Reviewerly/exHarmony huggingface dataset:
Invertible encryption network for optical image cryptosystem

In this paper, an invertible encryption network (IENet) is firstly proposed for optical image cryptosystem, which applies a novel learning way to optical image encryption for the first time, to the best of our knowledge. Here, the quadratic phase and double phase method are firstly applied to generate the phase-only hologram, and then it is encrypted by IENet. The ciphertext is finally obtained by learning the probability distribution from the most uniform matrix with an entropy of 8.0. Notably, the decryption process is the inverse process of the encryption. Due to the invulnerability of the ciphertext-only attack, chosen-ciphertext attack, and cryptosystem leakage, a high security is achieved by the proposed encryption method. Moreover, the optical video decryption is accomplished by combining optics and deep learning owing to the fast decryption of the neural network. The effectiveness and superiority of the proposed encryption method is verified by the simulation and experiment results. The proposed method provides a new approach and perspective for the learning-based encryption research.
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming

Recent studies have revealed that network-assisted techniques, by providing a comprehensive view of the network, improve HTTP Adaptive Streaming (HAS) system performance significantly. This paper leverages the capability of Software-Defined Networking, Network Function Virtualization, and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming (CSDN) framework. We employ virtualized edge entities to collect various information items and run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers. In case of a cache miss, a client’s request is served by an optimal replacement quality from a cache server, by a quality transcoded from an optimal replacement quality at the edge, or by the originally requested quality from the origin server. Comprehensive experiments conducted on a large-scale testbed demonstrate that CSDN outperforms other approaches in terms of the users’ QoE and network utilization.
Estimations of time-varying birth cardinality distribution and birth intensity in Gaussian mixture CPHD filter for multi-target tracking

Cardinalized probability hypothesis density (CPHD) filter is a promising algorithm for multi-target tracking. However, the performance of standard CPHD is degraded when the target birth process is unknown and dynamically changing. To remove this limitation, a discrete kernel estimator in conjunction with exponential weighted moving average scheme is introduced in this study to estimate the time-varying target birth cardinality distribution (i.e., probability distribution on number of newborn targets appearing during one sampling time) at each processing step. The target birth intensity is then updated according to the resulting estimated birth cardinality distribution. The estimated birth intensity and cardinality distribution can be employed by a tracker based on Gaussian mixture CPHD (GMCPHD) to modulate its filtering strength for target tracking. The performance of the proposed framework is demonstrated by both numerical simulations and experiment on a video surveillance dataset. Results show that the proposed algorithm can reduce the delay of standard GMCPHD filter's response to the changes in the number of targets, and thus improve the accuracy of cardinality estimates. Satisfactory estimation of cardinality can be obtained even when there is a fast change in the rate of birth.
A sparse coded composite descriptor for human activity recognition

Abstract This paper proposes a novel algorithm for computing discriminative descriptors named as a sparse coded composite descriptor (SCCD) for robust human activity recognition. The proposed method blends the state‐of‐the‐art handcrafted features and the discriminative nature of the sparse representation of visual information. The human activity is firstly modelled using any handcrafted feature, and then the sparse codes computed on a discriminative sparse dictionary of these features are embedded to provide discrimination in the feature set. Finally, a support vector machine (SVM) is trained using the proposed SCCDs to perform classification of different human activities. A new feature named as differential motion descriptor (DMD) is also proposed to extract the motion as well as spatial information from an activity video. The simulation results reveal that in comparison with the handcrafted feature, the corresponding SCCD improves the recognition accuracy significantly. The proposed method is compared with state‐of‐the‐art methods on KTH, Ballet, UCF50, and HMDB51 datasets and the proposed methodology of composite features outperforms these methods in terms of recognition accuracy.
Sequence-Level Training for Non-Autoregressive Neural Machine Translation

Abstract In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregressive mechanism leads to high translation latency of the NMT and restricts its low-latency applications. Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup by generating target words independently and simultaneously. Nevertheless, NAT still takes the word-level cross-entropy loss as the training objective, which is not optimal because the output of NAT cannot be properly evaluated due to the multimodality problem. In this article, we propose using sequence-level training objectives to train NAT models, which evaluate the NAT outputs as a whole and correlates well with the real translation quality. First, we propose training NAT models to optimize sequence-level evaluation metrics (e.g., BLEU) based on several novel reinforcement algorithms customized for NAT, which outperform the conventional method by reducing the variance of gradient estimation. Second, we introduce a novel training objective for NAT models, which aims to minimize the Bag-of-N-grams (BoN) difference between the model output and the reference sentence. The BoN training objective is differentiable and can be calculated efficiently without doing any approximations. Finally, we apply a three-stage training strategy to combine these two methods to train the NAT model. We validate our approach on four translation tasks (WMT14 En↔De, WMT16 En↔Ro), which shows that our approach largely outperforms NAT baselines and achieves remarkable performance on all translation tasks. The source code is available at https://github.com/ictnlp/Seq-NAT.
Global Relation-Aware Attention Network for Image-Text Retrieval

