Reviewerly/exHarmony huggingface dataset:
Quantification of the Impact of Popularity Bias in Multi-stakeholder and Time-Aware Environments

Popularity bias is one of the main biases present in recommendation algorithms, which means most popular items are over-recommended by the algorithms, while items with less interactions are invisible. In this paper we analyze the impact of the popularity bias over time for different stakeholders considering different recommendation algorithms. The datasets used were Last.FM-1B and KASANDR, while the algorithms compared were ALS and BPR, with Most Popular and random recommendations used as a baseline. The main contribution of this paper provides a new way of measuring popularity in a variable way over time and analyzes which algorithms maintain a popularity bias over time that negatively impacts stakeholders. The analysis of the unfairness metrics shows that the popularity bias is not static over time and that the ALS algorithm obtains more stable results with less unfairness than BPR among different groups of stakeholders.
New Distribution Strategies of Korean SMEs in Post COVID-19 Pandemic Era: Focusing on the Innovation of Official Distribution Channels

Purpose - In this study, we aim to explore new distribution strategies for sustainable growth in the era of the 4th industrial revolution, focusing on SMEs (small and medium-sized enterprises) in Korea, and suggest ways to upgrade the government s official distribution channel to the next level. Design/methodology - First of all, this paper explored the prior research, the current status of sales support for SMEs, and the changes in the distribution industry due to COVID-19 pandemic. Based on Moon (2016)’s ABCD strategic model - Agility, Benchmarking, Convergence, and Dedication, the study then derived directions in which official distribution channels should move and the new distribution strategy for Korean SMEs to secure competitive advantage. Findings - First, in terms of ‘Agility’, in order to upgrade official distribution channels, which are currently at some competitive disadvantages compared to private distribution companies, we must quickly introduce technologies for the 4th industrial revolution, such as AI, Big Data, etc., and establish precise strategies to strengthen the capabilities of SMEs. Second, in terms of ‘Benchmarking’, the use of “Chamelezones” has been increasing to enhance the competitiveness of offline stores in line with recent ontact trends. Therefore, official distribution channels should also benchmark such cases, strengthening their competitiveness by utilizing offline spaces more efficiently and effectively. Third, in terms of ‘Convergence’, in line with the rapidly changing trend of the times, official distribution channels should also promote active partnerships with media commerce, e-commerce and ICT platforms, as well as cooperation with private retailers, and focus on creating synergy effects through them. Finally, from the perspective of ‘Dedication’, digitalization should be promoted step by step, finding the sector that can accelerate digital among the value chains of official distribution channels, and continuing to discuss how to digitize it realistically. Originality/value - Based on this analysis, we have presented strategies and implications for innovating official distribution channels for SMEs, which will contribute to enhancing the competitive advantage of official distribution channels in the post COVID-19 pandemic era.
Attention, Emotion and Attendance Tracker with Question Generation System Using Deep Learning

Learning requires attention, and attention trackers have the potential to be very useful tools for educators. The attention of students can be tracked through various techniques. The promising technologies and related studies on attention, attendance, emotion, and question generation are discussed in this paper. The proposed system tackles the predicament of obtaining the attention of students during online lectures. This system incorporates a camera to capture input image and an algorithm which detects facial features from the image. The expected output is an attention score, emotion report, attendance marking for students using face recognition, and another model for attention evaluation by conducting a test where questions will be generated from the text provided by the teacher. The accuracy attained for the question generator model is 76%. Emotions are determined by the model at 68% precision.
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors

With the increasing interest in low-resource languages, unsupervised morphological segmentation has become an active area of research, where approaches based on Adaptor Grammars achieve state-of-the-art results.We demonstrate the power of harnessing linguistic knowledge as priors within Adaptor Grammars in a minimally-supervised learning fashion.We introduce two types of priors: 1) grammar definition, where we design language-specific grammars; and 2) linguistprovided affixes, collected by an expert in the language and seeded into the grammars.We use Japanese and Georgian as respective case studies for the two types of priors and introduce new datasets for these languages, with gold morphological segmentation for evaluation.We show that the use of priors results in error reductions of 8.9 % and 34.2 %, respectively, over the equivalent state-of-the-art unsupervised system.
Effective Slot Filling via Weakly-Supervised Dual-Model Learning

