Reviewerly/exHarmony huggingface dataset:
Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem

The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO) to enhance the diversity and convergence performance and improve the performance in high-dimensional cases for 0-1 knapsack problems. The proposed algorithm adopts quantum computing principles such as quantum superposition states and quantum gates. It also uses adaptive mutation operations of differential evolution, crossover operations of differential evolution, and quantum observation to generate new solutions as trial individuals. Selection operations are used to determine the better solutions between the stored individuals and the trial individuals created by mutation and crossover operations. In the event that the trial individuals are worse than the current individuals, the adaptive grey wolf optimizer and quantum rotation gate are used to preserve the diversity of the population as well as speed up the search for the global optimal solution. The experimental results for 0-1 knapsack problems confirm the advantages of QDGWO with the effectiveness and global search capability for knapsack problems, especially for high-dimensional situations.
Linear Semantics in Generative Adversarial Networks

Generative Adversarial Networks (GANs) are able to generate high-quality images, but it remains difficult to explicitly specify the semantics of synthesized images. In this work, we aim to better understand the semantic representation of GANs, and thereby enable semantic control in GAN’s generation process. Interestingly, we find that a well-trained GAN encodes image semantics in its internal feature maps in a surprisingly simple way: a linear transformation of feature maps suffices to extract the generated image semantics. To verify this simplicity, we conduct extensive experiments on various GANs and datasets; and thanks to this simplicity, we are able to learn a semantic segmentation model for a trained GAN from a small number (e.g., 8) of labeled images. Last but not least, leveraging our finding, we propose two few-shot image editing approaches, namely Semantic-Conditional Sampling and Semantic Image Editing. Given a trained GAN and as few as eight semantic annotations, the user is able to generate diverse images subject to a user-provided semantic layout, and control the synthesized image semantics. We have made the code publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A privacy protection scheme for telemedicine diagnosis based on double blockchain

With the development of the electronic healthcare industry, telemedicine diagnosis (TMD) has become a more flexible and convenient way. In the general remote diagnosis scheme, the collected medical data is stored in the cloud which may be leaked or tempered with. Since blockchain has the characteristics of centralized storage, some schemes employ blockchains to overcome the security problem of cloud storage, but most of them lack a supervision mechanism. The identity information provided by the doctor to the user must be accurate. However, the digital certificate issued by the certificate authority (CA) may have the problem of information update latency. To solve the above questions, we propose DBTMD, a TMD privacy protection scheme based on double blockchain. We have constructed two types of blockchains, Userchain and Medicalchain, and designed five transactions. Users, doctors, and hospitals can implement secure data interaction, and hospitals can supervise doctors effectively. We have developed an identity authentication chain to ensure the real-time accuracy of the doctor’s identity information. Security analysis and experimental analysis show that our scheme greatly reduces the communication costs of keys’ transaction compared with existing schemes.
Debiasing Learning based Cross-domain Recommendation

As it becomes prevalent that user information exists in multiple platforms or services, cross-domain recommendation has been an important task in industry. Although it is well known that users tend to show different preferences in different domains, existing studies seldom model how domain biases affect user preferences. Focused on this issue, we develop a casual-based approach to mitigating the domain biases when transferring the user information cross domains. To be specific, this paper presents a novel debiasing learning based cross-domain recommendation framework with causal embedding. In this framework, we design a novel Inverse-Propensity-Score (IPS) estimator designed for cross-domain scenario, and further propose three kinds of restrictions for propensity score learning. Our framework can be generally applied to various recommendation algorithms for cross-domain recommendation. Extensive experiments on both public and industry datasets have demonstrated the effectiveness of the proposed framework.
Deepfakes Creation and Detection Using Deep Learning

Deep learning has been used in a wide range of applications like computer vision, natural language processing and image detection. The advancement in deep learning algorithms in image detection and manipulation has led to the creation of deepfakes, deepfakes use deep learning algorithms to create fake images that are at times very hard to distinguish from real images. With the rising concern around personal privacy and security, Many methods to detect deepfake images have emerged, in this paper the use of deep learning for creating as well as detecting deepfakes is explored, this paper also propose the use of deep learning image enhancement method to improve the quality of deepfakes created.
Path planning of AUV during diving process based on behavioral decision-making