The cross-modal image-text retrieval has attracted extensive attention in recent years, which contributes to the development of search engine. Fine-grained features and cross-attention have been widely used in past researches to reach the goal of cross-modal image-text matching. Although cross-related methods have achieved remarkable results, the features must be encoded again in evaluation phase due to the interaction of the two modalities, which is unsuitable for actual scenarios of search engine development. In addition, the aggregated feature does not contain sufficient semantics since it is merely obtained by simple mean pooling. Furthermore, connecting weights of self-attention blocks are target position invariant, which lacks the expected adaptability. To tackle these limitations, in this paper, we propose a novel Global Relation-aware Attention Network (GRAN) for image-text retrieval by designing Global Attention Module (GAM) and Relation-aware Attention Module (RAM) which play an important role in modeling the global feature and the relationships of local fragments. Firstly, we propose Global Attention Module (GAM) followed the fine-grained features to obtain meaningful global feature. Secondly, we use several stacked transformer encoders to further encode features separately. Finally, we propose Relation-aware Attention Module (RAM) to generate a vector which represents the relation information to infer the attention intensity of pairwise fragments. The local features, the global feature, and their relations are considered jointly to conduct an efficient image-text retrieval. Extensive experiments are conducted on the benchmark datasets of Flickr30K and MSCOCO, demonstrating the superiority of our method. On the Flickr30K, compared to the state-of-the-art method TERAN, we improve [email protected](K=1) metric by 5.8% and 4.0 on the image and text retrieval tasks, respectively.
NMPC-MP: Real-time Nonlinear Model Predictive Control for Safe Motion Planning in Manipulator Teleoperation

Motion control and planning for the manipulator are critical components in manipulator teleoperation. Online (real-time) motion control is challenging for active obstacle avoidance and often results in fluctuating and unsafe motion. Offline motion planning, on the other hand, generates precise and secure trajectories for complex manipulation. In this paper, a real-time nonlinear model predictive control based motion planner (NMPC-MP) is designed for teleoperated manipulation. In contrast to traditional NMPC-based approaches, our model considers a complex environment with dynamic obstacles. Our multi-threaded NMPC-MP allows for real-time planning, including dynamic objects. We evaluate our approach both in a simulated environment and with real-world experiments using the Kinova <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Movo platform. The comparison to state-of-the-art approaches (e.g., RRT-Connect, CHOMP, and STOMP) shows a significant improvement in real-time motion planning using NMPC-MP. In real-world tests, the proposed planner was applied on a human-shaped dual manipulator setup. Our results show that the NMPC-MP runs in real-time and generates smooth and reliable trajectories. The experiments validate that the planner is able to precisely track active goals from the teleoperator while avoiding self-collision and obstacles.
Deep representation alignment network for pose-invariant face recognition

With the recent developments in convolutional neural networks and the increasing amount of data, there has been great progress in face recognition. Nevertheless, unconstrained situations remain challenging, given their variations in illumination, expression, and pose. To handle such pose variation, we propose the deep representation alignment network (DRA-Net), which aligns the deep representation of the profile face with that of the frontal face. Comprised of a denoising autoencoder (DAE) and a deep representation transformation (DRT) block, DRA-Net uses end-to-end training. DAE recovers deep representations of large pose angle in not visible face areas, and the DRT block transforms the recovered deep representation from profile into near-frontal poses. Also, we implement cosine loss and use pairwise training to mitigate the gap between frontal and profile representations and reduce intra-class variation. In experimental results, DRA-Net outperforms other state-of-the-art methods, particularly for large pose angle on LFW, YTF, Multi-PIE, CFP, IJB-A, and M 2 FPA benchmarks.
Evolutionary reinforcement learning via cooperative coevolutionary negatively correlated search

Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed Negatively Correlated Search (NCS) provides a distinct parallel exploration search behavior and is expected to facilitate RL more effectively. Considering that the commonly adopted neural policies usually involves millions of parameters to be optimized, the direct application of NCS to RL may face a great challenge of the large-scale search space. To address this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC) framework to scale-up NCS while largely preserving its parallel exploration search behavior. The issue of traditional CC that can deteriorate NCS is also discussed. Empirical studies on 10 popular Atari games show that the proposed method can significantly outperform three state-of-the-art deep RL methods with 50% less computational time by effectively exploring a 1.7 million-dimensional search space.
The structure and volume of large geysers in Yellowstone National Park, USA and the mineralogy and chemistry of their silica sinter deposits

Siliceous sinter is formed by biogenic and abiogenic opal deposition around hot springs and geysers. Using Structure-from-Motion photogrammetry we generated three-dimensional models of Giant and Castle Geysers from the Upper Geyser Basin of Yellowstone National Park. We use these models to calculate an approximate mass of sinter for each (~2 and ~ 5 kton, respectively) and estimate a range of plausible long-term deposition rates for Castle Geyser (470 to 940 kg·yr−1). We estimate ~2% of the silica discharged from Castle Geyser is deposited as sinter in the cone and proximal terraces. We collected 15 sinter samples following the stratigraphy of each geyser from the older terrace to the younger cone and examined them using a variety of analytical methods. We find that young opaline sinter with high water content (<12 wt% from loss on ignition) contains higher concentrations of major and trace elements, notably As, Sb, Rb, Ga and Cs, relative to older dehydrated sinter. Rare earth element (REE) concentrations in sinter are 2–3 orders of magnitude higher than in the thermal water from which they are deposited. Sinter deposits are enriched in light REE, Gd and Yb when normalized to concentrations in thermal water and enriched in Eu, Tm, and Yb when normalized to the underlying rhyolite. Sinter samples with the highest REE concentrations are also enriched in organic material, implying either microbial uptake of REE, or that organic molecules are efficient ligands that form metal complexes.
Governance in the era of Blockchain technology in Qatar: a roadmap and a manual for Trade Finance

Abstract Nations worldwide have sought to capitalize on the benefits of distributed ledger technology (DLT) including Blockchain, but struggled to strike a balance between encouraging investment and innovation in the technology while addressing the challenges and uncertainties through regulation. Through its FinTech (Financial Technology) Strategy, Qatar has sought to embrace DLT, but its regulatory approach also remains cautious. Trade Finance is an ideal business process to be disrupted through the benefits of DLT and especially Blockchain technology, since its processes remain antiquated, inefficient and lack digitization. Blockchain as a form of DLT particularly offers the Trade Finance process not only more rapid, secure, cost-effective and efficient procedures, but importantly completely assures trust between importers and exporters and removes the requirement to place such trust in third-party intermediaries. Qatar can reap considerable economic benefits through the enhancement of its Trade Finance regulations enabling the adoption of such Blockchain technology. As such, the authors propose a roadmap and manual for the governance of the Trade Finance Blockchain ecosystem in Qatar. The authors propose multi-layered governance approach to the regulation of Blockchain in Qatar by (1) embracing international regulations and standards; (2) replicating foreign regional and national rules that are appropriate and innovative; and (3) applying sandbox regulations to Blockchain products and services.
Enhancing the robustness of the convolutional neural networks for traffic sign detection

The detection of traffic signs in clean and noise-free images has been investigated by numerous researchers; however, very few of these works have focused on noisy environments. While in the real world, for different reasons (e.g. the speed and acceleration of a vehicle and the roughness around it), the input images of the convolutional neural networks (CNNs) could be extremely noisy. Contrary to other research works, in this paper, we investigate the robustness of the deep learning models against the synthetically modeled noises in the detection of small objects. To this end, the state-of-the-art architectures of Faster-RCNN Resnet101, R-FCN Resnet101, and Faster-RCNN Inception Resnet V2 are trained by means of the Tsinghua-Tencent 100K database, and the performances of the trained models on noisy data are evaluated. After verifying the robustness of these models, different training scenarios (1 – Modeling various climatic conditions, 2 – Style randomization, and 3 – Augmix augmentation) are used to enhance the model robustness. The findings indicate that these scenarios result in up to 13.09%, 12%, and 13.61% gains in the mentioned three networks by means of the mPC metric. They also result in 11.74%, 8.89%, and 7.27% gains in the rPC metric, demonstrating that improvement in robustness does not lead to performance drop on the clean data.
Are You Really Complaining? A Multi-task Framework for Complaint Identification, Emotion, and Sentiment Classification

