Reviewerly/exHarmony huggingface dataset:
Information-Based Particle Flow With Convergence Control

A new formulation of the Gaussian particle flow filter is presented using an information theoretic approach. The developed information-based form advances the Gaussian particle flow framework in two ways: it imparts physical meaning to the flow dynamics and provides the ability to easily include modifications for a non-Bayesian update. An orbit determination simulation with high initial uncertainty is used to demonstrate the consistent, robust performance of the information flow filter in situations where the extended Kalman filter fails.
Multi-Input CNN for molecular classification in breast cancer

Molecular classification in pathological anatomy is an important task as it is extremely convenient for the diagnosis of cancer and its subtypes for adequate therapeutic choice. With the development of computer vision, cancer classification has become an interdisciplinary subject in both medicine and computer vision.A multi-input convolutional neural network is designed for the molecular classification of cancer based on a collected dataset, which contains four tissues treated with four antibodies; each one of them is composed of 33 images. The proposed model achieves a satisfactory accuracy of 90.43% after data augmentation. Even though the data augmentation contributes to the model, the accuracy is still limited by the lack of sample diversity.
Smoothed Generalized Dirichlet: A Novel Count-Data Model for Detecting Emotional States

In this article, we propose novel approaches to deal with the problem of burstiness, the challenge of count-data sparseness, and the curse of dimensionality. We introduce a smoothed generalized Dirichlet distribution that is a smoothed variant of the generalized Dirichlet distribution and a generalization of the smoothed Dirichlet. We provide different learning methods based on mixture models and agglomerative clustering-based geometrical information: Kullback–Leibler divergence, Fisher metric, and Bhattacharyya distance. Moreover, we show that the new smoothed generalized Dirichlet could be considered as a prior to the multinomial, which generates a new distribution for count data that we call the smoothed generalized Dirichlet multinomial. In particular, we present an approximation based on Taylor series expansion for better performance and optimized running time in the case of high-dimensional count data. The proposed models are evaluated through two emotion detection applications: disaster-tweet-related emotions and pain intensity estimation. Experiments show the efficiency and the robustness of our approaches when dealing with texts, videos, and images.
On the Neural Backdoor of Federated Generative Models in Edge Computing

Edge computing, as a relatively recent evolution of cloud computing architecture, is the newest way for enterprises to distribute computational power and lower repetitive referrals to central authorities. In the edge computing environment, Generative Models (GMs) have been found to be valuable and useful in machine learning tasks such as data augmentation and data pre-processing. Federated learning and distributed learning refer to training machine learning models in the edge computing network. However, federated learning and distributed learning also bring additional risks to GMs since all peers in the network have access to the model under training. In this article, we study the vulnerabilities of federated GMs to data-poisoning-based backdoor attacks via gradient uploading. We additionally enhance the attack to reduce the required poisonous data samples and cope with dynamic network environments. Last but not least, the attacks are formally proven to be stealthy and effective toward federated GMs. According to the experiments, neural backdoors can be successfully embedded by including merely 5\% poisonous samples in the local training dataset of an attacker.
Anonymous Lattice Identity-Based Encryption with Traceable Identities

An anonymous identity-based encryption with tracing identities (AIBET) system enjoys that same strong privacy for receivers as a normal anonymous identity-based encryption system. Additionally, an AIBET system offers an identity tracing mechanism, which allows a tracer, who has an identity-associated tracing key, to uncover the recipient’s identity from the ciphertext. In this paper, we present an AIBET system based on plain lattices by exploiting a hierarchical power of lattice trapdoors in a novel way. We prove the security of the system under the conservative learning-with-errors assumption in the standard model. This is the first AIBET system provably secure under quantum-resistant assumptions. Our construction’s efficiency is comparable to the state-of-the-art lattice anonymous identity-based encryption system.
Relation-Aware Entity Matching Using Sentence-BERT

