Reviewerly/exHarmony huggingface dataset:
Reinforcement Routing on Proximity Graph for Efficient Recommendation

We focus on Maximum Inner Product Search (MIPS), which is an essential problem in many machine learning communities. Given a query, MIPS finds the most similar items with the maximum inner products. Methods for Nearest Neighbor Search (NNS) which is usually defined on metric space do not exhibit the satisfactory performance for MIPS problem since inner product is a non-metric function. However, inner products exhibit many good properties compared with metric functions, such as avoiding vanishing and exploding gradients. As a result, inner product is widely used in many recommendation systems, which makes efficient Maximum Inner Product Search a key for speeding up many recommendation systems. Graph-based methods for NNS problem show the superiorities compared with other class methods. Each data point of the database is mapped to a node of the proximity graph. Nearest neighbor search in the database can be converted to route on the proximity graph to find the nearest neighbor for the query. This technique can be used to solve MIPS problem. Instead of searching the nearest neighbor for the query, we search the item with a maximum inner product with query on the proximity graph. In this article, we propose a reinforcement model to train an agent to search on the proximity graph automatically for MIPS problem if we lack the ground truths of training queries. If we know the ground truths of some training queries, our model can also utilize these ground truths by imitation learning to improve the agent’s searchability. By experiments, we can see that our proposed mode which combines reinforcement learning with imitation learning shows the superiorities over the state-of-the-art methods.
Factors that Affects the Use of AI Agents in Adaptive Learning: A Sociomaterial and Mcdonaldization Approach in the Higher Education Sector

In the Higher Education Sector (HES), we see increasingly Artificial Intelligent (AI) agents in the form of chatbots or interactive virtual agents indistinguishable from people and a unique example of human-machine interaction using natural language processing. They are becoming one of the main technological tools to ensure accreditation and e-learning, while providing better adaptive learning. This conceptual paper aims to examine the factors that affect the intention to use AI agents/chatbots for adaptive learning in HEI from a sociomateriality perspective taking into consideration the mcdonaldization effect. An extended UTAUT (Unified Theory of Acceptance and Use of Technology) model is proposed to be evaluated in the HES context.
Digital education futures in Irish educational policy: tempering commercial influence through an exploration of emerging ethical, environmental and educational realities

This paper explores future digital technology policy for education with reference to the Irish context. Framed within the global context of commercial influence on education and related discourses, it addresses the Irish context with particular reference to identifiable ‘push’ and ‘pull’ factors seen as influencing policy provision in any next phase. The paper then proceeds to outline a number of intersecting factors or issues that will likely influence digital technology policy and practice in Irish schools in the future, taking into consideration the political, corporate, social, economic and educational backdrop. Likely ethical, environmental and educational factors are proposed and discussed. Looking across these factors, the paper concludes that establishing a clear vision of the role and purpose of technology in education, coupled with a more complex understanding and appreciation of the interaction between technology and education (which is contextually bound and relational, and reflects greater teacher rather than corporate agency) are significant prerequisites to any consideration of the future of digital education in the Irish educational policy context.
Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion

Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) into a geometric space and thus have gained increasing attentions. In addition, many recent Knowledge Graphs involve evolving data, e.g., the fact (\textit{Obama}, \textit{PresidentOf}, \textit{USA}) is valid only from 2009 to 2017. This introduces important challenges for knowledge representation learning since such temporal KGs change over time. In this work, we strive to move beyond the complex or hypercomplex space for KGE and propose a novel geometric algebra based embedding approach, GeomE, which uses multivector representations and the geometric product to model entities and relations. GeomE subsumes several state-of-the-art KGE models and is able to model diverse relations patterns. On top of this, we extend GeomE to TGeomE for temporal KGE, which performs 4th-order tensor factorization of a temporal KG and devises a new linear temporal regularization for time representation learning. Moreover, we study the effect of time granularity on the performance of TGeomE models. Experimental results show that our proposed models achieve the state-of-the-art performances on link prediction over four commonly-used static KG datasets and four well-established temporal KG datasets across various metrics.
A Sentiment-Based Approach to Predict Learners’ Perceptions Towards YouTube Educational Videos