In recent times, given the competitive nature of corporates, customer support has become the core of organizations that can strengthen their brand image. Timely and effective settlement of customer's complaints is vital in improving customer satisfaction in different business organizations. Companies experience difficulties in automatically identifying complaints buried deep in enormous online content. Emotion detection and sentiment analysis, two closely related tasks, play very critical roles in complaint identification. We hypothesize that the association between emotion and sentiment will provide an enhanced understanding of the state of mind of the tweeter. In this paper, we propose a Bidirectional Encoder Representations from Transformers (BERT) based shared-private multi-task framework that aims to learn three closely related tasks, viz. complaint identification (primary task), emotion detection, and sentiment classification (auxiliary tasks) concurrently. Experimental results show that our proposed model obtains the highest macro-F1 score of 87.38%, outperforming the multi-task baselines as well as the state-of-the-art model by indicative margins, denoting that emotion awareness and sentiment analysis facilitate the complaint identification task when learned simultaneously.
Two ICS Security Datasets and Anomaly Detection Contest on the HIL-based Augmented ICS Testbed

Security datasets with various operating characteristics and abnormal situations of industrial control system (ICS) are essential to develop artificial intelligence (AI)-based control system security technology. In this study, we built a hardware-in-the-loop (HIL)-based augmented ICS (HAI) testbed and developed ICS security datasets. Here, we introduce the second dataset (HAI 21.03), which was developed with the user feedback of the first released version (HAI 20.07). All HAI datasets are publicly available at https://github.com/icsdataset/hai. HAI 21.03 was expanded by adding data points and normal/attack scenarios to HAI 20.07. We also held an AI-based anomaly detection contest (HAICon 2020) utilizing the HAI datasets developed so far, giving many AI researchers an opportunity to discuss and share ideas for ICS anomaly detection research. This paper presents the results of the HAICon 2020. The results of the top teams in the competition can be used as a performance comparison criterion when using HAI 21.03.
Beyond the Bitcoin: Analysis of Challenges to Implement Blockchain in the Jordanian Public Sector

Blockchain technology is the most advanced "disruptive innovation" that has caught researchers' attention. This cutting-edge concept is the underlying technology for digital currencies (e.g., Bitcoin). Blockchain technology (BCT) holds immense potential for e-services by performing transactions in a permanent and verifiable manner. However, the adoption and implementation of Blockchain technology in various services and industries are a challenging task. The adoption and use of BCT require the consideration of a wide range of determinants, over and above the dominant technology focus of most existing research efforts. While academic literature on BCT is only beginning to emerge, the majority is concentrated on the technicalities of this latest technology and is inclined to ignore the complexities of technology adoption in private and public organizations. Drawing from data collected from 213 IT experts and a focused review of extant literature, this study identified a range of challenges (technological, institutional/organizational, and market) to the successful implementation of BCT across different government organizations. The proposed factors can be used by the public sector as a reference point for the efficient adoption of BCT and by scholars for further practical and theoretical research into BCT technology.
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings

Adversarial robustness of deep models is pivotal in ensuring safe deployment in real world settings, but most modern defenses have narrow scope and expensive costs. In this paper, we propose a self-supervised method to detect adversarial attacks and classify them to their respective threat models, based on a linear model operating on the embed-dings from a pretrained self-supervised encoder. We use a SimCLR encoder in our experiments, since we show the SimCLR embedding distance is a good proxy for human perceptibility, enabling it to encapsulate many threat models at once. We call our method SimCat since it uses SimCLR encoder to catch and categorize various types of adversarial attacks, including ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> and non-ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> evasion attacks, as well as data poisonings. The simple nature of a linear classifier makes our method efficient in both time and sample complexity. For example, on SVHN, using only five pairs of clean and adversarial examples computed with a PGD-ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> attack, SimCat’s detection accuracy is over 85%. More-over, on ImageNet, using only 25 examples from each threat model, SimCat can classify eight different attack types such as PGD-ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , PGD-ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> , CW-ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , PPGD, LPA, StAdv, ReColor, and JPEG-ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> , with over 40% accuracy. On STL10 data, we apply SimCat as a defense against poisoning attacks, such as BP, CP, FC, CLBD, HTBD, halving the success rate while using only twenty total poisons for training. We find that the detectors generalize well to unseen threat models. Lastly, we investigate the performance of our detection method under adaptive attacks and further boost its robustness against such attacks via adversarial training.