Reviewerly/exHarmony huggingface dataset:
Ciphertext-Policy Attribute-Based Encryption with Outsourced Set Intersection in Multimedia Cloud Computing

In a multimedia cloud computing system, suppose all cloud users outsource their own data sets to the cloud in the encrypted form. Each outsourced set is associated with an access structure such that a valid data user, Bob, with the credentials satisfying the access structure is able to conduct computing over outsourced encrypted set (e.g., decryption or other kinds of computing function). Suppose Bob needs to compute the set intersection over a data owner Alice’s and his own outsourced encrypted sets. Bob’s simple solution is to download Alice’s and Bob’s outsourced encrypted sets, perform set intersection operation, and decrypt the set intersection ciphertexts. A better solution is for Bob to delegate the cloud to calculate the set intersection, without giving the cloud any ability in breaching the secrecy of the sets. To solve this problem, this work introduces a novel primitive called ciphertext-policy attribute-based encryption with outsourced set intersection for multimedia cloud computing. It is the first cryptographic algorithm supporting a fully outsourced encrypted storage, computation delegation, fine-grained authorization security for ciphertext-policy model, without relying on an online trusted authority or data owners, and multi-elements set, simultaneously. We construct a scheme that provably satisfies the desirable security properties, and analyze its efficiency.
Reference-based Image Super-Resolution by Dual-Variational AutoEncoder

Due to severe information loss of low-resolution images, the development of single-image super-resolution methods is limited. Recently, the reference-based image super-resolution methods, which super-resolve the low-resolution inputs with the guidance of high-resolution reference images are emerging. In this paper, we design a Dual-Variational AutoEncoder (DVAE) for reference-based image super-resolution task, which can learn the high-frequency information and latent distribution of the high-resolution reference images as priors to improve the restoration quality of image super-resolution. Moreover, a hierarchical variational autoencoder strategy is exploited to further study latent space. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed approach.
What Is a Game Mechanic?

The term "game mechanic" is often used when discussing games, but are we all talking about the same thing? While game studies and related fields have produced several notable definitions, there is currently no accepted standard for the term within the broader community. Through a systematic literature review spanning six academic venues and several prominent books, we identify and analyze 49 explicit definitions for the concept of "game mechanics". Though some of the definitions are similar, they are all fundamentally distinct. Our work demonstrates the importance of providing or citing a definition when discussing game mechanics, and we provide a wide range of options to choose from.
Development of Early Real-Time Disaster Mitigation Warning System Landslide with Gyroscope ADXL345 Sensor

Abstract The purpose of the research is to develop a Real-Time Landslide Early Disaster Mitigation Warning System with the ADXL345 Gyroscope Sensor and to know the exact system designed in a prototype. The method used is research and development with a prototyping model which is divided into several stages, namely stage (1) problems, (2) data collection, (3) prototype design, (4) prototype testing, and (5) production. The resulting system is a prototype based on an Arduino microcontroller called “ERT-Ladister” with an ADXL345 Gyroscope sensor. Other features include SMS-Gateway, buzzer, and Monitoring via Monitor. Based on the feasibility test and the effectiveness test on a lab scale and field scale, it can be concluded that the system is feasible and effective. The value of the effectiveness test shows that there is a significant difference between before the system is installed and after the system is installed in field trials. The Real-Time Landslide Early Disaster Mitigation Warning System can increase community awareness around landslide-prone areas.
Virtual-Reality Based Crisis Management Training for Teachers: An Overview of the VRTEACHER Project

The coronavirus pandemic has created an educational crisis without precedent, posed new challenges, and highlighted the need for digital skills empowerment among teachers along with the use of technology-based approaches to support the delivery of education and ensure the future of learning. As a return to normality is far from over, the implementation of novel training approaches for teacher training is more imperative than ever. In this context, this paper presents the VRTEACHER project that aims to promote the use of digital technologies and innovative and open pedagogies as part of teachers’ training methodology. The vision of the project is to empower teacher’s skills (such as empathy, perspective-taking, self-efficacy, adaptability, etc.) and readiness related to class management in crisis situations, through Virtual Reality (VR) immersive and experiential training approaches that reflect real-life scenarios and situations. The aim and challenge of the project are not just to adapt the training provided to the digital era and connect it with new technologies but to build a holistic new approach to teacher training through the use of VR.
Development of Re-commerce Online Web-based Platform

