Reviewerly/exHarmony huggingface dataset:
Upaya Menunjang Keterampilan Berbicara Bahasa Inggris Anak dengan Permainan Anak Ayam

This study aims to describe how “chicks games” can be a game to demonstrate children’s speaking skill. In this research, the children are about aged 5-6 years. Qualitative approach is chosen with the research subjects is 20 of 1st graders of Madrasah Ibtidaiyah Al Ashriyah at Banjarmasin. Interviews, observation, and documentation were implemented in this research. While the technique used in data analysis is using interactive models from Miles and Huberman. The stages of data collection from Miles and Huberman are the data reduction stage, the data display stage, and the data analysis conclusion stage. The results of this study state that the teacher provides the opportunity for children to demonstrate English language skills by repetition methods, playing methods and singing methods. These three things are in line with the nature of children where children will more easily grasp learning.
Design Methodology and Metrics for Robust and Highly Qualified Security Modules in Trusted Environments

Cyberattacks and cybercriminal activities constitute one of the biggest threats in the modern digital era, and the frequency, efficiency, and severity of attacks have grown over the years. Designers and producers of digital systems try to counteract such issues by exploiting increasingly robust and advanced security mechanisms to provide secure execution environments aimed at preventing cyberattacks or, in the worst case, at containing intrusions by isolation. One of the most significative examples comes from General Purpose Processor (GPP) manufacturers such as Intel, AMD, and ARM, which in the last years adopted the integration of dedicated resources to provide Trusted Execution Environments (TEEs) or secure zones. TEEs are built layer by layer on top of an implicitly trusted component, the Root-of-Trust (RoT). Since each security chain is only as strong as its weakest link, each element involved in the construction of a TEE starting from the RoT must be bulletproof as much as possible. In this work, we revise and propose a design methodology to implement in both hardware (HW) and software (SW) highly featured and robust security blocks by highlighting the key points that designers should take care of, and the key metrics that should be used to evaluate the security level of the developed modules. We also include an analysis of the state of the art concerning RoT-based TEEs, and we illustrate a case study that documents the implementation of a cryptographic coprocessor for the secure subsystem of the Rhea GPP from the European Processor Initiative (EPI) project, according to the presented methodology. This work can be used by HW/SW security module designers as a cutting-edge guideline.
Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection

In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect, posing a challenge to multiview clustering. A clustering algorithm based on multiview feature selection clustering (MFSC), which combines similarity graph learning and unsupervised feature selection, is designed in this study. During the MFSC implementation, local manifold regularization is integrated into similarity graph learning, with the clustering label of similarity graph learning as the standard for unsupervised feature selection. MFSC can retain the characteristics of the clustering label on the premise of maintaining the manifold structure of multiview data. The algorithm is systematically evaluated using benchmark multiview and simulated data. The clustering experiment results prove that the MFSC algorithm is more effective than the traditional algorithm.
Automatic Roadside Camera Calibration with Transformers

Previous camera self-calibration methods have exhibited certain notable shortcomings. On the one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach.
PMFFRC: a large-scale genomic short reads compression optimizer via memory modeling and redundant clustering

Genomic sequencing reads compressors are essential for balancing high-throughput sequencing short reads generation speed, large-scale genomic data sharing, and infrastructure storage expenditure. However, most existing short reads compressors rarely utilize big-memory systems and duplicative information between diverse sequencing files to achieve a higher compression ratio for conserving reads data storage space.We employ compression ratio as the optimization objective and propose a large-scale genomic sequencing short reads data compression optimizer, named PMFFRC, through novelty memory modeling and redundant reads clustering technologies. By cascading PMFFRC, in 982 GB fastq format sequencing data, with 274 GB and 3.3 billion short reads, the state-of-the-art and reference-free compressors HARC, SPRING, Mstcom, and FastqCLS achieve 77.89%, 77.56%, 73.51%, and 29.36% average maximum compression ratio gains, respectively. PMFFRC saves 39.41%, 41.62%, 40.99%, and 20.19% of storage space sizes compared with the four unoptimized compressors.PMFFRC rational usage big-memory of compression server, effectively saving the sequencing reads data storage space sizes, which relieves the basic storage facilities costs and community sharing transmitting overhead. Our work furnishes a novel solution for improving sequencing reads compression and saving storage space. The proposed PMFFRC algorithm is packaged in a same-name Linux toolkit, available un-limited at https://github.com/fahaihi/PMFFRC .
Blockchain-Enabled Hyperledger Fabric to Secure Data Transfer Mechanism for Medical Cyber-Physical System: Overview, Issues, and Challenges

