Reviewerly/exHarmony huggingface dataset:
Smart Policy Control for Securing Federated Learning Management System

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants' side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.
Learning Logical Pauli Noise in Quantum Error Correction

The characterization of quantum devices is crucial for their practical implementation but can be costly in experimental effort and classical postprocessing. Therefore, it is desirable to measure only the information that is relevant for specific applications and develop protocols that require little additional effort. In this Letter, we focus on the characterization of quantum computers in the context of stabilizer quantum error correction. For arbitrary stabilizer codes, subsystem codes, and data syndrome codes, we prove that the logical error channel induced by Pauli noise can be estimated from syndrome data under minimal conditions. More precisely, for any such code, we show that the estimation is possible as long as the code can correct the noise.
A Proposal for Enhancing the Security and Privacy of Digital Currencies

Regarding digital currencies, the authors focus on 1) the security issues with the advent of quantum computers and 2) the privacy issues in relation with CBDC's wider collection of personal data. Many legal issues can be solved using technology. Thus, they propose central banks to adopt their privacy-enhancing technologies in cryptography, called “SK4SC,” which performs KYC (know your customer) while keeping PKI (public key infrastructure)-registered trading partners anonymous. Security and privacy in digital currency can be trade-offs. For example, the AML/CFT effort requires the collection of personal information about innocent people, which can be detrimental to their privacy. On the other hand, excessive restrictions on the collection of personal information to protect privacy may weaken the AML/CFT. However, adopting SK4SC can resolve the trade-offs by anonymizing KYC, which is essential for the AML/CFT.
Actor Model Frameworks: An Empirical Performance Analysis

The Actor model exploits large-scale concurrency by modelling logic in terms of computational primitives, or actors, that function in parallel. We are interested to understand the impact and performance of adopting the Actor model for developing scalable and resilient information systems. We give a brief overview of the Actor model and describe some of its mainstream implementations. Then, we design and implement a benchmark harness to empirically evaluate the performance of three Actor model implementations: Akka, Akka.NET and Microsoft Orleans. Our benchmarks, running on commodity hardware in a public cloud provider, show that Akka.NET and Orleans are comparable in non-distributed setups, and both outperform Akka. Orleans outperforms the other two significantly in a distributed setup.
Audit AI search tools now, before they skew research

Generative AI could be a boon for literature search, but only if independent groups scrutinize its biases and limitations. Generative AI could be a boon for literature search, but only if independent groups scrutinize its biases and limitations.
Developing of English Teaching Materials for the Communication Study Program

The objective of this study is to construct a communicative language that may be used to preserve the existence and to measure the quality of English for communication science students. The preparation of this design is intended so that learning English in communication sciences can run according to the initial goals and objectives. Data collection was carried out in three ways, namely: observation, questionnaires and focus group discussions (FGD). From the data obtained, there is a fact that 100% have never attended English training in communication sciences, 80% have difficulty applying communicative English, and 100% very often find things difficult in finding English vocabulary in communication sciences. The design of learning English in communication science is as follows: looking at factual conditions, learning by analysis, design, development, implementation, and evaluation, or ADDIE and the output is the implementation of communicative English in communication science for students.
People, Process, and Technology Maturity Levels for Successful Technology Implementation by State Departments of Transportation

