Reviewerly/exHarmony huggingface dataset:
"Explorers of Unknown Planets"

Alongside recent advances in artificial intelligence (AI), a new art practice has emerged in recent years that borrows and transforms these advances in the production of artworks. The actors of this emergent practice are coming from contemporary art, media and digital arts. These artists have developed an original practice of AI within their creative field. In this article, we propose a qualitative study to explore the nature of this practice. We interviewed five internationally renowned artists about how AI is integrated into their work. Through a thematic analysis of the interviews, we first find that their practice relies on crafting algorithms and data as materials. We uncover how they explicitly use this material unpredictability rather than avoid it. Secondly, we highlight the politics of their practice that consist of resisting the culture of AI research, as well as its inherent power dynamics. We also highlight how their relationship with the technology is imbued with ethics and how they rethink their role with respect to the technology. In this paper, we aim to provide the CSCW community with a way to expand the framework in which AI can be understood not only as a tool but also as cultural and political design material.
Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment

While Deep Neural Networks (DNNs) are deriving the major innovations through their powerful automation, we are also witnessing the peril behind automation as a form of bias, such as automated racism, gender bias, and adversarial bias. As the societal impact of DNNs grows, finding an effective way to steer DNNs to align their behavior with the human mental model has become indispensable in realizing fair and accountable models. While establishing the way to adjust DNNs to "think like humans'' is in pressing need, there have been few approaches aiming to capture how "humans would think'' when DNNs introduce biased reasoning in seeing a new instance. We propose Interactive Attention Alignment (IAA), a framework that uses the methods for visualizing model attention, such as saliency maps, as an interactive medium that humans can leverage to unveil the cases of DNN's biased reasoning and directly adjust the attention. To realize more effective human-steerable DNNs than state-of-the-art, IAA introduces two novel devices. First, IAA uses Reasonability Matrix to systematically identify and adjust the cases of biased attention. Second, IAA applies GRADIA, a computational pipeline designed for effectively applying the adjusted attention to jointly maximize attention quality and prediction accuracy. We evaluated Reasonability Matrix in Study 1 and GRADIA in Study 2 in the gender classification problem. In Study 1, we found applying Reasonability Matrix in bias detection can significantly improve the perceived quality of model attention from human eyes than not applying Reasonability Matrix. In Study 2, we found using GRADIA significantly improves (1) the human-assessed perceived quality of model attention and (2) model performance in scenarios where the training samples are limited. Based on our observation in the two studies, we present implications for future design in the problem space of social computing and interactive data annotation toward achieving a human-centered steerable AI.
Heterogeneity-aware fair federated learning

The fairness of federated learning means that the global model cannot discriminate against any group. Due data heterogeneity, the update direction of some clients will hinder other clients, so the global model is difficult to treat each user fairly. The current fair federated learning methods usually use the variance of model performance to measure the fairness, which can not quantify the fairness of federated learning process; And, they all intervene from the beginning, which will reduce the convergence speed and model performance; Moreover, the weights they applied to clients are designed based on experience, so it is difficult to trade-off between model performance and fairness. To address the above problems, firstly, we use Gini coefficient to quantify the fairness of federated learning, which can reflect the fairness of federated learning process in each round. Secondly, we divide federated learning into two different training stages: label fitting and data fitting, and propose fairness intervention in the data fitting stage. Thirdly, we propose a fairness federated optimization algorithm, which introduces fairness penalty term into the objective function, and obtains the relationship between clients' gradients and fairness through gradient descent. Experimental results show the effectiveness and fairness of the proposed method.
A novel feature integration and entity boundary detection for named entity recognition in cybersecurity

Owing to continuous cyberattacks, a large amount of threat intelligence is generated online every day. However, threat intelligence is mostly unstructured and multisource heterogeneous text. It is difficult for security analysts to understand the implicit threat in time. Knowledge Graph (KG) is an important research topic in recent years, which can perform automated and real-time analysis of threat intelligence in cybersecurity. As one of the critical technologies of KG, named entity recognition (NER) can identify cyberattack-related entities. It has been proved that long-distance structured information captured by dependency trees provides a rich semantic expression for the neural network. However, existing research works are more focused on the simple linear stack of neural networks when utilizing structured features. The interaction between different types of neural networks is vague. In addition, the existing models are insensitive to the boundaries of complex entity terms in cybersecurity. In this study, we propose a new feature integration and entity boundary detection (FIEBD) model. In our model, a new pretrained language model, PERT, is applied to obtain word embedding of cyber texts. Moreover, a novel neural network cell, namely GARU, is developed to incorporate different types of features extracted from graph neural networks and recurrent neural networks. It combines the graph encoder with the gate mechanism, aiming to obtain better hidden representation by explicit interaction. Furthermore, considering a large number of complex entities in cybersecurity, we contribute an entity boundary detection module to perform entity head and tail prediction as an augmentation task. We conduct extensive experiments on cybersecurity datasets. The results demonstrate that the proposed model achieves better performance than existing baseline methods.
No More “Solutionism” or “Saviourism” in Futuring African HCI: A Manyfesto

