Reviewerly/exHarmony huggingface dataset:
Cross-domain learning using optimized pseudo labels: toward adaptive car detection in different weather conditions and urban cities

Convolutional neural networks based object detection usually assumes that training and test data have the same distribution, which, however, does not always hold in real-world applications. In autonomous vehicles, the driving scene (target domain) consists of unconstrained road environments which cannot all possibly be observed in training data (source domain) and this will lead to a sharp drop in the accuracy of the detector. In this paper, we propose a domain adaptation framework based on pseudo-labels to solve the domain shift. First, the pseudo-labels of the target domain images are generated by the baseline detector (BD) and optimized by our data optimization module to correct the errors. Then, the hard samples in a single image are labeled based on the optimization results of pseudo-labels. The adaptive sampling module is approached to sample target domain data according to the number of hard samples per image to select more effective data. Finally, a modified knowledge distillation loss is applied in the retraining module, and we investigate two ways of assigning soft-labels to the training examples from the target domain to retrain the detector. We evaluate the average precision of our approach in various source/target domain pairs and demonstrate that the framework improves over 10% average precision of BD on multiple domain adaptation scenarios on the Cityscapes, KITTI, and Apollo datasets.
FastMDE: A Fast CNN Architecture for Monocular Depth Estimation at High Resolution

A depth map helps robots and autonomous vehicles (AVs) visualize the three-dimensional world to navigate and localize neighboring obstacles. However, it is difficult to develop a deep learning model that can estimate the depth map from a single image in real-time. This study proposes a fast monocular depth estimation model named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastMDE</i> by optimizing the deep convolutional neural network according to the encoder-decoder architecture. The decoder needs to obtain partial and semantic feature maps from the encoding phase to improve the depth estimation accuracy. Therefore, we designed FastMDE with two effective strategies. The first one involved redesigning the skip connection with the features of the squeeze-excitation module to obtain partial and semantic feature maps of the encoding phase. The second strategy involved redesigning the decoder by using the fusion dense block to permit the usage of high-resolution features that were learned earlier in the network before upsampling. The proposed FastMDE model utilizes only 4.1 M parameters, which is much lesser than the parameters utilized by state-of-art models. Thus, FastDME has a higher accuracy and lower latency than previous models. This study also demonstrates that MDE can leverage deep neural networks in real-time (i.e., 30 fps) with the Linux embedded board Nvidia Jetson Xavier NX. The model can facilitate the development and applications with superior performances and easy deployment on an embedded platform.
FRIDA — Generative feature replay for incremental domain adaptation

We tackle the novel problem of incremental unsupervised domain adaptation (IDA) in this paper. We assume that a labeled source domain and different unlabeled target domains are incrementally observed with the constraint that data corresponding to the current domain is only available at a time. The goal is to preserve the accuracies for all the past domains while generalizing well for the current domain. The IDA setup suffers due to the abrupt differences among the domains and the unavailability of past data including the source domain. Inspired by the notion of generative feature replay, we propose a novel framework called Feature Replay based Incremental Domain Adaptation (FRIDA) which leverages a new incremental generative adversarial network (GAN) called domain-generic auxiliary classification GAN (DGAC-GAN) for producing domain-specific feature representations seamlessly. For domain alignment, we propose a simple extension of the popular domain adversarial neural network (DANN) called DANN-IB which encourages discriminative domain-invariant and task-relevant feature learning. Experimental results on Office–Home, Office-CalTech, and DomainNet datasets confirm that FRIDA maintains superior stability-plasticity trade-off than the literature.
Decision-making under uncertainty: be aware of your priorities

Abstract Self-adaptive systems (SASs) are increasingly leveraging autonomy in their decision-making to manage uncertainty in their operating environments. A key problem with SASs is ensuring their requirements remain satisfied as they adapt. The trade-off analysis of the non-functional requirements (NFRs) is key to establish balance among them. Further, when performing the trade-offs it is necessary to know the importance of each NFR to be able to resolve conflicts among them. Such trade-off analyses are often built upon optimisation methods, including decision analysis and utility theory. A problem with these techniques is that they use a single-scalar utility value to represent the overall combined priority for all the NFRs. However, this combined scalar priority value may hide information about the impacts of the environmental contexts on the individual NFRs’ priorities, which may change over time. Hence, there is a need for support for runtime, autonomous reasoning about the separate priority values for each NFR, while using the knowledge acquired based on evidence collected. In this paper, we propose Pri-AwaRE, a self-adaptive architecture that makes use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to perform decision-making for SASs while offering awareness of NFRs’ priorities. MR-POMDP is used as a priority-aware runtime specification model to support runtime reasoning and autonomous tuning of the distinct priority values of NFRs using a vector-valued reward function. We also evaluate the usefulness of our Pri-AwaRE approach by applying it to two substantial example applications from the networking and IoT domains.
Improving Deep Forest by Exploiting High-order Interactions

