Reviewerly/exHarmony huggingface dataset:
Designing the Virtual Product Experience: Learnings from Shenzhen, China and the ESUN Solutions

At the borders of academic and research fields of psychology, IT&C, advertising, marketing and many more, the notions of virtual experience and virtual product experience developed because technological advances are directing towards a movement involving multi-sensory interactions that introduce high-quality images, sound and 3D images. However, online stores are limited in terms of the sensory information they can provide. In a virtual environment, people simply cannot physically look at products before buying them and have to rely on information provided by the online seller. At the same time, the fact that there is no direct contact with the product can reduce the consumer’s pleasure, which has proven to be a very important thing in the online shopping experience. Starting from these considerations, the case study presented in this paper outline the developments introduced by ESUN 3D+, a company affiliated to Shenzhen ESUN Display Co., Ltd., which dedicates to 3D digitizing technology research and related ecosystem construction, offering solutions for 3D scanning, 3D printing, virtual reality, augmented reality, 3D design and manufacturing services, as well as 3D cloud platform.
The Need for Cyber Resilient Enterprise Distributed Ledger Risk Management Framework

Critical infrastructure sectors are increasingly adopting enterprise distributed ledgers (DLs) to host long-term assets, systems, and information that is considered vital to an organization's ability to operate without clear or public plans and strategies to migrate safely and timely to post-quantum cryptography (PQC).A quantum computer (QC) compromised DL would allow eavesdropping, unauthorized client authentication, signed malware, cloak-in encrypted session, a man-in-themiddle attack (MITM), forged documents, and emails.These attacks can lead to disruption of service, damage of reputation and trust, injury to human life, and the loss of intellectual property, assets, regulated data, and global economic security.In 2018, Gartner revealed that a QC is a digital disruption that organizations may not be ready and prepared for, and CIOs may not see it coming. 1On September 18, 2019, IBM announced that the largest universal QC for commercial use would be available in October 2019. 2 On October 23, 2019, Google officially announced "Quantum Supremacy," "by performing a calculation in 200 seconds that would take a classical supercomputer approximately 10,000 years." 3 DL cyber resilience requires "reasonable" measures, policies, procedures, strategies, and risk management before large-scale deployment.Cyber resilience implementations must be a critical component during the design and building phase, or during the initialization phase.The most significant existing attack vector for enterprise DLs is the public key infrastructure (PKI), which is fundamental in securing the Internet and enterprise DLs and is a core component of authentication, data confidentiality, and data and system integrity [1] [2].Effectively implementing and managing a quantum-resistant PKI solution requires adherence to PKI standards, industry requirements, potential government mandates, certificate management policies, training personnel, and data recovery policies that currently do not exist.This research discusses security risks in enterprise DL PKI, areas that can be compromised, and provides an idea of what should be in a PKI DL Risk Management Framework plan.
Any-to-one Face Reenactment Based on Conditional Generative Adversarial Network

Face reenactment refers to the process of transferring the expressions and postures of a given face to the target face. We present a novel Any-to-one Face Reenactment Model based on Conditional Generative Adversarial Network, which has a simple dual converter structure: Any-to-one Face Landmarks Map Converter(AFLC) and Landmark-to-face Converter based on Conditional Generative Adversarial Network(LFC). The former transfers any source face into the landmarks map of the target face, and the map has the expression and posture attributes of the source face. The latter has a generator that transfers the landmarks map of the target face into the realistic and identity-preserving target facial image. The whole model is purely learning-based without any 3D model, and can generate high quality transferred face comparable to the state-of-the-art. What's more the model is highly robust to wild faces, including various faces of different complexions, ages, and genders. We performed an ablation study on our proposed AFLC to verify its importance for face reenactment of any object. AFLC helps the overall model to achieve an effective facial reenactment.
Self-Constructing Graph Convolutional Networks for Semantic Labeling

Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs. SCG can automatically obtain optimized non-local context graphs from complex-shaped objects in aerial imagery. We optimize SCG via an adaptive diagonal enhancement method and a variational lower bound that consists of a customized graph reconstruction term and a Kullback-Leibler divergence regularization term. We demonstrate the effectiveness and flexibility of the proposed SCG on the publicly available ISPRS Vaihingen dataset and our model SCG-Net achieves competitive results in terms of F1-score with much fewer parameters and at a lower computational cost compared to related pure-CNN based work. Our code will be made public soon.
A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining

Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e.,support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between the arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources have been created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in Argument Mining. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task. Thus, our baselines can be employed by the Argument Mining community to compare more effectively how well a method performs on the argumentative relation prediction task.
Current Problems of Forensic Authorship Analysis and the Possibility of Their Solution with the Use of Computer Methods: Problems and Prospects

Active development of Internet communication in recent years caused an increase in the number of forensic text examinations aimed at identifying and profiling (i.e. inferring gender, age, personality, etc. of the author from textual analysis) the author of written texts. Despite the availability of proven methodological recommendations for the production of such examinations, in this area there are many unresolved problems associated mainly with the emergence of new research objects. In addition, the existing expert practice does not fully utilize the achievements of corpus, computer, and quantitative linguistics. In this situation, there is a gap between the "qualitative" and "quantitative" methods of textual authorship analysis, which hinders further development of both theoretical research in the area of authorship attribution and profiling and an increase in the level of objectivity and reproducibility of forensic authorship analysis. The paper represents some typical tasks solved by a forensic expert; describes the characteristics of the objects of forensic authorship analysis, and determines the main difficulties forensic experts face in the course of this analysis. The possibilities of using existing computer methods to solve these tasks are analyzed. It is shown that not all the existing computer methods are useful for forensic authorship analysis. We also highlight the ways of development of forensic authorship analysis related to further theoretical research in the field of idiolect using corpus data and natural language processing techniques.
A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography

We proposed a two-branch multitask learning convolutional neural network to solve two different but related tasks at the same time. Our main task is to predict occult invasive disease in biopsy proven Ductal Carcinoma in-situ (DCIS), with an auxiliary task of segmenting microcalcifications (MCs). In this study, we collected digital mammography from 604 patients, 400 of which were DCIS. The model used patches with size of 512&times;512 extracted within a radiologist masked ROIs as input, with outputs including noisy MC segmentations obtained from our previous algorithms, and classification labels from final diagnosis at patients’ definite surgery. We utilized a deep multitask model by combining both Unet segmentation networks and prediction classification networks, by sharing first several convolutional layers. The model achieved a patch-based ROC-AUC of 0.69, with a case-based ROC-AUC of 0.61. Segmentation results achieved a dice coefficient of 0.49.
The intersection of ethics and AI

Artificial intelligence is a rapidly advancing field with the potential to revolutionize health care, transportation, and national security. Although the technology has been ubiquitous in every day society for a while, the advent of self-driving cars and smart home devices have propelled a discussion of the associated ethical risks and responsibilities. Since the usage of AI can have significant impacts on people, it is essential to establish a set of ethical values to follow when developing and deploying AI.
Error Bounds and Applications for Stochastic Approximation with Non-Decaying Gain

This work analyzes the stochastic approximation algorithm with non-decaying gains as applied in time-varying problems. The setting is to minimize a sequence of scalar-valued loss functions $f_k(\cdot)$ at sampling times $\tau_k$ or to locate the root of a sequence of vector-valued functions $g_k(\cdot)$ at $\tau_k$ with respect to a parameter $\theta\in R^p$. The available information is the noise-corrupted observation(s) of either $f_k(\cdot)$ or $g_k(\cdot)$ evaluated at one or two design points only. Given the time-varying stochastic approximation setup, we apply stochastic approximation algorithms with non-decaying gains, so that the recursive estimate denoted as $\hat{\theta}_k$ can maintain its momentum in tracking the time-varying optimum denoted as $\theta_k^*$. Chapter 3 provides a bound for the root-mean-squared error $ \sqrt{E(\|\hat{\theta}_k-\theta_k^*\|^2})$. Overall, the bounds are applicable under a mild assumption on the time-varying drift and a modest restriction on the observation noise and the bias term. After establishing the tracking capability in Chapter 3, we also discuss the concentration behavior of $\hat{\theta}_k $ in Chapter 4. The weak convergence limit of the continuous interpolation of $\hat{\theta}_k$ is shown to follow the trajectory of a non-autonomous ordinary differential equation. Both Chapter 3 and Chapter 4 are probabilistic arguments and may not provide much guidance on the gain-tuning strategies useful for one single experiment run. Therefore, Chapter 5 discusses a data-dependent gain-tuning strategy based on estimating the Hessian information and the noise level. Overall, this work answers the questions "what is the estimate for the dynamical system $\theta_k^*$" and "how much we can trust $\hat{\theta}_k $ as an estimate for $\theta_k^*$."
Relative generalized Hamming weights of affine Cartesian codes

