Reviewerly/exHarmony huggingface dataset:
Image encryption based on permutation polynomials over finite fields

In this paper, we propose an image encryption algorithm based on a permutation polynomial over finite fields proposed by the authors. The proposed image encryption process consists of four stages: i) a mapping from pixel gray-levels into finite field, ii) a pre-scrambling of pixels’ positions based on the parameterized permutation polynomial, iii) a symmetric matrix transform over finite fields which completes the operation of diffusion and, iv) a post-scrambling based on the permutation polynomial with different parameters. The parameters used for the polynomial parameterization and for constructing the symmetric matrix are used as cipher keys. Theoretical analysis and simulation demonstrate that the proposed image encryption scheme is feasible with a high efficiency and a strong ability of resisting various common attacks. In addition, there are not any round-off errors in computation over finite fields, thus guaranteeing a strictly lossless image encryption. Due to the intrinsic nonlinearity of permutation polynomials in finite fields, the proposed image encryption system is nonlinear and can resist known-plaintext and chosen-plaintext attacks.
Eye Tracking and ROI Detection within a Computer Screen Using a Monocular Camera

The region of interest will change according to the task, even in the same situation. In the study, a method for region of interest detection within a computer screen using a monocular camera is proposed. In contrast to gaze tracking techniques that require particular devices (e.g., an eye tracker and RGB-D device) meanwhile complex calibration, a cheap and more convenient monocular camera is used in this study to solves the eye gaze tracking problem. Firstly, Human face is detected in a real-time video sequence using HoG features. Then, the landmarks around the eyes, which reflect the gaze position, are extracted. Next, the iris centers are detected in the eye region. In order to reduce the gaze error caused by head movement, a three-dimensional head model is proposed to estimate head pose. Finally, the eye region is tracked by calculating the eye vectors and head movement. Experiments were performed to evaluate the face detection, landmarks, iris detection, eye movement estimation, and head pose estimation on databases such as the Hong Kong, BioID, and Boston University head pose databases. Besides, experiments for gaze tracking were performed for a real-time video sequence. Deviation is calculated using Euclidean distance between the real and estimated points. The results show that the method achieves an average error of 1.85∘ with head fixed and 3.58∘ with head movement in the range of −45∘ and 45∘. The requirement is detecting the user’s attention in the screen area. Our method can reach the same level to the other methods, even though the accuracy is not state-of-the-art. Meanwhile, as we all know not only a specific point is concerned but also a region area according to the characteristics of human eye imaging, thus the proposed method can meet the requirements of demand.
A cross-sectional population survey of physicians in Alberta, Canada about a novel provincial contact tracing smartphone app

Abstract Background The Canadian province of Alberta released the ABTraceTogether smartphone app in May 2020 to assist in contact tracing during the SARS-CoV-2 pandemic. Public engagement with this public health tool has been low, limiting the effectiveness of the intervention. This study examines physician knowledge of the app and practice patterns in relation to the app. Methods We conducted a cross-sectional self-administered online English language survey of physicians and medical students in Alberta, Canada. The survey link was sent to all registered members of the College of Physicians and Surgeons of Alberta and was distributed by other provincial physician organizations and health zone leaders. Results The survey received 317 responses. 96% of participants were aware of the app but only 27% had recommended the app to patients. The most common reason provided for not downloading or recommending the app was that participants had security concerns about the app. 23% of participants indicated they did not believe they had a responsibility to recommend the app to others. Conclusions Our study provides insights into participants’ knowledge and beliefs about the ABTraceTogether app. This information may be valuable to public health officials who wish to engage physicians in future public health campaigns.
Fintech Revolution in the Financial Industry

Today, businesses operate in a highly dynamic and highly volatile market that requires timely and accurate responses. It is precisely the modern and dynamic financial environment that puts many challenges in front of the business of the company, confronts them with the advancement of modern information and communication technologies, which also raises the need for many innovative financial solutions. Financial market creates the new term FinTeh” that includes some of the significant areas such as Bitcoin and Blockchain technology that have been evaluated in the last 5 years. With mentioned technologies, novelty in the financial market are definitely so-called „Neobanks“, banks without physical locations. It is for this reason that we can argue that these forms of new technologies are evolving over time, being embraced and incorporated into companies business processes to maximize the added-value and gain a competitive advantage on the market.This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Library Services Provided During COVID-19 Pandemic: Content Analysis of Websites of Premier Technological Institutions of India

