Reviewerly/exHarmony huggingface dataset:
A-BFPN: An Attention-Guided Balanced Feature Pyramid Network for SAR Ship Detection

Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and inland facilities in SAR images increase the difficulty for SAR ship detection. To increase the detection performance, in this paper, a novel deep learning network for SAR ship detection, termed as attention-guided balanced feature pyramid network (A-BFPN), is proposed to better exploit semantic and multilevel complementary features, which consists of the following two main steps. First, in order to reduce interferences from complex backgrounds, the enhanced refinement module (ERM) is developed to enable BFPN to learn the dependency features from the channel and space dimensions, respectively, which enhances the representation of ship objects. Second, the channel attention-guided fusion network (CAFN) model is designed to obtain optimized multi-scale features and reduce serious aliasing effects in hybrid feature maps. Finally, we illustrate the effectiveness of the proposed method, adopting the existing SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0). Experimental results show that the proposed method is superior to the existing algorithms, especially for multi-scale small ship targets under complex background.
Factors affecting healthcare providers to accept digital marketing: The moderating role of subjective norms

This research aimed at identifying the impact of (Perceived ease of use, Perceived usefulness, Perceived enjoyment and perceived trust), and examine the moderating role of subjective norms, on healthcare providers acceptance for digital marketing provided by the medical field companies. To achieve the goals of this research, the researcher relied on a descriptive and analytical approach. The research dealt with 400 healthcare providers, where the population consisted of all healthcare providers working in Amman, a convenience sample was chosen from the healthcare providers. Moreover, the questionnaire was the main tool for collecting data. Analyzing data was conducted using a set of statistical methods including exploratory factor analysis EFA, confirmatory factor analysis CFA, multiple regression, interactive hierarchical regression and process procedures method using (SPSS-V20) and (AMOS-V23). The key finding was there an impact of Perceived ease of use, Perceived usefulness, Perceived enjoyment and perceived trust on healthcare providers acceptance for digital marketing provided by the medical field companies. Recommendations and future research also were discussed.
The Face Deepfake Detection Challenge

Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable of working in an "in the wild" scenario; (ii) creating a method capable of reconstructing original images from Deepfakes. Real images from CelebA and FFHQ and Deepfake images created by StarGAN, StarGAN-v2, StyleGAN, StyleGAN2, AttGAN and GDWCT were collected for the competition. The winning teams were chosen with respect to the highest classification accuracy value (Task I) and "minimum average distance to Manhattan" (Task II). Deep Learning algorithms, particularly those based on the EfficientNet architecture, achieved the best results in Task I. No winners were proclaimed for Task II. A detailed discussion of teams' proposed methods with corresponding ranking is presented in this paper.
An IoT-Platform-Based Deep Learning System for Human Behavior Recognition in Smart City Monitoring Using the Berkeley MHAD Datasets

Internet of Things (IoT) technology has been rapidly developing and has been well utilized in the field of smart city monitoring. The IoT offers new opportunities for cities to use data remotely for the monitoring, smart management, and control of device mechanisms that enable the processing of large volumes of data in real time. The IoT supports the connection of instruments with intelligible features in smart cities. However, there are some challenges due to the ongoing development of these applications. Therefore, there is an urgent need for more research from academia and industry to obtain citizen satisfaction, and efficient architecture, protocols, security, and services are required to fulfill these needs. In this paper, the key aspects of an IoT infrastructure for smart cities were analyzed. We focused on citizen behavior recognition using convolution neural networks (CNNs). A new model was built on understanding human behavior by using the berkeley multimodal human action (MHAD) Datasets. A video surveillance system using CNNs was implemented. The proposed model’s simulation results achieved 98% accuracy for the citizen behavior recognition system.
Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

Seismology is among the intrinsic sciences that strictly affect human lives. Many research efforts are presented in the literature aiming at achieving risk mitigation and disaster management. More particularly, modern technologies have been employed in such a pivot. However, the day-to-day challenges and complexities of such natural science that face the stack holders still need more reliable and intelligent solutions. The solution can depend on a partial or integrated system of modern technologies. In this paper, we extensively survey the co-related modern technologies aiming to gather the major efforts exerted in this regard. It also outlines the desirability of seismology to modern technologies. Then, we present a detailed analysis of remote sensing and data communication networks (DCNs), which are considered the backend of seismic networks. Furthermore, for seismology, we depict both classical and non-classical approaches based on DCN principles, such as optical fiber-based acoustic sensors, social media, and the internet of things (IoT). Following that, a comprehensive description of the various optimization techniques utilized for seismic wave analysis and for prolonging network lifetime is offered. A description of the important functions that artificial intelligence (AI) can play in different fields of seismology is also included. Finally, we present some recommendations for stack holders to prevent natural calamities and preserve human lives.
A Fast Image Encryption Algorithm Based on Improved 6-D Hyper-Chaotic System