Research in HCI4D has continuously advanced a narrative of “lacks” and “gaps” of the African perspective in technoscience. In response to such misguided assumptions, this article attempts to reformulate the common and perhaps unfortunate thinking about African practices of design in HCI4D – i.e., largely as a function of African societal predicaments and Western technocratic resolutions. Through critical reflection on a range of issues associated with post-colonialism and post-development, I examine the possibilities that various historical tropes might offer to the reinvention of the African perspective on innovation. This leads to the consideration of how engaging in critical discussions about the future dimensions of African HCI can allow for grappling with the effect of the coloniality of being, power, and knowledge. Developing on the ideas of futuring as a way of dealing with the complexities of the present—in this case, the coloniality of the imagination—the article ends by discussing three tactical propositions for “remembering” future identities of African innovation where the values of autonomy are known and acted upon.
Unsupervised Feature Selection via Neural Networks and Self-Expression with Adaptive Graph Constraint

Unsupervised feature selection (UFS), which selects the most important feature subset and eliminates the unnecessary information for the upcoming data analysis, is a significant problem in machine learning and has been explored for years. Most UFS methods map features into a pseudo label space by multiplying a projection matrix constrained with sparsity to learn the mapping from the features to the labels. However, the mapping relationship is usually not linear, and linear regression may result in a suboptimal selection. To address this issue, we propose a novel UFS method, called neural networks embedded self-expression (NNSE). NNSE replaces the linear regression of traditional spectral analysis methods with neural networks to learn the pseudo label space. Besides, we embed neural networks into the self-expression model to improve the representative ability by preserving the local structure with an adaptive graph regularization module. Then we propose an efficient alternative iterative algorithm to solve the proposed model. Experimental results on 8 public datasets show NNSE outperforms the other state-of-the-art methods. Moreover, experimental results are also presented to show the convergence of the proposed method. The source code is available at: https://github.com/misteru/NNSE.
Brain-like retinex: A biologically plausible retinex algorithm for low light image enhancement

Retinex theory was first proposed by Land and McCann [1], where retinex is a portmanteau derived from the words of retina and cortex, implying that both the retina and cerebral cortex may participate in the perception of lightness and color. However, there are no recent reports on how the retina and visual cortex perform retinex decomposition. In this paper, we propose a biologically plausible solution to retinex decomposition. We develop an algorithm motivated by the primate's retinal circuit to detect textural gradients, design an algorithm originating from the visual cortex to extract image contours, and thus split image edges into image contours and textural gradients. Then, we establish a variational model for retinex decomposition by using image contours and textural gradients to encode discontinuities in illumination and variations in reflectance, respectively. We also apply the proposed retinex model to low light image enhancement, high dynamic resolution image toning, and color constancy. Experiments show consistent superiority of the proposed algorithm. The code is available at Github.
Antecedent, behaviour, and consequence (a-b-c) of deploying the contact tracing app in response to COVID-19: Evidence from Europe

In response to the current coronavirus (COVID-19) pandemic, countries have or intend to deploy contact tracing apps as a way of containing and or reducing the community spread of the virus. Whilst a few studies have so far been conducted on the acceptability of the app, little is known about the antecedent, behaviour, and consequence (a-b-c) of deploying the app and its success thereof. This study, therefore, proposes and validates an integrated a-b-c and technology acceptance model of deploying the contract tracing app in four European countries. The study adopts a quantitative approach and uses publicly available cross country survey data from the Center for Open Science. An extract of 2512 data is analysed using SEM-PLS. The results confirmed the integrated a-b-c and technology acceptance model that underpins the study and revealed that the chance of achieving a positive outcome with citizens complying with recommendations of the app was only 17.1 % or R2 = 0.171 (±0.020) whilst the chance of negative consequent or deviant response of uninstallation of the app by the citizens was 54.3 % or R2 = 0.543 (±0.021). The results have huge implications for governments and public health institutions in their attempt to deploy the contract tracing app.
Counting People and Bicycles in Real Time Using YOLO on Jetson Nano

