Reviewerly/exHarmony huggingface dataset:
An Approach to Generating Diverse Personas for Children and the Elderly for Software Development

Children and elderly users typically interact very differently with the same software interfaces. These should be taken into consideration while designing and developing software that will be used by these users. In order to better understand the characteristics of children and elderly users of software, we systematically reviewed the relevant literature. We identified different facets of children and the elderly that are relevant to their interaction with the software. The facets for children are- Special needs children, Interaction with technology and General characteristics. Facets for elderly users are- Technical proficiency, Interaction with technology, Technology expectations and General characteristics. We developed a tool with different descriptions of those facets collected from our review. We collected feedback on the tool from experts and refined the tool accordingly. The personas generated using the tool can be used as is or can be extended by adding more context-specific information to the personas. The tool will help designers and developers of software interfaces to better understand the requirements of these user groups and to incorporate their specific needs during design and development. This will result in reducing biases arising from age-related facets and help to create more inclusive software.
Privacy Protection Federated Learning Framework Based on Blockchain and Committee Consensus in IoT Devices

With the staggering growth of data on modern IoT devices, privacy concerns on IoT networks are becoming increasingly prominent. Thus, federated learning emerged. It trains the model by enabling multiple participants to employ data from multiple parties, making data accessible but not visible. However, user data may encounter issues with data privacy leakage in the existing federated learning environment. Thus, this article suggests BFLPP, a privacy protection federated learning framework based on blockchain and committee consensus in IoT devices. It leverages blockchain to verify local updates, rather than a centralized server and uses it to generate and store global models. It also implements local differential privacy to further secure data privacy. In this paper, we use committee nodes to validate the model parameters, and if we receive enough replies during the validation process. It will submit and upload the validated updates to the update block, then trigger the smart contract to aggregate updates and broadcast it to the nodes for the next training round. Eventually, this paper employs the BFLPP framework on convolutional neural networks to conduct experiments on the MNIST dataset, grounded on blockchain and federated learning. The experimental results indicate the security and effectiveness of the BFLPP framework.
Discrepancy Scaling for Fast Unsupervised Anomaly Localization

Computer vision systems can automatically find and segment anomalies in images even without ever seeing anomalous observations during training. Many methods for such unsupervised anomaly detection (AD) and localization (AL) tasks have been introduced in recent years, but the most accurate methods tend to be computationally heavy. In this paper, we propose Discrepancy Scaling, a method that significantly improves the accuracy of a very fast AD and AL approach called Student-Teacher Feature Pyramid Matching. We show that with Discrepancy Scaling, even a small, mobile-friendly convolutional neural network can perform well on AD and AL tasks.
Machine Excellence Tradeoffs to Ethical and Legal Perspectives

We appreciate well-functioning technology being able to also personalize its services.However, to protect privacy and avoid a potential misuse of personal data, we are encouraged to limit the amount of personal data we share through apps and Internet services.While some services do not really need all the data they ask us to provide, others depend on it to provide the best possible performance of its service.That regards systems that apply data in machine learning for tasks like medical diagnostics.Especially deep learning algorithms perform better by using a large amount of data and are now able to benefit from the large amount as well with limited training time given access to high-performance computing resources.This paper address and discuss the tradeoffs like the one we have between data sharing minimalization for increased privacy and data maximization for machine learning systems.Perspectives related to ethics, legal, and social issues are considered in the paper.There is no single conclusion on the challenge, but attention to it can increase the awareness that the best balance differs depending on the application addressed.
Topology Construction with Minimum Total Time for Geo-Distributed Decentralized Federated Learning

Decentralized federated learning (DFL) enables multiple clients to collaboratively train a machine learning model by exchanging parameters with each other while protecting privacy. Clients may be deployed in geo-distributed sites and communicate through varying significantly wide area networks (WANs). After the topology is constructed, the clients iteratively update the local model and exchange it with neighboring clients until the required accuracy is achieved. A more connected topology usually has fewer iterations but may suffer from a longer time per iteration. How to construct the topology with minimum total time by making a trade-off between the number of iterations and the time per iteration is a crucial issue to be studied. In this paper, we evaluate the number of iterations and the time per iteration of arbitrary topology and then propose a heuristic algorithm to construct the best one by evaluating a series of possible topologies. Our extensive experiments have shown that the other common topologies require 1.88 × to 4.53 × the total time to achieve the same accuracy compared to the topology obtained by our algorithm.
An Analysis of Extended and Dilated Filters in Sharpening Algorithms

