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Copyright of Anales de Geografía de la Universidad Complutense is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite the myriad benefits offered by blockchain technology, most of them still face several interrelated issues, such as limited transaction throughput, exorbitant transaction fees, and protracted confirmation times. Payment channel networks have emerged as a promising scalability solution, allowing two mutually distrustful users to engage in multiple off-chain transactions. However, existing schemes based on Hash Time Lock Contract or Anonymous Multi-hop Lock generally cannot ensure strong unlinkability of payments, due to the fact that the time-lock information still remains on the blockchain. To enhance on-chain privacy, a versatile tool was recently proposed by Thyagarajan et al. (CCS’20), named Verifiable Timed Signatures, but it suffers from the dual insufficiencies of linear-increasing performance and time unverifiability (i.e., performance is linear to the number of signature shares, and signatures cannot be ensured recoverable after the specified time). In this paper, we first propose an approach to reduce computational overhead of VTS, which can be applied to enhance other established schemes, such as VTD (S&P’22) and VTLRS (ESORICS’22). To further reduce the computational complexity from $\mathcal {O}(n)$ to $\mathcal {O}(1)$ , we introduce a new cryptographic primitive called Verifiable Timed Adaptor Signatures. Moreover, we extend the VTAS to VTAS+ which provides the security property of verifiable recovery. We demonstrate the practicality of our proposal via presenting a concrete instantiation and constructing a privacy-enhanced payment channel network. Finally, the comprehensive evaluation reveals that our solutions exhibit superior performance than the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Copyright of Dirasat: Educational Sciences is the property of University of Jordan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large-size images cannot be directly put into GPU for training and need to be cropped to patches due to GPU memory limitation. The commonly used cropping methods before are random cropping and sequential cropping, which are crude and fatally inefficient. First, categories of datasets are often imbalanced, and just simple cropping misses an excellent opportunity to make the data distribution balanced. Second, the training needs to crop a large number of patches to cover all patterns, which greatly increases the training time. This problem is of great practical hazards but is often overlooked by previous works. The optimal solution is to generate valuable patches. Valuable patches refer to the value to network training, i.e., the value of this patch for the convergence of the network, and the improvement of the accuracy. To this end, we propose a data-related patch proposal strategy to sample high valuable patches. The core idea is to score each patch according to the accuracy of each category, so as to perform balanced sampling. Compared with random cropping or sequential cropping, our method can improve the segmentation accuracy and accelerate the training vastly. Moreover, our method also shows great advantages over the loss-based balanced approaches. Experiments on Deepglobe and Potsdam show the excellent effect of our method. [ABSTRACT FROM AUTHOR]

Most of the existing object detection methods have complicated hand-designed components, such as nonmaximum suppression procedures and manual resizing of anchor boxes. Based on detection transformer (DETR), this letter not only eliminates the need for manual component adjustment but also solves three problems of poor remote sensing image for directional object capture, slow DETR convergence, and the same attention allocated by different layers of decoder. First, the D-angle module is used to align the rotating object region while accelerating the convergence using the a priori angle. Then, the overall computation of the model is reduced by using adaptive proposal selection (APS) in the cascade structure. Finally, the adaptive query selection (AQS) module is applied so that the decoder in different layers gets different attention weights to optimize the layer-by-layer fine-tuning process. In this letter, the effectiveness of the proposed method is verified using two public datasets, DOTA and HRSC2016. [ABSTRACT FROM AUTHOR]

In view of the current situation of low visibility at night and increasing poaching activities in wildlife reserves, the use of unmanned aerial vehicle (UAV) monitoring equipped with thermal infrared (TIR) cameras has become a trend. To overcome the difficulties of small objects and low resolution in infrared images, this letter proposes a nonsalient object detection algorithm in infrared images based on deep learning. Based on Grid R-CNN, we designed a feature extraction network, improved the region proposal network by guided anchoring (GA-RPN), and introduced the slice inference mechanism. Our method makes the network selectively fuse multiscale features to generate high-quality proposals and improve detection accuracy. Experimental results show our method is superior to other object detection algorithms for nonsalient elephant images on the BIRDSAI dataset. [ABSTRACT FROM AUTHOR]

Classifying remotely sensed hyperspectral images (HSIs) became a computationally demanding task given the extensive information contained throughout the spectral dimension. Furthermore, burgeoning data volumes compound inherent computational and storage challenges for data processing and classification purposes. Given their distributed processing capabilities, cloud environments have emerged as feasible solutions to handle these hurdles. This encourages the development of innovative distributed classification algorithms that take full advantage of the processing capabilities of such environments. Recently, computational-efficient methods have been implemented to boost network convergence by reducing the required training calculations. This letter develops a novel cloud-based distributed implementation of the extreme learning machine (CC-ELM) algorithm for efficient HSI classification. The proposal implements a fault-tolerant and scalable computing design while avoiding traditional batch-based backpropagation. CC-ELM has been evaluated over state-of-the-art HSI classification benchmarks, yielding promising results and proving the feasibility of cloud environments for large remote sensing and HSI data volumes processing. The code available at https://github.com/mhaut/scalable-ELM-HSI. [ABSTRACT FROM AUTHOR]

