Reviewerly/exHarmony huggingface dataset:
Active Proxy Dashboard: Binding Physical Referents and Abstract Data Representations in Situated Visualization through Tangible Interaction

Myriad systems have been proposed in recent years involving situated visualization with tangible scale models as proxies for physical referents. Most of these focus on static placements of scale models that mimic their real-world arrangement. While such static placement is useful to show the spatial context of referents, it does not take full advantage of the potential interaction affordances of the proxies. Our approach, Active Proxy, binds the data representation and referent through physical manipulations of scale models. We introduce a conceptual design space defining four quadrants as combinations of two dimensions: spatial to abstract representation; and passive to active proxy. Our focus is the novel quadrant defined by active proxies and abstract representations. Designing active proxy techniques is non-trivial as users are accustomed to common interaction modalities. This paper presents an initial exploration toward a better understanding of the active proxy concept, and designs of active proxy systems.
“Briefly Entertaining but Pointless”: Perceived Benefits &amp; Risks of Social Robots in the Home

In contrast to the adoption of personal assistants, social robots have yet to break into the domestic market. Several manufacturers have introduced and quickly retracted their social robots for the home. We report on a survey study (N = 50) to understand potential users' perceptions towards these social robots. Participants were presented with videos of three domestic social robots and subsequently provided their perception of these in terms of perceived benefits, attraction, privacy risk, usage intention, and capabilities. While participants perceived hedonic and utilitarian benefits, we found a low intention of future adoption of these devices. Further, our findings showed that owners of personal assistants perceived significantly higher hedonic benefits, fewer privacy risks, and higher intention to use domestic social robots. Our work provides an initial step towards understanding perceptions towards social robots and how previous exposure to domestic AI shapes users' perceptions.
Towards Accessible Sports Broadcasts for Blind and Low-Vision Viewers

Blind and low-vision (BLV) people watch sports through radio broadcasts that offer a play-by-play description of the game. However, recent trends show a decline in the availability and quality of radio broadcasts due to the rise of video streaming platforms on the internet and the cost of hiring professional announcers. As a result, sports broadcasts have now become even more inaccessible to BLV people. In this work, we present Immersive A/V, a technique for making sports broadcasts —in our case, tennis broadcasts— accessible and immersive to BLV viewers by automatically extracting gameplay information and conveying it through an added layer of spatialized audio cues. Immersive A/V conveys players' positions and actions as detected by computer vision-based video analysis, allowing BLV viewers to visualize the action. We designed Immersive A/V based on results from a formative study with BLV participants. We conclude by outlining our plans for evaluating Immersive A/V and the future implications of this research.
Digital Twin Application and Bibliometric Analysis for Digitization and Intelligence Studies in Geology and Deep Underground Research Areas

As deep underground digital twins have not yet been established worldwide, this study extracted keywords from national or city-led digital twin practices and elements of digital twins and through these keywords selected research papers and topics that could contribute to the establishment of deep underground digital twins in the future. We applied the concept of digital twins in geology and underground research to collect 1702 papers from the Web of Science and conducted semantic network analysis and topic modeling. The keywords digital, three dimensions, and real time were placed in the middle and have many links in the word network. Artificial intelligence, deep learning, and neural networks all showed a low degree of centrality. As a result of topic modeling using Latent Dirichlet allocation (LDA), topics related to topography, geological structure, and rock distribution, which are the basic data for building a deep underground digital twin, were noted, and topics related to earthquakes/vibrations, landslides, groundwater, and volcanoes were identified. Energy resources and space utilization have emerged as the main themes.
Bagging: An Ensemble Approach for Recognition of Handwritten Place Names in Gurumukhi Script

In this article, the authors present an effort to recognize handwritten Gurumukhi place names for use in postal automation. Five feature extraction techniques (zoning, horizontal peak extent, vertical peak extent, diagonal, and centroid) have been analyzed and optimized using Principal Component Analysis (PCA). Four classification methods ( k -Nearest Neighbor ( k -NN), decision tree, random forest, and Convolutional Neural Network (CNN)) have been utilized to classify the handwritten word images. To enhance the recognition results, the authors have employed Bootstrap Aggregation (Bagging) with a majority voting scheme. The authors used a public benchmark dataset of 40,000 handwritten place-name samples in the Punjabi language for their experimental work. The experiments were conducted using a 70:30 partitioning approach, where 70% of the data was utilized for training and the remaining 30% for testing. The system achieved a maximum recognition accuracy of 96.98% by utilizing a combination of zoning, vertical peak extent, and diagonal features, and a minimum Mean Squared Error (MSE) of 0.86% based on a combination of zoning and horizontal peak extent features with a majority voting scheme through ensemble (Bagging) methodology.
Tracking the Experience of Self in Everyday Life