Slot filling is a challenging task in Spoken Language Understanding (SLU). Supervised methods usually require large amounts of annotation to maintain desirable performance. A solution to relieve the heavy dependency on labeled data is to employ bootstrapping, which leverages unlabeled data. However, bootstrapping is known to suffer from semantic drift. We argue that semantic drift can be tackled by exploiting the correlation between slot values (phrases) and their respective types. By using some particular weakly labeled data, namely the plain phrases included in sentences, we propose a weakly-supervised slot filling approach. Our approach trains two models, namely a classifier and a tagger, which can effectively learn from each other on the weakly labeled data. The experimental results demonstrate that our approach achieves better results than standard baselines on multiple datasets, especially in the low-resource setting.
Genetic Algorithm Based Global and Local Feature Selection Approach for Handwritten Numeral Recognition

Although various methods have been proposed by the researchers over the years to carry out isolated handwritten numeral recognition, this is still considered as a challenging research problem. The primary challenge occurs because of sizable differences in writing styles of the digit patterns. Literature reveals that several feature extraction and classification methods have been researched upon to optimize the said recognition system but there is still room for improvement. Here, we introduce a two-stage handwritten numeral recognition system for three most popular scripts used in Indian subcontinent—Devanagari, Bangla and Roman. Initially, the global features that are estimated based on Histogram of Oriented Gradients (HOG) feature descriptor help in the formation of inter-numeral groups having nearly similar structure. Then, the optimal subset of features is selected using Genetic Algorithm (GA) on the combination of HOG and local distance features. These optimal features, thus produced, are employed for the classification of handwritten digits within the intra-numeral groups. Finally, the Multi-layer Perceptron (MLP) classifier is used to recognize the numerals. The strength of the present approach is efficient feature selection and the comprehensive classification scheme due to which notable recognition accuracies have been attained which are found to be better than some previous approaches.
Analysis of Soft Skills Implementation in the System of Foreign Language Training in Higher School

The article substantiates the necessity to promote the process of professional self-determination of migrant-students. The analysis of theoretical research on the problem reveals the reasons that impedes this process among students in general (insufficient school work aiming at organizing career guidance, the change of paradigms from “one job for life” to the concept of “diversity”), and migrant-students in particular (the influence of external factors, “administrative nationalism”). Some real successful practices during admission campaigns, and the possibilities of using the experience of foreign universities (applicant’s portfolio, structured interview), described in the article, can significantly contribute to the efficiency of the process of professional self-determination of migrant-students. The purpose of this article is to identify the possibilities of the admission campaign to promote professional self-determination of applicants with migrant background. Methodology. The study was carried out on the basis of a set of complementary methods: analysis of pedagogical literature; generalization of scientific information obtained in the course of the study; diagnostic methods. Conclusion. The organization of an admission campaign, aiming at overcoming the problems considering the possibilities of using foreign experience, has a significant potential for providing support for the professional self-determination of a migrant student.
Early Detection of Failed Bank Through Analysis of Financial Ratios and Bank Shareholders Ratios Using Data Mining For Rural Banks

This study aims to determine the effect of 8 bank financial ratios such as BOPO (operational efficiency ratio), CAR (Capital Adequacy Ratio), NPL (Non Performing Loan), ROA (Return On Assets), CR (Cash Ratio), KAP (quality of productive assets), PPAP (provision for loan losses) and LDR (Loan Deposit Ratio) and another ratio, namely Bank’s Shareholder ratio towards bank predictions whether a rural bank will be declared as failed bank or not. Eight financial ratios and another ratio that comparing BOD and BOC to Bank's Shareholders can be obtained from quarterly rural bank’s financial reports that have been published on the IFSA website from 2014 until 2018. The data in this research is approximately 1000 rural banks for training dataset. The method to predict rural bank become failed bank is data mining. The training dataset used is an imbalanced dataset. In order to be balanced, the SMOTE method is used. The balance dataset was then analyzed with the data mining process. The data mining methods used are KNN and Naïve Bayes, both are classification method.
Identification of a Person From Live Video Streaming Using Machine Learning in the Internet of Things (IoT)

In this paper, the authors develop an efficient face recognition algorithm from images or live video streaming for IoT systems based on K-nearest neighbor and support vector machine learning to recognize the person from the local database and extract the features of the face. Because of the complexity of the conditions, there might be some factors of facing errors like the size; the angle; the distance from the ear, nose, and eyes; etc. This sustainable machine learning-based IoT system is designed for sovereign face recognition with features extraction with improved accuracy near about 96%. The experimental study is done to test the performance of the face recognition in the changes of number of persons in video or images. Finally, this manuscript recognized persons from live video or images with accuracy approximately 96% by using the SVM and KNN classifiers and discussed with the block diagram and proposed algorithm.
PRNU Estimation from Encoded Videos Using Block-Based Weighting