A key aspect of Knowledge fusion is Entity Matching.The objective of this study was to investigate how to identify heterogeneous expressions of the same real-world entity.In recent years, some representative works have used deep learning methods for entity matching, and these methods have achieved good results.However, the common limitation of these methods is that they assume that different attribute columns of the same entity are independent, and inputting the model in the form of paired entity records will cause repeated calculations.In fact, there are often potential relations between different attribute columns of different entities.These relations can help us improve the effect of entity matching, and can perform feature extraction on a single entity record to avoid repeated calculations.To use attribute relations to assist entity matching, this paper proposes the Relation-aware Entity Matching method, which embeds attribute relations into the original entity description to form sentences, so that entity matching is transformed into a sentence-level similarity determination task, based on Sentence-BERT completes sentence similarity calculation.We have conducted experiments on structured, dirty, and textual data, and compared them with baselines in recent years.Experimental results show that the use of relational embedding is helpful for entity matching on structured and dirty data.Our method has good results on most data sets for entity matching and reduces repeated calculations.
Construction and Training of Cloud Computing-Based Smart Grid Operation Risk Early Warning Model

With the development of market economy, the demand for safe, stable and reliable power grid operation is increasing. At the same time, with the increasing complexity of domestic power grids and the gradual increase in the voltage of transmission lines, how to early warn the operation risks of smart grids in different environments has become a problem that power grid practitioners must study. Due to the complexity of the smart grid system itself, cloud computing can facilitate the complex calculation of the smart grid operation risk early warning system. The purpose of this paper is to study the construction and training of cloud computing-based smart grid operation risk early warning model. Starting from the requirements of the power grid operation risk early warning model, this paper analyzes that the model should have three functions: risk early warning, early warning emergency management, and risk emergency response. This paper designs the model and conducts system performance testing and power grid transmission line risk prediction testing. The test results show that the cloud computing-based training model has a maximum prediction accuracy of 99.8% and 98.1% on the training set and test set. It can be seen that this system has good stability and reliability in the early warning of galloping risk of power grid transmission lines.
The Multi-Modal Video Reasoning and Analyzing Competition

In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021. This competition is composed of four different tracks, namely, video question answering, skeleton-based action recognition, fisheye video-based action recognition, and person re-identification, which are based on two datasets: SUTD-TrafficQA and UAV-Human. We summarize the top performing methods submitted by the participants in this competition, and show their results achieved in the competition.
Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling

Existing unsupervised person re-identification methods only rely on visual clues to match pedestrians under different cameras. Since visual data is essentially susceptible to occlusion, blur, clothing changes, etc., a promising solution is to introduce heterogeneous data to make up for the defect of visual data. Some works based on full-scene labeling introduce wireless positioning to assist cross-domain person re-identification, but their GPS labeling of entire monitoring scenes is laborious. To this end, we propose to explore unsupervised person re-identification with both visual data and wireless positioning trajectories under weak scene labeling, in which we only need to know the locations of the cameras. Specifically, we propose a novel unsupervised multimodal training framework (UMTF), which models the complementarity of visual data and wireless information. Our UMTF contains a multimodal data association strategy (MMDA) and a multimodal graph neural network (MMGN). MMDA explores potential data associations in unlabeled multimodal data, while MMGN propagates multimodal messages in the video graph based on the adjacency matrix learned from histogram statistics of wireless data. Thanks to the robustness of the wireless data to visual noise and the collaboration of various modules, UMTF is capable of learning a model free of the human label on data. Extensive experimental results conducted on two challenging datasets, i.e., WP-ReID and Campus4K demonstrate the effectiveness of the proposed method.
Collaborative Experts Discovery in Social Coding Platforms

The popularity of online social coding (SC) platforms such as GitHub is growing due to their social functionalities and tremendous support during the product development lifecycle. The rich information of experts' contributions on repositories can be leveraged to recruit experts for new/existing projects. In this paper, we define the problem of collaborative experts finding in SC platforms. Given a project, we model an SC platform as an attributed heterogeneous network, learn latent representations of network entities in an end-to-end manner and utilize them to discover collaborative experts to complete a project. Extensive experiments on real-world datasets from GitHub indicate the superiority of the proposed approach over the state-of-the-art in terms of a range of performance measures.
Breast Cancer Classification Based on CNN and ESIM Model

