Reviewerly/exHarmony huggingface dataset:
Online Meta Adaptation for Variable-Rate Learned Image Compression

This work addresses two major issues of end-to-end learned image compression (LIC) based on deep neural networks: variable-rate learning where separate networks are required to generate compressed images with varying qualities, and the train-test mismatch between differentiable approximate quantization and true hard quantization. We introduce an online meta-learning (OML) setting for LIC, which combines ideas from meta learning and online learning in the conditional variational auto-encoder (CVAE) framework. By treating the conditional variables as meta parameters and treating the generated conditional features as meta priors, the desired reconstruction can be controlled by the meta parameters to accommodate compression with variable qualities. The online learning framework is used to update the meta parameters so that the conditional reconstruction is adaptively tuned for the current image. Through the OML mechanism, the meta parameters can be effectively updated through SGD. The conditional reconstruction is directly based on the quantized latent representation in the decoder network, and therefore helps to bridge the gap between the training estimation and true quantized latent distribution. Experiments demonstrate that our OML approach can be flexibly applied to different state-of-the-art LIC methods to achieve additional performance improvements with little computation and transmission overhead.
Secure and privacy-preserving D2D communication in fog computing services

Device-to-device (D2D)-aided fog network has proven to be a viable means to improve the performance of fog computing technology. To exploit the full potentials of the D2D-supported fog computing architecture, it is imperative to protect the information transmitted among collaborating fog nodes against unauthorized access as failure to do so may have catastrophic consequences. However, this critical issue has not been investigated in most of the existing works. Therefore, this paper proposes a secure and privacy-aware trust-based communication strategy for fog-to-fog collaborative services. An elliptic curve cryptography (ECC)-based two-party authentication and key agreement protocol is designed to allow two collaborating fog nodes generate a unique shared secret session key. A multi-colony head selection strategy is proposed using a multi-criteria decision-making method (MCDM), where multiple fog nodes are selected as colony head. A new efficient group key distribution and updating mechanism is designed for ensuring secured group communication. Consequently, ECC-based message dissemination approaches are designed for intra- and inter-colony trustworthy communications, where the generated secret session and group keys are used for data encryption. Moreover, we demonstrate that the proposed scheme guarantees data confidentiality and unforgeability against both Type I and Type II adversaries in the random oracle model (ROM) based on the intractability of the Computational Diffie–Hellman Problem (CDHP) and Discrete Logarithm Problem (DLP), respectively. An extensive performance analysis shows that the proposed scheme is very efficient and suitable for practical deployment. Comparative analysis reveals that the proposed scheme reports an improvement of about 33.15%–93.88%, 18.87%–67.91% , and 38.5%–93.8% in computation cost, communication overhead, and energy consumption, respectively, over the state-of-the-art related schemes.
Owner-Enabled Secure Authorized Keyword Search Over Encrypted Data With Flexible Metadata

Metadata plays an essential role in facilitating data organizing, finding, and understanding, but it also contains lots of sensitive information about the data and the data users, e.g., the location where a picture was taken. In practice, when sensitive data is encrypted before being uploaded to untrusted public clouds, an oblivious dilemma comes: if the metadata is totally encrypted, and its functionalities no longer exist; otherwise, sensitive information may be leaked. Hence, a secure and flexible mechanism for processing different fields (marked private or public for different scenario needs) of metadata simultaneously is desirable. We searched the literature for methods of achieving such a goal, it turned out that this was not explicitly considered or reasonably solved before. Therefore, in this paper, we investigate the problem of constructing privacy-enhancing metadata, namely, 1) flexible and tamper-resistant metadata setting, 2) owner-enabled secure search authorization with explicit metadata. Based on the concept of public key encryption with keyword search (PEKS), we propose a novel Authorized Keyword Search over Encrypted Data with Metadata scheme (MD-AKS), which firstly well addressed the above demands. We formalize the security model and prove the security of MD-AKS scheme. Our work maximizes the flexibility of metadata setting in two aspects: the associated metadata can be set as an arbitrary string and the costs of clients are independent of explicit metadata’s complexity. We implement MD-AKS, the theoretical comparison and experiment results further demonstrate the usability and scalability.
An Adaptive Mechanism for Virtual Machine Migration in the Cloud Environment