Given the increasing proliferation of educational resources on YouTube, this social media platform has become an optimal space to which students usually resort to improve their learning in various subjects. It has also turned into an online community in which all users can voice their opinion about videos they have watched. However, getting the perceptions of learners on these learning materials is not always an easy task. Our objective in this paper is, therefore, to propose a machine learning approach that can classify learners’ feedback as being positive or negative. For this purpose, textual data was retrieved from YouTube educational videos, pre-processed and then classified using different machine learning algorithms. Results of the present study demonstrated that the Support Vector Machines algorithm performs better than other models. In fact, with an accuracy score of 92.82% on combinations of unigrams and bigrams and 92.67% for associations of unigrams and trigrams, this supervised machine learning approach outperforms other implemented models, namely Naïve Bayes, Random Forest and Logistic Regression.
Augmented Reality Applications Development using Android-Based Marker-less Method as Tourism Promotion Media: A Case Study

Augmented reality has been used in many fields for modeling in worldwide. One of those fields is tourism promotion. Most of the augmented reality applications in tourism promotion fields use marker method. This research aimed to develop augmented reality application using android-based marker-less method for tourism promotion for a tourist park in Indonesia (Karang Resik – Tasikmalaya, Indonesia). The application feasibility testing uses ISO 25010 with four aspects. The results of this research are 100% for functional suitability testing, 81% for usability testing, good running in 10 diverse devices for compatibility testing, performance efficiency testing using three aspects resulting 77.5 seconds for threads access. 11.3% for CPU consumption, and 532.1 MB on average for memory consumption.
NoInjection: Preventing Unsafe Queries on NoSQL-Document-model Databases

Cyber-attack has today become a habitual practice of remotely destroying computer systems across the globe. The behavior of these systems is solely controlled by data which is nowadays stored in NoSQL databases. As a result, numerous techniques were introduced to protect this data when it&#x0027;s in use and in transit while leaving the at rest part (databases) in the hands of novice programmers without any enforcements. The NoSQL-Injection problem emerged due to this flexibility where malicious queries that mimic the legal queries are appended together for possible execution. In this paper, a new method is introduced into the NoSQL design practice. This method utilizes the Asymmetric encryption algorithm, RSA in particular. Using this method, key pairs are generated and exchanged during the design. The database server decrypts the legal queries before effecting any changes. An experiment was conducted to assess the performance of the proposed method. The proposed method proved to be safe from NoSQLInjection attacks when compared with formal methods. Based on this, the proposed method has the aptitude to prevent the occurrence of NoSQLInjection attacks on NoSQL applications.
Breast Cancer Classification from Histological Images using Multi-scale Dense Network

Early diagnosis and treatment of breast cancer is essential to increase the survival rate. Currently, histological analysis performed by pathologists is critical to breast cancer diagnosis. Compared with pathologists, computer-aided system utilizes machine learning techniques can reduce workload and increase the classification accuracy. In this paper, a method based on the multi-scale dense network (MSDNet) was proposed for breast cancer histological image classification. Networks with variable depths were used to increase computational efficiency on the premise of prediction accuracy. The BACH 2018 dataset were used and classified into four classes: normal tissue, benign lesion, in situ carcinoma and invasive carcinoma. Experimental results show that the overall accuracy is 98.28% and 97.31 % for binary and multi-class classification in patch level, respectively. The image-wise classification accuracy reaches 100% by majority vote fusion decision after patch prediction. Meanwhile, the computational time is reduced almost 50% compared with other methods.
LinArc - Deep Face Recognition Using LinCos And ArcFace

Data is overflowing day by day. The use of face recognition is rapidly picking up pace due to the boom in data and available computation power. The research done in this field is at an unimaginable pace, and accuracies of more than 99% have been achieved, which are possibly less only by Baye&#x2019;s error. However, there is still room for experimentation. This paper tries to build a model by mixing two novel ideas of face recognition - ArcFace and LinCos. In this paper, the target is to manipulate the Additive Angular Margin Loss used by ArcFace by incorporating the ideas of LinCos. We re-train the pre-trained ArcFace model using Mobile FaceNet with a modified loss function. The results suggest that our model optimizes at a faster rate as compared to the ArcFace and LinCos models.
Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.
Vehicle Distance and Speed Estimation Algorithm for Computer Vision Sensor System

