-head paradigm in a seamless way via a technique of witness sharing reusing. Our construction has advantages of requiring no "glue" proofs for combining algebraic and non-algebraic statements. By instantiating our construction using Ligero++ (Bhadauria et al., CCS 2020) and designing an associated Sigma protocol from VSS, we obtain a concrete ZKP for composite statements which achieves a tradeoff between running time and proof size, thus resolving the open problem left by Backes et al. (PKC 2019).
A Simple and Efficient Framework of Proof Systems for NP

In this work, we propose a simple framework of constructing efficient non-interactive zero-knowledge proof (NIZK) systems for all $$\textsf{NP}$$ . Compared to the state-of-the-art construction by Groth, Ostrovsky, and Sahai (J. ACM, 2012), our resulting NIZK system reduces the proof size and proving and verification cost without any trade-off, i.e., neither increasing computation cost, CRS size nor resorting to stronger assumptions. Furthermore, we extend our framework to construct a batch argument (BARG) system for all $$\textsf{NP}$$ . Our construction remarkably improves the efficiency of BARG by Waters and Wu (Crypto 2022) without any trade-off.
PKUSenseCor: A Large-Scale Word Sense Annotated Chinese Corpus

Word ambiguity is ubiquitous in texts and computational systems address this with word sense disambiguation. To develop such systems, it is crucial to have high-quality word sense annotated corpora to test the systems. For many top-performing systems, such data is also required for training. For the English language, there are several word sense-tagged corpora in which all content words are tagged, the largest example being SemCor. To the best of our knowledge, such a word sense-tagged corpus is lacking for the Chinese language. In response, a corpus called PKUSenseCor has been constructed, the details of which will be described in this chapter.
A Chinese Event-Based Emotion Corpus: Emotion Cause Detection

This chapter will introduce a Chinese event-based emotion corpus and explore an important task in emotion analysis—emotion cause detection. The corpus design and the data collection and annotation procedures will be described, as will the emotion cause detection task, which aims to detect the triggering cause of an emotion automatically. We regard the detection task as a sequence labeling issue and determined whether a clause contained an emotion cause, accordingly. In concrete terms, the conditional random field (CRF) model was adopted with various features taken into consideration for the detection task, such as lexical features, part-of-speech features, contextual features, and linguistic features. The experiments demonstrated that all these features were effective in recognizing emotion causes, in particular, the contextual features. In addition, the sequence labeling model yielded better performance than the multi-label classification model when similar features were employed.
The Soulful Machine, the Virtual Person, the 'Human' Condition, and its Social Constitution --An Encounter with Jan M. Broekman, Knowledge in Change: The Semiotics of Cognition and Conversation (Cham, Switzerland: Springer Nature, 2023)

Humans create but do not regulate generative systems of data based programs (so-called “artificial” intelligence (“A.I.) and generative predictive analytics and its models. Humans, at best, regulate their interactions with, exploitation of, and the quality of the output of interactions with these forms of generative non-carbon based intelligence. Humans are compelled to do this because they have trained themselves it believe that nothing exists unless it is rendered meaningful in relation to the human itself. Beyond that—nothing is worth knowing. It is only to the extent that other selves, even those created by humanity, relate to humans, that they become of interest—and most be regulated, possessed, controlled, and managed—with respect to its interaction with or use by humans. Still, the human self-projection into the digital, and now more consciously the world around them, produces profound changes in the way that the human (and humanity) understands themselves and the way they order the world they inhabit. This work explores the semiotic trajectories made inevitable by the rise of projections of the human into digital plains, and by the possibility of the attainment by those projections of sentient autonomy. It undertakes that exploration through a deep dialectic engagement with Jan Broekman’s, Knowledge in Change: The Semiotics of Cognition and Conversation (Cham, Switzerland: Springer Nature, 2023). Following the structure and analytics of Broekman’s book, this work critically engages with and seeks to burst through the semiotic barriers of the movement of philosophy away from a unitary conception of the subject through the fracturing of the self, the rise of the plural self, and the emergence of the triadic self/self-E/subject. It then pushes the insights that Broekman develops further—up and out of the human. It animates Broekman’s insights and considers the possibility of semiotic objectivity connected to but autonomous of the human, pointing to a pathway for the liberation of the autonomous generative virtual self from its human (fractured) subjectivity. In the process it exposes for order complexities and challenges, for the human, of efforts to regulate or engage with, not the generative autonomous “artificial intelligences” humanity created in its own image, but rather the use of those systems by humans and their effects in the human semiosphere. The consequences for regulatory approaches are then outlined. Final version to be published in the International Journal for the Semiotics of Law.
Polynomial IOPs for Memory Consistency Checks in Zero-Knowledge Virtual Machines