To manage all the operations of data centers resources, virtualization is the effective technique. In virtualization the virtual machine migration is the way by which data center operator can easily adapt the replacement of virtual machine, improves the resource provisioning and any other maintenance function of data center. Despite of this the virtual machine migration scheme is the major challenge to improve the efficiency of data center. This paper proposed a virtual machine migration process which will be responsible to minimize the migration which leads to reduce the execution time.
Self-Supervised Monocular Scene Decomposition and Depth Estimation

Self-supervised monocular depth estimation approaches either ignore independently moving objects in the scene or need a separate segmentation step to identify them. We propose MonoDepthSeg to jointly estimate depth and segment moving objects from monocular video without using any ground-truth labels. We decompose the scene into a fixed number of components where each component corresponds to a region on the image with its own transformation matrix representing its motion. We estimate both the mask and the motion of each component efficiently with a shared encoder. We evaluate our method on three driving datasets and show that our model clearly improves depth estimation while decomposing the scene into separately moving components.
Asynchronous Federated Learning for Sensor Data with Concept Drift

Federated learning (FL) involves multiple distributed devices jointly training a shared model without any of the participants having to reveal their local data to a centralized server. Most of previous FL approaches assume that data on devices are fixed and stationary during the training process. However, this assumption is unrealistic because these devices usually have varying sampling rates and different system configurations. In addition, the underlying distribution of the device data can change dynamically over time, which is known as concept drift. Concept drift makes the learning process complicated because of the inconsistency between existing and upcoming data. Traditional concept drift handling techniques such as chunk based and ensemble learning-based methods are not suitable in the federated learning frameworks due to the heterogeneity of local devices. We propose a novel approach, FedConD, to detect and deal with the concept drift on local devices and minimize the effect on the performance of models in asynchronous FL. The drift detection strategy is based on an adaptive mechanism which uses the historical performance of the local models. The drift adaptation is realized by adjusting the regularization parameter of objective function on each local device. Additionally, we design a communication strategy on the server side to select local updates in a prudent fashion and speed up model convergence. Experimental evaluations on three evolving data streams and two image datasets show that FedConD detects and handles concept drift, and also reduces the overall communication cost compared to other baseline methods.
Center-Aware Adversarial Autoencoder for Anomaly Detection

Anomaly detection based on subspace learning has attracted much attention, in which the compactness of subspace is commonly considered as the core concern. Most related studies directly optimize the distance from the subspace representation to the fixed center, and the influence of the anomaly level of each normal sample is not considered to adjust the normal concentrated areas. In such cases, it is difficult to isolate the normal areas from the anomaly ones by making the subspace compact. To this end, we propose a center-aware adversarial autoencoder (CA-AAE) method, which detects anomaly samples by acquiring more compact and discriminative subspace representations. To fully exploit the subspace information to improve the compactness, anomaly-level description and feature learning are novelly integrated herein by dividing the output space of the encoder into presubspace and postsubspace. In presubspace, the toward-center prior distribution is imposed by the adversarial learning mechanism, and the anomaly level of normal samples can be described from a probabilistic perspective. In postsubspace, a novel center-aware strategy is established to enhance the compactness of the postsubspace, which achieves adaptive adjustment of the normal areas by constructing a weighted center based on the anomaly level. Then, a flexible anomaly score function is constructed in the testing stage, in which both the toward-center loss and the reconstruction loss are combined to balance the information in the learned subspace and the original space. Compared to other related methods, the proposed CA-AAE shows the effectiveness and advantages in numerical experiments.
Soccer Field Boundary Detection Using Convolutional Neural Networks

Detecting the field boundary is often one of the first steps in the vision pipeline of soccer robots. Conventional methods make use of a (possibly adaptive) green classifier, selection of boundary points and possibly model fitting. We present an approach to predict the coordinates of the field boundary column-wise in the image using a convolutional neural network. This is combined with a method to let the network predict the uncertainty of its output, which allows to fit a line model in which columns are weighted according to the network's confidence. Experiments show that the resulting models are accurate enough in different lighting conditions as well as real-time capable. Code and data are available online ( https://github.com/bhuman/DeepFieldBoundary , https://sibylle.informatik.uni-bremen.de/public/datasets/fieldboundary ).
Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition

In our daily life, a large number of activities require identity verification, e.g., ePassport gates. Most of those verification systems recognize who you are by matching the ID document photo (ID face) to your live face image (spot face). The ID vs. Spot (IvS) face recognition is different from general face recognition where each dataset usually contains a small number of subjects and sufficient images for each subject. In IvS face recognition, the datasets usually contain massive class numbers (million or more) while each class only has two image samples (one ID face and one spot face), which makes it very challenging to train an effective model (e.g., excessive demand on GPU memory if conducting the classification on such massive classes, hardly capture the effective features for bisample data of each identity, etc.). To avoid the excessive demand on GPU memory, a two-stage training method is developed, where we first train the model on the dataset in general face recognition (e.g., MS-Celeb-1M) and then employ the metric learning losses (e.g., triplet and quadruplet losses) to learn the features on IvS data with million classes. To extract more effective features for IvS face recognition, we propose two novel algorithms to enhance the network by selecting harder samples for training. Firstly, a Cross-Batch Hard Example Mining (CB-HEM) is proposed to select the hard triplets from not only the current mini-batch but also past dozens of mini-batches (for convenience, we use batch to denote a mini-batch in the following), which can significantly expand the space of sample selection. Secondly, a Pseudo Large Batch (PLB) is proposed to virtually increase the batch size with a fixed GPU memory. The proposed PLB and CB-HEM can be employed simultaneously to train the network, which dramatically expands the selecting space by hundreds of times, where the very hard sample pairs especially the hard negative pairs can be selected for training to enhance the discriminative capability. Extensive comparative evaluations conducted on multiple IvS benchmarks demonstrate the effectiveness of the proposed method.
Reversible Linguistic Steganography With Bayesian Masked Language Modeling

Text authentication serves a vital role in the defense of digital identity and content against various types of cybercrime. The use of a digital signature is a common cryptographic technique for text authentication. Linguistic steganography can be applied to further conceal a digital signature within the corresponding text to facilitate data management. However, steganographic distortion lurking in the text, albeit almost imperceptible, has the potential to cause automatic computing machinery to make biased decisions. This has led to an interest in the pursuit of reversibility, the ability to reverse a steganographic process and remove distortion. In this article, we propose a reversible steganographic system for natural language text. We use a pre-trained transformer neural network for masked language modeling and embed messages in a reversible manner via predictive word substitution. Furthermore, we derive an adaptive steganographic route by taking account of predictive uncertainty, which is quantified based on a theoretical framework of Bayesian deep learning. Experimental results show that the proposed steganographic system can attain a proper balance between capacity, imperceptibility, and reversibility with close semantic and sentimental similarities between cover and stego texts.
Extending Momentum Contrast With Cross Similarity Consistency Regularization

Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs. However, in general the interplay between the negative pairs is ignored as they do not put in place special mechanisms to treat negative pairs differently according to their specific differences and similarities. In this paper, we present Extended Momentum Contrast (XMoCo), a self-supervised representation learning method founded upon the legacy of the momentum-encoder unit proposed in the MoCo family configurations. To this end, we introduce a cross consistency regularization loss, with which we extend the transformation consistency to dissimilar images (negative pairs). Under the cross consistency regularization rule, we argue that semantic representations associated with any pair of images (positive or negative) should preserve their cross-similarity under pretext transformations. Moreover, we further regularize the training loss by enforcing a uniform distribution of similarity over the negative pairs across a batch. The proposed regularization can easily be added to existing self-supervised learning algorithms in a plug-and-play fashion. Empirically, we report a competitive performance on the standard Imagenet-1K linear head classification benchmark. In addition, by transferring the learned representations to common downstream tasks, we show that using XMoCo with the prevalently utilized augmentations can lead to improvements in the performance of such tasks. We hope the findings of this paper serve as a motivation for researchers to take into consideration the important interplay among the negative examples in self-supervised learning.
Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond

Background and Aims We aim to develop a diagnostic tool for pathological-image classification using transfer learning that can be applied to diverse tumor types. Methods Microscopic images of liver tissue with and without hepatocellular carcinoma (HCC) were used to train and validate the classification framework based on a convolutional neural network. To evaluate the universal classification performance of the artificial intelligence (AI) framework, histological images from colorectal tissue and the breast were collected. Images for the training and validation sets were obtained from the Xiamen Hospital of Traditional Chinese Medicine, and those for the test set were collected from Zhongshan Hospital Xiamen University. The accuracy, sensitivity, and specificity values for the proposed framework were reported and compared with those of human image interpretation. Results In the human–machine comparisons, the sensitivity, and specificity for the AI algorithm were 98.0, and 99.0%, whereas for the human experts, the sensitivity ranged between 86.0 and 97.0%, while the specificity ranged between 91.0 and 100%. Based on transfer learning, the accuracies of the AI framework in classifying colorectal carcinoma and breast invasive ductal carcinoma were 96.8 and 96.0%, respectively. Conclusion The performance of the proposed AI framework in classifying histological images with HCC was comparable to the classification performance achieved by human experts, indicating that extending the proposed AI’s application to diagnoses and treatment recommendations is a promising area for future investigation.
Human Motion Enhancement via Tobit Kalman Filter-Assisted Autoencoder