Abstract We explicitly determine all the relative generalized Hamming weights of affine Cartesian codes using the notion of footprints and results from extremal combinatorics. This generalizes the previous works on the determination of relative generalized Hamming weights of Reed–Muller codes by Geil and Martin, as well as the determination of all the generalized Hamming weights of the affine Cartesian codes by Beelen and Datta.
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms

There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem as it makes training data heterogeneous but so far no attempts have been made to explicitly capture this variation. Here, we propose an approach capable of mimicking different styles of segmentation, which potentially can improve quality and clinical acceptance of automatic segmentation methods. In this work, instead of training one neural network on all available data, we train several neural networks on subgroups of data belonging to different segmentation variations separately. Because a priori it may be unclear what styles of segmentation exist in the data and because different styles do not necessarily map one-on-one to different observers, the subgroups should be automatically determined. We achieve this by searching for the best data partition with a genetic algorithm. Therefore, each network can learn a specific style of segmentation from grouped training data. We provide proof of principle results for open-sourced prostate segmentation MRI data with simulated observer variations. Our approach provides an improvement of up to 23% (depending on simulated variations) in terms of Dice and surface Dice coefficients compared to one network trained on all data.
PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning

Robots can now learn how to make decisions and control themselves, generalizing learned behaviors to unseen scenarios. In particular, AI powered robots show promise in rough environments like the lunar surface, due to the environmental uncertainties. We address this critical generalization aspect for robot locomotion in rough terrain through a training algorithm we have created called the Path Planning and Motion Control (PPMC) Training Algorithm. This algorithm is coupled with any generic reinforcement learning algorithm to teach robots how to respond to user commands and to travel to designated locations on a single neural network. In this paper, we show that the algorithm works independent of the robot structure, demonstrating that it works on a wheeled rover in addition the past results on a quadruped walking robot. Further, we take several big steps towards real world practicality by introducing a rough highly uneven terrain. Critically, we show through experiments that the robot learns to generalize to new rough terrain maps, retaining a 100% success rate. To the best of our knowledge, this is the first paper to introduce a generic training algorithm teaching generalized PPMC in rough environments to any robot, with just the use of reinforcement learning.
The Process Noise Model of Kalman Filter for Chirp Radar

This paper provides a process noise model of a two-state Kalman filter for tracking with linear frequency modulated (LFM) waveforms. The steady-state gains and error covariance of this Kalman filter with process noise model are derived. The derived steady-state gains are such that sensor-noise only (SNO) covariance matrix of $\alpha\beta$-filter with these steady-state gains equals an estimate covariance matrix of a first-degree fixed-memory smoothing algorithm. Thus, the Kalman filter with proposed process noise model approximates the fixed-memory polynomial filter in terms of tracking accuracies. The first-degree fixed-memory smoothing algorithm is a first-degree fixedmemory polynomial filter based on least-squares estimation. Also the range and range rate lag error expressions are derived for the fixed-memory polynomial filter.
Low-Complexity Power Amplifier Model Driven Wireless Emitter Identification Using Random Forest

Due to the widespread use of open wireless access points, the criminals easily access the Internet through these distributed points for illegal activities. However, the software layer of illegal devices can easily be tampered with, which leads to the inaccurate localization using IP address detection method. In this paper, we proposes a low-complexity nonlinear power amplifier (PA) model driven wireless emitter identification method, where a unified receiving model is built by fusing the proposed PA model named simplify generalized memory polynomial (SGMP) with the signal model and a Renyi entropy weighted random forest (RRF) is utilized to make the classification decisions. The proposed method consists of two stages, the training stage and the classification stage. In the training stage, we obtain the corresponding feature vectors by least squares method and train the RRF classifier. Moreover, a Renyi entropy weighted random forest algorithm is designed to enhance the classification accuracy. In the classification stage, the classification decision is made by the trained classifier using the feature vectors of the measured samples. Through simulation results, we demonstrate that the proposed scheme can be superior than recent methods and have more moderate computational complexity.
An Analysis for the Qualitative Improvement of Education and Learning based on the Way of Learner Errors in Descriptive Questions

