Reviewerly/exHarmony huggingface dataset:
Issues, problems and potential solutions when simulating continuous, non-normal data in the social sciences

Computer simulations have become one of the most prominent tools for methodologists in the social sciences to evaluate the properties of their statistical techniques and to offer best practice recommendations. Amongst the many uses of computer simulations, evaluating the robustness of methods to their assumptions, particularly univariate or multivariate normality, is crucial to ensure the appropriateness of data analysis. In order to accomplish this, quantitative researchers need to be able to generate data where they have a degree of control over its non-normal properties. Even though great advances have been achieved in statistical theory and computational power, the task of simulating multivariate, non-normal data is not straightforward. There are inherent conceptual and mathematical complexities implied by the phrase ``non-normality'' which are not always reflected in the simulations studies conduced by social scientists. The present article attempts to offer a summary of some of the issues concerning the simulation of multivariate, non-normal data in the social sciences. An overview of common algorithms is presented as well as some of the characteristics and idiosyncrasies that implied in them which may exert undue influence in the results of simulation studies. A call is made to encourage the meta-scientific study of computer simulations in the social sciences in order to understand how simulation designs frame the teaching, usage and practice of statistical techniques within the social sciences.
Formation of professional identity of an international diplomat

The article deals with issues related to the purpose, features, state and ways to improve the training of graduates of the master's program in the direction of 41.04.05-International relations.
Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment

Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.
A Decentralized Copyright Protection Method Supporting Validity Period Control

In response to the requirements of digital copyright protection measures that ensure digital content security and support validity period management, the paper introduces a decentralized copyright protection solution that incorporates validity period control. Firstly, we propose to establish the digital copyright protection architecture based on the consortium blockchain, which ensures the authenticity of transaction information by leveraging the merits of blockchain in terms of decentralization and immutability. Secondly, a validity period control model is proposed to solve current issues in the field by applying InterPlanetary File System address mapping to digital content storage and control of mapped addresses. As the last step, content distribution and usage protocols are designed based on the above methods, which adopt asymmetric encryption methods to help eliminate the reliance on third-party key management while ensuring the security of digital contents. Experimental results prove that our solution can realize effective decentralized copyright protection with support for copyright validity period control, which is expected to have good prospects in real-world applications.
Visualizing digital discourse

An influential book for me as a post-graduate student was Digital Discourse: Language in the New Media by Crispin Thurlow and Kristine Mroczek (2011). This was a book that raised questions about th...
Bayesian Model Selection for High-Dimensional Ising Models, With Applications to Educational Data

Doubly-intractable posterior distributions arise in many applications of statistics concerned with discrete and dependent data, including physics, spatial statistics, machine learning, the social sciences, and other fields. A specific example is psychometrics, which has adapted high-dimensional Ising models from machine learning, with a view to studying the interactions among binary item responses in educational assessments. To estimate high-dimensional Ising models from educational assessment data, $\ell_1$-penalized nodewise logistic regressions have been used. Theoretical results in high-dimensional statistics show that $\ell_1$-penalized nodewise logistic regressions can recover the true interaction structure with high probability, provided that certain assumptions are satisfied. Those assumptions are hard to verify in practice and may be violated, and quantifying the uncertainty about the estimated interaction structure and parameter estimators is challenging. We propose a Bayesian approach that helps quantify the uncertainty about the interaction structure and parameters without requiring strong assumptions, and can be applied to Ising models with thousands of parameters. We demonstrate the advantages of the proposed Bayesian approach compared with $\ell_1$-penalized nodewise logistic regressions by simulation studies and applications to small and large educational data sets with up to 2,485 parameters. Among other things, the simulation studies suggest that the Bayesian approach is more robust against model misspecification due to omitted covariates than $\ell_1$-penalized nodewise logistic regressions.
Logical word learning: The case of kinship

In this paper, we propose a framework for conceptual development through the lens of program induction. We implement this framework to model the acquisition of kinship term concepts, resulting in the first formal developmental model for kinship acquisition. We demonstrate that our model can learn several kinship systems of varying complexity using cross-linguistic data from English, Pukapuka, Turkish and Yanomamö. More importantly, the behavioral patterns observed in children learning kinship terms, under-extension and over-generalization, fall out naturally from our learning model. We conducted interviews to simulate realistic learning environments and demonstrate that the characteristic-to-defining shift is a consequence of our learning model in naturalistic contexts containing abstract and concrete features. We use model simulations to discuss the influence of simplicity and learning environment on the order of acquisition of kinship terms, positing novel predictions for the learning trajectories of kinship terms. We conclude the paper with a discussion of how this model framework generalizes beyond kinship terms and the limitations of our model.
A Spectral Clustering on Grassmann Manifold via Double Low Rank Constraint

