Reviewerly/exHarmony huggingface dataset:
Accuracy Analysis of ANN Back Propagation, Neuro-Fuzzy, and Radial Basis Function: A Case of HDI Forecasting

One measure of the progress of a region or country is the increase in the Human Development Index (HDI) which includes life expectancy, per capita income, and old school expectations. HDI becomes an essential reference in time-series data, so it needs to be done forecasting process with reliable method. We use HDI data as much as the last nine years in West Nusa Tenggara province, which is one of the regions with the highest HDI acceleration in Indonesia in recent years. We do forecasting by comparing three methods namely Back Propagation (BP), Neuro-Fuzzy (NF), and Radial base Function (RBF), covering forecasting with 3 models of training and testing on the Back Propagation method, 9 training and testing models on A Neuro-Fuzzy method, and 1 training and testing model in the Radial base Function method. While the parameter accuracy (error) used in this forecasting is Mean Square Error (MSE). Based on the results of the simulation obtained NTB province predictions in 2019 using the Back Propagation (BP) method of 67.46 (increased by 0.23%); The RBF method amounted to 67.3 (fixed); and the NF method of 67.18 (decreased by 0.17%). From these results, the conclusion that in this case, the BP method is very good at doing simulation and decision-making results. The results were obtained from simulated data witt type training TRAINGDA, TRAINGDX, and TRAINRP. But simulation using type TRAINRP has the best parameter output with a performance (R) of 0.99194, a validation check of 1000, a gradient of 13.8, and a level of accuracy of 99.39%.
A Q-learning based UAV Path Planning Method with Awareness of Risk Avoidance

Without considering problem of low availability caused by training cost in a large exploration space, many path planning methods of unmanned aerial vehicle (UAV) based on reinforcement learning have poor convergence. In this paper, a Risk-free UAV planning method based on Q-learning algorithm is proposed. The method introduces risk awareness training into path planning, enabling UAV to use real-time distance between target location and UAV as guidance information for training, so that the UAV can reduce the ineffective exploration space and learning costs and improve the algorithm convergence for large space exploration. Simulation results have shown that compared with standard Q-learning (SQL) algorithm, the proposed Risk- free Q-learning (RFQL) method achieves better results in terms of convergence and security.
Digital Risk Management for Data Attacks against State Evaluation

Understanding reasonable framework cyber attacks is essential for creating material assurance and recuperation measures. Propelled attacks follow exploited contact at diminished expenses and recognize capacity. This paper behaviors chance investigation of joined data trustworthiness and handiness attacks against the office framework state evaluation. We will in general contrast the consolidated attacks and unadulterated honesty attacks - false data infusion attacks. A safety record for defenselessness appraisal to those two sorts of attacks is arranged and created because a blended number connected science drawback. We will in general demonstrate that such joined attacks will prevail with less assets than false data infusion attacks. The consolidated attacks with confined data of the framework design also open gifts to keep camouflage against the undesirable data location. At last, the risk of joined attacks to dependable framework activity is assessed abuse the outcomes from defenselessness evaluation and attacks sway examination. The discoveries during this paper are substantial and upheld by a top to bottom contextual investigation
Bone Cancer Detection from X-Ray and MRI Images through Image Segmentation Techniques

This paper is based on integration of the biomedical field and computer science. Paper contains the study of bone cancer and features to predict the type of the same. Related work to find cancer in human body using computer vision is discussed in this paper. Image segmentation technique like sobel, prewitt, canny, K-means and Region Growing are described in this paper which can be stimulated for X-Ray and MRI image interpretation. Paper also shows the result of edge based and region based image segmentation techniques applied on X-Ray image to detect osteosarcoma cancer present on bone using MATLAB. Finally, paper concluded by finding best suited segmentation method for grey scaled image with future aspects.
Planning Individual Educational Trajectory in Continuing Education

Introduction. The current socio-economic development of Russia is characterized by intense changes in the field of interaction between the employment sector and the education system. The society positively perceives the measures of the state to support and develop education, which has become the most important area of social policy, going beyond the interests of the pedagogical community. Methods. Today, professional education institutions operate in challenging conditions. It is necessary to provide advanced training of specialists for innovation economy taking into consideration long-term demographic situation. Results. Development of individual educational trajectories is a multifaceted process. The goal of an individual educational trajectory is to ensure the development of self-reliance and individual initiatives, the possibility of the fullest realization of the creative potential for successful activities in the professional sphere. Discussion. The classification of external factors was developed as a result of the study. The external factors were classified by the type of activity (political, economic, social, legal, educational, etc.); by the source of action (state, cultural, technical, financial, spiritual, etc.); by the effect of exposure (direct/indirect action); by value relation (favorable, neutral, unfavorable, threatening); by the content of action (wide and narrow). The main external factors, influencing the development and implementation of educational trajectories in the continuous education system, in accordance with current legislation, are the general state educational policy, the economic viability of the education system, provision of social guarantees for the realization of citizens' rights to education. Conclusion. Planning of educational trajectory is a joint activity of a student and a teacher, consisting of several interrelated stages.
Performance Analysis of Advanced Encryption Standard (AES) S-boxes

