Reviewerly/exHarmony huggingface dataset:
FO-like Combiners and Hybrid Post-Quantum Cryptography

Combining several primitives together to offer greater security is an old idea in cryptography. Recently, this concept has resurfaced as it could be used to improve trust in new Post-Quantum (PQ) schemes and smooth the transition to PQ cryptography. In particular, several ways to combine key exchange mechanisms (KEMs) into a secure hybrid KEM have been proposed. In this work, we observe that most PQ KEMs are built using a variant of the Fujisaki-Okamoto (FO) transform. Thus, we propose several efficient combiners that take OW-CPA public-key encryption schemes (PKEs) and directly build hybrid IND-CCA KEMs. Our constructions are secure in the ROM and QROM and can be seen as generalizations of the FO transform. We also study how the hash functions (ROs) used in our transforms can be combined in order to improve efficiency and security. In a second part, we implement a hybrid KEM using one of our combiners as a proof-of-concept and benchmark it. More precisely, we build a hybrid IND-CCA KEM from the CPA-secure versions of HQC and LAC, two NIST Round 2 PQ proposals. We show that the resulting KEM offers comparable performances to HQC, thus improving security at a small cost. Finally, we discuss which PQ schemes should be combined in order to offer the best efficiency/security trade-off.
RF-DNN<sup>2</sup>: An ensemble learner for effective detection of PHP Webshells

The explosion of web services has been accompanied by a rapid development of dangerous cyberattack methods. Webshells are considered among the easiest and most persistent cyberattack methods targeting web servers in the last few years. Webshells are malicious scripts injected into web servers to gain illegal persistent and remote access through simple and benign HTTP requests. Through webshells, hackers can access confidential data, execute system commands and compromise more machines connected to the target server. All those dangerous actions can be performed without being noticed by administrators and malware detectors. In this paper, we propose an ensemble learner model named RF-DNN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the detection of webshells written in PHP scripting language. The proposed model combines the predictions of two deep neural network models and uses Random Forest as a stacking meta classifier. The first deep neural network model is trained on vectorized source codes of webshells and the second deep neural network model is trained on vectorized opcode sequences generated from webshell sources. Individual deep neural network models and RF-DNN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are compared with a set of traditional classifiers and other ensemble learners. The experiments show that the RF-DNN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> model has the best accuracy 98% and macro F1-score 97.45%.
Hybrid‐attention densely connected U‐Net with GAP for extracting livers from CT volumes

Liver segmentation is an important step in the clinical treatment of liver cancer, and accurate and automatic liver segmentation methods are extremely important. U-Net has been used as the benchmark for many medical segmentation networks, but it cannot fully utilize low-resolution information and global contextual information. To solve these problems, we propose a new network architecture named the hybrid-attention densely connected U-Net (HDU-Net).The proposed HDU-Net has three main changes relative to U-Net, as follows: (1) It uses a densely connected structure and dilated convolution to achieve feature reuse and avoid information loss. (2) A global average pooling block is proposed to further augment the receptive field and improve the segmentation accuracy of the network for small or disconnected liver regions. (3) By combining the spatial attention and channel attention mechanisms, a hybrid attention structure is proposed to replace the skip connection component to filter and integrate low-resolution information.Experiments conducted on the LITS2017, 3Dircadb and Sliver07 datasets show that the proposed model can segment the liver accurately and effectively. Dice scores reach 96.5%, 96.18%, and 97.57% on these datasets, respectively, constituting results that are superior to many previously proposed methods.The experimental liver segmentation results have demonstrated that our proposed network provides improved segmentation performance in comparison with other networks. The experimental results without postprocessing confirmed that our network solves the oversegmentation and undersegmentation problems to some extent. The proposed model is effective, robust, and efficient in terms of liver segmentation without requiring extensive training time or a very large dataset.
GAN for Generating User-Specific Human Activity Data From An Incomplete Training Corpus

