Reviewerly/exHarmony huggingface dataset:
A transformative shift toward blockchain‐based <scp>IoT</scp> environments: Consensus, smart contracts, and future directions

Abstract Recently, Internet‐of‐Things (IoT) based applications have shifted from centralized infrastructures to decentralized ecosystems, owing to user data's security and privacy limitations. The shift has opened new doors for intruders to launch distributed attacks in diverse IoT scenarios that jeopardize the application environments. Moreover, as heterogeneous and autonomous networks communicate, the attacks intensify, which justifies the requirement of trust as a key policy. Recently, blockchain‐based IoT solutions have been proposed that address trust limitations by maintaining data consistency, immutability, and chronology in IoT environments. However, IoT ecosystems are resource‐constrained and have low bandwidth and finite computing power of sensor nodes. Thus, the inclusion of blockchain requires an effective policy design regarding consensus and smart contract environments in heterogeneous IoT applications. Recent studies have presented blockchain as a potential solution in IoT, but an effective view of consensus and smart contract design to meet the end application requirements is an open problem. Motivated by the same, the survey presents the integration of suitable low‐powered consensus protocols and smart contract design to assess and validate the blockchain‐IoT ecosystems. We present blockchain‐IoT's emerging communication and security aspects with performance issues of consensus protocols, interoperability, and implementation platforms. A case study of a smart contract‐based blockchain‐driven ecosystem is presented with a comparative analysis of mining cost and latency, which shows its suitability in real‐world setups. We also highlight attacks on blockchain IoT, open issues, potential findings, and future directions. The survey intends to drive novel solutions for future consensus and safe, smart contract designs to support applicative IoT ecosystems.
Knowledge representation and acquisition for ethical AI: challenges and opportunities

Abstract Machine learning (ML) techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit-risk analysis, and insurance pricing. Likewise, in the physical world, ML models are critical components in autonomous agents such as robotic surgeons and self-driving cars. Among the many ethical dimensions that arise in the use of ML technology in such applications, analyzing morally permissible actions is both immediate and profound. For example, there is the potential for learned algorithms to become biased against certain groups. More generally, in so much that the decisions of ML models impact society, both virtually (e.g., denying a loan) and physically (e.g., driving into a pedestrian), notions of accountability, blame and responsibility need to be carefully considered. In this article, we advocate for a two-pronged approach ethical decision-making enabled using rich models of autonomous agency: on the one hand, we need to draw on philosophical notions of such as beliefs, causes, effects and intentions, and look to formalise them, as attempted by the knowledge representation community, but on the other, from a computational perspective, such theories need to also address the problems of tractable reasoning and (probabilistic) knowledge acquisition. As a concrete instance of this tradeoff, we report on a few preliminary results that apply (propositional) tractable probabilistic models to problems in fair ML and automated reasoning of moral principles. Such models are compilation targets for certain types of knowledge representation languages, and can effectively reason in service some computational tasks. They can also be learned from data. Concretely, current evidence suggests that they are attractive structures for jointly addressing three fundamental challenges: reasoning about possible worlds + tractable computation + knowledge acquisition. Thus, these seems like a good starting point for modelling reasoning robots as part of the larger ecosystem where accountability and responsibility is understood more broadly.
ProtoGAN: Towards high diversity and fidelity image synthesis under limited data

Recent years have witnessed tremendous progress with regard to few-shot learning methods, especially for classification and segmentation tasks. However, the challenging few-shot image generation has garnered less attention. Training GANs with limited data is a formidable task since discriminator overfitting and memorizing degrade synthesis quality. Existing approaches endeavor to expand the training set with data augmentation techniques. In this paper, we propose ProtoGAN, a GAN that incorporates the metric-learning-based prototype mechanism into adversarial learning. Concretely, we align the prototypes and features of real and generated images in the embedding space, facilitating the fidelity of the generated images. We include a variance term in the objective function in order to promote the synthesis diversity of the generative network. As an intuitive yet effective method, ProtoGAN is easy to implement and is complementary to existing methods. We perform extensive experiments on nineteen datasets spanning a variety of domains and resolutions. Both quantitative and qualitative results demonstrate the effectiveness of our proposed method for high-quality image synthesis under limited data. Our code is available at https://github.com/kobeshegu/ProtoGAN.
An improved differential evolution algorithm based on basis vector type and its application in fringe projection 3D imaging