Breast cancer is a major cause of death among women worldwide, and invasive ductal carcinoma (IDC) is the most common form of breast cancer. However, the recognition of images of hematoxylin and eosin (H&E) stained breast histopathology samples has the limitations of low efficiency and low accuracy in previous studies. Also, there is a lack of network platforms that can automatically interact with patients. To tackle these issues, this paper proposed a deep learning-based method for the classification of H&Estained breast tissue images released from Andrew Janowczyk's website by fine-tuning the VGG convolutional neural network (CNN). Moreover, the enhanced LSTM for natural language inference (ESIM) model is also proposed to process text matching in website intelligent question answering. As a result, we can achieve 80% accuracy on the validation set regarding image recognition, which significantly improves upon a previous benchmark. Furthermore, in terms of text matching, the ESIM model achieves 74% accuracy on the verification set, which meets the website's requirements for intelligent questions and answers. Therefore, patients can interact with the website that combines CNN and ESIM models to quickly obtain accurate preliminary breast cancer diagnosis results.
Image Outpainting with Depth Assistance

In some scenarios such as autonomous diriving, we can get a sparse point cloud with a large field of view, but an RGB image with a limited FoV. This paper studies the problem of image expansion using depth information converted from sparse point cloud projection. General image expansion tasks only use images as input for expansion. Such expansion is only carried out by RGB information, and the expanded content has limitations and does not conform to reality. Therefore, we propose introducing depth information into the image expansion task, and offering a reasonable image expansion model using the depth information of the sparse point cloud. Our model can generate more realistic and more reliable image expansion content than general image expansion. The results generated by our work must be authentic, which further enhances the practical significance of the image expansion problem. Furthermore, we also designed a variety of experimental research schemes on how to perform interactive matching between depth and RGB information further to enhance the help of depth for image expansion.
Optimizing Scatterplot-Matrices for Decision-Support:

The scatterplot matrix is defined to be a standard method for multivariate data visualization; nonetheless, their use for decision-support in a corporate environment is scarce. Amongst others, longstanding criticism lies in the lack of empirical testing to investigate optimal design specifications as well as areas of application from a business related perspective. Thus, on the basis of an innovative approach to assess a visualization’s fitness for efficient and effective decision-making given a user’s situational cognitive load, this study investigates the usability of a scatterplot matrix while performing typical tasks associated with multidimensional datasets (correlation and distribution assessment). A laboratory experiment recording eye-tracking data investigates the design of the matrix and its influence on the decision-maker’s ability to process the presented information. Especially, the information content presented in the diagonal as well as the size of the matrix are tested and linked to the user’s individual processing capabilities. Results show that the design of the scatterplot as well as the size of the matrix influenced the decision-making greatly.
A Survey of Data Mining Techniques in the Field of Cyborg Mining

The growing user population and open nature of social media have attracted a huge number of programs which are automated, also known as bots, which is considered to be a double-edged sword. But, in between human and bot, there is another organism known as cyborg. Cyborg can be defined as bot-assisted human or human-assisted bot. The spread of misinformation in the social media has greatly increased due to social bots as well. We mainly review about different techniques that have been used up until now to detect difference between human, bot or cyborg, fake accounts or spoofing accounts assisted by bot or cyborg with the help of data mining.
Knowledge distillation on neural networks for evolving graphs

Abstract Graph representation learning on dynamic graphs has become an important task on several real-world applications, such as recommender systems, email spam detection, and so on. To efficiently capture the evolution of a graph, representation learning approaches employ deep neural networks, with large amount of parameters to train. Due to the large model size, such approaches have high online inference latency. As a consequence, such models are challenging to deploy to an industrial setting with vast number of users/nodes. In this study, we propose DynGKD, a distillation strategy to transfer the knowledge from a large teacher model to a small student model with low inference latency, while achieving high prediction accuracy. We first study different distillation loss functions to separately train the student model with various types of information from the teacher model. In addition, we propose a hybrid distillation strategy for evolving graph representation learning to combine the teacher’s different types of information. Our experiments with five publicly available datasets demonstrate the superiority of our proposed model against several baselines, with average relative drop $$40.60\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>40.60</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> in terms of RMSE in the link prediction task. Moreover, our DynGKD model achieves a compression ratio of 21:100, accelerating the inference latency with a speed up factor $$\times 30$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>×</mml:mo><mml:mn>30</mml:mn></mml:mrow></mml:math> , when compared with the teacher model. For reproduction purposes, we make our datasets and implementation publicly available at https://github.com/stefanosantaris/DynGKD .
Creative Solutions and Professional Culture of Prison Staff