Digital imaging is omnipresent nowadays due to the expansion of the mobile device industry and its use cases. However, the diversity of sensors and hardware limitations often lead to images not meeting desired quality standards. To counteract this, significant research has been invested in image de-noising and sharpening to enhance the quality of captured images, making image sharpening an integral step in professional image processing. Nonetheless, certain challenges persist, including the frequent occurrence of over-enhancement in parts of the image, giving rise to artefacts such as "jaggies" or jagged edges, and a "halo" effect. Recognizing the successful outcomes achieved when dilated or extended filters are employed in other image processing tasks, like edge detection, our study is set to investigate a variety of sharpening algorithms using these filter extensions. A comprehensive evaluation was conducted on synthetic and natural images using a variety of image quality metrics, like PSNR or BRISQUE. In most cases, dilated kernels outperformed extended kernels, suggesting their potential superiority in image sharpening tasks while minimizing the introduction of undesired artefacts. However, the choice between dilated and extended kernels would ultimately depend on the specific application and algorithm used.
Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.
Photographic Tone Reproduction for Digital Images

A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.
Secure and efficient blockchain-based consensus scheme for MWSNs with clustered architecture

Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.
EnvText: A Chinese text mining tool for environmental domain with advanced BERT model

EnvText is a user-friendly Natural Language Processing (NLP) software package designed to analyze and mine Chinese text in environmental domain for NLP beginners. EnvText integrates essential NLP tools, such as domain-specific word lists, entity lists, corpus, pre-trained models and so on. It provides an excellent interface for neural network models, such as Bidirectional Encoder Representations from Transformers (BERT) and Recurrent Neural Networks (RNNs), which can automatically carry out functions like text classification, entity extraction, relationship extraction, and etc. Users can use multiple pre-trained models in EnvText or simply train their own model and perform text analysis.
Contrastively enforcing distinctiveness for multi-label image classification

Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of contrastive learning in single-label classifications motivates us to leverage this learning framework to enhance distinctiveness for better performance in multi-label image classification. In this paper, we show that a direct application of contrastive learning can hardly improve in multi-label cases. Accordingly, we propose a novel framework for multi-label classification with contrastive learning in a fully supervised setting, which learns multiple representations of an image under the context of different labels. This introduces a simple yet intuitive adaption of contrastive learning into our model to boost its performance in multi-label image classification. Extensive experiments on four benchmark datasets show that the proposed framework achieves state-of-the-art performance in the comparison with the advanced methods in multi-label classification.
Edge computing-based Generative Adversarial Network for photo design style transfer using conditional entropy distance

In recent years, cell phone photography systems have made significant advancements due to their widespread adoption. However, processing the large amount of data generated by these systems on the cloud often leads to high network latency and bandwidth requirements, limiting computational efficiency. To address this challenge, this study introduces a novel design style transfer method for generating high-quality stylized photos in cell phone photography systems using Generative Adversarial Networks (GANs) with an edge computing-based approach. The proposed method aims to enhance the visual appeal of cell phone images by mimicking the artistic style of paintings through abstraction and emphasis on key features. To achieve this objective, a conditional GAN is trained on meticulously labeled data pairs that identify crucial regions in images. This approach offers an efficient solution for computationally intensive tasks by utilizing inference on edge computing nodes. This effectively reduces network latency and bandwidth requirements while improving computational efficiency. Additionally, a penalty is introduced using conditional entropy distance to ensure consistent entropy levels across different photo samples, resulting in higher quality generated photos. Experimental results demonstrate that the proposed CEDGAN model achieves a detection accuracy ranging from 95% to 98%, with consistent results, thus laying a solid foundation for effective photo enhancement. When collecting important portions of materials, CEDGAN maintains a detection accuracy of around 90% to 95% without any significant difference in overall performance. As a result, this method opens up new possibilities for cell phone photography enthusiasts who desire to effortlessly create artistic and stylized photos using edge computing-based GANs. It offers a practical solution for combining technological innovation with creativity in photography.
Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security

With the rapid development of big data, artificial intelligence, and Internet technologies, human–human contact and human–machine interaction have led to an explosion of voice data. Rapidly identifying the speaker’s identity and retrieving and managing their speech data among the massive amount of speech data have become major challenges for intelligent speech applications in the field of information security. This research proposes a vocal recognition technique based on information adversarial training for speaker identity recognition in massive audio and video data, as well as speaker identification when oriented to the information security domain. The experimental results show that the method projects data from different scene channels all onto the same space and dynamically generates interactive speaker representations. It solves the channel mismatch problem and effectively improves the recognition of the speaker’s voice patterns across channels and scenes. It is able to separate overlapping voices when multiple people speak at the same time and reduce speaker separation errors. It realizes speaker voice recognition for the information security field and achieves a recall rate of 89% in a large database, which is of practical value for the intelligent application field.
Generative Adversarial Shaders for Real‐Time Realism Enhancement

