Reviewerly/exHarmony huggingface dataset:
Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics

Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants’ cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design.
Cyber Safety in the Digital Educational Environment: External and Internal Risks

The paper describes a novel project aimed at developing the concept and technique of cyber safety system of the education process’ participants in the digital learning environment (integration of human and cyber-physical systems approach). A striking feature of the proposed approach lies in its notable difference from the one and focus on a human in the network’s cybersecurity loop rather than technical aspects or human personal data. Besides, it discusses the method and way of conducting experimental studies.
Co-design of Gesture-Based Arabic Sign Language (ArSL) Recognition

Deaf and hard of hearing (DHH) individuals face barriers to communicating with others using sign language. Accordingly, the need for sign language translators is growing significantly. Technology plays an important role in our lives because it allows communication to be more convenient and effective, especially for DHH individuals, who desire privacy and independence. Many rely on technology to communicate. In this paper, we propose SignSpeak, an Arabic sign language (ArSL) recognition system using a leap motion controller. SignSpeak provides a framework for two-way communication by translating ArSL into spoken language and transcribing spoken language into text.
AMOUN: Asymmetric lightweight cryptographic scheme for wireless group communication

Multi-recipient cryptographic schemes provide secure communication, between one sender and multiple recipients, in a multi-party group. Providing secure multi-party communication is very challenging, especially in dynamic networks. Existing multi-recipient cryptographic schemes pose a variety of limitations. These include high computational overhead for both encryption and decryption, additional communication overhead and high setup cost due to change in membership, and collusion among recipients. In order to overcome these limitations, this paper introduces an efficient asymmetric multi-recipient cryptographic scheme, AMOUN. In the proposed scheme, to better utilize network resources, the sender transmits a ciphertext containing different messages to multiple recipients, where each recipient is only allowed to retrieve its own designated message. Security analysis demonstrates that the proposed scheme is indistinguishable under adaptive chosen plaintext attack. Quantitative analysis reveals that lightweight AMOUN shows lower average computational cost than both RSA and Multi-RSA, for both encryption and decryption, even when the key sizes are four times larger. For a given prime size, in case of encryption, AMOUN shows 98% and 99% lower average computational cost than RSA and Multi-RSA, respectively. For decryption, AMOUN shows a performance improvement of 99% compared to RSA and Multi-RSA.
Breaking Barriers: Educating Design Students about Inclusive Design through an Authentic Learning Framework

Abstract Current studies in design education suggest that students and educators base their designs on what they already know about themselves and their peers, or on stereotypical notions of others. This article presents a critical examination of a pedagogical approach employed in several architecture and interior design studios to determine how best to develop student understanding of how to design for real users and users with abilities different from themselves. This authentic learning approach with spatial design students and teachers from the School of Design, Queensland University of Technology, Australia and with people with differing abilities, used qualitative and quantitative questionnaires, student journals and design studio projects to create a multimodal data set. While there are no simple conclusions, or easy answers to unravel the complexity in creating inclusive designs, our findings point towards enabling new engagements and knowledge processes and scaffolding these activities around authentic learning, so that design students and educators can begin to understand the differing ways of designing for/with people with disabilities. The significance of this research is that it opens up new approaches for teaching design students about inclusive design beyond fake personas, building codes and anthropometric data, and provides evidence of the need for a more holistic, authentic and scaffolded approach.
TopiQAL: Topic-aware Question Answering using Scalable Domain-specific Supercomputers

We all have questions.About today's temperature, scores of our favorite baseball team, the Universe, and about vaccine for COVID-19.Life, physical, and natural scientists have been trying to find answers to various topics using scientific methods and experiments, while computer scientists have built language models as a tiny step towards automatically answering all of these questions across domains given a little bit of context.In this paper, we propose an architecture using stateof-the-art Natural Language Processing language models namely Topic Models and Bidirectional Encoder Representations from Transformers (BERT) that can transparently and automatically retrieve articles of relevance to questions across domains, and fetch answers to topical questions related to COVID-19 current and historical medical research literature.We demonstrate the benefits of using domain-specific supercomputers like Tensor Processing Units (TPUs), residing on cloud-based infrastructure, using which we could achieve significant gains in training and inference times, also with very minimal cost.
FHPT: Fine-Grained EHR Sharing in E-Healthcare Cloud with Hidden Policy and Traceability

E-healthcare cloud remarkably facilitates patients to outsource their electronic health record (EHR) to achieve large-scale information sharing in real-time for improving the efficiency of diagnosis and treatment. Unfortunately, compared with other outsourced data, EHR contains more personal privacy, which makes it more preferred by the adversaries. Besides, in the e-healthcare cloud, there may exist malicious users who deliberately disclose their access privileges to obtain financial benefits. In this paper, we propose a fine-grained EHR sharing scheme in e-healthcare cloud with hidden policy and traceability. Specifically, we present a new black-box traitor tracing scheme based on partial policy hiding attribute-based encryption and it performs fine-grained access control on the encrypted EHR, which not only prevents privacy leakage from access policy but also effectively track malicious users who leak decryption privileges to construct a black-box. Our scheme is proved to be fully secure under the standard model. Performance analysis shows that the scheme can achieve the design goals in terms of storage and computation overhead. In the meantime, it is more efficient than the existing schemes under the composite order group.
Monitoring of road damage detection systems using image processing methods and Google Map