The novel coronavirus (COVID-19) disease presented unique challenges to all the stakeholders of education system The sudden and unexpected outbreak of the virus forced the library professionals, to ascertain ways of working in a rapid time frame like shifting to digital platform wherever possible and to provide adequate remote services to the users Given the extrinsic challenges in providing services during a public health emergency, the purpose of this paper is to find out how technology became a saviour for the premier technological institutions of India during the COVID -19 pandemic The study explores the type of services provided by libraries of Indian Institutes of Technology (IITs) based on the contents published on their webpages The study highlights the significant initiatives taken by the libraries which can be followed by others to meet the post lockdown needs © 2020 All Rights Reserved
Smart Design for Resources Allocation in IoT Application Service based on multi-agent system and CSP

In the present paper, we aim at solving two problems; the first problem occurring in the transformation of the IoT devices (sensors, actuators, …) to cloud service. Therefore, we work on maintaining a smooth and efficient data transmission for the cloud and support customer applications like: data sharing, storage and processing. The second problem has two dimensions. In the first dimension, the problem is arisen in the submission of cloudlets (customer requested jobs) to Virtual Machines (VMs) in the hosts. To solve this problem, we propose scheduling algorithm for resource allocation according to the lowest cost and load. In the second dimension, the problem lies in the hosting of new VMs in the hosts. To overcome this problem, we need take into account the loads when housing new VMs in different datacenters. In this work, we suggest a resource allocation approach for services oriented IoT applications. The architecture of this approach is based on two technics: Multi Agent System (MAS) and Distributed Constraint Satisfaction Problems (DCSP). The MAS manages the physical resources, making decision and the communication between datacenters, while DCSP used to simplify the policy of the resources provisioning in Datacenters. Variables and constraints are distributed among multiple agents in different layers. The experimental results show that the efficiency of our approach is manifested in: Average System Load, Cost augmentation Rate and Available Mips.
Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN)

Jambi Province is a producer of palm oil as a mainstay of commodities. However, the limited insight of farmers in Jambi to oil palm pests and diseases affects oil palm productivity. Meanwhile, knowing the types of pests and diseases in oil palm requires an expert, but access restrictions are a problem. This study offers a diagnosis of oil palm disease using the most popular concept in the field of artificial intelligence today. This method is deep learning. Various recent studies using CNN, say the results of image recognition accuracy are very good. The data used in this study came from oil palm image data from the Jambi Provincial Plantation Office. After the oil palm disease image data is trained, the training data model will be stored for the process of testing the oil palm disease diagnosis. The test evaluation is stored as a configuration matrix. So that it can be assessed how successful the system is to diagnose diseases in oil palm plants. From the testing, there were 2490 images of oil palm labeled with 11 disease categories. The highest accuracy results were 0.89 and the lowest was 0.83, and the average accuracy was 0.87. This shows that the results of the classification of oil palm images with CNN are quite good. These results can indicate the development of an automatic and mobile oil palm disease classification system to help farmers.
The power of quantum neural networks

Fault-tolerant quantum computers offer the promise of dramatically improving machine learning through speed-ups in computation or improved model scalability. In the near-term, however, the benefits of quantum machine learning are not so clear. Understanding expressibility and trainability of quantum models-and quantum neural networks in particular-requires further investigation. In this work, we use tools from information geometry to define a notion of expressibility for quantum and classical models. The effective dimension, which depends on the Fisher information, is used to prove a novel generalisation bound and establish a robust measure of expressibility. We show that quantum neural networks are able to achieve a significantly better effective dimension than comparable classical neural networks. To then assess the trainability of quantum models, we connect the Fisher information spectrum to barren plateaus, the problem of vanishing gradients. Importantly, certain quantum neural networks can show resilience to this phenomenon and train faster than classical models due to their favourable optimisation landscapes, captured by a more evenly spread Fisher information spectrum. Our work is the first to demonstrate that well-designed quantum neural networks offer an advantage over classical neural networks through a higher effective dimension and faster training ability, which we verify on real quantum hardware.
Cosine-Distance Virtual Adversarial Training for Semi-Supervised Speaker-Discriminative Acoustic Embeddings