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has attracted a lot of attention because it solely employs image-level labels to drive the model training. Most of the WSOD methods incline to mine salient object as positive instance, and the less salient objects are considered as negative instances, which will cause the problem of missing instances. In addition, the quantity of hard and easy instances is usually imbalanced, and consequently, the cumulative loss of a large amount of easy instances dominates the training loss, which limits the upper bound of WSOD performance. To handle the first problem, a complementary detection network (CDN) is proposed, which consists of a complementary multiple instance detection network (CMIDN) and a complementary feature learning (CFL) module. The CDN can capture robust complementary information from two basic multiple instance detection networks (MIDNs) and mine more object instances. To handle the second problem, an instance difficulty evaluation metric named instance difficulty score (IDS) is proposed, which is employed as the weight of each instance in the training loss. Consequently, the hard instances will be assigned larger weights according to the IDS, which can improve the upper bound of WSOD performance. The ablation experiments demonstrate that our method significantly increases the baseline method by large margins, i.e., 23.6% (10.2%) mean average precision (mAP) and 32.4% (13.1%) correct localization (CorLoc) gains on the NWPU VHR-10.v2 (DIOR) dataset. Our method obtains 58.1% (26.7%) mAP and 72.4% (47.9%) CorLoc on the NWPU VHR-10.v2 (DIOR) dataset, which achieves better performance compared with seven advanced WSOD methods. [ABSTRACT FROM AUTHOR]

Substantial progress has been made in detecting ships of arbitrary orientation in synthetic aperture radar (SAR) images. However, the mainstream method is still limited by the horizontal bounding box (HBB) boundary, which cannot provide scaling information for the length and width of the oriented bounding box (OBB) in an intuitive way. In this study, we propose a novel encode representation to describe the OBB by breaking through the border restriction of the HBB. Specifically, we derive an inclination factor from two left-top point offsets (LTPO), which enables us to directly infer the coordinates of the four OBB vertices and obtain an oriented rectangular proposal. To obtain high-quality oriented semantic features, we utilize a feature adaptive module (FAM) to learn the shape and orientation implied by arbitrary-oriented ships through spatial transformation. Our comparative experiments demonstrate that our proposed method achieves superior performance and detection accuracy on two commonly-used benchmark datasets for oriented SAR ship detection dataset named SSDD and high-resolution SAR images dataset (HRSID). [ABSTRACT FROM AUTHOR]

Semantic instance reconstruction attracts increasing attention in several areas such as mobile mapping, scene reconstruction, and robot navigation. Although much progresses have been made in recent years, the reconstruction performance is highly sensitive to occlusions and noises. To address these issues, we incorporate point cloud completion into a novel semantic instance reconstruction network PMNet, which consists of a 3-D object detection module, a point cloud completion module, and a mesh generation module. Based on the candidate instance proposals and their proposal features obtained in the object detection module, a point encoder layer is proposed to learn the local geometric features from the point cloud belonging to the detected instances, and a feature transformation layer is utilized to align the proposal features with the local geometric features. These two types of features are then fused and fed into the point cloud decoder to predict the complete point cloud of each instance. The mesh is finally reconstructed for each instance by the mesh generation module. Quantitative and qualitative experiments conducted on the ScanNetv2 dataset demonstrate that the proposed PMNet achieves the best reconstruction performance on real-world point clouds. [ABSTRACT FROM AUTHOR]

We study the problem of efficient object detection in 3-D point clouds with the voxel-point framework. Considering a large number of redundant and dense proposals are usually generated for small-sized objects during inference in voxel-based single-stage detectors, the existing detectors usually introduce extra subnetworks to filter and further refine the redundancy proposals. Albeit feasible, the computational and memory cost also increase during inference. In this letter, we introduce a novel voxel-to-point 3-D detector, termed V2P-SSD, which is a novel and lightweight pipeline that jointly integrates the voxel backbone and point head together in a single-stage framework. Different from dense predictions in feature maps, voxels related to objects in our framework are sampled with a fixed number and then transformed into points. Consequently, the point head is used to dynamically generate object proposals. Our voxel-to-point detection paradigm demonstrates a significant precision improvement on small-sized objects without introducing extra memory footprints. Extensive experiments conducted on KITTI and ONCE benchmarks validate the superiority of our method. [ABSTRACT FROM AUTHOR]

Surface defect detection plays an important role in manufacturing and has aroused widespread interests. However, it is challenging as defects are highly similar to non-defects. To address this issue, this paper proposes a Region of Interest Attention (RoIA) network based on deep learning for automatically identifying surface defects. It consists of three parts: multi-level feature preservation (MFP) module, region proposal attention (RPA) module, and skip dense connection detection (SDCD) ones, where MFP is designed to differentiate defect features and texture information by feature reserved block, RPA is developed to locate the position of the defects by capturing global and local context information, and SDCD is proposed to better predict defect categories by propagating the fine-grained details from low-level feature map to high-level one. Experimental results conducted on three public datasets (e.g., NEU-DET, DAGM and Magnetic-Tile) demonstrate that the proposed method can significantly improve the detection performance than state-of-the-art ones and achieve an average defect detection accuracy of 99.49%. [ABSTRACT FROM AUTHOR]

The war and Russian aggression against Ukraine require government and society long-term extraordinary efforts. It's not only in the field of the destroyed economy. The primary necessity is to restore a dignified life to every person affected by the war, military or civilian. The fate of each of us, our society, and our country in general will depend on efforts effectiveness in this direction. Objective. To develop and substantiate proposals for a systematic approach to the provision of medical rehabilitation assistance in the country. Results. The principles on which it is expedient to create a system of rehabilitation assistance in Ukraine have been formulated and substantiated. They are the principles of statehood, modernity, science, continuity, phasing and unified tactics, regionalism, specializations, self-rehabilitation. Each principle is briefly described, the experience of the world's leading countries is given. The state and society role in the rehabilitation process is noted. Proposals. Develop a strategy, concept and government program for creating a rehabilitation assistance system in Ukraine. Provide a construction and equipment of regional rehabilitation centers (RC) within the post-war country renovation program. It has to be used worldwide principles of new hospital's design and construction regarding the 200 inpatient beds in the unit. To locate RC outside of big cities, usually. Training of rehabilitation specialists should be provided in various levels medical educational institutions. Modified vehicles to manual control for disable people and training in its use. To prevent the liquidation of Ukrainian Research Prosthetics Institution. To transfer this institution functions and the property complex to Sytenko Institute of Spine and Joint Pathology National Ukrainian Academy of Medical Sciences. To transfer research medical rehabilitation organizer and executor functions to the National Ukrainian Academy of Medical Sciences, in generally. [ABSTRACT FROM AUTHOR]