This paper proposes a model to address the challenges faced by medical cyber-physical systems (MCPS) by implementing a permissioned blockchain platform. The platform incorporates the unique properties of blockchain into the network of affected systems, including decentralization, transparency, and immutability. The platform also includes a novel technique to secure MCPS through an automated access-control manager. This manager allows users to control who has access to their data, and can be configured to trust a third party if desired. The paper also extends into networked medical device systems, and discusses how the platform can be used to address critical is-sues specific to this field, such as network design. Finally, the paper discusses how various security features can be integrated into ultra-small devices, enhancing the protection of embedded systems. The overall objective of this research is to develop a secure and efficient data transfer mechanism for MCPS. The proposed platform addresses the challenges faced by MCPS by incorporating the unique properties of blockchain.
Bi-Unitary Superperfect Polynomials over 𝔽2 with at Most Two Irreducible Factors

A divisor B of a nonzero polynomial A, defined over the prime field of two elements, is unitary (resp. bi-unitary) if gcd(B,A/B)=1 (resp. gcdu(B,A/B)=1), where gcdu(B,A/B) denotes the greatest common unitary divisor of B and A/B. We denote by σ**(A) the sum of all bi-unitary monic divisors of A. A polynomial A is called a bi-unitary superperfect polynomial over F2 if the sum of all bi-unitary monic divisors of σ**(A) equals A. In this paper, we give all bi-unitary superperfect polynomials divisible by one or two irreducible polynomials over F2. We prove the nonexistence of odd bi-unitary superperfect polynomials over F2.
Stable and Fast Deep Mutual Information Maximization Based on Wasserstein Distance

Deep learning is one of the most exciting and promising techniques in the field of artificial intelligence (AI), which drives AI applications to be more intelligent and comprehensive. However, existing deep learning techniques usually require a large amount of expensive labeled data, which limit the application and development of deep learning techniques, and thus it is imperative to study unsupervised machine learning. The learning of deep representations by mutual information estimation and maximization (Deep InfoMax or DIM) method has achieved unprecedented results in the field of unsupervised learning. However, in the DIM method, to restrict the encoder to learn more normalized feature representations, an adversarial network learning method is used to make the encoder output consistent with a priori positively distributed data. As we know, the model training of the adversarial network learning method is difficult to converge, because there is a logarithmic function in the loss function of the cross-entropy measure, and the gradient of the model parameters is susceptible to the "gradient explosion" or "gradient disappearance" phenomena, which makes the training of the DIM method extremely unstable. In this regard, we propose a Wasserstein distance-based DIM method to solve the stability problem of model training, and our method is called the WDIM. Subsequently, the training stability of the WDIM method and the classification ability of unsupervised learning are verified on the CIFAR10, CIFAR100, and STL10 datasets. The experiments show that our proposed WDIM method is more stable to parameter updates, has faster model convergence, and at the same time, has almost the same accuracy as the DIM method on the classification task of unsupervised learning. Finally, we also propose a reflection of future research for the WDIM method, aiming to provide a research idea and direction for solving the image classification task with unsupervised learning.
A Blockchain-Based Method for Power-Related Data Quality Auditing

Power big data technology is playing an increasingly important role in the regulation and data analysis of smart grids, which face “data silos” and privacy issues. Federated Learning is a distributed machine learning framework and allows model training to be done without compromising user raw data. To protect private information, the model parameters uploaded by the user are usually trained in cipher text. However, the presence of ciphertext data makes it difficult to audit the quality of data uploaded by users. Smart grid federated analysis tasks are vulnerable to attackers launching attacks such as data poisoning and free-riding, which can have a serious impact on the global model trained. To defend against data poisoning and free-riding attacks, there is a need to audit encrypted grid data uploaded by users. In this paper, we propose a blockchain-based power-related data quality audit method that can ensure the correctness of the smart grid federated analysis model. In particular, we design an efficient noise addition mechanism that makes the aggregation model parameter noise sum to zero, which can protect user data privacy while ensuring the usability of the aggregation model. In addition, we propose a grouped aggregated data quality audit algorithm that can quickly locate users who upload malicious data. We conducted experimental evaluations on both the Real Power dataset and the MNIST dataset, the results showed that our approach is effective against data poisoning and free-riding attacks.
Deep Reinforcement Learning-Based Intelligent Decision-Making for Orbital Game of Satellite Swarm

