Reviewerly/exHarmony huggingface dataset:
Theorizing ‘The Gap’ Twenty Years Later: Global Development, Design, and Speculative Ethics in Edtech Research

Abstract Drawing from Feminist Science and Technology Studies, this paper explores how we might revisit and recuperate past academic research projects, theories, and relationships to design futures that matter for social good. As context, I begin by outlining a decade of research in Information and Communication Technologies for Development (ICTD), which linked the United Nations Millennium Development Goals to innovations in telecommunications and computing. I then introduce the ‘theory of design-reality gaps’ that was proposed by Heeks to study ’wicked problems’ in this domain (2002). I revisit two strands of research that I carried out in relation to the ‘design-reality gap’. The first involved an ethnographic study of a participatory mobile phone based learning intervention for Kenyan health workers. I argued that instead of a singular ‘gap’ explained by geographic, sociocultural, or economic ‘divides’, there was a messy entanglement, constituted by sociomaterial practices that enacted a multiplicity (Mol in The body multiple: Ontology in medical practice, Duke University Press, 2002) of desired futures. In the second strand, I attempted to care for the practices that were abandoned by the learning intervention when one kind of justice was prioritized over others. This explored how the research could be more ‘speculative’ and how this ‘speculative commitment’ could generate new ethical questions and logics for living with technology (Puig de la Bellacasa in Social Studies of Science, 41(1), 85–106, 2011 and Matters of care: Speculative ethics in more than human worlds, University of Minnesota Press, 2017). I argue that approaching the design-reality ‘gap’ as a multiplicity instead of a void can support Tuck’s call for educational interventions that turn away from damage oriented theories of change to ones based on desire – approaching difference not as a lack, but as an ever-growing assemblage (2009). Tinkering with the original Heeks model, I conclude that in the postdigital era, the design-reality gap is now better-understood as a fluid space of multiplicities, and what is arguably most pressing is to study the differences in competing objectives and values, rather than disparities in information and technology.
Algorithmic futures: an analysis of teacher professional digital competence frameworks through an algorithm literacy lens

Algorithmic systems shape every aspect of our daily lives and impact our perceptions of the world. The ubiquity and profound impact of algorithms mean that algorithm literacy—awareness and knowledge of algorithm use, and the ability to evaluate algorithms critically and exercise agency when engaging with algorithmic systems—is a vital competence for navigating life in the 21st century. Professional digital competence (PDC) frameworks for teachers include technological, pedagogical, and social competence areas and are intended to illustrate the necessary knowledge, skills, and attitudes for digitally competent teachers. Using document analysis, we undertook a systematised review and evaluation of selected PDC frameworks through the lens of algorithm literacy. This analysis demonstrated that although some aspects of algorithm literacy could be inferred within the PDC frameworks analysed, there is a need for further explicit integration. Just as the DigComp framework for citizens has been updated to recognise the vital importance of understanding algorithmic systems’ impact, so should PDC frameworks be revised. Recommendations are provided for incorporating understandings of algorithmic governance and bias and ensuring digital Bildung development in PDC frameworks. Implications for teacher education programmes are also discussed.
Optimized the Performance of Super Resolution Images by Salt and pepper Noise Removal based on a Modified Trimmed Median Filter

Image processing is an interesting area where noisy images can be restored from salt and pepper noise. Various filtering algorithms can be used in the restoration process. Pixels in the original image should not be affected by the restoration process. Despite changes in dimensions and image format, the problem of the existing work persists. A hybrid technique used Ant colony Optimization to remove high-density salt and pepper noise from images. This hybrid technique would remove salt and pepper noise in corrupted images. Ant Colony Optimization (ACO) identifies and selects noisy pixels from corrupted images. It eliminates salt and pepper noise (SP Noise), which causes black and white spots in the original image. All the processes are explained to prove the theory, and the simulation results are presented.
The weak-form efficiency of cryptocurrencies

