Reviewerly/exHarmony huggingface dataset:
Vulnerability Analysis of 2500 Docker Hub Images

The use of container technology has skyrocketed during the last few years, with Docker as the leading container platform. Docker's online repository for publicly available container images, called Docker Hub, hosts over 3.5 million images at the time of writing, making it the world's largest community of container images. We perform an extensive vulnerability analysis of 2500 Docker images. It is of particular interest to perform this type of analysis because the vulnerability landscape is a rapidly changing category, the vulnerability scanners are constantly developed and updated, new vulnerabilities are discovered, and the volume of images on Docker Hub is increasing every day. Our main findings reveal that (1) the number of newly introduced vulnerabilities on Docker Hub is rapidly increasing; (2) certified images are the most vulnerable; (3) official images are the least vulnerable; (4) there is no correlation between the number of vulnerabilities and image features (i.e., number of pulls, number of stars, and days since the last update); (5) the most severe vulnerabilities originate from two of the most popular scripting languages, JavaScript and Python; and (6) Python 2.x packages and jackson-databind packages contain the highest number of severe vulnerabilities. We perceive our study as the most extensive vulnerability analysis published in the open literature in the last couple of years.
Digital Learning Interventions in Higher Education

This scoping review was undertaken to synthesize and describe research related to digital learning interventions in higher education, focusing on technological outcomes. Five electronic databases were searched, and 86 articles were included in the review. The data related to positive and negative technological outcomes and authors' suggestions were analyzed using inductive content analysis. The articles represented six disciplines across six continents and included quantitative (n = 65), qualitative (n = 3), and mixed-methods (n = 18) intervention studies. For positive technological outcomes, digital formats of learning were considered effective and participatory forms of learning in a majority of the articles. The students appreciated individualized and self-paced learning, and the digital form increased their motivation to learn. Automatized technical solutions that enabled learning and teaching had several advantages, and digital learning was believed to save the resources of students, teachers, and organizations. For negative technological outcomes, the technical difficulties in using the digital devices or platforms were described the most, and a need for resources was identified. Feedback from teachers was considered important from positive and negative viewpoints. Authors' suggestions for future digital teaching and learning as well as related interventions consisted of various activities, resources, environments, and methods.
VBLFI: Visualization-Based Blind Light Field Image Quality Assessment

Light field image (LFI) contains the intensity and direction information of the scene. The huge amount of data and different visualization methods of LFI brings great challenges to LFI processing and its blind LFI quality assessment. This paper analyzes the human visual perception from the LFI's visualization, and proposes a novel Visualization-based Blind Light Field Image quality assessment (VBLFI) model. With LFI's visualization and its depth cues, we compute mean difference image from LFI to reduce redundant information of LFI and to describe depth and structural information of LFI. LFI's multi-scale expression with curvelet transform is used to reflect the multi-channel characteristics of human visual system. So, the corresponding natural scene statistical features and energy features are extracted from the mean difference image and sub-aperture images of LFI in curvelet transform domain to form the feature vector, further used to predict the LFI quality. Compared to the representative 2D image quality assessment models and the state-of-the-art LFIQA models, the proposed VBLFI model has better prediction accuracy and stability in the public LFI databases.
Learning Stereo High Dynamic Range Imaging From A Pair of Cameras With Different Exposure Parameters

It is possible to generate stereo high dynamic range (HDR) images/videos by using a pair of cameras with different exposure parameters. In this article, a learning-based stereo HDR imaging (SHDRI) method with three modules is proposed. In the proposed method, we construct three convolutional neural network (CNN) modules that perform specific tasks, including exposure calibration CNN (EC-CNN) module, hole-filling CNN (HF-CNN) module and HDR fusion CNN (HDRF-CNN) module, to combine with traditional image processing methods to model SHDRI pipeline. To avoid ambiguity, we assume that the left-view image is under-exposed and the right-view image is over-exposed. Specifically, the EC-CNN module is first constructed to convert stereo multi-exposure images into the same exposure to facilitate subsequent stereo matching. Then, based on the estimated disparity map, the right-view image is forward-warped to generate the initial left-view over-exposure image. After that, extra exposure information is utilized to guide hole-filling. Finally, the HDRF-CNN module is constructed and employed to extract fusion features to fuse the hole-filled left-view over-exposure image with the original left-view under-exposure image into the left-view HDR image. Right-view HDR images can be generated in the same way. In addition, we propose an effective two-phase training strategy to overcome the lack of a sufficient large stereo multi-exposure dataset. The experimental results demonstrate that the proposed method can generate stereo HDR images with high visual quality. Furthermore, the proposed method achieves better performance in comparison with the latest SHDRI method.
Two novel semi-quantum-reflection protocols applied in connected vehicle systems with blockchain