This study proposes a path planning method based on behavioral decision-making (PPM-BBD). An optimized path is planned for autonomous underwater vehicle (AUV) to save energy during the diving process. The key idea is to apply success-history based adaptive differential evolution algorithm including linear population size reduction (L-SHADE) to the optimization of energy. In addition, motion constraints are considered during the diving process to ensure that the path points are reachable. We take the sequence of motion angles as the population, and propose an angle modification strategy to apply to the population evolution process, so that the angle sequence remains in the space of feasible solutions. The modification strategy lays a theoretical foundation for the application of L-SHADE in this path planning problem. The performance of PPM-BBD is evaluated on data from simulation test. The most suitable control parameters are determined through simulation experiments. The effects of PPM-BBD with L-SHADE and five types of DE are compared. The experimental evaluation was performed through sea trials in Tuandao Bay, Qingdao, China. Results show that PPM-BBD saves at least 9% energy compared to the other algorithms. It is concluded that it is workable to obtain a reachable path with optimized energy consumption during diving process.
Discovering the global landscape of fraud detection studies: a bibliometric review

Purpose This paper aims to present a bibliometric analysis of publications from the Scopus database on fraud detection studies. Design/methodology/approach The current research used Microsoft Excel to conduct the frequency analysis, VOSviewer for data visualisation and Harzing’s Publish or Perish for citation metrics and analysis. Findings In alignment with these research results, the publications on fraud detection studies have been consistently increasing since 2005. India was rated first as the most active country in fraud detection research. Tongji University from China was the most active institution that published significant publications related to fraud detection research. A total of 160 scholars from 89 various countries and 160 different institutions published several fraud detections studies with multi-authors’ participation in different languages. Originality/value To the best of the authors knowledge, this study is the first study to review fraud detections publications in the Scopus science database.
Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention

Due to the complex background and spatial distribution, it brings great challenge to object detection in high-resolution remote sensing images. In view of the characteristics of various scales, arbitrary orientations, shape variations, and dense arrangement, a multiscale object detection method in high-resolution remote sensing images is proposed by using rotation invariance deep features driven by channel attention. First, a channel attention module is added to our feature fusion and scaling-based single shot detector (FS-SSD) to strengthen the long-term semantic dependence between objects for improving the discriminative ability of the deep features. Then, an oriented response convolution is followed to generate feature maps with orientation channels to produce rotation invariant deep features. Finally, multiscale objects are predicted in a high-resolution remote sensing image by fusing various scale feature maps with multiscale feature module in FS-SSD. Five experiments are conducted on NWPU VHR-10 dataset and achieve better detection performance compared with the state-of-the-art methods.
From warm to hot starts

The serverless computing model leverages high-level languages, such as JavaScript and Java, to raise the level of abstraction for cloud programming. However, today's design of serverless computing platforms based on stateless short-lived functions leads to missed opportunities for modern runtimes to optimize serverless functions through techniques such as JIT compilation and code profiling.
Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models

We study two state of the art deep generative networks, the Introspective Variational Autoencoder and the Style-Based Generative Adversarial Network, for the generation of new diffusion-weighted magnetic resonance images. We show that high quality, diverse and realistic-looking images, as evaluated by external neuroradiologists blinded to the whole study, can be synthesized using these deep generative models. We evaluate diverse metrics with respect to quality and diversity of the generated synthetic brain images. These findings show that generative models could qualify as a method for data augmentation in the medical field, where access to large image database is in many aspects restricted.
High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptation

3D video avatars can empower virtual communications by providing compression, privacy, entertainment, and a sense of presence in AR/VR. Best 3D photo-realistic AR/VR avatars driven by video, that can minimize uncanny effects, rely on person-specific models. However, existing person-specific photo-realistic 3D models are not robust to lighting, hence their results typically miss subtle facial behaviors and cause artifacts in the avatar. This is a major drawback for the scalability of these models in communication systems (e.g., Messenger, Skype, FaceTime) and AR/VR. This paper addresses previous limitations by learning a deep learning lighting model, that in combination with a high-quality 3D face tracking algorithm, provides a method for subtle and robust facial motion transfer from a regular video to a 3D photo-realistic avatar. Extensive experimental validation and comparisons to other state-of-the-art methods demonstrate the effectiveness of the proposed framework in real-world scenarios with variability in pose, expression, and illumination. Our project page can be found at this website.
Look Before you Speak: Visually Contextualized Utterances

