Reviewerly/exHarmony huggingface dataset:
AI-assisted on-chip nanophotonic convolver based on silicon metasurface

Abstract Convolution operation is of great significance in on-chip all-optical signal processing, especially in signal analysis and image processing. It is a basic and important mathematical operation in the realization of all-optical computing. Here, we propose and experimentally implement a dispersionless metalens for dual wavelengths, a 4f optical processing system, and then demonstrate the on-chip nanophotonic convolver based on silicon metasurface with the optimization assistance of inverse design. The characteristic size of the dispersionless metalens device is 8 × 9.4 μm, and the focusing efficiency is up to 79% and 85% at wavelengths of 1000 and 1550 nm, respectively. The feature size of the convolver is 24 × 9.4 μm, and the proposed convolver allows spatial convolution operation on any desired function at dual wavelengths simultaneously. This work provides a potential scheme for the further development of on-chip all-optical computing.
Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is considered as a very challenge computer vision problem due to the variations in camera point of view, distance from pedestrian, changes in illuminations and weather conditions, variation in the surrounding objects, as well as present of human-like objects. Recently, deep learning-based models are getting more attention, and they have proven a great success in many object detection problems such as the detection of faces, breast masses, and vehicles. As such, this work aims to develop a deep learning-based model that will be applied for the problem of pedestrian detection from a drone-based images. Particularly, faster region-based convolutional neural network (Faster R-CNN) will be used to search for the present of a pedestrian inside the captured drone-based images. To assess the performances, a total of 1500 images were collected by S30W drone and these images were captured at different places, with various views and weather conditions, and at daytime and night-time. Results show that Faster R-CNN was able to achieve a promising result with 98% precision, 99% recall, and 98% F1 measure. Further analysis has been conducted by comparing the outcomes of Faster R-CNN with YOLO deep model on UAV123 publicly available dataset. The reported results indicated that both detection models almost reported very similar results.
Learning to Model Relationships for Zero-Shot Video Classification

With the explosive growth of video categories, zero-shot learning (ZSL) in video classification has become a promising research direction in pattern analysis and machine learning. Based on some auxiliary information such as word embeddings and attributes, the key to a robust ZSL method is to transfer the learned knowledge from seen classes to unseen classes, which requires relationship modeling between these concepts (e.g., categories and attributes). However, most existing approaches ignore to model the explicit relationships in an end-to-end manner, resulting in low effectiveness of knowledge transfer. To tackle this problem, we reconsider the video ZSL task as a task-driven message passing process to jointly enjoy several merits including alleviated heterogeneity gap, low domain shift, and robust temporal modeling. Specifically, we propose a prototype-sample GNN (PS-GNN) consisting of a prototype branch and a sample branch to directly and adaptively model all the relationships between category-attribute, category-category, and attribute-attribute. The prototype branch aims to learn robust representations of video categories, which takes as input a set of word-embedding vectors corresponding to the concepts. The sample branch is designed to generate features of a video sample by leveraging its object semantics. With the co-adaption and cooperation between both branches, a unified and robust ZSL framework is achieved. Extensive experiments strongly evidence that PS-GNN obtains favorable performance on five popular video benchmarks consistently.
Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment

Objective: Alzheimer's disease (AD) is a neurodegenerative disorder that initially presents with memory loss in the presence of underlying neurofibrillary tangle and amyloid plaque pathology. Mild cognitive impairment is the initial symptomatic stage, which is an early window for detecting cognitive impairment prior to progressive decline and dementia. We recently developed the Visuospatial Memory Eye-Tracking Test (VisMET), a passive task capable of classifying cognitive impairment in AD in under five minutes. Here we describe the development of a mobile version of VisMET to enable efficient and widespread administration of the task. Methods: We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eye gaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals. Results: Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2 cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking. Conclusion: This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment. Significance: Given the ubiquity of tablet devices, our approach has the potential to scale globally.
Extending machine learning classification capabilities with histogram reweighting