This study aimed to examine the weak-form efficiency of some of the most capitalised cryptocurrencies. The sample consisted of 24 cryptocurrencies selected out of 30 cryptocurrencies with the highest market capitalisation as of October 19, 2022. Stablecoins were not considered. The study covered the period from January 1, 2018 to August 31, 2022. The results of robust martingale difference hypothesis tests suggest that the examined cryptocurrencies were efficient most of the time. However, their efficiency turned out to be time-varying, which validates the adaptive market hypothesis. No evidence was found for the impact of the coronavirus outbreak and the Russian invasion of Ukraine on the weak-form efficiency of the examined cryptocurrencies. The differences in efficiency between the most efficient cryptocurrencies and the least efficient ones were noticeable, but not large. The results also allowed to observe some slight differences in efficiency between the cryptocurrencies with the largest market cap and cryptocurrencies with the lowest market cap. However, the differences between the two groups were too small to draw any far-reaching conclusions about a positive relationship between the market cap and efficiency. The obtained results also did not allow us to detect any trends in efficiency.
Extrovert or Introvert? GAN-Based Humanoid Upper-Body Gesture Generation for Different Impressions

Abstract Gestures, a form of body language, significantly influence how users perceive humanoid robots. Recent data-driven methods for co-speech gestures have successfully enhanced the naturalness of the generated gestures. Moreover, compared to rule-based systems, these methods are more generalizable for unseen speech input. However, many of these methods cannot directly influence people’s perceptions of robots. The primary challenge lies in the intricacy of constructing a dataset with varied impression labels to develop a conditional generation model. In our prior work ([22]) Controlling the impression of robots via gan-based gesture generation. In:Proceedings of the international conference on intelligent robots and systems. IEEE, pp 9288-9295), we introduced a heuristic approach for automatic labeling, training a deep learning model to control robot impressions. We demonstrated the model’s effectiveness on both a virtual agent and a humanoid robot. In this study, we refined the motion retargeting algorithm for the humanoid robot and conducted a user study using four questions representing different aspects of extroversion. Our results show an improved capability in controlling the perceived degree of extroversion in the humanoid robot compared to previous methods. Furthermore, we discovered that different aspects of extroversion interact uniquely with motion statistics
Student Behavior Detection in the Classroom Based on Improved YOLOv8

Accurately detecting student classroom behaviors in classroom videos is beneficial for analyzing students' classroom performance and consequently enhancing teaching effectiveness. To address challenges such as object density, occlusion, and multi-scale scenarios in classroom video images, this paper introduces an improved YOLOv8 classroom detection model. Firstly, by combining modules from the Res2Net and YOLOv8 network models, a novel C2f_Res2block module is proposed. This module, along with MHSA and EMA, is integrated into the YOLOv8 model. Experimental results on a classroom detection dataset demonstrate that the improved model in this paper exhibits better detection performance compared to the original YOLOv8, with an average precision (mAP@0.5) increase of 4.2%.
Noise-induced Parrondo's paradox in discrete-time quantum walks

Parrondo's paradox refers to the apparently paradoxical effect whereby two or more dynamics in which a given quantity decreases are combined in such a way that the same quantity increases in the resulting dynamics. We show that noise can induce Parrondo's paradox in one-dimensional discrete-time quantum walks with deterministic periodic as well as aperiodic sequences of two-state quantum coins where this paradox does not occur in the absence of noise. Moreover, we show how the noise-induced Parrondo's paradox affects the time evolution of quantum entanglement for such quantum walks.
Research on Automatic Classification of Coal Mine Microseismic Events Based on Data Enhancement and FCN-LSTM Network

Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety, disaster prevention and early warning. In the early stage, the classification of microseismic events relies on human experiences, which is not only inefficient but also often causes some misclassifications. In recent years, the neural network-based classification method has become more favored by people because of its advantages in modeling procedures. A microseismic signal is a kind of time-series signal and the application of the classification method is widely optimistic. The number and the balance of the training data samples have an important impact on the accuracy of the classification result. However, the quality of the training data set obtained from the production cannot be guaranteed. A long short-term memory (LSTM) network can analyze the time-series input data, where the image classification at the pixel level can be achieved by the fully convolutional network (FCN). The two structures in the network can not only use the advantages of the FCN for extracting signal details but also use the characteristics of LSTM for conveying and expressing the long time-series information effectively. In this paper, a time-series data enhancement combination process is proposed for the actual poor microseismic data. A hybrid FCN-LSTM network structure was built, the optimal network parameters were obtained by experiments, and finally a reasonable microseismic data classifier was obtained.
THANet: Transferring Human Pose Estimation to Animal Pose Estimation