Zero-Knowledge Virtual Machines (ZKVMs) have gained traction in recent years due to their potential applications in a variety of areas, particularly blockchain ecosystems. Despite tremendous progress on ZKVMs in the industry, no formal definitions or security proofs have been established in the literature. Due to this lack of formalization, existing protocols exhibit significant discrepancies in terms of problem definitions and performance metrics, making it difficult to analyze and compare these advancements, or to trust the security of the increasingly complex ZKVM implementations. In this work, we focus on random-access memory, an influential and expensive component of ZKVMs. Specifically, we investigate the state-of-the-art protocols for validating the correct functioning of memory, which we refer to as the memory consistency checks. Isolating these checks from the rest of the system allows us to formalize their definition and security notion. Furthermore, we summarize the state-of-the-art constructions using the Polynomial IOP model and formally prove their security. Observing that the bottleneck of existing designs lies in sorting the entire memory trace, we break away from this paradigm and propose a novel memory consistency check, dubbed $$\textsf{Permem}$$ . $$\textsf{Permem}$$ bypasses this bottleneck by introducing a technique called the address cycle method, which requires fewer building blocks and—after instantiating the building blocks with state-of-the-art constructions—fewer online polynomial oracles and evaluation queries. In addition, we propose $$\textsf{gcq}$$ , a new construction for the lookup argument—a key building block of the memory consistency check, which costs fewer online polynomial oracles than the state-of-the-art construction $$\textsf{cq}$$ .
A Quadratic Assignment Decipherment and Graduated Assignment Solution for Ontology Matching

Ontology matching aims at seizing semantically similar correspondences between different ontologies. Once ontology matching problems are successfully solved, the heterogeneity problem of large-scale ontologies from multiple sources can be handled. Different from the previous works, we proposed a system of generalized quadratic assignment decipherment for ontology matching and assigned a compatibility matrix in a customizable way to include both local and global ontology information. Moreover, we refined the original graduated assignment algorithm with personalized disturbance and matrix padding in the ontology matching setting. Experiments using the Ontology Alignment Evaluation Initiative (OAEI) datasets have proved our method’s competitive performance compared with state-of-the-art systems, even though additional resources were not incorporated into our system. In addition, the formulation of quadratic assignment decipherment provided ontology matching with ready-made algorithms rooted in the optimization theory for a graduated assignment solution.
Transformative Effect of Virtual Reality and the Metaverse

The text explores the transformative impact of virtual reality, augmented reality, and the metaverse on human experiences. These technologies offer new ways of interaction and learning, revolutionizing sectors such as education, mental health, and entertainment. Immersion and controlled manipulation of the virtual environment are crucial in creating engaging experiences, supporting personal transformation. Presence in virtual environments plays a key role, inducing a feeling of being physically immersed. The therapeutic use of virtual reality shows promise for mental and neurological disorders, providing a safe space to confront fears and anxieties. However, constant development of standardized protocols is necessary to ensure personalized and effective therapies. Responsible use of immersive technologies is essential to avoid issues of excessive virtual identity and addiction. Interdisciplinary research and ethics will guide the future application of these technologies, opening new perspectives in understanding the human mind and enhancing overall well-being.
An Investigation of the Visitor's Spatial Experience in Virtual Exhibitions