Counting objects in video images has been an active area of computer vision for decades. For precise counting, it is necessary to detect objects and follow them through consecutive frames. Deep neural networks have allowed great improvements in this area. Nonetheless, this task is still a challenge for edge computing, especially when low-power edge AI devices must be used. The present work describes an application where an edge device is used to run a YOLO network and V-IOU tracker to count people and bicycles in real time. A selective frame-downsampling algorithm is used to allow a larger frame rate when necessary while optimizing memory usage and energy consumption. In the experiments, the system was able to detect and count the objects with 18 counting errors in 525 objects and a mean inference time of 112.82 ms per frame. With the selective downsampling algorithm, it was also capable of recovering and reduce memory usage while maintaining its precision.
Reinforcement recommendation reasoning through knowledge graphs for explanation path quality

Numerous Knowledge Graphs (KGs) are being created to make Recommender Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the recommendation process allows the underlying model to extract reasoning paths between recommended products and already experienced products from the KG. These paths can be leveraged to generate textual explanations to be provided to the user for a given recommendation. However, the existing explainable recommendation approaches based on KG merely optimize the selected reasoning paths for product relevance, without considering any user-level property of the paths for explanation. In this paper, we propose a series of quantitative properties that monitor the quality of the reasoning paths from an explanation perspective, based on recency, popularity, and diversity. We then combine in- and post-processing approaches to optimize for both recommendation quality and reasoning path quality. Experiments on three public data sets show that our approaches significantly increase reasoning path quality according to the proposed properties, while preserving recommendation quality. Source code, data sets, and KGs are available at https://tinyurl.com/bdbfzr4n.
A blockchain based access control scheme with hidden policy and attribute

Attribute-based access control, which can provide fine-grained and flexible privacy protection, is widely used in practice. Traditional attribute-based access control schemes require the intervention of a trusted third party, which will cause single point of failure. Due to its decentralized nature, blockchain is now considered as a promising way to solve the above issue. The basic idea of existing blockchain-based access control schemes is to code the access policy into a smart contract. However, the transparency of the blockchain makes it easier for the adversary to explore the public information to launch a privacy attack. In this paper, we propose an attribute-based access control scheme which benefits from the smart contract technology while ensuring the privacy of attributes and policies. An additive homomorphic cryptosystem is utilized to encrypt attributes and access policies. The scheme uses multiple blockchain nodes to collectively decrypt the data, and uses zero-knowledge proof technology to ensure the correctness of the decryption result. Therefore, the smart contract is able to make the authorization decision without actually seeing attributes or policies. We have implemented the proposed access control scheme on Hyperledger Fabric. Simulation results show that the computation overhead introduced by encryption/decryption and zero-knowledge proof is reasonable.
The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents

Recommender Systems (RSs) have played an important role in online retailing portals and customers’ decision-making processes. Recommender systems that are based on the conventional Collaborative Filtering (CF) approach rely on single customers’ ratings on retailing websites. Multi-criteria CF (MCCF) approaches that rely on multi-aspects of the products have provided more reliable and effective recommendations on retailing websites. However, these approaches should be improved in terms of accuracy by solving sparsity issues and incorporating criteria ratings. In addition, most of the recommendation agents that are based on MCCF cannot learn automatically from the features of the products to model customers’ preferences and generate accurate recommendations on retailing websites. Besides, although previous studies have utilized single and multi-criteria ratings in recommendation agents of tourism websites, still, if there is a lack of ratings of items, most of these systems will fail to generate accurate recommendations to users. In this research, we develop a new recommendation agent based on a MCCF approach to effectively improve the performance of previous recommendation systems for tourism websites. The results demonstrated that the method can predict the most relevant products to users, particularly when the dataset is sparse.
LDNet: Lightweight dynamic convolution network for human pose estimation