The demand for safe, reliable, and higher-quality infrastructure systems often increases the complexity of transportation construction projects and necessitates more comprehensive evaluation methods. This often leads to the incorporation of technologies for oversight and management. With a focus on effective technology implementation, this research stretches beyond simply evaluating technologies to consider integrating the technology with personnel and procedures at departments of transportation (DOTs). Drawing from literature and a survey of state DOT personnel, this study applies and evaluates maturity models for people, process, and technology with regard to technology implementation at DOTs. The findings support the need for understanding technology implementation in the aspects of people, process, and technology maturity to improve the probability of implementation success. The framework allows DOTs to move toward higher maturity levels and improve the likelihood of implementation success. The study’s objective is to assess the importance of people, process, and technology as implementation dimensions that state DOTs need to focus on for successfully implementing technologies. The idea and scope of the paper can be extended to any new technology implementation efforts at DOTs, including Automatic Identification and Data Capture technologies, emerging sensing and wireless technologies, safety technologies, and others. The maturity models presented enable organizations to audit and benchmark their status for technology implementation, and the relative levels allow these organizations to advance from basic to advanced stages. This paper introduces three maturity models for people, process, and technology, their adaptation to DOT operations, and their possible future use, as presented through preliminary survey results. The final result of this study provides a maturity model methodology for successful technology implementation and a self-assessment tool for state DOTs to gauge and improve their current maturity levels. Preliminary validation of these products was conducted via a workshop of subject matter experts, and the results indicate that state DOTs can use these products to self-assess their organization’s maturity and devise a plan as required for improved technology implementation.
A MACHINE LEARNING APPROACH FOR HATE SPEECH AND OFFENSIVE LANGUAGE DETECTION ON SOCIAL MEDIA

The problem of hate speech has gained significant prominence in recent times, and its detrimental impact on individuals and communities cannot be ignored.One probable solution to tackle this issue is by utilizing machine learning algorithms to automatically detect and flag hate speech in text-based data.The process of hate speech detection through machine learning involves training a model on a dataset of labelled examples, where each example has been labelled as either hate speech or non-hate speech.The text data is examined for various features such as the usage of particular words or phrases, grammar, and syntax, which are then extracted for the model to learn the distinction between hate speech and non-hate speech.After successful training, the model can classify new text data as either hate speech or non-hate speech.However, it is crucial to note that hate speech detection using machine learning is not infallible and can be influenced by biases present in the training data or the algorithm itself.Researchers continue to work on enhancing the accuracy and fairness of hate speech detection algorithms.In conclusion, machine learning-based hate speech detection has the potential to be an effective tool in the fight against hate speech, but it is imperative to pay attention to its limitations and biases.In the proposed approach, we created a web application in Python and Streamlit, and we utilized naïve bays and SVM to recognize speech on social media with an accuracy of more than 90%.
Exploration on the Classroom Reform of Residential Decoration Space Design in Higher Vocational Colleges in the Post Epidemic Era

In the post epidemic period, the teaching mode of higher vocational education presents a new trend of teaching transformation and development.In particular, many higher vocational colleges reflect the teaching development model of the Internet in their teaching activities, and the cloud based teaching model reflects important value and significance in the application of higher vocational education.Objectively, online teaching is closely related to the current epidemic prevention and control.From the perspective of the overall development trend of teaching, it adapts to the new idea of the current development of Internet plus teaching.From the perspective of practice, this Internet teaching mode is consistent with the teaching situation of students.Taking the key courses of typical majors as an example, we can tap the advantages and disadvantages of online teaching mode.This paper mainly discusses the reform and development mode of teaching design in higher vocational education in the post epidemic era, mainly taking the teaching of residential decoration space design as an example.
From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights

Abstract Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.
From Three-Body game to Metaverse

&lt;p&gt;This paper aims to study the concept of the metaverse as reflected in the Three-Body game in the TV series &lt;em&gt;The Three-Body Problem&lt;/em&gt;, as well as the current development status of the metaverse in the real world. Firstly, the Three-Body game in &lt;em&gt;The Three-Body problem&lt;/em&gt; is analyzed and explained to uncover the underlying metaverse knowledge. Then, through a literature review, the implementation and application scenarios of the metaverse in various industries are investigated from the databases Web of Science and Scopus, using keywords such as “metaverse”, “Three-Body”, “virtual world”, “virtual reality games”, and “human-computer interaction”. Nearly 10,000 relevant articles were retrieved, and 20 articles were selected for in-depth qualitative and quantitative analysis. Subsequently, the content of the literature is summarized from three aspects: the current development status of the metaverse, advancements in virtual reality technology, and advancements in human-computer interaction technology. The application status and technological progress of the metaverse in various industries and the existing technological limitations are discussed. By extending the concept of the metaverse from science fiction, this paper provides research ideas for the future development of the metaverse.&lt;/p&gt;
A Novel Approach for Optimization of Convolution Neural Network with Particle Swarm Optimization and Genetic Algorithm for Face Recognition