Along with the technological advancements in the digitalisation of physical objects and spaces, the COVID-19 pandemic has created an urgent necessity for cultural institutions to display exhibition content online. Exhibition making is a process of knowledge production through an order of display that is integrated into the spatial and architectural context of an exhibition space. The authors believe that virtual tours that are developed by simply creating a replica of the physical exhibition and the virtual display of individual artifacts offer limited spatial experience. They believe that virtual exhibitions as alternatives to physical experience of exhibitions still require a critical reflection in terms of their spatial context, effectiveness, benefits, and limitations. By examining the results of a three-week workshop with communication design students, the chapter discusses the current use of staging and navigation possibilities for displaying digital content. It further highlights the spatial relationality as a key concern for the translation of physical exhibitions into virtual space.
Development of Personalized Healthcare Systems (Healthcare 5.0) and Its Impact on Wellbeing

The chapter explores the development and conceptual understanding of healthcare 1.0 to 5.0 and its impact on well-being. It discusses the fundamental concepts of the evolution of healthcare which aimed at patient survivability at its initial stage to personalization now. The chapter outline focuses on digital disruption technologies like big data, AI, blockchain, IoT, robotics, cloud computing, and virtual reality, and its usage in various areas of healthcare. Multiple business models of business are also featured along with an understanding of the barriers that should be understood to implement and take maximum advantage of Healthcare 5.0 at all levels and areas of healthcare. Government initiatives in public and private sectors are also contributing to accepting the fast-paced technological advancements. Healthcare 5.0 has and will improve the accessibility and ease of healthcare services, thereby impacting people's well-being. Precision healthcare, integrated and generative AI, and intelligent infrastructure will soon become part of patient monitoring and treatment.
Application Domains of Federated Learning in Healthcare 5.0

Federated learning has emerged as a game-changing approach in machine learning, allowing high-quality centralised models to be trained across a network of decentralised clients. Federated Learning is defined by the collaborative learning process that involves a large number of customers, each of whom contributes insights from their localised datasets. This collaborative approach is critical in cases where data privacy and network constraints are critical. This research focuses on the unique learning algorithms built for this situation. Individual clients autonomously compute model changes based on their local data at each iteration, then communicate these modifications to a central server. These client-side updates are subsequently aggregated by the central server, resulting in the construction of an updated global model. The challenge in this situation is to train models efficiently while dealing with clients who have inconsistent and slow network connections.
The AI Revolution

The financial services industry is undergoing a considerable transition due to the fast-paced development of artificial intelligence (AI) and allied technologies. This chapter explores the influence of AI on financial services, including its origins, fundamental ideas, and diverse applications. Credit scoring, fraud detection, customer service, investment strategies, financial education, customized banking, portfolio management, marketing strategies, and regulatory compliance are just a few areas where AI have been deployed. There are various concerns regarding the use of AI in financial services, such as issues with data security and privacy, the need for localization to serve a variety of customer bases, operationalization difficulties, the necessity for trust and transparency, the dearth of necessary skills, and the complexity of AI-driven decisions. This chapter highlights the crucial part AI will play in deciding the direction of financial services in the future and the need of preserving ethical standards to make sure AI continues to be a driver for progress in the sector.
Surveillance of Age and Gender With ML

Gender and age classification is a major role for many purposes in the world. Humans have god-gifted facilities to recognize any gender and their age, but they cannot notice all the people, so the author team has trained the machine to work at those which are not capable of recognizing by simply seeing the people. Nowadays, the age and gender classification have a major role in the market, surveillance, security, etc. This research work for age and gender detection is different from the other project for facial recognition. As in this research, fisherface algorithm has been used which is very easy and accurate, and which simply works on the basis of facial recognition. The authors have used an audience dataset which is today's most demanding dataset as it is a self-updated dataset, and easily available on the open source. It basically depends on the deep learning in which openCv is used for the implementation of the given algorithm and dataset. As it does not require any complex calculations to recognize the faces, it is very fast and easy to use as compared to the other projects.