This study explores the potential benefits of an interactive system that supports individuals in collecting data and reflecting on their self-concept and self-aspects in daily life. Through a think-aloud study (N = 10) and in-situ deployment (N = 7), we design, deploy, and evaluate a self-tracking technology probe. The results suggest that participants found benefit in participating in the study and tracking their self-aspects, with all seven participants in the in-situ deployment expressing interest in continuing to use the system after the study. The study highlights the usefulness of supporting self-reflection at various temporal scales, and has implications for the design of personal informatics systems utilizing the multiple self-aspects framework and Day Reconstruction Method. This research contributes to the understanding of the potential benefits of interactive systems in supporting self-tracking of the experience of self-aspects in daily life.
Research and Implementation of Low-Power Anomaly Recognition Method for Intelligent Manhole Covers

This paper addresses the difficulty of balancing a real-time response and low power consumption in intelligent manhole cover application scenarios. It proposes a method to distinguish normal and abnormal events by segmenting the boundary at which the acceleration of the intelligent manhole cover deviates from a set threshold and lasts for a certain period, based on the difference in the intelligent manhole cover’s vibration patterns when a normal event and an abnormal event occur. This paper uses the autonomous data fusion of digital output motion sensor data to implement a pattern recognition algorithm for the above-mentioned pattern, which reduces the MCU computing and working time and the overall power consumption of the system while meeting real-time response requirements. The test results demonstrate that the method has a high rate of anomaly recognition accuracy. The method ensures the system’s real-time response capability, and the actual low power consumption test demonstrates that the device can operate continuously for 9.5 years. The low power consumption index exceeds the requirements of the existing national standard, thereby resolving the issue that it is challenging to balance intelligent manhole cover abnormality recognition and low power consumption.
An Integrated Fast Hough Transform for Multidimensional Data

Line, plane and hyperplane detection in multidimensional data has many applications in computer vision and artificial intelligence. We propose Integrated Fast Hough Transform (IFHT), a highly-efficient multidimensional Hough transform algorithm based on a new mathematical model. The parameter space of IFHT can be represented with a single k-tree to support hierarchical storage and "coarse-to-fine" search strategy. IFHT essentially changes the least square data-fitting in Li's Fast Hough transform (FHT) to the total least squares data-fitting, in which observational errors across all dimensions are taken into account, thus more practical and more resistant to data noise. It has practically resolved the problem of decreased precision of FHT for target objects mapped to boundaries between accumulators in the parameter space. In addition, it enables a straightforward visualization of the parameter space which not only provides intuitive insight on the number of objects in the data, but also helps with tuning the parameters and combining multiple instances if needed. In all simulated data with different levels of noise and parameters, IFHT surpasses Li's Fast Hough transform in terms of robustness and precision significantly.
Trends and motivational factors of media consumption of students of the St. Petersburg University of Management and Economics Technologies in 2022-2023: Results of a pilot sociological study

The article presents the results of a media consumption sociological study among the students of the St. Petersburg University of Management and Economics Technologies. The purpose of the study was to assess the impact of external factors (sanctions, a distinct polarization of the information agenda) on the attitude of “digital youth” to the common media channels, on change of the nature of media consumption. The results of the study revealed that, according to the overwhelming majority of respondents, external factors had not affected their information sources selection process. At the same time, according to in-depth interviews, respondents note significant changes in the motivation for choosing information channels and attitudes to information published on the web. It seems interesting to focus further research on the digital youth media behavior in terms of forming Internet regulatory mechanisms.
ContractBox: Realizing accountable data sharing on the edge using a small scale blockchain

The utilization of IoT devices is becoming omnipresent in industrial settings. However, adoption in more rural areas still poses challenges. Especially when processing data on the edge, privacy, data integrity, accountability and data ownership pose challenges since the devices may be easily accessed and manipulated. We present ContractBox, a system that provides accountable and trusted data sharing based on a publisher–subscriber system, as well as trusted computing on the edge. ContractBox uses a Trusted Execution Environment to guarantee the confidentiality and integrity of clients' data and code. Furthermore, it provides security by using WebAssembly to execute the smart contracts in their own sandboxed environment. This design protects the host as well as co-located smart contracts from misbehaving executions. Lastly, it ensures the immutability and accountability of the published data by storing it in a blockchain. We show that ContractBox can process several thousand publications per second with various payloads and host multiple smart contract runtimes on a single edge device. ContractBox also achieves up to 35 times higher throughput than a comparable deployment of Hyperledger Fabric.
A real-time augmented reality system to see through forklift components