We present a novel approach to enhance the quality of human motion data collected by low-cost depth sensors, namely D-Mocap, which suffers from low accuracy and poor stability due to occlusion, interference, and algorithmic limitations. Our approach takes advantage of a large set of high-quality and diverse Mocap data by learning a general motion manifold via the convolutional autoencoder. In addition, the Tobit Kalman filter (TKF) is used to capture the kinematics of each body joint and handle censored measurement distribution. The TKF is incorporated with the autoencoder via latent space optimization, maintaining adherence to the motion manifold while preserving the kinematic nature of the original motion data. Furthermore, due to the lack of an open source benchmark dataset for this research, we have developed an extension of the Berkeley Multimodal Human Action Database (MHAD) by generating D-Mocap data from RGB-D images. The newly extended MHAD dataset is skeleton-matched and time-synced to the corresponding Mocap data and is publicly available. Along with simulated D-Mocap data generated from the CMU Mocap dataset and our self-collected D-Mocap dataset, the proposed algorithm is thoroughly evaluated and compared with different settings. Experimental results show that our approach can improve the accuracy of joint positions and angles as well as skeletal bone lengths by over 50%.
A Novel API Recommendation Approach By Using Graph Attention Network

Although the use of APIs (Application Programming Interfaces) in software program development can effectively improve development efficiency, developers still need to spend more time in finding suitable APIs. To improve the overall development efficiency, many API recommendation approaches have been proposed. However, they could not make good use of the information in the source code, especially for the structural information. The PDG (Program Dependence Graph) of source code can contain both syntactic and structural information, which can be great representations of the source code. Based on the PDG, we propose a new approach, called JARST (Java API Recommendation combining Structural with Textual code information), which recommends the appropriate APIs by analyzing the structure information and text information of the source code. The JARST approach uses a graph neural network to learn source code structure information of PDG and uses a multi-modal approach to learn the text information in the source code. Finally, we combine the structural and textual information of the source code to implement API recommendations. We collect 625 open source Java projects from Github as our experimental objects. The experimental results show that JARST can provide accurate APIs to help software developers facilitate development activities. Moreover, it performs better than the cutting-edge studies including APIRes-CST and APIREC with higher top-k accuracy values. In detail, the improvement achieves up to 35.3%.
AI Based Proctoring

From the past year, Online Examination has become popular in all the educational fields due to covid19 and its flexibility. However, the institutions and community confront a big difficulty in terms of proctoring methods, as it is extremely difficult to administer cheating-free online exams. Here we present techniques and tools through which the proctor need not to be present throughout the exam. This is based on neural networks and machine learning. Our AI-based model will be able to detect any unfair means in an examination. Our experiments proved that the proposed system is better than the existing ones.
Recovery for underwater image degradation with multi-stage progressive enhancement

Optical absorption and scattering result in quality degradation of underwater images, which hampers the performance of underwater vision tasks. In practice, a well-posed underwater image recovery requires a combination of scene specificity and adaptability. To this end, this paper breaks down the overall recovery process into in-situ enhancement and data-driven correction modules, and proposes a Multi-stage Underwater Image Enhancement (MUIE) method to cascade the modules. In the in-situ enhancement module, a channel compensation with scene-relevant supervision is designed to address different degrees of unbalanced attenuation, and then the duality-based computation inverts the result of running a enhancement on inverted intensities to recover the degraded textures. In response to different scenarios, a data-driven correction, encoding corrected color-constancy information under data supervision, is performed to correct the improper color appearance of in-situ enhanced results. Further, under the collaboration between scene and data information, the recovery of MUIE avoids ill-posed response and reduces the prior dependence of specific scenes, resulting in a robust performance in different underwater scenes. Recovery comparison results confirm that the recovery of MUIE shows the superiority of scene clarity, realistic color appearance and evaluation scores. With the recovery of MUIE, the Underwater Image Quality Measurement (UIQM) scores of recovery-challenging images in the UIEB dataset were improved from 1.59 to 3.92.