Recent studies on deep forests have shown that deep learning frameworks can be built on non-differentiable modules without a backpropagation training process. However, the feature representations of deep forests only consist of predicted class probabilities. The information these class probabilities deliver is very limited and lacks diversity, especially when the number of output labels is far less than the number of input features. Besides, the prediction-based representations require us to save multiple layers of random forests to use them during testing, which is high-memory and high-time cost. In this paper, we propose a novel deep forest model that utilizes high-order interactions of input features to generate more informative and diverse feature representations. Specifically, we design a generalized version of Random Intersection Trees (gRIT) to discover stable high-order interactions and apply Activated Linear Combination (ALC) to transform them into hierarchical distributed representations. These interaction-based representations obviate the need to store random forests in the front layers, thus greatly improving the computational efficiency. Our experiments show that our method achieves highly competitive predictive performance with significantly reduced time and memory cost.
Despeckling of SAR Images Using BEMD-Based Adaptive Frost Filter

In image processing, removal of speckle noise from a satellite image is a challenging task for the researchers. There are various approaches for speckle noise reduction. Generally, the speckle noises are scattered, in satellite images, medical images and synthetic aperture radar (SAR) images. This paper introduces a bidimensional empirical mode decomposition (BEMD)-based adaptive filtering method for despeckling of SAR image. The noisy SAR image is decomposed into different bidimensional intrinsic mode function (BIMF) levels using BEMD and then filtering is performed on the first BIMF level, as it contains the high-frequency noise component. This adaptation process effectively filters out the noisy image component without destroying the original image component. A BEMD-based adaptive Frost filter is introduced in this paper for despeckling of SAR images. The despeckling performances of our proposed filtering method are further analyzed by visual evaluation and also using performance parameters comparatively. Our reconstructed images show better performance quantitatively and qualitatively compared to other filters.
Requirements document relations

Abstract Relations between requirements are part of nearly every requirements engineering approach. Yet, relations of views, such as requirements documents, are scarcely considered. This is remarkable as requirements documents and their structure are a key factor in requirements reuse, which is still challenging. Explicit formalized relations between documents can help to ensure consistency, improve completeness, and facilitate review activities in general. For example, this is relevant in space engineering, where many challenges related to complex document dependencies occur: 1. Several contractors contribute to a project. 2. Requirements from standards have to be applied in several projects. 3. Requirements from previous phases have to be reused. We exploit the concept of “layered traceability”, explicitly considering documents as views on sets of individual requirements and specific traceability relations on and between all of these representation layers. Different types of relations and their dependencies are investigated with a special focus on requirement reuse through standards and formalized in an Object-Role Modelling (ORM) conceptual model. Automated analyses of requirement graphs based on this model are able to reveal document inconsistencies. We show examples of such queries in Neo4J/Cypher for the EagleEye case study. This work aims to be a step toward a better support to handle highly complex requirement document dependencies in large projects with a special focus on requirements reuse and to enable automated quality checks on dependent documents to facilitate requirements reviews.
Derivation predicting inflection

Abstract In this paper, we investigate the value of derivational information in predicting the inflectional behavior of lexemes. We focus on Latin, for which large-scale data on both inflection and derivation are easily available. We train boosting tree classifiers to predict the inflection class of verbs and nouns with and without different pieces of derivational information. For verbs, we also model inflectional behavior in a word-based fashion, training the same type of classifier to predict wordforms given knowledge of other wordforms of the same lexemes. We find that derivational information is indeed helpful, and document an asymmetry between the beginning and the end of words, in that the final element in a word is highly predictive, while prefixes prove to be uninformative. The results obtained with the word-based methodology also allow for a finer-grained description of the behavior of different pairs of cells.
Self-supervised Universal Domain Adaptation with Adaptive Memory Separation

Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain where both domains share a common label space and hold a private label space respectively. One of the most challenging goals in UniDA is to separate target samples from common classes and these from private classes without any prior knowledge on the target label space. In this paper, we propose a novel self-supervised adaptive memory network with consistency regularization for UniDA. The adaptive memory includes all target samples and source class centers, which dynamically divides target samples into common area, uncertain area, and unknown area based on the entropy. Our proposed framework jointly assigns a specific neighborhood to each target sample and clusters the target sample to its neighbor from the neighborhood. Most importantly, the proposed framework adopts consistency regularization that gradually makes the output of the classifier more reliable. This simple strategy is proved to be very effective for UniDA problem. Experimental results on two UniDA benchmarks demonstrate the effectiveness of our method.
Evaluating Upgradable Smart Contract

Smart contracts have been developed and employed in both permissioned and permissionless blockchains recently, mainly to enforce agreements among parties without the need for intermediaries. This achievement is the result of blockchain immutability which guarantees that no party can alter the conditions of an already deployed contract. However, immutability also makes patching or updating contracts impossible even when incorrectness, unfairness, or security flaws are spotted in them. So far, researchers in academia and industry have developed two main methods, data segregation and proxy storage, with six patterns to make deployed contracts upgradable. However, until now, there has been no comprehensive framework that can simultaneously offer upgradability, security resilience, and scalability features. For example, none of the existing solutions have implemented any security mechanism that can resist attacks such as the DAO one. Through extensive analysis and implementation of all these patterns, and taking state-of-the-art attacks on the Ethereum network into consideration, we propose our innovative framework, “Comprehensive-Data-Proxy pattern” which uses data segregation on the top of proxy pattern, that can completely defend against any types of Reentrancy attacks. Additionally, this solution mitigates the scalability issue of the proxy pattern. Our experiments show that the framework can address these two issues with negligible impact on performance.
A Personalized Recommendation System for English Teaching Resources Based on Multi-K Nearest Neighbor Regression Algorithm

In order to ensure the quality of resource recommendation and solve the problems of low recommendation accuracy, long recommendation time, and high data loss rate in the process of resource recommendation in traditional methods, a personalized recommendation system of English teaching resources based on the multi-K nearest neighbor regression algorithm is designed. According to the overall architecture of the personalized recommendation system of teaching resources, this study designs the resource browsing function module, teaching resource detailed page recommendation module, and teaching resource database. Based on the basic idea of the multi-K nearest neighbor regression algorithm, in order to avoid the loss of important data in English teaching resource recommendation and reduce the data loss rate, a missing data reconstruction algorithm of English teaching resources is proposed. Finally, the path interest of student users is considered from the selection of browsing path and access time to realize the personalized recommendation of English teaching resources. The experimental results show that the system has high resource recommendation accuracy, short recommendation time, and low data loss rate.
Multi-hop Knowledge Graph Reasoning Based on Hyperbolic Knowledge Graph Embedding and Reinforcement Learning

Some unseen facts in knowledge graphs (KGs) can be complemented by multi-hop knowledge graph reasoning. The process of multi-hop reasoning can be presented as a serialized decision problem, and then be solved with reinforcement learning (RL). However, existing RL-based reasoning methods cannot deal with the hierarchical information in KGs effectively. To solve this issue, we propose a novel RL-based multi-hop KG reasoning model Path Additional Action-space Ranking (PAAR). Our model first proposes a hyperbolic knowledge graph embedding (KGE) model which can effectively capture hierarchical information in KGs. To introduce hierarchical information into RL, the hyperbolic KGE vector is subsequently added to the state space and helps to expand the action space. In order to alleviate the reward sparsity problem in RL, our model utilizes the score function of hyperbolic KGE model as a soft reward. Finally, the metric of hyperbolic space is added to the training of RL as a penalty strategy to constrain the sufficiency of multi-hop reasoning paths. Experimental results on two real-world datasets show that our proposed model not only provides effective answers but also offers sufficient paths.
A Deterministic Polynomial Public Key Algorithm over a Prime Galois Field GF(p)

The ancient Vieta's formulas reveal the relationships between coefficients of an nth-degree polynomial and its roots. It is surprisingly found that there exists a hidden secret for a potential public key exchange: decoupling the product of all roots or constant term from summations of root products or coefficients of a polynomial to establish a keypair. The proposed deterministic polynomial public key algorithm or DPPK is built on the fact that a polynomial cannot be factorized without its constant term. DPPK allows the keypair generator to combine a base polynomial, eliminable during the decryption, with two solvable polynomials and creates two entangled polynomials. Two coefficient vectors of the entangled polynomials form a public key, and their constant terms, together with the two solvable polynomials, form the private key. By only publishing coefficients of polynomials without their constant terms, we greatly restrict polynomial factoring techniques for the private key extraction. We demonstrate that the time complexity, in terms of field operations, of the private key extraction from the known public key is a super-exponential difficulty O(p2) for classical attacks and an exponential difficulty O(p) for quantum attacks, respectively, in comparison with the low sub-exponential complexity for the PQC algorithms. The best-known deterministic complexity of the polynomial factoring problem for the secret key extraction from intercepted ciphertexts is O(npl/2) for classical attacks and O(pl/2) for quantum attacks, respectively, at the same complexity level as Grover's search algorithm. Performance comparisons with the PQC finalists for keypair generations, encryptions, and decryptions are presented.
Image alignment using mixture models for discontinuous deformations