Lightweight implementation of existing human pose estimation networks limits the model representation capability, and it cannot effectively deal with problems such as changeable poses, complex backgrounds, and occlusion in practical applications. To address this problem, a lightweight human pose estimation network with dynamic convolution, called LDNet, is proposed in this study. First, we start from a lightweight feature extraction head to reduce the number of image preprocessing parameters. Then, we employ a high-resolution parallel subnetwork to predict precise keypoint heatmaps. To reduce the complexity due to high-resolution representations while maintaining good network performance, we propose a lightweight dynamic convolution. It can cope with changing human poses by adaptively learning different convolution parameters. Finally, to further exploit the relationship between the high-level semantic and spatial structure features for accurately locating different keypoints, we propose a keypoint refinement module based on our lightweight dynamic convolution to improve the keypoint detection and location results. Overall, accurate keypoint prediction results are obtained and compared with those of many existing networks, such as HRNet, the number of parameters is reduced by 82.1% and the calculation complexity is reduced by 47.9%. The model achieves an average precision of 73.5% and 88.7% on the COCO 2017 and MPII datasets, respectively. LDNet also shows good prediction accuracy and robustness on the CrowdPose dataset. The proposed network is superior to existing outstanding lightweight networks and is comparable to existing large-scale human pose estimation networks.
An exploitation-boosted sine cosine algorithm for global optimization

The sine cosine algorithm (SCA) has drawn significant attention from researchers in different fields because of fewer control parameters, excellent global optimization ability, and simple principles. However, there are some drawbacks of the SCA which needed immediate attention. The position-updated equation of the SCA is good at exploration but poor at exploitation, which leads to slow converging speed and low converging accuracy in some complex cases. Therefore, an exploitation-boosted sine cosine algorithm (namely, EBSCA) is proposed in this study. In order to enhance the ability of exploitation, a new position-updated equation is designed by emphasizing the position information of the best individual, thereby guiding the updating of new candidate individuals. Meanwhile, the information weights of the best individual and the current individual in the new equation are dynamically adjusted by a balance factor b to avoid over-exploitation of the information from the best individual. Furthermore, a new integration way of combining the quantization orthogonal crossover strategy with the SCA is proposed to improve the utilizing efficiency of the searching space. The performance of the EBSCA is evaluated by being tested on 13 classical benchmark functions, IEEE CEC 2015 problems, and four well-known engineering applications. The comparisons demonstrate that the EBSCA algorithm obviously improves the performance of the SCA. Additionally, the experimental results show that , the proposed EBSCA algorithm exhibits higher competitiveness compared to other algorithms participated in this research.
Protecting world leaders against deep fakes using facial, gestural, and vocal mannerisms

Since their emergence a few years ago, artificial intelligence (AI)-synthesized media-so-called deep fakes-have dramatically increased in quality, sophistication, and ease of generation. Deep fakes have been weaponized for use in nonconsensual pornography, large-scale fraud, and disinformation campaigns. Of particular concern is how deep fakes will be weaponized against world leaders during election cycles or times of armed conflict. We describe an identity-based approach for protecting world leaders from deep-fake imposters. Trained on several hours of authentic video, this approach captures distinct facial, gestural, and vocal mannerisms that we show can distinguish a world leader from an impersonator or deep-fake imposter.
Monte Carlo Ensemble Neural Network for the diagnosis of Alzheimer’s disease

Convolutional neural networks (CNNs) have been increasingly used in the computer-aided diagnosis of Alzheimer's Disease (AD). This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network (MCENN) by introducing Monte Carlo sampling and an ensemble neural network in the integration with ResNet50. Our goals are to improve the 2D-slice CNN performance and to design the MCENN model insensitive to image resolution. Unlike traditional ensemble approaches with multiple base learners, our MCENN model incorporates one neural network learner and generates a large number of possible classification decisions via Monte Carlo sampling of feature importance within the combined slices. This can overcome the main weakness of the lack of 3D brain anatomical information in 2D-slice CNNs and develop a neural network to learn the 3D relevance of the features across multiple slices. Brain images from Alzheimer's Disease Neuroimaging Initiative (ADNI, 7199 scans), the Open Access Series of Imaging Studies-3 (OASIS-3, 1992 scans), and a clinical sample (239 scans) are used to evaluate the performance of the MCENN model for the classification of cognitively normal (CN), patients with mild cognitive impairment (MCI) and AD. Our MCENN with a small number of slices and minimal image processing (rigid transformation, intensity normalization, skull stripping) achieves the AD classification accuracy of 90%, better than existing 2D-slice CNNs (accuracy: 63%∼84%) and 3D CNNs (accuracy: 74%∼88%). Furthermore, the MCENN is robust to be trained in the ADNI dataset and applied to the OASIS-3 dataset and the clinical sample. Our experiments show that the AD classification accuracy of the MCENN model is comparable when using high- and low-resolution brain images, suggesting the insensitivity of the MCENN to image resolution. Hence, the MCENN does not require high-resolution 3D brain structural images and comprehensive image processing, which supports its potential use in a clinical setting.