Data clustering is a fundamental topic in machine learning and data mining areas. In recent years, researchers have proposed a series of effective methods based on Low Rank Representation (LRR) which could explore low-dimension subspace structure embedded in original data effectively. The traditional LRR methods usually are designed for vectorial data from linear spaces with Euclidean distance. However, high-dimension data (such as video clip or imageset) are always considered as non-linear manifold data such as Grassmann manifold with non-linear metric. In addition, traditional LRR clustering method always adopt single nuclear norm as low rank constraint which would lead to suboptimal solution and decrease the clustering accuracy. In this paper, we proposed a new low rank method on Grassmann manifold for video or imageset data clustering task. In the proposed method, video or imageset data are formulated as sample data on Grassmann manifold first. And then a double low rank constraint is proposed by combining the nuclear norm and bilinear representation for better construct the representation matrix. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods.
High performance computers: from parallel computing to quantum computers and biocomputers

Abstract Various programming methods are considered. Particular attention is paid to parallel programming, quantum computers and biocomputers. This attention is due to the fact that in recent years, high-performance computing has been intensively developing. One of the main ideas for increasing the speed of information processing is to carry out calculations in parallel. For classical programming methods this is achieved thanks to the advent of multiprocessor computers. Such computers allow computational tasks to be parallelized by introducing parallelization elements into classical programming languages. Another approach to speed up computation is based on the idea of a quantum computer. The use of qubits in quantum computers leads to the fact that all possible states of the system are simultaneously processed. Another approach leading to increased computing performance is based on the development of biocomputers. This approach is based on the idea of using DNA chains consisting of a sequence of four nitrogenous bases (adenine, guanine, thymine, and cytosine). The information is stored and processed as a sequence of these nitrogenous bases. An increase in the speed of calculations is carried out due to the fact that biochemical reactions can take place simultaneously on different parts of the DNA - chains.
How could Neural Networks understand Programs?

Semantic understanding of programs is a fundamental problem for programming language processing (PLP). Recent works that learn representations of code based on pre-training techniques in NLP have pushed the frontiers in this direction. However, the semantics of PL and NL have essential differences. These being ignored, we believe it is difficult to build a model to better understand programs, by either directly applying off-the-shelf NLP pre-training techniques to the source code, or adding features to the model by the heuristic. In fact, the semantics of a program can be rigorously defined by formal semantics in PL theory. For example, the operational semantics, describes the meaning of a valid program as updating the environment (i.e., the memory address-value function) through fundamental operations, such as memory I/O and conditional branching. Inspired by this, we propose a novel program semantics learning paradigm, that the model should learn from information composed of (1) the representations which align well with the fundamental operations in operational semantics, and (2) the information of environment transition, which is indispensable for program understanding. To validate our proposal, we present a hierarchical Transformer-based pre-training model called OSCAR to better facilitate the understanding of programs. OSCAR learns from intermediate representation (IR) and an encoded representation derived from static analysis, which are used for representing the fundamental operations and approximating the environment transitions respectively. OSCAR empirically shows the outstanding capability of program semantics understanding on many practical software engineering tasks.
Integrating Historical States and Co-attention Mechanism for Visual Dialog

Visual dialog is a typical multi-modal task which involves both vision and language. Nowadays, it faces two major difficulties. In this paper, we propose the Integrating Historical States and Co-attention (HSCA) for visual dialog to solve them. It includes two main modules, Co-ATT and MATCH. Specifically, the main purpose of the Co-ATT module is to guide the image with questions and answers in the early stage to get more specific objects. It tackles the first difficulty of the temporal sequence issue in historical information which may influence the precise answer for multi-round questions. The MATCH module is, based on a question with pronouns, to retrieve the best matching historical information block. It overcomes the second difficulty of the visual reference problem which requires to solve pronouns referring to unknowns in the text message and then to locate the objects in the given image. We quantitatively and qualitatively evaluate our model on VisDial v1.0, at the same time, ablation studies are carried out. The experimental results demonstrate that HSCA outperforms the state-of-the-art methods in many aspects.
Aggregating Object Features Based on Attention Weights for Fine-Grained Image Retrieval

Object localization and local feature representation are key issues in fine-grained image retrieval. However, the existing unsupervised methods still need to be improved in these two aspects. For conquering these issues in a unified framework, a novel unsupervised scheme, named DSAW for short, is presented in this paper. Firstly, we proposed a dual-selection (DS) method, which achieves more accurate object localization by using an adaptive threshold method to perform feature selection on local and global activation map. Secondly, a novel and faster self-attention weights (AW) method is developed to weight local features by measuring their importance in the global context. Finally, we also evaluated the performance of the proposed method on five fine-grained image datasets and the results showed that our DSAW outperformed the existing best method.
NDS: an interactive, web-based system to visualize urban neighborhood dynamics in United States

NDS is an interactive, web-based system, for the visualization of multidimensional neighborhood dynamics across the 50 largest US Metropolitan Statistical Areas (MSAs) from 1980 to 2010 (http://neighborhooddynamics.dreamhosters.com). Four different visualization tools are developed: (1) an interactive time slider to show neighborhood classification changes for different years; (2) multiple interactive bar charts for each variables of each neighborhood; (3) an animated neighborhood’s trajectory and sequence cluster on a self-organizing map (SOM) output space; and (4) a synchronized visualization tool showing maps for four time stamps at once. The development of this interactive online platform for visualizing dynamics overcomes many of the challenges associated with communicating changes for multiple variables, across multiple time stamps, and for a large geographic area when relying upon static maps. The system enables users to select and dive into details on particular neighborhoods and explore their changes over time.
Cognitive Analytics of Apple Products from Crowdsourced Tweets

