Reviewerly/exHarmony huggingface dataset:
Seventh Workshop on Data Management for End-to-End Machine Learning (DEEM)

The DEEM'23 workshop (Data Management for End-to-End Machine Learning) is held on Sunday June 18th, in conjunction with SIGMOD/PODS 2023. DEEM brings together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in ML application scenarios. The workshop solicits regular research papers (10 pages) describing preliminary and ongoing research results, including industrial experience reports of end-to-end ML deployments, related to DEEM topics. In addition, DEEM 2023 has a category for short papers (4 pages) as a forum for sharing interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, preliminary results, and descriptions of system components and tools related to end-to-end ML pipelines. The workshop received 13 high-quality submissions on diverse topics relevant to DEEM, of which 6 regular papers and 7 short papers.
Disaggregated Database Systems

Disaggregated database systems achieve unprecedented excellence in elasticity and resource utilization at the cloud scale and have gained great momentum from both industry and academia recently. Such systems are developed in response to the emerging trend of disaggregated data centers where resources are physically separated and connected through fast data center networks. Database management systems have been traditionally built based on monolithic architectures, so disaggregation fundamentally challenges the designs. On the other hand, disaggregation offers benefits like independent scaling of compute, memory, and storage. Nonetheless, there is a lack of systematic investigation into new research challenges and opportunities in recent disaggregated database systems.
Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM)

Recent advances in AI techniques, as well as enabling hardware and infrastructure, has led to the integration of AI in wide-ranging domains and tasks. In particular, AI has been used to handle various types of data (including numerical, textual and image data) and has been adopted in large-scale distributed systems. From a data management perspective, this calls for the harnessing of state-of-the-art AI solutions for data management tasks and systems. aiDM is a full-day workshop that offers a stage for innovative interdisciplinary research that studies the interaction between AI and data management and develops new AI technologies for data-related tasks. This year, aiDM'23 particularly focuses on the transparent exploitation of AI techniques in existing enterprise-level data management workloads.
DataChat: An Intuitive and Collaborative Data Analytics Platform

Enterprises invest in data platforms with the aim of extracting meaningful information through analytics. Typically, experts create analytics pipelines that feed into dashboards and provide answers to predetermined questions. This approach makes analytics a spectator sport for most people and introduces operational bottlenecks to leveraging those investments. To improve the value derived from data, many organizations are opting to open up their data assets and allow access to a wider range of users. However, using programming languages such as SQL and Python for analytics can be difficult for most enterprise users. DataChat provides a simplified data science approach that is intuitive, powerful, and accessible to all data users. The platform is built on a library of data functions that are cleanly abstracted to maximize efficiency and ease of use while maintaining a rich suite of tools necessary for data science. With these functions, users can create data analysis pipelines by using a simple point-and-click interface in a spreadsheet view or by using natural English interfaces. Modern sharing and collaboration features are central to all aspects of the platform, allowing teams to easily bridge expertise gaps. A deeper understanding of results is facilitated by providing automatically-generated English explanations of how they were derived. By enhancing these aspects of data science and human-to-human communication, the platform addresses the needs that many organizations are encountering as their analytics needs mature.
Fast Natural Language Based Data Exploration with Samples

The ability to extract insights from large amounts of data in a timely manner is a crucial problem. Exploratory Data Analysis (EDA) is commonly used by analysts to uncover insights using a sequence of SQL commands and associated visualizations. However, in many cases, this process is carried out by non-programmers who must work within tight time constraints, such as in a marketing campaign where a marketer must quickly analyse large amounts of data to reach a target revenue. This paper presents ApproxEDA - a system that combines a natural language processing (NLP) interface for insight discovery with an underlying sample-based EDA engine. The NLP interface can convert high-level questions into contextual SQL queries of the dataset, while the backend EDA engine significantly speeds up insight discovery by selecting the most optimum sample from among many pre-created samples using various sampling strategies. We demonstrate that ApproxEDA addresses two key aspects: converting high-level NLP inputs to contextual SQL and intelligently selecting samples using a reinforcement learning agent. This protects users from diverging from their original intent of analysis, which can occur due to approximation errors in results and visualizations, while still providing optimal latency reduction through the use of samples.
ATENA-PRO: Generating Personalized Exploration Notebooks with Constrained Reinforcement Learning

One of the most common, helpful practices of data scientists, when starting the exploration of a given dataset, is to examine existing data exploration notebooks prepared by other data analysts or scientists. These notebooks contain curated sessions of contextually-related query operations that together demonstrate interesting hypotheses and conjectures on the data. Unfortunately,relevant such notebooks, that had been prepared on the same dataset, and in light of thesame analysis task, are often nonexistent or unavailable.
Analyzing Online Media Articles on Diabetes using Natural Language Processing: A Comparative Study of Indian Ocean Region and France