Background: In everyday conversations, a person with aphasia (PWA) compensates for their language impairment by relying on multimodal and material resources, as well as on their conversation partners. However, some social actions people perform in authentic interaction, proposing a joint future activity, for example, ordinarily rely on a speaker producing a multi‐word utterance. Thus, the language impairment connected to aphasia may impede the production of such proposals, consequently hindering the participation of PWAs in the planning of future activities. Aims: To investigate (1) how people with post‐stroke chronic aphasia construct proposals of joint future activities in everyday conversations compared with their familiar conversation partners (FCPs); and (2) how aphasia severity impacts on such proposals and their uptake. Methods & Procedures: Ten hours of video‐recorded everyday conversations from seven persons with mild and severe aphasia of varying subtypes and their FCPs were explored using conversation analysis. We identified 59 instances where either party proposed a joint future activity and grouped such proposals according to their linguistic format and sequential position. Data are in Finnish. Outcomes & Results: People with mild aphasia made about the same number of proposals as their FCPs and used similar linguistic formats to their FCPs when proposing joint future activities. This included comparable patterns associated with producing a time reference, which was routinely used when a proposal initiated a planning activity. Mild aphasia manifested itself as within‐turn word searches that were typically self‐repaired. In contrast, people with severe aphasia made considerably fewer proposals compared with their FCPs, the proposal formats being linguistically unidentifiable. This resulted in delayed acknowledgement of the PWAs' talk as a proposal. Conclusions & Implications: Mild aphasia appears not to impede PWAs' ability to participate in the planning of joint future activities, whereas severe aphasia is a potential limitation. To address this possible participatory barrier, we discuss clinical implications for both therapist‐led aphasia treatment and conversation partner training. WHAT THIS PAPER ADDS: What is already known on the subject: PWAs use multimodal resources to compensate for their language impairment in everyday conversations. However, certain social actions, such as proposing a joint future activity, cannot ordinarily be accomplished without language. What this paper adds to existing knowledge: The study demonstrates that proposing joint future activities is a common social action in everyday conversations between PWAs and their family members. People with mild aphasia used typical linguistic proposal formats, and aphasic word‐finding problems did not prevent FCPs from understanding the talk as a proposal. People with severe aphasia constructed proposals infrequently using their remaining linguistic resources, a newspaper connecting the talk to the future and the support from FCPs. What are the potential or actual clinical implications of this work?: We suggest designing aphasia treatment with reference to the social action of proposing a joint future activity. Therapist‐led treatment could model typical linguistic proposal formats, whereas communication partner training could incorporate FCP strategies that scaffold PWAs' opportunities to construct proposals of joint future activities. This would enhance aphasia treatment's ecological validity, promote its generalization and ultimately enable PWAs to participate in everyday planning activities. [ABSTRACT FROM AUTHOR]

An event-triggered control technique has been developed recently. This technique explicitly reduced the signal transmission by introducing a flexible design of threshold inequalities. It was later extended to event-triggered model-predictive control for power converter systems. In this letter, by incorporating this control technique into an extended state-observer-based finite-control-set model-predictive control framework, we have developed a new model-predictive control architecture for power converter systems with parametric uncertainties. Meanwhile, a novel cost function with respect to the angle minimization term is embedded into this proposal. The novelty of our development lies not only in integrating the event-triggered mechanism with the suggested finite-control-set model-predictive control architecture for facilitating the alleviation of performance deterioration caused by parameter variations and model uncertainties, but also in a multiobjective optimization design that allows the switching frequency in a low value. Finally, extensive simulative and experimental investigations for a modular multilevel converter confirm the interest and the viability of the proposed design methodology. [ABSTRACT FROM AUTHOR]

Science experts play important role in spreading the science-related knowledge that can improve societal outcomes by guiding policy in transformative way. Experts communication and its influence on policy decision-makers is particularly important topic when it comes to problems of climate change. This paper explore how European citizens perceive the role of experts and policy makers in science communication in climate change topic. It is based on the international research analysing European citizen’s perception of science communication to better understanding of how beliefs, perceptions and knowledge of science-related issues originate among EU citizens and to enquire their proposals for enhancing the quality of science communication. The findings of this paper come from studies performed within a European project entitled CONCISE which was carried out in five European countries (Portugal, Spain, Italy, Slovakia, Poland) based on qualitative research method – public consultation. The purpose of this paper is to review the existing structural obstacles that experts and policymakers, currently face when attempting to communicate the climate change successfully and to present the main findings from public consultation about the main proposals for experts and policy makers in communicating theclimate change to public.

This policy brief provides an overview of current legislation that discriminates against parents with disabilities. It also considers non discriminatory legislation that has been enacted or is currently being enacted at the state level, with the hope of encouraging more states—eventually all states—to adopt similar legislation. It is our strong belief that such legislative changes are both needed and deserved by the at least 4.1 million disabled parents currently raising children under the age of 18 in the US.

