Reviewerly/exHarmony huggingface dataset:
Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of patients, which may limit the diagnosis accuracy fundamentally. In this paper, we propose a metadata and image features co-aware personalized federated learning scheme for smart healthcare. Specifically, we construct an intelligent diagnosis model, by which users can obtain fast and accurate diagnosis services. Meanwhile, a personalized federated learning scheme is designed to utilize the knowledge learned from other edge nodes with larger contributions and customize high-quality personalized classification models for each edge node. Subsequently, a Naïve Bayes classifier is devised for classifying patient metadata. And then the image and metadata diagnosis results are jointly aggregated by different weights to improve the accuracy of intelligent diagnosis. Finally, the simulation results illustrate that, compared with the existing methods, our proposed algorithm achieves better classification accuracy, reaching about 97.16% on PAD-UFES-20 dataset.
FFN: Fountain Fusion Net for Arbitrary-Oriented Object Detection

Arbitrary-oriented object detection (AOOD) is widely used in aerial images because of its efficient object representation. However, current detectors employ the over-standardized feature extraction structure, resulting in detectors has no ability to adaptively readjust feature representations of detection units. Meanwhile, we observe that many detection units could not focus on the objects of interest in their receptive field and are easily affected by the background information and interference targets, leading to the weaking of feature expression ability. We call them sub-optimal detection units. To address this issue, we propose a novel feature enhancement module called fountain feature enhancement module (FFEM). FFEM ingeniously uses the fountain-like structure to reconstruct the features of sub-optimal detection units, generating fountain features that can automatically condense spatial regional features, which effectively enhances detectors’ overall representation ability. Then, a high-performance AOOD detector called fountain fusion net (FFN) is proposed with FFEM embedded, and many novel AOOD components are tested for their progressiveness. We validated our FFN and FFEM using three remote sensing datasets ‒ DOTA, HRSC2016, and UCAS-AOD as well as one scene text dataset‒ICDAR 2015. Extensive experiments demonstrate the effectiveness of our proposed method on improving current detectors to achieve state-of-the-art performance based on this novel idea.
CoGNN: An Algorithm-Hardware Co-Design Approach to Accelerate GNN Inference With Minibatch Sampling

As a new algorithm of graph embedding, Graph Neural Networks (GNNs) have been widely used in many fields. However, GNN computing has the characteristics of both sparse graph processing and dense neural network, which make it difficult to be deployed efficiently on the existing graph processing accelerators or neural network accelerators. Recently, some GNN accelerators have been proposed, but the following challenges have not been fully solved: 1) The mini-batch GNN inference scenario has the potential of software and hardware co-design, which can bring 30% computation amount reduction, and this is not well utilized. Besides, the cost of Message Flow Graph construction is large and may account for more than 50% of the total delay. 2) The feature aggregation has a large amount of data access and relatively small amount of computation, which leads to low on-chip data reuse, only 10% of dense computing. 3) Without the optimization of sparse computing units, simple memory bank and cross bar architecture can easily lead to bank access conflict and load imbalance, reducing the utilization of computing units to less than 60%. In order to solve the above problems, we propose a algorithm-hardware co-design scheme to accelerate GNN inference, which includes three technologies: 1) A reuse-aware sampling method is proposed for mini-batch inference scenarios, which reduces 30% of the calculation and improves the on-chip reusability of local data. 2) Through the node-wise parallelism-aware quantization, the features and weights are quantized to integers with eight or four bits, which reduces the amount of memory access by at least four times. 3) An accelerator supporting the above technologies is designed and evaluated, and different operations are supported by the sampling-inference integration architecture. The multi-bank on-chip memory pool is designed to support data reuse, and edge stream reordering is used to reduce data access conflicts, improving the utilization of computing units by 1.5x. Combined with the above technologies, the experiments show that our design achieves 9.2× speedup and 29× energy efficiency improvement compared with Deep Graph Library framework running on servers equiped with CPU and GPU.
Human-Centered AI: A Framework for Green and Sustainable AI

A human-centered artificial intelligence (AI) approach is proposed as a theoretical foundation and a practical guideline to achieve green and sustainable AI. The goal of this study is to contribute to the discussion of AI as both more sustainable and greener.
Becoming a Target: Journalists’ Perspectives on Anti-Press Discourse and Experiences with Hate Speech