This study proposes and examines an analytical method with the aim of improving the quality of education and learning by situating the answers to full descriptive questions in probability and statistics to make variables of learners’ comprehension of learned content as answer characteristics, based on actual student mistakes. First, we proposed and examined a method for extracting answer characteristics from the answers to the questions in probability and statistics as variables. Second, we proposed a method for obtaining answer characteristics to accurately describe learners’ comprehension of each problem and indicate learning and educational policies for learners to improve learning by using regression trees. In addition, the relationship between learners’ general ability and answer characteristics was visualized in an item characteristic chart to indicate the general comprehension of the learners. Further, the relationship between learners’ learning strategy and answer characteristics was structuralized using Bayesian network models, and effective learning strategies for both learners as a whole and individual learners were extracted and evaluated towards the qualitative improvement of their comprehension using probabilistic reasoning. Our findings showed that the effectiveness of a learning strategy varies with each concept treated in a given problem; with the degree of basical or applied answer characteristics, indicating that the required learning strategy varies according to a given learner’s stage of learning. Moreover, the improvement of hours studying dispersion for both mid-term and final examinations was revealed as effective for a wide range of subjects.
Economic Relationship between Brazil and China: An Empirical Assessment Using Sentiment and Content Analysis

This paper uses sentiment and content analysis techniques to investigate treaties and other international documents signed by Brazil and China.It identifies the strong presence of values such as solidarity orienting their economic relations, and therefore suggests an alternative approach in observing international relations involving Global South countries, taking into consideration the Spirit of Bandung.The research used the software RStudio for text mining and ATLAS.ti for discursive analysis.
An empirical comparison of distance/similarity measures for Natural Language Processing

Text Classification is one of the tasks of Natural Language Processing (NLP). In this area, Graph Convolutional Networks (GCN) has achieved values higher than CNN's and other related models. For GCN, the metric that defines the correlation between words in a vector space plays a crucial role in the classification because it determines the weight of the edges between two words (represented by nodes in the graph). In this study, we empirically investigated the impact of thirteen measures of distance/similarity. A representation was built for each document using word embedding from word2vec model. Also, a graph-based representation of five dataset was created for each measure analyzed, where each word is a node in the graph, and each edge is weighted by distance/similarity between words. Finally, each model was run in a simple graph neural network. The results show that, concerning text classification, there is no statistical difference between the analyzed metrics and the Graph Convolution Network. Even with the incorporation of external words or external knowledge, the results were similar to the methods without the incorporation of words. However, the results indicate that some distance metrics behave better than others in relation to context capture, with Euclidean distance reaching the best values or having statistical similarity with the best.
A Fast Face Recognition CNN Obtained by Distillation

Nowadays, the trend of the latest research in face recognition model shows that “the complex—the better” paradigm can be directly applied to these systems, whose accuracy effectively depends on both a large number of well-trained parameters and a complex functional structure. If this approach is sustainable for an offline processing on a consumer PC, it is far less appealing in the mobile environment, where processing power, as well as a high amount of onboard RAM could not be available. The distillation technique, applied on the cumbersome dlib-resnet-v1 face recognition model results in a lighter version that, while maintaining a comparable accuracy, can achieve a faster processing rate (>10×) and a lower memory occupation (1/6). The final model has been implemented on a single board PC, also using a neural hardware accelerator.
Design Thinking as a Framework for Innovation in Libraries

Abstract This article presents human-centered design methods as a way to create relevant and vibrant libraries. Dokk1–the main library in Aarhus-is co-created with users, partners, and stakeholders. It has become an open, flexible library space that also uses technology as a way to invite the users to become part of the space. Using design thinking as a new way of working, co-creation also becomes a part of developing new services and spaces and a new way of working for the library staff.