Reviewerly/exHarmony huggingface dataset:
Welding path planning method based on grid arc welding robot

In order to solve the problems of low planning efficiency and accuracy in traditional robot welding path planning methods, a grid model based welding path planning method for arc welding robot is proposed. This method simplifies the linkage structure of arc welding robot and constructs the kinematics equation of arc welding robot. According to the kinematics equation, the grid model is constructed to calculate the slope information, roughness and step elevation difference information to complete the welding path planning of arc welding robot. The experimental results show that the proposed method has higher planning accuracy and efficiency than the traditional welding path planning methods.
SmartSSO - A Deep Learning Platform for Automated Account Linkage in Federated Identity Management

User identity linkage (UIL) refers to linking users’ assets and identities across social networks. With the rapid growth of social media in our day-to-day life, UIL’s importance has gone beyond being just a research topic, and it has become a necessary precondition for critical tasks like fraud detection.However, only a few models have been proposed to tackle UIL in different domains than social networks. This limitation becomes even more evident in academic federated identity management (FIM) domains. Service providers (SP) deal with restricted users’ data in these environments, and often data related to connections between entities and resources (i.e., network-based information) such as friends associations or the clients’ inclinations is not available.This research addresses the account linkage (AL) problem for organizations inside federated environments with limited or no access to users’ data. In the proposed model, we focus on analyzing users’ habits and behavior during login processes by utilizing a Variational Autoencoder’s (VAE) latent space. The learned structure in this space is used to derive related accounts owned by one user.To the best of our knowledge, the proposed model is the first approach attempting to solve the AL problem inside an academic FIM domain with its high requirements regarding data security and limited users’ information. Preliminary results show that the proposed model could achieve almost 90% accuracy in linking accounts possessed by one user.
A Primitive Cipher with Machine Learning

Multi-access edge computing (MEC) equipped with artificial intelligence is a promising technology in B5G wireless systems. Due to outsourcing and other transactions, some primitive security modules need to be introduced. In this paper, we design a primitive cipher based on double discrete exponentiation and double discrete logarithm. The machine learning methodology is incorporated in the development. Several interesting results are obtained. It reveals that the number of key-rounds is critically important.
The relationship between vitality and the level of subjectivity in future pedagogues

The article presents the results of a study of subjectivity and vitality among students – future pedagogues. Topicality of the research is determined by the need to increase the subjectivity of students as one of the predictors that determine the vitality of a person. The theoretical analysis of the concepts of "subjectivity", "vitality" is carried out. The authors examine the results of the study of the ratio of the components of subjectivity and indicators of vitality in students. The purpose of the study is to identify the relationship between indicators of viability and the level of subjectivity and its components among students – future pedagogues. It was found that the indicators of subjectivity (activity, ability to reflect, freedom of choice, awareness of uniqueness, understanding and acceptance of another as well as self-development) in this sample are at the average value. Analysis of the data of the study of viability and its indicators, it was found that in this sample of subjects self-motivation, social competency, self-esteem and adaptive ways of behaviour are most pronounced. The author has established significant correlations between the indicators of vitality and the components of subjectivity. The empirical data obtained in the study and described in the article can be used in the development of programmes in the formation of professional competences in first-year students.
A New Photographic Reproduction Method Based on Feature Fusion and Virtual Combined Histogram Equalization

A desirable photographic reproduction method should have the ability to compress high-dynamic-range images to low-dynamic-range displays that faithfully preserve all visual information. However, during the compression process, most reproduction methods face challenges in striking a balance between maintaining global contrast and retaining majority of local details in a real-world scene. To address this problem, this study proposes a new photographic reproduction method that can smoothly take global and local features into account. First, a highlight/shadow region detection scheme is used to obtain prior information to generate a weight map. Second, a mutually hybrid histogram analysis is performed to extract global/local features in parallel. Third, we propose a feature fusion scheme to construct the virtual combined histogram, which is achieved by adaptively fusing global/local features through the use of Gaussian mixtures according to the weight map. Finally, the virtual combined histogram is used to formulate the pixel-wise mapping function. As both global and local features are simultaneously considered, the output image has a natural and visually pleasing appearance. The experimental results demonstrated the effectiveness of the proposed method and the superiority over other seven state-of-the-art methods.
Reiterative Domain Aware Multi-target Adaptation