Limited visibility during the operation of forklifts is one of the most significant sources of danger in in-plant material handling. Existing systems record concealed areas via cameras and display them directly on monitors in the operator's cab. The operator has to temporarily turn his attention to a screen and is unable to perceive the real information necessary for the driving task. We developed the first augmented reality based driver assistance system for safety improvement in intralogistics. The results show the capability to eliminate view restrictions directly in the operator's field of view and create the illusion of transparent vehicle components.
An asymmetric topology-preserving Neighborhood Retrieval Visualizer

We propose an asymmetric topology-preserving version of the Neighborhood Retrieval Visualizer (NeRV) dimensionality reduction and data visualization method. We introduce an asymmetric dissimilarity using the asymmetric coefficients. This dissimilarity is employed in the traditional NeRV, and in this way, a novel asymmetric form of this approach is formulated. The asymmetric coefficients convey the information on asymmetry in the input data. They are determined on the basis of the graph representing the input data. This allows for discovering of the geometry and topology of the input data. The experiments conducted on three real datasets confirm the effectiveness of the proposed approach on the basis of the comparative analysis of our solution against the six reference dimensionality reduction and data visualization algorithms.
Division and composition of 360°videos for the generation of personalized tours for tourism, hotel industry and education based on immersive VR

Emerging technologies have made it possible for communication strategies with customers and users to be re-focused towards very important innovative changes, such as the use of 360° videos and immersive virtual reality in the tourism, educational sectors, among others. The advantages of these new tools lie in the fact that they can immerse users in experiences very close to reality and those that could correspond to their profiles using adaptive approaches based on Artificial Intelligence, elements that make it possible to use various areas of business and education. In this work, the use of immersive virtual reality based on spherical video is proposed to create a mobile application that, using the appropriate interfaces and functionalities, generates immersive experiences, which offer potential users virtual tours or tours for which a segmentation by scenes and sequencing is carried out, application aimed at the promotion of hotels, tourist centers and educational activities.
Teaching Effective and Gamified Cybersecurity using the Metaverse: Challenges and Opportunities

The COVID-19 pandemic caused a tectonic shift in the way that institutions, ranging from k-12 to higher education, approach their teaching and learning strategies. It is an understatement to say that teaching effective and gamified Cybersecurity courses amidst the COVID19 pandemic placed an extreme pressure on faculty. While Zoom, WebEx, and Google Classroom virtual meeting were sufficient for some, others were in pursuit of platforms which could address the hands-on interaction component that happens in a class setting while isolating and/or removing some of the distractive and engagement-adverse ingredients that occur in traditional remote meeting platforms. To this end we present some challenges and opportunities which exist in the intersection of Cybersecurity learning and teaching through the Metaverse. As an extension we plan to measure the effectiveness of Metaverse towards Cybersecurity gamified remote learning by providing future research directions towards the ultimate goal of metamorphosing traditional Cybersecurity classrooms.
Research on Model Optimization Technology of Federated Learning

Federated learning is a new distributed machine learning model training method, which can protect user privacy while enabling a large number of edge devices to train a shared model based on their local data sets, and enable participants to benefit from each other. However, federated learning also faces some serious challenges, such as resource constraints, uneven data distribution, edge dynamics, and so on. Moreover, the existing federated learning methods may lead to long training time, consume a lot of bandwidth and other resources, and the heterogeneity of edge devices further worsens the inherent synchronization barrier problem of federated learning. In response to the above problems, this paper studies the existing model optimization technologies of federated learning, including node selection mechanism, local data enhancement, model compression, local update, gradient compression and other model optimization technologies, and summarizes and analyzes them.
Tec Virtual Campus, a metaverse for engineering learning