Animal pose estimation (APE) boosts the understanding of animal behaviors. Recent vision-based APE has attracted extensive attention due to the advantages of contactless and sensorless applications. One of the main challenges in APE is the lack of high-quality keypoint annotations for different animal species since manually annotating the animal keypoints is very expensive and time-consuming. Existing works alleviate this problem by synthesizing APE data and generating pseudo-labels for unlabeled animal images. However, feature representations learned from synthetic images could not be directly transferred to real-world scenarios, and the generated pseudo-labels are usually noisy, which limits the model’s performance. To address the above challenge, we propose a novel cross-domain vision transformer for APE to Transfer Human pose estimation to Animal pose estimation, termed THANet, as humans share skeleton similarities with some animals. Inspired by the success of ViTPose in HPE, we design a unified vision transformer encoder to extract universal features for both animals and humans followed by two task-specific decoders. We further introduce a simple but effective cross-domain discriminator to bridge the domain gaps between the human pose and the animal pose. We evaluated the proposed THANet on the AP-10K and Animal-Pose benchmarks, and the extensive experiments show that our method achieves a promising performance. Specifically, the proposed vision transformer and cross-domain method significantly improve the model’s accuracy and generalization ability for APE.
MOTIVATIONAL MECHANISM OF EMPLOYEE'S INITIATIVE BEHAVIOR

In today’s business, the readiness and ability of employees to voluntarily and proactively contribute to the creation and implementation of innovations through continuous improvements in processes, work and other aspects of joint activities becomes an extremely important source of efficiency and development of the organization. This ability is manifested in initiative behavior, the management of which requires the use of methods based on understanding the nature of employees’ proactivity. The article puts forward a theoretical justification for the specific motivational mechanisms of the employee’s initiative behavior, which differ significantly from the mechanisms of normative behavior; in the development of ideas about the intra-motivational nature of proactivity, the process of launching initiative behavior is described, including the stages of creating an impulse-challenge, self-actualization and self-organization; describes the main parameters of the organizational environment that supports staff initiatives. The presented results will be useful for conducting scientific research in the field of proactive behavior, as well as for the development of HR-policy and practical measures to motivate staff.
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition

Sensor-based Human Activity Recognition facilitates unobtrusive monitoring of human movements. However, determining the most effective sensor placement for optimal classification performance remains challenging. This paper introduces a novel methodology to resolve this issue, using real-time 2D pose estimations derived from video recordings of target activities. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. We validate our approach through a feasibility study, applying inertial sensors to monitor 13 different activities across ten subjects. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach, demonstrating its efficacy. This research significantly advances the field of Human Activity Recognition by providing a lightweight, on-device solution for determining the optimal sensor placement, thereby enhancing data anonymization and supporting a multimodal classification approach.
A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain

The Non-Fungible Token (NFT) market in the Ethereum blockchain experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022. However, concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially. Our research examines the effects of wash trading on the NFT market in Ethereum from the beginning until January 2022, using multiple approaches. We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774. We look at two ways to profit from wash trading: Artificially increasing the price of the NFT and taking advantage of the token reward systems provided by some marketplaces. Our findings show that exploiting the token reward systems of NFTMs is much more profitable (mean gain of successful operations is $1.055M on LooksRare), more likely to succeed (more than 80% of operations), and less risky than reselling an NFT at a higher price using wash trading (50% of activities result in a loss). Our research highlights that wash trading is frequent in Ethereum and that NFTMs should implement protective mechanisms to stop such illicit behavior.
Entanglement Routing Over Quantum Networks Using Greenberger-Horne-Zeilinger Measurements

Generating a long-distance quantum entanglement is one of the most essential functions of a quantum network to support quantum communication and computing applications. The successful entanglement rate during a probabilistic entanglement process decreases dramatically with distance, and swapping is a widely-applied quantum technique to address this issue. Most existing entanglement routing protocols use a classic entanglement-swapping method based on Bell State measurements that can only fuse two successful entanglement links. This paper appeals to a more general and efficient swapping method, namely n-fusion based on Greenberger-Horne-Zeilinger measurements that can fuse n successful entanglement links, to maximize the entanglement rate for multiple quantum-user pairs over a quantum network. We propose efficient entanglement routing algorithms that utilize the properties of n-fusion for quantum networks with general topologies. Evaluation results highlight that our proposed algorithm under n-fusion can greatly improve the network performance compared with existing ones.
Secure Collaborative Learning in Mining Pool via Robust and Efficient Verification

