Reviewerly/exHarmony huggingface dataset:
Evaluation of educational applications in natural sciences

In this paper, a variety of mobile, tablet or desktop educational applications designed to teach physics concepts to 3- to 5-year-old preschoolers are presented and evaluated. The primary purpose was to investigate these applications' effect on kindergarten children and their interest in effectively learning physics concepts. The REVEAC application evaluation tool was used to evaluate the applications, which evaluates the educational content, design, functionality and technical characteristics of the application.
Density and Context Aware Network with Hierarchical Head for Traffic Scene Detection

We investigate traffic scene detection from surveillance cameras and UAVs. This task is rather challenging, mainly due to the spatial nonuniform gathering, large-scale variance, and instance-level imbalanced distribution of vehicles. Most existing methods that employed FPN to enrich features are prone to failure in this scenario. To mitigate the influences above, we propose a novel detector called Density and Context Aware Network(DCANet) that can focus on dense regions and adaptively aggregate context features. Specifically, DCANet consists of three components: Density Map Supervision(DMP), Context Feature Aggregation(CFA), and Hierarchical Head Module(HHM). DMP is designed to capture the gathering information of objects supervised by density maps. CFA exploits adjacent feature layers' relationships to fulfill ROI-level contextual information enhancement. Finally, HHM is introduced to classify and locate imbalanced objects employed in hierarchical heads. Without bells and whistles, DCANet can be used in any two-stage detectors. Extensive experiments are carried out on the two widely used traffic detection datasets, CityCam and VisDrone, and DCANet reports new state-of-the-art scores on the CityCam.
Elastic Resource Allocation for Coded Distributed Computing Over Heterogeneous Wireless Edge Networks

Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.
Application of Blockchain Technology to Podcast-Based Enterprise Content Marketing

This article explores the potential of blockchain technology to enhance podcast content marketing. In the post-pandemic era, more and more enterprises are using podcasts for content marketing. However, the rapid growth of the podcast industry has also raised concerns. This study looks at current podcast advertising methods and uses a 10-step process to examine the need for a blockchain platform. With the decentralization, transparency and traceability of the blockchain and five smart contracts, the new distributed podcast platform improves content marketing metrics and reduces transaction costs. It provides enterprises with a more robust podcast platform, as well as more opportunities for niche creators to showcase and optimize the podcast platform as a useful advertising and marketing tool.
Combating Drug Counterfeiting by Tracing Ownership Transfer Using Blockchain Technology

Supply chain management in healthcare sector plays significant role in the lives of people. Presence of intermediaries and lack of traceability has lured it towards drug counterfeiting, which reduces drug efficacy, thus hampering patients' trust and putting their lives in danger. Blockchain has emerged as a groundbreaking technology which provide a system that is immutable and transparent without the requirement of a trusted third party. This paper depicts how blockchain can provide a solution to reduce drug counterfeit in the pharmaceutical supply chain. This is achieved by creating a Blockchain Network in which each participant and the drug is registered to maintain transparency of transactions among all the participants. Realtime transfer of ownership in the blockchain creates all-round visibility among the registered participants to retrieve information like the current owner, timestamp of ownership transfer and authenticity of drugs. This paper implements PharmaChain for real-time traceability and trackability information of drugs from end-to-end of the pharmaceutical supply chain.
Decision Diagrams for Quantum Computing

Quantum computing promises to solve some important problems faster than conventional computations ever could. Currently available NISQ devices on which first practical applications are already executed demonstrate the potential -- with future fault-tolerant quantum hardware for more demanding applications on the horizon. Nonetheless, the advantages in computing power come with challenges to be addressed in the design automation and software development community. In particular, non-quantum representations of states and operations, which provide the basis, e.g., for quantum circuit simulation or verification, require an exponential amount of memory. We propose to use decision diagrams as data structure to conquer the exponential memory requirements in many cases. In this chapter, we review the fundamentals on decision diagrams and highlight their applicability in the tasks of quantum circuit simulation with and without errors as well as in verification of quantum circuits. The tools presented here are all available online as open source projects.
Heterogeneous Reservoir Computing Models for Persian Speech Recognition