Multi-Target Domain Adaptation (MTDA) is a recently popular powerful setting in which a single classifier is learned for multiple unlabeled target domains. A popular MTDA approach is to sequentially adapt one target domain at a time. While only one pass is made through each target domain, the adaptation process for each target domain may consist of many iterations. Inspired by the spaced learning in neuroscience, we instead propose a reiterative approach where we make several passes/reiterations through each target domain. This leads to a better episodic learning that effectively retains features for multiple targets. The reiterative approach does not increase total number of training iterations, as we simply decrease the number of iterations per domain per reiteration. To build a multi-target classifier, it is also important to have a backbone feature extractor that generalizes well across domains. Towards this, we adopt Transformer as a feature extraction backbone. We perform extensive experiments on three popular MTDA datasets: Office-Home, Office-31, and DomainNet, a large-scale dataset. Our experiments separately show the benefits of both reiterative approach and superior Transformer-based feature extractor backbone.
Data Augmentation vs. PyraD-DCNN: A Fast, Light, and Shift Invariant FCNN for Text Recognition

Modern OCRs rely on the use of recurrent deep neural networks for their text recognition task. Despite its known capacity to achieve high accuracies, it is shown that these networks suffer from several drawbacks. Mainly, they are highly dependent on the training data-set and particularly non-resistant to shift and position variability. A data augmentation policy is proposed to remedy this problem. This policy allows generating realistic variations from the original data. A novel fast and efficient fully convolutional neural network (FCNN) with invariance properties is also proposed. Its structure is mainly based on a multi-resolution pyramid of dilated convolutions. It can be seen as an end-to-end 2D signal to sequence analyzer that does not need recurrent layers. In this work, extensive experiments have been held to study the stability of recurrent networks. It is shown that data augmentation significantly improves network stability. Experiments have also confirmed the advantage of the PyraD-DCNN based system in terms of performance but also in terms of stability and resilience to position and shift variability. A private data-set composed of more than 600k images has been used in this work.
Know Yourself and Know Others: Efficient Common Representation Learning for Few-shot Cross-modal Retrieval

Learning the common representations for various modalities of data is the key component in cross-modal retrieval. Most existing deep approaches learn multiple networks to independently project each sample into a common representation. However, each representation is only extracted from the corresponding data, which totally ignores the relationships between other data. Thus it is challenging to learn efficient common representations when lacking sufficient supervised multi-modal data for training, e.g., few-shot cross-modal retrieval. How to efficiently exploit the information contained in other examples is underexplored. In this work, we present the Self-Others Net, a few-shot cross-modal retrieval model that fully exploits information contained both in its own and other samples. First, we propose a self-network to fully exploit the correlations that lurk in the data itself. It integrates the features at different layers and extracts the multi-level information in the self-network. Second, an others-network is further proposed to model the relationships among all samples, which learns the Mahalanobis tensor and mixes the prototypes of all data to capture the non-linear dependencies for common representation learning. Extensive experiments are conducted on three benchmark datasets, which demonstrate clear improvements of the proposed method over the state-of-the-arts.
Unsupervised Domain Adaption based on metric learning for Person Re-Identification

Person re-identification(ReID) with deep convolutional neural networks(CNNs) has attracted increasing interest in computer vision due to its wide potential applications in visual surveillance and has achieved high performance in recent years using a lot of techniques to overcome the challenges such as variations in view angle, lighting, image occlusion. Another main challenge in person re-identification(ReID) is the cross domain adaptation. Due to different domains, a person re-identification model trained on one dataset with good performance often fails to achieve same or better performance on other datasets. We propose a method which is about both the source and target datasets. We fine-tune the deep CNN model on the labeled source dataset in a supervised manner by using distance metric learning and the unlabeled target dataset in an unsupervised manner simultaneously.
“Will Central Bank Digital Currencies (CBDC’s) Eliminate the Need for Cryptocurrencies?”

As of February 2021 there were 4,501 crypto coins on the market (Best 2021). In response to the explosion of blockchain development, The Bank of England are considering the introduction of a Central Bank Digital Currency (CBDC) (Bank of England 2021a). This would give the central bank the ability to build on their traditional centralised role in the economy, ensuring stability by providing certainty and liquidity in the financial system, as the UK’s monetary and fiscal policy manager. This paper is sectioned into three parts addressing the question: Will a CBDC eliminate the need for cryptocurrency? The historical and contemporary role of the Bank of England is considered, crypto coins and CBDC are assessed against their competencies, and finally a comparison between the capability of crypto coins and a CBDC, comprises of an evaluation to whether either can meet the Bank of England’s policy objectives. This paper finds that, if crypto assets become progressively governed by regulation through incorporation into existing and developing legal frameworks (Ferreira 2021; Agnikhotram and Kouroutakis 2018; Bank of England 2021a; Department of Treasury 2021), far from being exclusive assets, both a CBDC and crypto-assets present a range of potential opportunities to contribute to a host of financial instruments, that can operate in an interoperable, inclusive FinTech sector. This will result in supporting an open, competitive, and free market, ultimately benefitting end users in an increasingly globalised financial system.
PENGARUH MEDIA GAWAI TERHADAP EFEKTIVITAS BELAJAR SISWA KELAS III SD NEGERI 091416 MASA PANDEMI COVID – 19