Abstract In the traffic environment, accidents often occur due to road conditions with holes. This can be fatal for motorcycle riders and car drivers. Therefore, this paper aims to provide a road contour damage information system by detecting potholes on the highway and reporting damage on Google’s maps. So road users, especially vehicle drivers, can be careful when they want to pass that road. The method used in this paper, which uses Werner D. Streidt algorithm threshold values and edge detection in digital image processing to detect holes in the road, as well as mark the coordinates of hole locations and uploads them on Google’s maps, using the Raspberry Pi two embedded devices as a management center data, with a 6M NEO U-box Global Positioning System (GPS) sensor, as geotagging location and CSI camera interface. A A raspberry pi 2 for taking pictures of road surface contours. System testing is carried out in several stages at different times. In daytime conditions, in sunny weather, the system’s success rate in detecting potholes is 83.2%. Whereas with tree barriers, the success rate is 67%, with detection failure 33%. In conclusion, this system succeeded in showing all points of the hole location by giving a mark on the Google Map display application.
Auction System in Food Supply Chain Management Using Blockchain

Blockchain has become very popular these days because of its decentralized nature. The applications of blockchain are spread out in all domains from agriculture to medical field. Food supply chain can be made transparent using blockchain. The need for transparency in food supply chain is increasing day by day because nowadays people are more cautious about the food they buy and consume. So every individual should be able to get complete details about the food they eat from farming to consumer end. In this area, blockchain is playing a vital role. Along with giving complete information of food products to end user, it is also required for a farmer to get good prices for the food products they had grown. Currently, food products are sold in centralized auction system with the involvement of a third party. Because of the biased nature of third party, farmers are not able to get valid price for food items they have even though there is a demand in market. So, this paper is discussing an auction system for food products without a third party using blockchain technology, which avoids biasing.
On Subpicture-based Viewport-dependent 360-degree Video Streaming using VVC

Virtual reality applications create an immersive experience using 360° video with high resolution and frame rate. However, since the user only views a portion of 360° video according to his/her current viewport, streaming the whole content with high resolution causes bandwidth wastage. To address this issue, viewport-dependent approaches have been proposed such that only the part of the video which falls within user's current viewport is transmitted in high quality while the rest of the content is transmitted in lower quality. The selection of high- and low-quality parts is constantly adapted according to the user's head motion, which requires frequent intra coded frames at switching points, leading to an increment in the overall streaming bitrate. In this paper a viewport-adaptive streaming scheme is introduced, which avoids intra frames at switching points by introducing long intra period for non-changing parts of the content during head motion. This scheme has been realized taking advantage of mixed Video Coding Layer (VCL) Network Abstraction Layer (NAL) unit feature of Versatile Video Coding (VVC) standard. This method reduces bitrate significantly, especially for the sequences with either no or only slow camera motion, which is common for 360° video capturing.
A Service Deployment Method Considering Application Reliability of Networks

Service deployment often needs to guarantee QoS of service compositions for networks which compose services to complete users' application requests. Because evaluation methods for QoS of service compositions rely on fixed-process-based formal models which are not suitable for dynamic service composition processes in service deployment, current service deployment methods can only guarantee QoS of single service and are insufficient to guarantee QoS of service compositions. In this article, a service deployment method considering application reliability is proposed. First, application reliability is introduced to measure QoS of service compositions and an evaluation method based on application flow is presented. Then a service deployment optimization model with the goal of application reliability is established. To solve this model efficiently, an improved genetic algorithm by a new method generating initial population is adopted. Finally, 5G network case study verifies that the improved genetic algorithm converges quickly and our proposed method significantly outperforms the existing method in terms of application reliability by comparison.
Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.
Context-Aware Systems Architecture (CaSA)

Context-aware systems are becoming increasingly mainstream as more and more technology allows real-time collection of daily life data and it is more and more affordable to provide useful services to citizens in various situations of need. However, developers in this field are not well supported. Naturally we have inherited a number of methods and tools from past software engineering efforts to create previous computing systems. However, the most recent generation of systems dominated by sensing supported context-awareness integrating a variety of data sources and with a higher expectation of personalized services delivered at the right time, place and in the right form, are not well supported. Developers need more guidance and support to pinpoint those valuable contexts and to work out ways of detecting them and activating the right services associated with these contexts. Our community has reported on various systems they created however not much is emerging in a way of a methodology, a standard, a transferable body of advice and guidance which can help teams next time they need to develop a new system. In this article we explain a couple of complementary methodologies which we have tried and tested through development of different context-aware projects. We argue these are of practical usefulness and provide an initial valid point of discussion for our community to create evolved versions of these which can be tested more widely to identify good practice in the area.
Detection of sharp objects using deep neural network based object detection algorithm