According to the different generation methods, the types of candidate position (basis vector) in the meta-heuristic algorithm can be divided into stochastic, deterministic, and probabilistic. The stochastic type is beneficial to the preservation of population diversity, deterministic model can accelerate convergence, and probabilistic style emphasizes the balance between population diversity and convergence speed. In order to make full use of various characteristics, an improved differential evolution algorithm based on basis vector type (DEBVT) is proposed. On the one hand, in the process of mutation, individuals in the population choose the appropriate basis vector form for evolution in light of their features and needs. On the other hand, the control parameters are adaptively adjusted for the purpose of balancing exploration and exploitation. Based on twenty-nine benchmark functions, DEBVT is compared with several meta-heuristics and differential evolution variants with different types of basis vectors. All experimental results demonstrate that compared with other competitive algorithms, the optimization performance of the proposed DEBVT is remarkable. In addition, an improved UNET network (IUnet) is proposed for absolute phase extraction in fringe projection 3D imaging technology, and the activation function configuration of the network is optimized by DEBVT to construct the IUnet-DEBVT network, which further reduces the error of absolute phase and verifies of the effectiveness of DEBVT.
Applying regression techniques to determine mathematical equations of exergy, electricity, and energy values of photovoltaic thermal collector

The highlight of this study is to produce mathematical equations that can calculate the electricity, energy, and exergy efficiency values of an air-type photovoltaic-thermal (PV-T) solar collector with the help of a regression-based artificial intelligence method. Contrary to classical predictive artificial intelligence models, the Elastic.Net regression method, which can produce a linear equation for the modeled parameter, was used. Six different data sets were obtained, with the trials carried out in two months for the PV-T efficiency values. The Elastic Net algorithm produced energy, electrical, and exergy efficiency equations with the first, third, and fifth datasets. Comparisons were made between experimental thermal efficiency ratings and the output of efficiency formulas. The other three data sets were then subjected to efficiency equations, and the results were contrasted with the experimental values. The thermal efficiency equations predicted the experimental data in the data sets 1, 3, 5 and, 2, 4, 6 with mean MAPEs of 2.9% and 4.16%, respectively. The experimental values in both group data sets have been said to be comparable to the efficiency values discovered using the equations. As a result, it is anticipated that the efficiency equations developed can be used in various experimental configurations or flat solar collectors. Additionally, prediction models were created utilizing datasets 1, 3, and 5 using the artificial neural network (ANN) technique. The best ANN network structure was obtained using different network types, training algorithms, transfer functions, and layer-neuron numbers in ANN models.
Digital infrastructural development and inclusive growth in Sub-Saharan Africa

The study assesses the effect of digital infrastructural development on inclusive growth in 44 Sub-Saharan African countries from 2000 to 2020. The study adopts the Driscoll-Kraay strategy to address cross-sectional dependence and the Newey-West standard errors to address errors-related problems. The study adopted the four indicators of digital infrastructures and their component score to determine their effect on inclusive growth which ensures equitable distribution of resources in an economy. The findings of the study reveal that inclusive growth in Sub-Saharan Africa is enhanced by the number of individuals using the internet, the number of fixed broadband subscribers, fixed telephone and mobile cellular subscriptions per 100 adults. The findings further reveal that digital infrastructures enhance the level of inclusive growth in Sub-Saharan African economies irrespective of whether the countries belong to the lower-, middle- and upper-income groups. The study recommends policymakers augment their investments in digital infrastructure and human capital to raise the level of inclusive growth.
Rapid offline detection and 3D annotation of assembly elements in the augmented assembly