Estimating the photo-response non-uniformity (PRNU) of an imaging sensor from videos is a challenging task due to complications created by several processing steps in the camera imaging pipeline. Among these steps, video coding is one of the most disruptive to PRNU estimation because of its lossy nature. Since videos are always stored in a compressed format, the ability to cope with the disruptive effects of encoding is central to reliable attribution. In this work, by focusing on the block-based operation of widely used video coding standards, we present an improved approach to PRNU estimation that exploits this behavior. To this purpose, several PRNU weighting schemes that utilize block-level parameters, such as encoding block type, quantization strength, and rate-distortion value, are proposed and compared. Our results show that the use of the coding rate of a block serves as a better estimator for the strength of PRNU with almost three times improvement in the matching statistic at low to medium coding bitrates as compared to the basic estimation method developed for photos.
Remote sensing target detection in a harbor area based on an arbitrary-oriented convolutional neural network

With the rapid development of optical remote sensing, it is urgent to find a reliable target detection method. Compared with traditional detection algorithms, a convolutional neural network has attracted considerable attention owing to its efficiency and high transitivity. However, different from general images, remote sensing images contain complex background information and dense small targets with changeable directions that make detection very challenging. To solve these problems and provide a stable and high-performance detection method, a rotated saliency fusion object detection (RSD) model based on “you only look once” (YOLO)v4 is established. First, salient image fusion technology is used to magnify target information. Second, the angle variable and rotated non-maximal suppression is introduced to improve the accuracy of rotated object detection by including the detection of dense objects. Third, the network structure is enhanced to improve the performance of small-target detection. Finally, the k-means algorithm and data enhancement are introduced to increase the robustness of the model. Extensive experiments demonstrate the superiority of the proposed model in detection speed and accuracy. The mean average precision of the proposed RSD model reaches 97.32% for the remote sensing images in a harbor area with an average detection speed of 13.41 s − 1.
A Piezoelectric Artificial Throat and Computer Vision Based on Smart Assistive System

Amyotrophic lateral sclerosis (ALS) patients will gradually lost their motion abilities and have huge inconvenience in their daily life. In this article, an artificial throat and eye tracking based smart assistive system is presented. Through detecting eye motions and throat vibrations, the established neural network can interpret the patient's intention, and perform simple operations by a robotic arm (e.g. grasp an object). The experimental results demonstrate that the developed system can help patients obtain basic self-care ability.
Meta-learning for symbolic hyperparameter defaults

Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem. In this work, we propose a zero-shot method to meta-learn symbolic default hyperparameter configurations that are expressed in terms of the properties of the dataset. This enables a much faster, but still data-dependent, configuration of the ML algorithm, compared to standard hyperparameter optimization approaches. In the past, symbolic and static default values have usually been obtained as hand-crafted heuristics. We propose an approach of learning such symbolic configurations as formulas of dataset properties from a large set of prior evaluations on multiple datasets by optimizing over a grammar of expressions using an evolutionary algorithm. We evaluate our method on surrogate empirical performance models as well as on real data across 6 ML algorithms on more than 100 datasets and demonstrate that our method indeed finds viable symbolic defaults.
A Reputation-Based Approach Using Consortium Blockchain for Cyber Threat Intelligence Sharing

The CTI (Cyber Threat Intelligence) sharing and exchange is an effective method to improve the responsiveness of the protection party. Blockchain technology enables sharing collaboration consortium to conduct a trusted CTI sharing and exchange without a trusted centralized institution. However, the distributed connectivity of the blockchain-based CTI sharing model proposed before exposes the systems into byzantine attacks, the compromised members of partner organizations will further decrease the accuracy and trust level of CTI by generating false reporting. To address the unbalance issues of performance in speed, scalability and security, this paper proposes a new blockchain-based CTI model, which combines consortium blockchain and distributed reputation management systems to achieve automated analysis and response of tactical threat intelligence. In addition, the novel consensus algorithm of consortium blockchain that is fit for CTI sharing and exchange introduced in this paper. The new consensus algorithm is called 'Proof-of Reputation' (PoR) consensus, which meets the requirements of transaction rate and makes the consensus in a creditable network environment through constructing a reputation model. Finally, the effectiveness and security performance of the proposed model and consensus algorithm is verified by experiments.
Tuning and amplifying the interactions in superconducting quantum circuits with subradiant qubits