This study is devoted to solving the perception problem of autonomous transport system by Convolutional Neural Network (CNN) application. The goal of this paper is to develop the method for single camera based computer vision sensor for intelligent transport systems to detect and recognize objects in real-time, as well as estimate the speed of detected vehicle. This paper aims at evaluating the workability of this sensor in different weather conditions for autonomous vehicle applications. This sensor can be applied for different levels of abstraction like road safety, autonomous vehicle navigation, guiding system for disabled, animal survey, surveillance and other purposes.
The Disclosure of Non-financial Risk. The Emerging of Cyber-Risk

The chapter aims to deepen the emerging issue of cyber-risk that, among non-financial (NF) risks, represents a major threat for organizations due to the growing sophistication and variety of data breaches and cyberattacks. The research provides a general background for NF risks disclosure illustrating the path that led from external voluntary to mandatory frameworks for the disclosure of NF risks following the EU Directive. Then, we move on to describe the role of internal auditors for the management and disclosure of the digital risk. In order to better understand the effectiveness of corporate governance mechanisms for digital risk disclosure, we conduct a review of literature and a visualization to provide the most frequent topics and their chronological evolution. The chapter contributes to the risk disclosure and corporate governance literature, by providing some theoretical and practical implications.
An Embedding Carrier-Free Steganography Method Based on Wasserstein GAN

Image has been widely studied as an effective carrier of information steganography, however, low steganographic capacity is a technical problem that has not been solved in non-embedded steganography methods. In this paper, we proposed a carrier-free steganography method based on Wasserstein GAN. We segmented the target information and input it into the trained Wasserstein GAN, and then generated the visual-real image. The core design is that the output results are converted into images in the trained network according to the mapping relationship between preset coding information and random noise. The experimental results indicated that the proposed method can effectively improve the ability of steganography. In addition, the results also testified that the proposed method does not depend on the complex neural network structure. On this basis, we further proved that by changing the length of noise and the mapping relationships between coding information and noise, the number of generated images can be reduced, and the steganography ability and efficiency of the algorithm can be improved.
FASSST: Fast Attention Based Single-Stage Segmentation Net for Real-Time Instance Segmentation

Real-time instance segmentation is crucial in various AI applications. This work designs a network named Fast Attention based Single-Stage Segmentation NeT (FASSST) that performs instance segmentation with video-grade speed. Using an instance attention module (IAM), FASSST quickly locates target instances and segments with region of interest (ROI) feature fusion (RFF) aggregating ROI features from pyramid mask layers. The module employs an efficient single-stage feature regression, straight from features to instance coordinates and class probabilities. Experiments on COCO and CityScapes datasets show that FASSST achieves state-of-the-art performance under competitive accuracy: real-time inference of 47.5FPS on a GTX1080Ti GPU and 5.3FPS on a Jetson Xavier NX board with only 71.6 GFLOPs.
Powerful Physical Adversarial Examples Against Practical Face Recognition Systems

It is well-known that the most existing machine learning (ML)-based safety-critical applications are vulnerable to carefully crafted input instances called adversarial examples (AXs). An adversary can conveniently attack these target systems from digital as well as physical worlds. This paper aims to the generation of robust physical AXs against face recognition systems. We present a novel smoothness loss function and a patch-noise combo attack for realizing powerful physical AXs. The smoothness loss interjects the concept of delayed constraints during the attack generation process, thereby causing better handling of optimization complexity and smoother AXs for the physical domain. The patch-noise combo attack combines patch noise and imperceptibly small noises from different distributions to generate powerful registration-based physical AXs. An extensive experimental analysis found that our smoothness loss results in robust and more transferable digital and physical AXs than the conventional techniques. Notably, our smoothness loss results in a 1.17 and 1.97 times better mean attack success rate (ASR) in physical white-box and black-box attacks, respectively. Our patch-noise combo attack furthers the performance gains and results in 2.39 and 4.74 times higher mean ASR than conventional technique in physical world white-box and black-box attacks, respectively.
Design of a novel non‐volatile hybrid spintronic true random number generator