Background: Diabetes is a global health concern affecting millions of people worldwide. However, knowledge, attitudes, and practices related to this disease vary widely across different regions. This article aims to investigate mediainfluenced perceptions about diabetes in France and the Indian Ocean countries using natural language processing (NLP) techniques applied to online news articles. Findings aims to provide expert in Health Literacy (HL) and health promotion to develop better communication strategies. Method: Constitute a datatset of Online news articles on Diabetes and apply NLP like Word2Vec for word integration, LDA for topic identification, and transformer-based classification models (e.g., BERT and its variants) for sentiment analysis. processing (NLP). Results: Sentiment analysis revealed more negative discussions about diabetes in the Indian Ocean region (48%) compared to France (32%), with neutral articles dominating in France (42%). In terms of topic Identification there were some topic which appeared for France which were not present for indian ocean region. Discussions: The findings of this study indicate that perceptions and discussions about diabetes differ between two regions, which have implications for public health interventions and communication strategies. However, the study is limited by the initial amount of information captured for analysis.
Cryptic Regulation of Crypto-Tokens

This paper investigates the alignment of existing securities regulations with the emerging landscape of crypto-tokens and blockchain technology. By examining the features of these digital assets, including decentralization, consensus mechanisms, and programmability, we analyze how they interact with existing financial rules. We compare approaches to regulation across countries, considering potential impacts on innovation. Furthermore, we explore the issues that may arise with blockchain networks, such as payment efficiency and market safety. The study aims to contribute to discussions about balancing innovation within the blockchain sphere and ensuring investor protection and market security, underlining areas that may necessitate regulatory improvements.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Originality Under Singapore Copyright Law

Situated within a cross-jurisdictional assessment of originality across multiple countries, this chapter offers insights into Singapore’s distinctive approach to conceptualizing and applying the concept of originality under copyright law. As a fundamental criterion for obtaining copyright protection, originality serves to delineate works that are worthy of copyright protection from those that do not qualify for such protection. Through an examination of the historical development of copyright law in Singapore and a careful analysis of key cases, this chapter provides an understanding about the specific standard of originality required under Singapore law. Moreover, it devotes some space to discussing the interaction between originality and developments in technology, such as artificial intelligence, within the context of Singapore’s copyright framework. By analyzing Singapore’s response to technological developments through the specific lens of originality, this chapter contributes to a comprehensive understanding of that concept, highlighting its significance within the broader discourse on copyright protection.
A Survey on Memory Assistive Technology for Elderly

Technology cannot solve all age-related problems; however, they have the potential to relieve a significant amount of stress on elderly and their family. In this paper, existing memory assistive techniques for elderly are reviewed, and the idea of a smart memory companion is presented. The application leverages the personalized digital journal curated using historical and daily life experiences of the elderly to aid the aged in early stages of Alzheimer. The implementation proposed in this paper focuses on using automatic speech recognition and natural language processing techniques to build an intelligent conversational agent, retrieval engine, and an alerting module to help the elderly person in recalling and outdoor safe navigation. The smart application is meant to reduce the burden on the family to continuously monitor their aged loved ones and avoid caretakers unless necessary. The companion can be used to collect memories in visual and textual format from elderly, family, and caretakers to utilize it when needed. The companion can also be integrated with existing virtual assistants like Alexa and Google Home.
Analysis of Metaheuristic Algorithms for Optimized Extreme Learning Machines in Various Sectors

Decision-making is a critical task in our day-to-day applications. Any problem in this real world needs accurate solution at any point of time. There are several machine learning algorithms that can be adopted specifically for particular purpose and extreme learning machine (EML) is one among these algorithms. This paper presents a brief study of extreme learning machine and its combination with various metaheuristic algorithms, and its application to many problems specific domain. The performance achieved by these metaheuristic algorithms fused extreme learning machines are discussed in terms of relevant metrics. The details of the dataset used in the applications are also discussed.
A Blockchain Framework for Investment Authorities to Manage Assets and Funds

