Reviewerly/exHarmony huggingface dataset:
Polynomial Image Enlargement based on Edge Detection

This paper proposes an image enlargement method that approximates the original image scene by constructing a polynomial surface and using edges information as a feature constraint to solve the problem that can not virtually guarantee edge sharpening during the image enlargement process. First, we use the detection operator to preprocess the image and save the high-frequency information of the edges. Second, we use the sampling formula and edge information of the image as constraints to construct the fitting surface patch. The new method uses edge high-frequency information as suggestions for interpolation polynomials. Experiments and the comparison between other methods show that the images generated by this method have extraordinary visual effects, which are beneficial to medical image processing and other engineering applications and practices.
Using options to improve robustness of imitation learning against adversarial attacks

Imitation learning has been shown to be a successful learning technique in scenarios where autonomous agents have to adapt their operation across diverse environments or domains. The main principle underlying imitation learning is to determine a state-to-action mapping, called a policy, from trajectories demonstrated by an expert. We consider the problem of imitation learning under adversarial settings where the expert could be malicious and intermittently give incorrect demonstrations to misguide the learning agent. We propose a technique using temporally extended policies called options to make a learning agent robust against adversarial expert demonstrations. Experimental evaluation of our proposed technique for a game playing AI shows that a learning agent using our options based technique can successfully resist deterioration in its task performance as compared to using conventional reinforcement learning, when an expert adversarially modifies the demonstrations either randomly or strategically.
Joint Attention for Multi-Agent Coordination and Social Learning

Joint attention - the ability to purposefully coordinate attention with another agent, and mutually attend to the same thing -- is a critical component of human social cognition. In this paper, we ask whether joint attention can be useful as a mechanism for improving multi-agent coordination and social learning. We first develop deep reinforcement learning (RL) agents with a recurrent visual attention architecture. We then train agents to minimize the difference between the attention weights that they apply to the environment at each timestep, and the attention of other agents. Our results show that this joint attention incentive improves agents' ability to solve difficult coordination tasks, by reducing the exponential cost of exploring the joint multi-agent action space. Joint attention leads to higher performance than a competitive centralized critic baseline across multiple environments. Further, we show that joint attention enhances agents' ability to learn from experts present in their environment, even when completing hard exploration tasks that do not require coordination. Taken together, these findings suggest that joint attention may be a useful inductive bias for multi-agent learning.
Traffic Light Controller using Image Processing

Traffic congestion at junctions is a serious issue on a daily basis. The prevailing traffic light controllers are unable to manage the different traffic flows. Most of the current systems operate on a timing mechanism that changes the signal after a particular interval of time. This may cause frustration and result in motorist's time waste. Traffic congestion is a major problem in the currently existing systems. Delays, safety, parking, and environmental problems are the main issues of current traffic systems that emit smoke and contribute to increasing Global Warming. Sensor-based systems reduce the waiting time and maximize the total number of vehicles that can cross an intersection. Our proposed system can control the traffic lights based on image processing without the need for traffic police. This can reduce congestion, delay, road accidents, need for manpower. Under image processing, we use sub techniques like RGB to Gray conversion, Image resizing, Image Enhancement, Edge detection, Image matching, and Timing allocation. A real-time image is captured for every 1 second. After edge detection procedure for both reference and real-time images, these images are compared using SURF Algorithm. Then the amount of traffic is detected and the details are stored in the server. Arduino is used for a traffic signal in the hardware part. It consists of a Wi-Fi module. The micro-controller used in the system Arduino. Four cameras are placed on respective roads and these cameras are used to capture images to analyze traffic density. Then the traffic signals are decided according to the density of traffic. Our technique can be effective to combat traffic on Indian Roads. A lot of time can be saved by deploying this system and also it conserves a lot of resources as well as the economy
Design Guide Prediction Model for the Legal Interactions (GPM-LI)

There is a lot of time and money wasted during incorrect interactions in many laboratories in different fields (chemical, physical, medical, biology). The process of finding a correct interaction before entering laboratories of various kinds is one of the most important challenges in the field of scientific research as a result of the progress achieved in the field of intelligent computing. Therefore, in this paper, we will seek to solve the above challenge by building an integrated software model to test the interactions and ensure their accuracy before they are performed in the laboratories. This paper introduces the GPM-LI model for predicting optimal interactions. GPM-LI consists of several stages, in first stage separates the group of interaction into their primary elements, then the model selects the roots to build the set of possible interactions, where the root is not chosen randomly, but rather specifically through using (DSelect), after that the best set of interactions is found (finding the set of optimal path of interaction) through the use of our new algorithm Legal Tracking Interaction by Whale Optimization (LTI-WO), this algorithm find An optimal set of possible interactions, and the optimal interaction with GPM-LI represents the least expensive and time-consuming interaction, and the last stage is the evaluation of results through the use of the conflict matrix.
An Improved under Sampling Approaches for Concept Drift and Class Imbalance Data Streams using Improved Cuckoo Search Algorithm