The purpose of this paper is to identifying conceptual dimensions of fantasy in the digital art of sinful advertising in the light of Merleau-Ponty's proposals. Due to the breadth of the research, it was not possible to count it statistically, so the two researchers used what is available on social networking sites, as well as on the international Internet, and to benefit from them in accessing the digital artistic productions and identifying them in line with the objective of the current study. The two researchers chose a sample for the research; rather, they were (2) intentionally, according to the following encyclopedias. The two researchers used the qualitative content analysis method as a method for the current research. One of the most important results reached by the researcher is that: Digital art is represented in accordance with the presentation of a fantasy style that depends on the metaphor of forms, which may be unfamiliar, have been placed in a familiar context, or their forms are familiar and have been placed in an unfamiliar context. On a specific meaning by combining the image and truth, reality and imagination, subjectivity and objectivity, these methods astonished and aroused the receiver’s insight and insight towards enriching him intellectually and making him in a state of contemplation, induction, and moral and semantic referrals, as in Models (1) and (2). The art of fantasy advertising was manifested through the creativity of the designer artist in combining the aesthetics of the photographic image and the professionalism of the drawn image to combine the objective and subjective, imaginary and reality to reveal a dialogic discourse that represents hidden conflicts that reveal the tragedy of reality trying to mitigate its impact (2) It was rich in content and psychological dimensions. One of the most important recommendations recommended by the researchers is that: It is necessary that it be a subject dedicated to digital design in the Department of Art Education because contemporary arts and mostly thanks to technological developments are the most popular and employed, organizing workshops and educational seminars for students, and taking care of holding exhibitions specialized in digital arts, to increase the students’ visual taste and spread their thoughts and knowledge. [ABSTRACT FROM AUTHOR]

Business accelerators play a key role in the initial critical stages of assessment of commercial viability, offering mentorship provision of funding and protection of intellectual property (IP) for product development and refinement. However, little is known about the decision-making criteria and detailed analysis of the underlying criteria and interdependencies between the key factors used by accelerator organizations to fund start-ups. This article focuses on the decision-making criteria utilized by a leading £21M accelerator program, largely funded by the European Regional Development Fund for initial stage funding and IP protection for product and innovation commercialization. We incorporate a multimethodological interpretive-based approach based on Day's “Real-Win-Worth” framework to develop the interrelationships and ranking between the factors. The results highlight the significance and weighting attached to the factors associated with the technical competency of the proposer and evidence of demand existing for the product. We propose a new framework that models the key factor interrelationships offering additional insight to accelerator-based decision making. [ABSTRACT FROM AUTHOR]

Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recently. However, most existing methods ignore the efficiency issues, e.g., high computational complexity and slow adaptation speed. Notably, efficiency has become an increasingly important evaluation metric for few-shot techniques due to an emerging trend toward embedded AI. To this end, we present an efficient pretrain-transfer framework (PTF) baseline with no computational increment, which achieves comparable results with previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed. Within the KI initializer, we propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights. Finally, our approach not only achieves the SOTA results across three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits high efficiency with $1.8-100\times $ faster adaptation speed against the other methods on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is the first work to consider the efficiency problem in FSOD. We hope to motivate a trend toward powerful yet efficient few-shot technique development. The codes are publicly available at https://github.com/Ze-Yang/Efficient-FSOD. [ABSTRACT FROM AUTHOR]

There exist a variety of visual relationships among entities in an image. Given a relationship query $\langle subject, predicate, object \rangle $ , the task of visual relationship referring (VRR) aims to disambiguate instances of the same entity category and simultaneously localize the subject and object entities in an image. Previous works of VRR can be generally categorized into one-stage and multi-stage methods. The former ones directly localize a pair of entities from the image but they suffer from low prediction accuracy, while the latter ones perform better but they are indirect to localize only a couple of entities by pre-generating a rich amount of candidate proposals. In this paper, we formulate the task of VRR as an end-to-end bounding box regression problem and propose a novel one-stage approach, called VRR-TAMP, by effectively integrating Transformers and an adaptive message passing mechanism. First, visual relationship queries and images are respectively encoded to generate the basic modality-specific embeddings, which are then fed into a cross-modal Transformer encoder to produce the joint representation. Second, to obtain the specific representation of each entity, we introduce an adaptive message passing mechanism and design an entity-specific information distiller SR-GMP, which refers to a gated message passing (GMP) module that works on the joint representation learned from a single learnable token. The GMP module adaptively distills the final representation of an entity by incorporating the contextual cues regarding the predicate and the other entity. Experiments on VRD and Visual Genome datasets demonstrate that our approach significantly outperforms its one-stage competitors and achieves competitive results with the state-of-the-art multi-stage methods. [ABSTRACT FROM AUTHOR]

To support collaborations between technical communicators and nonprofits, this article outlines a framework for composing public events. The article develops a technical and professional communication (TPC) lens for public events and then draws that together with a case study of a nonprofit's strategies for their public event. Through this work, the article outlines a framework for organizing and managing public events that can engage challenging publics around complex information. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

In large-scale chip multiprocessors (CMPs), the scalability of a coherence directory becomes more important as the number of cores increases. However, previously proposed scalable coherence directories typically reduce the directory storage overhead at the cost of one or more aspects of performance, accuracy, and complexity. In this article, we propose the tag-sharer-fusion (TSF) directory, a scalable coherence directory with low hardware complexity, as well as with high performance and accuracy. Each directory entry has just enough bits to store a single sharer pointer and is divided into two primary formats: tag and sharer, where sharer entries store sharers but not tags. Each private block is tracked by a tag entry, and each shared block is tracked by a combination of a tag entry and a sharer entry in the same set. Simulation of a 128-core chip-multiprocessor with the PARSEC and SPLASH-2x benchmarks shows that the TSF directory requires only a quarter of the area of a non-scalable full-map sparse directory to achieve similar performance and network traffic, both with an average overhead within 1%. The TSF directory outperforms the state-of-the-art Pool and way-combining directory proposals in terms of storage overhead, performance, and network traffic. [ABSTRACT FROM AUTHOR]