The increasing evolution of Internet of Vehicles (IoV) is bringing about a crucial challenge in information security, which is typically classified into network and large data storage security areas. A quantum cryptosystem is secure against cloning, and is thus immune to many types of traditional network attacks. Blockchain is a distributed ledger technology based on a game theory consensus, and has already shown significant advantages in terms of data security. In this study, a method to extend the use of quantum and blockchain technologies in the security of IoV is considered. Herein, we present a semi-quantum system with a blockchain architecture and provide two lightweight approaches for its application to IoV with unconditional security and a single round of completion. Moreover, with our two protocols, only two modular exponentiations are applied, and the remaining computations are hash or XOR operations, which can be ignored. The results of our study may provide a guide for future applications toward IoV in the era of quantum and blockchain technologies.
Solving text clustering problem using a memetic differential evolution algorithm

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.
Should you be using mobile technologies in teaching? Applying a pedagogical framework

The extent of how mobile devices, such as smartphones and tablets, are seamlessly incorporated into the personal day-to-day life is not often considered by University instructors. Unfocused incorporation of mobile technologies into the classroom can de-emphasize intended learning objectives if students struggle to use the technology itself or by acting as a distraction. The effective inclusion of mobile technology is not a simple process as the intervention needs to be purposeful and have the potential to improve the student learning environment while working alongside more traditional face-to-face learning. This paper presents a pathway to help instructors address both pedagogical and technological considerations of incorporating mobile learning into the curriculum. The pathway developed through the adaptation of the iPAC framework, feedback from international practitioners and tested with worked examples. In all cases, the instructors' reflective responses to the eight pathway questions indicate a clear structured activity, engaged students and considers equal access, prior experience and contingency planning. This pathway indicates an effective methodology for instructors to assess whether the mobile learning intervention is appropriate and adds value to their teaching. Further external evaluation of the pathway with additional teaching examples will enhance the effectiveness of the methodology.
Text to Image Synthesis With Bidirectional Generative Adversarial Network

Generating realistic images from text descriptions is a challenging problem in computer vision. Although previous works have shown remarkable progress, guaranteeing semantic consistency between text descriptions and images remains challenging. To generate semantically consistent images, we propose two semantics-enhanced modules and a novel Textual-Visual Bidirectional Generative Adversarial Network (TVBi-GAN). Specifically, this paper proposes a semanticsenhanced attention module and a semantics-enhanced batch normalization module. These modules improve consistency of synthesized images by involving precisely semantic features. What's more, an encoder network is proposed to extract semantic features from images. During the adversarial process, the encoder could guide our generator to explore corresponding features behind descriptions. With extensive experiments on CUB and COCO datasets, we demonstrate that our TVBi-GAN outperforms state-of-the-art methods.
A Novel Coverless Information Hiding Method Based on the Most Significant Bit of the Cover Image

With the rapid development of information technology recently, information security has become the focus of public concern. Information Hiding (IH) technology is an effective method to tackle the problem of information leakage incidents. In this paper, a novel coverless information hiding method based on the Most Significant Bit (MSB) of cover image is proposed (CIHMSB). Firstly, the cover image is segmented into a number of fragments. Secondly, in order to use the MSB of cover image to represent the secret information, the average intensity of each fragment is calculated. Thirdly, a one-to-one mapping between the MSB of the image fragment and the secret information is established using the mapping sequence (denote as Kr), decided by the sender and the receiver in advance. This process produces a mapping flag (denote as Kf ), which is sent by the sender along with the stego image. The objective of the proposed work is to increase hiding capacity, curtail the distortion of the stego image to improve its quality and reduce the Bit Error Rate (BER) of stego image in the case of distortion. Experimental results show that the proposed method can conceal 2601 bits secret information per carrier with peak signal-to-noise ratio (PSNR) of ∞ dB. What's more, some stego image quality assessment parameters, such as structural similarity (SSIM) index and universal image quality index (Qi), are slightly better than existing information hiding methods. Furthermore, the proposed method has good performance against such as AGWN, salt & pepper noise, low-pass filtering and JPEG compression attacks.
Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote

The lack of robustness shown by deep neural networks (DNNs) questions their deployment in safety-critical tasks, such as autonomous driving. We pick up the recently introduced redundant teacher-student frameworks (3 DNNs) and propose in this work a novel error detection and correction scheme with application to semantic segmentation. It obtains its robustnesss by an online-adapted and therefore hard-to-attack student DNN during vehicle operation, which builds upon a novel layer-dependent inverse feature matching (IFM) loss. We conduct experiments on the Cityscapes dataset showing that this loss renders the adaptive student to be more than 20% absolute mean intersection-over-union (mIoU) better than in previous works. Moreover, the entire error correction virtually always delivers the performance of the best non-attacked network, resulting in an mIoU of about 50% even under strongest attacks (instead of 1...2%), while keeping the performance on clean data at about original level (ca. 75.7%).
Efficient Yolo: A Lightweight Model For Embedded Deep Learning Object Detection

It is essential to pursue efficiency for on-road object detection task. To incorporate deep model into embedded devices while maintaining high accuracy, in this paper, an Efficient YOLO framework is rebuilt based on traditional YOLOv3. Firstly, an iterative initialization strategy is designed to ensure the network sparsity in the initial training. Then comprehensive pruning schemes including layer-level and channel-wise pruning are proposed to lighten the model parameters.With the support of external dataset, the detection accuracy remains at a high level. Compared with the orignal version, our model shrinks the model size by 96.93% and calculation amount by 84.36%. The inference speed is improved 2.23 times on NVIDIA Jetson TX2 platform. Finally, we achieve a mAP of 0.492 on the testing dataset, and rank the top accuracy of ICME competition.
DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data

Large detection datasets have a long tail of lowshot classes with very few bounding box annotations. We wish to improve detection for lowshot classes with weakly labelled web-scale datasets only having image-level labels. This requires a detection framework that can be jointly trained with limited number of bounding box annotated images and large number of weakly labelled images. Towards this end, we propose a modification to the FRCNN model to automatically infer label assignment for objects proposals from weakly labelled images during training. We pose this label assignment as a Linear Program with constraints on the number and overlap of object instances in an image. We show that this can be solved efficiently during training for weakly labelled images. Compared to just training with few annotated examples, augmenting with weakly labelled examples in our framework provides significant gains. We demonstrate this on the LVIS dataset 3.5 gain in AP as well as different lowshot variants of the COCO dataset. We provide a thorough analysis of the effect of amount of weakly labelled and fully labelled data required to train the detection model. Our DLWL framework can also outperform self-supervised baselines like omni-supervision for lowshot classes.
Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables

Sensor data streams from wearable devices and smart environments are widely studied in areas like human activity recognition (HAR), person identification, or health monitoring. However, most of the previous works in activity and sensor stream analysis have been focusing on one aspect of the data, e.g. only recognizing the type of the activity or only identifying the person who performed the activity. We instead propose an approach that uses a weakly supervised multi-output siamese network that learns to map the data into multiple representation spaces, where each representation space focuses on one aspect of the data. The representation vectors of the data samples are positioned in the space such that the data with the same semantic meaning in that aspect are closely located to each other. Therefore, as demonstrated with a set of experiments, the trained model can provide metrics for clustering data based on multiple aspects, allowing it to address multiple tasks simultaneously and even to outperform single task supervised methods in many situations. In addition, further experiments are presented that in more detail analyze the effect of the architecture and of using multiple tasks within this framework, that investigate the scalability of the model to include additional tasks, and that demonstrate the ability of the framework to combine data for which only partial relationship information with respect to the target tasks is available.
Assessment of the ecological and geochemical conditions in surface sediments of the Varzob river, Tajikistan

