Reviewerly/exHarmony huggingface dataset:
A case learning-based differential evolution algorithm for global optimization of interplanetary trajectory design

The problem of optimally designing an interplanetary trajectory for a space mission is considered in this paper. To tackle the extreme non-linearity of the search space, a case learning-based differential evolution algorithm, named CLDE, is proposed. It stores successful control parameters (scaling factor and crossover possibility) and retrieve the available reference information according to a geographic similarity in each generation. To depart from the basin of attraction of a local optimum, CLDE will give up learning from the successful cases once no better offsprings have been obtained within a certain number of generations and generate new control parameters. Two versions of CLDE have been developed, for global optimization (G-CLDE) and local optimization (L-CLDE), respectively. Their performance has been tested on GTOP benchmarks and real mission design. Experimental results show that G-CLDE performs better than related algorithms, including PYGMO algorithms and recently published L-SHADE variants. L-CLDE can improve upon the best known solution for the Messenger benchmark (full version). By connecting G-CLDE and L-CLDE together, CLDE finds promising results in acceptable computational time on the GTOP benchmark.
The Graph Based Benchmark Suite (GBBS)

In this demonstration paper, we present the Graph Based Benchmark Suite (GBBS), a suite of scalable, provably-efficient implementations of over 20 fundamental graph problems for shared-memory multicore machines. Our results are obtained using a graph processing interface written in C++, extending the Ligra interface with additional functional primitives that have clearly defined cost bounds. Our approach enables writing high-level codes that are simultaneously simple and high-performance by virtue of using highly-optimized primitives. Another benefit is that optimizations, such as graph compression, are implemented transparently to high-level user code, and can thus be utilized without changing the implementation. Our approach enables our codes to scale to the largest publicly-available real-world graph containing over 200 billion edges on a single multicore machine.
A Reinforcement Learning Framework for Relevance Feedback

We present RML, the first known general reinforcement learning framework for relevance feedback that directly optimizes any desired retrieval metric, including precision-oriented, recall-oriented, and even diversity metrics: RML can be easily extended to directly optimize any arbitrary user satisfaction signal. Using the RML framework, we can select effective feedback terms and weight them appropriately, improving on past methods that fit parameters to feedback algorithms using heuristic approaches or methods that do not directly optimize for retrieval performance. Learning an effective relevance feedback model is not trivial since the true feedback distribution is unknown. Experiments on standard TREC collections compare RML to existing feedback algorithms, demonstrate the effectiveness of RML at optimizing for MAP and α-n DCG, and show the impact on related measures.
ASiNE

Motivated by a success of generative adversarial networks (GAN) in various domains including information retrieval, we propose a novel signed network embedding framework, ASiNE, which represents each node of a given signed network as a low-dimensional vector based on the adversarial learning. To do this, we first design a generator G+ and a discriminator D+ that consider positive edges, as well as a generator G - and a discriminator D- that consider negative edges: (1) G+/G- aim to generate the most indistinguishable fake positive/negative edges, respectsupively; (2) D+/D aim to discriminate between real positive/negative edges and fake positive/negative edges, respectively. Furthermore, under ASiNE, we propose two new strategies for effective signed network embedding: (1) an embedding space sharing strategy for learning both positive and negative edges; (2) a fake edge generation strategy based on the balance theory. Through extensive experiments using five real-life signed networks, we verify the effectiveness of each of the strategies employed in ASiNE. We also show that ASiNE consistently and significantly outperforms all the state-of-the-art signed network embedding methods in all datasets and with all metrics in terms of accuracy of sign prediction.
Art, antiquities, and blockchain: new approaches to the restitution of cultural heritage

Objects of cultural heritage present a unique and important opportunity for the use of blockchain technology. Specifically, blockchain, a distributed ledger technology, can be used to disincentivize the sale of looted objects and to manage shared stewardship, ownership, and exhibition of these contested artifacts taken though war or colonialism. We offer background on repatriation of antiquities using the Byzantine Fresco Foundation as a core case study; introduce a working model of stakeholders in antiquities markets in both contemporary and historical context; and propose a blockchain solution using four different cases. The paper draws on newly sourced archival documents, game-theory interpretations of stakeholder behavior and application of this new technology in regulatory context. These blockchain applications are especially timely with the publication of the Sarr Savoy Report and the Arts Council England’s rewriting of its restitution guidelines for museums and galleries.
A Novel Blockchain-Based Framework for Vehicle Life Cycle Tracking: An End-to-End Solution