In this paper, we propose three protocols to share, among a set of $N$ competing entities, the responsibility to grant anonymous access to a resource. The protocols we propose vary in their settings to take into account central or distributed registration. We prove that any subset of guardian authorities can neither tamper with, nor forge, new access-key tokens. Besides, two of the methods we propose are resistant to the eventual appearance of quantum computers. The protocols we propose permit new approaches for cryptographic applications such as electronic voting or blockchain access. [ABSTRACT FROM AUTHOR]

One of the main challenges in affective computing is the development of models to represent the information that is inherent to emotions. It is necessary to consider that the terms used by humans to name emotions depend on the culture and language used. This article presents an experiment-based method to represent and adapt emotion terms to different cultural environments. We propose using circular boxplots to analyze the distribution of emotions in the Pleasure-Arousal space. From the results of this analysis, we define a new cross-cultural representation model of emotions in which each emotion term is assigned to an area in the Pleasure-Arousal space. An emotion is represented by a vector in which the direction indicates the type, and the module indicates the intensity of the emotion. We propose two methods based on fuzzy logic to represent and express emotions: the emotion representation process in which the term associated with the recognized emotion is defuzzified and projected as a vector in the Pleasure-Arousal space; and the emotion expression process in which a fuzzification of the vector is produced, generating a fuzzy emotion term that is adapted to the culture and language in which the emotion will be used. [ABSTRACT FROM AUTHOR]

Weakly supervised object detection (WSOD) aims to train object detectors by using only image-level annotations. Many recent works on WSOD adopt multiple instance detection networks (MIDN), which usually generate a certain number of proposals and regard proposal classification as a latent model learning within image classification. However, these methods tend to detect salient object, salient object parts and clustered objects due to lack of instance-level annotations during training. Thus a core issue is how to guarantee that the network learn as many objects with precise bounding boxes as possible. In this paper, we address this issue by exploiting the potential of proposal scores during training. We propose an adaptive instance refinement (AIR) framework with three novel designs, which can be integrated with MIDN into a single network. Specifically, adaptive instance mining attempts to discover all positive instances according to the score distribution of proposals and their spatial similarity. Adaptive score modulation dynamically adjusts proposal scores to make the network focus more on instances with different difficulties in different training iterations. Adaptive knowledge refinement distills important information from all previous stages by the weighted average of proposal scores. The experimental results on the PASCAL VOC 2007 and 2012 benchmarks and the MS COCO benchmark demonstrate that AIR significantly improves the performance of the original MIDN and achieves the state-of-the-art results. [ABSTRACT FROM AUTHOR]

For the remote sensing object detection task, two-stage networks are widely used due to their high accuracy. These networks roughly predict the proposal regions containing potential objects. It is assumed in these methods that the sizes of these regions are close to that of the corresponding real object. However, this assumption is not always true. Consequently, the detector is affected by the size-unfitting proposal regions. In this letter, a hierarchical information enhancing detector (HIE-Det) is advocated to deal with this issue. First, the important semantic reinjection (ISR) module is proposed to mitigate the lack of object semantics caused by the size-unfitting problem. Compared with the normal detectors, the ISR module increases the proportion of information on objects and improves the effectiveness of the detection model. Second, the object boundary enhancing (OBE) module is proposed to improve the robustness of the regression. The OBE module introduces the convolutional branch stacking multigranularity grids for the same proposal region. Multiple granularity levels improve the robustness of the model to the different degrees of proposal size unfitting. Finally, to evaluate the effectiveness of the HIE-Det on multiscale datasets in a balanced and effective manner, we propose the scale-modulating scores (S-scores), i.e., scale-modulating average precision (sAP) and scale-modulating average recall (sAR). Compared with the other comprehensive scores, the S-scores are rid of the sample amounts and give priority to weaker indices. Implementing the proposed HIE-Det, S-scores {sAP, sAR} are, respectively, improved from {17.3%, 29.7%} to {34.8%, 43.2%}, reaching the state-of-the-art performance on the HRRSD dataset. These experiments verify the effectiveness of the proposed HIE-Det. [ABSTRACT FROM AUTHOR]

Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion. To overcome this challenge, one recent work builds an action-click supervision framework. It requires similar annotation costs but can steadily improve the localization performance when compared to the conventional weakly supervised methods. In this paper, by revealing that the performance bottleneck of the existing approaches mainly comes from the background errors, we find that a stronger action localizer can be trained with labels on the background video frames rather than those on the action frames. To this end, we convert the action-click supervision to the background-click supervision and develop a novel method, called BackTAL. Specifically, BackTAL implements two-fold modeling on the background video frames, i.e., the position modeling and the feature modeling. In position modeling, we not only conduct supervised learning on the annotated video frames but also design a score separation module to enlarge the score differences between the potential action frames and backgrounds. In feature modeling, we propose an affinity module to measure frame-specific similarities among neighboring frames and dynamically attend to informative neighbors when calculating temporal convolution. Extensive experiments on three benchmarks are conducted, which demonstrate the high performance of the established BackTAL and the rationality of the proposed background-click supervision. [ABSTRACT FROM AUTHOR]