While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it’s available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. Our task involves predicting the next utterance in a video, using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Leveraging recent advances in multimodal learning, our model consists of a novel co-attentional multimodal video transformer, and when trained on both textual and visual context, outperforms baselines that use textual inputs alone. Further, we demonstrate that our model trained for this task on unlabelled videos achieves state-of-the-art performance on a number of downstream VideoQA benchmarks such as MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA.
Feature learning and patch matching for diverse image inpainting

We present an image inpainting approach to generate diverse high-quality inpainting results. Recent advances in deep adversarial networks have led to significant improvements in the challenging task of filling large holes in natural images. Although deep generative models can generate visually plausible structures and textures, most of them are not interpretable, making it difficult to control the inpainting output. In addition, deep generative models do not have capacity to produce diverse results for each input. To address such limitations, we design a novel free-form image inpainting framework with two sequential steps: the first step formulates the inpainting process as a regression problem and utilizes a U-Net-like convolutional neural network to map an input to a coarse inpainting output, and the second step utilizes the nearest neighbor based pixel-wise matching to map the coarse output to diverse high-quality outputs. The second step allows our approach to compose novel high-quality content by copy-pasting high-frequency missing information from different training exemplars. Experiments on multiple datasets, i.e., CelebA-HQ, AFHQ, and Paris StreetView, show that our approach is able to offer multiple natural outputs with higher diversity in a controllable manner.
Expressive Bilateral Access Control for Internet-of-Things in Cloud-Fog Computing

As a versatile system architecture, cloud-fog Internet-of-Things~(IoT) enables multiple resource-constrained devices to communicate and collaborate with each other. By outsourcing local data and immigrating expensive workloads to cloud service providers and fog nodes (FNs), resource-constrained devices can enjoy data services with low latency and minimal cost. To protect data security and privacy in the untrusted cloud-fog environment, many cryptographic mechanisms have been invented. Unfortunately, most of them are impractical when directly applied to cloud-fog IoT computing, mainly due to the large number of resource-constrained end-devices (EDs). In this paper, we present a secure cloud-fog IoT data sharing system with bilateral access control based on a new cryptographic tool called lightweight matchmaking encryption. Our system enforces both sender access control and receiver access control simultaneously and adapts to resource-constrained EDs by outsourcing costly workloads to FNs. We conduct extensive experiments to demonstrate the superior performance of our system to the most relevant solutions in the literature.
LPSNet: A lightweight solution for fast panoptic segmentation

Panoptic segmentation is a challenging task aiming to simultaneously segment objects (things) at instance level and background contents (stuff) at semantic level. Existing methods mostly utilize a two-stage detection network to attain instance segmentation results, and a fully convolutional network to produce a semantic segmentation prediction. Post-processing or additional modules are required to handle the conflicts between the outputs from these two nets, which makes such methods suffer from low efficiency, heavy memory consumption and complicated implementation. To simplify the pipeline and decrease computation/memory cost, we propose an one-stage approach called Lightweight Panoptic Segmentation Network (LPSNet), which does not involve a proposal, anchor or mask head. Instead, we predict a bounding box and semantic category at each pixel upon the feature map produced by an augmented feature pyramid, and design a parameter-free head to merge the per-pixel bounding box and semantic prediction into panoptic segmentation output. Our LPSNet is not only efficient in computation and memory, but also accurate in panoptic segmentation. Comprehensive experiments on COCO, Cityscapes and Mapillary Vistas datasets demonstrate the promising effectiveness and efficiency of the proposed LPSNet.
Semiautomatic robust regression clustering of international trade data