We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural network as an observable in a statistical system, enabling its extrapolation over continuous ranges in parameter space. We demonstrate our proposal using the phase transition in the two-dimensional Ising model. By interpreting the output of the neural network as an order parameter, we explore connections with known observables in the system and investigate its scaling behaviour. A finite size scaling analysis is conducted based on quantities derived from the neural network that yields accurate estimates for the critical exponents and the critical temperature. The method improves the prospects of acquiring precision measurements from machine learning in physical systems without an order parameter and those where direct sampling in regions of parameter space might not be possible.
Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances

Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions according to the results obtained for early NIST SRE (Speaker Recognition Evaluation) datasets. From the practical point of view, taking into account the increased interest in virtual assistants (such as Amazon Alexa, Google Home, AppleSiri, etc.), speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks. This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances. For these purposes, we considered deep neural network architectures based on TDNN (TimeDelay Neural Network) and ResNet (Residual Neural Network) blocks. We experimented with state-of-the-art embedding extractors and their training procedures. Obtained results confirm that ResNet architectures outperform the standard x-vector approach in terms of speaker verification quality for both long-duration and short-duration utterances. We also investigate the impact of speech activity detector, different scoring models, adaptation and score normalization techniques. The experimental results are presented for publicly available data and verification protocols for the VoxCeleb1, VoxCeleb2, and VOiCES datasets.
Economic Freedom and its Impact on Foreign Direct Investment: Global Overview

Abstract The purpose of this research is to examine the economic freedom (EF) along with its macroeconomic determinants impact on Foreign Direct Investment (FDI) inflow in South Asia, East Asia, Latin America, Middle East, and North Africa, Northern Europe, Southern Europe, Western Europe, Eastern Europe and Sub Saharan Africa. We use Heritage Foundation economic freedom index data over the period of 1999 to 2018 and employ the stepwise multi regression on variables of business freedom, government spending, tax burden, government integrity, property rights, investment freedom, trade freedom and monetary freedom. The results show that EF has a significant positive impact in South Asia, Latin America, East Asia, North Europe and West Europe. However, for the Middle East and North Africa, East European and South European economies EF has an insignificant influence on FDI inflow.
Building City Dashboards for Different Types of Users

City dashboard websites are a common modality for bringing opengovernment philosophies into the public domain.Yet, there has been little research concerning the optimum design for city dashboards that takes account of users' expectations and skills.Indeed, there has been minimal exploration of user-centered design (UCD) to improve the usability and utility of smart city technologies in general.This study sought to conduct a user evaluation analysis to inform a UCD approach to city dashboards.Interviews with different types of users were conducted that applied a protocol analysis to gain insight into user perspectives and experiences of city dashboards.Along with critical incident technique procedures, interaction data of critical significance to the user was collected and a content analysis was conducted.These qualitative data were used to determine representations of users, as identified through observed behaviors, attitudes, needs, and goals.Targeted-scope user experience personas for the design process were then constructed to represent and build empathy towards three potential users of city dashboard systems: novices, end-users, and advanced users.The collected user requirements and the personas formulated are underpinning the re-design of an existing city dashboard.
Attention-Based Deep Learning Framework for Human Activity Recognition With User Adaptation

Sensor-based human activity recognition (HAR) requires to predict the action of a person based on sensor-generated time series data. HAR has attracted major interest in the past few years, thanks to the large number of applications enabled by modern ubiquitous computing devices. While several techniques based on hand-crafted feature engineering have been proposed, the current state-of-the-art is represented by deep learning architectures that automatically obtain high level representations and that use recurrent neural networks (RNNs) to extract temporal dependencies in the input. RNNs have several limitations, in particular in dealing with long-term dependencies. We propose a novel deep learning framework, TrASenD, based on a purely attention-based mechanism, that overcomes the limitations of the state-of-the-art. We show that our proposed attention-based architecture is considerably more powerful than previous approaches, with an average increment, of more than 7% on the F1 score over the previous best performing model. Furthermore, we consider the problem of personalizing HAR deep learning models, which is of great importance in several applications. We propose a simple and effective transfer-learning based strategy to adapt a model to a specific user, providing an average increment of 6% on the F1 score on the predictions for that user. Our extensive experimental evaluation proves the significantly superior capabilities of our proposed framework over the current state-of-the-art and the effectiveness of our user adaptation technique.
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling

Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking. We propose an answer ranking model named MUSE which carefully models multiple semantic relations among the question, answers, and relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph with the question, each answer, and each review snippet as nodes. Then a customized graph convolutional neural network is designed for explicitly modeling the semantic relevance between the question and answers, the content consistency among answers, and the textual entailment between answers and reviews. Extensive experiments on real-world E-commerce datasets across three product categories show that our proposed model achieves superior performance on the concerned answer ranking task.
Analysis of cyber-crime effects on the banking sector using the balanced score card: a survey of literature

Purpose The purpose of this paper is to review the effect of cybercrime in the banking sector. Design/methodology/approach This study uses a survey of literature and the balanced scorecard (BSC) to analyse the effect of cybercrime on the banking sector. Findings The literature reviewed confirms an increasing wave of cybercrime that has impacted negatively on the good will and economic growth of financial institutions, indirectly through loss of trust in the digital infrastructure or directly through fraud and extortion in both developing and developed countries. Research limitations This study is limited to the application of BSC to analyse the effect of cybercrime in the banking sector only. Practical implications To avert on going massive losses owing to cybercrime, the authors quest for development of an alert system that can create the awareness of both the banks and the customers by effectively implementing and integrating big data technology into their system to mitigate the negative impacts of cybercrime. Originality/value The novelty of this study lies in the fact that this study uses the BSC for the analysis of cybercrime in the banking sector, a problem that has not been sufficiently highlighted in the existing literature.
Cluster-based zero-shot learning for multivariate data

Supervised learning requires a sufficient training dataset which includes all labels. However, there are cases that some class is not in the training data. Zero-shot learning (ZSL) is the task of predicting class that is not in the training data (unseen class). The existing ZSL method is done for image data. However, the zero-shot problem should happen to every data type. Hence, considering ZSL for other data types is required. In this paper, we propose the cluster-based ZSL method, which is a baseline method for multivariate binary classification problems. The proposed method is based on the assumption that if data is far from training data, the data is considered as unseen class. In training, clustering is done for training data. In prediction, the data is determined belonging to a cluster or not. If data does not belong to a cluster, the data is predicted as unseen class. The proposed method is evaluated and demonstrated using the KEEL datasets.
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

Abstract Obstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.
Robust Single-Image Haze Removal Using Optimal Transmission Map and Adaptive Atmospheric Light

Haze removal is an ill-posed problem that has attracted much scientific interest due to its various practical applications. Existing methods are usually founded upon various priors; consequently, they demonstrate poor performance in circumstances in which the priors do not hold. By examining hazy and haze-free images, we determined that haze density is highly correlated with image features such as contrast energy, entropy, and sharpness. Then, we proposed an iterative algorithm to accurately estimate the extinction coefficient of the transmission medium via direct optimization of the objective function taking into account all of the features. Furthermore, to address the heterogeneity of the lightness, we devised adaptive atmospheric light to replace the homogeneous light generally used in haze removal. A comparative evaluation against other state-of-the-art approaches demonstrated the superiority of the proposed method. The source code and data sets used in this paper are made publicly available to facilitate further research.
Eschewing Gender Stereotypes in Voice Assistants to Promote Inclusion

