Reviewerly/exHarmony huggingface dataset:
Multi-level Deep Learning Vehicle Re-identification using Ranked-based Loss Functions

Identifying vehicles across a network of cameras with non-overlapping fields of view remains a challenging research problem due to scene occlusions, significant inter-class similarity and intra-class variability. In this paper, we propose an end-to-end multi-level re-identification network that is capable of successfully projecting same identity vehicles closer to one another in the embedding space, compared to vehicles of different identities. Robust feature representations are obtained by combining features at multiple levels of the network. As for the learning process, we employ a recent state-of-the-art structured metric learning loss function previously applied to other retrieval problems and adjust it to the vehicle re-identification task. Furthermore, we explore the cases of image-to-image, image-to-video and video-to-video similarity metric. Finally, we evaluate our system and achieve great performance on two large-scale publicly available datasets, CityFlow-ReID and VeRi-776. Compared to most existing state-of-art approaches, our approach is simpler and more straightforward, utilizing only identity-level annotations, while avoiding post-processing the ranking results (re-ranking) at the testing phase.
Virtual zero-photon catalysis for improving continuous-variable quantum key distribution via Gaussian post-selection

Quantum catalysis is a feasible approach to increase the performance of continuous-variable quantum key distribution (CVQKD), involving the special zero-photon catalysis (ZPC) operation. However, in the practical point of view, the improvement effect of this operation will be limited by the imperfection of the photon detector. In this paper, we show that the ZPC operation at the sender can be simulated by a post-selection method without implementing it in practical devices. While performing this virtual version of ZPC in CVQKD, we can not only reach the ideal case of its practical implementation with minimal hardware requirement, but also keep the benefit of Gaussian security proofs. Based on Gaussian modulated coherent state protocols with achievable parameters, we enhance the security of the proposed scheme from the asymptotical case to the finite-size scenario and composable framework. Simulation results show that similar to the asymptotical case, both the maximal transmission distance and the tolerable excess noise of virtual ZPC-involved CVQKD outperform the original scheme and the scheme using virtual photon subtraction while considering finite-size effect and composable security. In addition, the virtual ZPC-involved CVQKD can tolerate a higher imperfection of the detector, enabling its practical implementation of the CVQKD system with state-of-the-art technology.
Transferrable Referring Expression Grounding with Concept Transfer and Context Inheritance

Referring Expression Grounding (REG) aims at localizing a particular object in an image according to a language expression. Recent REG methods have achieved promising performance, but most of them are constrained to limited object categories due to the scale of current REG datasets. In this paper, we explore REG in a new scenario, where the REG model can ground novel objects out of REG training data. With this motivation, we propose a Concept-Context Disentangled network (CCD) which transfers concepts from auxiliary classification data with new categories meanwhile inherits context from REG data to ground new objects. Specially, we design a subject encoder to learn a cross-modal common semantic space, which can bridge the semantic and domain gap between auxiliary classification data and REG data. This common space guarantees CCD can transfer and recognize novel categories. Further, we learn the correspondence between image proposal and referring expression upon location and relationship. Benefiting from the disentangled structure, the context is relatively independent of the subject, so it can be better inherited from the REG training data. Finally, a language attention is learned to adaptively assign different importance to subject and context for grounding target objects. Experiments on four REG datasets show our method outperforms the compared approach on the new-category test datasets.
Image Super-Resolution Using Lightweight Multiscale Residual Dense Network

The current super-resolution methods cannot fully exploit the global and local information of the original low-resolution image, resulting in loss of some information. In order to solve the problem, we propose a multiscale residual dense network (MRDN) for image super-resolution. This network is constructed based on the residual dense network. It can integrate the multiscale information of the image and avoid losing too much information in the deep level of the network, while extracting more information under different receptive fields. In addition, in order to reduce the redundancy of the network parameters of MRDN, we further develop a lightweight parameter method and deploy it at different scales. This method can not only reduce the redundancy of network parameters but also enhance the nonlinear mapping ability of the network at different scales. Thus, it can better learn and fit the feature information of the original image and recover the satisfactory super-resolution image. Extensive experiments are conducted, which demonstrate the effectiveness of the proposed method.
Exploring Language Prior for Mode-Sensitive Visual Attention Modeling