Deep learning algorithms have the ability to learn complex functions and provide state-of-the-art results for com-puter vision problems. In recent times, these algorithms far exceeded the existing computer vision based techniques for object detection in X-ray imaging systems. So far, in literature single class of object namely gun and its parts were considered for detection using the SIXray10 database. We propose deep learning-based solution for the detection of sharp objects namely knife, scissors, wrench, pliers in the SIXray 10database. We propose two models namely model A and model B using a common object detection algorithm- YOLOv3 (You Only Look Once) with InceptionV3 and ResNet-50. YOLO is a deep neural network based object detection algorithm that performs the task in one-shot which allows real time inference in video of 15-30 fps. The model is FCN (Fully Convolutional Network) as has the capacity to perform both regression and classification by sharing weights for both the tasks. The network predicts a rectangular box called bounding box around the predicted object of interest along with the associated class. We analyze the performance of both model in terms of mAP. We achieve mean accuracy of 59.95% for model-A and 63.35% for Model-B. The most daunting part of the project is the low ratio of harmful to nonharmful items. By performing rigorous experiments we came up with the best set of possible results which uses varied pretrained neural networks for feature extraction in tandem with YOLO model for object detection. We endeavor to improve on these existing results so as these systems can be successfully deployed in airports to minimize human error and improve security in such environments.
The Political Economy of Blockchain Governance

We develop a theory of blockchain governance using tools from formal political theory. The software underlying blockchain projects is frequently updated (forked) to implement new policies, and these forks are the subject of our inquiry. We investigate the ways in which the decentralized governance structure and preferences of users influence which policies are implemented, considering network effects as well as user preferences for different policies. We describe several types of forks and identify the strategic conditions necessary for each. We show that network-effects can motivate less moderate policy proposals, and highlight the role of market frictions created by cryptocurrency exchanges in promoting non-contentious forks that are adopted by all users. The model explains counter-intuitive phenomena that have been observed in the governance of blockchain systems.
Rotation Axis Calibration of Laser Line Rotating-Scan System for 3D Reconstruction

The laser line rotating-scan system has been a popular method for 3D reconstruction. But the rotating axis of the scanner usually does not coincide with the camera origin point, which makes this technique a bit more complex. This paper proposes a rotation axis calibration method to solve the problem for more accurate 3D imaging. The calibration parameters are defined as the external parameters (rotation and translation) between the camera coordinate system and the world coordinate system of the rotation axis. With the rotation axis calibration, 3D results of a single frame are transformed into the world coordinate system of the rotation axis. Finally, we realize multi-frame splicing only with the rotation angle provided by the scanner. Experiments on real data show our method is basically correct and practical.
Edible speculations: designing everyday oracles for food futures

Digital food technologies carry promise for better food futures but they are often problematic in their impact on food cultures. While proponents suggest that food-tech products such as smart kitchenware or diet personalisation services can support efficient food practices, critics highlight various risks. This paper presents our findings from Edible Speculations, a long-term design research project exploring the contested space of food-tech innovation through a series of speculative design (SD) events situated in everyday public contexts. We illustrate the opportunities and limits of eventful SD in supporting critical engagements with food-tech issues through an Edible Speculations case study called the Parlour of Food Futures. Our discussion of selected Parlour events can inform readers interested in food-tech themes as well as those keen on experimenting with eventful approaches to SD research.
Basic Consideration of Collaborative Filtering Based on Rough C-Means Clustering

Collaborative filtering (CF) is a technique for realizing recommender systems found in e-commerce sites and video streaming services. Appropriate content recommendations to individual users will improve usability, purchase rates, video ratings, and corporate profits. Clustering is a technique for automatically classifying and summarizing the data by extracting clusters composed of similar objects. Clustering-based CF extracts clusters composed of users with similar interests and preferences, and recommends contents with high preference degree within the cluster. Rough C-means (RCM)-type methods can extract overlapped clusters dealing with the certainty, possibility, and uncertainty of belonging of object to clusters. In this study, we propose two-types of RCM-based CF, namely, GRCM-based CF (GRCM-CF) and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> GRCM-based CF ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> GRCM-CF). Furthermore, we verified the recommendation performance of the proposed methods, GRCM-CF and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> GRCM-CF, through numerical experiments using two real-world datasets, namely, NEEDS-SCAN/PANEL dataset and MovieLens dataset.
Supervision of master theses based on Scrum: A case study

The success rate of a master program on Information Systems and Computer Engineering (MEIC) in a Portuguese university is very unsatisfactory, showing that less than half of the students complete the assigned work in their first term. However, the success rate of a group of students that were supervised based on the Scrum framework was much higher. So, in this study we assess the current situation and identify the benefits of using Scrum to manage master's theses. Evidence suggests that this approach increases the number of students successfully completing their projects. So, this paper discusses the issues at stake and reports on a qualitative study with focus on the actual practices and benefits reported by students in the master program. We found that some methods in the Scrum framework can address existing problems in the development of theses with very positive results.