Investment authorities are broad financial institutions that carefully manage investments on behalf of the national government using a long-term value development approach. To provide a stronger structure or framework for Investment Authorities to govern the distribution of funds to public and private markets, we have started research to create a blockchain-based prototype for managing and tracking numerous finances of such authorities. We have taken the case study of Oman Investment Authority (OIA) of Sultanate of Oman. Oman’s wealth is held in OIA. It is the organization that oversees and utilizes the additional capital generated by oil and gas profits in public and private markets. Unlike other Omani funds, this one focuses primarily on assets outside the Sultanate. The operation of the OIA entails a large number of transactions, necessitating a high level of transparency and administration among the parties involved. Currently, OIA relies on various manuals to achieve its goals, such as the Authorities and Responsibilities manual, the Investment Manual, and the Code of Business Conduct, among others. In this paper, we propose a blockchain-based framework to manage the operations of OIA. Blockchain is a part of the Fourth Industrial Revolution, and it is reshaping every industry. The main components of every blockchain are assets and participants. The funds are the major assets in the proposed study, and the participants are the various fund shareholders/recipients. The blockchain’s transactions are all safe, secure, and immutable, and it is part of a trust less network. The transactions are simple to follow and verify. By replacing intermediary firms with smart contracts, blockchain-based solutions eliminate any middlemen in the fund allocation process.
Stopwords Aware Emotion-Based Sentiment Analysis of News Articles

It may be quite challenging to develop suitable techniques for analyzing the enormous amount of unstructured content of news items and extracting opinions from it. Understanding the attitudes, opinions, and feelings expressed in an online mention generally involves figuring out the emotional undertone of a string of words. Problems resulting in incoherent sentiment analysis are encountered in many of the existing approaches for sentiment analysis due to the presence of punctuation, ironic sentences, etc. Negative stopwords carry significant information about the sentiment of the sentence, but it is found that most of the approaches concerned with sentiment analysis remove these stopwords during the pre-processing stage. Thus, a suitable approach using negative stopwords has been proposed to minimize the information loss and improve the accuracy of the results obtained from sentiment analysis. The proposed approach has been evaluated using the dataset of various categories of news articles obtained from BBC, and it is found to yield an average accuracy of 85.75 for providing the sentiment polarity of various news items.
Enhanced Dense Layers Using a Quadratic Transformation Function

Despite the intense improvements in existing architectures and the development of new deep-learning models, the core of the dense layers remains the same. Here, we are referring to the function used to process the input from the neurons of previous layers, also called the transformation function. In this study, we propose a new methodology for the computation of a neuron’s output, by adding a quadratic term to the conventional linear operation. With the aim of achieving a higher accuracy, we test the proposed process on a number of well-known data sets (MNIST, CIFAR). There is a significant improvement in the performance of a simple dense neural network. The initial accuracy itself is much higher when the new neurons are used. Initial convergence to higher accuracies is always much faster in the proposed model, and the computational time of the model is reduced to half (or even less). This enhancement can potentially be reflected in every deep learning architecture that uses a dense layer and will be remarkably higher in larger architectures that incorporate a very high number of parameters and output classes. The proposed model also fights issues like the vanishing gradient. Toward the end of the paper, we discuss the vast future scope of this study.
Novel Machine-Learning-Based Decision Support System for Fraud Prevention

Due to the popularity of cloud services, it is unavoidable, that not just legitimate, but fraudulent registrations will happen. For a service with good reputation, it is essential to prevent fraud users. A common way is to filter these cases during the registration process by analysts. This chapter presents a novel decision support system that can recognize anomalous behavioral patterns and classify accounts based on the available data thus implementing an automated fraud prevention system. The process uses both supervised and unsupervised approaches, thus avoiding errors due to inaccurate labeling. As a supervised machine learning algorithm, random forest classifier and logistic regression are used, and as an unsupervised, auto encoder is used. The developed flow gives a recommendation to the analyst whether a new user is potentially fraud or not and provides feedback on the accuracy of analysts’ work based on the results of the unsupervised approach. The newly developed process is able to supervise the decisions made by analysts thus improving the labeling process. The main goal of this chapter is to present a new, more deterministic labeling workflow with the ability to provide feedback so it can improve the correctness of the training data set.
Hybrid intelligence-based cryptosystem

In healthcare informatics, telemedicine is a vital topic especially in the covid19 situation. Telemedicine means the remote delivery of medical services with the help of telecommunication. It has proven that this type of medical service is very helpful in any pandemic like COVID-19 or in the situation of chronic disease. Telemedicine itself functions via physical devices, software, and applications working together to connect remote patients and even multiple networks to a healthcare provider. While the benefits of the telemedicine are clear for the healthcare industry, it also poses the cyber risks that could lead to a misdiagnosis and also bad for well-being of the patients. For secure communication, any telehealth system must address the issues like multifactor authentication, data loss prevention, advanced threat protection, secure telephony, and electronic health record (EHR) security in wireless communication. In this paper we have introduced a hybrid intelligence-based cryptosystem for secure data communication in the telehealth system. Our system is developed by integrating three modules: Hopfield model, Cramer's rule, and DNA sequence–based dynamic S-box; round key generation; and the encryption-decryption process. Different types of experimental results and its analysis and observation proves the reality and effectiveness of our technique.
Formal signoff on real projects