Over the last decade, deep-learning methods have been gradually incorporated into conventional automatic speech recognition (ASR) frameworks to create acoustic, pronunciation, and language models. Although it led to significant improvements in ASRs' recognition accuracy, due to their hard constraints related to hardware requirements (e.g., computing power and memory usage), it is unclear if such approaches are the most computationally- and energy-efficient options for embedded ASR applications. Reservoir computing (RC) models (e.g., echo state networks (ESNs) and liquid state machines (LSMs)), on the other hand, have been proven inexpensive to train, have vastly fewer parameters, and are compatible with emergent hardware technologies. However, their performance in speech processing tasks is relatively inferior to that of the deep-learning-based models. To enhance the accuracy of the RC in ASR applications, we propose heterogeneous single and multi-layer ESNs to create non-linear transformations of the inputs that capture temporal context at different scales. To test our models, we performed a speech recognition task on the Farsdat Persian dataset. Since, to the best of our knowledge, standard RC has not yet been employed to conduct any Persian ASR tasks, we also trained conventional single-layer and deep ESNs to provide baselines for comparison. Besides, we compared the RC performance with a standard long-short-term memory (LSTM) model. Heterogeneous RC models (1) show improved performance to the standard RC models; (2) perform on par in terms of recognition accuracy with the LSTM, and (3) reduce the training time considerably.
Crypto Regulation and the Case for Europe

The blockchain phenomenon has seen an extraordinary rise to prominence. The technology has grown at a revolutionary speed across many sectors and within little over a decade. It is no longer a niche technology for geeks, but a formidable innovation capable of triggering a paradigm shift, not only in finance, but within the society as a whole. While the blockchain industry has experienced an unprecedented growth, regulators struggled to keep pace with this innovation, both in terms of understanding this phenomenon and adapting or providing adequate legal and regulatory frameworks. As a result, legal and regulatory uncertainties are some of the main obstacles for blockchain innovation. This paper seeks to analyze regulators’ and policymakers’ efforts to understand and develop an adequate regulatory approach to crypto assets, tokens, and the distributed ledger technology (DLT) in Europe and illustrates the evolution of regulatory perception and recognition of this innovation. As the EU regulator remained passive for some time toward blockchain innovation except for a few inconsequential statements or reports, the EU countries tried to address this innovation individually and mostly attempted to apply existing legal framework to blockchain, with limited success. This paper gives an example of Liechtenstein as a jurisdiction that developed a comprehensive, bespoke and unique law that creates an entirely new legal architecture and principles to enable the token economy. It also outlines the EU latest initiative to create unique and bespoke regulation to govern markets in crypto assets and highlights the challenge of regulating the dynamically developing blockchain technology for the entire European region.
Evolving Data Space Technologies: Lessons Learned from an IDS Connector Reference Implementation

The establishment of sovereign data spaces is a cutting-edge topic in industrial data sharing scenarios. For instance, organizations like the International Data Spaces (IDS) Association and the Gaia-X European Association for Data and Cloud design standards for sovereign data exchanges and involved governance structures. As an implementation of these standards, data space technologies are of central importance for ensuring secure and sovereign processes and thus establishing trust between data space participants. In this paper, we present our approach, an IDS Connector reference implementation, to integrate data sharing and processing systems into data spaces by providing identity and data management, secure communication protocols, easy-to-use digital contract administration, and prototypical data usage control enforcement. After presenting the software architecture and design, we outline the successful application in selected use cases and derive lessons learned for further developments of data space technologies.
The Effects of Hand Tracking on User Performance: an experimental study of an object selection based memory game

Until recently, Virtual Reality (VR) applications relied on controllers to enable user interaction in virtual environments. With advances in tracking technology, HMDs are now able to track the movements of users’ hands in real-time with significantly greater accuracy, allowing us to interact with the digital world directly with our hands. However, it is not entirely clear how hand tracking affects users’ performance. In this study, we investigate user performance using an in-game analytics-based assessment methodology for a VR memory puzzle task. We conducted a within-subjects experiment with 30 participants in three conditions: 1- Hand-tracking, 2-Controller Without Haptics, and 3- Controller With Haptics. In all our measurements (correct order and pattern, correct pattern only, and trial completion) except for the initial selection time, participants performed best with hand tracking. The use of controllers with haptics did not outperform controllers without haptics in most measures, possibly because other feedback cues compensated for the lack of haptics. This study helps us better understand the three selected interactivity methods when used in VR, as well as the importance of naturalistic experience in interaction design.
Learning Topological Interactions for Multi-Class Medical Image Segmentation

Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions among different classes (e.g., containment and exclusion). These constraints naturally arise in biomedical images and can be crucial in improving segmentation quality. In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network. The implementation is completely convolution-based and thus can be very efficient. This empowers us to incorporate the constraints into end-to-end training and enrich the feature representation of neural networks. The efficacy of the proposed method is validated on different types of interactions. We also demonstrate the generalizability of the method on both proprietary and public challenge datasets, in both 2D and 3D settings, as well as across different modalities such as CT and Ultrasound. Code is available at: https://github.com/TopoXLab/TopoInteraction .
Server Consolidation Algorithms for Cloud Computing

In recent years, companies and researchers have hosted and rented computer resources over ‎the ‎‎internet due to cloud computing, which led to an increase in the energy consumed by ‎data centers. This ‎‎consumption is considered one of the world's highest, ‎which pushed many ‎researchers to propose ‎several techniques such as server ‎consolidation (SC) to solve the‎‏ ‏trade‏-‏off‏ ‏‏‎between energy saving and ‎quality of service ‎‎(QoS). SC requires maintaining service level ‎agreements (SLA) violations and ‎minimizing ‎the number of active physical machines (PMs). ‎Furthermore, to achieve this balance and ‎‎avoid ‎increasing hardware costs, the SC challenge targets ‎placing new virtual machines ‎‎(VMs) in ‎suitable PMs. This work explored the existing SC algorithms ‎that include ‎CloudSim as a simulator ‎environment and PlanetLab as a dataset. The authors compared ‎the well-known optimization methods ‎and extracted the weaknesses of the main three deployed ‎‎approaches involved in the consolidation ‎process: bin-packing model, metaheuristics, ‎and machine ‎learning-based solutions.‎
A Critical Discourse Analysis of Different News Reports on the Same Event: Illustrated with Examples from &amp;lt;i&amp;gt;China Daily&amp;lt;/i&amp;gt; and &amp;lt;i&amp;gt;The Guardian&amp;lt;/i&amp;gt;

Since the 1970s, Critical Discourse Analysis (CDA) has been widely applied to the analysis of news reports.News report, as a kind of public discourse, conveys its state's political position and ideology and influences its readers' opinions.The study takes Fairclough's Three-dimensional Model as theoretical framework, analyzing and comparing 10 reports from The Guardian and China Daily (5 samples each) from the perspectives of wording, modality, news sources, and sociohistorical contexts.Through analysis, the study attempts to illustrate different images constructed in the two media and the two newspapers' different attitudes towards the Extradition Bill Incident.The results show that the news reports in China Daily and The Guardian do differ a lot.The Guardian uses a bunch of derogatory words to describe the police, while deliberately filters information to glorify the criminal actions of the protesters; it uses "may" and "would" to impose groundless speculation on Chinese central government and adopts protesters' remarks for many times.The Guardian shapes a righteous and brave image of protesters, and a brutal image of police, expressing its support for protesters.China Daily shows the efforts the police have made for the peace of Hong Kong and the wrongdoings of protesters with substantial behavioral descriptions; it uses "will", "should" and "must" to show China's resolution and action to wind down the riots, and express strong condemnation for what the protesters have done, building a positive and responsible image of the police.
Adaptive Control Algorithm for Uncalibrated Position-based Visual Servoing

The paper proposes an adaptive control algorithm for the uncalibrated position-based visual servoing to complete the tracking task of robot system. The desired and current poses of the end-effector are obtained by the camera and the system error is described in the camera coordination, which can avoid the work of hand-eye calibration. The connection matrix between the robot and the end-effector as well as the camera measurement bias are estimated together by the input/output data in the online control process, which can avoid the calibration between the robot terminal and the end-effector and reduce the influence of the measurement bias. By our proposed algorithm, the controller can update the model. Moreover, the control law and the estimation updated law are designed by the Lyapunov theory, which is proved that the system error and the parameter estimation error can be closed to zero. To verify the performance of the proposed controller, a numerical simulation experiment on a 6 degrees-of-freedom robot manipulator equipped with the end-effector and the eye-to-hand camera under our proposed control method is designed and presented.
Blockchain and Autonomous Vehicles: Architecture, Security and Challenges

