Reviewerly/exHarmony huggingface dataset:
PPAQ: Privacy-Preserving Aggregate Queries for Optimal Location Selection in Road Networks

Aggregate nearest neighbor (ANN) query, which can find an optimal location with the smallest aggregate distance to a group of query users’ locations, has received considerable attention and been practically useful in many real-world location-based applications. Nevertheless, query users still hesitate to use these applications due to privacy concerns, as there is a worrisome that the location-based service (LBS) providers may abuse their locations after collecting them. In this article, to tackle this issue, we propose a novel privacy-preserving aggregate query (PPAQ) scheme to select an optimal location for query users in road networks. Specifically, we first analyze the problem of the ANN query in road networks and identify two basic operations, i.e., addition and comparison, in the query. Then, we carefully design efficient addition and comparison circuits to securely add and compare two bit-based inputs, respectively. With these two secure circuits, we propose our PPAQ scheme, which can simultaneously protect the users’ locations, query results, and access patterns from leaking. Detailed security analysis shows that our proposed scheme is indeed privacy-preserving. In addition, extensive performance evaluations are conducted, and the results indicate that our proposed scheme has an acceptable efficiency for non-real-time applications.
Systematic Mapping: Artificial Intelligence Techniques in Software Engineering

Artificial Intelligence (AI) has become a core feature of today’s real-world applications, making it a trending topic within the software engineering (SE) community. The rise in the availability of AI techniques encompasses the capability to make rapid, automated, impactful decisions and predictions, leading to the adoption of AI techniques in SE. With industry revolution 4.0, the role of software engineering has become critical for developing productive, efficient, and quality software. Thus, there is a major need for AI techniques to be applied to enhance and improve the critical activities within the software engineering phases. Software is developed through intelligent software engineering phases. This paper concerns a systematic mapping study that aimed to characterize the publication landscape of AI techniques in software engineering. Gaps are identified and discussed by mapping these AI techniques against the SE phases to which they contributed. Many systematic mapping review papers have been produced only for a specific AI technique or a specific SE phase or activity. Hence, no previous systematic mapping review has explored the overall trends in AI techniques and their application to all SE phases.
Attention-Based Cross-Modality Feature Complementation for Multispectral Pedestrian Detection

Multispectral pedestrian detection based on deep learning can provide a robust and accurate detection under different illumination conditions, which has important research significance in safety.In order to reduce the log-average miss rate of the object under different illumination conditions, a new one-stage detector suitable for multispectral pedestrian detection is proposed.First, in order to realize the complementarity between the information flows of the two modalities in the feature extraction stage to reduce the object loss, a low-cost cross-modality feature complementary module (CFCM) is proposed.Second, in order to suppress the background noise in different environments and enhance the semantic information and location information of the object, so as to reduce the error detection of the object, an attention-based feature enhancement fusion module (AFEFM) is proposed.Thirdly, through the feature complementarity of colorthermal image pair and the multi-scale fusion of depth feature layer, the horizontal and vertical multidimensional data mining of parallel deep neural network is realized, which provides effective data support for object detection algorithm.Finally, through the reasonable arrangement of proposed modules, a robust multispectral detection framework is proposed.The experimental results on the Korea Advanced Institute of Science and Technology (KAIST) pedestrian benchmark show that the proposed method has the lowest logaverage miss rate compared with other state-of-the-art multispectral pedestrian detectors, and has a good balance in speed and accuracy.
Expressive Data Sharing and Self-Controlled Fine-Grained Data Deletion in Cloud-Assisted IoT

Expressive data sharing and efficient data deletion are essential to drive the development of cloud-assisted IoT. But insecure transmission and the vulnerability of the cloud server may cause potential threats to IoT data, attribute-based encryption (ABE) is widely applied to ensure data confidentially. Nonetheless, the potential data exposure caused by the compromised long-term key and the contradiction between conventional access structures in ABE and the various demands of data owners are still two huge challenges. To overcome these challenges, this article designs an unbounded and puncturable ciphertext-policy ABE with arithmetic span program ( <inline-formula><tex-math notation="LaTeX">$\mathcal {UP}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {CP}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {ABE}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {ASP}$</tex-math></inline-formula> ) scheme and presents an expressive data sharing and self-controlled fine-grained data deletion solution in cloud-assisted IoT, which allows data owners to efficiently encrypt and share data with various computable access policies, but also enables data owners and data users to independently delete specific data stored in the cloud. The designed <inline-formula><tex-math notation="LaTeX">$\mathcal {UP}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {CP}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {ABE}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {ASP}$</tex-math></inline-formula> leverages unbounded ABE and puncturable encryption to support the flexible update of system parameters and the deletion of specific data. Also, the arithmetic span program access structure is combined to realize expressive data sharing. Moreover, the <inline-formula><tex-math notation="LaTeX">$\mathcal {UP}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {CP}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {ABE}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathcal {ASP}$</tex-math></inline-formula> is adaptively secure in the standard model, and comprehensive performance evaluations demonstrate its practicability and scalability in cloud-assisted IoT.
Fusion of Periocular Deep Features in a Dual-Input CNN for Biometric Recognition

