Reviewerly/exHarmony huggingface dataset:
Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform

An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters.
Evolutionary Synthesis of Failure-Resilient Analog Circuits

Analog circuit design requires large amounts of human knowledge. A special case of circuit design is the synthesis of robust and failure-resilient electronics. Evolutionary algorithms can aid designers in exploring topologies with new properties. Here, we show how to encode a circuit topology with an upper-triangular incident matrix and use the NSGA-II algorithm to find computational circuits that are robust to component failure. Techniques for robustness evaluation and evolutionary algorithm guidances are described. As a result, we evolve square root and natural logarithm computational circuits that are robust to high-impedance or short-circuit malfunction of an arbitrary rectifying diode. We confirm the simulation results by hardware circuit implementation and measurements. We think that our research will inspire further searches for failure-resilient topologies.
A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs

Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.
Multi Feedback LFSR Based Watermarking of FSM

Many techniques are available nowadays, for Intellectual Property(IP) protection of Digital circuits. Out of these techniques, the popular one is watermarking. Similar to the watermarking used in case of text, image and video, watermarking of digital circuits also modifies a digital circuit design in such a way, that only the IP owner of design is able to extract the watermark form the design. In this paper, Multi – Feedback configuration of Linear Feedback Shift Register(LFSR) is used to watermark a FSM based design. This watermarking technique improves the watermark strength of already existing LFSR based watermarking technique. In terms of hardware utilization, it is significantly efficient than some popular watermarking techniques. The proposed technique has been implemented using Verilog HDL in Xilinx ISE and the simulation is done using ModelSim.
Hybridization of Harmony and Cuckoo Search for Managing the Task Scheduling in Cloud Environment

Cloud computing plays a vital role in gathering the physical and virtual resources given to the client on-demand and on a pay as per uses basis using the internet. In today’s world, cloud computing has been considered as the best concept for the virtualization of various resources. There are various approaches that have been available for improvising the load balancing and also to improvise the job scheduling in the concept of cloud. But there are two most important thing in cloud computing, that is, task scheduling and allocation of resources. So, to achieve better performance of resources, task scheduling and allocation of resources must be organized in a more optimized way and hence cloud computing provides great performance in less maintenance cost. In this paper, cuckoo search and harmony search is hybrid and proposed cuckoo harmony search algorithm (CHSA) for enhancing the scheduling process and make it more optimized. As per proposed CHSA, a new hybrid function is developed based on cost, memory, energy consumption, credit, and penalty and the proposed algorithm is compared with cuckoo search and harmony search on all these parameters. After the analysis of proposed CHSA, comparing to cuckoo search and harmony search, it has been find out that, it attains minimum cost, memory usage, energy consumption, penalty, and maximum credit.
Implementing kNearest Neighbor Methods to Predict Car Prices

The demand for automotive in Indonesia has never subsided, considering that the human need for transportation greatly affects people's daily lives. Various attempts are made by manufacturers to produce cars of a quality that is comparable to the costs incurred and following market demand. Prediction is a process that can be done to achieve this goal. One of the prediction methods that can be used in this case is the kNearest Neighbor. The prediction process consists of a preprocessing stage that cleans and filters unnecessary variables, followed by a variable multicollinearity test stage with Variance Inflation Factor (VIF). The multicollinearity test found 4 variables that had a specific influence in predicting the VIF value of these variables, respectively 2.22, 2.08, 1.53, 1.10 for Horse Power, Car Width, Highend, and, Hatchback respectively. The four variables of the VIF test results have a positive correlation with the price variable as the dependent variable. The prediction model is made using 4 variables selected based on the VIF test, to determine the accuracy of the method used, the Linear Regression model and, the kNearest Neighbor through the validation test with Mean Absolute Error (MAE) and R2. The kNearest Neighbor method produces an MAE test of 0.06 and R2 results are 0.843. This can be concluded if the overall kNearest Neighbor method has qualified performance in making predictions with continuous value variables or in other words using the concept of regression.
Nutrition education incorporation into mainstream primary school curriculum in Ghana: Stakeholders’ sources of nutrition information and perceived barriers