The purpose of this paper is to show in regression clustering how to choose the most relevant solutions, analyze their stability, and provide information about best combinations of optimal number of groups, restriction factor among the error variance across groups and level of trimming. The procedure is based on two steps. First we generalize the information criteria of constrained robust multivariate clustering to the case of clustering weighted models. Differently from the traditional approaches which are based on the choice of the best solution found minimizing an information criterion (i.e. BIC), we concentrate our attention on the so called optimal stable solutions. In the second step, using the monitoring approach, we select the best value of the trimming factor. Finally, we validate the solution using a confirmatory forward search approach. A motivating example based on a novel dataset concerning the European Union trade of face masks shows the limitations of the current existing procedures. The suggested approach is initially applied to a set of well known datasets in the literature of robust regression clustering. Then, we focus our attention on a set of international trade datasets and we provide a novel informative way of updating the subset in the random start approach. The Supplementary material, in the spirit of the Special Issue, deepens the analysis of trade data and compares the suggested approach with the existing ones available in the literature.
Analysis of Perceptual Hashing Algorithms in Image Manipulation Detection

Perceptual image hashing is a family of algorithms that generate content-based image hashes. Unlike cryptographic hashes, perceptual hashes are designed to not change much when an image undergoes minor modifications such as compression, color-correction, and brightness. Therefore, these algorithms have been found useful in detecting redundant images, perform reverse image search, and flagging/filtering inappropriate imagery by comparing image hashes with a dataset of known perceptual hashes. While these applications have been served well, there is a new threat to visual information in the form of misinformation through content changing image manipulation using techniques such as object addition/removal and copy-move. While perceptual hashing algorithms can very accurately identify copies of similar images and their legitimately modified versions, their effectiveness in detecting content changing manipulations is not studied well. Existing research and advanced image libraries propose various image hashing algorithms, and several datasets of manipulated images are available in the public domain. In this work, a review and performance analysis of several perceptual hashing algorithms against two such datasets is presented. Results show less than ideal performance in distinguishing maliciously manipulated images from legitimate ones. Based on the results, limitations of perceptual hashing algorithms are identified, and future research directions are proposed.
An Automatic Detection Algorithm of Metro Passenger Boarding and Alighting Based on Deep Learning and Optical Flow

Urban rail transit is one of the important application areas of autonomous driving, which will require an algorithm to monitor the exchange of passengers between metro carriages and stations. To this end, this article first proposes a metro passenger detection (MPD) algorithm to track passengers getting on or off, namely the MPD algorithm. The algorithm is composed of two MetroNexts and an optical flow algorithm. The two MetroNexts are based on a novel multiple scales attention convolution (MSAC) block and detect metro carriages and passengers fast and accurately with small model size. The optical flow algorithm predicts the direction of moving passengers, which help the MPD algorithm filter out unrelated ones. Combining the coordinates and moving direction of passengers, the MPD algorithm can determine whether passengers are boarding or alighting. Based on various benchmark data sets and a new metro station data set, the experimental results have demonstrated that the MSAC block is effective to improve detection accuracy. Compared with the existing state-of-the-art detection networks, the two MetroNexts have achieved competitive results with good time/memory efficiency on pedestrian data sets. On the metro station data set, the MPD algorithm robustly recognizes target passengers, the detection speed of which remains competitive even on the embedded platform, thus becoming an intelligent instrument of the metro station.
A meta-heuristic density-based subspace clustering algorithm for high-dimensional data

Subspace clustering is one of the efficient techniques for determining the clusters in different subsets of dimensions. Ideally, these techniques should find all possible non-redundant clusters in which the data point participates. Unfortunately, existing hard subspace clustering algorithms fail to satisfy this property. Additionally, with the increase in dimensions of data, classical subspace algorithms become inefficient. This work presents a new density-based subspace clustering algorithm (S_FAD) to overcome the drawbacks of classical algorithms. S_FAD is based on a bottom-up approach and finds subspace clusters of varied density using different parameters of the DBSCAN algorithm. The algorithm optimizes parameters of the DBCAN algorithm through a hybrid meta-heuristic algorithm and uses hashing concepts to discover all non-redundant subspace clusters. The efficacy of S_FAD is evaluated against various existing subspace clustering algorithms on artificial and real datasets in terms of F_Score and rand_index. Performance is assessed based on three parameters: average ranking, SRR ranking, and scalability on varied dimensions. Statistical analysis is performed through the Wilcoxon signed-rank test. Results reveal that S_FAD performs considerably better on the majority of the datasets and scales well up to 6400 dimensions on the actual dataset.