Modeling human visual attention mechanism is a fundamental problem for the understanding of human vision, which has also been demonstrated as an important module for various multimedia applications such as image captioning and visual question answering. In this paper, we propose a new probabilistic framework for attention, and introduce the concept ofmode to model the flexibility and adaptability of attention modulation in complex environments. We characterize the correlations between the visual input, the activated mode, the saliency and the spatial allocation of attention via a graphical model representation, based on which we explore the lingual guidance from captioning data for the implementation of a mode-sensitive attention (MSA) model. The proposed framework explicitly justifies the usage of center bias for fixation prediction and can convert an arbitrary learning-based backbone attention model to a more robust multi-mode version. Experimental results on the York120, MIT1003 and PASCAL datasets demonstrate the effectiveness of the proposed method.
Attention-driven Unsupervised Image Retrieval for Beauty Products with Visual and Textual Clues

Beauty and personal care product retrieval (BPCR) aims to match a query image of an item to examples of the same item in a large database. The task is extremely challenging because a small number of ground-truth examples have to be found in a large search space. Previous works mostly search only with visual representations and have not made full use of the product descriptions. Since many noisy examples only have subtle visual differences comparing to the ground-truth examples (e.g. similar packaging but different brands) and those differences (e.g. product brands) are especially hard to be captured only by visual features, methods merely based on visual feature similarities can easily regard those noisy examples as examples of the same item in the query image. We notice that the product descriptions are good sources for capturing those subtle visual differences. Therefore, we propose a search method utilizing both images and product descriptions in this work. Before searching, we not only prepare attention-based visual features for each database image but also a textual index (TI) that matches each database example to other examples with similar product descriptions. During searching, the visual feature of the query image is firstly searched in the whole database and then searched in a subset obtained by looking up the TI. Finally, the second result is used to refine the initial result. Since the subset examples usually have similar properties (e.g. brands and type), the noisy examples in the initial result can be effectively replaced. We have experimentally proved the effectiveness of the proposed method on the validation set of the Perfect-500K dataset. Our team (NTU-Beauty) achieved the 3rd place in the leader board of the Grand Challenge of AI Meets Beauty in ACM Multimedia 2020. Our code is available at: https://github.com/jingwenh/2020-ai-meets-beauty_ntubeauty.git.
The Systemic Concept of Contextual Truth

Abstract In this paper the truth is studied in the frame of autonomous systems theory. The method of the truth verification is worked out in its functional aspect. The verification is based on comparison of the predicted inner state of the autonomous agent, that is the cognitive subject, to the achieved inner state of the agent. The state is achieved as the result of performing the action in the real world—the agent’s environment. The action design is created on the basis of the agent’s model of the world. The truth is defined as the adequacy of the model of the real world in the context of the goal that is assumed to be reached as the effect of the performed action. The concepts of the cognitive subject, the truth bearings and the knowledge are redefined. The classical problems of aletheiology and epistemology are discussed in the light of the proposed approach. The cybernetic construct of an autonomous agent allows the researcher to consider a wide class of cognitive entities, which, in the previous approaches, have been limited only to human beings as cognitive subjects.
Towards Modeling Visualization Processes as Dynamic Bayesian Networks

Visualization designs typically need to be evaluated with user studies, because their suitability for a particular task is hard to predict. What the field of visualization is currently lacking are theories and models that can be used to explain why certain designs work and others do not. This paper outlines a general framework for modeling visualization processes that can serve as the first step towards such a theory. It surveys related research in mathematical and computational psychology and argues for the use of dynamic Bayesian networks to describe these time-dependent, probabilistic processes. It is discussed how these models could be used to aid in design evaluation. The development of concrete models will be a long process. Thus, the paper outlines a research program sketching how to develop prototypes and their extensions from existing models, controlled experiments, and observational studies.
Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval

How to estimate the distance between data instances is a fundamental problem in many artificial intelligence algorithms, and critical in diverse multimedia applications. A major challenge in the estimation is how to find an appropriate distance function when labeled data are insufficient for a certain task. Multi-task metric learning (MTML) is able to alleviate such data deficiency issue by learning distance metrics for multiple tasks together and sharing information between the different tasks. Recently, heterogeneous MTML (HMTML) has attracted much attention since it can handle multiple tasks with varied data representations. A major drawback of the current HMTML approaches is that only linear transformations are learned to connect different domains. This is suboptimal since the correlations between different domains may be very complex and highly nonlinear. To overcome this drawback, we propose a deep heterogeneous MTML (DHMTML) method, in which a nonlinear mapping is learned for each task by using a deep neural network. The correlations of different domains are exploited by sharing some parameters at the top layers of different networks. More importantly, the auto-encoder scheme and the adversarial learning mechanism are integrated and incorporated to help exploit the feature correlations in and between different tasks and the specific properties are preserved by learning additional task-specific layers together with the common layers. Experiments demonstrated that the proposed method outperforms single-task deep metric learning algorithms and other HMTML approaches consistently on several benchmark datasets.
Educational Robotics in Practice of Modern School