Nutrition literacy has been cited as a crucial life skill. Nutrition education as a primary school subject has been treated inconsequentially when compared to other subjects. We investigated an aspect of the current state of nutrition education in Ghana by engaging stakeholders about their sources of nutrition information and the perceived barriers in implementing nutrition education in mainstream primary schools.Three hundred and fifty one (351) primary school children, 121 homebased caregivers, six schoolteachers, two headteachers, two Ghana Education Service (GES) officials, and six school cooks were involved in the study. Surveys were used to collect data on nutrition information acquisition behaviors and to record perceived barriers. Key Informant Interviews were conducted among GES officials, headteachers, schoolteachers and school cooks, while Focus Group Discussions were used among homebased caregivers and children to gather qualitative information.Only 36.3% of the primary school children had heard about nutrition, and 71% of those got nutrition information from their family members. About 70% of homebased caregivers had heard or seen nutrition messages, and their source of nutrition information was predominantly traditional media. Schoolteachers mostly received their nutrition information from non-governmental organizations and the Internet, while most of the school cooks stated their main source of nutrition information was hospital visits. Perceived barriers included schoolteachers' knowledge insufficiency, and lack of resources to adequately deliver nutrition education. Lack of a clear policy appeared to be an additional barrier.The barriers to the implementation of nutrition education in the mainstream curriculum at the primary school level that were identified in this study can be resolved by: providing schoolteachers with learning opportunities and adequate nutrition education resources for practical delivery, having specific national policy framework, and including family members and school cooks in the nutrition education knowledge and information dissemination process.
Robust <i>H<sub>∞</sub> </i> Deep Neural Network-Based Filter Design of Nonlinear Stochastic Signal Systems

Recently, deep neural network (DNN) schemes based on big data-driven methods have been successfully applied to image classification, communication, translation of language, speech recognition, etc.However, more efforts are still needed to apply them to complex robust nonlinear filter design in signal processing, especially for the robust nonlinear H ∞ filter design for robust state estimation of nonlinear stochastic signal system under uncertain external disturbance and output measurement noise.In general, the design problem of robust nonlinear H ∞ filter needs to solve a complex Hamilton-Jacobi-Isaacs equation (HJIE), which is not easily solved analytically or numerically.Further, robust nonlinear H ∞ filter is not easily designed by training DNN directly via conventional big data schemes.In this paper, a novel robust H ∞ HJIE-embedded DNN-based filter design is proposed as a co-design of H ∞ filtering algorithm and DNN learning algorithm for the robust state estimation of nonlinear stochastic signal systems with external disturbance and output measurement noise.In the proposed robust H ∞ DNN-based filter design, we have proven that when the approximation error of HJIE by the trained DNN through Adam learning algorithm approaches to 0, the HJIE-embedded DNN-based filter will approach the robust nonlinear H ∞ filter of nonlinear stochastic signal system with uncertain external disturbance and output measurement noise.Finally, a trajectory estimation problem of 3-D geometry incoming nonlinear stochastic missile system by the proposed robust H ∞ HJIE-embedded DNN-based filter scheme through the measurement by the sensor of radar system with external disturbance and measurement noise is given to illustrate the design procedure and validate its robust H ∞ filtering performance when compared with the extended Kalman filter and particle filter.
Leading ideas of the acmeological concept of social and pedagogical assistance to the employment of university graduates

Л. Г. Пак Е. Г. Каменева Л. А
Foundations of the Socio-Physical Model of Activities (SOMA) for Autonomous Robotic Agents1

