Reviewerly/exHarmony huggingface dataset:
Blockchain-Aided Keyword Search over Encrypted Data in Cloud

Attribute-based keyword search (ABKS) achieved significant attention for data privacy and fine-grained access control of outsourced cloud data. However, most of the existing ABKS schemes are designed based on a semi-honest and curious cloud storage system in which the search fairness between two parties becomes questionable. Hence, it is vital to building a protocol that provides mutual trust between the cloud and its users. This paper proposes a blockchain-aided keyword search over encrypted data, which achieves search fairness between the cloud and its users using Ethereum blockchain and smart contracts. Additionally, the system accomplishes fine-grained access control, limiting access to the data to only those who have been given permission. Besides, the scheme allows multi keyword search by the users. The security analysis shows that our scheme is indistinguishable against chosen-plaintext attack and other malicious attacks. The performance analysis shows that the scheme is efficient .
Automation with Blockchain: Creating a Marketplace for IoT Based Irrigation System

The next revolutionary technology to emerge since the creation of bitcoin in 2008 is blockchain technology. The Internet of Things (IoT), security, and other industries have all adopted blockchain technology. The article discusses the use of smart contracts, an Ethereum blockchain feature, to automate transactions in the Ethereum blockchain-based marketplace platform for Internet of Things devices. The following is demonstrated as a proof of concept by building a prototype of the suggested platform utilising Ethereum, Ganache, Web3, and Metamask. This exemplifies how transactions can be automated using smart contracts on the blockchain. When the land’s moisture content is less than some critical value, the prototype concentrates on automating the watering of the land. Real-time monitoring of the moisture level of the consumer’s land is possible. On the basis of the centralised paradigm, attempts are being made to construct such a system. The goal is to automate every activity. In the blockchain, the smart contract functions like a living thing. Because of this, automating is made possible. The blockchain also gives each gadget a unique account, considering them as participants in the transaction rather than just the sold goods, increasing the potential for automation.
A Guide to become NIST Compliant

The National Institute of Standards and Technology (NIST) develops cybersecurity standards, guidelines, best practices, and other tools to meet the needs of American business, government organizations, and the general public. Their work spans from giving businesses accurate information that they can utilize immediately to longer-term research that anticipates technology developments and future challenges. Moreover, the needs of American industry and the general public are what drive their cybersecurity endeavors. They collaborate closely with stakeholders to set priorities and ensure that our resources are focused on the most urgent issues they face. NIST also advances the management of privacy risks, some of which are directly connected to cybersecurity, and broadens our understanding of those risks. Among the main areas on which NIST contributes and focuses are cryptography, education and workforce, emerging technologies, risk management, identity and access management, measurements, privacy, trustworthy networks, and trustworthy platforms. This research paper aims to focus on discussing the various controls of the NIST. It also emphasizes on the processes to be followed by the federal agencies to ensure that they are NIST compliant.
Designing English Instructional Model: An Integrated Technology in ESP for Biological Students

This study was based on the students’ needs regarding the instructional model of English teaching at the Biology Department. The objective of this study was to design English materials by combining YouTube and ABA (American and British Academy) Applications based on the students' needs analysis. The study was mixed-method research with the subject of the research was Biological students of Muhammadiyah University of Parepare, Indonesia. The result of the study was the instructional model focused on input, process, and output. In the input, there are goals and skills; in the process, there are learning activities and venues and in the output there is learning outcomes. The skills are listening, speaking, reading, and writing. Every skill consists of five activities. Listening skill consisted of five activities, namely hearing, understanding, remembering, responding, and evaluating. Speaking skill consisted of apprehending, synthesizing, analyzing, and evaluating. Reading skill consisted of reading, generating, comprehending, discussing, and evaluating. Writing consisted of pre-writing, composing, feedbacking, revising, and evaluating. There were two venues of the model. They were indoor and outdoor. Besides, the learning outcomes focused on cognitive, affective, and psychomotor
EdgeAI: Concept and Architecture

In this chapter, we introduce the concept of EdgeAI, followed by a brief talk on how AI benefits edge computing and vice versa. We then move to discuss the variety of architectures that may be adopted for implementing AI at the edge, explicitly elaborating on training and inference architectures separately. Next, we provide a holistic view of the different criteria to evaluate the processes of Model Training and Model Inference at the edge. We then review emerging frameworks and technologies which assist the training and inference processes by targeting and improving specific Key Performance Indicators (KPIs). We also analyze how each enabling technology impacts the different KPIS for both the training and inference process. Finally, we summarize all the architectures, criteria for evaluating AI model workflow, and the enabling technologies for model training and inference at the edge in the form of a comparative analysis.
A Real-time Multi-Person 3D Pose Estimation System from Multiple RGB-D Views for Live Streaming of 3D Animation