The features of the influence of cognitive training methods on the improvement of the level of professional culture of prison staff and the growth of their creative potential are considered. The features and peculiarities of the penal system employees, who are in close contact with convicts, and engaged in the prevention of conflicts, criminal aggression, escapes, drug abuse, and riots have been investigated. The author concludes that the regular use of cognitive technologies contributes significantly to the professionalism of the penitentiary staff, to creative thinking, quick social/psychological adaptation to unfavorable situations, and prevention of burnout. The author's program of cognitive training contains six thematic sections related to specific methods that are aimed at: (1) the formation of creative thinking; (2) the formation of a high subjective significance of professional culture and effectiveness; (3) constructive change of interpersonal (in the broadest sense) relationships; the acquisition of communication skills; (4) the study of adaptive coping strategies, methods of self-analysis and autogenic training; (5) study of methods of rapid diagnosis of psychological qualities of a person and assessment of his emotional state, as well as the most effective ways to influence a person; (6) development of organizational skills. This program will be useful for such purposes as the selection of candidates, training and retraining of law enforcement officers, as well as the prevention of their professional deformation.
On Social Network De-Anonymization With Communities: A Maximum A Posteriori Perspective

A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated cross-domain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through varied cost functions. However, many cost functions are empirically proposed without sufficient theoretical support. For some other works probing the theoretical bound, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to minimize the cost functions. Besides, only few prior works have discussed the de-anonymization of social networks with communities. We address those concerns in a social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. Based on the Maximum A Posteriori (MAP) estimation, our first contribution is a series of MAP-based cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized. We prove the general multiplicative inapproximability and thus propose two heuristics, which, respectively, enjoy an <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are also empirically validated under classical synthetic and real-wrold social networks. Both theoretical and empirical observations manifest the importance of community in enhancing privacy inferencing.
Experimental Modal Analysis of Violin Bodies with Different Structural Patterns of Resonance Spruce

Abstract The paper aims to highlight the modal response of violin bodies made of four quality classes of resonant wood, following the anatomical structure of the wood. The four categories of musical instruments, made at the S.C. Gliga Instrumente Muzicale S.A., were excited by an impact hammer type B&amp;K 8204 and its response was captured by an accelerometer type B&amp;K 4517-002 (Brüel &amp; Kjær, Denmark, Nærum). The received signals were transmitted through a conditioning device to a Dynamic Signal Acquisition System (DAQ) - NI USB-9233 (National Instruments, USA, Austin), connected to a laptop. The obtained signals were in the form of the natural frequencies and the damping factor. The results highlighted the nonlinear and inhomogeneous behavior of violins with different structural patterns, proving that their vibrations depend on many factors.
Optimizing the Design of Recommendation Systems

With the development of digital technology and internet applications, the massive market data and information overload make it hard for users to organize and make good use of comments to choose their preferred items. In this connection, recommendation systems are widely developed with high expectations. While there have been substantial researches focusing on the recommendation methods and implementations, this paper provides an up-to-date synthesis of the essential factors towards a more rigorous, user-helping system design in consolidation of collaborative filtering, content-based model, and hybrid model in a fast-developing web and social network environment. Aspects of evaluation metrics are analyzed and compared in connection to particular applicabilities in the system design. Further explorations in terms of credit rating are discussed in a real-business setting.
A Formal System for the Universal Quantification of Schematic Variables

We advocate the use of de Bruijn’s universal abstraction \lambda {\mathord \infty }{}{}{} for the quantification of schematic variables in the predicative setting, and we present a typed \lambda {}{}{}{} -calculus featuring the quantifier \lambda {\mathord \infty }{}{}{} accompanied by other practically useful constructions like explicit substitutions and expected type annotations. Our calculus stands just on two notions, i.e., bound rt-reduction and parametric validity, and has the expressive power of \lambda \mathord \rightarrow . Thus, while not aiming at being a logical framework by itself, it does enjoy many desired invariants of logical frameworks including confluence of reduction, strong normalization, preservation of type by reduction, decidability, correctness of types and uniqueness of types up to conversion. This calculus belongs to the \lambda \delta family of formal systems, which borrow some features from the pure type systems and some from the languages of the Automath tradition, but stand outside both families. In particular, our calculus includes and evolves two earlier systems of this family. Moreover, a machine-checked specification of its theory is available.