Reviewerly/exHarmony huggingface dataset:
Toward Scalable and Robust AIoT via Decentralized Federated Learning

As Artificial Intelligence of Things (AIoT) has become increasingly important for modern AI applications, federated learning (FL) is envisioned to be the enabling technology for AIoT, especially for large-scale, data privacy-preserving scenarios. However, most existing FL is managed in a centralized manner (CFL), which confronts the limitations of scalability given the AioT device explosion. The key challenge faced by CFL is the communication bottleneck at the central model aggregation server, which leads to a high server-to-worker communication delay and thus severely slows down the model convergence. To address this challenge, this article introduces a generic decentralized FL (DFL) framework that can operate in either synchronous (Sync-DFL) mode or asynchronous (Async-DFL) mode to alleviate the high communication congestion around the central server. Moreover, Async-DFL is the first DFL in the literature to provide a generic FL framework that is fully asynchronous and able to completely avoid worker waiting, which leads to robust distributed model training in the inherently heterogeneous IoT environments, where stragglers (i.e., slow devices) are very common due to the largely varying computing/networking speeds of IoT devices. Our DFL framework is implemented, deployed, and experimented with in both simulation and physical testbeds. The results show that Async-DFL can accelerate the convergence speed of model training twice as fast as CFL, while maintaining convergence accuracy and effectively combating the impact of the stragglers.
YOLOv3_ReSAM: A Small-Target Detection Method

Small targets in long-distance aerial photography have the problems of small size and blurry appearance, and traditional object detection algorithms face great challenges in the field of small-object detection. With the collection of massive data in the information age, traditional object detection algorithms have been gradually replaced by deep learning algorithms and have an advantage. In this paper, the YOLOV3-Tiny backbone network is augmented by using the pyramid structure of image features to achieve multi-level feature fusion prediction. In order to eliminate the loss of spatial feature information and hierarchical information caused by pooling operations in convolution processes and multi-scale operations in multi-layer structures, a spatial attention mechanism based on residual structure is proposed. At the same time, the idea of reinforcement learning is introduced to guide bounding box regression on the basis of the rough positioning of the native boundary regression strategy, and the variable IoU calculation method is used as the evaluation index of the reward function, and the boundary regression model based on the reward mechanism is proposed for fine adjustment. The VisDrone2019 data set was selected as the experimental data support. Experimental results show that the mAP value of the improved small-object detection model is 33.15%, which is 11.07% higher than that of the native network model, and the boundary regression accuracy is improved by 23.74%.
Grouping Bilinear Pooling for Fine-Grained Image Classification

Fine-grained image classification is a challenging computer visual task due to the small interclass variations and large intra-class variations. Extracting expressive feature representation is an effective way to improve the accuracy of fine-grained image classification. Bilinear pooling is a simple and effective high-order feature interaction method. Compared with common pooling methods, bilinear pooling can obtain better feature representation by capturing complex associations between high-order features. However, the dimensions of bilinear representation are often up to hundreds of thousands or even millions. In order to get compact bilinear representation, we propose grouping bilinear pooling (GBP) for fine-grained image classification in this paper. Firstly, by dividing the feature layers into different groups, and then carrying out intra-group bilinear pooling or inter-group bilinear pooling. The representation captured by GBP can achieve the same accuracy with less than 0.4% parameters compared with full bilinear representation when using the same backbone. This extreme compact representation largely overcomes the high redundancy of the full bilinear representation, the computational cost and storage consumption. Besides, it is because GBP compresses the bilinear representation to the extreme that it can be used with more powerful backbones as a plug-and-play module. The effectiveness of GBP is proved by experiments on the widely used fine-grained recognition datasets CUB and Stanford Cars.
Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering

The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover informative representations and define relevant articles from the rapidly growing biomedical literature due to the unsupervised nature of document clustering. Moreover, empirical investigations demonstrated that traditional text clustering methods produce unsatisfactory results in terms of non-contextualized vector space representations because that neglect the semantic relationship between biomedical texts. Recently, pre-trained language models have emerged as successful in a wide range of natural language processing applications. In this paper, we propose the Gaussian Mixture Model-based efficient clustering framework that incorporates substantially pre-trained (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) BioBERT domain-specific language representations to enhance the clustering accuracy. Our proposed framework consists of main three phases. First, classic text pre-processing techniques are used biomedical document data, which crawled from the PubMed repository. Second, representative vectors are extracted from a pre-trained BioBERT language model for biomedical text mining. Third, we employ the Gaussian Mixture Model as a clustering algorithm, which allows us to assign labels for each biomedical document. In order to prove the efficiency of our proposed model, we conducted a comprehensive experimental analysis utilizing several clustering algorithms while combining diverse embedding techniques. Consequently, the experimental results show that the proposed model outperforms the benchmark models by reaching performance measures of Fowlkes mallows score, silhouette coefficient, adjusted rand index, Davies-Bouldin score of 0.7817, 0.3765, 0.4478, 1.6849, respectively. We expect the outcomes of this study will assist domain specialists in comprehending thematically cohesive documents in the healthcare field.
Digital Assyriology—Advances in Visual Cuneiform Analysis