This paper presents the development of a recommence online web-based platform. The main goal of this research paper is to design a web-based online interface for reselling used gadgets. The re-commerce market is currently underutilized. Re-commerce generates $7.5 billion globally, compared to the $3.25 trillion retail industry. The web-based online re-commerce industry has great opportunities in Bangladesh. A re-commerce web application has been developed. Using this proposed web-based online platform, users can sell any of their used gadgets within 60 seconds from their home. The web application is user friendly and efficient. An android mobile application has also been developed in this paper for the recommence system.
Quantum Computer Music: Foundations and Initial Experiments

Quantum computing is a nascent technology, which is advancing rapidly. There is a long history of research into using computers for music. Nowadays computers are absolutely essential for the music economy. Thus, it is very likely that quantum computers will impact in the music industry in the future. This chapter lays the foundations of the new field of Quantum Computer Music. It begins with an introduction to algorithmic computer music and methods to program computers to generate music, such as Markov chains and random walks. Then, it presents quantum computing versions of those methods. The discussions are supported by detailed explanations of quantum computing concepts and walk-through examples. A novel bespoke generative music algorithm in presented, the Basak-Miranda algorithm, which leverages a property of quantum mechanics known as constructive and destructive interference to operate a musical Markov chain.
An Approach for Maintaining Safety at Work Places During the COVID-19 Pandemic Using Deep Learning and Contact Tracing Applications

The outbreak of "COVID_19" has affected our lives and more so, our economy. One question that comes to mind is how to prevent the spread of this virus, and one possible solution to this is to work from home. However, there are certain industries which cannot work from home, for example, manufacturing, health care and production. For such workplaces, in this paper, we have proposed a mask detection module that uses the YOLOv3 algorithm to classify whether an employee is wearing a mask or not. A training accuracy of 96% and testing accuracy of 93.36% were obtained using the YOLOv3 algorithm. Along with the designed algorithm, four standard convolutional neural network architectures—VGG-16, DenseNet, InceptionNet and MobileNet—are also tested on the given dataset. The highest test accuracy of 91.21% was obtained from the MobileNet classifier. The paper also proposes an additional stage consisting of a company Web site that can keep a daily track on the employee's health and sanitization using a multipurpose wearable as well as an application that can be used to contact tracing purpose.
A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories

Missing marker information is a common problem in Motion Capture (MoCap) systems. Commercial MoCap software provides several methods for reconstructing incomplete marker trajectories; however, these methods still rely on manual intervention. Current alternatives proposed in the literature still present drawbacks that prevent their widespread adoption. The lack of fully automated and universal solutions for gap filling is still a reality. We propose an automatic frame-wise gap filling routine that simultaneously explores restrictions between markers’ distance and markers’ dynamics in a least-squares minimization problem. This algorithm constitutes the main contribution of our work by simultaneously overcoming several limitations of previous methods that include not requiring manual intervention, prior training or training data; not requiring information about the skeleton or a dedicated calibration trial and by being able to reconstruct all gaps, even if these are located in the initial and final frames of a trajectory. We tested our approach in a set of artificially generated gaps, using the full body marker set, and compared the results with three methods available in commercial MoCap software: spline, pattern and rigid body fill. Our method achieved the best overall performance, presenting lower reconstruction errors in all tested conditions.
Performance Comparison of Convolutional Neural Network Models on Cervical Cell Classification

Cervical cancer is regarded as one of the most common type of cancer suffered by women in the world. However, it can be treated if being detected early. The main method of diagnosing this type of cancer is through Papanicolaou test, in which cervical cells are collected and examined under a microscope. The process of examining the cervical cells is often done manually and is labourious. Several methods have been proposed to automate this process using machine learning. This study explores three Convolutional Neural Network models (i.e., AlexNet, Inception-V3 and ResNet50-V2) in classifying cervical cells. Transfer learning was implemented with different sets of varying number of images. Herlev dataset was used in this study. In the Herlev dataset, images were classified into seven classes (i.e. four abnormal and three normal). The classification done was based on seven-class classification. Performance of each model was measured based on accuracy, precision and recall. Based on the result obtained, ResNet50-V2 performed the best with the accuracy, precision and recall compared to the other two models.
CloudSim Exploration: A Knowledge Framework for Cloud Computing Researchers