The era of the Covid 19 pandemic has changed the teaching and learning process from the face-to-face stage at school to distance learning at home. This effort is to break the spread of covid 19. Ofcourse a means is needed to support an effective teaching and learning process, this study aims to see the influence of gawai learning media. on student learning in the Covid 19  pandemic era. The formulation of the problem in this study is to determine the effect of gawai learning media in the covid-19 pandemic era, The research was conducted on third grade students at SD Negeri 091416 Afd. I Bah Birong Ulu which will be held on April 26, 2021 until May 10, 2021. The methodology in this research is descriptive qualitative. By collecting data using observation, interviews  and documentation. From the research results, it was found that gawai learning media was very influential in the teaching and learning process in the pandemic era, and this is one of the effective media for distance learning as it is today, and the benefits that students get by using gawais a) as student learning media b). As an information center c). Adding student insight d) and tools to facilitate communication. And the obstacles faced by students a) students have to spend money to buy internet packages when studying b) students who do not have gawais c) students lack motivation when studying at home. The benefit of this research is to provide information on learning media using gawais which are very effective in the era of the Covid 19 pandemic.
Detection of the Inflammatory Bowel Diseases via Machine Learning Methods

The current standard of inflammatory bowel disease (IBD), especially, Crohn’s disease (CD), diagnosis is set through an invasive endoscopy procedure. However, serum metabolites hold potential as useful biomarkers for non-invasive diagnosis and treatment of IBDs. The goal of this research was to elucidate the biomarkers including metabolites and genes related to IBDs, to show their distinguishing and common features, and to create a machine-learning (ML) model for recognition of each disease. We explored metabolic pathways and gene–metabolite networks related to unspecified-IBD (uIBD), Crohn’s disease (CD), and ulcerative colitis (UC). P38 MAPK, ERK1/2, AMPK, and proinsulin were found to be closely related to the pathology of IBDs. The best performing ML model, trained on filtered disease-specific metabolite datasets, was able to predict metabolite class with 92.17% accuracy. Through examination of IBD-related serum, significant relationships between the inputted metabolites and certain metabolic and signaling pathways were found, which can be pinpointed and used to increase accuracy of disease diagnoses. Development of a ML model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of the IBDs. Our results elucidate a large variety of metabolites, genes, and pathways that could be used for better understanding of IBDs’ molecular mechanisms.
Case Study of University Ecosystem Development

The high educational institute’s automation systems for the student and staff management was described and compared in the article; also, was explained why HEIs are forced to develop their own systems to automate the work with the student information, to support the educational process, with the possibility to generate reports and obtain statistical information. Next, the case study of university ecosystem development was considered in the article. The flexible information system named University Student Management system was developed which allows importing data from existing software, to conveniently process data on the higher education applicant’s information changes, to control the processing of student orders in The Unified State Electronic Database on Education, to quickly receive all types of statistical reports.
Security estimates of the NTRUCipher and NTRUCipher+ encryption schemes against BKW-attack

Due to the need of creation a symmetric encryption scheme for practical usage, the security of which (similarly to asymmetric cryptosystems) is based on the difficulty of solving only one computational problem, in 2017 the NTRUCipher encryption scheme was proposed. Preliminary researches of this encryption scheme have been conducted, but the question of NTRUCipher’s security to certain specific attacks is open. This article provides estimates of the complexity of chosen-plaintext attack on the NTRUCipher encryption scheme and even on its natural improvement NTRUCipher+. The given analytical estimates allow to compare the security of these encryption schemes and to make a conclusion about inexpediency of usage the NTRUCipher+ encryption scheme for its increase.
EGIM: Evolution Graph Based Interest Modeling for Click-Through Rate Prediction

It is essential for the click-through rate prediction to learn informative representations. Some studies exploit user behavior data to learn user interest representation. These models usually integrate user historical data into the represented embedding without considering user relevance. Such an approach easily leads to suboptimal representations since it fails to capture the high-order collaboration signal. In this paper, we propose the Evolution Graph-based Interest Modeling (EGIM) to transform user behavior data into a dynamic structure. the user-item interaction is presented as an evolution graph. Graph Convolutional Network is applied as the interest extractor and the Long Short-Term Memory is adopted to learn the evolution of user interest. In the end, the relevance of the user interest sequence with the target item is introduced by the attention-based interest-aggregation layer. Extensive experimental results on three real-world datasets demonstrate that EGIM has significant improvements in terms of Recall@20 and NDCG@20 over several state-of-the-art models.
Unified quality assessment of natural and screen content images via adaptive weighting on double scales

