Reviewerly/exHarmony huggingface dataset:
Federated learning-based air quality prediction for smart cities using BGRU model

Nowadays, Internet of Things (IoT) has become very popular due to its applications in various fields such as industry, commerce, and education. Cities become smart cities by utilizing lots of applications and services of IoT. However, these intelligent applications and services significantly threaten the environment regarding air pollution. Therefore, high accuracy in air pollution monitoring and future air quality predictions have become our primary concern to save human beings from health issues coming from air pollution. In general, deep learning (DL) and federated learning (FL) techniques are suitable for solving various forecasting problems and dealing with the high volatile air components in heterogeneous big data scenarios. This ambiance of DL and FL motivates us to exploit the DL-based Bidirectional Gated Recurrent Unit (BGRU) method for future air quality prediction using big data and federated learning (FL) to train our model in a distributed, decentral, and secure ways. This paper proposes a novel distributed and decentralized FL-based BGRU model to accurately predict air quality using the smart city's big data. The effectiveness of the FL-based BGRU Model is estimated with other machine learning (ML) models by using various evaluation metrics.
NeuLens

Convolutional neural networks (CNNs) play an important role in today's mobile and edge computing systems for vision-based tasks like object classification and detection. However, state-of-the-art methods on CNN acceleration are trapped in either limited practical latency speed-up on general computing platforms or latency speed-up with severe accuracy loss. In this paper, we propose a spatial-based dynamic CNN acceleration framework, NeuLens, for mobile and edge platforms. Specially, we design a novel dynamic inference mechanism, assemble region-aware convolution (ARAC) supernet, that peels off redundant operations inside CNN models as many as possible based on spatial redundancy and channel slicing. In ARAC supernet, the CNN inference flow is split into multiple independent micro-flows, and the computational cost of each can be autonomously adjusted based on its tiled-input content and application requirements. These micro-flows can be loaded into hardware like GPUs as single models. Consequently, its operation reduction can be well translated into latency speed-up and is compatible with hardware-level accelerations. Moreover, the inference accuracy can be well preserved by identifying critical regions on images and processing them in the original resolution with large micro-flow. Based on our evaluation, NeuLens outperforms baseline methods by up to 58% latency reduction with the same accuracy and by up to 67.9% accuracy improvement under the same latency/memory constraints.
Black-box Bayesian adversarial attack with transferable priors

Deep neural networks are vulnerable to adversarial attacks, even in the black-box setting, where the attacker only has query access to the model. The most popular black-box adversarial attacks usually rely on substitute models or gradient estimation to generate imperceptible adversarial examples, which either suffer from low attack success rates or low query efficiency. In real-world scenarios, it is extremely improbable for an attacker to have unlimited bandwidth to query a target classifier. In this paper, we proposed a query efficient gradient-free score-based attack, named BO-ATP, which combines Bayesian optimization strategy with transfer-based attacks and searches for perturbation in low-dimensional latent space. Different from the gradient-based method, in the search process, our attack makes full use of the prior information obtained from the previous query to sample the next optimal point instead of local gradient approximation. Results on MNIST, CIFAR10, and ImageNet show that even at a low 1000 query budget, we still achieve high attack success rates in both targeted and untargeted attacks, and the query efficiency is dozens of times higher than the previous state-of-the-art attack methods. Furthermore, we show that BO-ATP can successfully attack some state-of-the-art defenses, such as adversarial training.
Visual tracking with dumbbell selection network

The Siamese network-based trackers aim to train the convolutional neural network offline to match target templates and search regions. Recent researches have managed to adopt deep neural networks to extract sufficient semantic information for the Siamese trackers. However, most of the current methods suffer drastic target appearance variations for 1) failing to encode sufficient localization information of targets and 2) neglecting powerful cross-channel interaction information, thus reducing the learned features’ discriminative and representative ability the tracking accuracy. To remedy these issues, in this article, we propose a Dumbbell Selection Network (DuStNet) by exploring the correlation of the hierarchies of convolutional layers. In concrete, an adaptively Dumbbell Selection mechanism is presented to deal with the targets’ appearance deformation by providing rich semantic and localization information. Furthermore, a CSResNet is developed to improve the residual unit in backbones by strengthening the interdependence between the convolution feature channels. We ingeniously employ the Generalized Intersection over Union (GIoU) to supervise the cross-layer feature-map selection, improving tracking accuracy when utilized as a regression loss simultaneously. Our results suggest that the proposed method is robust to significant appearance variations and can generate more accurate bounding boxes in complicated scenarios. Extensive experimental results on large-scale benchmark datasets prove our method’s effectiveness, which achieves excellent performance on OTB2015, VOT2017, LaSOT, and VOT2019.
Object-aware navigation for remote embodied visual referring expression