Human activity recognition (HAR), the task of predicting the activities performed by an individual using mobile sensor data, is an active and important area of research. Unfortunately, it is very costly to collect the data required to train robust HAR classifiers. To tackle this issue, there has been an increasing focus on generating synthetic HAR data for augmentation purposes. The state-of-the-art generative HAR approaches utilize Generative Adversarial Networks (GANs) to produce realistic synthetic HAR data. However, these solutions can not generate personalized data that matches the behavior of particular users, limiting their potential use cases. This is particularly problematic in the mobile health domain, where the target users are often elderly or disabled and are thus likely to have activity signals that are unique from the general population. To overcome this drawback, we propose a novel controllable GAN solution Control-HAR-GAN. Our approach learns user and activity signals independently, and when generating synthetic instances, practitioners can specify both the activity to be generated as well as the user the data should match. This has the added benefit in that our model supports novel user-activity pairs by generating examples that match the data that would have been recorded by a particular user if they had performed the target activity, even if the user never performed that activity during the data collection process. We show that our model outperforms the existing HAR GAN approach in generating observed user-activity pairs by up to 10%. Additionally, our approach can also perform the task of novel user-activity pair generation, which is impossible for existing approaches.
An Interdisciplinary Literature Classifier Based on Multi-task Multi-label Learning

Interdisciplinary integration is one of the motive power of scientific innovation and development. In order to improve the classification effect of interdisciplinary literature, this paper adopts multi-task learning method to learn interdisciplinary literature categories with similar research topic. Aiming at the imbalance and intersectionality of the distribution of the categories of the literature in the field of Library and Information Science, this paper proposes a classification framework for interdisciplinary literature based on multi-task learning. The framework is based on BERT and improves the classification effect of the model in minority categories by introducing the machine reading comprehension task, which predicts the position of keywords in titles and abstracts. The results show that the multi-task learning method is more effective than decision tree, support vector machine, convolutional neural network, recurrent neural network and pre-trained models. In addition, compared with cost-sensitive method, the proposed method is more helpful for the minority class, and its Macro-F1 value has reached 74.84%.
When Deep Learning Meets Differential Privacy: Privacy,Security, and More

Over the past decade, we have witnessed unprecedented development in deep learning (DL) and its contributions to modern networking systems. Along with its wide adoption, however, are growing concerns over the broad attack surfaces toward learning systems and the intrinsic vulnerabilities on privacy, security, robustness, and more. As a countermeasure to mitigate the threats or formalize a better defense, a widely adopted approach is to introduce a certain level of random perturbation (a.k.a. calibrated artificial noise) at either the training or prediction phase. Noteworthy examples include effective defenses against model inference attacks and notions of certified robustness. As such, differential privacy (DP), originally established as a privacy-preserving framework for data publishing, has drawn great interest from the learning community. Given a target utility and the acceptable trade-off, DP&#x0027;s formalization on the amount of noise needed has been shown to be widely applicable to a broad range of DL vulnerability mitigations. In this article, we present to our readers the recent representative advancements intersecting DL and DP, ranging from privacy enhancements for DL systems to security and robustness improvements and other novel extensions. Furthermore, we discuss the ongoing challenges and propose a number of future directions where DP has great potential to positively contribute to future DL systems.
Correcting Face Distortion in Wide-Angle Videos

Video blogs and selfies are popular social media formats, which are often captured by wide-angle cameras to show human subjects and expanded background. Unfortunately, due to perspective projection, faces near corners and edges exhibit apparent distortions that stretch and squish the facial features, resulting in poor video quality. In this work, we present a video warping algorithm to correct these distortions. Our key idea is to apply stereographic projection locally on the facial regions. We formulate a mesh warp problem using spatial-temporal energy minimization and minimize background deformation using a line-preservation term to maintain the straight edges in the background. To address temporal coherency, we constrain the temporal smoothness on the warping meshes and facial trajectories through the latent variables. For performance evaluation, we develop a wide-angle video dataset with a wide range of focal lengths. The user study shows that 83.9% of users prefer our algorithm over other alternatives based on perspective projection. The video results can be found at https://www.wslai.net/publications/video_face_correction/.
Sketch Generation From Real Object Images Using Generative Adversarial Network and Deep Reinforcement Learning

