Reviewerly/exHarmony huggingface dataset:
Development of a sociological way of thinking with the help of multimedia technologies on the example of Navigitel tales

The sociological way of thinking is defined as a key mode of perceiving and evaluate social actions and as a regulator of a socialised actor performance. A model of development of the sociological way of thinking under an influence of Navigitel tales is built. Structural and functional description of the model is presented. Multimedia technologies are substantiated as a technological base for practical implementation of the built model. The potential of multimedia technologies at the development of the sociological way of thinking has been disclosed. Keys thesis of axiology and sociological essence of the Navigitel tales are formulated.
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

White blood cell (WBC) is an important indicator of inflammation in the body, and it can help distinguish between bacterial and viral infections. At present, most primary medical institutions in China have a poor percentage of adoption of blood-testing technology, and a hematology detection system with a high price to performance ratio and easy operation is urgently needed in primary healthcare centers. This paper introduces the principle and operation procedures of a point-of-care testing (POCT) card-based leukocyte analyzer (evaluated system), which was used to detect WBC indexes such as neutrophils, lymphocytes, and intermediate group cells (including eosinophils, basophils, and monocytes) in whole blood. The results from the evaluated system were compared to those from two commercial automatic hematology analyzers (reference system). The correlation and consistency between the evaluated system and the commercial reference systems were analyzed. The results showed that WBC count and number of granulocytes detected by the evaluated and reference systems showed a strong positive correlation (rs = 0.972 and 0.973, respectively), while the number of lymphocytes showed a relatively low correlation (rs = 0.851). A Bland-Altman plot showed that the major difference between the values detected by the evaluated system and the reference systems is within 95% limits of agreement (LoA), indicating that the two systems are in good agreement. In conclusion, the evaluated system has an excellent correlation, robust consistency, and a reliable comparison with the results of the widely used automatic hematology analyzers. It is ideal for WBC detection in primary medical institutions where a full-automatic five-category hematology analyzer is unavailable, especially during the COVID-19 normalized prevention and control period.
Topics in robust statistical learning

Some recent contributions to robust inference are presented. Firstly, the classical problem of robust M-estimation of a location parameter is revisited using an optimal transport approach - with specifically designed Wasserstein-type distances - that reduces robustness to a continuity property. Secondly, a procedure of estimation of the distance function to a compact set is described, using union of balls. This methodology originates in the field of topological inference and offers as a byproduct a robust clustering method. Thirdly, a robust Lloyd-type algorithm for clustering is constructed, using a bootstrap variant of the median-of-means strategy. This algorithm comes with a robust initialization.
Direct implementation of a perceptron in superconducting circuit quantum  hardware

The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in [Torrontegui and Garc\'ia-Ripoll 2019 EPL 125 30004], we demonstrate a superconducting qubit implementation of an adiabatic controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. We show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the adiabatic gate length, the coupling between the qubits and the frequency of the applied drive, respectively. In its general form, the gate realizes a multi-qubit entangling operation in a single step, whose decomposition into single- and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.
Hinge Policy Optimization: Reinterpreting PPO-Clip and Attaining Global  Optimality

Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and one example is the proximal policy optimization algorithm with a clipped surrogate objective (PPO-clip), which has been popularly used in deep reinforcement learning due to its simplicity and effectiveness. Despite its superior empirical performance, PPO-clip has not been justified via theoretical proof up to date. This paper proposes to rethink policy optimization based on the principle of hinge policy optimization (HPO), called to achieve policy improvement by solving a large-margin classification problem with hinge loss, and thereby reinterprets PPO-clip as an instance of HPO. From this perspective, we present HPO algorithms and establish the following convergence guarantees: (i) We first study HPO with direct parameterization and establish almost-sure asymptotic convergence in the tabular setting; (ii) We present the NeuralHPO algorithm, which substantiates the HPO principle in the neural function approximation setting via a regression-based policy update scheme, and thereafter characterize the convergence rate of NeuralHPO. To the best of our knowledge, this is the first-ever that can prove global convergence to an optimal policy and convergence rate for a variant of PPO-clip.
CoVA: Context-aware Visual Attention for Webpage Information Extraction