In this paper, we propose a semi-supervised learning (SSL) technique for training deep neural networks (DNNs) to generate speaker-discriminative acoustic embeddings (speaker embeddings). Obtaining large amounts of speaker recognition train-ing data can be difficult for desired target domains, especially under privacy constraints. The proposed technique reduces requirements for labelled data by leveraging unlabelled data. The technique is a variant of virtual adversarial training (VAT) [1] in the form of a loss that is defined as the robustness of the speaker embedding against input perturbations, as measured by the cosine-distance. Thus, we term the technique cosine-distance virtual adversarial training (CD-VAT). In comparison to many existing SSL techniques, the unlabelled data does not have to come from the same set of classes (here speakers) as the labelled data. The effectiveness of CD-VAT is shown on the 2750+ hour VoxCeleb data set, where on a speaker verification task it achieves a reduction in equal error rate (EER) of 11.1% relative to a purely supervised baseline. This is 32.5% of the improvement that would be achieved from supervised training if the speaker labels for the unlabelled data were available.
Forming Ensembles at Runtime: A Machine Learning Approach

Smart system applications (SSAs) built on top of cyber-physical and socio-technical systems are increasingly composed of components that can work both autonomously and by cooperating with each other. Cooperating robots, fleets of cars and fleets of drones, emergency coordination systems are examples of SSAs. One approach to enable cooperation of SSAs is to form dynamic cooperation groups—ensembles—between components at runtime. Ensembles can be formed based on predefined rules that determine which components should be part of an ensemble based on their current state and the state of the environment (e.g., “group together 3 robots that are closer to the obstacle, their battery is sufficient and they would not be better used in another ensemble”). This is a computationally hard problem since all components are potential members of all possible ensembles at runtime. In our experience working with ensembles in several case studies the past years, using constraint programming to decide which ensembles should be formed does not scale for more than a limited number of components and ensembles. Also, the strict formulation in terms of hard/soft constraints does not easily permit for runtime self-adaptation via learning. This poses a serious limitation to the use of ensembles in large-scale and partially uncertain SSAs. To tackle this problem, in this paper we propose to recast the ensemble formation problem as a classification problem and use machine learning to efficiently form ensembles at scale.
Feature selection to increase the random forest method performance on high dimensional data

Random Forest is a supervised classification method based on bagging (Bootstrap aggregating) Breiman and random selection of features. The choice of features randomly assigned to the Random Forest makes it possible that the selected feature is not necessarily informative. So it is necessary to select features in the Random Forest. The purpose of choosing this feature is to select an optimal subset of features that contain valuable information in the hope of accelerating the performance of the Random Forest method. Mainly for the execution of high-dimensional datasets such as the Parkinson, CNAE-9, and Urban Land Cover dataset. The feature selection is done using the Correlation-Based Feature Selection method, using the BestFirst method. Tests were carried out 30 times using the K-Cross Fold Validation value of 10 and dividing the dataset into 70% training and 30% testing. The experiments using the Parkinson dataset obtained a time difference of 0.27 and 0.28 seconds faster than using the Random Forest method without feature selection. Likewise, the trials in the Urban Land Cover dataset had 0.04 and 0.03 seconds, while for the CNAE-9 dataset, the difference time was 2.23 and 2.81 faster than using the Random Forest method without feature selection. These experiments showed that the Random Forest processes are faster when using the first feature selection. Likewise, the accuracy value increased in the two previous experiments, while only the CNAE-9 dataset experiment gets a lower accuracy. This research’s benefits is by first performing feature selection steps using the Correlation-Base Feature Selection method can increase the speed of performance and accuracy of the Random Forest method on high-dimensional data.
Design of Embedded Target Tracking System Based on MobileNet and KCF

With the development of computer technology, people pay more attention to target detection and tracking technology. The computer vision technology can be used to detect and track the target in the monitoring scene. However, the tracking algorithms are usually programed on the computer platform and cannot be utilized for automatically tracking, which severely limits the application scope of the target tracking system. In this paper, we construct a target detection and tracking system based on the embedded development board and image analysis technology. The int8 quantized MobileNetSSD network model is utilized and the improved kernel correlation filtering (KCF) target tracking algorithm is transplanted to the embedded development board. The video data is collected through the external camera and the tracking information is output to the display screen in a real time. At last, an experimental platform is built to verify the feasibility of the tracking algorithm based on the embedded development board.
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach

Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains common in fields such as cognitive impairment (CI) detection.Manually engineering features from noisy text is time and resource consuming, and can potentially result in features that do not enhance model performance.To combat this, we describe a new approach to feature engineering that leverages sequential machine learning models and domain knowledge to predict which features help enhance performance.We provide a concrete example of this method on a standard data set of CI speech and demonstrate that CI classification accuracy improves by 2.3% over a strong baseline when using features produced by this method.This demonstration provides an example of how this method can be used to assist classification in fields where interpretability is important, such as health care.
Deep Energy-based Modeling of Discrete-Time Physics

Physical phenomena in the real world are often described by energy-based modeling theories, such as Hamiltonian mechanics or the Landau theory, which yield various physical laws. Recent developments in neural networks have enabled the mimicking of the energy conservation law by learning the underlying continuous-time differential equations. However, this may not be possible in discrete time, which is often the case in practical learning and computation. Moreover, other physical laws have been overlooked in the previous neural network models. In this study, we propose a deep energy-based physical model that admits a specific differential geometric structure. From this structure, the conservation or dissipation law of energy and the mass conservation law follow naturally. To ensure the energetic behavior in discrete time, we also propose an automatic discrete differential algorithm that enables neural networks to employ the discrete gradient method.
Disentangle-based Continual Graph Representation Learning

Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data.However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming data.To address this issue, we study the problem of continual graph representation learning which aims to continually train a graph embedding model on new data to learn incessantly emerging multi-relational data while avoiding catastrophically forgetting old learned knowledge.Moreover, we propose a disentangle-based continual graph representation learning (DiC-GRL) framework inspired by the human's ability to learn procedural knowledge.The experimental results show that DiCGRL could effectively alleviate the catastrophic forgetting problem and outperform state-of-the-art continual learning models.
Performance Enhancement of Customer Segmentation Using a Distributed Python Framework, Ray

Over the years, there has been a huge popularity of the recommender systems worldwide. Recommender systems have been implemented over several domains ranging from recommendations for videos and movies to that for products and applications, and many more. The algorithms, which are used for recommender systems, implement segmentation of the customer based on several attributes. These algorithms are time-consuming and require comparatively high computation power. This work deals with the parallelization of different algorithms for simple customer segmentation in the Python environment using the framework, Ray. The dataset for this work includes a huge list of purchases that are carried out by 4000 customers, over a year. The parallelization is carried out throughout the multicores of CPU and the cores of GPU. Additionally, the work also shows the speedup that is obtained after parallelization, for analyzing the overall increase in performance.
Can Automatic Post-Editing Improve NMT?

Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort. APE has had notable success when used with statistical machine translation (SMT) systems but has not been as successful over neural machine translation (NMT) systems. This has raised questions on the relevance of APE task in the current scenario. However, the training of APE models has been heavily reliant on large-scale artificial corpora combined with only limited human post-edited data. We hypothesize that APE models have been underperforming in improving NMT translations due to the lack of adequate supervision. To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT. We empirically show that a state-of-art neural APE model trained on this corpus can significantly improve a strong in-domain NMT system, challenging the current understanding in the field. We further investigate the effects of varying training data sizes, using artificial training data, and domain specificity for the APE task. We release this new corpus under CC BY-NC-SA 4.0 license at https://github.com/shamilcm/pedra.
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora

What kind of basic research ideas are more likely to get applied in practice?There is a long line of research investigating patterns of knowledge transfer, but it generally focuses on documents as the unit of analysis and follow their transfer into practice for a specific scientific domain.Here we study translational research at the level of scientific concepts for all scientific fields.We do this through text mining and predictive modeling using three corpora: 38.6 million paper abstracts, 4 million patent documents, and 0.28 million clinical trials.We extract scientific concepts (i.e., phrases) from corpora as instantiations of "research ideas", create concept-level features as motivated by literature, and then follow the trajectories of over 450,000 new concepts (emerged from 1995-2014) to identify factors that lead only a small proportion of these ideas to be used in inventions and drug trials.Results from our analysis suggest several mechanisms that distinguish which scientific concept will be adopted in practice, and which will not.We also demonstrate that our derived features can be used to explain and predict knowledge transfer with high accuracy.Our work provides greater understanding of knowledge transfer for researchers, practitioners, and government agencies interested in encouraging translational research.