Aiming at the security and efficiency of image transmission, a fast image encryption algorithm based on an improved 6-D chaotic system is proposed.Firstly, we design a hyper-chaotic system with more complex chaotic behavior, analyze the Lyapunov exponential spectrum, chaotic attractor and randomness of the system, and generate random sequences through randomness enhancement operation.Secondly, image preprocessing is used to select pixels from the original image to form a thumbnail, the size of the key space can be changed by adjusting the thumbnail.Thirdly, the hash value of the original image is used as the initial values of the hyper-chaotic system to realize the uniqueness of the key.The row encryption matrix and column encryption matrix are generated according to the maximum and minimum values of row and column pixels in the thumbnail.These two encryption matrices are composed of the full arrangement of random sequences, which refers to the random combination of random sequences in a certain order.Before the encryption, the Arnold transformation is performed on the original image and then the cipher image is obtained by row encryption and column encryption respectively.The experimental results illustrate that the proposed algorithm has excellent security performance, robustness and the speed of encryption and decryption is very fast.
Recurrent Dynamic Embedding for Video Object Segmentation

Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However, 1) the hardware cannot withstand the ever-increasing memory requirements as the video length increases. 2) Storing lots of information inevitably introduces lots of noise, which is not conducive to reading the most important information from the memory bank. In this paper, we propose a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size. Specifically, we explicitly generate and update RDE by the proposed Spatio-temporal Aggregation Module (SAM), which exploits the cue of historical information. To avoid error accumulation owing to the recurrent usage of SAM, we propose an unbiased guidance loss during the training stage, which makes SAM more robust in long videos. Moreover, the predicted masks in the memory bank are inaccurate due to the inaccurate network inference, which affects the seg-mentation of the query frame. To address this problem, we design a novel self-correction strategy so that the network can repair the embeddings of masks with different qualities in the memory bank. Extensive experiments show our method achieves the best tradeoff between performance and speed. Code is available at https://github.com/Limingxing00/RDE-VOS-CVPR2022.
Blockchain Technology and Artificial Intelligence Together: A Critical Review on Applications

It is undeniable that the adoption of blockchain- and artificial intelligence (AI)-based paradigms is proceeding at lightning speed. Both paradigms provide something new to the market, but the degree of novelty and complexity of each is different. In the age of digital money, blockchains may automate installments to allow for the safe, decentralized exchange of personal data, information, and logs. AI and blockchains are two of the most talked about technologies right now. Using a decentralized, secure, and trustworthy system, blockchain technology can automate bitcoin payments and provide users access to a shared ledger of records, transactions, and data. Through the use of smart contracts, a blockchain may also regulate user interactions without the need for a central authority. As an alternative, AI provides robots with the ability to reason and make decisions and human-level intellect. This revelation led to a thorough assessment of the AI and blockchain combo created between 2012 and 2022. This critical review contains 121 articles from the recent decade that investigate the present situation and rationale of the AI and blockchain combination. The integration’s practical application is the emphasis of this overview’s meatiest portion. In addition, the gaps and problems of this combination in the linked literature have been studied, with a focus on the constraints.
Readout of a quantum processor with high dynamic range Josephson parametric amplifiers

We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in which the active nonlinear element is implemented using an array of rf-SQUIDs. The device is matched to the 50 Ω environment with a Klopfenstein-taper impedance transformer and achieves a bandwidth of 250–300 MHz with input saturation powers up to −95 dBm at 20 dB gain. A 54-qubit Sycamore processor was used to benchmark these devices, providing a calibration for readout power, an estimation of amplifier added noise, and a platform for comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. We find that the high power rf-SQUID array design has no adverse effect on system noise, readout fidelity, or qubit dephasing, and we estimate an upper bound on amplifier added noise at 1.6 times the quantum limit. Finally, amplifiers with this design show no degradation in readout fidelity due to gain compression, which can occur in multi-tone multiplexed readout with traditional JPAs.
A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.
Reasoning and Tuning: Graph Attention Network for Occluded Person Re-Identification

Occluded person re-identification (re-id) aims to match occluded person images to holistic ones. Most existing works focus on matching collective-visible body parts by discarding the occluded parts. However, only preserving the collective-visible body parts causes great semantic loss for occluded images, decreasing the confidence of feature matching. On the other hand, we observe that the holistic images can provide the missing semantic information for occluded images of the same identity. Thus, compensating the occluded image with its holistic counterpart has the potential for alleviating the above limitation. In this paper, we propose a novel Reasoning and Tuning Graph Attention Network (RTGAT), which learns complete person representations of occluded images by jointly reasoning the visibility of body parts and compensating the occluded parts for the semantic loss. Specifically, we self-mine the semantic correlation between part features and the global feature to reason the visibility scores of body parts. Then we introduce the visibility scores as the graph attention, which guides Graph Convolutional Network (GCN) to fuzzily suppress the noise of occluded part features and propagate the missing semantic information from the holistic image to the occluded image. We finally learn complete person representations of occluded images for effective feature matching. Experimental results on occluded benchmarks demonstrate the superiority of our method.
Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images

Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. However, little attention has been paid to this topic in Optical Remote Sensing Images (O-RSIs). To this end, we focus on this research, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., PAs on object detection in O-RSIs, and propose a more Threatening PA without the scarification of the visual quality, dubbed TPA. Specifically, to address the problem of inconsistency between local and global landscapes in existing patch selection schemes, we propose leveraging the First-Order Difference (FOD) of the objective function before and after masking to select the sub-patches to be attacked. Further, considering the problem of gradient inundation when applying existing coordinate-based loss to PAs directly, we design an IoU-based objective function specific for PAs, dubbed Bounding box Drifting Loss (BDL), which pushes the detected bounding boxes far from the initial ones until there are no intersections between them. Finally, on two widely used benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., DIOR and DOTA, comprehensive evaluations of our TPA with four typical detectors (Faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable effectiveness. To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic.
Tutorial on Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents capable of understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in healthcare and robotics, multimodality has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this tutorial is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. Building upon a new edition of our survey paper on multimodal ML and academic courses at CMU, this tutorial will cover three topics: (1) what is multimodal: the principles in learning from heterogeneous, connected, and interacting data, (2) why is it hard: a taxonomy of six core technical challenges faced in multimodal ML but understudied in unimodal ML, and (3) what is next: major directions for future research as identified by our taxonomy.
A PDDL+ Benchmark Problem: The Batch Chemical Plant

The PDDL+ language has been mainly devised to allow modelling of real-world systems, with continuous, time-dependant dynamics. Several interesting case studies with these characteristics have been also proposed, to test the language expressiveness and the capabilities of the support tools. However, most of these case studies have not been completely developed so far. In this paper we focus on the batch chemical plant case study, a very complex hybrid system with nonlinear dynamics that could represent a challenging benchmark problem for planning techniques and tools. We present a complete PDDL+ model for such system, and show an example application where the UPMurphi universal planner is used to generate a set of production policies for the plant.
Self-Taught Decision Theoretic Planning with First Order Decision Diagrams

We present a new paradigm for planning by learning, where the planner is given a model of the world and a small set of states of interest, but no indication of optimal actions in these states. The additional information can help focus the planner on regions of the state space that are of interest and lead to improved performance. We demonstrate this idea by introducing novel model-checking reduction operations for First Order Decision Diagrams (FODD), a representation that has been used to implement decision-theoretic planning with Relational Markov Decision Processes (RMDP). Intuitively, these reductions modify the construction of the value function by removing any complex specifications that are irrelevant to the set of training examples, thereby focusing on the region of interest. We show that such training examples can be constructed on the fly from a description of the planning problem thus we can bootstrap to get a self-taught planning system. Additionally, we provide a new heuristic to embed universal and conjunctive goals within the framework of RMDP planners, expanding the scope and applicability of such systems. We show that these ideas lead to significant improvements in performance in terms of both speed and coverage of the planner, yielding state of the art planning performance on problems from the International Planning Competition.
Clustering-Based Compression for Population DNA Sequences

Due to the advancement of DNA sequencing techniques, the number of sequenced individual genomes has experienced an exponential growth. Thus, effective compression of this kind of sequences is highly desired. In this work, we present a novel compression algorithm called Reference-based Compression algorithm using the concept of Clustering (RCC). The rationale behind RCC is based on the observation about the existence of substructures within the population sequences. To utilize these substructures, <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means clustering is employed to partition sequences into clusters for better compression. A reference sequence is then constructed for each cluster so that sequences in that cluster can be compressed by referring to this reference sequence. The reference sequence of each cluster is also compressed with reference to a sequence which is derived from all the reference sequences. Experiments show that RCC can further reduce the compressed size by up to 91.0 percent when compared with state-of-the-art compression approaches. There is a compromise between compressed size and processing time. The current implementation in Matlab has time complexity in a factor of thousands higher than the existing algorithms implemented in C/C++. Further investigation is required to improve processing time in future.
AAANE: Attention-Based Adversarial Autoencoder for Multi-scale Network Embedding

Network embedding represents nodes in a continuous vector space and preserves structure information from a network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding (AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: (1) an attention-based autoencoder that effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training, and (2) an adversarial regularization guides the autoencoder in learning robust representations by matching the posterior distribution of the latent embeddings to a given prior distribution. Experimental results on real-world networks show that the proposed approach outperforms strong baselines.
On the Detection of Passive Malicious Providers in Cloud Federations

Cloud federation has emerged as a new business architecture which aims to help cloud providers cope with the increased waves of demands on their resources and services. Although plenty of solutions have been proposed trying to ensure the optimal formation of cloud federations, these approaches ignore the problem of encountering malicious providers that join federations to destroy them from inside and exclude some strong competitors from the market. To tackle this challenge, we propose, in this letter, a maximin game theoretical model which assists the broker, responsible for creating and managing federations, with maximizing the detection of such malicious providers. The challenge here is to deal with providers that try to minimize the detection maximization through distributing their misbehavior over several federations and changing their identities from time to time. Experiments conducted using real data from the CloudHarmony dataset reveal that our solution maximizes the detection of malicious providers and improves the profit and quality of service of the federations compared with the sky federation model.