Cuneiform tablets appertain to the oldest textual artifacts used for more than three millennia and are comparable in amount and relevance to texts written in Latin or ancient Greek. These tablets are typically found in the Middle East and were written by imprinting wedge-shaped impressions into wet clay. There is an increasing demand in the Digital Humanities domain for handwriting recognition, i.e., machine reading of handwritten script, focusing on historic documents. Current practice in text analysis of cuneiform script relies heavily on transliteration and translation, which are incomplete and influenced by the knowledge and experience of the expert that created them. The development of computational tools for cuneiform analysis presents many opportunities. An efficient and accurate sign spotting enables cross-referencing and statistical analyzes that are infeasible to perform manually. Furthermore, a wedge constellation spotting tool, provides experts with a significantly broader base of references to create more accurate and less time consuming transliterations and translations. Yet, cuneiform script has since resisted efforts to computational processing on basis of its basic constituents, its 3D wedge-shaped impressions and their free-form arrangements into signs. In this work, we review the literature on computational processing and recognition in the domain of cuneiform script. We introduce the different heterogeneous sources of cuneiform script, namely, manual ink-on-paper drawings, digital vector graphics drawings, photographs, and 3D scans of tablets. We describe the development of methods, beginning with the first computational classification using a hybrid manual encoding and computational comparison, to the latest methods making use of Generative Adversarial Neural Networks to recognize characters automatically. Finally, we give an overview of applications of these methods that enable quantitative mining in the small, e.g., patterns of wedge constellations, and in the large, e.g., networks of economic activity.
Privacy-preserving quantum protocol for finding the maximum value

Abstract In this paper, we first define a primitive protocol of secure multiparty computations to privately compute the logic operator “OR” (SMC_OR). Accordingly, we design a feasible quantum SMC_OR protocol by using single photons, which can achieve information-theoretical security in the semi-honest model. Furthermore, we adopt the proposed quantum SMC_OR protocol to solve an interesting but important privacy-preserving problem, i.e., finding the maximum value among many secrets. Finally, we simulate the related quantum protocols in Qiskit and verify the correctness and the feasibility of the proposed protocols.
A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.
Using Augmented Reality to Enhance Students’ Representational Fluency: The Case of Linear Functions

Using multiple representations is advocated and emphasized in mathematics and science education. However, many students have difficulty connecting multiple representations of linear functions. Augmented Reality (AR) may affect these teaching and learning difficulties by offering dynamically linked representations. Inspired by this, our study aims to develop, implement, and evaluate an AR-based multi-representational learning environment (MRLE) with three representations of linear functions. The data were collected from 82 seventh graders from two high-performing classes in an urban area in China, through a pre-test, a post-questionnaire, and follow-up interviews. The results reveal that students were satisfied with the AR-based MRLE, which assisted in enhancing their understanding of the real-life, symbolic, and graphical representations and connections among them. Regarding students’ interactions with multiple representations, apparent differences in learning sequences and preferences existed among students in terms of their representational learning profile. In sum, learning in the AR-based MRLE is a complex interaction process between the mathematics content, forms of representations, digital features, and students’ representational learning profile.
Time-on-task metrics for predicting performance

research-article Open Access Share on Time-on-task metrics for predicting performance Authors: Juho Leinonen Aalto University Aalto UniversityView Profile , Francisco Enrique Vicente Castro University of Massachusetts Amherst University of Massachusetts AmherstView Profile , Arto Hellas Aalto University Aalto UniversityView Profile Authors Info & Claims ACM InroadsVolume 13Issue 2June 2022 pp 42–49https://doi.org/10.1145/3534564Online:17 May 2022Publication History 0citation68DownloadsMetricsTotal Citations0Total Downloads68Last 12 Months68Last 6 weeks25 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