This paper aims to help find solutions for questions an early researcher may have to set up experiments in their development environment. Simultaneously, while identifying the steps required for experimenting, the authors narrowed on an experimenting toolkit for Cloud Computing as an area of their study. Because of such simulators, the cloud computing environment itself is available easily at the comfort of one’s desktop resources instead of visiting an actual physical data center to collect trace and log files as data sets for real workloads. This paper acts as an experience sharing to naive researchers who are interested in how to go about to start cloud computing setups. A new framework called Cloud Computing Simulation Environment (CCSE) is presented with inspiration from Procure Apply Consider and Transform (PACT) model to ease the learning process. The literature survey in this paper shares the path taken by researchers for understanding the architecture, technology, and tools required to set up a resilient test environment. This path also depicts the introduced framework CCSE. The parameters found out of the experiments were Virtual Machines (VMs), Cloudlets, Host, and Cores. The appropriate combination of the values of the parameters would be horizontal scaling of VMs. Increasing VMs does not influence the average execution time after a specific limit on the number of VMs allocated. Nevertheless, in vertical scaling, appropriate combinations of the cores and hosts yield better execution times. Thereby maintaining the optimal number of hosts is an ultimate saving of resources in case of VM allocations.
Explore, Filter and Distill

Reinforcement learning (RL) has been verified in real-world list-wise recommendation. However, RL-based recommendation suffers from huge memory and computation costs due to its large-scale models. Knowledge distillation (KD) is an effective approach for model compression widely used in practice. However, RL-based models strongly rely on sufficient explorations on the enormous user-item space due to the data sparsity issue, which multiplies the challenges of KD with RL models. What the teacher should teach and how much the student should learn from each lesson need to be carefully designed. In this work, we propose a novel Distilled reinforcement learning framework for recommendation (DRL-Rec), which aims to improve both effectiveness and efficiency in list-wise recommendation. Specifically, we propose an Exploring and filtering module before the distillation, which decides what lessons the teacher should teach from both teachers' and students' aspects. We also conduct a Confidence-guided distillation at both output and intermediate levels with a list-wise KL divergence loss and a Hint loss, which aims to understand how much the student should learn for each lesson. We achieve significant improvements on both offline and online evaluations in a well-known recommendation system. DRL-Rec has been deployed on WeChat Top Stories for more than six months, affecting millions of users. The source codes are released in https://github.com/modriczhang/DRL-Rec.
Geometric Heuristics for Transfer Learning in Decision Trees

Motivated by a network fault detection problem, we study how recall can be boosted in a decision tree classifier, without sacrificing too much precision. This problem is relevant and novel in the context of transfer learning(TL), in which few target domain training samples are available. We define a geometric optimization problem for boosting the recall of a decision tree classifier, and show it is NP-hard. To solve it efficiently, we propose several near-linear time heuristics, and experimentally validate these heuristics in the context of TL. Our evaluation includes 7 public datasets, as well as 6 network fault datasets, and we compare our heuristics with several existing TL algorithms, as well as exact mixed integer linear programming(MILP) solutions to our optimization problem. We find that our heuristics boost recall in a manner similar to optimal MILP solutions, yet require several orders of magnitude less compute time. In many cases the F1 score of our approach is competitive, and often better, than other TL algorithms. Moreover, our approach can be used as a building block to apply transfer learning to more powerful ensemble methods, such as random forests.
Performance Comparison of EKF and UKF for Offshore Boom Crane System

In this paper, a performance comparison between the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) is shown for nonlinear underactuated systems. The filters are designed and tested for the benchmark cart pendulum system and the underactuated offshore boom crane system. The EKF and UKF are designed for both state estimation and noise removal of measured output in the presence of disturbance in the system. Simulation results from the MATLAB environment have shown that both estimators can produce the required result, but UKF gives higher accuracy than that of EKF when system nonlinearity is higher.
Relation-Based Knowledge Distillation for Anomaly Detection

Anomaly detection is a binary classification task, which is to judge whether the input image contains an anomaly or not and the difficulty is that only normal samples are available at training. Due to this unsupervised nature, the classic supervised classification methods will fail. Knowledge distillation-based methods for unsupervised anomaly detection have recently drawn attention as it has shown the outstanding performance. In this paper, we present a novel knowledge distillation-based approach for anomaly detection (RKDAD). We propose to use the “distillation” of the “FSP matrix” from adjacent layers of a teacher network, pre-trained on ImageNet, into a student network which has the same structure as the teacher network to solve the anomaly detection problem, we show that the “FSP matrix” are more discriminative features for normal and abnormal samples than other traditional features like the latent vectors in autoencoders. The “FSP matrix” is defined as the inner product between features from two layers and we detect anomalies using the discrepancy between teacher’s and student’s corresponding “FSP matrix”. To the best of our knowledge, it is the first work to use the relation-based knowledge distillation framework to solve the unsupervised anomaly detection task. We show that our method can achieve competitive results compared to the state-of-the-art methods on MNIST, F-MNIST and surpass the state-of-the-art results on the object images in MVTecAD.
Red flag behaviors in financial services frauds: a mixed-methods study