Abstract True random number generators (TRNGs) are mainly used in cryptographic applications for the generation of various keys. A standard CMOS‐based TRNG utilizes physical phenomena such as thermal noise, metastability, and oscillator jitter to generate fully non‐deterministic random outputs but has high power dissipation and a large overhead area. In this work, we develop a novel low power non‐volatile TRNG circuit that utilizes the inherent stochastic switching nature of magnetic tunnel junction (MTJ) as a randomness source. Thanks to the capabilities of MTJ and carbon nanotube field‐effect transistors (CNTFETs), the proposed TRNG circuit consumes low power when compared with conventional CMOS‐based TRNGs. The reliability of the proposed circuit is checked by running the corner case tests that considers all the process and environmental variations. The quality of the output bitstream is evaluated by running the twelve statistical randomness tests by the U.S National Institute of Standards and Technology (NIST). The circuit passes all the tests, meaning the output bit‐stream is truly random. The circuit is designed in such a way that, even if the power is turned off, the quality of the randomness is not affected. The proposed TRNG structure has a less complex design and generates truly random output.
Championing the future of ghost pot recovery through the implementation of remotely operated vehicles and community science models

This research explores using an ROV (Remotely Operated Vehicle) and community science models to aid ghost pot location and recovery efforts in coastal communities. Ghost pots are a form of ghost fishing gear and, commonly refer to crab pots that are lost, discarded, or abandoned. This creates a cycle of marine habitat destruction through the continued entrapment of marine life. Current ghost pot location and recovery methods are expensive and coastal communities often lack access to side scan sonars, recovery permits, trained divers, and GIS datasets. This project addresses these challenges by proposing a new method to solve this growing problem in a more accessible and inexpensive way through marine technology innovation and community science.This research is being conducted over two years as part of a multi-stage pilot project with the support of the Jefferson County Marine Resources Committee. The technologies implemented in the project include an award winning custom ROV and crowd sourced app. This app, which specifically targets divers, fishermen, and beach-goers, is being developed to incentivize community engagement in the location of ghost pots through interactive challenges. Though many derelict gear hotlines currently exist, countless incidents go unreported. By gamifying a reporting system using leaderboards, community dive events, and award systems, the goal is to amplify the number of ghost pot reports and select study sites without having to undertake extensive surveying measures. Once a search zone has been narrowed, using data collected through the app, the ROV can complete a search transect to locate and recover ghost pots. By utilizing a volunteer network, engaging student roboticists, and building community partnerships, we are creating a community-driven location and recovery model.
Search on Encrypted COVID-19 Healthcare Data in Blockchain-Assisted Distributed Cloud Storage

In present circumstances, Coronavirus Disease-2019 (COVID-19) is a very significant health issue affecting human life all over the world. In an Internet of Medical Things (IoMT) environment, the deployed wearable Internet of Things (IoT)-enabled smart devices in a patient&#x0027;s body sense vital information. COVID19-related information is very confidential and private. Thus, the data can be stored into private-blockchain-based distributed cloud storage. For big data analysis, it becomes difficult to decrypt each block residing in the blockchain. Hence, only useful blocks can be searched and then decrypted in order to lessen the computational burden. To deal with this, we discuss a search technique on encrypted COVID-19 data for a blockchain-assisted distributed encrypted database. Finally, blockchain implementation has been also provided to show its effectiveness.
Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations

We present a framework that, given a set of skills a robot can perform, abstracts sensor data into symbols that are used to automatically encode the robot’s capabilities in Linear Temporal Logic (LTL). We specify reactive high-level tasks based on these capabilities, for which a strategy is automatically synthesized and executed on the robot, if the task is feasible. If a task is not feasible given the robot’s capabilities, our framework automatically suggests additional skills for the robot that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table.
Two-stage insulator self-explosion defect detection method based on Mask R-CNN

With the continuous development of power systems, the application of UAVs in the inspection of power systems has become more and more extensive. Aiming at the problem of few defect insulator samples and complex background, a detection method of Two-stage insulator self-explosion defect detection method based on Mask R-CNN is proposed. This paper uses Mask R-CNN to obtain the mask of the insulator and the corresponding original image part, and send them into the classification model with SPP network. The final insulator detection accuracy reached 87.5%.
Masked Face Detection and Calibration with Deep Learning Models

Abstract Under the COVID-19 pandemic, the demand that face detection devices should be enhanced to detect masked faces is imperative. In this study, we utilize several state-of-the-art face detection models and compare them on various unmasked and masked human face datasets. Moreover, by analyzing the results we obtain, we evaluate these disparate models and discover some problems. Attempting to overcome the problems discovered, we propose and implement several improvements, and acquire more results for analysis. At length, we propose some ideas for future research directions.