Image alignment is a challenging problem for the cases when objects in the scene appear discontinuous displacements in images captured from different view points. In this paper, a mixture model approach based on mesh warping is proposed. Given a set of corresponding feature points between images, variational Bayesian linear regression framework is adopted to estimate mixture models and determine the number of components. To improve the alignment accuracy, warping model of each component is refined separately by minimizing the photometric error. Then, all pixels in the overlapping region are assigned to one of the components in terms of the photometric similarity, the distribution of feature points and the consistency of adjacent pixels, which contributes to the final prediction of warping. Experiment demonstrates that the proposed method is effective on real data and achieves higher alignment accuracy than state-of-the-arts.
Supporting Demand-Response strategies with the DELTA ontology

The increasing interest of demand response (DR) strategies has fostered the funding of new research projects in Europe. The development of DR in these projects requires a bi-directional data exchange, which revolves around diverse overlapping topics, between infrastructures and providers. As a result, ontologies are required to enable a transparent exchange and consumption of data, ensuring its semantic correctness. Nevertheless, currently, there is no ontology that can be used for such data exchange covering all these topics. In the context of the EU project DELTA, which focuses on DR, an ontology has been developed with the goal of filling the aforementioned lack. This paper describes the approach followed to develop this ontology, specifies its requirements, and also includes a practical example of how to use it.
Inverse Design of High-Dimensional Nanostructured 2×2 Optical Processors Based On Deep Convolutional Neural Networks

Optical computation, especially the optical neural network, has gained great attention recently due to their high computation throughput and low energy consumption. The 2×2 optical processor, as a building block of high-order matrix multiplication circuit, can be an essential part for optical neural networks. For the first time, we demonstrate a compact and general optical processor on silicon-on-isolator platform with a footprint of 1.68×3.6 μm². The transfer matrix of an optical processor is determined by its nanostructured pattern, which has 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">420</sup> possible combinations. To accelerate the design process of arbitrary optical processors, a two-step trained tandem model consisting of a forward model and an inverse model based on deep convolutional neural networks is proposed. After training, the forward model is able to predict the transfer matrix for a given optical processor with prediction accuracy of 98.8%, while the calculation speed is more than one thousand times faster than the electromagnetic simulation. The inverse model can predict the geometry of an optical processor for a target transfer matrix with prediction accuracy of 96.5% and its prediction time is also within a second. This two-step trained tandem model paves a new way for optical processors design.
An augmented reality-based system for improving quality of services operations: a study of educational institutes

Purpose This study aims to evaluate the usability of the augmented reality-based Evoke Education System (EES) to improve service operations in educational settings. The EES uses an animated character (Moe) to interact with children in a classroom by reproducing their teacher's movements and speech. Design/methodology/approach This study uses a quantitative approach for the system usability evaluation. The ESS was evaluated by 71 children aged 6–8 years old, from two primary schools. After interacting with the EES, they completed a system usability questionnaire and participated in a knowledge acquisition test. Findings The knowledge acquisition test undertaken on the initial day showed statistically significant improvements for children taught with the EES, compared to children taught through traditional teaching approaches. However, the retest nine days later was not statistically significant (as only one school participated) due to low power. This study used confirmatory factor analysis (CFA), resulting in the identification of five essential factors (likeability, interactiveness, retention, effectiveness/attractiveness and satisfaction) that contribute to the EES's usability. The comparison with existing literature shows that these factors are consistent with the definition of system usability provided by the International Organization for Standardization and current academic literature in this field. Research limitations/implications The findings presented in this study are based on the data from only two schools. The research can be extended by involving children from a greater number of schools. Mixed methods and qualitative research approaches can be used for future research in this area to generalise the results. Originality/value This study proposes an innovative augmented reality-based education system to help teachers deliver their key messages to the children in a fun way that can potentially increase their knowledge retention.