We propose a tunable coupler consisting of $N$ fixed-frequency qubits, which can tune and even amplify the effective interaction between two superconducting quantum circuits. The tuning range of the interaction is proportional to $N$, with a minimum value of zero and a maximum that can exceed the physical coupling rates between the coupler and the circuits. The effective coupling rate is determined by the collective magnetic quantum number of the qubit ensemble, which takes only discrete values and is free from collective decay and decoherence. Using single-photon $\ensuremath{\pi}$-pulses, the coupling rate can be switched between arbitrary choices of the initial and final values within the dynamic range in a single step without going through intermediate values. A cascade of the couplers for amplifying small interactions or weak signals is also discussed. These results should not only stimulate interest in exploring the collective effects in quantum information processing, but also enable development of applications in tuning and amplifying the interactions in a general cavity-QED system.
Quantum and Randomised Algorithms for Non-linearity Estimation

Non-linearity of a Boolean function indicates how far it is from any linear function. Despite there being several strong results about identifying a linear function and distinguishing one from a sufficiently non-linear function, we found a surprising lack of work on computing the non-linearity of a function. The non-linearity is related to the Walsh coefficient with the largest absolute value; however, the naive attempt of picking the maximum after constructing a Walsh spectrum requires Θ (2 n ) queries to an n -bit function. We improve the scenario by designing highly efficient quantum and randomised algorithms to approximate the non-linearity allowing additive error, denoted λ, with query complexities that depend polynomially on λ. We prove lower bounds to show that these are not very far from the optimal ones. The number of queries made by our randomised algorithm is linear in n , already an exponential improvement, and the number of queries made by our quantum algorithm is surprisingly independent of n . Our randomised algorithm uses a Goldreich-Levin style of navigating all Walsh coefficients and our quantum algorithm uses a clever combination of Deutsch-Jozsa, amplitude amplification and amplitude estimation to improve upon the existing quantum versions of the Goldreich-Levin technique.
South Korea

South Korea took a ‘no shutdown, no lockdown’ approach to ensure the least disruption to the lives of its public. Notably, not only did the country take measures to combat COVID-19 by implementing drive-through testing, but also public put efforts to voluntarily wear masks. From a communications perspective, the government immediately responded by quickly forming the Central Disaster and Safety Countermeasures Headquarters (CDSCHQ) to carry out consistent and transparent communications twice a day. However, the message from the government was not always consistent such as in the example of the type of mask that should be worn. Nevertheless, the government’s real-time notification on the status of confirmed cases and tracking the movement of those people through smartphones, by utilising apps that were developed in private and public sectors, played a major role in preventing a more dramatic spread of COVID-19. Despite that it may be too soon to pass judgement on the success of South Korea’s early stage response to COVID-19, South Korea paved the way in combatting COVID-19 through novel test procedures, the utilisation of technology to monitor the spread of the contagion within the country and the public’s drive to fight as one.
Learning Multimodal Affinities for Textual Editing in Images

Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent. Unlike natural images that capture physical objects, document-images contain a significant amount of text with critical semantics and complicated layouts. In this work, we devise a generic unsupervised technique to learn multimodal affinities between textual entities in a document-image, considering their visual style, the content of their underlying text, and their geometric context within the image. We then use these learned affinities to automatically cluster the textual entities in the image into different semantic groups. The core of our approach is a deep optimization scheme dedicated for an image provided by the user that detects and leverages reliable pairwise connections in the multimodal representation of the textual elements to properly learn the affinities. We show that our technique can operate on highly varying images spanning a wide range of documents and demonstrate its applicability for various editing operations manipulating the content, appearance, and geometry of the image.
Impact of Automotive System Safety Design on Machine Learning Based Perception Systems

Perception in autonomous driving is a prominent safety-critical application of machine learning (ML). Recent attention towards ML safety has contributed to a variety of guidelines for establishing assurance in ML based systems - however these guidelines are agnostic to automotive system safety design. This paper bridges the gap between top level safety decomposition and its impact on ML based perception systems. We study system design for different driving scenarios as examples. We then explore the relationship between the decomposition of safety goals to camera based perception and its impact on model learning and deployment aspects of the perception system, namely accuracy measures, ensemble methods, and distribution shift. We show how the safety decomposition plays an integral role in creating guidelines for ML design and deployment.