3D pose estimation of multi-person in real-time is a challenging problem which is essential for a various virtual reality (VR) applications. In this paper, we propose a real-time multi-person 3D pose estimation system for live streaming of 3D animation with multiple RGB-D cameras. The proposed system comprises the several edge devices connected to a central server via networks. 2D pose detection and depth sensing is locally conducted on each edge device. The edge device transmits the results to the central server. 3D pose reconstruction is performed on the central server. The central server aligns the multiple camera coordinates with a specific world plane. Then, the central server matches the 2D pose result across the multiple cameras to a person based on the distance. Finally, the 3D poses are reconstructed based on multi-view triangulation. The proposed system is capable of processing in real-time. To demonstrate the proposed system estimates multi-person 3D poses in real-time, we implement the prototypes and show the proposed system can be applied to the live streaming of 3D animation such as PC and Web.
Distance-based Visual Scanpath Estimation and Applications

Existing studies used to estimate visual attention with the bottom-up (e.g., image salient features) and top-down methods (e.g., task-driven information searching patterns) and have gained progressive results in computing probable visual attention with various image types, user groups, and viewing durations. However, these works are mostly based on fixed distances of viewing and cannot be generalised to visual scanpath estimation at dynamic viewing distances. Therefore, the research fills this gap by 1) investigating user's visual attention patterns at different viewing distances, and 2) developing the distance-based visual scanpath estimation model that can generate human-like visual scanpath at different distances. The research also prepares a large-scale eye tracking dataset to support the visual scanpath model and evaluates it with applications such as graphic design and advertisement. The research's main contributions are two-fold. Firstly, it provides the novel visual scanpath estimation model that works at different viewing distances and secondly, draws insights into how effective of the model in different applications.
DECI: A Tutorial on Designing Effective Conversational Interfaces

Conversational User Interfaces (CUIs) have been argued to have advantages over traditional GUIs due to having a more human-like interaction. The growing popularity of conversational agents has enabled humans to interact with machines more naturally. There is an increasing familiarity among people with conversational interactions mediated by technology due to the widespread use of mobile devices and messaging services and a hungry market for conversational agents. Based on the recent advances in conversational AI, as a result of the proliferation of large language models, the signs are that the future of human-computer interaction will have a significant conversational component. Today, over two-thirds of the population on our planet has access to the Internet, with ever-lowering barriers to accessibility. This tutorial will showcase the benefits of employing novel conversational interfaces for crowd computing, human-AI decision making, health and well-being, and information retrieval. Given the widespread adoption of AI systems across several domains, we will discuss the potential of conversational interfaces in facilitating and mediating people’s interactions with AI systems. The tutorial will include interactive elements and discussions and provide participants with insights to inform the design of effective conversational interfaces.
SketchRec 2023: 3rd Workshop on Sketch Recognition

Sketch recognition is the interpretation of hand-drawn diagrams, and seeks to understand the users' intent while allowing them to draw unconstrained diagrams. Research in sketch recognition has been on-going for approximately half a century, and has experienced iterative advances due to the difficulty of the problem. As pen- and touch-capable devices such as smartphones, tablets, touch-driven monitors, and large touchscreen devices have become ubiquitous, and as emergent technologies such as virtual and augmented reality-driven computing technologies are becoming more advanced, sketch recognition-related research remains an open field for researchers to explore in approaching continuing interaction and recognition challenges with these technologies. The SketchRec workshop was hosted at the ACM Annual Conference on Intelligent User Interfaces (IUI) and served the sketch recognition research community for sharing, presenting, and discussion research work that relates to the topics in the research field.
Interactive Model Refinement in Relational Domains with Inductive Logic Programming

This paper presents an interactive system for exploring and editing logic-based machine learning models specialised for the relational reasoning problem domain. Prior work has highlighted the value of visual interfaces for enabling effective user interaction during model training. However, these existing systems require two-dimensional tabular data and are not well-suited to relational machine learning tasks. Logic-based methods, such as those developed in the field of Inductive Logic Programming, can address this; they retain relational information by operating directly on raw relational data while remaining inherently interpretable and editable to allow for human intervention. However, such systems require logical expertise to operate effectively and do not enable visual exploration. We aim to address this; taking design inspiration from equivalent interfaces for propositional learning, we present a visual interface that enhances the usability of inductive logic programming systems for domain experts without a background in computational logic.
VIVA: Visual Exploration and Analysis of Videos with Interactive Annotation

