Reviewerly/exHarmony huggingface dataset:
Novel beta function-based image encryption with fractional sine transform

Due to the rapidly growing technological advancements especially in the field of data science and associated technologies like the Internet of things, the safety concern of important data is widespread. Data collection and exchange have achieved greater levels in this cyber era so the fishing activities. Digitized visual content security is therefore much needed here because the major part of digital data contains images or video also due to the rapid growth in smart mobile communication and allied instruments and gadgets. Different image protection strategies are therefore already proposed to ensure this. A hybrid technique combining discrete fractional sine transformation with Beta function is therefore proposed here in the sequence. This hybrid combination is remarkable in the sense that compared to its ancestor algorithm; it has greater PSNR, least reconstruction error and promising statistical results viz. NPCR, UACI and correlation coefficient.
Banking system stability: A prerequisite for financing the Sustainable Development Goals in Nigeria

The banking system, which has been the fulcrum of funding for Nigeria’s economy, is plagued by instability in the face of a growing amount of non-performing loans. This is examined in the current milieu of the need for funding the Sustainable Development Goals (SDGs). Using a number of proxies for SDGs 8 and 9, annual time series data covering 1992 to 2019 were used with variables such as GDP per capita, commercial banks’ loans to small-scale enterprises, banking system stability indicators and liquid assets to total assets of banks. The study utilized the Autoregressive Distributed Lag. Findings showed that banking system stability has a significant positive effect on funding the SDGs 8 and 9 beyond the five per cent level of significance within the study period. Non-performing loans remained negative throughout the study. The result suggests that banking stability would enhance funding of the SDGs, and banks would be stable if they finance the SDGs. The policy implication explains the importance of banks actively pursuing opportunities to build sustainable enterprises and developing strategies that will enable their core banking business to be more venture-driven rather than consumer-oriented. In conclusion, there is a need to completely eliminate or reduce the quantum of non-performing loans from the system and establish a regulatory framework that will facilitate its expected role of intermediation in the economy profitably and successfully. AcknowledgmentThe authors would like to appreciate Covenant University for financial support to publish this paper.
Autocorrect in the Process of Translation — Multi-task Learning Improves Dialogue Machine Translation

Tao Wang, Chengqi Zhao, Mingxuan Wang, Lei Li, Deyi Xiong. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. 2021.
A study of English teaching based on the effectiveness of the cultivation of vocational technical talents

As a big country of population and strong education, the importance of education is self-evident. Especially in recent years, with the deepening of the reform of education system and quality education, the demand of the society for the application-oriented and skilled talents is increasing, which greatly promotes the development of higher vocational education. The main goal of higher vocational education is to cultivate students with professional skills and vocational ability, and the employment quality and employment rate of students are the important standards to measure the quality of higher vocational education. For higher vocational English, it has stronger applicability, so constructing a scientific and reasonable teaching mode based on vocational ability is quite necessary, which can effectively cultivate students’ practical ability and promote the effectiveness of classroom teaching. This paper makes an in-depth analysis and discussion on the occupation skill-oriented vocational English teaching mode, and according to the needs of professional talents in higher vocational education in our country, the author deeply studies the existing problems in the teaching of English in contemporary society and solves them actively, so that the English teaching in higher vocational colleges in China can achieve better results.
Development of an algorithm for optimization of continuous technological process of cultivation of microorganisms

When implementing the tasks of controlling technological processes, finding the optimal control actions, and creating control algorithms that implement the optimal modes of technological processes, it is necessary to present the criterion of optimality in the form of a goal function, the extremum of which best meets the purpose of this object and expressed as - Relevant technical and economic indicators. The criterion of optimality should be an integral indicator that reflects the main aspects of production. Profit is most often taken as such a criterion for typical microbiological industries - as the most generalized indicator, reflecting almost all aspects of the enterprise. Possible criteria of optimality are analyzed in the form of technical and economic indicators of the process of cultivation of microorganisms, the extremum of which best meets the objectives of production and reflects the main aspects of the functioning of the control object. The analysis of possible modes of microalgae cultivation has been carried out. Two optimization algorithms are substantiated. The first one is based on random search method with an absolute bias, an algorithm for optimizing the process of cultivating microorganisms with continuous regeneration of the flow in one cultivator. The second is an algorithm for determining the optimal residence time of chlorella particles in multistage cultivators, focused on the method of dynamic programming implemented in Wellman's recurrence relation. The developed algorithm for operational forecasting and automatic control of the chlorella cultivation process allows, under given production conditions and the composition of nutrients, to increase the productivity of technological equipment and improve the quality of the target product, as well as to prevent in advance various unforeseen and emergency production situations.
Depth Super-Resolution by Texture-Depth Transformer

Depth maps have been still suffering from some non-negligible effects, resulting from the consumer-level sensors. The limited resolution of the acquired depth maps is one of these annoying issues. Many prominent researchers have recently made a lot of efforts, such as traditional filters, as well as the deep learning paradigms. However, depth super-resolution is still an open challenge. In this paper, we design a texture-depth transformer for depth super-resolution task, which can learn the corresponding structural information of the high-resolution texture images and the corresponding interpolated depth maps. Moreover, a multi-scale feature fusion strategy is exploited to further enhance the fusion feature. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed approach.
Certificateless Public Auditing Scheme with Data Privacy Preserving for Cloud Storage