Periocular recognition has attracted attention in recent times. The advent of the COVID-19 pandemic and the consequent obligation to wear facial masks made face recognition problematic due to the important occlusion of the lower part of the face. In this work, a dual-input Neural Network architecture is proposed. The structure is a Siamese-like model, with two identical parallel streams (called base models) that process the two inputs separately. The input is represented by RGB images of the right eye and the left eye belonging to the same subject. The outputs of the two base models are merged through a fusion layer. The aim is to investigate how deep feature aggregation affects periocular recognition. The experimentation is performed on the Masked Face Recognition Database (M $$^2$$ FRED) which includes videos of 46 participants with and without masks. Three different fusion layers are applied to understand which type of merging technique is most suitable for data aggregation. Experimental results show promising performance for almost all experimental configurations with a worst-case accuracy of 90% and a best-case accuracy of 97%.
Black-Box Based Limited Query Membership Inference Attack

Conventional membership inference attacks usually require a large number of queries of the target model when training shadow models, and this task becomes extremely difficult when the number of queries is limited.Aiming at the problem of insufficient training data for shadow models due to the limited number of queries, we propose a membership inference attack method based on generative adversarial networks (GAN).First, we use generative adversarial networks to augment the samples obtained by a small number of queries to expand the training data of the model; Secondly, we use the improved CNN to obtain shadow models that have a higher degree of fitting on different target model structures; Finally, we evaluate the accuracy of the proposed algorithm on XgBoost, Logistic, and neural network models using public datasets MNIST and CIFAR10 in a black-box setting, and the results show that our model has an average attack accuracy of 62% and 83%, respectively.It can be seen that, compared with the existing research methods, our model can obtain better attack effects under the condition of significantly reducing the number of queries, which shows the feasibility of our proposed method in membership inference attacks.
An End-to-End Curriculum Learning Approach for Autonomous Driving Scenarios

In this work, we combine Curriculum Learning with Deep Reinforcement Learning to learn without any prior domain knowledge, an end-to-end competitive driving policy for the CARLA autonomous driving simulator. To our knowledge, we are the first to provide consistent results of our driving policy on all towns available in CARLA. Our approach divides the reinforcement learning phase into multiple stages of increasing difficulty, such that our agent is guided towards learning an increasingly better driving policy. The agent architecture comprises various neural networks that complements the main convolutional backbone, represented by a ShuffleNet V2. Further contributions are given by (i) the proposal of a novel value decomposition scheme for learning the value function in a stable way and (ii) an ad-hoc function for normalizing the growth in size of the gradients. We show both quantitative and qualitative results of the learned driving policy.
Underwater Image Enhancement Using Pre-trained Transformer

The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical example of the important role of the machine learning algorithms to support marine exploration and monitoring, reducing the need for human intervention like the manual processing of the images, thus saving time, effort, and cost. This paper is the first application of the image transformer-based approach called "Pre-Trained Image Processing Transformer" to underwater images. This approach is tested on the UFO-120 dataset, containing 1500 images with the corresponding clean images.
Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

Federated learning (FL) represents a promising distributed machine learning paradigm that allows smart devices to collaboratively train a shared model via providing local data sets. However, problems considering multiple co-existing FL services and different types of service providers are rarely studied. In this paper, we investigate a multiple FL service trading problem in Unmanned Aerial Vehicle (UAV)-aided networks, where FL service demanders (FLSDs) aim to purchase various data sets from feasible clients (smart devices, e.g., smartphones, smart vehicles), and model aggregation services from UAVs, to fulfill their requirements. An auction-based trading market is established to facilitate the trading among three parties, i.e., FLSDs acting as buyers, distributed located client groups acting as data-sellers, and UAVs acting as UAV-sellers. The proposed auction is formalized as a 0-1 integer programming problem, aiming to maximize the overall buyers’ revenue via investigating winner determination and payment rule design. Specifically, since two seller types (data-sellers and UAV-sellers) are considered, an interesting idea integrating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">seller pair</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint bid</i> is introduced, which turns diverse sellers into virtual seller pairs. Vickrey-Clarke-Groves (VCG)-based, and one-sided matching-based mechanisms are proposed, respectively, where the former achieves the optimal solutions, which, however, is computationally intractable. While the latter can obtain suboptimal solutions that approach to the optimal ones, with low computational complexity, especially upon considering a large number of participants. Significant properties such as truthfulness and individual rationality are comprehensively analyzed for both mechanisms. Extensive experimental results verify the properties and demonstrate that our proposed mechanisms outperform representative methods significantly.
Comparative Study of the Firefly Algorithm and the Whale Algorithm

The firefly algorithm is one of the relatively new algorithms based on swarm intelligence. This algorithm has various applications, it is especially good at solving combinatorial optimization problems. Contemporary research on optimization algorithms assumes the introduction of frequent modifications in order to increase the efficiency of algorithms and adjust their operation to the complexity of a given research problem. In this work, the firefly algorithm and the whale algorithm were subjected to the research procedure in order to solve an optimization problem. The achieved results enabled the comparison of both methods.
Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user’s privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.
End-to-end Argument Mining with Cross-corpora Multi-task Learning