Point cloud instance segmentation has achieved huge progress with the emergence of deep learning. However, these methods are usually data-hungry with expensive and time-consuming dense point cloud annotations. To alleviate the annotation cost, unlabeled or weakly labeled data is still less explored in the task. In this paper, we introduce the first semi-supervised point cloud instance segmentation framework (SPIB) using both labeled and unlabelled bounding boxes as supervision. To be specific, our SPIB architecture involves a two-stage learning procedure. For stage one, a bounding box proposal generation network is trained under a semi-supervised setting with perturbation consistency regularization (SPCR). The regularization works by enforcing an invariance of the bounding box predictions over different perturbations applied to the input point clouds, to provide self-supervision for network learning. For stage two, the bounding box proposals with SPCR are grouped into some subsets, and the instance masks are mined inside each subset with a novel semantic propagation module and a property consistency graph module. Moreover, we introduce a novel occupancy ratio guided refinement module to refine the instance masks. Extensive experiments on the challenging ScanNet v2 dataset demonstrate our method can achieve competitive performance compared with the recent fully-supervised methods. [ABSTRACT FROM AUTHOR]

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively expensive, we introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera. At the heart of our approach lies the observation that object segmentation and background reconstruction are linked tasks, and that, for structured scenes, background regions can be re-synthesized from their surroundings, whereas regions depicting the moving object cannot. We encode this intuition into a self-supervised loss function that we exploit to train a proposal-based segmentation network. To account for the discrete nature of the proposals, we develop a Monte Carlo-based training strategy that allows the algorithm to explore the large space of object proposals. We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods. [ABSTRACT FROM AUTHOR]

The progress of Russian–Japanese negotiations in 1903–1904 is covered; the positions of the parties are analyzed; , the reasons for the intransigence of the Japanese side in the negotiations on the conclusion of a new agreement are examined; and the reaction in the ruling spheres of Russia to the amendments of the Japanese government to the Russian text of the agreement and the nature of Russian–Korean relations on the eve of the Russo–Japanese War are studied. The author focuses on Japan's responsibility for starting the war, showing Japan's aggressive policy in Korea. The essence of the Japanese proposals, tantamount to a demand from the Russian government for the formal recognition of Japan's protectorate over Korea, is analyzed; attempts by Japanese representatives in Seoul to intimidate the Korean Emperor Gojong and impose an agreement on the appointment of a Japanese resident in Seoul to manage domestic and foreign policy to ensure the "moral right" to establish its undivided dominance on the Korean Peninsula and the request of the Korean emperor for the protection and patronage of Russia are shown, the position of the Russian side during the negotiations, based on the preference to continue negotiations with Japan to avoid an armed clash, its perception and evaluation are studied. The article is based on newly discovered archival materials and published sources. Their study indicates that Russian diplomacy showed great pliability, seeking to delay the aggravation of a military conflict. By the final period of the negotiations, the Russian side had agreed to meet Japan halfway on almost all points concerning the Korean issue, seeking only to preserve the mutual obligation to respect the independence, territorial integrity, and inviolability of the Korean Empire. [ABSTRACT FROM AUTHOR]

In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their performance on old classes deteriorates significantly. A few efforts have been reported to address this limitation, all of which apply variants of knowledge distillation to avoid catastrophic forgetting. We note that although distillation helps to retain previous learning, it obstructs fast adaptability to new tasks, which is a critical requirement for incremental learning. In this pursuit, we propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. This ensures a seamless information transfer via a meta-learned gradient preconditioning that minimizes forgetting and maximizes knowledge transfer. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection. We evaluate our approach on a variety of incremental learning settings defined on PASCAL-VOC and MS COCO datasets, where our approach performs favourably well against state-of-the-art methods. Code and trained models: https://github.com/JosephKJ/iOD. [ABSTRACT FROM AUTHOR]

This paper presents a deep architecture, called pyramidal semantic correspondence networks (PSCNet), that estimates locally-varying affine transformation fields across semantically similar images. To deal with large appearance and shape variations that commonly exist among different instances within the same object category, we leverage a pyramidal model where the affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed. Different from the previous methods which directly estimate global or local deformations, our method first starts to estimate the transformation from an entire image and then progressively increases the degree of freedom of the transformation by dividing coarse cell into finer ones. To this end, we propose two spatial pyramid models by dividing an image in a form of quad-tree rectangles or into multiple semantic elements of an object. Additionally, to overcome the limitation of insufficient training data, a novel weakly-supervised training scheme is introduced that generates progressively evolving supervisions through the spatial pyramid models by leveraging a correspondence consistency across image pairs. Extensive experimental results on various benchmarks including TSS, Proposal Flow-WILLOW, Proposal Flow-PASCAL, Caltech-101, and SPair-71k demonstrate that the proposed method outperforms the lastest methods for dense semantic correspondence. [ABSTRACT FROM AUTHOR]

Dealing with an uncertain and dynamic environment when facing multiple and fast-paced challenges forces professionals to adopt agile practices in different environments, resulting in the use of hybrid project management models. However, identifying the right practice to adopt can be challenging, given the variety of project types and environmental factors. In this article, a recommendation method that would allow for the identification of patterns of project management practices for different environments—using an agility indicator—is proposed. The proposed method is tested with a dataset of 856 projects. A cluster analysis is applied to divide the projects into three groups according to environmental characteristics, called scenarios: waterfall, agile, and hybrid. Then, we apply the association rule technique for each group separately, identifying specific patterns of practice for each group. Through a comparative analysis, we verify the consistency between the recommendations of the proposed method for each scenario and the literature on project management. The results indicate the feasibility of the proposed method, thus opening up new research opportunities for hybrid models that can be customized for different projects. This article can help project management professionals apply the agile method beyond its use in software development and improve the process of combining project management practices. We also suggest directions for new research to advance the knowledge of useful decision support tools for hybrid model customization. [ABSTRACT FROM AUTHOR]