Purpose This study aims to enlist the red flag behaviors exhibited in financial services frauds. Design/methodology/approach A pluralistic mixed methodology was adopted in this study. Data collected via semi-structured interviews were coded, quantified and subjected to descriptive analysis to identify the most frequently exhibited red flag behaviors in financial services frauds. The relative risk of exhibition of the identified red flag behaviors was assessed by intuitively comparing the red flag behaviors identified in financial services frauds (experimental group, n = 24) with the red flag behaviors identified in a heterogeneous control sample of non-financial services frauds (control group, n = 28). Findings This study identifies six red flag behaviors likely to be more frequently exhibited in financial services frauds than in non-financial services frauds. Practical implications Results of this study can be used to develop a typical behavioral profile of a financial services fraud perpetrator. Active communication of this profile in fraud awareness training can help make fraud conspicuous in the financial services industry. Originality/value This study is unique because human behavior as a possible fraud indicator is an under-researched area. Further, this study examines first level of evidence and attempts an ex-post analysis of actual red flag behaviors exhibited in acknowledged fraud cases in which the perpetrator/perpetrators has/have been clearly identified.
Machine Learning Algorithm to Predict Fraudulent Loan Requests

Machine learning is a strategy that enable computers to automatize information-driven model building and programming through a scientific discovery of statistically important patterns within the obtainable data. The learning capability of a machine and the ability to do predictive analysis is very obligatory in this age of vast information. In this study, we focused on banking sector where too many individuals are applying for bank credits. Though, it is really troublesome to determine whom loan should be granted or whom should be rejected. For banking organizations acceptance of loan is a main task. The prediction model that we formed in this paper for predicting fraudulent loan requests. In this paper, we were working with six algorithms - Decision tree, Support vector machine, Random forest, K nearest neighbors, Ada-Boost, and Logistic regression to predict the fraudulent loan request from customers. We got 83.75% accuracy from K-Nearest Neighbors algorithm which was better than other five machine learning approaches.
An ontology of masters of the Babylonian Talmud

Abstract The purpose of this research is to build an ontology of the masters appearing in the Babylonian Talmud (BT). The ontology built so far has been shared as a Linked Open Data and it will be linked to existing vocabularies. This work has been developed in the context of the Babylonian Talmud Translation Project, where more than eighty Talmudists are working together, since 2012, at the translation (comprehensive of explicative notes and glossaries) of the Talmud into Italian. The construction of the resource has involved the application of tools leveraging on computational linguistics approaches. The ontology, already describing more than 500 masters, constitutes the first portion of a more comprehensive Talmudic Knowledge Base where the text itself, the terminology, the entities, and the concepts constituting the BT will be formalized and linked to each other.
Managing the Shift in the Enterprise Perimeter in order to delay a Cybersecurity Breach

In the year, 2019, South African enterprises have seen a substantial increase in the number of cybersecurity incidents. From the public-sector to the private-sector, many enterprises have had to redirect large sums of their budgets towards responding to cybersecurity incidents that could have been averted. For most enterprises, having experienced, or seen the impact of a number of disclosed cybersecurity incidents in South Africa (SA) during 2019, the majority of enterprises had begun to strengthen their cybersecurity defenses in order to avert future cybersecurity incidents. To a large extent, most enterprises have implemented security measures such as firewalls, proxy gateways, patch management solutions, intrusions prevention or detection systems, encryption, network access control and so on. However, whilst these measures are implemented, in 2020, security architects saw a shift in the enterprise perimeter due to COVID-19 pandemic. Owing to the pandemic, on the 26th of March 2020, the South African government implemented a countrywide lock-down effectively enforcing all its citizens to stay at homes thus translating to an unintended remote workforce. During this time, employees found themselves overnight without any training working outside of the secured layered cybersecurity defenses of their enterprises. This arrangement negatively affected the security posture of many organizations. It effectively forced all enterprise employees to work from home, away from the secure enterprise network forcing information technology administrators to find ways to extend the enterprise perimeter in order to allow these inexperienced overnight remote workers to continue to securely work remotely. This article explores the consequences of the shift in the enterprise perimeter and also suggests practical technical steps that enterprises can apply in order to avert possible cybersecurity incidents.