The aims of present study consist of collecting more information on the geochemistry of the Varzob river unconsolidated sediments as reflecting the diversity of the South-Hissar range together with a comprehensive evaluation of the anthropogenic contamination of the same sediments. Accordingly, the content of nine major, rock forming elements - Na, Mg, Al, Si, K, Ca, Ti, Mn and Fe as well as of other 27 trace ones - Sc, V, Cr, Co, Ni, Zn, As, Br, Rb, Sr, Zr, Sb, Cs, Ba, La, Ce, Nd, Sm, Eu, Gd, Tb, Yb, Hf, Ta, W, Th, and U was determined by Epithermal Neutron Activation Analysis. A special attention was given to potential heavy contaminants - V, Cr, Mn, Co, Ni, Zn, As, Sb and Ba whose content was used to calculate more environmental contamination proxies i.e. contamination factor, geoaccumulation, and pollution load indices. At the same time, the content of Cr, Ni, Sc, Zr, REE, Th and U were used to establish the nature and origin of recent sediments. The increased values of some pollution indices characteristic of industrial activity in the upper part of the Varzob River indicate an anthropogenic origin of As, Sb and Mn while the increased contents of Th and U in the lower reaches of the Varzob River could be attributed to the pegmatite field of the Odjuk tributary.
Hashtag our stories: Hashtag recommendation for micro-videos via harnessing multiple modalities

Due to the short attention span and instant gratification phenomenon, micro-videos are growing exponentially while gaining more and more concerns. Yet the sheer number of micro-videos leads to severe information overload issues, making it difficult for users to identify their desired micro-videos. The hashtag, mainly utilized in the domain of the microblog or the image, is the indicator or the core idea of the target content and can be applied to various information retrieval scenarios (e.g., search, browse, and categorization). So far, however, little attention has been paid to perform the hashtag recommendation for micro-videos via harnessing multiple modalities. In this article, we devise a neural network-based solution, LOGO (short for "muLti-mOdal-based hashtaG recOmmendation"), to recommend hashtags for micro-videos by utilizing multiple modalities. The proposed LOGO approach first represents each modality as the combination of sequential units in it, weighted by the attention mechanism. In this way, the sequential and attentive features are captured simultaneously. After that, the LOGO integrates the representations of all modalities via a multi-view representation learning framework, which projects the representations into a common space under the restriction of the modality similarity. Ultimately, the LOGO feed the projections of three modalities in the common space and the embeddings of hashtags into a customized neural collaborative filtering framework to perform the hashtag recommendation. Extensive experiments on the scope of both overall performance comparison and micro-level analyses have well-justified the effectiveness and rationality of our proposed approach.
TomoFluid: Reconstructing Dynamic Fluid From Sparse View Videos

Visible light tomography is a promising and increasingly popular technique for fluid imaging. However, the use of a sparse number of viewpoints in the capturing setups makes the reconstruction of fluid flows very challenging. In this paper, we present a state-of-the-art 4D tomographic reconstruction framework that integrates several regularizers into a multi-scale matrix free optimization algorithm. In addition to existing regularizers, we propose two new regularizers for improved results: a regularizer based on view interpolation of projected images and a regularizer to encourage reprojection consistency. We demonstrate our method with extensive experiments on both simulated and real data.
Nonlinear Robust Discrete Hashing for Cross-Modal Retrieval

Hashing techniques have recently been successfully applied to solve similarity search problems in the information retrieval field because of their significantly reduced storage and high-speed search capabilities. However, the hash codes learned from most recent cross-modal hashing methods lack the ability to comprehensively preserve adequate information, resulting in a less than desirable performance. To solve this limitation, we propose a novel method termed Nonlinear Robust Discrete Hashing (NRDH), for cross-modal retrieval. The main idea behind NRDH is motivated by the success of neural networks, i.e., nonlinear descriptors, in the field of representation learning, and the use of nonlinear descriptors instead of simple linear transformations is more in line with the complex relationships that exist between common latent representation and heterogeneous multimedia data in the real world. In NRDH, we first learn a common latent representation through nonlinear descriptors to encode complementary and consistent information from the features of the heterogeneous multimedia data. Moreover, an asymmetric learning scheme is proposed to correlate the learned hash codes with the common latent representation. Empirically, we demonstrate that NRDH is able to successfully generate a comprehensive common latent representation that significantly improves the quality of the learned hash codes. Then, NRDH adopts a linear learning strategy to fast learn the hash function with the learned hash codes. Extensive experiments performed on two benchmark datasets highlight the superiority of NRDH over several state-of-the-art methods.