Abstract Application of realism enhancement methods, particularly in real‐time and resource‐constrained settings, has been frustrated by the expense of existing methods. These achieve high quality results only at the cost of long runtimes and high bandwidth, memory, and power requirements. We present an efficient alternative: a high‐performance, generative shader‐based approach that adapts machine learning techniques to real‐time applications, even in resource‐constrained settings such as embedded and mobile GPUs. The proposed learnable shader pipeline comprises differentiable functions that can be trained in an end‐to‐end manner using an adversarial objective, allowing for faithful reproduction of the appearance of a target image set without manual tuning. The shader pipeline is optimized for highly efficient execution on the target device, providing temporally stable, faster‐than‐real time results with quality competitive with many neural network‐based methods.
The praxis of radical placemaking

Emerging from the social disparities of the COVID-19 pandemic and contestations over marginal bodies in space during the global Black Lives Matter movement, Radical Placemaking is proposed as a digital placemaking design practice and investigated as part of a 3-year design study. This practice involves marginalized bodies highlighting social issues through the ephemerality and spectacularity of digital technologies in public space in [smart] cities. Radical Placemaking methodology, as demonstrated through three design interventions, engages participatory action research, slow design, and open pedagogies for marginal bodies to create place-based digital artifacts. Through the making and experience of the artifacts, Radical Placemaking advances and simulates a virtual manifestation of the marginal beings' bodies and knowledge in public spaces, made possible through emerging technologies. Through nine key strategies, the paper offers a conceptual framework that imbibes a relational way of co-designing within the triad of people-place-technology.
Class Incremental Learning based on Identically Distributed Parallel One-Class Classifiers

Class incremental learning requires models to learn new-class knowledge without forgetting old-class information. As a natural solution, the parallel one-class framework (POC) has attracted extensive attention. However, POC is prone to suffer the problem of lacking comparability between classifiers due to their inconsistent output distributions. To address this drawback, we propose an incremental learning method based on Identically Distributed Parallel One-class Classifiers (IDPOC). The core of IDPOC is a novel one-class classifier with Gaussian distributed output, referred to as Deep-SVD2D. Deep-SVD2D encourages the distribution of sample representations to follow the standard multivariate Gaussian. Consequently, the distance between the representation and its class center will approximately follow a chi-square distribution with some freedom degree. IDPOC further eliminates the freedom degree to ensure the output of all classifiers to follow an identical distribution, thus enhancing the comparability between different classifiers. We evaluate IDPOC on four popular benchmarks: MNIST, CIFAR10, CIFAR100, and Tiny-ImageNet. The experimental results show that IDPOC achieves state-of-the-art performance, e.g., it outperforms the best baseline by 1.6% and 2.8% on two large-scale benchmarks of CIFAR100 and Tiny-ImageNet, respectively 1.
User Evaluation of Conversational Agents for Aerospace Domain

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DDG: Dependency-difference gait based on emotional information attention for perceiving emotions from gait

Perceiving human emotions is crucial in the realm of affective computing. As a nonverbal biological feature, gait plays a significant role in this field, owing to its resistance to manipulation or replication. In this paper, we propose a gait-based emotion perception framework called Dependency-Difference Gait (DDG), which can extract emotional features from gait patterns comprehensively and efficiently. We also introduce a method of spatial–temporal difference representation, which constructs the static spatial difference information within frames and dynamic temporal difference information between frames. We abstract these details as difference information and fuse them with the dependency information extracted from the original sequence. Our approach not only breaks the limitations of hand-crafted features, but also enables the extraction of a broader spectrum of emotional features. Additionally, we present the Emotional Information Attention (EIA) mechanism, allowing DDG to focus on key joints and frames based on the quantity of emotional information. Experimental and visualization results substantiate the effectiveness of the DDG and EIA. In the quality analysis, we find that selecting a few number of joints with a substantial amount of emotional information is beneficial for emotion classification. However, selecting a few frames can disrupt the temporal structure of the sequence, resulting in suboptimal performance.
Abstract interpretation, Hoare logic, and incorrectness logic for quantum programs

Abstract interpretation, Hoare logic, and incorrectness (or reverse Hoare) logic are powerful techniques for static analysis of computer programs. They have all been successfully extended to the quantum setting, but developed largely in parallel. This paper explores the relationship between these techniques in the context of verifying quantum while-programs, where the abstract domain and the set of assertions for quantum states are well-structured. We show that any complete quantum abstract interpretation induces a quantum Hoare logic and a quantum incorrectness logic, both being sound and relatively complete. Moreover, the induced logic systems operate in a forward manner, making them more suitable for certain applications. Conversely, any sound and relatively complete quantum Hoare logic or incorrectness logic induces a complete quantum abstract interpretation. As an application, we show the non-existence of any sound and relatively complete quantum Hoare logic or incorrectness logic if tuples of local subspaces are taken as assertions.