Human cognition is stimulated with the nature and structure of our brain. The emotions expressed by the humans are heavily dependent on the brain-stimuli. The product manufacturers are showing their keen interest to know the feedback from the customers about the products. In the Internet-era, the customers are sharing their views, opinions, reviews in publicly available resources such as Online Social Networks (OSN). These reviews have correlation between their mood or emotion and the time at which the customer express their review comments in such platforms. This research is focused on identifying the emotions, as human cognition, which are expressed in a crowdsourced-tweets of Apple products. The dataset used in this research has sentiment values based on the customer reviews. Moreover, the dataset was crowdsourced to mention whether the review belongs to the Apple company or not. It is being found that there is a significant difference between the results available in the sentiment values and the results obtained by this research – cognitive analytics. It is also noted that though the sentiment values are high for some of the opinions, whereas the emotion values are neutral. The novelty of this research is to map the opinions to the eight different human cognition and also to finally find whether the opinion is positive or negative.
Performance of reference-frame-independent quantum key distribution in underwater channel

During free-space quantum key distribution, the rotation and fluctuation of reference frame degrades the performance of quantum key distribution (QKD). Reference-frame-independent QKD (RFI-QKD) overcomes this issue effectively. To date, much theoretical and experimental research has been conducted on the performance of free-space RFI-QKD. However, these studies are all based on free-space air and satellite ground, and none have investigated the performance of RFI-QKD in an underwater channel. Therefore, this paper constructed a channel model that considered both scattering and optical attenuation to obtain an RFI-QKD secret key rate in an underwater channel. The simulation results confirm that even in a relatively harsh underwater scenario, RFI-QKD maintains good performance.
Toward Free Head Motion for Visible-Spectrum Gaze Tracking Systems

The visible-spectrum gaze tracker gains the advantage of excellent user experience compared with the traditional infra-ray based one. But the head motion compensation becomes extremely difficult due to the lack of stable reference feature points on the eye images. The system performance degrades a lot if the user head is not fixed at the chin rest. This paper presents the compensation mechanism of free head motion for a visible-spectrum gaze tracker. The proposed approach aims at allowing the user to move their heads back and forth. The experimental results show that the accuracy as well as precision can be significantly improved.
Parametric optimization the automated plan of extracurricular activities of students by means of information technologies

Abstract This article explains problems of individualization of education and extracurricular activities’ programs’ development. Main problems that need to be solved at various stages of the organization of extracurricular activities, from its methodological study to organizational and technical planning are analyzed. An overview of the directions of extracurricular activities, the forms in which it can be organized as well as the specifics of its implementation at various levels of education is offered. In order to reduce the labor intensity of time costs taking into account the time allocated for its implementation as well as the ability to generate plans taking into account individual preferences an intelligent information system was developed. Multi-criteria mathematical models from the class of optimization methods for solving the posed problem of decision support, functional and software-architectural developed model of a web service and examples of its work on the stages of planning general activities in the optimization formulation are presented. The proposed approach can be widely used in educational institutions of various levels interested in reducing the cost of organizing extracurricular work, automatic checking for compliance with requirements, as well as generating a work plan taking into account the opinions of various categories of experts.
Traditional Learning versus E-Learning

The axis of this research paper is to compare and contrast the methods of traditional learning in classroom and E-Learning. The topic of this research paper appeared while considering the constant growing trend of technology and as a consequence of the current trends, the need for change to the methods of learning and teaching appears. With changes made to methods of learning there will be an increase in the learning choices, leading to an advancement, improvement, and progression in education. Besides the methods of traditional learning and E-Learning, this paper will make an analysis on the advantages and disadvantages of these learning methods, on the learning styles of traditional learning, which includes a physical place, usually a classroom, where students and educators or teachers can interact and on the learning styles of E-Learning, which includes an e-space with a server and a web browsing interface, based on wireless communication networks.
A multi‐modal heterogeneous data mining algorithm using federated learning

In disease diagnosis, the classification accuracy based on multi-modal models is usually higher than single-modal models. However, in the process of multimodal data fusion, how to reasonably solve the heterogeneity problem and better extract the information between different modal data has attracted the attention of scholars. Federated learning is an efficient machine learning method that can expand between multiple parameters or multiple computing nodes. It has been applied successfully in the financial industry and cross-industry cooperation. In this paper, a novel algorithm to disease diagnosis model based on federated learning is proposed. The model not only cleverly solves heterogeneous problems, but also excavates information between different modal data to promote the model to be more robust and discriminative. The experiment results show that the proposed model has better performance than traditional fusion algorithms. Notably, compared with other models, this model converges faster and requires less computation.