Reviewerly/exHarmony huggingface dataset:
A Refined Single-Stage Detector With Feature Enhancement and Alignment for Oriented Objects

Compared to traditional object detection of horizontal bounding box, detecting rotated objects with arbitrary orientations and various scales is a critical yet challenging task especially in remote sensing images.Although considerable progresses have been made through the utilization of deep CNN, there still exists space for exploration of oriented object detection.In this paper, we propose a refined Single-Stage Detector for Oriented objects (OSSDet), which is equipped with the enhanced feature extraction network and an adaptive feature alignment module for finer detection.For feature enhancement, a Bidirectional and Inner Residual Feature Pyramid Network (BIRFPN) and a Multi-scale Feature Aggregation (MFA) module are devised for getting more representative features.Then, to address the problem of feature misalignment, we propose the Adaptive Feature Alignment Module (AFAM) to reallocate the sampling locations and weight the positive and negative feature points during the refined detection process.The reallocation and weighting will be adjusted adaptively according to the results from the coarse detection.Experiments conducted on public remote sensing databases show the effectiveness of our method.
An end-to-end deep learning model for robust smooth filtering identification

Smooth filtering, a common blurring and denoising operator, has often been utilized postoperatively to diminish the traces left by malicious manipulations. Most of the existing forensic methods only focus on one specific filtering artifact such as median filtering, which is insufficient to reveal the manipulation history of digital images. Unlike traditional convolutional neural network (CNN)-based networks, which normally introduce handcrafted features, including frequency domain features and median filtering residuals, into the preprocessing layer, this paper proposes an end-to-end deep learning model for robust smooth filtering identification. First, a distinctive network structure named the Squeeze-and-Excitation (SE) block is introduced to select discriminative features adaptively and suppress the irrelevant features to the smooth filtering effect. Then, as the network depth increases, multiple inception-residual blocks are stacked to extract discriminative features and reduce the information loss. Finally, different smooth filtering operations can be classified through learning hierarchical features. The experimental results on a composite database show that the proposed model outperforms the state-of-the-art methods, especially in small size and JPEG compression scenarios.
Knowledge-Guided Disentangled Representation Learning for Recommender Systems

In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.
Solo-to-Collaborative Dual-Attention Network for One-Shot Object Detection in Remote Sensing Images

In this article, we attempt to achieve one-shot object detection by mimicking the human ability to learn new concepts under limited reference, which aims at detecting all object instances of an unseen class in a target image when given a query image of the same unseen class. However, this one-shot learning ability of human benefits from the fact that human brain can quickly extract and process the associated information between the query–target images, which is an issue for the one-shot object detection framework to overcome. Moreover, the feature extraction of the query class in target images is intractable due to the complex and diversified background of remote sensing images. To solve these issues, we propose a solo-to-collaborative dual-attention network (SCoDANet) to hierarchically (image itself/pairs) enhance image feature representations. It consists of three components: 1) solo-attention head that strengthens the compactness of intraclass feature representations of an image and avoids background interference by selectively aggregating the similar features from the spatial and channel dimensions, respectively; 2) dual coattention module that guides RPN to generate an expected set of region proposals related to the query class by mining the coinformation of each query–target feature pair; and 3) nonlinear matching that provides a measure of similarity between the query feature and proposals of the target image to further learn a more robust detector. Our extensive experiments over two benchmarks demonstrate the effectiveness of our method under the one-shot scenario of detecting seen and unseen object categories.
Hardware Efficient Pseudo-Random Number Generator Using Chen Chaotic System on FPGA

This paper introduces an FPGA implementation of a pseudo-random number generator (PRNG) using Chen’s chaotic system. This paper mainly focuses on the development of an efficient VLSI architecture of PRNG in terms of bit rate, area resources, latency, maximum length sequence, and randomness. First, we analyze the dynamic behavior of the chaotic trajectories of Chen’s system and set the parameter’s value to maintain low hardware design complexity. A circuit realization of the proposed PRNG is presented using hardwired shifting, additions, subtractions, and multiplexing schemes. The benefit of this architecture, all the binary multiplications (except [Formula: see text] and [Formula: see text] operations are performed using hardwired shifting. Moreover, the generated sequences pass all the 15 statistical tests of NIST, while it generates pseudo-random numbers at a uniform clock rate with minimum hardware complexity. The proposed architecture of PRNG is realized using Verilog HDL, prototyped on the Virtex-5 FPGA (XC5VLX50T) device, and its analysis has been done using the Matlab tool. Performance analysis confirms that the proposed Chen chaotic attractor-based PRNG scheme is simple, secure, and hardware efficient, with high potential to be adopted in cryptography applications.
High-Performance Reservoir Computing With Fluctuations in Linear Networks