Technology in Robotics and machine learning have been applied in numerous fields including the arts. Paul The Robot is able to draw sketches from human faces using the conventional convolution filter method. Generative Adversarial Network (GAN) has been successful in generating synthetic images. Researches in sketch generation have been conducted either by using Recurrent Neural Network (RNN) or by using Deep Reinforcement Learning, with step-by-step stroke drawing. This research proposes a system to generate sketches from real object images using GAN dan Deep Reinforcement Learning. The training framework used is based on Doodle-SDQ (Doodle with Stroke Demonstration and Deep Q-Network) that combines supervised learning and reinforcement learning. Real object images are converted into contour images by GAN to be the reference images by the reinforcement learning agent to generate the sketch. The experiment is done by modifying pooling layers during the supervised learning stage and rare exploration scenarios during the reinforcement learning stage. The result of this research is a model that can reach an average total reward of 2558.98 with an average pixel error of 0.0489 using 200 as the maximum step in an average time of 3.29 seconds for the sketch generation.
Deep autoencoder-based fuzzy c-means for topic detection

Topic detection is a process for determining topics from a collection of textual data. One of the topic detection methods is a clustering-based method, which assumes that the centroids are topics. The clustering method has the advantage that it can process data with negative representations. Therefore, the clustering method allows a combination with a broader representation learning method. In this paper, we adopt deep learning for topic detection by using a deep autoencoder and fuzzy c-means called deep autoencoder-based fuzzy c-means (DFCM). The encoder of the autoencoder performs a lower-dimensional representation learning. Fuzzy c-means groups the lower-dimensional representation to identify the centroids. The autoencoder's decoder transforms back the centroids into the original representation to be interpreted as the topics. Our simulation shows that DFCM improves the coherence score of eigenspace-based fuzzy c-means (EFCM) and is comparable to the leading standard methods, i.e., nonnegative matrix factorization (NMF) or latent Dirichlet allocation (LDA).
Denoising Smart Grid data using Fuzzy Transform based Thresholding on EMD

A soft thresholding mechanism based on Fuzzy membership function (MF) is proposed in this work to remove the noise from smart grid signals. First, EMD is applied to break down the signal into various components. Then level dependent soft thresholding parameters, which are determined from the intrinsic mode functions (IMFs), are used into the fuzzy modifiedS-MF for the noise reduction. A parameter is used to determine how many IMFs are to be thresholded and then the rest are kept unthresholded. In order to evaluate the denoising performance, a comparative analysis is presented with wavelet-transform-based state of art technique.
Comparative Study of Face Detection Methods for Robust Face Recognition Systems

Face detection systems are used in various computer vision-based applications such as biometrics, security, surveillance, etc. Computationally immoderate face detection methods may not be convenient for devices with inadequate resources. On the other hand, an appropriate face detection approach should be considered in order to achieve high accuracy and substantial performance. This paper deliberates different methods of facial detection and contrasts them to find a better approach for a robust facial recognition system. Five methods of face detection were used in this comparison namely, ViolaJones, Histogram of Oriented Gradient with Support Vector Machine (HOG-SVM), Multi-task Cascaded Convolutional Network (MTCNN), Single Shot Multibox Detector (SSD) and Maxmargin Object Detection (MMOD). Each method was evaluated by varying illumination intensity, angle of the face, the scale of the face and different occlusion types. Video data and WIDERFACE image samples were used for the analysis. Obtained experimental results depict that SSD performs better on the task of face detection with high accuracy and performance, while MMOD has the lowest performance and Viola-Jones gives the lowest accuracy.
Role of the Social Constructivist Theory, Andragogy, and Computer-Mediated Instruction (CMI) in Adult ESL Learning and Teaching Environments

The purpose of this chapter is to explore whether social constructivism promotes mobile technology rich, student-centered learning/teaching practices, leading adult English as a second language (ESL) students to transform into self-directed learners. Under this theory, a shift from teacher-centered English language learning to a student-oriented approach to how students acquire basic English skills is promoted and students' possible transformation into independent and autonomous learners is fostered because, by use of mobile technologies, they may successfully transfer information from their previous experiences to their current knowledge. Incorporating mobile learning (m-Learning) into their instruction and making social constructivism an integral part of their curriculum, ESL teachers can contribute to their students transformation into self-directed learners as an active, knowledge-building community and in charge of their own learning processes, which explains that this theory is highly compatible with the principles grounded in what Knowles proposed as a progressive learning approach for adult education: andragogy.
Drone Detection Based on Multi-scale Feature Fusion