Rapid development of cloud storage services, users are allowed to upload heavy storage and computational cost to cloud to reduce the local resource and energy consumption. While people enjoy the desirable benefits from the cloud storage service, critical security concerns in data outsourcing have been raised seriously. In the cloud storage service, data owner loses the physical control of the data and these data are fully controlled by the cloud server. As such, the integrity of outsourced data is being put at risk in reality. Remote data integrity checking (RDIC) is an effective solution to checking the integrity of uploaded data. However, most RDIC schemes are rely on traditional public key infrastructure (PKI), which leads communication and storage overhead due to the certificate management. Identity-based RDIC scheme is not need the storage management, but it has a drawback of key escrow. To solve these problems, we propose a practical certificateless RDIC scheme. Moreover, many public auditing schemes authorize the third party auditor (TPA) to check the integrity of remote data and the TPA is not fully trusted. Thus, we take the data privacy into account. The proposed scheme not only can overcome the above deficiencies but also able to preserve the data privacy against the TPA. Our theoretical analyses prove that our mechanism is correct and secure, and our mechanism is able to audit the integrity of cloud data efficiently.
DIACR-Ita @ EVALITA2020: Overview of the EVALITA2020 Diachronic Lexical Semantics (DIACR-Ita) Task.

This paper describes the first edition of the “Diachronic Lexical Seman-tics” (DIACR-Ita) task at the EVALITA2020 campaign. The task challenges participants to develop systems that can automatically detect if a given word has changed its meaning over time, given con-textual information from corpora.The task, at its first edition, attracted 9 participant teams and collected a total of 36 sub-mission runs
A Zero-Shot Method for 3D Medical Image Segmentation

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, existing methods based on outstanding 2.5D and 3D segmentation strategies are time-consumption and hardware-consumption while ensuring high accuracy. In order to reduce the high demand for automatic segmentation of tumor images and avoid the noise interference in a single input image, we propose an end-to-end zero-shot CNN segmentation method. Our method only utilizes two adjacent images, instead of the target image, as the input data of deep neural network to predict the brain tumor area in the target image. Avoiding noise interference in the target image, this method makes full use of the spatial context feature between adjacent slices in order to obtain accurate zero-shot segmentation results. We compare with the state-of-the-art segmentation frameworks on the same benchmark and notice that our method has strong competitiveness.
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice

This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning/symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski’s general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition.
Hybrid graph-based Sybil detection with user behavior patterns

Online social networks (OSNs) are known to be vulnerable to Sybil Attack, where attackers leverage the openness to create multiple fake identities for launching many malicious activities. In this paper, we define a weighted-strong-social (WSS) graph that integrates the OSN structure and user behavior patterns and propose a novel hybrid graph-based sybil detection approach. The hybrid approach estimates the trustworthiness of users and user pairs based on user behaviors that can be obtained locally and add them to the OSN structure to construct a WSS graph for sybil detection. The evaluation results show that the AUC of the hybrid approach is 0.954, which is significantly higher than that of previous sybil detection methods.
Efficacy of Oversampling Over Machine Learning Algorithms in Case of Sentiment Analysis

Text classification is a very important problem in artificial intelligence domain and covers a wide portion in natural language processing, which can be called as sentiment analysis. Sentiment analysis is basically extracting the tone or emotion of the writer, by understanding the text sequence. This way of approach is to understand the sentiment of a text considering as a boon in the customer management system and can easily be applied to the social media sites, such as twitter or e-commerce websites, like amazon to get the customer review and analyze. Sentiment analysis can be binary or multiclass, here in our approach, we will consider both of them, by doing a comparative study between long short-term memory (LSTM), random forest, support vector machine(SVM), and XGBoost, to check if they can be as good as LSTM in any case. Also, as we discover the data distribution problem in our datasets, so we will be applying oversampling to make the distribution in a stabilized form.
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration

ACTIVEANNO is a novel annotation tool focused on document-level annotation tasks developed both for industry and research settings.It is designed to be a general-purpose tool with a wide variety of use cases.It features a modern and responsive web UI for creating annotation projects, conducting annotations, adjudicating disagreements, and analyzing annotation results.ACTIVEANNO embeds a highly configurable and interactive user interface.The tool also integrates a RESTful API that enables integration into other software systems, including an API for machine learning integration.ACTIVEANNO is built with extensible design and easy deployment in mind, all to enable users to perform annotation tasks with high efficiency and high-quality annotation results.
Reimagining the Future of Healthcare Industry through Internet of Medical Things (IoMT), Artificial Intelligence (AI), Machine Learning (ML), Big Data, Mobile Apps and Advanced Sensors