This paper presents VIVA, a novel interactive tool for visually exploring long videos and searching for specific moments. Previous work on video data exploration and analytics often assumes that manually-created, rich annotations are available. However, such metadata may not be easily obtained. We design an interactively machine learning workflow for users to rapidly create annotations along a timeline. Combined with VIVA’s focus+context visualization that effectively displays frame snapshots in the context of a video stream, VIVA enables users to explore and analyze long video clips by incrementally make sense of them. We present usage scenarios that demonstrate how users would use VIVA for video-related tasks.
Designing Efficient and Customizable Radar-based Gesture Interfaces

Radar sensors have many advantages compared to traditional vision-based sensors for gesture recognition. They work in poor lighting and weather conditions, come with less privacy concerns than vision-based sensors, and can be integrated into everyday objects. However, due to the size and complexity of radar data, most radar-based systems rely on deep-learning techniques for gesture recognition. These systems take time to train and make it challenging to support quick customization by the user, such as changing the gesture set of an application. In this thesis, we want to investigate whether we can create efficient and customizable radar-based gesture interfaces, by reducing the size of raw radar data and relying on simple template matching algorithms for gesture recognition. We have already implemented a pipeline that handles these steps and tested its performance on a dataset of 20 gestures performed by three participants in front of a cheap, off-the-shelf FMCW radar. The next steps include developing a software environment for testing recognition techniques on radar gestures, optimizing our pipeline for real-time gesture recognition, and investigating two new use cases: environments where the radar is obstructed by some materials (wood, glass, and PVC) and breathing patterns recognition.
Secure Data Aggregation in Federated Learning

In the previous chapter, we introduced secure multi-party computation with the basic knowledge of the popular building blocks and how to take the advantage of SMPC to implement the goal of federated learning through masking. Moreover, we presented how researchers use secret sharing techniques to address the dropout issue during training.
PORDE: Explaining Data Poisoning Attacks Through Visual Analytics with Food Delivery App Reviews

Artificial intelligence (AI) gives many benefits to our lives. However, biased AI models created by receiving data poisoning attacks may induce social problems. Therefore, developers must consider carefully whether the training data received a poison attack when developing an AI model. Data visualization is one of the methods to facilitate the analysis of the data required for checking if the training data received a poisoning attack. However, prior studies did not visualize real-world AI training data. Restaurant reviews in delivery apps are one of the cases of a poisoned dataset. Restaurants hold review events on delivery apps to encourage customers to write a positive review in return for certain rewards, thereby creating reviews with bias. In this study, we propose POisoned Real-world Data Explainer (PORDE) that explains data poisoning attacks through visual analytics with food delivery app reviews. The findings of this study suggest implications for securing safe training data and developing less biased AI models.
Serendipity into session-based recommendation: Focusing on unexpectedness, relevance, and usefulness of recommendations

Conventional recommender systems have a potential problem of hyper-personalization that produces biased recommendations, decreasing the diversity of items. Concerning this issue, serendipity-oriented recommender systems have been introduced as an alternative to increasing recommendation diversity. However, these systems have a drawback in potentially recommending uninteresting items from a target user's perspective. To overcome this limitation, the present study introduces a novel algorithm to recommend diverse items that are likely to be favored via fusing serendipity into existing algorithms with maintaining accuracy. Specifically, we incorporate elements of serendipity into session- and graph neural network-based recommender systems by utilizing not only unexpected but also relevant data from historical data. Furthermore, our algorithm reflects the personalized balancing for trade-off via a user-controllable parameter. In the application of our algorithm, the experimental results on two real-world datasets show the algorithm's trade-off between accuracy and diversity and its potential from the user-centric perspective. This study contributes to proposing a new algorithm that provides not solely useful but also varied recommendations to broaden users’ horizons and to simple and clear algorithmic improvement by its application.
GAN Attacks and Counterattacks in Federated Learning

In this chapter, we will present the related content of generative adversarial networks (GANs) and their applications in the federated learning environment. GANs have gained extraordinary attention from both academia and industry, and they naturally possess the gene of security as an attacking tool, although its original purpose was not for security.
Introduction to Federated Learning

In this chapter, we plan to present some basic knowledge of federated learning and offer an overview of the security and privacy attacks and counter methods for beginners. Some sections could be skipped for readers who have understood the concepts already.
Event Detection Explorer: An Interactive Tool for Event Detection Exploration