In the Remote Embodied Visual Referring Expression (REVERIE) task, an agent needs to navigate through an unseen environment to identify a referred object following high-level instructions. Despite recent efforts of vision-and-language navigation (VLN), previous methods commonly rely on detailed navigational instructions, which might not be available in practice. To address this issue, we present a method that strengthens vision-and-language (V&L) navigators with object-awareness. By combining object-aware textual grounding and visual grounding operations, our technique helps the navigator recognize the relationship between instructions and the contents of captured images. As a generic method, the proposed solution can be seamlessly integrated into other V&L navigators with different frameworks (for example, Seq2Seq or BERT). In order to alleviate the problem of data scarcity, we synthesize augmented data based on a simple yet effective prompt template that retains object information and destination information. Experimental results on REVERIE and R2R datasets demonstrate the proposed methods’ applicability and performance improvement across different domains.
Optimal path planning of Unmanned Aerial Vehicles (UAVs) for targets touring: Geometric and arc parameterization approaches

The path planning problem for unmanned aerial vehicles (UAVs) is important for scheduling the UAV missions. This paper presents an optimal path planning model for UAV to control its direction during target touring, where UAV and target are at the same altitude. Geometric interpretation of the given model is provided when the vehicles consider connecting an initial position to the destination position with specific target touring. We develop a nonlinear constrained model based on an arc parameterization approach to determine the UAV's optimal path touring a target. The model is then extended to touring finite numbers of targets and optimizing the routes. The model is found reliable through several simulations. Numerical experiments are conducted and we have shown that the UAV's generated path satisfies vehicle dynamics constraints, tours the targets, and arrives at its destination.
Threshold secret sharing scheme of quantum images based on least significant bit theory

An (r,n) threshold secret sharing scheme for quantum images based on the least significant bit (LSB) theory is proposed in this paper. First of all, we embed the quantum secret image into four different quantum carrier images by using LSB theory to form four embedded images, which has no visual difference from the original carrier images. Then, we adjust the pixels of the embedded carrier images in a range of [0,250], so as to obtain the adjusted images. Finally, we use r−1 degree Lagrange polynomials to divide the four adjusted images into groups of shadow images, which are safely transmitted to the four groups of participants. Each group has n participants. Any r(r≤n) participants are together in each group, both the secret image and the four carrier images can be recovered. The experimental results show that our scheme not only has good recovery quality, but also has high security and can resist external attack. In addition, our scheme does not involve any encryption steps, which greatly reduces the computational complexity.
CUNA: A privacy preserving medical records storage in cloud environment using deep encryption

Securing the medical images in cloud environment becomes mandatory because of massive image accumulation and information storage in cloud premises. Large accommodation of information in the cloud brings another risk of data security. Privacy preserving data mining techniques investigate privacy of data to be maintained before or either after the data mining technique. Lightweight technique to store the patient data securely in the cloud is focused. The proposed study is focused on designing a robust model in which medical records privacy is preserved using Customizable Unique node access (CUNA) architecture. The system design undergoes data preparation, encryption, cloud transfer, decryption and analysis modules. The input to the proposed system is patient health care data such as CT images, Medical records, Clinical treatment procedures etc. The CT images are encrypted with respect to their medical records, further transferred into the cloud by hiding the text based confidential data into the images. Inspite of normal cloud storage process, the proposed approach utilized deep cryptography technique to make authenticated storage mechanism adaptive in the cloud. CUNA is the customized commonly allocated key assigned at every node. The unique key word is encrypted, and utilized to transfer the privacy data of the patients. The data can be retrieved from the cloud, after the decryption of CUNA code. The correlation of the key is systematically achieved using Gaussian regression process algorithm (GRA). The proposed approach is compared with various state-of-art approaches and obtained the accuracy of 92% with image MSE of 0.0012%.
Artificial Intelligence Systems in CKD: Where Do We Stand and What Will the Future Bring?