Transactions related to vehicles include manufacturing, buying, selling, paying insurance(takaful), obtaining regular inspection, leasing a vehicle from banks, getting in an accident, engaging in a traffic violation, calculating price predictions and renting a vehicle.Many people perform transactions related to vehicles in their daily life; transportation authorities also perform vehicle transactions as part of managing vehicle fleets.But tracking these transactions is a challenging task.There are countrywide solutions that uses centralized systems.However, these solutions have problems with trust management, transparency, and access control.Therefore, we believe there is still room for integrated automation of various vehicle-related transactions.In this paper, we present a blockchain-based framework for vehicle tracking that incorporates the mentioned features.Moreover, blockchain is customized to enable usage control for additional transactions, such as inspection, renting and islamic insurance.The usage control model is integrated with IoT devices to continuously monitor the vehicles for certain conditions and remotely revoke access if needed.The complete transaction set is recorded over an immutable ledger that provides trust, transparency and a complete history of record.In this paper, we also presents a prototype implementation of a permissioned blockchain, which will be made available under the GNUv3 General Public License.Performance analysis is performed on the newly proposed framework implementation over the permissioned blockchain to measure its adoption and suitability.
Searching for Actions on the Hyperbole

In this paper, we introduce hierarchical action search.Starting from the observation that hierarchies are mostly ignored in the action literature, we retrieve not only individual actions but also relevant and related actions, given an action name or video example as input.We propose a hyperbolic action network, which is centered around a hyperbolic space shared by action hierarchies and videos.Our discriminative hyperbolic embedding projects actions on the shared space while jointly optimizing hypernymhyponym relations between action pairs and a large margin separation between all actions.The projected actions serve as hyperbolic prototypes that we match with projected video representations.The result is a learned space where videos are positioned in entailment cones formed by different subtrees.To perform search in this space, we start from a query and increasingly enlarge its entailment cone to retrieve hierarchically relevant action videos.Experiments on three action datasets with new hierarchy annotations show the effectiveness of our approach for hierarchical action search by name and by video example, regardless of whether queried actions have been seen or not during training.Our implementation is available at https: //github.com/Tenglon/hyperbolic_action
Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction

Deep Interest Network (DIN) is a state-of-the-art model which uses attention mechanism to capture user interests from historical behaviors. User interests intuitively follow a hierarchical pattern such that users generally show interests from a higher-level then to a lower-level abstraction. Modelling such interest hierarchy in an attention network can fundamentally improve the representation of user behaviors. We therefore propose an improvement over DIN to model arbitrary interest hierarchy: Deep Interest with Hierarchical Attention Network (DHAN). In this model, a multi-dimensional hierarchical structure is introduced on the first attention layer which attends to individual item, and the subsequent attention layers in the same dimension attend to higher-level hierarchy built on top of the lower corresponding layers. To enable modelling of multiple dimensional hierarchy, an expanding mechanism is introduced to capture one to many hierarchies. This design enables DHAN to attend different importance to different hierarchical abstractions thus can fully capture a user's interests at different dimensions (e.g. category, price or brand). To validate our model, a simplified DHAN is applied to Click-Through Rate (CTR) prediction and our experimental results on three public datasets with two levels of one-dimensional hierarchy only by category. It shows DHAN's superiority with significant AUC uplift from 12% to 21% over DIN. DHAN is also compared with another state-of-the-art model Deep Interest Evolution Network (DIEN), which models temporal interest. The simplified DHAN also gets slight AUC uplift from 1.0% to 1.7% over DIEN. A potential future work can be combination of DHAN and DIEN to model both temporal and hierarchical interests.
Improved Soccer Action Spotting using both Audio and Video Streams

In this paper, we propose a study on multi-modal (audio and video) action spotting and classification in soccer videos. Action spotting and classification are the tasks that consist in finding the temporal anchors of events in a video and determine which event they are. This is an important application of general activity understanding. Here, we propose an experimental study on combining audio and video information at different stages of deep neural network architectures. We used the SoccerNet benchmark dataset, which contains annotated events for 500 soccer game videos from the Big Five European leagues. Through this work, we evaluated several ways to integrate audio stream into video-only-based architectures. We observed an average absolute improvement of the mean Average Precision (mAP) metric of 7.43% for the action classification task and of 4.19% for the action spotting task.
Learning Unseen Concepts via Hierarchical Decomposition and Composition