As an auxiliary assembly guidance method, the augmented assembly has been gradually applied in modern manufacturing. However, with current product assembly developing toward customization and diversification, static augmented assembly guidance instructions that mainly depend on position measurement are hard to meet the needs of complex assembly cases. The position of assembly elements such as tools and essential parts may change frequently. This paper proposes a novel approach for detecting and annotating assembly elements using AR mobile devices in an offline environment to solve the problem. An improved deep learning object detection network YOLOv4-Lite is proposed. It achieves further lightweight while improving detection accuracy than YOLOv4-Tiny, running well in offline AR mobile devices with limited computing power. A spatial 3D annotation algorithm is presented to calculate the location of the center of the target object and generate markers for annotation. To verify the feasibility of the proposed approach, an offline detection system for assembly elements named ARODAS (augmented reality offline detection and annotation system) is developed and deployed into HoloLens2. Experimental results show that the proposed approach can complete detection and annotation with reasonable accuracy, assist the operator in detecting key assembly elements, and has a significant advantage in operation speed. To verify the usability and usefulness of the ARODAS, we performed quantitative and qualitative analyses by conducting a user study: searching for several kinds of small parts in a real environment. Compared with the traditional marker-based AR system, the ARODAS system shows a better overall performance in the user test.
A novel cluster validity index based on augmented non-shared nearest neighbors

The true cluster number of the dataset in practical applications is rarely known in advance. Therefore, it is necessary to use a cluster validity index to evaluate the clustering results and determine the optimal cluster number. However, the performance of existing cluster validity indices is vulnerable to various factors such as cluster shape and density. To solve the above issues, this paper proposes a new cluster validity index based on augmented non-shared nearest neighbors (ANCV). The ANCV index is based on the following principles: (1) Within-cluster compactness can be measured by the distance between the pairs of data points with fewer shared nearest neighbors. (2) The distances between the pairs of data points at the intersection of clusters can be used to estimate the between-cluster separation. On this basis, the above point pairs are further extended to their augmented non-shared nearest neighbors, thereby forming small clusters. Then, the average distance within and between these clusters is calculated respectively to estimate the within-cluster compactness and between-cluster separation. Finally, the optimal number of clusters is determined by the difference between the between-cluster separation and the within-cluster compactness. Experimental results on both 12 two-dimensional synthetic datasets and 10 real datasets from UCI have shown that the ANCV index performs the best among all compared indices.
A Measurement-Derived Functional Model for the Interaction Between Congestion Control and QoE in Video Conferencing

Video Conferencing Applications (VCAs) that support remote work and education have increased in use over the last two years, contributing to Internet bandwidth usage. VCA clients transmit video and audio to each other in peer-to-peer mode or through a bridge known as a Selective Forwarding Unit (SFU). Popular VCAs implement congestion control in the application layer over UDP and accomplish rate adjustment through video rate control, ultimately affecting end user Quality of Experience (QoE). Researchers have reported on the throughput and video metric performance of specific VCAs using structured experiments. Yet prior work rarely examines the interaction between congestion control mechanisms and rate adjustment techniques that produces the observed throughput and QoE metrics. Understanding this interaction at a functional level paves the way to explain observed performance, to pinpoint commonalities and key functional differences across VCAs, and to contemplate opportunities for innovation. To that end, we first design and conduct detailed measurements of three VCAs (WebRTC/Jitsi, Zoom, BlueJeans) to develop understanding of their congestion and video rate control mechanisms. We then use the measurement results to derive our functional models for the VCA client and SFU. Our models reveal the complexity of these systems and demonstrate how, despite some uniformity in function deployment, there is significant variability among the VCAs in the implementation of these functions.
Platform governance and sociological participation

The positive and negative effects of the participation of digital platform companies in governance are a major issue of the times. Starting from the three subfields of internal governance, external governance, and co-governance, we construct an analytical framework for digital platform governance to understand the four-sided relationship among digital platform companies, the state, the market, and society. From the perspective of enterprise autonomy, we should view the effectiveness of platform company participation in social governance dialectically. The key of promoting good platform governance is to improve the external structural pressure from the state, promote the reform of the endogenous governance of enterprises, and build a sustainable architecture for co-governance. The mission of sociologists includes not only explanation but also intervention.
Proactive Contact Tracing