The Advanced Encryption Standard (AES) algorithm is available in a wide scope of encryption packages and is the single straightforwardly accessible cipher insisted by the National Security Agency (NSA), The Rijndael S-box is a substitution box S-Box assumes a significant job in the AES algorithm security. The quality of S-Box relies upon the plan and mathematical developments. Our paper gives an outline of AES S-Box investigation, the paper finds that algebraic attack is the most security gap of AES S-Box, likewise give a thought regarding distinctive past research to improve the static S-confines that has been utilized AES, to upgrade the quality of AES Performance by shocking the best S-box.
FENL: an ISE to mitigate analogue micro-architectural leakage

Ge et al. [GYH18] propose the augmented ISA (or aISA), a central tenet of which is the selective exposure of micro-architectural resources via a less opaque abstraction than normal. The aISA proposal is motivated by the need for control over such resources, for example to implement robust countermeasures against microarchitectural attacks. In this paper, we apply an aISA-style approach to challenges stemming from analogue micro-architectural leakage; examples include power-based Hamming weight and distance leakage from relatively fine-grained resources (e.g., pipeline registers), which are not exposed in, and so cannot be reliably controlled via, a normal ISA. Specifically, we design, implement, and evaluate an ISE named FENL: the ISE acts as a fence for leakage, preventing interaction between, and hence leakage from, instructions before and after it in program order. We demonstrate that the implementation and use of FENL has relatively low overhead, and represents an effective tool for systematically localising and reducing leakage.
Towards Empowering Educators to Create their own Smart Personal Assistants

Despite a growing body of research about the design and use of Smart Personal Assistants such as Amazon's Alexa or Google's Assistant, little is known about their ability to help educators offering individual support in large-scale learning environments. Smart Personal Assistant ecosystems empower educators to develop their own agents without deep technological knowledge. The objective of this paper is to design and validate a method that helps educators to create SmartPersonal Assistants for their learning environments.Using a design science research approach, we first gather requirements from students and educators as well as from information systems and education theory.Next, we create an alpha version of our method and evaluate it with a focus group before we instantiate our artifact in an everyday learning environment.The findings indicate that our method is able to empower educators to design Smart Personal Assistants that significantly improve students' learning success.
Cyber Security

Abstract Cyber Security accepts a vigorous role in the area of information technology. Safeguarding the information has become an enormous problem in the current day. The cybersecurity the main thing that originates in mind is ‘cyber crimes’ which are aggregate colossally daily. Different governments and organizations are taking numerous measures to keep these cyber wrongdoings. Other than different measures cybersecurity is as yet a significant worry to many. This paper mostly emphases on cyber security and cyber terrorism. The significant trends of cybersecurity and the consequence of cybersecurity discuss in it. The cyber-terrorism could make associations lose billions of dollars in the region of organizations. The paper also explains the components of cyber terrorism and motivation of it. Two case studies related to cybersecurity also provide in this paper. Some solution about cyber security and cyber terrorism also explain in it.
Shedding Light on Shadowbanning

For social media services, content moderation — the policies, practices and tools providers have in place to address online abuse — is their primary tool to defend users from everything from mis- and disinformation to hate speech and online extremism. As the list of potential abuses continues to grow, online service providers have commensurately built new systems to enforce their content policies, largely through removing or reducing the visibility of potentially abusive content. But social media services don’t always inform users when they are moderating their content: a user’s comment may appear normally to themselves but be hidden to others; a user’s handle may disappear in search; a user’s original content may get ranked so low in a recommendation algorithm that, for all intents and purposes, it becomes undiscoverable. On the internet, people refer to this broad range of undisclosed content moderation actions as shadowbanning.The goal of this paper is to bridge the gap between social media companies, end users, and the broader public in how each understands shadowbanning in order to help social media companies better manage disclosures to users about content moderation. We aim to do this by critically examining three questions: (1) What is shadowbanning? (2) Who is affected by shadowbanning? (3) What larger effects does shadowbanning have?The first section of this paper defines the term shadowbanning, reviews specific shadowbanning practices in areas such as search, comments, and recommendations, and discusses the reasons social media companies give for why they engage in shadowbanning. The second section looks at which groups may be disproportionately affected by shadowbanning and describe how users diagnose and respond to their own shadowbanning. The third section explores the consequences of shadowbanning on individuals, groups, and society at large. The fourth and final section recommends three ways social media services can mitigate the harms of shadowbanning: sharply limiting the circumstances in which they shadowban, “shedding light” on shadowbanning by publishing their policies about when they shadowban and data about content or accounts subject to opaque content moderation, and creating mechanisms to allow researchers to learn more about the potential harms shadowbanning may cause. URL: https://cdt.org/insights/shedding-light-on-shadowbanning/
Image Sensing and Processing with Convolutional Neural Networks