In this paper, we present foundations of the Socio-physical Model of Activities (SOMA). SOMA represents both the physical as well as the social context of everyday activities. Such tasks seem to be trivial for humans, however, they pose severe problems for artificial agents. For starters, a natural language command requesting something will leave many pieces of information necessary for performing the task unspecified. Humans can solve such problems fast as we reduce the search space by recourse to prior knowledge such as a connected collection of plans that describe how certain goals can be achieved at various levels of abstraction. Rather than enumerating fine-grained physical contexts SOMA sets out to include socially constructed knowledge about the functions of actions to achieve a variety of goals or the roles objects can play in a given situation. As the human cognition system is capable of generalizing experiences into abstract knowledge pieces applicable to novel situations, we argue that both physical and social context need be modeled to tackle these challenges in a general manner. The central contribution of this work, therefore, lies in a comprehensive model connecting physical and social entities, that enables flexibility of executions by the robotic agents via symbolic reasoning with the model. This is, by and large, facilitated by the link between the physical and social context in SOMA where relationships are established between occurrences and generalizations of them, which has been demonstrated in several use cases in the domain of everyday activites that validate SOMA.
Explainable Job-Posting Recommendations Using Knowledge Graphs and Named Entity Recognition

The growth of online job-posting repositories provided job-seekers with access to a large number of potential jobs. User assessment of recommended jobs becomes especially a tedious and time-consuming task with the overwhelming number of job recommendations. To enhance the job-seeker’s ability to evaluate the suitability of a recommended job, we propose an explainable job recommendation system, which matches the user to the most relevant jobs based on their profile. Then, the system explains to the user why each job-posting has been recommended to them. The proposed system uses a knowledge graph (KG) structure to model job-postings and user profiles in one homogeneous structure. Graph relations between the job-seekers and job-postings are mined through natural language processing (NLP) of the textual content from job-postings and user-profiles. Based on the graph structure itself and a customized named entity classifier, a human-readable explanation is generated for each recommendation and provided to the job-seeker. The explanation includes information about the matching factors that led the system to recommend a certain job-posting to the user. The proposed system is implemented and tested on a sample data-set of user profiles and job-postings from open online repositories. We use BELU and Rouge-L scores to show that the proposed systems generated relevant explanations for recommended jobs.
Application of AI And Computer Vision To Face Mask And Social Distance Detection in CCTV Video Streams

According to the figures obtained by the Department of Health (DOH), COVID-19, the worldwide pandemic, has had an enormous global impact, infecting a growing population and causing over three million fatalities.Because of the global epidemic, governments all over the world were obliged to institute lockdowns in order to prevent virus transmission. The use of face masks and safe social distance, according to sources, are two of the best safety precautions to be observed by the public to avoid the transmission of the virus. Modern deep neural network models are combined with geometrical approaches to develop a strong model that incorporates three components of the system’s detection, monitoring, and testing. As a consequence, the technique presented saves time while reducing corona virus transmission.It might be used efficiently in the current circumstances, where lockdown is being loosened in order to inspect people at public meetings, retail malls, and other locations. Automated inspection saves time and money by reducing the number of people needed to examine the public, and it can be used everywhere to ensure safety.
E-Learning System Development: Challenges and Threats in the Digital Economy

In the context of the digital transformation of all socio-economic processes of society and the complex situation with «live» communication (as a result of the coronavirus pandemic), in Russia and other countries of the world, the problem of organizing an effective e-learning system at all levels of education is first on the agenda. In the Russian Federation, the solution to this problem is currently being exacerbated by the digital inequality of territories that has developed for various reasons. This situation is explained by many factors, including the level of availability of high-speed Internet, the level of digitalization of the local telephone network, the cost of IT services in various regions of our country. The article identified the current problems of the modern system of electronic (distance) education in the Russian Federation, revealed the impact of these problems on the practice of digital learning, examined the challenges and threats of implementing the e-learning system in practice, including those related to the digital inequality of Russian territories. The authors presented an analysis of statistical data that characterize the level of students’ access to broadband internet in various regions of Russia, which is required for the normal functioning of modern educational platforms. As a result of the analysis, the authors of the article formulated proposals to reduce the risks of distance education, as well as possible ways of its improvement.
Virtual Undergraduate Internships: One COVID-19 Side Effect THat Academic Libraries Should Keep