The aim of the work is to consider an urgent problem - the introduction of educational robotics in the practice of a modern school. The inclusion of educational robotics schools in educational practice is determined by the current situation and the factors indicated by the authors in the article. The main aspects of the organization of classes in robotics are indicated. The positive aspects of studying robotics for students are considered: the acquisition of independent labor skills, the formation of a holistic view of modern technologies, the development of social, communication skills. Pointing to the principles of teaching robotics, the authors highlight the positive educational effects: the formation of educational motivation, the growth of cognitive activity, the formation of objective criteria for self-esteem, a positive emotional background, and health-saving activity. Based on the principle of educational robotics - learning through the game - the authors identify the methods of activity of the teacher. According to the authors, educational robotics is an integrative subject area and this circumstance allows us to consider educational robotics as a stimulator of the educational activity of schoolchildren, motivating them to study disciplines related to robotics. Based on a study among schoolchildren engaged in a circle of robotics, the authors conclude that studying the basics of robotics creates the prerequisites for socializing students and continuing technical education, and opens up opportunities for promising professions in the modern labor market.
Active Learning for Multimedia Computing: Survey, Recent Trends and Applications

The widespread emergence and deployment of inexpensive sensors has resulted in the generation of enormous amounts of digital data in today's world. While this has expanded the possibilities of solving real world problems using computational learning frameworks, selecting the salient data samples from such huge collections of data has proved to be a significant and practical challenge. Further, to train a reliable classification model, it is important to have a large quantity of labeled training data. Manual annotation of large amounts of data is an expensive process in terms of time, labor and human expertise. This has set the stage for research in the field of active learning. Active learning algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and thereby tremendously reduce human annotation effort in training an effective classifier. It can be applied across all existing classification / regression methods and with any kind of data, thus making it a very generalizable approach. The success of active learning in several applications (such as image retrieval, image recognition) has resulted in the extension of the framework to problem settings beyond regular classification / regression. Active learning concepts have been extended to newer problem settings (such as feature selection, video summarization, matrix completion) and have also been combined with other learning paradigms such as deep learning and transfer learning. This tutorial will seek to present a comprehensive overview of active learning with a focus on multimedia computing applications, including historical perspectives, theoretical analysis and novel paradigms. The novelty of this tutorial lies in its focus on the emerging trends, algorithms and applications of active learning. It will aim at introducing concepts and open perspectives that motivate further work in this domain, ranging from fundamentals to applications and systems.
AffectI

An important application of affective image annotation is affective image content analysis, which aims to automatically understand the emotion being brought to viewers by image contents. The so-called subjective perception issue, i.e., different viewers may have different emotional responses to the same image, makes it difficult to link image features with the expected perceived emotion. Due to the ability to learn features, recent deep learning technologies have opened a new window on affective image content analysis, which has led to a growing demand for affective image annotation technologies to build large reliable training datasets. This paper proposes a novel affective image annotation technique, AffectI, for efficiently collecting diverse and reliable emotional labels with the estimate emotion distribution for images based on the concept of Game With a Purpose (GWAP). AffectI features three novel mechanisms: a selection mechanism for ensuring all emotion words being fairly evaluated for collecting diverse and reliable labels; an estimation mechanism for estimating the emotion distribution by aggregating partial pairwise comparisons of the emotion words for collecting the labels effectively and efficiently; an incentive mechanism shows the comparison between current player and her opponents as well as all past players to promote the interest of players and also contributes the reliability and diversity. Our experimental results demonstrate that AffectI is superior to existing methods in terms of being able to collect more diverse and reliable labels. The advantage of using GWAP for reducing the frustration of evaluators was also confirmed through subjective evaluation.
Faithful Multihop Two-Qubit Transmission Through GHZ-GHZ Channel