Abstract Mining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they usually suffer from a shortage of training data. To address this issue, we expand the training data with various auxiliary argument mining corpora and propose an end-to-end cross-corpus training method called Multi-Task Argument Mining (MT-AM). To evaluate our approach, we conducted experiments for the main argument mining tasks on several well-established argument mining corpora. The results demonstrate that MT-AM generally outperformed the models trained on a single corpus. Also, the smaller the target corpus was, the better the MT-AM performed. Our extensive analyses suggest that the improvement of MT-AM depends on several factors of transferability among auxiliary and target corpora.
Lecciones de la vigilancia de la COVID-19. Necesidad urgente de una nueva vigilancia en salud pública. Informe SESPAS 2022

In this article we provide the most important epidemiological aspects in the first phases of the pandemic and some preliminary reflections from the Coordinating Centre for Health Alerts and Emergencies, the unit that has coordinated surveillance at the national level. COVID-19 has brought to light the weaknesses in the surveillance system and how difficult it is to manage a health crisis in the absence of a robust public health structure. The commitment of public health professionals during this epidemic has made up for the lack of resources in many occasions, and has evidenced the need to incorporate new professional profiles to surveillance teams. The need to rapidly adapt has achieved an improvement in existing systems and the development of new tools and new systems. These need to turn into structural changes that improve the quality of surveillance, decreasing territorial gaps and ensuring a better and coordinated response to future health crises. It is urgent to incorporate tools for process automation and to grant timely availability of data. To that end, public health and epidemiological surveillance must participate in the process of digital development within the National Health System. Profound changes are needed in public health surveillance, which has to be integrated in all healthcare levels. It is also important to strengthen the capacity for analysis by promoting alliances and joint actions. During this alert, the importance of coordination in public health in a decentralized country has been evident. At international level, it is necessary to review the tools to share data to coordinate an alert from the early stages.
Modulation Recognition Using Signal Enhancement and Multistage Attention Mechanism

Robustness against noise is critical for modulation recognition (MR) approaches deployed in real-world communication systems. In MR systems, a corrupted signal is normally enhanced using low-level signal enhancement (SE) before signal classification (SC). Many existing approaches address signal distortion problems by compartmentalizing SE from SC. While those approaches allow for efficient development, they also dictate compartmentalized performance metrics, without feedback from the SC module. For example, SE modules are designed using perceptual signal quality metrics but not with SC in mind. To improve the effectiveness of SE on MR, this paper proposes a joint learning framework consisting of three cascaded modules: dual-channel spectrum fusion, SE, and SC. Instead of separately processing SE and SC, these three modules are integrated into one framework and jointly trained with a single recognition loss. In contrast to estimating clean signals, the SE module in the proposed joint learning framework is trained to predict a ratio mask and find important time-frequency bins for the SC module. We integrate a multistage attention mechanism into the framework to further increase the robustness. The multistage attention mechanism is deployed to strengthen the recognition-related features learned from context information in channel, time, and frequency domains. We evaluate the recognition performance of the proposed framework and its modules on two benchmark datasets: RadioML2016.10a and RadioML2016.10b. The experiment results show that the proposed joint learning framework outperforms the separate learning framework. Moreover, comparisons are performed with several existing learning-based MR methods in the literature. The proposed joint learning framework leads to significant performance improvement, especially for modulated signals corrupted by channel noise.
Blockchain as a Service (BCaaS): A Value Modeling Approach in the Education Business Model

The adoption of Blockchain has caused the organization to rethink how it operates and adds value to its current processes.Some scholars refer to Blockchain as the Black Swan of the 21 st century, which will have a greater impact than the introduction of the Internet.Currently, there is little discussion about Blockchain technology and the value model approach and how organizations can close the gap between strategy and operational planning to successfully implement this technology in organizations.This paper explores the relationship and implications of Blockchain technology in relation to the value chain model in education.This paper proposes a value modeling approach for implementing Blockchain in the education sector and demonstrates how a solution works in practice.The novelty of the research will be from both theoretical and practical perspectives.The study will appraise enhancement that can be made within the existing literature on Blockchain and the value chain model, and how the alignment of the two concepts can be used to improve the use of the current value chain model in the education sector.
Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist.Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum.This is partly due to the rise in the use of license-free and managed shared bands as well as other opportunistic spectrum access solutions.As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important.In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as mitigation approaches based on expert systems.More recently, researchers have successfully explored artificial intelligence/machine learning enabled physical layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it.In this paper, we address the knowledge gap in literature with respect to the state-of-the-art in deep learning-based interference suppression.Specifically, we review a wide range of techniques that have used deep learning to suppress interference by learning interference characteristics directly from data, rather than relying on expert systems.We provide a thorough technical discussion of the prominent deep learning algorithms that have been proposed in the literature and provide comparison and guidelines regarding their successful implementation in this application.In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in this critical field.
Cyber Bullying Detection for Hindi-English Language Using Machine Learning

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