Abstract The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process.
Data Legacies, Epistemic Anxieties, and Digital Imaginaries in Archaeology

Archaeology operates in an increasingly data-mediated world in which data drive knowledge and actions about people and things. Famously, data has been characterized as “the new oil”, underpinning modern economies and at the root of many technological transformations in society at large, even assuming a near-religious power over thought and action. As the call for this Special Issue recognizes, archaeological research is socially and historically situated and consequently influenced by these same broader developments. In archaeology, as in the wider world, data is the foundation for knowledge, but its capacity is rarely reflected upon. This paper offers just such a reflection: a meditation on the nature of archaeological digital data and the challenges for its (re)use. It asks what we understand by data: its etymology and comprehension, its exceptionality and mutability, its constructs and infrastructures, and its origins and consequences. The concept of the archaeological data imaginary is introduced to better understand approaches to the collection and use of archaeological data, and a case study examines how knowledge is mediated and remediated through the data embedded in grey literature. Appreciating the volatility and unpredictability of digital data is key in understanding its potential for use and reuse in the creation of archaeological knowledge.
FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device

With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, there is a trend to push models down to edges and end devices. However, due to the limitation of computing resource, it is difficult for end devices to complete complex computing tasks alone. Therefore, this article divides the model into two parts and deploys them on multiple end devices and edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead, forming a hierarchical distributed architecture. In order to enable the distributed model to continuously evolve by using new data generated by end devices, we comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework FLEE , which can realize dynamical updates of models without redeploying them. Through image and sentence classification experiments, we verify that it can improve model performances under all kinds of data distributions, and prove that compared with other frameworks, the models trained by FLEE consume less global computing resource in the inference stage.
Cross-virtuality analysis of rich X-ray computed tomography data for materials science applications

We demonstrate a collaborative visualisation framework using a traditional desktop-based setup together with a virtual reality-based system for the analysis of rich X-Ray Computed Tomography Data (XCT) from composite materials. The framework supports experts in the time-consuming and cognitively demanding task of analysing objects, in particular, curved fibres and pores, along with their characteristics, such as length, orientation, or shape. Our framework employs visualisation methods in virtual reality focusing on a detailed spatial impression, combined with desktop-based methods on a large-screen multi-touch display focusing on analysing characteristics and their distributions. We provide two case studies on fibre-reinforced polymer (FRP) datasets, showcasing the potential of our framework. One case study analyses a FRP containing short, strongly curved polyethylene terephthalate fibres. A second study explores small elliptic pores inside a carbon FRP dataset. Our preliminary results show that the addition of virtual reality to the classical desktop-based workflow can enhance the spatial understanding of the dataset, encourage collaboration, as well as foster engagement with the data.
A scoping review of distributed ledger technology in genomics: thematic analysis and directions for future research

Abstract Objective Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. Materials and Methods We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. Results Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. Discussion Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people’s attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. Conclusion While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners.
A novel game theory based reliable proof‐of‐stake consensus mechanism for blockchain

Abstract The prominent achievement of blockchain technology stimulates exceptional innovation. The major component of blockchain is the consensus mechanism. The standard consensus mechanisms specifically Proof‐of‐Work (PoW) rely on mining procedures and stake‐based mechanisms such as Proof‐of‐Stake (PoS) rely on massive stake investment as the sole criteria for selection of leader nodes. However, PoW impose huge computational power requirements and latter may incorporate malicious nodes as leader nodes in anonymous blockchain. These issues might fuel the way for distrust among the participants in blockchain. Henceforth, a novel game theory based reliable PoS mechanism for blockchain has been proposed. Federated learning has been used to compute trust_score for each node. The nodes are trained on locally generated dataset. Further, a game theoretic approach has been proposed that uses a reward and punishment scheme to ensure threshold level of trust_score maintenance by each node. Finally, a crop insurance use case has been developed with the consensus mechanism and blockchain coded in python. The insurance claims are made to operate through smart contract based mobile app system to impart more authenticity. The system is tested and results show an intrusion accomplishment rate reduced by approximate 40% when compared to the standard PoS mechanism and by approximately 33% for algorand, 29% for ouroboros and 20% for tendermint. The mean absolute error also decreases by 30% within specific time. Furthermore, the proposed federated learning‐based system is compared with basic neural network‐based machine learning model and the results reveal that a significant reduction in average training time amounting to 8.35 second is achieved. Test accuracy has also been analyzed for various learning mechanisms.