With the widespread application of drones in commercial and industrial fields, drone detection has received increasing attention in public safety and others. However, due to significant differences in target scales, complex flight backgrounds, and the existence of interfering targets, drone detection based on vision technology remains a challenging task nowadays. Convolutional neural networks(CNN) can learn target features with strong expressive ability. However, its robustness for detecting large, medium, and small targets is not stable due to the lack of low-level feature semantic information and the insufficiency of high-level feature details. For this challenge, we propose a drone detection network that extracts features of the target from multiple receptive fields via Res2net and realizes hierarchical multi-scale feature fusion with a novel mixed feature pyramid structure. We also build a drone detection dataset to evaluate our approach. Our method outperforms RetinaNet on our dataset, and our model&#x0027;s mean average precision (mAP) is more than 93&#x0025;.
Dynamic performance–Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds

Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73-43.44%, 44.06-92.11%, and 16.38-24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures.
Journalistic Storytelling for Complexity

The evolution of digital media has fostered the emergence of new and adapted forms of storytelling. Journalism combines classic narrative resources with technology and interactive strategies to inform, explain and represent reality. After an initial stage of adaptation and experimentation, it is necessary to reflect on the contribution of the new formats to contemporary, quality journalism for new active audiences. This chapter summarizes the evolution of digital journalistic narrative to identify the differentiating elements that have come from the intersection of documentary and journalism, as well as the new approaches that have arisen from immersive technologies. A panoramic vision will allow us to establish a roadmap to advance the production and research of storytelling for a complex world.
A Survey on Human Action Recognition and Detection Techniques

The recognition and detection of activities of human have gathered a significant research importance in the field of computer vision. In the current paper, a performance evaluation on various techniques of Human Action Recognition (HAR) is carried out in detail. The applications of HAR are widespread and they range from Security Surveillance to Patient Health Monitoring Systems, Human-to-Computer interaction to Robotics, etc. In the recent past, many research articles have been published on HAR systems and HAR system is broadly divided into Pre-processing stage, Segmentation stage, Feature extraction stage, Action classifier stage and Action identification stage. The goal of the paper is to consider different stages of HAR system and to compare various algorithms used in these stages for input video datasets.
Single-Skeleton and Dual-Skeleton Hypergraph Convolution Neural Networks for Skeleton-Based Action Recognition

In the last several years, the graph convolutional networks (GCNs) have shown exceptional ability on skeleton-based action recognition. Currently used mainstream methods often include identifying the movements of a single skeleton and then fusing the features. But in this way, it will lose the interactive information of two skeletons. Moreover, since there are some interactions between people (such as handshake, high-five, hug, etc.), the loss will reduce the accuracy of skeleton-based action recognition. To address this issue, we propose a two-stream approach (SD-HGCN). On the basis of single-skeleton stream (S-HGCN), a dual-skeleton stream (D-HGCN) is added to recognizing actions with interactive information between skeletons. The model mainly includes a multi-branch inputs adaptive fusion module (MBAFM) and a skeleton perception module (SPM). MBAFM can make the input features more distinguishable through two GCNs and an attention module. SPM may identify relationships between skeletons and build topological knowledge about human skeletons, through adaptive learning of the hypergraph distribution matrix based on the semantic information in the skeleton sequence. The experimental results show that the D-HGCN consumes less time and has higher accuracy, which meets the real-time requirements. Our experiments demonstrate that our approach outperforms state-of-the-art methods on the NTU and Kinetics datasets.
Designing Dissemination and Validation of a Framework for Teaching Cloud Fundamentals

Three previous Working Groups (WGs) met at ITiCSE conferences to explore ways to help educators incorporate cloud computing into their courses and curricula by mapping industry job skills to knowledge areas (KAs). These WGs identified, organized, and grouped together student learning objectives (LOs) and developed these KAs and LOs in a repository of learning materials and course exemplars.