Reservoir computing has emerged as a powerful machine learning paradigm for harvesting nontrivial information processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become nonlinear functions of the input history. We show that encoding the input to quantum or classical fluctuations of a network of interacting harmonic oscillators can lead to a high performance comparable to that of a standard echo state network in several nonlinear benchmark tasks. This equivalence in performance holds even with a linear Hamiltonian and a readout linear in the system observables. Furthermore, we find that the performance of the network of harmonic oscillators in nonlinear tasks is robust to errors both in input and reservoir observables caused by external noise. For any reservoir computing system with a linear readout, the magnitude of trained weights can either amplify or suppress noise added to reservoir observables. We use this general result to explain why the oscillators are robust to noise and why having precise control over reservoir memory is important for noise robustness in general. Our results pave the way toward reservoir computing harnessing fluctuations in disordered linear systems.
A Moderated Mediation Model of the Relationship Between Primary and Secondary School Teachers’ Digital Competence and Online Teaching Behavior

A growing body of work has been devoted to studying teachers’ digital competence, but not much is known about how teachers’ digital competence affects online teaching behavior. Guided by the technological pedagogical content knowledge (TPACK) framework, this study investigated how teachers’ digital competence affects online teaching behavior based on Chinese primary and secondary teachers. A total of 1833 teachers completed self-report scales measuring digital competence, online teaching behavior, use intention of online teaching, and students’ online learning difficulties using the online platform Questionnaire Star. The results indicated that the level of online teaching intentions mediated the relationship between teachers’ digital competence and online teaching behavior. Students’ learning difficulties in online learning moderated the relationship between teachers’ digital competence and the intention to use online teaching, such that the relationship became stronger as students’ online learning difficulties decreased. Similarly, the indirect relationship between teachers’ digital competence and online teaching behavior was stronger at decreased levels of students’ online learning difficulties. The findings contribute to a better understanding of the impact of teachers’ digital competence on online teaching behavior.
AI under great uncertainty: implications and decision strategies for public policy

Decisions where there is not enough information for a well-informed decision due to unidentified consequences, options, or undetermined demarcation of the decision problem are called decisions under great uncertainty. This paper argues that public policy decisions on how and if to implement decision-making processes based on machine learning and AI for public use are such decisions. Decisions on public policy on AI are uncertain due to three features specific to the current landscape of AI, namely (i) the vagueness of the definition of AI, (ii) uncertain outcomes of AI implementations and (iii) pacing problems. Given that many potential applications of AI in the public sector concern functions central to the public sphere, decisions on the implementation of such applications are particularly sensitive. Therefore, it is suggested that public policy-makers and decision-makers in the public sector can adopt strategies from the argumentative approach in decision theory to mitigate the established great uncertainty. In particular, the notions of framing and temporal strategies are considered.
A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data

This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection.
Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important to verify the effectiveness of the clustering algorithm in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of two popular clustering mechanisms by comparing their performance over publicly available benchmarking data sets that capture a variety of data point distributions as well as the number of attributes, especially from a computational point of view by incorporating techniques that alleviate some of the issues that plague these algorithms. Sensitivity to initialization conditions and stagnation to local minima are explored. Further, an implementation of a feedforward neural network utilizing a fully connected topology in particle swarm optimization is introduced. This serves to be a guided random search technique for the neural network weight optimization. The algorithms utilized here are studied and compared, from which their applications are explored. The study aims to provide a handy reference for practitioners to both learn about and verify benchmarking results on commonly used real-world data sets from both a supervised and unsupervised point of view before application in more tailored, complex problems.
COST-SENSITIVE MULTI-CLASS ADABOOST FOR UNDERSTANDING DRIVING BEHAVIOR BASED ON TELEMATICS

ABSTRACT Using telematics technology, insurers are able to capture a wide range of data to better decode driver behavior, such as distance traveled and how drivers brake, accelerate, or make turns. Such additional information also helps insurers improve risk assessments for usage-based insurance, a recent industry innovation. In this article, we explore the integration of telematics information into a classification model to determine driver heterogeneity. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero accidents, a lower proportion with exactly one accident, and a far lower proportion with two or more accidents. We here introduce a cost-sensitive multi-class adaptive boosting (AdaBoost) algorithm we call SAMME.C2 to handle such class imbalances. We calibrate the algorithm using empirical data collected from a telematics program in Canada and demonstrate an improved assessment of driving behavior using telematics compared with traditional risk variables. Using suitable performance metrics, we show that our algorithm outperforms other learning models designed to handle class imbalances.
Cross DQN: Cross Deep Q Network for Ads Allocation in Feed

E-commerce platforms usually display a mixed list of ads and organic items in feed. One key problem is to allocate the limited slots in the feed to maximize the overall revenue as well as improve user experience, which requires a good model for user preference. Instead of modeling the influence of individual items on user behaviors, the arrangement signal models the influence of the arrangement of items and may lead to a better allocation strategy. However, most of previous strategies fail to model such a signal and therefore result in suboptimal performance. In addition, the percentage of ads exposed (PAE) is an important indicator in ads allocation. Excessive PAE hurts user experience while too low PAE reduces platform revenue. Therefore, how to constrain the PAE within a certain range while keeping personalized recommendation under the PAE constraint is a challenge.
Refresh When You Wake Up: Proactive Threshold Wallets with Offline Devices