ABSTRACTABSTRACTIn many parts of the world, journalists work in increasingly hostile environments. To better understand the characteristics and implications of hostility against the press in a hybrid regime, this study explores how journalists in Serbia perceive and experience anti-press hate speech. Based on 20 interviews with diverse reporters, we describe hate speech against journalists as an ad hominem attack triggered by the very nature of journalistic conduct. We discuss two identified mechanisms of hate speech propagation. The first, machinery of hate speech, refers to orchestrated attacks on independent journalists by ruling party officials, pro-government news outlets, and troll armies. In contrast, the lone-wolf-haters mechanism refers to authentic and sporadic expressions of hate by extreme partisan audiences to which all journalists are susceptible, regardless of their affiliation. Describing their personal experiences, the participants report various responses to encountering hate speech, ranging from immediate emotions to less immediate psychological reactions to physical health problems. To prevent hateful acts from interfering with their journalistic performance, they rely on a variety of coping strategies and mechanisms, including psychological and professional strategies, as well as social and institutional support.KEYWORDS: Hate speechanti-press discoursejournalistspersonal experiencescoping strategiesflawed democracies AcknowledgementsThis research was approved by the University of Belgrade – Institute for Philosophy and Social Theory IRB, number 329, on 30 November 2022.Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingOur work was realized with the support of the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, according to the Agreement on the realization and financing of scientific research, and was partly funded by Grant 60/2022-DO-SĆF from the Slavko Ćuruvija Foundation.
ElectAnon: A Blockchain-based, Anonymous, Robust, and Scalable Ranked-choice Voting Protocol

Remote voting has become more critical in recent years, especially since the COVID-19 outbreak. Blockchain technology and its benefits such as decentralization, security, and transparency have given rise to proposals for blockchain-based voting systems. However, the traceability of blockchain transactions violates voter anonymity in existing proposals. Besides, transaction costs also need to be considered. Solutions that may cause repeated elections should be avoided for a low-cost scalable voting system. In this work, we propose ElectAnon, a blockchain-based, self-tallying, and ranked-choice voting protocol focusing on anonymity, robustness, and scalability. ElectAnon achieves anonymity by enabling voters to register with identity commitments and cast their votes via zero-knowledge proofs. Robustness is realized by removing the direct control of the authorities in the voting process by using timed-state machines. Each voter encodes the ballot into a single integer and blinds the vote off-chain while making the verification on-chain. This makes the protocol infinitely scalable in the number of voters. ElectAnon is also a solution for governance in Decentralized Autonomous Organizations (DAO): It includes a candidate proposal module and an algorithm-agnostic mechanism to plug-in different tallying methods easily. The Merkle forest extension is proposed for conducting even more trustless elections. ElectAnon is implemented with smart contracts based on Ethereum Virtual Machine (EVM) and a zero-knowledge gadget, Semaphore. The implementation also includes two different sophisticated tallying methods, Borda Count and Tideman. Experimental results show that a 40-voter and 10-candidate election can be implemented with the gas consumption reduced up to 89% compared to previous works. While other studies could not exceed a 25,000-voter setup, ElectAnon has been observed to run safely for 1,000,000 voters. The implementation can be found at https://github.com/ceyonur/electanon .
Securing health care data through blockchain enabled collaborative machine learning

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.
Machine Learning-Based Methods for Identifying Bug Severity Level from Bug Reports

Bug reports constitute an important source of information that may be utilized to remedy bugs. One aspect of bug report analysis is determining the severity level of the reported problem. Nevertheless, manually assigning severity to a large number of bug reports is time-consuming, and human analysis of the severity of software bugs may be subject to biased. Consequently, automated analysis is required for detecting the severity of software defects concealed inside bug reports. Even though there have been several attempts to tackle the problem and a number of machine learning and text mining approaches have been applied, the performance of the solutions may vary depending on the datasets employed. As a result, this is the objective of our research, in which we intend to evaluate the efficacy of many machine learning approaches (i.e. Logistic Regression, Random Forest, Support Vector Machine, and Long Short-Term Memory) for severity analysis on multiple bug report datasets (i.e. Core, Firefox, Thunderbird, and Bugzilla) from the Mozilla bug tracking system. It should be emphasized that our research is based on a binary classification scheme in which bug reports are classified into two classes: severe class and non-severe class. The experimental results revealed that the LR algorithm performed well in analyzing the severity of bug reports in the Core, Firefox, and Bugzilla datasets, whereas the LSTM approach performed well for the Thunderbird dataset.
Role of IoT in Industrial Transformation and Marketing Management

This is an extended research article discussing the role and the applications of the internet of things (IoT) in industrial transformation (ITN) in general and in marketing management (MMgnt) under five research proposals. IoT has already reached its threshold of success in all the disciplines and MMgnt has also been applying it in many of its areas for optimum growth, development, and safe working. This chapter is a case-based qualitative analysis which involves the role of IoT in three fields of MMgnt, namely customer relationship management (CRM), business process models (BPM), and product life cycle (PLC). For study, many instances from the real retail firms are compared before and after the application of IoT and presented the distinguished achievement of classification of IoT. This study proposes five IoT plans for measuring the effectiveness of IoT for the above-mentioned three sections of MMgnt that depend on the collection and sharing of data from external and internal environments.
Exploring Dependence of Human Resource Management (HRM) on Internet of Things (IoT) and Digital Marketing in the Digital Era