Convolutional neural networks are contemporary deep learning models that are employed for many various applications. In general, the filter size, number of filters, number of convolutional layers, number of fully connected layers, activation function and learning rate are some of the hyperparameters that significantly determine how well a CNN performs.. Generally, these hyperparameters are selected manually and varied for each CNN model depending on the application and dataset. During optimization, CNN could get stuck in local minima. To overcome this, metaheuristic algorithms are used for optimization. In this work, the CNN structure is first constructed with randomly chosen hyperparameters and these parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. A CNN with optimized hyperparameters is used for face recognition. CNNs optimized with these algorithms use RMSprop optimizer instead of stochastic gradient descent. This RMSprop optimizer helps the CNN reach global minimum quickly. It has been observed that optimizing with GA and PSO improves the performance of CNNs. It also reduces the time it takes for the CNN to reach the global minimum.
A Data Space for Climate Science in the European Open Science Cloud

The increasing volume of data in many scientific fields demands a transformative approach to data management and analysis. The data space concept, i.e., a digital ecosystem promoting sustainable and FAIR data use, has emerged to address key challenges. This paper introduces the ENES Data Space, a domain-specific implementation for climate scientists within the European Open Science Cloud. This integrated environment offers datasets, tools, and services for data science applications. We present the motivations, architecture, and a scientific application example, demonstrating the data space’s capabilities.
Educators’ Digital Literacy Competence in a Hybrid Learning Environment

Digital literacy represents the ability to use the information and communications technologies safely, critically, and purposefully. This is an essential component in the delivery and attainment of quality education in the educational system. However, due to the pandemic, digital literacy in the educational process have led to a change in the role of teachers, pupils, and resources used in teaching and nature of teacher instruction. This study aims to examine the digital literacy competence of educators conducting classes in hybrid learning environment using the qualitative research design. Data sources include abstracts and field notes. Data are analyzed using thematic analysis. Results revealed that the digital competence areas of educators in a hybrid learning environment include devices and software operations, communication and collaboration, digital content creation, information and data literacy, and problem-solving. Further study is recommended to assess the implication of the digital literacy competence to improve learning outcomes.
Multiple Instance Differentiation Learning for Active Object Detection

Despite the substantial progress of active learning for image recognition, there lacks a systematic investigation of instance-level active learning for object detection. In this paper, we propose to unify instance uncertainty calculation with image uncertainty estimation for informative image selection, creating a multiple instance differentiation learning (MIDL) method for instance-level active learning. MIDL consists of a classifier prediction differentiation module and a multiple instance differentiation module. The former leverages two adversarial instance classifiers trained on the labeled and unlabeled sets to estimate instance uncertainty of the unlabeled set. The latter treats unlabeled images as instance bags and re-estimates image-instance uncertainty using the instance classification model in a multiple instance learning fashion. Through weighting the instance uncertainty using instance class probability and instance objectness probability under the total probability formula, MIDL unifies the image uncertainty with instance uncertainty in the Bayesian theory framework. Extensive experiments validate that MIDL sets a solid baseline for instance-level active learning. On commonly used object detection datasets, it outperforms other state-of-the-art methods by significant margins, particularly when the labeled sets are small. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WanFang13/MIDL</uri> .
Using Standard Machine Learning Language for Efficient Construction of Machine Learning Pipelines