Webpage information extraction (WIE) is an important step to create knowledge bases. For this, classical WIE methods leverage the Document Object Model (DOM) tree of a website. However, use of the DOM tree poses significant challenges as context and appearance are encoded in an abstract manner. To address this challenge we propose to reformulate WIE as a context-aware Webpage Object Detection task. Specifically, we develop a Context-aware Visual Attention-based (CoVA) detection pipeline which combines appearance features with syntactical structure from the DOM tree. To study the approach we collect a new large-scale dataset of e-commerce websites for which we manually annotate every web element with four labels: product price, product title, product image and background. On this dataset we show that the proposed CoVA approach is a new challenging baseline which improves upon prior state-of-the-art methods.
Recommendation System of Component Selection for Aquascape With SMART Method

AbstractThe increasing enthusiasm of aquascape hobbyists is one of the factors in the development of the beauty industry of artificial underwater ecosystems in the aquarium. The aesthetic value offered by aquascape is one of the allures for its connoisseurs. The art of organizing ecosystems in water is a simple definition of an aquascape. The complexity of the arrangement can then be described through the presence of various themes. The selection of tools, flora and fauna, and other ornaments to form a unified ecosystem are things that are of concern so that an ecosystem in artificial water has good aesthetic value and can be categorized in a certain theme. By using a decision support system (DSS) or decision-making system, the results can provide better decisions and minimize the possibility of errors. One method of decision making in the scope of decision support system (DSS) or decision making system is Simple Multi Attribute Rating Technique (SMART), where this method can determine product recommendations based on several product variants offered as a form of marketing analysis. This method is able to provide recommendations for more than one decision and provide results with the best accuracy. System testing was conducted to 50 users and it was found that the recommendation system based on the questionnaire application was able to provide a good percentage of conformity between the user's wishes and the recommendations given. That is, 51% of correspondents rated it very appropriate, 27.5% of correspondents rated accordingly, 19.5% of correspondents rated neutral, and only 2% of correspondents rated it inappropriateKeywords: Aquascape, Decision Support System, SMART, Recommendation
Epidemic Management and Control Through Risk-Dependent Individual  Contact Interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City prove the concepts. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population ($\gtrsim 75\%$) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.
The Effect of LMX Ambivalence on Creativity : The Mediating Role of Feedback Seeking Behavior and the Moderating Effect of Core Self-Evaluation

LMX 이론은 직장 내 리더와의 관계에 대한 인식이 리더와 조직에 대한 인식과 더불어 구성원의 업무행동, 나아가 조직성과에 큰 영향을 미침을 보여주며, 이는 리더십의 관계기반 연구의 중심으로서 많은 학문적 기여를 해왔다. 그러나 선행연구들은 리더-구성원의 관계를 단가적 속성으로 가정함에 따라 이들 간의 관계가 가지는 양가적 측면을 포착하지 못했다는 한계가 있다. 직장 내 상사-구성원 관계가 본질적으로 높은 양가성(즉, 좋은 관계와 그렇지 않은 관계가 혼재)을 가진다는 점을 고려할 때, LMX 양가성의 영향을 탐색하는 것은 보다 현실적인 상사-구성원 관계에 대한 이해를 높일 수 있다. 그에 따라 본 연구는 세 가지 목적을 가진다. 첫째, LMX 양가성과 피드백 추구행동 간의 관계를 알아보는 것이다. 둘째, LMX 양가성이 피드백 추구행동을 통해 창의성에 미치는 영향을 알아보는 것이다. 셋째, 이 관계에서 구성원 핵심자기평가의 조절 및 조절된 매개효과를 살펴보는 것이다. 육군 간부로 구성된 상사-부하 171쌍의 자료를 토대로, 위계적 다중 회귀분석을 실시하여 가설을 검증하였다. 분석 결과, LMX 양가성은 피드백 추구행동과 부적 관계, 피드백 추구행동은 창의성과 정적 관계를 가졌다. 간접효과 검증 결과, LMX 양가성은 피드백 추구행동을 경유하여 창의성에 부정적인 영향을 미치는 것으로 나타났다. 조절효과 검증결과, LMX 양가성과 피드백 추구행동의 부적 관계는 핵심자기평가가 높을수록 완화되었으며, LMX 양가성이 피드백 추구행동을 통하여 창의성에 미치는 간접효과 또한 핵심자기평가가 높을수록 약해지는 것으로 확인되었다. 본 연구는 리더와의 관계에 대한 양가적 인식이 구성원의 피드백 추구행동과 더불어 창의성에 미치는 해악적 영향을 조명하고, 나아가 이를 완화시켜주는 핵심자기평가의 조절효과를 규명함으로써 관심변수들의 역학관계에 대한 지식을 확장하는 데 기여하고 있다. 아울러 LMX 양가성의 부정적 효과와 이를 예방 및 최소화하기 위한 조직적, 관리적 노력이 필요하다는 점과, 인적자원의 관리 및 개발의 중요함을 짚어주고 있다. Previous studies assume the leader-member relationship as a unitary attribute, and there is a limitation in that they did not capture the ambivalent aspects of the LMX. Accordingly, this study has three purposes. First, it is to find out the relationship between LMX ambivalence and feedback-seeking behavior. Second, it is to investigate the effect of LMX ambivalence on creativity through feedback-seeking behavior. Third, in this relationship, the moderating and moderated mediating effects of employee’s core self-evaluation are examined. Based on the data of 171 pairs of supervisors-employees in military settings, hierarchical multiple regression analysis was performed to verify the hypotheses. As a result of the analysis, LMX ambivalence had a negative relationship with feedback seeking behavior, and feedback seeking behavior had a positive relationship with creativity. As a result of indirect effect test, it was found that LMX ambivalence negatively affects creativity through feedback seeking behavior. In the case of moderating effect, it was confirmed that the negative relationship between LMX ambivalence and feedback seeking behavior was weakened as the core self-evaluation increased, and the indirect effect of LMX ambivalence on creativity through feedback seeking behavior also weakened as the core self-evaluation increased. This study contributes to expanding knowledge of the dynamics of the variables of interest by illuminating the harmful effects of ambivalent perceptions of the relationship between leaders and followers on creativity along with the feedback seeking behavior of members and further investigating the moderating effects of core self-evaluation.
Reinforcement Learning-powered Semantic Communication via Semantic  Similarity