Convolutional neural networks are a class of deep neural networks that leverage spatial information, and they are therefore well suited to classifying images for a range of applications [...].
Effect of Credit to Farmers and Agricultural Productivity in Nigeria

In Nigeria, there has been a continuous decline in farmers access to credit facilities. This study examined the effect of farmers credit on agricultural productivity from 1981 to 2016 using data from World Bank Development Index (WDI). The result of the Ordinary Least Squares (OLS) estimation shows that agricultural bank credit (β=0.667173, t=5.961095 &amp; P&lt;0.05) exerts a significant positive effect on agricultural output. Bank lending rate (β=1.094792, t=1.295874 &amp; P&gt;0.05) and foreign exchange rate (β=0.124297, t=0.437929 &amp; P&gt;0.05) do not show a significant effect on agricultural output. It was submitted that bank credit has a significant positive effect on agricultural productivity in Nigeria. The need for government to promote savings and bank credit to farmers was recommended.
One-Class LSTM Network for Anomalous Network Traffic Detection

Artificial intelligence-assisted security is an important field of research in relation to information security. One of the most important tasks is to distinguish between normal and abnormal network traffic (such as malicious or sudden traffic). Traffic data are usually extremely unbalanced, and this seriously hinders the detection of outliers. Therefore, the identification of outliers in unbalanced datasets has become a key issue. To help solve this challenge, there is increasing interest in focusing on one-class classification methods that train models based on the samples of a single given class. In this paper, long short-term memory (LSTM) is introduced into one-class classification, and one-class LSTM (OC-LSTM) is proposed based on the traditional one-class support vector machine (OC-SVM). In contrast with other hybrid deep learning methods based on auto-encoders, the proposed method is an end-to-end training network that uses a loss function such as the OC-SVM optimization objective for model training. A comprehensive experiment on three large complex network traffic datasets showed that this method is superior to the traditional shallow method and the most advanced deep method. Furthermore, the proposed method can provide an effective reference for anomaly detection research in the field of network security, especially for the application of one-class classification.
Privacy-Preserving KNN Classification Algorithm for Smart Grid

With the development of Internet of Things (IOT), outsourcing data and tasks to a cloud server has become a popular and economical way for small devices with restricted ability. The k-nearest neighbors (KNNs) classification algorithm have been commonly applied to medical image classification, abnormal detection, defective product identification, and so on. The previous privacy-preserving KNN algorithms are based on two cloud servers, which have high computational and communication costs. In this paper, we design a privacy-preserving KNN classification (PPKC) algorithm with single cloud server for smart grid. Specifically, each smart meter and control center encrypted their data with Paillier cryptosystem and the cloud server does some calculation on the encrypted data. We prove that PPKC can protect the privacy of both smart meters and control centers, and the classification results are also private for the server. Besides, both smart meters and control centers can stay offline after uploading their data. The experiment results demonstrate that the proposed PPKC algorithm is more efficient than the previous algorithms and it can obtain almost the same accuracy as the original KNN, which means the PPKC algorithm is more applicable for small devices in IOT.
Learning to Retrieve Videos by Asking Questions