Recent years have witnessed the rapid development of aerospace science and technology, and the orbital game technology has shown great potential value in the field of failed satellite maintenance, debris removal, etc. In this case, orbital game is often characterized by nonlinear dynamic model, unknown state information, high randomness, but the existing approaches to deal with game problem are difficult to be applied. The analytical method based on game theory is only applicable to simple scenarios, and it is challenging to find the optimal strategy for such complex scenarios as satellite swarm game. It should be noted that deep reinforcement learning has some research basis in the cooperative decision-making and control of multi-agents. In view of its powerful perception and decision ability, this paper applies deep reinforcement learning to solve the orbital game problem of satellite swarm. Firstly, the game scenario is modeled, where typical constraints, e.g., minimum time, optimal fuel, and collision avoidance, are taken into consideration in the game process, and then the multi-agent reinforcement learning algorithm is developed to solve the optimal maneuver strategy. The algorithm is based on the Actor-Critic architecture and uses a centralized training and decentralized execution approach to solve the optimal joint maneuver strategy. For different task scenarios, the action space, state observation space, and reward space are designed to introduce more rewards that match the specific game tasks to make the algorithm converge quickly, so that the satellite swarm emerges and executes better intelligent strategies to complete the corresponding game task.
FEDSET: Federated Random Forest Based on Differential Privacy

The rapid development of the federated machine learning paradigm has broken down the data barriers in technology between different organizations and individuals, allowing data that was previously difficult to analyze collaboratively to be used to unimaginable value. Among them, federated random forest has gradually gained popularity among collaborative analysis users for its good adaptability to structured data. However, the contradiction between the demand for collaborative analysis services and users’ awareness of privacy-preserving is becoming increasingly prominent and has become a bottleneck hindering the healthy and orderly development of the big data industry. To this end, this paper comprehensively analyzes the privacy preservation and usability requirements faced by federated random forest, and investigates a differential privacy-based federated random forest approach FEDEST. Users design privacy budgets locally, build CART decision trees with noise through a selective noise addition mechanism, and then upload them to the server for aggregation into a random forest, and optimize the random forest model through multiple rounds of iterations. FEDEST can improve the accuracy of business analysis models while safeguarding the privacy information of collaborative analysis participants. Experiments on the Adult and BRFSS datasets show that FEDEST has the highest classification accuracy of 91% and 87%, respectively, which is close to the classification accuracy of non-federated forest.
DSBT: Research on Soulbound Token Mechanism Based on Consortium Blockchain and Decentralized Identity

Issues such as technology, application and future development based on blockchain have always been hot topics. Future forms such as Web3.0 and the Metaverse are inseparable from digital identities and virtual tokens. This paper focuses on the recently popular Soulbound Token (SBT), introduces the concepts of digital identity, blockchain, and Decentralized Identity (DID) related to it, analyzes the shortcomings and challenges of SBT, and combines the consortium blockchain and DID. A new concept of Soulbound Token (DSBT) based on the decentralized identity of the consortium blockchain is proposed, which aims to make up for the technical deficiencies of SBT, and through the new concept of Displayable Credentials (DCs), DSBT combines DID for identity authentication, The advantages of access, in turn, can realize the concept of “programmable privacy” expected by SBT. Finally, the coping mechanism is proposed for the possible risks of DSBT, the application scenarios of DSBT in the active health intelligent care platform for the elderly are listed, and the future development of blockchain, DID and DSBT is expected.
‘I am not a number’: on quantification and algorithmic norms in translation

ABSTRACTNumbers and measurements enable transactions and communication in translation in ways that are helpful and indisputably necessary. However, as deployment of quantification and mathematisation has become more complex and opaque, it is important to interrogate the validity of measures and predictions, especially if they are to be used as a basis for action. This article takes a critical look at the various types of quantification and mathematisation used in translation and considers the effects of these on translators working in highly technologized workflows. It introduces the concept of algorithmic norms, whereby translators feel pressured to reverse engineer and conform to the demands of algorithmic management.KEYWORDS: Translation technologytranslation normsalgorithmsmathematisation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was conducted with the financial support of Science Foundation Ireland at ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology at Dublin City University [grant number 13/RC/2106_P2].Notes on contributorsJoss MoorkensJoss Moorkens is an Associate Professor at the School of Applied Language and Intercultural Studies in Dublin City University (DCU), Challenge Leader at the ADAPT Centre, and member of DCU’s Institute of Ethics and Centre for Translation and Textual Studies. He has written on the topics of translation technology, machine translation, translation quality evaluation, translator precarity, and translation ethics. He is General Coeditor of Translation Spaces with Prof. Dorothy Kenny and coeditor of a number of books and journal special issues. He is also a coauthor of the textbook Translation Tools and Technologies (Routledge 2023) and a forthcoming textbook on Machine Translation and Automation.
Why don’t we trace? A study on the barriers to software traceability in practice