Quantum teleportation is an important method for transmitting quantum states between two quantum communication nodes. In this paper, we propose a low-latency quantum communication scheme based on Greenberger-Horne-Zeilinger (GHZ) state. To implement two-qubit transmission, the source and all intermediate nodes perform quantum measurements simultaneously and the measurement results are transmitted to the destination node through classical channel independently. The destination node performs appropriate unitary operation on its particles to recover the original two-qubit state based on received measurement results. Our scheme effectively reduces the end-to-end quantum communication delay.
DeSmoothGAN: Recovering Details of Smoothed Images via Spatial Feature-wise Transformation and Full Attention

Recently, generative adversarial networks (GAN) have been widely used to solve image-to-image translation problems such as edges to photos, labels to scenes, and colorizing grayscale images. However, how to recover details of smoothed images is still unexplored. Naively training a GAN like pix2pix causes insufficiently perfect results due to the fact that we ignore two main characteristics including spatial variability and spatial correlation as for this problem. In this work, we propose DeSmoothGAN to utilize both characteristics specifically. The spatial variability indicates that the details of different areas of smoothed images are distinct and they are supposed to be recovered differently. Therefore, we propose to perform spatial feature-wise transformation to recover individual areas differently. The spatial correlation represents that the details of different areas are related to each other. Thus, we propose to apply full attention to consider the relations between them. The proposed method generates satisfying results on several real-world datasets. We have conducted quantitative experiments including smooth consistency and image similarity to demonstrate the effectiveness of DeSmoothGAN. Furthermore, ablation studies are performed to illustrate the usefulness of our proposed feature-wise transformation and full attention.
MGAAttack

Recent studies have shown that deep neural networks (DNNs) are susceptible to adversarial attacks even in the black-box settings. However, previous studies on creating black-box based adversarial examples by merely solving the traditional continuous problem, which suffer query efficiency issues. To address the efficiency of querying in black-box attack, we propose a novel attack, called MGAAttack, which is a query-efficient and gradient-free black-box attack without obtaining any knowledge of the target model. In our approach, we leverage the advantages of both transfer-based and scored-based methods, two typical techniques in black-box attack, and solve a discretized problem by using a simple yet effective microbial genetic algorithm (MGA). Experimental results show that our approach dramatically reduces the number of queries on CIFAR-10 and ImageNet and significantly outperforms previous work. In the untargeted attack, we can attack a VGG19 classifier with only 16 queries and give an attack success rate more than 99.90% on ImageNet. Our code is available at https://github.com/kangyangWHU/MGAAttack.
Identification of Risk Factors Characteristics of Transfusion Reaction

Blood transfusion is an important part of health care. A blood transfusion can be carried out after careful consideration of its risks and benefits. One of the important considerations for blood transfusion is adverse transfusion reaction. Several risk factors for a transfusion reaction are age, gender, repeated transfusion, disease diagnosis, type of blood component, and blood incompatibility. This research is a descriptive study performed at Blood Bank Dr. Wahidin Sudirohusodo Hospital, Makassar, from January to December 2017. The subjects were all patients reported to have an acute transfusion, totaling 104 subjects. The descriptive method was carried out by the calculation of frequency distribution. From January to December 2017, 104 patients with transfusion reactions were obtained, predominantly were male (53.8%), with the lowest age range of â‰¥ 60 years (27.9%), packed red cell as the most found blood components (82.4%), a history of previous transfusion reactions (72.1%), and a diagnosis of malignancy (53.9%). The history of repeated transfusions is the most common risk factor compared to age, gender, blood component, and patient diagnosis.
The impact of isolation kernel on agglomerative hierarchical clustering algorithms

Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. AHC generates a dendrogram that provides richer information and insights from a dataset than partitioning clustering. However, a major problem with existing distance-based AHC methods is: it fails to effectively identify adjacent clusters with varied densities, regardless of the cluster extraction methods applied to the resultant dendrogram. This paper aims to reveal the root cause of this issue and provides a solution by using a data-dependent kernel. We analyse the condition under which existing AHC methods fail to effectively extract clusters, and give the reason why the data-dependent kernel is an effective remedy. This leads to a new approach to kernerlise existing hierarchical clustering algorithms including the traditional AHC algorithms, HDBSCAN, GDL, PHA and HC-OT. Our extensive empirical evaluation shows that the recently introduced Isolation Kernel produces a higher quality or purer dendrogram than distance, Gaussian Kernel and adaptive Gaussian Kernel in all the above mentioned AHC algorithms.