The wide adoption of conversational voice assistants has shaped how we interact with this technology while simultaneously highlighting and reinforcing negative stereotypes. For example, conversational systems often use female voices in subservient roles. They also exclude marginalized groups, such as non-binary individuals, altogether. Speech recognition systems also have significant gender, race and dialectal biases [14, 15, 19]. Instead, there is an opportunity for these systems to help change gender norms and promote inclusion and diversity as we continue to struggle with gender equality [10], and progress towards LGBTQ+ rights across the globe [13]. However, prior research claims that users strongly dislike voices without clear gender markers or misalignments between a voice and personality [12]. This calls for additional research to understand how voice assistants may be designed to not perpetuate gender bias while promoting user adoption.
Socio-psychological factors of students` satisfaction in the context of digitalization of education during the COVID-19 pandemic and self-isolation

As you know, digitalization of many aspects of modern society became relevant in the XX1 century for most developed countries. This process is also typical for the Russian Federation, and it has received a special acceleration during the current period of the COVID-19 pandemic. The Rapid development of digitalization is now taking place in the field of education, where distance learning is becoming one of the Central forms. This impetuosity has created a number of problems, both organizational and psychological, since the real reality has not yet been fully prepared for radical changes in both the teaching methodology and the methods of obtaining knowledge by the student audience. Accordingly, this transformation of such an important social sphere as education requires the efforts of representatives of many scientific disciplines, including philosophers, sociologists, cultural scientists, and psychologists. The latter, in addition to theoretical justifications, conduct a number of empirical studies on the psychological adaptability of young people studying to new conditions. One of them is the proposed study, which is based on the assumption that the level of satisfaction with work in the distance format of students during the period of self-isolation depends a lot on the degree of their subjective well-being. The socio-psychological factors that characterize the subjective well-being of students in this situation include: skills for distance learning, satisfaction with the results of this form of education, maintaining relationship with classmates, and the effectiveness of remote interaction with teachers. The empirical part of the study involved 100 students of student age. It has been revealed that the educational environment with a low level of quality (lack of electronic equipment on self-isolation, necessary for providing distance learning) and uncoordinated contacts with teachers and classmates, can negatively affect some components of satisfaction with distance education of students.
A Homomorphic Encryption and Privacy Protection Method Based on Blockchain and Edge Computing

With its decentralization, reliable database, security, and quasi anonymity, blockchain provides a new solution for data storage and sharing as well as privacy protection. This paper combines the advantages of blockchain and edge computing and constructs the key technology solutions of edge computing based on blockchain. On one hand, it achieves the security protection and integrity check of cloud data; and on the other hand, it also realizes more extensive secure multiparty computation. In order to assure the operating efficiency of blockchain and alleviate the computational burden of client, it also introduces the Paillier cryptosystem which supports additive homomorphism. The task execution side encrypts all data, while the edge node can process the ciphertext of the data received, acquire and return the ciphertext of the final result to the client. The simulation experiment proves that the proposed algorithm is effective and feasible.
AN HRM SYSTEM FOR SMALL AND MEDIUM ENTERPRISES (SME)S BASED ON CLOUD COMPUTING TECHNOLOGY

Technology has changed our life and the way we work; however, technology has affected several methods of working in Small and Medium Enterprises (SME)s. Human Resource (HR) is one of the core components in businesses, and nowadays most businesses are using technology for daily basis tasks. However, it still is not used all over the world. In Kurdistan Region-Iraq (KRI), most of the SMEs still use the old way of working and follow the paper-based method for their daily basis tasks. According to a survey, more than seventy percent of SMEs in Kurdistan are not using software to manage human resource management tasks. However, some big companies are using HRMS; but even then, there is a lack of use of Cloud Technology. In this study, a model of the Enterprise Human Resource Management System (EHRMS) is proposed and implemented to solve the HR problems in this area using Cloud Technology. The proposed system consists of sixteen standard modules which used usually with famous HRM systems. The system has been developed by using several technologies such as CodeIgniter as a software framework. The system is launched and deployed on Amazon Web Service (AWS) Elastic Compute Cloud (EC2).