Composing and recognizing new concepts from known sub-concepts has been a fundamental and challenging vision task, mainly due to 1) the diversity of sub-concepts and 2) the intricate contextuality between sub-concepts and their corresponding visual features. However, most of the current methods simply treat the contextuality as rigid semantic relationships and fail to capture fine-grained contextual correlations. We propose to learn unseen concepts in a hierarchical decomposition-and-composition manner. Considering the diversity of sub-concepts, our method decomposes each seen image into visual elements according to its labels, and learns corresponding sub-concepts in their individual subspaces. To model intricate contextuality between sub-concepts and their visual features, compositions are generated from these subspaces in three hierarchical forms, and the composed concepts are learned in a unified composition space. To further refine the captured contextual relationships, adaptively semi-positive concepts are defined and then learned with pseudo supervision exploited from the generated compositions. We validate the proposed approach on two challenging benchmarks, and demonstrate its superiority over state-of-the-art approaches.
Implicit Discourse Relation Classification: We Need to Talk about Evaluation

Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations.However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature.In this work, we highlight these inconsistencies and propose an improved evaluation protocol.Paired with this protocol, we report strong baseline results from pretrained sentence encoders, which set the new state-of-the-art for PDTB 2.0.Furthermore, this work is the first to explore fine-grained relation classification on PDTB 3.0.We expect our work to serve as a point of comparison for future work, and also as an initiative to discuss models of larger context and possible data augmentations for downstream transferability.
Designing a digital image encryption scheme using chaotic maps with prime modular

This study proposes a scale-invariant digital image encryption method that includes three main steps rows-columns diffusion, the 3D scale-invariant modular chaotic map, and Hill diffusion. Pixels substitution, and mixing adjacency pixels of the plain image are performed using rows-columns diffusion and Hill diffusion. The 3D scale-invariant chaotic maps are used to permutation image pixels without image size restriction. Modular arithmetic is used in the 3D scale-invariant chaotic maps and Hill diffusion to increase keyspace and enhance security parameters. With repeat at least two rounds of main steps, the proposed encryption scheme reaches to optimum parameter values and it is highly sensitive to minor differences in both secret key and plain image, resulting in resisting chosen/known plaintext attacks effectively. Experimental results show that the proposed scheme demonstrates excellent security capacity with high efficiency, compared with existing approaches in digital image encryption.
Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback

This letter presents a reactive planning system that enriches the topological representation of an environment with a tightly integrated semantic representation, achieved by incorporating and exploiting advances in deep perceptual learning and probabilistic semantic reasoning. Our architecture combines object detection with semantic SLAM, affording robust, reactive logical as well as geometric planning in unexplored environments. Moreover, by incorporating a human mesh estimation algorithm, our system is capable of reacting and responding in real time to semantically labeled human motions and gestures. New formal results allow tracking of suitably non-adversarial moving targets, while maintaining the same collision avoidance guarantees. We suggest the empirical utility of the proposed control architecture with a numerical study including comparisons with a state-of-the-art dynamic replanning algorithm, and physical implementation on both a wheeled and legged platform in different settings with both geometric and semantic goals.
Employing Personal Word Embeddings for Personalized Search

Personalized search is a task to tailor the general document ranking list based on user interests to better satisfy the user's information need. Many personalized search models have been proposed and demonstrated their capability to improve search quality. The general idea of most approaches is to build a user interest profile according to the user's search history, and then re-rank the documents based on the matching scores between the created user profile and candidate documents. In this paper, we propose to solve the problem of personalized search in an alternative way. We know that there are many ambiguous words in natural language such as 'Apple', and people with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, for different users, such a word should own different semantic representations. Motivated by this idea, we design a personalized search model based on personal word embeddings, referred to as PEPS. Specifically, we train personal word embeddings for each user in which the representation of each word is mainly decided by the user's personal data. Then, we obtain the personalized word and contextual representations of the query and documents with an attention function. Finally, we use a matching model to calculate the matching score between the personalized query and document representations. Experiments on two datasets verify that our model can significantly improve state-of-the-art personalization models.
Projection &amp; Probability-Driven Black-Box Attack

Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer from the need for excessive queries, as it is non-trivial to find an appropriate direction to optimize in the high-dimensional space. In this paper, we propose Projection & Probability-driven Black-box Attack (PPBA) to tackle this problem by reducing the solution space and providing better optimization. For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial. We then propose a simple method to construct a low-frequency constrained sensing matrix, which works as a plug-and-play projection matrix to reduce the dimensionality. Such a sensing matrix is shown to be flexible enough to be integrated into existing methods like NES and Bandits <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TD</sub> . For better optimization, we perform a random walk with a probability-driven strategy, which utilizes all queries over the whole progress to make full use of the sensing matrix for a less query budget. Extensive experiments show that our method requires at most 24% fewer queries with a higher attack success rate compared with state-of-the-art approaches. Finally, the attack method is evaluated on the real-world online service, i.e., Google Cloud Vision API, which further demonstrates our practical potentials.
Guided Frequency Separation Network for Real-World Super-Resolution

Training image pairs are unavailable generally in real- world super-resolution. Although the LR images can be down-scaled from HR images, some real-world characteristics (such as artifacts or sensor noise) have been removed from the degraded images. Therefore, most of state-of-the- art super-resolved methods often fail in real-world scenes.In order to address aforementioned problem, we proposed an unsupervised super-resolved solution. The method can be divided into two stages: domain transformation and super-resolution. A color-guided domain mapping network was proposed to alleviate the color shift in domain transformation process. In particular, we proposed the Color Attention Residual Block (CARB) as the basic unit of the domain mapping network. The CARB which can dynamically regulate the parameters is driven by input data. Therefore, the domain mapping network can result in the powerful generalization performance. Moreover, we modified the discriminator of the super-resolution stage so that the network not only keeps the high frequency features, but also maintains the low frequency features. Finally, we constructed an EdgeLoss to improve the texture details. Experimental results show that our solution can achieve a competitive performance on NTIRE 2020 real-world super-resolution challenge.
Contextual Adversarial Attacks For Object Detection

The recent advances in adversarial attack techniques have witnessed the success of attacking high-quality CNN-based object detectors. However, in literature, the adversarial attack algorithms on object detection mainly focus on disturbing optimization objectives (i.e., classification and regression loss), which are sub-optimal due to ignoring contextual information. Novelly, we propose contextual adversarial perturbation (CAP) to attack the contextual information, which is more effective to degrade the mAP and recall of object detectors. Particularly, our CAP does not rely on ground-truth information to generate adversarial examples and thus leads to stronger generalization ability. Remarkably, we further design a contextual background loss and degrade the mAP and recall to almost 0.00%. Extensive experiments on PASCAL VOC and MS COCO datasets demonstrate the effectiveness of our attacks on both fully and weakly supervised object detectors.
Occlusion-Aware GAN for Face De-Occlusion in the Wild

Occluded faces-as a common scene in real life-have a significant negative impact on most face recognition systems. Existing methods try to remove the occlusions by a single-stage generative adversarial network (GAN), which is unaware of the occlusion and thus has difficulties in generalizing to a large variety of occlusion types, e.g., different objects at various positions. To this end, we propose the two-stage Occlusion-Aware GAN (OA-GAN), where the first GAN is for disentangling the occlusions, which will be served as the additional input of the second GAN for synthesizing the final de-occluded faces. In this way, our two-stage model can handle diverse occlusions in the wild and is naturally more explainable because of its awareness of the occluded objects. Extensive experiments on both synthetic and real-world datasets validate the superiority of the two-stage OAGAN design. Furthermore, by applying the generated de-occluded faces to facial expression recognition (FER) systems, we find that our two-stage de-occlusion process significantly increases the accuracy of FER under occlusion.
SAM: The Sensitivity of Attribution Methods to Hyperparameters

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation is its robustness to input hyperparameter changes that are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust by end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! In contrast, explanations generated for robust classifiers that are trained to be invariant to pixel-wise perturbations are surprisingly more robust. Interestingly, such sensitivity is not reflected in the average explanation correctness scores over the entire dataset as commonly reported in the literature.