The field of human resource management (HRM) has been impacted by the rise of the internet of things (IoT) in recent years. IoT technology allows for the collection and analysis of data on employee behaviour, performance, and engagement. This data can be used to improve HRM processes such as recruitment, training, and retention. Additionally, IoT technology can be used to improve communication and collaboration between employees and managers. However, there are also concerns about privacy and security when using IoT technology in the workplace. Overall, HRM is becoming increasingly dependent on IoT technology to gather important data and improve processes, but it is important to consider the potential negative impacts and take steps to mitigate them. This chapter has an implication on the organisations of all sizes that may utilise the IoT and digital marketing in strengthening their HRM practices, that shall help them to attract good talent and lead to satisfaction and loyalty among employees.
Learning Dense Consistent Features for Aerial-to-Ground Structure-From-Motion

The integration of aerial and ground images is known to be effective for enhancing the quality of 3-D reconstruction in complex urban scenarios.However, directly applying the structurefrom-motion (SfM) technique for unified 3-D reconstruction with aerial and ground images is particularly difficult, due to the large differences in viewpoint, scale, and appearance between those two types of images.Previous studies mainly rely on viewpoint rectification or view rendering/synthesis to improve the feature matching quality for aligning the aerial and ground models.Nevertheless, these approaches still fail to address the inherent information differences between aerial and ground images.In this article, we propose a learning-based matching framework for direct SfM with ground and aerial images.The key idea of our method is to learn the pixel-wise consistent features between aerial and ground images to handle the large heterogeneity of these two types of images.Specifically, we deploy a learning-based matching framework to robustly correspond the aerial and ground images.With the high-quality feature matching, learned feature maps are used for refining keypoint locations and fusing featuremetric error into bundle adjustment with the consideration of geometric error, both of which can further improve the accuracy and completeness of the recovered 3-D scene.Extensive experiments conducted on six datasets demonstrate that the proposed method can reconstruct high-fidelity 3-D models with direct aerial-to-ground SfM, which cannot be achieved by existing methods.In addition, our method also shows outstanding performance in subtasks of feature matching and point cloud recovery.
A blockchain-based privacy-preserving and collusion-resistant scheme (PPCR) for double auctions

Electronic auctions (e-auctions) remove the physical limitations of traditional auctions and bring this mechanism to the general public. However, most e-auction schemes involve a trusted auctioneer, which is not always credible in practice. Some studies have applied cryptography tools to solve this problem by distributing trust, but they ignore the existence of collusion. In this paper, a blockchain-based Privacy-Preserving and Collusion-Resistant scheme (PPCR) for double auctions is proposed by employing both cryptography and blockchain technology, which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy. A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance. A Dispute Resolution agreement (DR) is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct. In addition, a Concise Dispute Resolution protocol (CDR) is designed to handle situations where the number of accused winners is small, significantly reducing the computation cost of dispute resolution. Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.
Would US citizens accept cybersecurity deresponsibilization? Perhaps not

Responsibilizing governments provide advice about how to manage a variety of risks. If citizens do not heed the advice and things go wrong, they are expected to accept the adverse consequences without complaint. However, in some cases, citizens are unable or unwilling to embrace these government-assigned responsibilities and to act on the advice, for a variety of valid reasons. It may be appropriate for governments to provide more direct support: in essence, deresponsibilizing citizens who struggle to embrace the responsibility. In this paper, we explore whether US citizens would be willing to accept more help from their government in the cyber realm. Using two studies, we find that perceptions related to the government's competence and benevolence are necessary pre-requisites for a willingness to be deresponsibilized, and also that many respondents did not have confidence that either of these were sufficient. This deficiency might well render governments’ well-intended deresponsibilization endeavours futile. We conclude by proposing deresponsibilization strategies that acknowledge and accommodate this.
Semantic modeling of cell damage prediction: a machine learning approach at human-level performance in dermatology

Machine learning is transforming the field of histopathology. Especially in classification related tasks, there have been many successful applications of deep learning already. Yet, in tasks that rely on regression and many niche applications, the domain lacks cohesive procedures that are adapted to the learning processes of neural networks. In this work, we investigate cell damage in whole slide images of the epidermis. A common way for pathologists to annotate a score, characterizing the degree of damage for these samples, is the ratio between healthy and unhealthy nuclei. The annotation procedure of these scores, however, is expensive and prone to be noisy among pathologists. We propose a new measure of damage, that is the total area of damage, relative to the total area of the epidermis. In this work, we present results of regression and segmentation models, predicting both scores on a curated and public dataset. We have acquired the dataset in collaborative efforts with medical professionals. Our study resulted in a comprehensive evaluation of the proposed damage metrics in the epidermis, with recommendations, emphasizing practical relevance for real world applications.
Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases

High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.
Fast Substructure Search in Combinatorial Library Spaces

We present an efficient algorithm for substructure search in combinatorial libraries defined by synthons, i.e., substructures with connection points. Our method improves on existing approaches by introducing powerful heuristics and fast fingerprint screening to quickly eliminate branches of nonmatching combinations of synthons. With this, we achieve typical response times of a few seconds on a standard desktop computer for searches in large combinatorial libraries like the Enamine REAL Space. We published the Java source as part of the OpenChemLib under the BSD license, and we implemented tools to enable substructure search in custom combinatorial libraries.
On the Implicit Bias in Deep-Learning Algorithms

Examining the implicit bias in training neural networks using gradient-based methods.