We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Starting by analyzing the defects of existing joint source channel coding (JSCC) methods, we show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. To address this problem, we take advantage of learning from semantic similarity, instead of relying on conventional paired bit-level supervisions like cross entropy and bit error rate. However, to develop such a semantic communication system is indeed a nontrivial task, considering the nondifferentiability of most semantic metrics as well as the instability from noisy channels. To further resolve these issues, we put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement by using the policy gradient technique, and to interact with the surrounding noisy environment in a natural way. We have testified the proposed method in the challenging European-parliament dataset. Experiments on both AWGN and phase-invariant fading channel have confirmed the superiority of our method in revealing the semantic meanings, and better handling the channel noise especially in low-SNR situations. Apart from the experimental results, we further provide an indepth look at how the semantics model behaves, along with its superb generalization ability in real-life examples. As a brand new method in learning-based JSCC tasks, we also exemplify an RL-based image transmission paradigm, both to prove the generalization ability, and to leave this new topic for future discussion.
INVIGORATE: Interactive Visual Grounding and Grasping in Clutter

This paper presents INVIGORATE, a robot system that interacts with human through natural language and grasps a specified object in clutter. The objects may occlude, obstruct, or even stack on top of one another. INVIGORATE embodies several challenges: (i) infer the target object among other occluding objects, from input language expressions and RGB images, (ii) infer object blocking relationships (OBRs) from the images, and (iii) synthesize a multi-step plan to ask questions that disambiguate the target object and to grasp it successfully. We train separate neural networks for object detection, for visual grounding, for question generation, and for OBR detection and grasping. They allow for unrestricted object categories and language expressions, subject to the training datasets. However, errors in visual perception and ambiguity in human languages are inevitable and negatively impact the robot's performance. To overcome these uncertainties, we build a partially observable Markov decision process (POMDP) that integrates the learned neural network modules. Through approximate POMDP planning, the robot tracks the history of observations and asks disambiguation questions in order to achieve a near-optimal sequence of actions that identify and grasp the target object. INVIGORATE combines the benefits of model-based POMDP planning and data-driven deep learning. Preliminary experiments with INVIGORATE on a Fetch robot show significant benefits of this integrated approach to object grasping in clutter with natural language interactions. A demonstration video is available at https://youtu.be/zYakh80SGcU.
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene  Context Graph and Relation-based Optimization

Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods. In this paper, we propose a novel method for panoramic 3D scene understanding which recovers the 3D room layout and the shape, pose, position, and semantic category for each object from a single full-view panorama image. In order to fully utilize the rich context information, we design a novel graph neural network based context model to predict the relationship among objects and room layout, and a differentiable relationship-based optimization module to optimize object arrangement with well-designed objective functions on-the-fly. Realizing the existing data are either with incomplete ground truth or overly-simplified scene, we present a new synthetic dataset with good diversity in room layout and furniture placement, and realistic image quality for total panoramic 3D scene understanding. Experiments demonstrate that our method outperforms existing methods on panoramic scene understanding in terms of both geometry accuracy and object arrangement. Code is available at https://chengzhag.github.io/publication/dpc.
Lossless Image Compression Using a Multi-Scale Progressive Statistical  Model

Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have been used in this field to achieve a higher compression rate. Methods based on pixel-wise autoregressive statistical models have shown good performance. However, the sequential processing way prevents these methods to be used in practice. Recently, multi-scale autoregressive models have been proposed to address this limitation. Multi-scale approaches can use parallel computing systems efficiently and build practical systems. Nevertheless, these approaches sacrifice compression performance in exchange for speed. In this paper, we propose a multi-scale progressive statistical model that takes advantage of the pixel-wise approach and the multi-scale approach. We developed a flexible mechanism where the processing order of the pixels can be adjusted easily. Our proposed method outperforms the state-of-the-art lossless image compression methods on two large benchmark datasets by a significant margin without degrading the inference speed dramatically.
Dynamic Network Quantization for Efficient Video Inference

Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational resources required to achieve robust recognition. Motivated by the effectiveness of quantization for boosting efficiency, in this paper, we propose a dynamic network quantization framework, that selects optimal precision for each frame conditioned on the input for efficient video recognition. Specifically, given a video clip, we train a very lightweight network in parallel with the recognition network, to produce a dynamic policy indicating which numerical precision to be used per frame in recognizing videos. We train both networks effectively using standard backpropagation with a loss to achieve both competitive performance and resource efficiency required for video recognition. Extensive experiments on four challenging diverse benchmark datasets demonstrate that our proposed approach provides significant savings in computation and memory usage while outperforming the existing state-of-the-art methods.
Spatio-Temporal Interaction Graph Parsing Networks for Human-Object  Interaction Recognition

For a given video-based Human-Object Interaction scene, modeling the spatio-temporal relationship between humans and objects are the important cue to understand the contextual information presented in the video. With the effective spatio-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also to directly capture inter-time dependencies. It is more critical to capture the position changes of human and objects over the spatio-temporal dimension when their appearance features may not show up significant changes over time. The full use of appearance features, the spatial location and the semantic information are also the key to improve the video-based Human-Object Interaction recognition performance. In this paper, Spatio-Temporal Interaction Graph Parsing Networks (STIGPN) are constructed, which encode the videos with a graph composed of human and object nodes. These nodes are connected by two types of relations: (i) spatial relations modeling the interactions between human and the interacted objects within each frame. (ii) inter-time relations capturing the long range dependencies between human and the interacted objects across frame. With the graph, STIGPN learn spatio-temporal features directly from the whole video-based Human-Object Interaction scenes. Multi-modal features and a multi-stream fusion strategy are used to enhance the reasoning capability of STIGPN. Two Human-Object Interaction video datasets, including CAD-120 and Something-Else, are used to evaluate the proposed architectures, and the state-of-the-art performance demonstrates the superiority of STIGPN.
Towards More Efficient Federated Learning with Better Optimization  Objects

Federated Learning (FL) is a privacy-protected machine learning paradigm that allows model to be trained directly at the edge without uploading data. One of the biggest challenges faced by FL in practical applications is the heterogeneity of edge node data, which will slow down the convergence speed and degrade the performance of the model. For the above problems, a representative solution is to add additional constraints in the local training, such as FedProx, FedCurv and FedCL. However, the above algorithms still have room for improvement. We propose to use the aggregation of all models obtained in the past as new constraint target to further improve the performance of such algorithms. Experiments in various settings demonstrate that our method significantly improves the convergence speed and performance of the model.