Managers continuously make decisions on investing in projects that aim to enhance organizational performance. Poor project investment decision making results in lost benefits (rejecting a good project) and inefficiency (approving a suboptimal project). Whereas decisions regarding what project proposals to approve heavily rely on their stated goals, such goals are frequently unrealistic. This article aims at enhancing project investment decision making by extending decision comprehensiveness theory to the project goal-setting context. In an initial survey study, we found empirical support that a comprehensive goal-setting process enhances decision quality under uncertainty. In a second longitudinal study, we found that comprehensive goal setting further enhances project investment decision making above and beyond other established goal-setting dimensions (e.g., SMART goals), and eventually improves project performance. Last, we found that extensive consultation with key project stakeholders is the most effective comprehensive goal-setting practice. Based on research and practice, we describe what should comprise an effective goal-setting consultation process. This article extends goal-setting theory by suggesting that in addition to established characteristics, the goal-setting process under uncertainty should also be comprehensive. [ABSTRACT FROM AUTHOR]

White-box block cipher (WBC) has been proposed by Chow et al. to prevent the secret key to be extracted from its implementation in an untrusted context. A pivotal technique behind WBC is to convert the iterated round functions into a series of look-up tables (LUTs) with encodings. The construction of encoded LUTs consists of matrix operations, such as multiplication and inversion. The widely-used matrix libraries in applications, such as open-source NTL and M4RI, are primarily designed for large dimensional matrix operations. Therefore, they might not be suitable for WBC implementations which are mainly based on small-scale matrices and vectors. In this paper, we propose a new matrix library named WBMatrix for the optimization of WBC implementations. WBMatrix reduces the operating steps of multiplication and simultaneously generates pairwise invertible matrices as encodings. The performance comparison supports that WBMatrix improves the table construction and encryption phases on Intel x86 and ARMv8 platforms. Moreover, WBMatrix also boosts the initialization and encryption phases of LowMC/LowMC-M block ciphers and enhances the performance for the generation of key-dependent Sbox. [ABSTRACT FROM AUTHOR]

This article studies the robust deep stereo vision in autonomous driving systems and counteracting adversarial attacks. The autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract the practical attacks in autonomous systems, novel methods based on simulated attacks are proposed in this article. Univariate and multivariate functions are adopted to represent the relationships between the left and right input images and the deep stereo model. A stereo regularizer is proposed to guide the model to learn the implicit relationship between the images and characterize the loss function’s local smoothness. The attacks are generated by maximizing the regularizer term to break the linearity and smoothness. The model then defends the attacks by minimizing the loss and regularization terms. Two techniques are developed in this article. The first technique, SmoothStereo, explores the basic knowledge from the physical world and smoothness, while the second technique, SmoothStereoV2, improves SmoothStereo through leveraging the smooth activation functions during the defense. SmoothStereoV2 can learn and utilize the gradient information concerning the attacks. The gradients of the smooth activation functions can handle attacks for improving the model robustness. Numerical experiments on KITTI datasets demonstrate that the proposed methods offer superior performance. [ABSTRACT FROM AUTHOR]

We present concepts and recipes for the anytime performance assessment when benchmarking optimization algorithms in a blackbox scenario. We consider runtime—oftentimes measured in the number of blackbox evaluations needed to reach a target quality—to be a universally measurable cost for solving a problem. Starting from the graph that depicts the solution quality versus runtime, we argue that runtime is the only performance measure with a generic, meaningful, and quantitative interpretation. Hence, our assessment is solely based on runtime measurements. We discuss proper choices for solution quality indicators in single- and multi-objective optimization, as well as in the presence of noise and constraints. We also discuss the choice of the target values, budget-based targets, and the aggregation of runtimes by using simulated restarts, averages, and empirical cumulative distributions which generalize convergence graphs of single runs. The presented performance assessment is to a large extent implemented in the comparing continuous optimizers (COCO) platform freely available at https://github.com/numbbo/coco. [ABSTRACT FROM AUTHOR]

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Classification algorithms have recently acquired great popularity due to their efficiency to generate models capable of solving high complexity problems. Specifically, black box models are the ones that offer the best results, since they greatly benefit from the enormous amount of data available to learn models in an increasingly accurate way. However, their main disadvantage compared to other simpler algorithms, e.g., decision trees, is the loss of interpretability for both the model and the individual classifications, which may become a major drawback because of the increasing number of applications where it is advisable and even compulsory to provide an explanation. A well-accepted practice is to build an explainable model that can mimic the behavior of the (more complex) classifier in the neighborhood of the instance to be explained. Nonetheless, the generation of explanations in such white box models is not trivial either, which has generated intense research. It is common to generate two types of explanations, factual explanations and counterfactual explanations, which complement each other to justify why the instance has been classified into a certain class or category. In this work, we propose the definition of factual and counterfactual explanations in the frame of fuzzy decision trees, where multiple branches can be fired at once. Our proposal is centered around the definition of factual explanations that can contain more than a single rule, in contrast to the current standard that is limited to considering a single rule as a factual explanation. Moreover, we introduce the idea of robust factual explanation. Finally, we provide procedures to obtain counterfactual explanations from the instance and also from a factual explanation. [ABSTRACT FROM AUTHOR]