Artificial intelligence (AI) systems in health care settings refer to fully integrated systems that use predictions from AI models to support clinical decisions, often within the context of a learning health system (Fig 1). An AI system can be defined by its 2 key capabilities: the ability to represent and compute different forms of knowledge about patient risk (eg, the AI model itself), and integration of these computations within the broader information technology environment, such as the electronic health record (EHR), that enable it to drive clinical actions.
Leader, Socializer or Specialist? Distinguishing Responders with the Cues of Questions

ABSTRACT Community question‐answering (CQA) enables responders to select questions, and respond to the questions by answering, commenting or voting. Accordingly, questions with different cues (i.e. complexity, specificity, emotional expressiveness, politeness, popularity, rewards) tend to attract different responders. However, the research is limited regarding the types of responders based on the questions they responded to. The gap inhibits us to form a complete understanding of how questions bridge askers with responders. Moreover, how different types of responders contribute to maintaining the ecosystem of the CQA has not been studied adequately. Accordingly, we conducted an online survey to organize responders by the cues of questions. Cluster analysis was used to group responders into three types: (1) “leaders” respond to complex and popular questions, attracting many followers in CQA; (2) “socializers” answer less complex and specific questions with emotion‐laden words; and (3) “specialists” respond to complex questions with high specificity but seldom use the social functions of CQAs. Finally, contributions and limitations are discussed.
Information Resilient Society in an<scp>AI</scp>World—Is<scp>XAI</scp>Sufficient?

ABSTRACT This paper discusses the role of organizational records management (ORM) with respect to the field of explainable artificial intelligence (XAI) and argues about its necessity and significance. The current trend is to utilize AI to explain AI, including both explanation production and provision. We argue that this kind of approach is not sufficient by itself as it lacks neutrality and localness to explanation recipients, thus ineffective in establishing public trust. We propose the addition of the ORM profession, as an informational 3rd party, to the current and future XAI. We envision that, by working together with XAI performers, ORM contributes to an information resilient AI society. To take on the related responsibilities, the ORM profession must improve its professional expertise and strengthen its professional independence. We call for consensus among individual information professionals and the advocacy of information professional networks to make this vision a reality.
A Longitudinal Study to Understand University Students' Searching as a Learning Process

Abstract Learning, or addressing a gap in one's knowledge, is an important motivator behind information‐seeking activities. The Search as Learning research community advocates that information search systems should be reconfigured to become educational platforms to foster learning and sensemaking. Modern search systems have yet to adapt to support this function. An important step to foster learning during search is to identify behavioral patterns that distinguish searchers gaining more vs. less knowledge during search. Previous efforts have primarily studied searchers in the short term, typically during a single lab session. Researchers are concerned over this ephemeral approach, as learning is not fleeting, and takes place over time. We propose an exploratory longitudinal study to analyze the long‐term searching behaviour of students enrolled in a university course, over the span of a semester. Our research aims are to identify if and how students’ searching behaviour changes over time, as they gain new knowledge on a subject; and how processes like motivation, metacognition, self‐regulation, and other individual differences moderate their “searching as learning” behaviour. Findings from this exploratory longitudinal study will help to build improved information search systems that foster human learning and sensemaking.
Turning Filter Bubbles into Bubblesphere with <scp>Multi‐Viewpoint KOS</scp> and Diverse Similarity

ABSTRACT The filter bubble phenomenon and its negative societal effects have been extensively explored in the literature in the past decade. However, the ability of modern AI‐based systems to create personalized information bubbles, that is, to classify similar contents and users into clusters according to their interests and behavior, can actually be quite beneficial if utilized and managed properly and ethically. In this article we present ongoing research that aims to refine such bubble‐building smart systems by adopting an ethical, multi‐perspective approach that allows for linking isolated bubbles into a consolidated bubblesphere and offering users a choice to explore diverse bubbles related to their topics of interest. To implement the proposed approach, content matching should be based on diverse similarity , which can be derived from a multi‐viewpoint KOS . In addition, the study explores how such a multi‐viewpoint KOS and bubblesphere can be constructed using Wikidata's ranks and qualifiers.
Optical visual encryption using focused beams and convolutional neural networks