This article describes how 3D technology platforms, also called metaverses, generate active learning, school motivation, and understanding of learning topics. It describes the implementation process of the platform in engineering courses; specifically, it records the perceptions of university students about the use of the Tec Virtual Campus platform to identify if students consider that it favors the educational process and to know if it motivates engineering learning in the first semesters of a university career. Qualitative data were collected from newly enrolled students <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{n}=\boldsymbol{96})$</tex> . The activity was carried out in the Tec21 educational model of the Tecnológico de Monterrey (Mexico), an educational model based on challenges. The results show that the Tec Virtual Campus platform contributed positively as a motivational element and as a cognitive resource, constituting an educational alternative in the area of engineering.
Improvement and Application of Blockchain Consensus Algorithm for Distributed Power Energy System

This article focuses on the application of blockchain consensus algorithms in the power energy trading system and the comparison of these algorithms such as PBFT(Practical Byzantine Fault Tolerance), Raft and IBFT(Istanbul Byzantine Fault Tolerance). The article introduces the concept of blockchain consensus algorithms and its application in power energy trading system. It then provides a brief overview of the three algorithms and their underlying structures. The comparison of the algorithms is done in terms of features such as scalability, reliability and security. The article also provides an overview of the advantages and disadvantages of each algorithm and its application in power energy trading system. We examine the blockchain partition algorithm improved by the integration of Raft consensus and IBFT consensus, with a special focus on the advantages of using a hybrid algorithm in distributed networks. We discuss the need for consensus algorithms that are well-suited to blockchain networks and the improved security provided by a consensus algorithm incorporating both Raft and IBFT.We also discuss the implications of incorporating Raft and IBFT into a blockchain partition algorithm and its potential scalability. In conclusion, we provide a synthesis of our research, with potentials for further studies and contributions in this field.
A Trustable and Traceable Blockchain-based Secondhand Market with Committee Consensus

The secondhand market has shown promise due to its resource-saving and idle interchange features, but the trust problem has hampered its growth. Currently, many studies employ third-party platforms to build secondhand markets. However, buyers are unable to identify the legitimacy of secondhand products because they lack specialized knowledge. It is very challenging to protect an individual’s rights if the third-party marketplace is malicious. In order to address these issues, consortium blockchain is used in this paper instead of the third-party platform. Additionally, to improve the efficiency of blockchain, a novel committee-based Byzantine consensus is designed for the secondhand market. The submitted transactions can be verified by committee nodes, and other client nodes can update the product’s states and verify the recorded transactions. The transactions are transparent, permanent, and traceable, and they are verified by the chosen committee nodes. Buyers and sellers can therefore have trust in our blockchain-based second- hand market. Our experiment has proven that the consortium blockchain-based secondhand market can be more effective and reliable. The use of committee consensus has effectively reduced transaction latency on our blockchain platform.
On Hierarchies of Balanced Sequences

Balanced sequences and balanced codes have attracted a lot of research in the last seventy years due to their diverse applications in information theory as well as other areas of computer science and engineering. There have been some methods to classify balanced sequences. This work suggests two new different hierarchies to classify these sequences. The first one is based on the largest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> for which each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> -tuple is contained the same amount of times in the sequence. This property is a generalization for the property required for de Bruijn sequences. The second hierarchy is based on the number of balanced derivatives of the sequence. Enumeration for each such family of sequences and efficient encoding and decoding algorithms are provided in this paper.
Relation-propagation meta-learning on an explicit preference graph for cold-start recommendation

The cold-start problem has been of great concern in the recommendation domain. To address this problem, meta-learning frameworks have been widely adopted due to their fast adaptation to new tasks. However, existing meta-learning-based methods always use higher-order graph structures to obtain global user preferences but neglect to consider local preferences at different rating levels in a fine-grained manner. Since differences in user ratings on items truly reflect differences in users' local preferences, we propose a relation-propagation meta-learning on explicit preference graph for cold-start recommendation (RPMLG-Rec) to improve the generalization performance of existing meta-learning-based methods. Specifically, we focus on capturing the relationships between local preferences. First, our RPMLG-Rec approach concatenates different local preferences to form the nodes of local user preference and further constructs an explicit preference graph. Second, the relationships between local preferences, including intraclass commonality and interclass uniqueness, are used to guide the propagation of relationships in the explicit preference graph with graph convolutional networks and produce more distinguishable local preference nodes. Third, the precise global user preference is obtained with an attention mechanism. Finally, prior knowledge is learned based on a set of training tasks and quickly adapted to make recommendations for new tasks, following the optimization-based meta-learning training strategy. To the best of our knowledge, this is the first time that the relationships between local user preference nodes have been explicitly considered in cold-start recommendation. In addition, we conducted extensive experiments on two real-world datasets, and the experimental results demonstrate the effectiveness of our proposed framework.