Event Detection (ED) is an important task in natural language processing. In the past few years, many datasets have been introduced for advancing ED machine learning models. However, most of these datasets are under-explored because not many tools are available for people to study events, trigger words, and event mention instances systematically and efficiently. In this paper, we present an interactive and easy-to-use tool, ED Explorer, for ED dataset and model exploration. ED Explorer consists of an interactive web application, an API, and an NLP toolkit, which can help both domain experts and non-experts to better understand ED tasks. We use ED Explorer to analyze a recently proposed large-scale ED dataset (referred to as MAVEN). With ED Explorer, we discovered several underlying issues of the dataset, including data sparsity, label bias, label imbalance, and debatable annotations. Such insights are essential for guiding the continuous improvement of existing ED datasets and the advances of ED models. The ED Explorer system1 and the demonstration video2 have both been made publicly available.
CheerUp: A Real-time Ambient Visualization of Cheerleading Pose Similarity

Cheerleading is a highly structured, synchronized activity to cheer the team up as a form of encouragement. However, it is challenging for the cheerleaders and the spectators to understand such meanings in their intensive movement. To our best knowledge, there is no real-time interactive system designed to aim to integrate multi-pose estimation, pose-similarity computations, and ambient visualization. To address the gap, we propose a real-time interactive visualization system, CheerUp, that can integrate these three components. CheerUp can import not only the pre-recorded video clips but also can support real-time 1080p at 60fps webcam live video. CheerUp can compare up to six cheerleaders’ pose similarities using a Body-part-level Pose Distance(BPD) and transform them into an abstract ambient visualization that symbolizes the intensity of the movement and similarity. We hope this work can support the cheerleading team’s dance practice and help the spectators can understand the meanings underlying in the intensive movement.
Inference Attacks and Counterattacks in Federated Learning

From the previous chapter, we have learned that federated learning (FL) can be used to protect data privacy since users no longer share their raw data during collaborative training. However, many studies have shown that even under the protection of federated learning, users’ privacy is not necessarily secure. Inference attack is one of the privacy infringement methods that can be executed in FL. Through the study of this chapter, readers will have a basic understanding of inference attacks in federated learning. We also present a discussion on how to counter or mitigate inference attacks.
An automated guide to COVID-19 and future pandemic prevention and management

Abstract In this paper, we present CoFighter, a mobile application for prevention and management of COVID-19 and other related pandemics in the globalized world. We took advantage of the proliferation of mobile smart devices in every home to design and implement an Android application for COVID-19 and similar pandemics. Since the outbreak of COVID-19 pandemic in 2019, there has been even more serious pressures on governments and health institutions on the best way to provide appropriate and reliable guide to individuals on how to contain the virus and similar pandemics in the future. Citizens have not been adequately informed of the various provisions and guides by their governments and the wide usage of social media had led to the spread of fake news, misinformation and conspiracy theories. It therefore becomes very necessary to develop a dynamic information repository in the form of a mobile application to help combat the spread of any pandemic whenever the need arises. The application provides information on COVID-19, vaccine challenges, prevention guides and cases management and timely updates to keep citizens properly and adequately informed. It makes provision for future similar pandemics that could throw the world into chaos as the CORONA virus did in 2019. The weaknesses and challenges observed in most popularly existing COVID-19 applications were highlighted and implemented in CoFighter. CoFighter provides users, governments and health workers with a platform not only to manage COVID-19 and other similar pandemics in the future, but also helps frontline health workers to better manage the pandemics. The developed application runs on an Android device with Android version 4.2 or higher and can be used not only to manage COVID-19 pandemic, but also to manage economic crisis and similar future pandemics. CoFighter is available via the Repository: https://github.com/OkeyIsOkay/CoFighter-Project .
Effects of Visual Explanation on Perceived Creative Autonomy in an AI-Based Generative Music System

This work aims to explore how a user’s understanding of a creative AI’s decision-making affects their experience when collaborating with it, through the inclusion of Explainable AI features in an interactive generative music system. We have created multiple versions of a generative music system that use different visualization tools to show how the user input motion, captured through a video camera, is being received and interpreted as the manipulation of audio effects. We have designed and conducted a study to measure participants’ perceptions of the system’s autonomy in the creative process, their sense of the system’s ability to support their creativity, and their trust and satisfaction in the visualizations as explanations of the system’s behavior. In this study, we found a range of musical expertise and knowledge of machine learning concepts among subjects, which correlated with their preferences and expectations for autonomy in AI behavior. Increased understanding of the system’s behavior, achieved through interacting with its visualizations, allowed users to perceive it as acting closer to their expectations.