Abstract Researchers have proposed numerous tools, methods, and techniques for establishing and maintaining software traceability. Despite its acknowledged importance, researchers argue that traceability is still “a sought-after, yet often elusive quality in software-intensive systems”. We have little evidence regarding how creating, managing, and using traceability links vary depending on factors such as organizational contexts, software development practices, and project types. We conduct an empirical study where software development practitioners express their perception regarding the value of software traceability. Via an online survey, 55 participants provided information related to their current traceability practices and needs. Furthermore, we interviewed 14 practitioners to gain a more in-depth understanding. Our study investigates the effect of two independent variables: the software development paradigm and the type of developed software system. Among the several identified findings, our analysis reveals that, although the traceability costs are an inhibitor for adopting more mature traceability practices, the respondents believe that the expected benefits still outweigh envisioned costs. Traceability is mainly performed manually: not only are automated trace retrieval tools scarce, but their offered automation is not expected to replace human involvement.
Evolving Conditional Disclosure of Secrets

In this work, we define and study the notion of evolving conditional disclosure of secrets (CDS) protocols. In this model, parties arrive infinitely in sequential order. Each party holds a private input, and when arrives, it sends a random message to a referee. In turn, at any stage of the protocol, the referee should be able to reconstruct a secret string, held by all the parties, from the messages it gets, if and only if the inputs of the parties that arrived satisfy some condition. A similar notion was previously presented for secret sharing, a closely related cryptographic primitive used in many secure protocols. In a secret sharing scheme, a dealer holds a secret string. It randomly generates shares and distributes them to a set of parties, one share for each party, such that only some predefined subsets of the parties would be able to reconstruct the secret from their shares. In addition to the initiation of evolving CDS, we present a few constructions of evolving CDS protocols, for different classes of conditions that should be satisfied by the inputs of the parties. We believe that our new notion can be used to better understand evolving secret sharing and other related cryptographic primitives, and that further applications and constructions of it will be found in the future.
Dynamic Multi-server Updatable Encryption

In this paper, we propose the Dynamic Multi-Server Updatable Encryption (DMUE) primitive as an extension of standard public-key updatable encryption. Traditional UE aims to have efficient ciphertext updates performed by an untrusted server such that the compromise of several cryptographic keys and update tokens does not reduce the standard security of encryption. The update token supports outsourced ciphertext updates without requiring the server to decrypt and re-encrypt the ciphertext and it is typically derived from old and new keys. To mitigate the risk of a single point of failure in single-server UE and thus improve the resilience of the scheme, we formalise a multi-server variant of UE to treat the issue of token leakage. We can achieve a distributed update process by providing each server with an update token and requiring a threshold of servers to engage honestly. However, servers may act dishonestly or need to be replaced over time, so our primitive must cater to dynamic committee changes in the servers participating across epochs. Inspired by the work of Benhamouda et al. (TCC’20) on dynamic proactive secret sharing, we propose a generic DMUE scheme built from public-key UE and dynamic proactive secret sharing primitives and prove the ciphertext unlinkability of freshly encrypted versus updated ciphertexts.
Robust Property-Preserving Hash Meets Homomorphism

Boyle et al. introduced the Property-Preserving Hash (PPH) concept in 2019, spawning a series of subsequent research endeavors over the following two years. A PPH function compresses an input x into a short hash value while revealing a pre-defined one-bit property of the input from that hash value. A PPH is robust (RPPH) if no probabilistic polynomial-time adversary can find an input whose hash value cannot correctly reveal the property of inputs. This research studies a homomorphic variant of RPPH, denoted as Homomorphic Robust Property-Preserving Hash (HRPPH). This variant is particularly beneficial in privacy computing scenarios, where certain computations of private data are required, but access to the original data is intentionally restricted. The contributions of our study are twofold: