Reviewerly/exHarmony huggingface dataset:
Qualitative Evaluation of Face Embeddings Extracted From well-known Face Recognition Models

This paper demonstrates a qualitative evaluation/comparison of face embeddings extracted from deep learning models, such as VGG-Face, Dlib, and OpenFace, on a face discrimination task. While conducting experiments, each of linear SVM (support vector machine) classifier, Euclidean distance, and Cosine distance algorithms was utilized to compare/analyze face vectors (embeddings) extracted from those 3 deep learning models. This resulted in 9 overall combinations of face recognition techniques to be compared. To implement a fair comparison of these 9 combinations, first, training and test datasets were gathered; these datasets were made up of complete frontal and well-cropped (NxN sized) face images of 33 persons (mostly Asian), with at least 10 different face images for each person. Then, face images in the training dataset were introduced into deep learning models to extract face vectors from them. Next, these face vectors were stored in a local directory as a reference database (to be used with Euclidean and Cosine distance methods) and were used to train SVM classifier. Subsequently, these face vectors were utilized to classify (recognize) face images (vectors) from the test dataset. As experiments proved, the best face recognition technique amongst 9 combinations was Dlib based face recognition model (with SVM classifier combined) as it showed the highest rate to distinguish people from each other.Although, this research work does not bring novelty to the domain, it took an effort to evaluate/compare well-known deep face models performances on Asian faces (mostly) and choose the best one to utilize as a basis for door access control application.
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Morph targets for 3D facial animation with webcam using Facemesh, Jeeliz-transfer APIs, and Three.js

This paper presents a face control system for 3D avatar with webcam using the Facemesh API for face tracking and Jeeliz-transfer API for eye tracking. 3D avatar face animation is developed as a responsive web application. It starts with face detection and tracking through the webcam. Face coordinate data is normalized to a vertical face view where the distance between the eyes and the level of the eyes are the same for each video frame. Then new face coordinates are calculated in both 2D and 3D to study the change of specific coordinates such as mouth shape and face shape. In addition, the coordinates of face structures in the video platform have also been added to the 3D model platform. Specific coordinates are studied to analyze distance changes to be applied in 3D avatar manipulation. The 3D models are designed and created in multiple blend-shapes or basic character facial features. However, due to the limitations of web browser-based 3D morphing, which Three.js is used for morph target displays, it allows only eight combination shapes to be displayed at the same time. Therefore, the required blending geometry must be pre-assembled. Blend-shape factors are based on an analysis of the coordinates of each moving face in order to eliminate the limitations. Our 3D facial animations with a webcam generate results of high quality, real-time and online simulation. Therefore, our work is a fundamental technology that can be applied to animate other 3D characters in blend-shape format.
A Novel Algorithm for Mining Minimal Generators of Closed Frequent Significance Itemsets

In data mining, association rule mining is one of the most important and well-studied techniques. Especially, the technique mine exact association rules and non-redundant. Some authors have proposed exact association rules mining from the minimal generators of closed frequent itemsets on transaction databases, where items have no weight (equal weight, as equal to 1). However, in real world applications, usually, each item has a different weight (the importance/significance of each item). In this paper, we propose an efficient algorithm called NOV-mGCFSI for mining minimal generators of closed frequent significance itemsets based on approach DOES NOT satisfy the downward closure property (a great challenge). The experimental results show that the proposed algorithms perform faster than other existing algorithms on both real-life datasets of UCI and synthetic datasets generated by IBM Almaden.
A Streamlined News Recommender System Using Variable Markov Model

Providing news to users in a news recommender system is a balancing act between delivering news that is recent and news that is relevant to their interests. Ideally, users would like to receive a stream of recommended similar articles that interest them, and in the meantime control their traversal through different topics of news articles in a stream-wise fashion. These recommended topics should not only match their previous interests, but also expose them to different views and opinions. A Variable Markov Model (VMM) built on trends in recently published news articles, is proposed as a solution to categorically cater news to all users with minimal overhead and maintenance. Our experiment has shown that, this single model provided to all users, though not built based on users' personal interests, is applicable as a basis to achieve catered and novel news recommendation experiences to individual users.
Leveraging Approximate Constraints for Localized Data Error Detection

Error detection is key for data quality management. AI techniques can leverage user domain knowledge to identifying sets of erroneous records that conflict with domain knowledge. To represent a wide range of user domain knowledge, several recent papers have developed and utilized soft approximate constraints (ACs) that a data relation is expected to satisfy only to a certain degree, rather than completely. We introduce error localization, a new AI-based technique for enhancing error detection with ACs.
Deep MCEM for Weakly-Supervised Learning to Jointly Segment and Recognize Objects Using Very Few Expert Segmentations

Typical methods for semantic image segmentation rely on large training sets comprising pixel-level segmentations and pixel-level classifications. In medical applications, a large number of training images with per-pixel segmentations are difficult to obtain. In addition, many applications involve images or image tiles containing a single object/region of interest, where the image/tile-level information about object/region class is readily available. We propose a novel deep-neural-network (DNN) framework for joint segmentation and recognition of objects relying on weakly-supervised learning from training sets having very few expert segmentations, but with object-class labels available for all images/tiles. For weakly-supervised learning, we propose a variational-learning framework relying on Monte Carlo expectation maximization (MCEM), inferring a posterior distribution on the missing segmentations. We design an effective Metropolis-Hastings posterior sampler coupled with sample reparametrizations to enable end-to-end learning. Our DNN first produces probabilistic segmentations of objects, and then their probabilistic classifications. Results on two publicly available real-world datasets show the benefits of our strategies of (i) joint object segmentation and recognition as well as (ii) weakly-supervised MCEM-based learning.
Predicting the Cycle Time at a Production Line Through the Development of the 3-3-1 Multilayer Perceptron Artificial Neural Networks with Formulated Momentum Rate

The uncertainty of cycle time due to manpower performance, material availability and machine constraint could affect the efficiency of completion time. Hence, the cycle time of a specific task must coordinate efficiently to ensure the smoothness of production operation. Thus, predicting cycle time is an essential issue in production operation and is deemed crucial to be foreseen. From previous studies, various techniques have been utilised to predict cycle time. It is found several works show that the smallest measurement error has been achieved through their proposed Artificial Neural Networks (ANN) models compared to the other predictive techniques. In this regard, the objective of this research is to develop an ANN model to predict the cycle time of a product based on several factors. A feed-forward multilayer perceptron (MLP) network was established and subsequently trained by the developed Backpropagation (BP) learning algorithm to predict cycle time. As a result, the predicted cycle time of the new audio products is 5 s based on the collected data at a selected case company in manufacturing audio speaker products. Consequently, the ANN model could assist production planner in predicting cycle time from historical data for producing new audio speaker products.
Research on Evaluation of Tower Vibration State Based on SCADA

Abstract Tower is very critical to the safe operation of wind turbines. In this paper, SCADA data is used to evaluate the vibration state of the tower. A tower vibration correlation analysis method based on denoising autoencoder (DAE) is proposed, which evaluates the impact of state parameters on tower vibration based on the reconstruction residual. The tower vibration is predicted based on the Long Short-Term Memory (LSTM) network, and then the tower vibration state is evaluated based on the Wasserstein distance. The actual SCADA data is used to verify the proposed method. The results show that the method accurately predicts the tower vibration trend and quantitatively evaluates the tower vibration state.
SneezeLove: Embodying Cultural Superstitions in Connected Devices

Devices for remote communication and sending digital gestures have been widely explored in the field of ubiquitous computing. With the lack of social contact during the COVID-19 pandemic and renewed interest in these devices in mind, we suggest the affordances of a culturally-informed design approach. We present SneezeLove, a device that uses sneezes to communicate that someone is missing you. Users tickle the device’s nose to send an electromechanical ‘sneeze’ to another device. The concept plays on a superstition in Chinese culture that attributes an additional layer of meaning to sneezes, and we found similar beliefs in other cultures that signify that someone out there is thinking of you: hiccups in India, “burning” ears in Ireland. The device, and its design approach, suggests how low-resolution messages can be enlivened and made more personally meaningful by tapping into expressive and specific cultural superstitions.
Assessment of the communicative competence in a productive collective activity within digital transformation

In the context of digital transformation, it is necessary to educate a person who will be able to work productively in an uncertain situation with the information deficit possessing soft skills and appropriate hard skills, without which it is impossible to proceed to the educational community with a high level of spiritual, legal and professional culture. The communicative competence is of great importance. The objectives of this research were to study components of communicative competence and diagnostic approach as well as to suggest the way of competence assessment/Search for the mechanisms to manage communicative competence of students is an important task in the development of higher education. The solution to this problem will increase the efficiency of training of future specialists in the period of active development of new communication technologies and create the conditions for successful professional and personal fulfillment. The research presented in the paper is based on the hypothesis that the formation of communicative competence of university students is determined by the interconnectedness of competencies which make up the structure of communicative competence through the sequencing of the stages of development of communicative competence and determination of the algorithm of formation of communicative competence in higher education
Design of RSM scheme against DPA suitable for LBlock algorithm

A RSM (rotating S-box masking) scheme suitable for the LBlock algorithm to improve the vulnerability of the algorithm before power attacks in this paper. The scheme takes advantage of the characteristics of the LBlock algorithm itself, inserts the mask when the initial intermediate value is calculated, reduces the connection between the intermediate value and the operation, and ensures that both the nonlinear operation and the linear operation are protected by the mask. It is proved that the proposed scheme can resist first-order DPA (differential power analysis) through security experiments.
UNIAXIAL COMPRESSIVE TEST FOR COMPRESSIVE DUCTILITY OF PRECAST CONCRETE PILE

In order to develop precast concrete pile which has compressive ductility, static uniaxial compressive test was conducted using precast concrete pile specimens. Some of the specimens are precast concrete solid pile with unbonded steel pipe. In the result, precast concrete solid pile with unbonded steel pipe had ductile behavior, ultimate strain of specimens was more than 4.0 %. Furthermore, compressive capacities of the specimens are evaluated using existing formulae. In the result, the existing formulae underestimated the compressive capacities of test results.
You Draft We Complete: A BERT-Based Article Generation by Keyword Supplement

In this paper, we investigate a text generation scenario that we are given a sequence of keywords and our goal is to automatically turn the sequence into a semantic coherent passage. Moving toward the goal, we propose a scheme called round-robin generation that produces semantic coherent results by overcoming the repeating word problem (commonly observed in story generation scenarios) through global attention and multi-tasking training. Experimental evaluation demonstrates that our scheme effectively mitigates the word repeating problems. We also show an improvement of BLEU 4 from 17.8 to 19.1 compared with a strong baseline on the benchmarking datasets.
The Impact of Remittance in Economic Growth: Empirical Evidence from the Western Balkan Countries

The objective of this paper is to study the impact of remittances upon the origin country of emigrants. To achieve our purpose, we have performed a quantitative analysis by using the ordinary least squares method (OLS), i.e., the Pooled Regression. The empirical research focused on the influence of remittances on the economic activity in WB countries, with emphasis on Kosovo. Our results are in accordance with the recent studies in the field and conclude that the effect of remittances on economic growth is statistically significant. We found that, for each 1% increase in remittance of Western Balkan countries, the GDP will increase on average by 0.12%, holding the other variables constant. The productive use of remittances has a direct impact on economic activity in the country of origin.
FORMATION OF SOCIAL EDUCATOR’S DIGITAL COMPETENCE AS A WAY TO PROFESSIONAL GROWTH

In the article, the impact of digitalization of education on the transformation of the professional competence of a social educators is analyzed; the need of the formation of digital competence of social educators in the context of the widespread implementation of information and communication technologies in the educational process is substantiated; the state of using educational Internet resources in the professional activities of social educators is analyzed; the importance of improving the level of digital competence as a component of the professional competence of social educators is substantiated; the use of digital tools as a practical toolkit for social educators is proposed; a brief overview of digital tools that will be useful in the work of a social educator is provided; a training course "Using Internet resources in the work of a social educator" developed by authors is presented; the course consists of four modules in accordance with the main directions of the social educator's activities, in particular, diagnostic, developmental, educational and preventive work; the course has a practical focus, for the implementation of practical tasks, adapted to the professional activity of a social educator, instructional and methodological materials are developed that make it possible to conduct the course both offline and online; the course is developed on the principles of accessibility, comprehensibility, professional orientation and motivation; to implement the developed course, it is recommended to use the Google Classroom platform, which makes it possible to combine traditional and innovative forms and methods of teaching, which is especially important in the context of the introduction of distance and blended learning; the results of approbation of the developed course, which was conducted for social educators of the Nadvirnyansky district of Ivano-Frankivsk region are presented; taking into account the results of approbation, the conclusion is given about the advisability of using the course for the professional growth of social educators, including for the formation of the readiness of a social educator for the implementation of digital tools into professional activities.
A mechanism for developing the professional potential of the professor-teaching composition in the higher school

Abstract This article discusses the relationship of ICT with the need for lifelong learning and the development of the professional potential of the faculty of the Higher School. The goal of the paper is to present a mechanism for developing the professional potential of the faculty of the Higher School.
Work-in-Progress-VR-Enabled Pedagogy in a First-Year Seminar

Immersive and spatial media are increasingly prevalent in education. However, the pedagogies associated with using these technologies in post-secondary general education contexts, their effectiveness in supporting student learning, and the role of student perception of immersive and spatial media are not well-described in the literature. This work-in-progress paper presents a descriptive case study of an undergraduate general education course, with a special focus on the pedagogical integration of immersive and spatial media tools, student learning trajectories, and perception of immersive and spatial media.
Design Family Health Management System Based on Ethereum Blockchain Interaction With MyEtherWallet Using Solidity

A family health management system deals with a safe and secure way of managing family member health details such as illness diagnosis, treatment, medical prescriptions, medical reports, and life insurance policies. All health records are encrypted and stored in chronological order utilizing blockchain technology so that authenticity, integrity, security, and privacy of the records are safeguarded. All stages of medical treatment are documented and stored for ease of future reference by the family. This becomes very handy if a person changes doctors and/or relocates to some other place/country for varied reasons. All the necessary health reports are shared with the life insurance company so that insurance claims become hassle free, if applicable. Medicine details are cross-verified with the manufactures to avoid fake drugs. This chapter explores the compatibility of using ethereum with the interactive MyEtherWallet to implement the proposed model.
Boosting the Transferability of Adversarial Examples with Translation Transformation

Abstract Although the adversarial examples have achieved an incredible white-box attack rate, they tend to show poor transferability in black-box attacks. Date augmentation is considered to be an effective means of enhancing the adversarial examples transferability. To this end, based on translation transformation we propose a new method to generate more mobile adversarial examples to attack the advanced defense model. By optimizing the original image, the input diversity is improved and the transferability of the adversarial examples generated by further training is enhanced. This method can also be combined with the attack method based on gradient, and the new method can make the attack success rate higher. Experiments on ImageNet data sets show that the proposed method is superior to gradient based methods such as MI-FGSM in black box attack, while maintaining a high success rate of white box attack. We hope that our proposed method of attack can serve as a benchmark for assessing the robustness of networks to opponents and the effectiveness of the different methods of defence.
Spotting celebrities among peers in a TV show: how to exploit web querying for weakly supervised visual diarization

In this paper, we propose a novel solution for popularity recognition. This methodology consists of categorizing and exploiting web image resources of people in terms of relevant identities. To demonstrate its usefulness, we also study the effects of incorporating this procedure into a visual diarization system.In our setting, training data is obtained by querying Google Images about the known identities of the participants in a TV show (we only know who participates but not when). As Google queries may return imprecise results, images retrieved for each query (or identity) are processed to distinguish true from false positives, keeping the former while filtering out the latter.Next, facial clustering is performed to drive a filtering process discarding noisy samples (false positives) from returned images (i.e. only images linked to the principal cluster are adopted as training data).For popularity recognition, Random Forest (RF) and support vector machine (SVM) classifiers have been tested. Three different types of features have been proposed to build the models upon: image related features, query related features and clustering related features.Feature ranking and feature selection techniques applied show that clustering related features are the most important for popularity recognition. In fact, the RF model based on the 3 top ranked features achieves an accuracy close to 100% on test set. Results also demonstrate that integrating the popularity solution into our visual diarization pipeline helps to reduce the Diarization Error Rate (DER) in a 2%, removing around a 15% of noisy identities, which confirms the quality of this procedure and its high performance in other scenarios.
Extraction of Elements of Protest Based on BERT Model and TextTeaser improved algorithm

Abstract A protest is a document that the procuratorial organ exercises the power of legal supervision and submits it to the judicial organ. Element extraction is the basis for tasks such as case card filling, document correction, cause analysis, and case recommendation. At present, there are no effective tools and methods for extracting the elements of criminal protests. Most of them are manually marked, with low accuracy and low efficiency. In response to this problem, this paper is to propose a method to extract the elements of the criminal protest by sub-category, based on Bert model, using natural language processing technology. The experiments are demonstrated to illustrate the effectiveness of the proposed method.