The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as "pingdemic," may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users' infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning

The Industrial Internet of Things (IIoT) revolution has emerged as a promising network that enhanced information dissemination about the city's resources. This city's resources are wirelessly connected to different constrained devices (such as sensors, robotics, and actuators). However, the communication of this wireless information is threatened by several malicious attacks, cyber-attacks, and hackers. This is due to unsecured IIoT networks that were exposed as a potential back door entry point for the attacks. Consequently, this study aims to develop a security framework for the smart cities’ sustainability edge computing vulnerabilities using Petri Net and Genetic Algorithm-Based Reinforcement Learning (GARL). First, a common trust model for addressing information outflows in the network using a distributed authorization algorithm is proposed. This algorithm is implemented on a secure framework modeling in Petri Net called secure trust-aware philosopher privacy and authentication (STAPPA) for mitigation of the privacy breach in the networks. Genetic Algorithm-based Reinforcement Learning (GARL) is used to optimize the search, detect anomalies, and shortest route during the agent learning in the environment. The detection and accuracy rate results obtained over a secure framework using reinforcement learning are 98.75, 99, 99.50, 99.75, and 100% during simulation in the network environment. The average sensitivity of the detection rate is 1.000, while the average specificity outcome is 0.868. The result of the GARL simulation model obtained shows the best distance of 238.84 * 10−3 fitness when the search space is optimized by reducing the number of chromosomes to 10 in the model. These approaches help to detect anomalies and prevent unauthorized users from accessing edge computing components in the city architecture.
Data augmentation to improve syndromic detection from emergency department notes

Using natural language processing algorithms requires a large amount of annotated data, which is difficult to obtain in clinical settings. In this work we aimed to develop and evaluate data augmentation methods to enhance the performance of classification tasks implemented in a medical domain. Three text augmentation methods were evaluated for generating febrile convulsion-like emergency department (ED) presentations. These were synthetic text generation, domain-specific data augmentation, and task-agnostic data augmentation techniques. We used a transformer-based classifier, first establishing a baseline by training with non-augmented data, then training on each set of augmented data to compare the results with the baseline. The synthetic text generation augmentation method was clearly superior, with the f-score improving by 12% compared with the baseline. The domain-specific approach improved the score by 6%, while the task-agnostic augmentation method failed to enhance classification performance. Our study shows that data augmentation methods can be used for generating artificial ED data to enhance classification of critical syndromes, thereby assisting syndromic surveillance to contribute to improving public health.
Feature enhancement and supervised contrastive learning for image splicing forgery detection

Image forgery detection remains a challenging task due to the variation in the scale of the tampered areas and the forensic clues that are obscured by various post-processing operations. Most existing deep learning methods rely only on single-scale high-level features and ignore the correlations of the pixels in intra-image and inter-image. Due to this limitation, previous methods are considered unsuitable for multi-scale splicing forgery detection. To fill this gap, we propose a novel model for improving multi-scale splicing forged regions localization by utilizing multi-level multi-scale feature enhancement and pixel-level supervised contrastive learning. First, we introduce a multi-level multi-scale feature enhancement module (MFEM) to integrate the multi-level information and capture the multi-scale global contextual representation by embedding an improved atrous spatial pyramid pooling (ASPP) mechanism into the non-local module. It strengthens the capability of the model to sense multi-scale tampered regions. Second, the pixel-level supervised contrastive learning mechanism is designed to separately cluster the pixel representations of tampered and real regions within and across images. It improves intra-class compactness and inter-class separability of the pixel embedding space significantly and enhances feature expression capabilities. Third, we design a multi-loss progressive learning (MPL) strategy to integrate the complementary advantages of multi-loss functions to optimize the scale and position parameters of the tampered regions during the training process. Extensive experiments have shown that the proposed model outperforms state-of-the-art methods. It can effectively detect and segment multi-scale tampered regions, even for noisy and JPEG-compressed images.