RadViz contributes to multidimensional analysis by using 2D points for encoding data elements and interpreting them along the original data dimensions. For these characteristics it is used in different application domains, like clustering, anomaly detection, and software visualization. However, it is likely that using the dimension arrangement that comes with the data will produce a plot that leads users to make inaccurate conclusions about points values and data distribution. This article attacks this problem without altering the original RadViz design: It defines, for both a single point and a set of points, the metric of effectiveness error, and uses it to define the objective function of a dimension arrangement strategy, arguing that minimizing it increases the overall RadViz visual quality. This article investigated the intuition that reducing the effectiveness error is beneficial for other well-known RadViz problems, like points clumping toward the center, many-to-one plotting of non-proportional points, and cluster separation. It presents an algorithm that reduces to zero the effectiveness error for a single point and a heuristic that approximates the dimension arrangement minimizing the effectiveness error for an arbitrary set of points. A set of experiments based on 21 real datasets has been performed, with the goals of analyzing the advantages of reducing the effectiveness error, comparing the proposed dimension arrangement strategy with other related proposals, and investigating the heuristic accuracy. The Effectiveness Error metric, the algorithm, and the heuristic presented in this article have been made available in a d3.js plugin at https://aware-diag-sapienza.github.io/d3-radviz. [ABSTRACT FROM AUTHOR]

The rapid progress in deep learning-based computer vision has opened unprecedented possibilities in computing various high-level analytics for sports. Artificial intelligence techniques such as predictive analysis, automatic highlight generation, and assistant coaching have been applied to improve performance and decision-making for teams and players. To perform any high-level analysis from a game match, collecting the locations (where) and identities (who) of players is crucial and challenging. In this paper, a universal JErsey number DEtector (JEDE) for player identification is presented that predicts players’ bounding boxes and keypoints, along with bounding boxes and classes of jersey digits and numbers in an end-to-end manner. Instead of generating digit proposals from pre-defined anchors, JEDE predicts more robust proposals guided by players’ features and pose estimation. Moreover, a dataset is collected from soccer and basketball matches with annotations on players’ bounding boxes and body keypoints, and jersey digits’ bounding boxes and labels. Extensive experimental results and ablation studies on the collected dataset show that the proposed method outperforms the state-of-the-art methods by a large margin. Both quantitative and qualitative results also demonstrate JEDE’s superior practicality and generalizability over different sports. [ABSTRACT FROM AUTHOR]

Person search, consisting of jointly or separately trained person detection stage and person Re-ID stage, suffers from significant challenges such as inefficiency and difficulty in acquiring discriminative features. However, certain work has either turned to the end-to-end framework whose performance is limited by task conflicts or has consistently attempted to obtain more accurate bounding boxes (Bboxes). Few studies have focused on the impact of sample-specificity in person search datasets for training a fine-grain Re-ID model, and few have considered obtaining discriminative Re-ID features from Bboxes in a more efficient way. In this paper, a novel sample-enhanced and instance-sensitive (SEIE) framework is designed to boost performance. By analyzing the structure of person search framework, our method refines the two stages separately. For the detection stage, we re-design the usage of Bbox and a sample enhancement combination is proposed to further enhance the quality and quantity of Bboxes. SEC can suppress false positive detection results and randomly generate high-quality positive samples. For the Re-ID stage, we contribute an instance similarity loss to exploit the similarity between classless instances, and an Omni-scale Re-ID backbone is employed to learn more discriminative features. We obtain a more efficient and discriminative person search framework by concatenating the two stages. Extensive experiments demonstrate that our method achieves state-of-the-art performance with a high speed, and significantly outperforms other existing methods. [ABSTRACT FROM AUTHOR]

The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one critical limitation, which conflicts with the real mechanism of our visual system — a typical long-term methodology. As a result, failure cases keep showing up in the results of the current SOTA models, and the short-term methodology becomes the major technical bottleneck. To solve this problem, this paper proposes a novel VSOD approach, which performs VSOD in a complete long-term way. Our approach converts the sequential VSOD, a sequential task, to a data mining problem, i.e., decomposing the input video sequence to object proposals in advance and then mining salient object proposals as much as possible in an easy-to-hard way. Since all object proposals are simultaneously available, the proposed approach is a complete long-term approach, which can alleviate some difficulties rooted in conventional short-term approaches. In addition, we devised an online updating scheme that can grasp the most representative and trustworthy pattern profile of the salient objects, outputting framewise saliency maps with rich details and smoothing both spatially and temporally. The proposed approach outperforms almost all SOTA models on five widely used benchmark datasets. [ABSTRACT FROM AUTHOR]

Video object detection (VOD) focuses on detecting objects for each frame in a video, which is a challenging task due to appearance deterioration in certain video frames. Recent works usually distill crucial information from multiple support frames to improve the reference features, but they only perform at frame level or proposal level that cannot integrate spatial-temporal features sufficiently. To deal with this challenge, we treat VOD as a spatial-temporal hierarchical features interacting process and introduce a Multi-level Spatial-Temporal (MST) feature aggregation framework to fully exploit frame-level, proposal-level, and instance-level information in a unified framework. Specifically, MST first measures context similarity in pixel space to enhance all frame-level features rather than only update reference features. The proposal-level feature aggregation then models object relation to augment reference object proposals. Furthermore, to filter out irrelevant information from other classes and backgrounds, we introduce an instance ID constraint to boost instance-level features by leveraging support object proposal features that belong to the same object. Besides, we propose a Deformable Feature Alignment (DAlign) module before MST to achieve a more accurate pixel-level spatial alignment for better feature aggregation. Extensive experiments are conducted on ImageNet VID and UAVDT datasets that demonstrate the superiority of our method over state-of-the-art (SOTA) methods. Our method achieves 83.3% and 62.1% with ResNet-101 on two datasets, outperforming SOTA MEGA by 0.4% and 2.7%. [ABSTRACT FROM AUTHOR]