In spring 2020, the California State University System and its 23 campuses became the first in the United States to commit to a year of remote instruction due to the COVID-19 pandemic. Remote instruction separated the system’s librarians from their users, whose needs were especially great within the system’s ethnically, economically, and academically diverse student body. At California State University Channel Islands (CSUCI), public health guidelines and closures prevented many students from fulfilling internship and field experience requirements needed for graduation. CSUCI’s John Spoore Broome Library established a work-around by implementing a virtual internship program for one undergraduate that benefited both the student and the library. This paper, coauthored by the intern, documents the virtual internship, describes the model and structure of the experience, and argues that such remote internships are one pandemic side effect that should remain to promote equity in library and information science.
A strategy to the reduction of communication overhead and overfitting in Federated Learning

Federated learning (FL) is a framework to train machine learning models using decentralized data, especially unbalanced and non-iid. Adaptive methods can be used to accelerate convergence, reducing the number of rounds of local computation and communication to a centralized server. This paper proposes an adaptive controller to adapt the number of epochs needed that employs Poisson distribution to avoid overfitting of the aggregated model, promoting fast convergence. Our results indicate that increasing the local update of the model should be avoided, but yet some complementary mechanism is needed to model performance. We evaluate the impact of an increasing number of epochs of FedAVG and FedADAM.
Conditional Generative Adversarial Network-Based Image Denoising for Defending Against Adversarial Attack

Deep learning has become one of the most popular research topics today. Researchers have developed cutting-edge learning algorithms and frameworks around deep learning, applying them to a wide range of fields to solve real-world problems. However, we are more concerned about the security risks associated with deep learning models themselves—such as adversarial attacks, which will be discussed in this article. Attackers can use the deep learning model to create the conditions for an attack, maliciously manipulating the input images to deceive the classification model and produce false positives. This paper proposes a method of pre-denoising all input images to prevent adversarial attacks by adding a purification layer before the classification model. The method in this paper is proposed based on the basic architecture of Conditional Generative Adversarial Networks. It adds the image perception loss to the original algorithm Pix2pix to achieve more efficient image recovery. Our method can recover noise-attacked images to a level close to the actual image to ensure the correctness of the classification results. Experimental results show that our approach can quickly recover noisy images, and the recovery accuracy is 20.22% higher than the previous state-of-the-art.
Spatial-Temporal Graph Neural Network for Regional Photovoltaic Power Forecasting Based on Weather Condition Recognition

Photovoltaic (PV) power generation can effectively utilize solar energy resources. The regional PV power forecasting is helpful for dispatchers to formulate dispatching plans in a more scientific and reasonable way. At present, the existing methods don&apos;t sufficiently consider the temporal correlation of power output and the physical connection caused by clouds movement between PV plants. Aiming at these shortcomings, this paper proposes a spatial-temporal graph neural network method for regional photovoltaic power forecasting based on weather condition recognition. Firstly, we select the spatial-temporal graph convolutional neural network as the forecasting model, which can capture spatial feature through the Graph Convolution Neural model and capture temporal feature through the Gated Convolution Neural unit. The input is the graph structure data consists of the feature matrix and the adjacency matrix representing the connection relationship between plants, the output is the regional power. Secondly, due to the correlations between PV plants varies with weather conditions, we divide the weather into three categories: cloudless, partial cloud coverage and total cloud coverage. Different adjacency matrices will set for different weather conditions to train the forecasting model. Finally, the actual operation data is used for simulation, and the results verify the effectiveness of classification forecasting.
WhyAct: Identifying Action Reasons in Lifestyle Vlogs

We aim to automatically identify human action reasons in online videos.We focus on the widespread genre of lifestyle vlogs, in which people perform actions while verbally describing them.We introduce and make publicly available the WHYACT dataset, consisting of 1,077 visual actions manually annotated with their reasons.We describe a multimodal model that leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.