In this chapter, we have attempted to integrate many of the lessons explored in the earlier chapters, and describe a methodology to enable formal signoff: primary ownership of validation for some parts of the design by the FV team, from the beginning of design through the final tapein. At a broad level, there are four main aspects to this methodology: plan and architect, apply and execute, cover and regress, and track and close. Each of these aspects of your validation planning and progress should be regularly discussed with and presented to management, to increase their confidence that FV isn't some arcane and obscure alternate method, but a fully integrated and supported element of your overall validation toolkit.
Does Virtual Hotel Shifting Realities or Just Daydreaming? A Wake-Up Call

Today’s tourism and hospitality managers face new challenges in dealing with travelers who are part of digital natives, brought up in a three-dimensional (3D) world of virtual communication, visual complexity, and online identities. There are plenty of ways hotels can incorporate virtual reality (VR) and augmented reality (AR) into the guest experience, and new ones are coming virtually constantly. The current review has demonstrated that virtual hotels could play a significant role in the hospitality and accommodation business. It need not just be considered “a gimmick,” but it also contains “hidden gems” that are important to know in doing future business. Therefore, this paper aims to define and recognize basic insights into the concept, best practices, types, characteristics, and components of virtual hotels according to virtual hotel operator objectives that have been implemented. Various thoughts and viewpoints present important definitions, categorizations, and a component of virtual hotels. The virtual hotel is categorized into two main types according to the number of players (users): single user or multiusers. Furthermore, the group is divided into various virtual hotel forms where the single user had been identified from (1) virtual hotel simulators, (2) virtual hotel showrooms, (3) hotel add-on reality, and (4) self-guided virtual tours, and for multiusers, it can take several forms such as (1) hybrid co-working spaces, (2) meta-version hotels, and (3) standalone virtual hotel. A virtual hotel should not be considered a competitor to a physical hotel. The assimilation of the physical and the digital environment will reinforce each other. This review discusses the paradigm and practices of a virtual hotel and takes it a step further to see if the virtual hotel can be regarded as a hotel in its own right.
Selecting Robots to Take Over Tasks in Hospitality Settings: Joining Two Research Fields

Robotic assistance for work processes in the hospitality industry is receiving increased attention both in the hospitality industry and in academic research. Unfortunately, academic literature about hospitality robotics is currently disjointed, making it hard for hospitality professionals to decide which processes to target, and which available robotic systems would result in benefits (or limitations) for the organisation, employees or guests. Similarly, it is hard to understand what robotic functionalities need to be developed for particular processes, or what future functionalities to expect from ongoing developments in the robotics field. Researching hospitality robotics requires expertise in both fields. With this chapter, we try to give an insight in the things that should be considered when designing and evaluating the effectiveness of robots in a hospitality work environment. We do this by illustrating how robots can influence many interactions in a work environment and we present a conceptual framework for mapping these different interactions.
Mineral exploration using multispectral and hyperspectral remote sensing data

In many environments where traditional field surveys are difficult and time-consuming, the remote sensing (RS) technology enables quick delineation of mineralized zones over wide areas at minimum cost and effort. Minerals and rocks are identified based on their mineral absorption properties present in the visible, near-infrared (VNIR), shortwave infrared (SWIR) (0.4–2.5 μm), and thermal infrared (TIR) (8–12 μm) wavelength regions. Iron oxides and hydroxides are charactered in the spectra region of VNIR (0.4–1.1 µm) region, whereas the SWIR absorption features (2.0–2.5 µm) distinguish the spectra of carbonates, clay minerals, and sulfates. The TIR distinguish the spectra of silicates. Multispectral (e.g., Landsat 7, Landsat 8, ASTER, and Sentinel-2A) and hyperspectral (e.g., AVIRIS and Hyperion) RS data are used for mineral exploration in various environments to delineate structural elements that may have controlled mineralization as well as alteration zones. Mineral exploration in these environments is examined using numerous image processing techniques, such as minimum noise fraction, principal component analysis, band ratio, matched filtering, and image classification. This chapter addresses several mineral exploration techniques and methods including spectral properties of minerals and rocks, and selected case studies.