The majority of traditional text-to-video retrieval systems operate in static environments, i.e., there is no interaction between the user and the agent beyond the initial textual query provided by the user. This can be suboptimal if the initial query has ambiguities, which would lead to many falsely retrieved videos. To overcome this limitation, we propose a novel framework for Video Retrieval using Dialog (ViReD), which enables the user to interact with an AI agent via multiple rounds of dialog. The key contribution of our framework is a novel multimodal question generator that learns to ask questions that maximize the subsequent video retrieval performance. Our multimodal question generator uses (i) the video candidates retrieved during the last round of interaction with the user and (ii) the text-based dialog history documenting all previous interactions, to generate questions that incorporate both visual and linguistic cues relevant to video retrieval. Furthermore, to generate maximally informative questions, we propose an Information-Guided Supervision (IGS), which guides the question generator to ask questions that would boost subsequent video retrieval accuracy. We validate the effectiveness of our interactive ViReD framework on the AVSD dataset, showing that our interactive method performs significantly better than traditional non-interactive video retrieval systems. Furthermore, we also demonstrate that our proposed approach also generalizes to the real-world settings that involve interactions with real humans, thus, demonstrating the robustness and generality of our framework.
What Is Significant in Modern Augmented Reality: A Systematic Analysis of Existing Reviews

Augmented reality (AR) is a field of technology that has evolved drastically during the last decades, due to its vast range of applications in everyday life. The aim of this paper is to provide researchers with an overview of what has been surveyed since 2010 in terms of AR application areas as well as in terms of its technical aspects, and to discuss the extent to which both application areas and technical aspects have been covered, as well as to examine whether one can extract useful evidence of what aspects have not been covered adequately and whether it is possible to define common taxonomy criteria for performing AR reviews in the future. To this end, a search with inclusion and exclusion criteria has been performed in the Scopus database, producing a representative set of 47 reviews, covering the years from 2010 onwards. A proper taxonomy of the results is introduced, and the findings reveal, among others, the lack of AR application reviews covering all suggested criteria.
Reference-Based Multi-Level Features Fusion Deblurring Network for Optical Remote Sensing Images

Blind image deblurring is a long-standing challenge in remote sensing image restoration tasks. It aims to recover a latent sharp image from a blurry image while the blur kernel is unknown. To solve this problem, many image priors-based algorithms and learning-based algorithms have been proposed. However, most of these methods are based on a single blurry image. Due to the lack of high frequency information, the images restored by these algorithms still have some deficiencies in edge and texture details. In this work, we propose a novel deep learning model named Reference-Based Multi-Level Features Fusion Deblurring Network (Ref-MFFDN), which registers the reference image and the blurry image in the multi-level feature space and transfers the high-quality textures from registered reference features to assist image deblurring. Comparative experiments on the testing set prove that our Ref-MFFDN outperforms many state-of-the-art single image deblurring approaches in both quantitative evaluation and visual results, which indicates the effectiveness of using reference images in remote sensing image deblurring tasks. More ablation experiments demonstrates the robustness of Ref-MFFDN to the input image size, the effectiveness of multi-level features fusion network (MFFN) and the effect of different feature levels in multi-feature extractor (MFE) on algorithm performance.
Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators

Machine learning algorithms have opened a breach in the fortress of the prediction of high-dimensional chaotic systems. Their ability to find hidden correlations in data can be exploited to perform model-free forecasting of spatiotemporal chaos and extreme events. However, the extensive feature of these evolutions constitutes a critical limitation for full-size forecasting processes. Hence, the main challenge for forecasting relevant events is to establish the set of pertinent information. Here, we identify precursors from the transfer entropy of the system and a deep Long Short-Term Memory network to forecast the complex dynamics of a system evolving in a high-dimensional spatiotemporal chaotic regime. Performances of this triggerable model-free prediction protocol based on the information flowing map are tested from experimental data originating from a passive resonator operating in such a complex nonlinear regime. We have been able to predict the occurrence of extreme events up to 9 round trips after the detection of precursor, i.e., 3 times the horizon provided by Lyapunov exponents, with 92 % of true positive predictions leading to 60 % of accuracy.
End-to-end entanglement in a polar-molecule array under intrinsic decoherence

Dynamic entanglement between two end molecules in a polar-molecule array is investigated in static electric fields. Specific initial states are set to probe the evolution of the molecular system under the influence of intrinsic decoherence. We analyze the degree of entanglement between the end molecules by use of concurrence. When the Bell states are initially shared between the end molecules, the end-to-end concurrence shows either a fragile or a robust behavior. The robust concurrence is less affected by decoherence, reflecting a situation that the initial Bell state can be shared between the end molecules for long periods of time. The stability of the entanglement shared between two arbitrary molecules in the array is further studied. Furthermore, we show how to induce a high end-to-end concurrence via transfer of a separable state. The state is transferred from one end molecule to the other. During the back-and-forth transfer processes, the concurrence cyclically reveals high peaks, corresponding to different Bell-like states. In addition, population inversion is observed to only occur at the end molecules in the array. We further examine the contribution of effective states to the concurrence and discuss the effects of decoherence, dipole-field interaction, and dipole-dipole interaction on its properties.