In multimedia communication systems, digital images may contain various visual contents, among which the natural scene images (NSIs) and screen content images (SCIs) are two important and common types. The existing full-reference image quality assessment (IQA) metrics are designed for only one type of images, but cannot precisely perceive the visual quality of another type. It is still unclear what the different characteristics are between NSIs and SCIs resulting in this failure. Inspired by some psychological studies, we figure out that it is due to the different structural scale levels between NSIs and SCIs. Given this observation, this paper introduces the gradient degradation of Gaussians (GDoG) to analyze the images’ structural scale level, proposes a fast unified IQA index for both NSIs and SCIs by incorporating an adaptive weighting strategy on double scales. Experimental results conducted on several databases verify the effectiveness and efficiency of the proposed unified IQA index for both types of images, also demonstrate that the adaptive weighting strategy based on GDoG can improve the existing models for cross-content-type images.
Multi-Objective Recommendations and Promotions at TOTAL

In this paper, we revisit the semantics of recommendations and promotional offers using multi-objective optimization principles. We investigate two formulations of product recommendation that go beyond traditional settings by optimizing simultaneously two conflicting objectives: Budget-Reco optimizes two customer-centric goals, namely utility and budget, and Business-Reco optimizes utility, a customer-centric goal, and profit margin, a business-oriented goal. To capture those objectives, we formulate knapsack problems and propose adaptations of exact and approximate algorithms. We also propose Group-Promo, the problem of generating product promotions that we model as a group discovery problem with multiple objectives and develop a Pareto-based solution. Our experiments on our TOTAL datasets demonstrate the importance of multi-objective optimization in the retail context, as well as the usefulness of our solutions when compared to their exact baselines. The results are valuable to TOTAL's marketing department that has been improving hand-crafted strategies by launching several promotional campaigns using our algorithms.
A Lower Bound for Regret in Logistic Regression

We study logistic regression with binary features in which the number (or degree) of occurring features determines the label probability. This model fits one of social networks, where the number of friends determines the likelihood of outcomes instead of the identity of the friends, or more generally, a graph model, where the degree of a node can determine its structure. It includes the case in which weights can be viewed as i.i.d. (e.g., in Bayesian modeling). For such a model, we introduce the maximal minimax regret that we analyze using a unique combination of analytic combinatorics and information theory. More importantly, the resulting regret is a general lower bound for the pointwise regret of a general logistic regression over all algorithms (learning distributions). We show that the introduced worst case (maximum over feature sequences) maximal minimax regret grows asymptotically as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(d/2)\log(T/d)+(d/2)\log(\pi/2)+O(d/\sqrt{T})$</tex> for dimensionality <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d=o(\sqrt{T})$</tex> , which is a lower bound for a regret of a general logistic regression. We extend our results to loss functions other than logistic loss and non-binary labels.
Pedagogy of Happiness: A Russian View

For thousands of years the topic of happiness has attracted attention from the representatives of various sciences. However, until now there has been no unity in understanding the essence, sources, and components of happiness. Quite often, the interpretation of the phenomenon of happiness is limited to the analysis of the works of philosophers of antiquity, the European Middle Ages, modern and recent history, and many researchers are in a kind of Eurocentric captivity. Consciously or accidentally, the works of Russian thinkers are often not considered. This hinders the creation of a holistic and more objective picture of such an important aspect of every person’s life. In order to overcome this Eurocentric bias, this study suggests tracing back the main stages of the development of the idea of happiness in Russia from the era of Peter the Great to the present. For this purpose, the authors used the methods of theoretical, comparative, and retrospective analysis. The authors believe that currently the pedagogy of happiness is being actively formed. To educate happy people, teachers should master the art of the pedagogy of happiness.
Text-Enhanced Attribute-Based Attention for Generalized Zero-Shot Fine-Grained Image Classification

We address the generalized zero-shot fine-grained image classification problem, in which classes are visually similar and training images for some classes are not available. We leverage auxiliary information in the form of textual descriptions to facilitate the task. Specifically, we propose a text-enhanced attribute-based attention mechanism to compute features from the most relevant image regions guided from the most relevant attributes. Experiments on two popular datasets of CUB and AWA2 show the effectiveness of the proposed method.