We use Standard Machine Learning Language (SML) to streamline the synthesis of machine learning pipelines in this research. The overarching goal of SML is to ease the production of machine learning pipeline by providing a level of abstraction which makes it possible for individuals in industry and academia to use machine learning to tackle challenges across a variety of fields without having to deal with low level details involved in creating a machine learning pipeline. We further probe into how a wide range of interfaces can be instrumental in interacting with SML. Lines of comparison are further drawn to analyze the efficiency of SML in practical use cases versus traditional approaches. As an outcome, we developed SML a query like language which serves as an abstraction from writing a lot of code. Our findings show how SML is competent in solving the problems that utilize machine learning.
Transparency and Sovereignty in Contact-tracing Networks

Threaded through Jean Baudrillard's writings on society, politics, and technology is the notion of transparency.Transparency features in several works critiquing the digital era: by rendering everything overexposed and proximate, media technology has facilitated a frictionless or, using Baudrillard's term, ecstatic flow of information.As transparency aligns itself semantically with frictionlessness and ecstasy, the question concerning the preconditions of transparency becomes pertinent.By advancing a reading of Singapore's TraceTogether application, this article proposes that embedded within Baudrillard's notion of transparency is the paradigm of friction: sovereignty.The TraceTogether team purports a non-invasive and privacy-preserving contact-tracing protocol.Circumventing the use of geo-location tracking, TraceTogether formulates a network of infected bodies based on proximity data by using Bluetooth Relative Signal Strength Indicator (RSSI) readings between devices with the application installed.The reduction of all markers of identification and differentiation-or, points of friction-into the same frictionless code belies an imperative invocation of sovereignty.Drawing on a range of thought that obliquely confronts the question of sovereignty and friction, this article frames the inquiry within the parameters of Jean Baudrillard and other theorists whose deliberations on infrastructure like architecture and networks are wrought upon the concept of transparency.The sovereign power to suspend or collapse the friction/frictionless binary calls into question the right to retain one's friction in this frictionless digital era.This article proposes a critical reading of topological transparency with a view to the political and biophilosophical implications of what is at stake in proximity-tracing technologies.
On the Truth Claims of Deepfakes: Indexing Images and Semantic Forensics

When news media shared a video of outgoing president Donald Trump acknowledging the victory of president-elect Joe Biden, some social media users conspired that it was a deepfake, a synthetic image made with machine learning (ML) algorithms, despite evidence to the contrary.Employing this example in the following, I focus on how images generate veracity through the interrelated actions of human and machine learning (ML) algorithms.I argue that ML presents an opportunity to revisit the semiotic infrastructures of images as an approach towards asking how photorealistic images produce truth claims in ways that exceed the purely visual.Drawing from photographic theories of the image index and diagrammatic understandings of ML, I argue that meaning, described here as what images do in the world, is a product of negotiation between multiple technological processes and social registers, spanning data sets, engineering decisions, and human biases.Focusing on Generative Adversarial Networks (GANs), I analyze sociopolitical and scientific discourses around deepfakes to understand the ways in which ML affords hegemonic ways of seeing.I conclude that ML operationalizes the evidentiary power of images, generating new thresholds of visibility to manage uncertainty.My aim is to critically challenge post-truth paranoias by analyzing how ML algorithms come to have ethicopolitical agency in visual culture, with implications for how images are made to matter in post-truth media ecologies.
A comfortable graph structure for Grover walk

Abstract We consider a Grover walk model on a finite internal graph, which is connected with a finite number of semi-infinite length paths and receives the alternative inflows along these paths at each time step. After the long time scale, we know that the behavior of such a Grover walk should be stable, that is, this model has a stationary state. In this paper our objectives are to give some characterization upon the scattering of the stationary state on the surface of the internal graph and upon the energy of this state in the interior. For the scattering, we concretely give a scattering matrix, whose form is changed depending on whether the internal graph is bipartite or not. On the other hand, we introduce a comfortability function of a graph for the quantum walk, which shows how many quantum walkers can stay in the interior, and we succeed in showing the comfortability of the walker in terms of combinatorial properties of the internal graph.