Reviewerly/exHarmony huggingface dataset:
A Tight Lower Bound on Adaptively Secure Full-Information Coin Flip

In a distributed coin-flipping protocol, Blum [ACM Transactions on Computer Systems '83], the parties try to output a common (close to) uniform bit, even when some adversarially chosen parties try to bias the common output. In an adaptively secure full-information coin flip, Ben-Or and Linial [FOCS '85], the parties communicate over a broadcast channel and a computationally unbounded adversary can choose which parties to corrupt along the protocol execution. Ben-Or and Linial proved that the n-party majority protocol is resilient to O(√n) corruptions (ignoring poly-logarithmic factors), and conjectured this is a tight upper bound for any n-party protocol (of any round complexity). Their conjecture was proved to be correct for single-turn (each party sends a single message) single-bit (a message is one bit) protocols Lichtenstein, Linial, and Saks [Combinatorica '89], symmetric protocols Goldwasser, Tauman Kalai, and Park [ICALP '15], and recently for (arbitrary message length) single-turn protocols Tauman Kalai, Komargodski, and Raz [DISC '18]. Yet, the question for many-turn protocols was left completely open. In this work we close the above gap, proving that no n-party protocol (of any round complexity) is resilient to ω(√n) (adaptive) corruptions.
Smart City Architecture Development Methodology (SCADM): A Meta-Analysis Using SOA-EA and SoS Approach

Architecture and methodology development for smart city are still being carried out together in clarifying the scope of smart city. This is because the application of Enterprise Architecture (EA) still does not accommodate its characteristics as a form of System of System. This study discusses the EA research overview on smart city design and the gaps in EA implementation for smart city architecture development. This research is intended to create a smart city architecture development methodology as a System of System for reference architecture with the collaboration of several systems. The system is an element of smart city designed and developed by the leaders of each coordinated system. In the end, this methodology can form the basis for building and coordinating the development of a collaborative smart city by several actors.
User gesture elicitation of common smartphone tasks for hand proximate user interfaces

The ubiquity of smartphone interactions along with the advancements made in mixed reality applications and gesture recognition present an intriguing space for novel interaction techniques using the hand as an interface. This paper explores the idea of using hand proximate user interfaces (UI), i.e. interactions with and display of interface elements on and around the hand. We conducted two user studies to gain a better understanding of the design space for such interactions. The first study identifies the possible ways in which various elements can be displayed on and around the hand in the context of common smartphone applications. We conduct a second study to build a gesture set for interactions with elements displayed on and around the hand. We contribute an analysis of the data and observations collected from the two studies, resulting in a layout set and a gesture set for interactions with hand proximate UIs.
Personalized Recommendation for Online Retail Applications Based on Ontology Evolution

The upcoming generation of World Wide Web is signified in semantic web technology that allows future applications to grasp and connect with numerous knowledge bases. Due to its exclusive function in modeling specific domain, Ontology has been playing an essential role in semantic web development. Recommender systems are an indispensable part of online site, which makes their use of high value in recommending items to users according to their interests. The semantic recommender systems recently aim to accomplish the website ontologies to generate semantic recommendations for users' profiles. Therefore, ontology-based semantic recommender systems are used to develop web recommendation. In this paper a recommendation system architecture based on ontology is proposed to give semantic recommendations for each user profile. The proposed system architecture applies two recommendation techniques, content-based filtering and collaborative filtering.
Authenticated Data Redaction With Accountability and Transparency

A common practice in data redaction is removing sensitive information prior to data publication or release. In data-driven applications, one must be convinced that the redacted data is still trustworthy. Meanwhile, the data redactor must be held accountable for (malicious) redaction, which could change/hide the meaning of the original data. Motivated by these concerns, we present a novel solution for authenticated data redaction based on a new Redactable Signature Scheme with Implicit Accountability (<inline-formula><tex-math notation="LaTeX">$\mathsf {RSS}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">RSS</mml:mi></mml:math><inline-graphic xlink:href="huang-ieq1-2998135.gif"/></alternatives></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathsf {IA}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">IA</mml:mi></mml:math><inline-graphic xlink:href="huang-ieq2-2998135.gif"/></alternatives></inline-formula>). In the event of a dispute, not only the original data signer but also the redactor can generate an evidence tag to unequivocally identify the party who produced the data/signature pair. Without the evidence tag, the redaction operation is transparent. Furthermore, the redactor can independently prove the trustworthiness of the redacted data, without any interaction with the original data signer. Our design is built on a new approach which adds accountability to any transparent redactable signature schemes. We show that the proposed design satisfies all the security goals with affordable cost. As an extension, we show how to realize accountable, transparent and authenticated data redaction in the multi-redactor setting.
The Development of Augmented Reality Applications for Chemistry Learning

This chapter describes the use of Augmented Reality (AR) technology in chemistry education. The chapter begins with definition analysis, development, component, working principles, steps in making AR media, and supporting applications that are related with AR in education particularly for chemistry teaching and learning process. The proposed AR system consists of three parts: computers, Head Mounted Display (HMD), and markers that use AR toolkit working principles as follows: making AR through Vuforia setting stage, making the target management, managing assets, and running processes. Additionally, Unity application also supports in AR making. There were researches in the field of education especially in chemistry teaching and learning that had used AR technology, such as the concept of crystal structure, molecular geometry, molecular chirality, and molecular hybridization.
A Novel Mobile Robot Navigation Method Based on Hand-Drawn Paths

In the real world, a human tourist can quickly arrive at a target along the route planned by his/her instinctive cognition of prepared maps. While on the mobile robot, it is required to know every way-points of the static or dynamic planning paths to reach a goal point. The previous navigation modes are mostly from sensor location, speech interaction, grid maps and so on. They rely on sensing performance, the speech instruction of site personnel or complex algorithms for the path searching and tracking. For improving the performance of interaction and tele-operated path tracking, in this paper, a novel interactive navigation based on the recognition of hand-drawn paths is proposed for mobile robots. Pen, paper, and camera are employed as the interface between users and the mobile robot. Initially, the image of a hand drawn path in the paper map is captured by a camera and segmented from the background by the color detection model. It is hypothesized that the captured frame of hand-drawn paths is slant to the original orthogonal map. Therefore, this method should firstly project the smoothed skeleton of slant path into the coordinate space of orthogonal map to guide the robot to perform path tracking. Then the hand-drawn path is rectified and remapped into the registered reference image of the orthogonal map through feature matching and perspective transformation. The combination of SIFT and RANSAC algorithms are employed for improving the accuracy of the projection of slant paths in the orthogonal map. Sequentially, a limited number of way-points are picked out from the corrected path in an orthogonal 2D map space usable for 2D navigation. The oriented sequential motion vectors are estimated by linking all successive way-points and employed for the path tracking along the predefined route step by step. Eventually, random navigation paths are designed to validate the robustness, effectiveness and accuracy of the constructed interactive navigation system.
Ethereum Transaction Performance Evaluation Using Test-Nets

Blockchain technologies can safely and neutrally store and process transaction data (including smart contracts) on the chain. Based on smart contracts, a special type of applications can be deployed, known as Decentralized Applications (DApps). Within existing Blockchain platforms, Ethereum is the most popular one adopted for DApp developments. The performance constraints of Ethereum dramatical impact usability of DApp. In this paper, we experimentally evaluate the performances of Ethereum from 3 types of tests: 1) Account balance query latency 2) Block generation time and 3) End-to-end transaction acceptance latency. The results show that the end-to-end transaction time in Ethereum is unstable. Consequently, the applications with low latency constraints and high frequency transaction requirements are not ready to be deployed unless off-chain transaction methods are considered.
Efficient Image Cipher Based on Baker Map in the Discrete Cosine Transform

Abstract This paper presents an efficient image cipher based on applying the chaotic Baker Map (BM) in the Discrete Cosine Transform (DCT). The encryption module of the proposed DCT-based BM image cipher employs a DCT on the original plain-image then, the DCT coefficients of the plain-image are shuffled with the BM. Finally, the inverse DCT is applied to the shuffled plain-image DCT coefficients to obtain the final cipher-image. The decryption module of the proposed DCT-based BM image cipher employs a DCT on the input cipher-image then, the DCT coefficients of the cipher-image are inversely shuffled with the BM. Finally, the inverse DCT is applied to the inversely shuffled cipher-image DCT coefficients to obtain the original plain-image. A set of experimental tests are performed to test the validity of the proposed DCT-based BM image cipher and the performed tests demonstrated the superiority of the proposed DCT-based BM image cipher in terms of statistical, differential, sensitivity and noise immunity.
Hub Labeling for Shortest Path Counting

The notion of shortest path is fundamental in graph analytics. While many works have devoted to devising efficient distance oracles to compute the shortest distance between any vertices s and t, we study the problem of efficiently counting the number of shortest paths between s and t in light of its applications in tasks such as betweenness-related analysis. Specifically, we propose a hub labeling scheme based on hub pushing and discuss several graph reduction techniques to reduce the index size. Furthermore, we prove several theoretical results on the performance of the scheme for some special graph classes. Our empirical study verifies the efficiency and effectiveness of the algorithms. In particular, a query evaluation takes only hundreds of microseconds in average for graphs with up to hundreds of millions of edges. We report our findings in this paper.
Conducting HCI Research with People Living with HIV Remotely

Conducting HCI research with people living with HIV in face-to-face settings can be challenging in terms of recruitment and data collection due to HIV-related stigma. In this case study, we share our experiences from conducting research remotely in two studies using the Asynchronous Remote Communities method with participants recruited from in-person and online support groups, respectively. Our findings and discussion around challenges, best practices, and lessons learned during the phases of recruitment and data collection expand and further support the suitability of the method to conduct research remotely with a highly stigmatized population.
Bat algorithm with Weibull walk for solving global optimisation and classification problems

Bat algorithm (BA) becomes the most widely employed meta-heuristic algorithm to interpret the diverse kind of optimisation and real-world classification problems. BA suffers from one of the influential challenges called local minima. In this study, we carry out two modifications in the original BA and proposed a modified variant of BA called bat algorithm with Weibull walk (WW-BA) to solve the premature convergence issue. The first modification involves the introduction of Weibull descending inertia weight for updating the velocity of bats. The second modification approach updates the local search strategy of BA by replacing the Random walk with the Weibull Walk. The simulation performed on 19 standard benchmark functions represent the competence and effectiveness of WW-BA compared to the state of the art techniques. The proposed WWBA is also examined for classification problem. The empirical results reveal that the proposed technique outperformed the classical techniques.
Emotional expressions of non-humanoid urban robots

Self-moving robots may soon be ubiquitous in urban spaces, thereby enabling a wide range of use cases. To increase their social acceptance, previous work has explored how non-humanoid robots can express emotional states through various modalities, such as lights and movement. However, the majority of research has been carried out in controlled lab experiments, raising questions about their ecological validity. In this work, we present the design of emotional expressions for an urban robot via low-resolution lighting patterns and motion cues. We present an in-the-wild evaluation using questionnaires and interviews (N=72). Our findings suggest that a range of contextual aspects can influence the perception of the robot and its emotional expressions, such as overall choreography and interactional context, immediate impact of the environment, coupled with people's own emotions and past experiences. Our analysis thus outlines challenges and provides guidance for designers and researchers working with urban robots.
Teddy: A System for Interactive Review Analysis

Reviews are integral to e-commerce services and products. They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services. Today, data scientists analyze reviews by developing rules and models to extract, aggregate, and understand information embedded in the review text. However, working with thousands of reviews, which are typically noisy incomplete text, can be daunting without proper tools. Here we first contribute results from an interview study that we conducted with fifteen data scientists who work with review text, providing insights into their practices and challenges. Results suggest data scientists need interactive systems for many review analysis tasks. Towards a solution, we then introduce Teddy, an interactive system that enables data scientists to quickly obtain insights from reviews and improve their extraction and modeling pipelines.
Segmentation and Recognition of Eating Gestures from Wrist Motion using Deep Learning

This paper describes a novel approach of segmenting and classifying eating gestures from wrist motion using a deep learning neural network. It is inspired by the approach of fully-convolutional neural networks in the task of image segmentation. Our idea is to segment 1D gestures the same way 2D image regions are segmented, by treating each inertial measurement unit (IMU) datum like a pixel. The novelty of our approach lies in training a neural network to recognize data points that describe an eating gesture just like it would be trained to recognize pixels describing an image region. The data for this research is known as the Clemson Cafeteria Dataset. It was collected from 276 participants that ate an unscripted meal at the Harcombe Dining Hall at Clemson University. Each meal consisted of 1 - 4 courses, and 488 such recordings were used for the experiments described in this paper. Sensor readings consist of measurements taken by an accelerometer (x, y, z) and a gyroscope (yaw, pitch, roll). A total of 51,614 unique gestures associated with different activities commonly seen during a meal were identified by 18 trained raters. Our neural network classifier recognized an average of 79.7% of ‘bite’ and 84.7% of ‘drink’ gestures correctly per meal. Overall 77.7% of all gestures were recognized correctly on average per meal. This indicates that a deep learning model can successfully be used to segment eating gestures from a time series recording of IMU data using a technique similar to pixel segmentation within an image.
Designing Exocentric Pedestrian Navigation for AR Head Mounted Displays

Augmented reality (AR) is gradually becoming used for navigation in urban environments, allowing users to see instructions in their physical environment. However, viewing this information through a smartphone's screen is not ideal, as it can cause users to become inattentive to their surroundings. AR head-mounted displays (HMD) have the potential to overcome this issue by integrating navigational information into the user's field of view (FOV). While work has explored the design of turn-by-turn egocentric AR navigation interfaces, little work has explored the design of exocentric interfaces, which provide the user with an overview of their desired route. In response to this, we examined the impact of three different exocentric AR map displays on pedestrian navigation performance and user experience. Our work highlights pedestrian safety concerns and provides design implications for future AR HMD pedestrian navigation interfaces.
Virtually-Extended Proprioception: Providing Spatial Reference in VR through an Appended Virtual Limb

Selecting targets directly in the virtual world is difficult due to the lack of haptic feedback and inaccurate estimation of egocentric distances. Proprioception, the sense of self-movement and body position, can be utilized to improve virtual target selection by placing targets on or around one's body. However, its effective scope is limited closely around one's body. We explore the concept of virtually-extended proprioception by appending virtual body parts mimicking real body parts to users' avatars, to provide spatial reference to virtual targets. Our studies suggest that our approach facilitates more efficient target selection in VR as compared to no reference or using an everyday object as reference. Besides, by cultivating users' sense of ownership on the appended virtual body part, we can further enhance target selection performance. The effects of transparency and granularity of the virtual body part on target selection performance are also discussed.
File integrity monitoring tools: Issues, challenges, and solutions

Summary Ensuring integrity of sensitive files in file systems is imperative to computer systems. The vast majority of attacks work through unapproved or unauthorized access to sensitive files to take secret data like secret keys, passwords, credit card numbers, and so on. After that, attackers generally conceal their traces by subverting critical files like system logs. File system integrity monitoring is a well‐known way to deal with ensuring integrity of sensitive and critical files. The file system is at the heart of any computing system as it stores the data and executable programs. Malicious users or malwares might change the content of a sensitive data file or insert malicious payload in an executable. Hence, monitoring the integrity of the files in the file system is of utmost importance. Significant research work has been done on file integrity monitoring tools. This article presents the currently available tools for file integrity monitoring and their underlying approaches.
Children of Color's Perceptions of Fairness in AI: An Exploration of Equitable and Inclusive Co-Design

When it comes to algorithmic rights and protections for children, designers will need to face new paradigms to solve problems they are targeting. The field of Design typically deals with form and function and is executed in molecules or pixels. But algorithms have neither. More importantly, algorithms may be biased in their execution against those without privileged status such as people of color, children, and the non-affluent. In this paper, we review our work on exploring perceptions of fairness in AI through co-design sessions with children of color in non-affluent neighborhoods of Baltimore City. The design sessions aimed at designing an artificially intelligent librarian for their local branch. Our preliminary findings showcase three key themes of this group's perceptions of fairness in the context of an artificially intelligent authority figure.
Body Follows Eye: Unobtrusive Posture Manipulation Through a Dynamic Content Position in Virtual Reality

While virtual objects are likely to be a part of future interfaces, we lack knowledge of how the dynamic position of virtual objects influences users' posture. In this study, we investigated users' posture change following the unobtrusive and swift motions of a content window in virtual reality (VR). In two perception studies, we estimated the perception threshold on undetectable slow motions and displacement during an eye blink. In a formative study, we compared users' performance, posture change as well as subjective responses on unobtrusive, swift, and no motions. Based on the result, we designed concept applications and explored potential design space of moving virtual content for unobtrusive posture change. With our study, we discuss the interfaces that control users and the initial design guidelines of unobtrusive posture manipulation.
FRAME-SDN

In recent years, cloud gaming (CG)'s market share has grown in both academic research and adoption by the industry. Despite its advantages, CG has its own challenges. Delay has one of the most detrimental effects on the QoE of players. This is more intense in multiplayer cloud gaming (MCG), where players interact, and the game conditions should be the same for all players to be able to play fairly. Hence, network resources should be allocated to players based on their requirements and current network conditions, so that inter-delays of interacting players would be relatively close. To remedy this and to improve the QoE of CG, this paper combines the two concepts of edge computing and software-defined networks (SDN). Edge Computing brings servers closer to clients to decrease end-user delay. SDN enables dynamic management of network, based on user requirements and network states. We propose a suboptimal dynamic server and path selection method that allows players to achieve their required delay and bandwidth. Moreover, this method minimizes the inter-player delays for players in the same group. Experimental results show that our resource provisioning reduces inter-player delay and total delay by 94%, 31%, respectively, compared to competing methods.
A Data Access Model for Privacy-Preserving Cloud-IoT Architectures

We propose a novel data collection and data sharing model for cloud-IoT architectures with an emphasis on data privacy. This model has been implemented in Privasee, an open source platform for privacy-aware web-application development, which provides a plug-in module to support IoT application development. Privasee uses a cloud-IoT architecture called DataBank. We provide examples and discuss future extensions.