The target of this paper is to implement an optically-based visual encryption system able to work with a large set of optical codes. The optical setup comprises a holographic system designed to generate spirally-polarized highly focused fields and an imaging module able to perform polarimetric analysis. In a previous stage, the optical system is numerically simulated in order to produce synthetic polarimetric distributions that are used to train a convolutional neural network. Interestingly, the way the network is trained depends on the selected state of polarization. Then, secret codes are split in two XOR-connected ones that are optically processed. The corresponding experimental polarimetric distribution is obtained and transmitted to the corresponding recipients, that can recover the code by interrogating the neural network. Finally, combining the two pieces of information, the encrypted message can be decoded.
Clustering mixed-type data using a probabilistic distance algorithm

Cluster analysis is a broadly used unsupervised data analysis technique for finding groups of homogeneous units in a data set. Probabilistic distance clustering adjusted for cluster size (PDQ), discussed in this contribution, falls within the broad category of clustering methods initially developed to deal with continuous data; it has the advantage of fuzzy membership and robustness. However, a common issue in clustering deals with treating mixed-type data: continuous and categorical, which are among the most common types of data. This paper extends PDQ for mixed-type data using different dissimilarities for different kinds of variables. At first, the PDQ for mixed-type data is defined, then a simulation design shows its advantages compared to some state of the art techniques, and ultimately, it is used on a real data set. The conclusion includes some future developments.
StreamingTag

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, the idea of piracy tracing is not widely used at the moment, since existing fingerprinting-based streaming systems fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, a polarized and randomized SVD watermarking scheme suitable for short segments, and a collusion-resistant fingerprinting scheme optimized for large-scale streaming services. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing SVD watermarking schemes, the proposed watermarking scheme improves the watermark extraction accuracy by 2.25x at most and 1.5x on average. Compared with existing collusion-resistant fingerprinting schemes, the proposed scheme catches more colluders and improves the recall rate by 26%.
Entification of Metadata for <scp>Knowledge‐Graph‐Guided</scp> Discovery

Abstract This paper reports on the application of entification to collections of archival metadata. Entification refers to the identification of the entities described in information objects and associated metadata records. The process presents issues about whether existing records‐style metadata records are suitable and how entification will affect user access and interaction with metadata and information objects. In this study, we developed an entification workflow that includes the following steps: data cleaning, entity reconciliation to create linked data through metadata enrichment, accuracy selection, and developing knowledge graphs to communicate the semantic relationships among entities. Additionally, we discuss the implications of entification for practitioners in the field of information science, especially current limitations in the process.
Transforming North-South research partnerships: Lessons learned from energy, technology &amp; enterprise global challenge research fund projects

The Global Challenges Research Fund (GCRF) provided a mechanism for academia to undertake projects relevant to the Sustainable Development Goals but there have been limited opportunities to critically interrogate such projects. In this paper we will use the Technology Implementation Model for Energy to deconstruct the purpose, assumptions and expectations, engagement strategies, and reflective processes of four GCRF projects in order to better understand relationships between researchers and those being researched. Thus, the aim of this paper is to explore and understand the lived experiences of four inter-disciplinary GCRF Primary Investigators implementing poverty alleviating technologies in a range of sectors to generate recommendations that can be applied to wider academic communities engaging with vulnerable populations. Our key findings show that despite the integration of Theory of Change models and the Responsible Research and Innovation (RRI) framework in GCRF-funded projects, project aims continue to be driven by researchers rather than reflecting end-user needs. Whilst some projects looked to generate feelings of ownership, adequate engagement strategies and reflective learning practices, these processes are often not formally embedded in project activities resulting in a decoupling of researcher expectations and end-user assumptions – ultimately derailing project outcomes. Our recommendations for academics operating within the International Development space are to 1) Talk early, often and transparently, 2) Keep Thinking – who benefits?, 3) Be reflective, responsive, and open to change and, 4) Use a systematic approach to facilitate this process.
The State of the Approval Plan in the Wake of the COVID-19 Pandemic

59 academic technical services librarians were surveyed about the state of their approval plans in the wake of the collections and acquisitions challenges posed by the COVID-19 pandemic. 40 responded, with a majority indicating a move away from print auto-shipments and toward e-preferred collection models. Libraries that continued to receive print auto-shipments tended to favor narrow, targeted profiles, such as faculty publications, international plans, best-sellers and award winners. Every response qualified as a movement away from print auto-shipments as an acquisitions strategy, even in cases where approval plans were maintained or, in one case, initiated for the first time.