Recently, collaborative learning is proposed to amortize massive computation costs of highly sophisticated artificial intelligence (AI) tasks. To attract lots of participants, researchers investigate blockchains ‘ economic incentives with proof of useful work (PoUW) consensus protocols to motivate substantial numbers of miners in a mining pool to complete AI tasks. However, participants might be untrusted and defraud rewards with as less as possible efforts. In the paper, we propose a robust and efficient proof of learning scheme called RPoL that enables pool managers to verify the training integrity of pool workers for secure pooled mining. Specifically, we devise an address-encoded model and employ a commitment-based secure sampling method to prevent malicious participants from abusing well-trained models or evading the sampling-based verification. Besides, we optimize RPoL via locality-sensitive hashing (LSH) to achieve communication-efficient verification while tolerating inherent reproduction errors of AI tasks. Furthermore, we conduct theoretical analysis and extensive evaluations. The results demonstrate that RPoL preserves high model performance against adversaries with acceptable costs and thus helps the pool win the mining competition among consensus nodes.
SEREDACT: Secure and Efficient Redactable Blockchain with Verifiable Modification

The immutability of blockchains is an important security feature, but applications and studies have shown that it poses some problems. For instance, harmful information and vulnerable programs can be permanently stored on public blockchains such as Bitcoin and Ethereum, causing continuous damage. Therefore, researchers proposed the redactable blockchain to delete or modify those harmful data. Existing schemes usually adopt the Chameleon hash function (CHF) to keep the block hash unchanged so that other blocks remain unaffected. However, these schemes suffer from two security problems: (i) (unknown-version) users cannot determine whether a received block is the up-to-date version because different versions have the same hash; and (ii) (lazy-redaction) miners have no motivations to update historical blocks, causing continuous spreading of data which should have been discarded. To solve the problems, we propose SEREDACT, a secure and efficient redactable blockchain protocol with verifiable modification. Specifically, we design a Merkle tree-based verification mechanism with efficient dynamic updating that supports quick version checks and forcible modification updates, and further integrate it with restricted redaction policies to guarantee security. Our security and performance analyses show that SEREDACT has adequate security as a redactable blockchain protocol and retains close efficiency compared with the immutable blockchain.
FaaSBatch: Enhancing the Efficiency of Serverless Computing by Batching and Expanding Functions

With high scalability and flexibility, serverless computing is becoming the most promising computing model. Existing serverless computing platforms initiate a container for each function invocation, which leads to a huge waste of computing resources. Our examinations reveal that (i) executing invocations concurrently within a single container can provide comparable performance to that provided by multiple containers (i.e., traditional approaches); (ii) redundant resources generated within a container result in memory resource waste, which prolongs the execution time of function invocations. Motivated by these insightful observations, we propose FaaSBatch - a serverless framework that reduces invocation latency and saves scarce computing resources. In particular, FaaSBatch first classifies concurrent function requests into different function groups according to the invocation information. Next, FaaSBatch batches the invocations of each group, aiming to minimize resource utilization. Then, FaaSBatch utilizes an inline parallel policy to map each group of batched invocations into a single container. Finally, FaaSBatch expands and executes invocations of containers in parallel. To further reduce invocation latency and resource utilization, within each container, FaaSBatch reuses redundant resources created during function execution. We conduct extensive experiments based on Azure traces to evaluate the effectiveness and performance of FaaSBatch. We compare FaaSBatch with three state-of-the-art schedulers Vanilla, SFS, and Kraken. Our experimental results show that FaaSBatch effectively and remarkably slashes invocation latency and resource overhead. For instance, when executing I/O functions, FaaSBatch cuts back the invocation latency of Vanilla, SFS, and Kraken by up to 92.18%, 89.54%, and 90.65%, respectively; FaaSBatch also slashes the resource overhead of Vanilla, SFS, and Kraken by 58.89% to 94.77%, 43.72% to 90.39%, and 42.99% to 78.88%, respectively.
Demo: PIEChain - A Practical Blockchain Interoperability Framework

A plethora of different blockchain platforms have emerged in recent years, but many of them operate in silos. As such, there is a need for reliable cross-chain communication to enable blockchain interoperability. Blockchain interoperability is challenging because transactions can typically not be reverted – as such, if one transaction is committed then the protocol must ensure that all related transactions are committed as well. Existing interoperability approaches, e.g., Cosmos and Polkadot, are limited in the sense that they only support interoperability between their own subchains, or require intrusive changes to existing blockchains. To overcome this limitation, we propose PIEChain, a general, Kafka-based cross-chain communication framework. We utilize PIEChain for a practical case study: a cross-chain auction in which users who hold tokens on multiple chains bid for a ticket sold on another chain. PIEChain is the first publicly available, practical implementation of a general framework for cross-chain communication.