From Bluetooth enabled hearing aids to robotic caretakers, wearable and smart devices industries are immensely contributing to the development of the healthcare industry with the help of Internet of Things (IoT). Latest technologies like Artificial Intelligence, 3D Printing, Big data, Machine Learning, Advanced Sensors, Mobile Applications and other technologies will continue to generate lot of opportunities for Medtech organizations. Some of the latest healthcare innovations practiced at present might have been seen or read by some of us only in science fiction movies or science fiction stories a long ago. Presently, IoT and Artificial Intelligence is creating a revolution in healthcare industry when it comes to diagnosis and treatment of varied diseases. From smartphones to robots, artificial intelligence is already making its presence felt in healthcare industry and as such it is progressively recognizing the transformative nature of IoT technologies which drives innovation in the development of connected medical devices. Gradual increase in the number of connected medical devices with the advent of technology advancements helps to capture and transmit medical related data wherever and whenever required to the concerned people and thus, it gave birth to the Internet of Medical Things (IoMT), where the Internet of Things (IoT) and healthcare meet. The IoMT helps to constantly monitor and alter (if required) the behvaiour of the patient and his/her health status in real time and also supports healthcare organizations to effectively streamline clinical processes, patient information and related work flows to enhance its operational productivity. The IoMT has made and continues to make the delivery of P4 Medicine (Predictive, Preventive, Personalized and Participatory) even for remote locations with the help of connected sensors and devices helping in real-time patient care. IoMT helps doctors and caregivers to provide patient care and support by constantly monitoring data related to patients through mobile apps and connected medical devices even when patient(s) or doctor(s) are located at remote locations. This research paper discusses about six use cases explaining how IoMT is applied in healthcare industry.
HiLAM-aligned kernel discriminant analysis for text-dependent speaker verification

Probabilistic Linear Discriminant Analysis (PLDA) has been a commonly used backend classifier for many text-dependent speaker verification (TDSV) systems. Lately, PLDA projections have been integrated with the traditional Dynamic Time Warping (DTW) template matching framework, resulting in the DTW Online i-vector/PLDA system. The system is shown to achieve state-of-the-art performance for TDSV task. PLDA model serves to train a subspace that compensates for channel and session variabilities. It assumes linear separability between speaker-phrase information and other components. However, this relationship is known to be non-linear. The non-linearity is more prominent in case of short speech extracts, as in the case of the online i-vectors. This results in loss of vital speaker-phrase information at PLDA modeling. To this end, this work explores Kernel Discriminant Analysis (KDA) for TDSV task. It further proposes to use Hierarchical Multi-Layer Acoustic Model (HiLAM) to complement KDA with a more effective speaker-text class definition. The proposed system is hypothesized to benefit on three counts — non-linear modeling ability of KDA, speaker idiosyncrasy information associated with HiLAM-defined speaker-text units and modeling of the exact context of the pass-phrase, as offered by HiLAM. It shows a relative Equal Error Rate (EER) reduction of up to 50.63% on Part 1 of the RSR2015 database when compared to the baseline DTW Online i-vector/PLDA system.
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis

How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized by a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depths or shapes. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression.
Estimating Historical Exposure to Respirable Crystalline Silica in the Mining Industry in Ontario, Canada Using a Newly Developed Exposure Database

Abstract Objectives To use the recently developed Ontario Mining Exposure Database (OMED) to describe historical silica exposure in the Ontario metal mining industry and identify predictors of historical silica exposure. Methods Personal respirable crystalline silica (RCS) data for metal mines were extracted from OMED and included both individual and summary measures, where multiple exposure measurements (n &amp;gt; 1) were aggregated and entered as a single exposure value (n = 1). Data were stratified by sample location (underground/surface) for analysis. Monte Carlo simulation was used to simulate individual measures from the summary measures. A fixed effects multiple linear regression model was used to assess the effects of commodity (ore mined), sample year, source of exposure data, and occupational group on RCS concentration. Parameter estimates (β), standard errors, and 95% upper and lower confidence intervals were reported. Results The OMED contained 12 995 silica measurements. After limiting to RCS measurements in metal mines, and measures with sufficient information for analysis, 2883 RCS measurements collected from 1974 to 1991 remained, including 2816 individual and 67 summary measurements. In total, 321 individual RCS measurements were simulated from the 67 summary measures. The analysis database contained 2771 (12% simulated) underground measurements and 366 surface measurements (0% simulated). In the underground group, an overall geometric mean (GM) of 0.05 [geometric standard deviation (GSD) 3.09] mg m−3 was estimated with a 6% annual decrease over time. In this group, the commodity with the highest average RCS level was zinc mines (GM = 0.07 mg m−3) and the lowest was iron mines (GM = 0.01 mg m−3). In the surface group, an overall GM of 0.05 (GSD 3.70) mg m−3 was estimated with an 8% decreased over time. In this group, the commodity with the highest average RCS level was gold mines (GM = 0.07 mg m−3) and the lowest was zinc mines (GM = 0.03 mg m−3). In both groups, company collected data had lower estimated RCS compared with regulator collected data. Conclusions Historical RCS levels decreased over time. Mean measurements exceeded the American Conference of Governmental Industrial Hygienists current health-based threshold limit value (0.025 mg m−3). The main predictors of exposure were commodity, source of exposure data, and sample year. However, low R2 and high GSD values suggest additional predictors of RCS exposures in Ontario’s metal mines exist that were unavailable in OMED.