Proactive security is the notion of defending a distributed system against an attacker who compromises different devices through its lifetime, but no more than a threshold number of them at any given time. The emergence of threshold wallets for more secure cryptocurrency custody warrants an efficient proactivization protocol tailored to this setting. While many proactivization protocols have been devised and studied in the literature, none of them have communication patterns ideal for threshold wallets. In particular a (t, n) threshold wallet is designed to have t parties jointly sign a transaction (of which only one may be honest) whereas even the best current proactivization protocols require at least an additional t−1 honest parties to come online simultaneously to refresh the system.In this work we formulate the notion of refresh with offline devices, where any t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ρ</inf> parties may proactivize the system at any time and the remaining n−t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ρ</inf> offline parties can non-interactively "catch up" at their leisure. However, many subtle issues arise in realizing this pattern. We identify that this problem is divided into two settings: (2, n) and (t, n) where t > 2. We develop novel techniques to address both settings as follows:•We show that the (2, n) setting permits a tight t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ρ</inf> for refresh. In particular we give a highly efficient t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ρ</inf> = 2 protocol to upgrade a number of standard (2, n) threshold signature schemes to proactive security with offline refresh. This protocol can augment existing implementations of threshold wallets for immediate use– we show that proactivization does not have to interfere with their native mode of operation. This technique is compatible with Schnorr, EdDSA, and with some effort even sophisticated ECDSA protocols. By implementation we show that proactivizing two different recent (2, n) ECDSA protocols incurs only 14% and 24% computational overhead respectively, less than 200 bytes, and no extra round of communication.•For the general (t, n) setting we prove that it is impossible to construct an offline refresh protocol with t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ρ</inf> < 2(t−1), i.e. tolerating a dishonest majority of online parties. Our techniques are novel in reasoning about the message complexity of proactive security, and may be of independent interest.Our results are positive for small-scale decentralization (such as 2FA with threshold wallets), and negative for large-scale distributed systems with higher thresholds. We thus initiate the study of proactive security with offline refresh, with a comprehensive treatment of the dishonest majority case.
Revisiting the role of inflation in financial development: unveiling non-linear and moderating effects

Purpose This paper aims to examine the non-linear impact of inflation on financial development, and the moderating role of GDP in the relationship between inflation and financial development in a panel of 125 countries. Design/methodology/approach It employs the dynamic common correlated effects (CCE) that can control for heterogeneity and cross-sectional dependence. This technique enables us to conduct both panel and country-specific analyses. Findings Though there is no significant evidence that inflation has a non-linear impact on financial development in the panel, the country-specific estimations reveal that inflation has a non-linear impact on financial development in 66 countries. The results also show that GDP mitigates the detrimental effect of inflation on financial development in 45 countries. An insight into the non-linear relationship between inflation and financial development is crucial for policy decision-making. Besides, knowledge of the moderating role of GDP in the relationship between financial development and inflation is fundamental for policy formulations. Originality/value Although the extant literature has shown that the inflation rate plays a negative role in financial development, the literature overlooked the non-linear relationship between the two variables. Besides, the studies have not considered the role of GDP in moderating the impact of the inflation rate on financial development. This study fills these gaps in the existing body of finance literature.
Receding Horizon Task and Motion Planning in Changing Environments

Complex manipulation tasks require careful integration of symbolic reasoning and motion planning. This problem, commonly referred to as Task and Motion Planning (TAMP), is even more challenging if the workspace is non-static, e.g. due to human interventions and perceived with noisy non-ideal sensors. This work proposes an online approximated TAMP method that combines a geometric reasoning module and a motion planner with a standard task planner in a receding horizon fashion. Our approach iteratively solves a reduced planning problem over a receding window of a limited number of future actions during the implementation of the actions. Thus, only the first action of the horizon is actually scheduled at each iteration, then the window is moved forward, and the problem is solved again. This procedure allows to naturally take into account potential changes in the scene while ensuring good runtime performance. We validate our approach within extensive experiments in a simulated environment. We showed that our approach is able to deal with unexpected changes in the environment while ensuring comparable performance with respect to other recent TAMP approaches in solving traditional static benchmarks. We release with this paper the open-source implementation of our method.
Gaussian Quantum Markov Semigroups on a One-Mode Fock Space: Irreducibility and Normal Invariant States

We consider the most general Gaussian quantum Markov semigroup on a one-mode Fock space, discuss its construction from the generalized GKSL representation of the generator. We prove the known explicit formula on Weyl operators, characterize irreducibility and its equivalence to a Hörmander type condition on commutators and establish necessary and sufficient conditions for existence and uniqueness of normal invariant states. We illustrate these results by applications to the open quantum oscillator and the quantum Fokker-Planck model.