In the smart culture where everything is going intelligently, there is a need to administer the intelligent transport system and improve the traffic congestion, accidents on road accidents, and above all suitable parking allocation. It is to be noted that intelligent vehicles are equipped with the internet that enables all types of communication with the surroundings. Automotive service providers like Uber and Ola are the inspiration behind Blockchain Technology and Autonomous Vehicles. In some countries, they provide automated autonomous vehicles for smooth travel management and are known as linked or connected autonomous vehicles. Although such services are susceptible to attacks like men-in-the-middle attacks, parodying of a global positioning system, DoS attacks, sniffing, and many similar attacks, those are creating hurdles in the development of fully automated vehicles wherein the parallel autonomous industry is growing so fast as self-driving cars. Most of the studies concentrate on a central system with a single point of failure. The paper discussed about blockchain and autonomous vehicles, architecture, security, and challenges.
Communication Efficient Federated Learning With Heterogeneous Structured Client Models

Federated learning (FL) has recently attracted much attention due to its superior performance in privacy protection when processing data from different terminals. However, homogeneous deep learning models are pervasively adopted without considering the difference between distinct data in various clients, resulting in low learning performance and high communication costs. This paper thus proposes a novel FL framework with heterogeneous structured client models for handling different data scales and investigates its superiority over canonical FL with homogeneous models. Additionally, singular value decomposition is adopted on the client models to reduce the amount of transmitted data, i.e., the communication costs. The aggregation mechanism with multiple models on the central server is then presented based on the heterogeneous characteristics of the uploaded parameters and models. The proposed framework is applied to four benchmark classification datasets and a trend following task on electromagnetic radiation intensity time series data. Experimental results demonstrate that the proposed method can effectively improve the accuracy of local learning models and significantly reduce communication costs.
Token Open Security Model for Updatable Encryption

Updatable encryption (UE) is suitable for key rotation and ciphertext data update in cloud storage environment. For most updatable encryption schemes, when the tokens are all open and the keys of the past epochs are corrupted by the adversary, the adversary can easily obtain the keys of all epochs, and the scheme is no longer safe. However, the existing security models for UE often use firewalls and insulated regions to limit the information obtained by the adversary when proving the security of the scheme, and require the adversary not to corrupt all the update tokens. We believe that this limitation is far from the real attack scenario, and in this paper we present a new security model for UE, token open security model, which allows the adversary to corrupt all update tokens and keys of the past epochs. We prove that a secure UE scheme under this model must have no-directional key updates and uni-directional ciphertext updates. We prove an equivalent condition of this model. We also present a general construction approach for UE schemes to satisfy token open security model.
SGSI – A Scalable GPU-Friendly Subgraph Isomorphism Algorithm

Due to the inherent hardness of subgraph isomorphism, the performance is often a bottleneck in various real-world applications. We address this by designing an efficient subgraph isomorphism algorithm leveraging features of GPU architecture. Existing GPU-based solutions adopt two-step output scheme, performing the same join twice in order to write intermediate results concurrently. They also lack GPU architecture-aware optimizations that allow scaling to large graphs. In this article, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> calable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> PU-friendly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ubgraph <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> somorphism algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SGSI</i> . SGSI incorporates a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prealloc-Combine</i> strategy based on the vertex-oriented framework, which avoids joining-twice in existing solutions. It uses a GPU-friendly data structure (called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PCSR</i> ) to represent an edge-labeled graph. We also study fine-grained load balance strategies and discuss how to handle enormous graphs that cannot be resident in GPU memory. A partition-based pipeline framework is proposed. Extensive experiments on both synthetic and real graphs show that SGSI outperforms the state-of-the-art algorithms by up to several orders of magnitude and has a good scalability with graph size scaling to billions of edges.