One of the biggest challenges in the recent times in the field of data stream learning is to mitigate the presence of concept drift. There are numerous challenges in overcoming the concept drift, such as changing class ratio, huge volume of data and real time processing for effective knowledge discovery. Evolutionary search techniques are one of the new paradigms to handle huge dimensionality and scalability of the data streams. One of the finest and least applied evolutionary search approaches is the cuckoo search technique for data streams. To solve both the concept drift and class imbalance issues simultaneously, in this paper we have proposed an approach using nature inspired evolutionary optimizing technique known as Cuckoo Feature and Instance Selection (CFIS) algorithm. The performance evaluation of the proposed approach is done on an exclusive experimental setup of 15 data streams formed and compared with two data stream approach. Moreover, a set of six evaluation criteria’s are considered for showing overall better performance of the proposed approach in the presence of concept drift and class imbalance.
Steganalysis Using Yedrodj-net net's Convolutional Neural Networks (CNN) Method on Steganography Tools

Steganalysis method is used to detect the presence or absence of steganography files or can be referred to anti-steganography. Steganalysis can be used for positive purposes, which is to know the weaknesses of a steganography method, so that improvements can be made. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. Blind steganalysis is difficult to implement, but then machine learning techniques emerged that could be used to create a detection model using experimental data, one of which is Convolutional Neural Networks (CNN). A study proposes that the CNN method can detect steganography files using the latest method with a low error probability value compared to other methods, CNN Yedroudj-net. As one of the steganalysis methods with the latest machine learning steganalysis techniques, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of many tools commonly used for steganography applications. Knowing the performance of CNN Yedroudj-net on several steganography tools is very important, to measure the level of ability in terms of steganalysis of some of these tools. Especially so far, machine learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This study will use five tools that are Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which are not known exactly what steganography methods are used on the tools. Yedroudj-net method will be implemented in the steganography file from the output of the five tools. Then a comparison with the popular steganalysis tool is used, StegSpy. The results show that Yedroudj-net is quite capable of detecting the presence of steganography files, slightly better than StegSpy.
SiamMOT: Siamese Multi-Object Tracking

In this paper, we focus on improving online multi-object tracking (MOT). In particular, we introduce a region-based Siamese Multi-Object Tracking network, which we name SiamMOT. SiamMOT includes a motion model that estimates the instance's movement between two frames such that detected instances are associated. To explore how the motion modelling affects its tracking capability, we present two variants of Siamese tracker, one that implicitly models motion and one that models it explicitly. We carry out extensive quantitative experiments on three different MOT datasets: MOT17, TAO-person and Caltech Roadside Pedestrians, showing the importance of motion modelling for MOT and the ability of SiamMOT to substantially outperform the state-of-the-art. Finally, SiamMOT also outperforms the winners of ACM MM'20 HiEve Grand Challenge on HiEve dataset. Moreover, SiamMOT is efficient, and it runs at 17 FPS for 720P videos on a single modern GPU. Codes are available in \url{https://github.com/amazon-research/siam-mot}.
Class Normalization for (Continual)? Generalized Zero-Shot Learning

Normalization techniques have proved to be a crucial ingredient of successful training in a traditional supervised learning regime. However, in the zero-shot learning (ZSL) world, these ideas have received only marginal attention. This work studies normalization in ZSL scenario from both theoretical and practical perspectives. First, we give a theoretical explanation to two popular tricks used in zero-shot learning: normalize+scale and attributes normalization and show that they help training by preserving variance during a forward pass. Next, we demonstrate that they are insufficient to normalize a deep ZSL model and propose Class Normalization (CN): a normalization scheme, which alleviates this issue both provably and in practice. Third, we show that ZSL models typically have more irregular loss surface compared to traditional classifiers and that the proposed method partially remedies this problem. Then, we test our approach on 4 standard ZSL datasets and outperform sophisticated modern SotA with a simple MLP optimized without any bells and whistles and having ~50 times faster training speed. Finally, we generalize ZSL to a broader problem -- continual ZSL, and introduce some principled metrics and rigorous baselines for this new setup. The project page is located at https://universome.github.io/class-norm.
Research Intuitions of Asymmetric Crypto System

The fast moving world full of data exchange and communication technology, with all sensitive information of an individual virtually available anywhere and anytime, make the Internet world more critical in security aspects. The areas of risks are attended and assured to be safe by means of some sought of crypto mechanisms. The strength and vulnerability of the crypto mechanism defines the durability of the system. The encryption on the communication channel can implement either public or private key algorithms based on the area of applications. The public key cryptography is specifically designed to keep the key itself safe between the sender and receiver themselves. There are plenty of public key cryptographic algorithms but only a few are renowned. This paper is aimed to collect all possible public key cryptographic methods and analyze its pros and cons so as to find a better algorithm to suite almost all conditions in Internet communication world and e-commerce. Research in quantum computers is booming now and it is anticipated that the supremacy of quantum computers will crack the present public key crypto algorithms. This paper highlights issues and challenges to be faced from quantum computing and draws the attention of network researchers to march towards researching on quantum-safe algorithms.
System Design and Data Governance for Environmental Disaster Management in Smart Scenic - A Case Study of Huangshan Mountain

Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.
The Role of Social Media in Shaping the Trends of University Youth towards the Requirements of the Knowledge Economy

The study aims to identify social media's role in shaping university youth's attitudes towards the knowledge economy's requirements, and the study belongs to the descriptive studies. The study conducted a survey on a sample of 400 individual adolescents by using questionnaire form. The study concluded that social media has contributed to the development of the knowledge economy by being an important source for obtaining information about the knowledge economy, directly contributing to the formation of awareness among individuals, working to raise the level of knowledge economy culture among community members, and working on forming specific trends for the individual towards knowledge economy requirements. The most important participation mechanisms that it undertakes through social media to spread the requirements of the knowledge economy is to interact with pages interested in transmitting news of the knowledge economy, publishing pictures, posts or news related to the knowledge economy in society, commenting on issues related to the knowledge economy. The most important obstacles to the requirements of the knowledge economy through social media are represented in the following: the lack of mechanisms for developing knowledge behaviors among young people, the lack of awareness of the knowledge economy culture in action and content, the lack of proficiency in the use of information and communication technology, the violation of the intellectual property rights of knowledge, the destruction, destruction or theft of electronic websites.
In Silico Study on the Effect of Heliannuol A, B, C, D, E Compounds of Sunflower (Helianthus annuus L.) on Dual PI3K/mTOR (5OQ4) Enzyme

Heliannuol is a sesquiterpene that has a benzoxepine ring, oxepin. Many derivatives of benzoxepine compounds show anticancer activity by inhibiting the phosphoinositide 3-kinase (PI3K) enzyme. These enzymes play a role in cell proliferation and growth. The study aims to predict the physicochemical properties using Lipinski’s Rule of Five parameters on phosphoinositide 3- kinase (PI3K/Mtor; PDB 5OQ4) enzyme and the toxicity of Heliannuol A, B, C, D, E compounds. The process uses the pkCSM online tool. The validation of receptor 5OQ4 is done using the value parameter RMSD &lt; 2 (Å). Protox online tool dan pkCSM online tool is employed to predict the toxicity using parameter LD50, skin sensitization, Ames toxicity, hepatotoxicity, and toxicity class. The interaction of ligan and enzyme is tested using Molegro Virtual Docker 6.0. Heliannoul A, B, C, D, E compounds fulfill Lipinski’s Rule of Five. The receptor 5OQ4 is known valid using the value of RMSD 0,923 (Å). Heliannuol A, B, C, D, E compounds inhibit Dual PI3K / mTOR enzyme less than Bimiralisib. As a result of the toxicity test of compounds Helliannouls A, B, C, E, and Bimiralisib compounds are included in class 4, while Helliannouls D compounds are included in class 5.
Development of a Web-Based Complaint Management Platform for a University Community

Complaint management has served as a unique and efficient method of assessing student and staff satisfaction in any university community.It has helped foster improvement and change in the way operations are carried out and ensure that the students and staff are served to the best of the University's ability.Although there have been massive improvements using complaint management, there is still a lack of efficiency when it comes to manual complaints management.This work is aimed at developing a web-based complaint management system for a University community with the use of JavaScript as the programming language and MongoDB server as the database, in order to improve the way complaints are handled and analyzed in the University.It was discovered from this research that students were genuinely interested in an online platform for submitting complaints as it helped to simplify the process.The web-based complaint management system is, however, subject to various improvements and developments as technology advances.
An Application of Emotion Detection in Sentiment Analysis on Movie Reviews

The research focus on the issue of accuracy for sentiment analysis. The researcher experimented on emotion detection result to be used in sentiment analysis. The emotions that were included in this research are happiness, sadness, anger, and fear. Once emotion was detected the system will then use it to know the sentiment of the person on a particular movie. This paper aims to measure the accuracy in sentiment analysis enhanced by emotion detection and to know whether emotion detection plays a key role in reading sentiment analysis.
IMPACT OF THE DIGITAL DIVIDE ON DIGITAL LEARNING IN ASSAM: ECONOMIC CAUSES BEHIND IT.

Digital learning is one of the most influential alternatives for mitigating the effects of a pandemic on the teaching-learning process. Especially in the present COVID-19 crisis, digital learning has emerged as a great alternative for continuing academic activities where the physical gathering is completely prohibited. Academic institutions and other online learning platforms take initiatives to promote digital learning process during these days, but the digital divide has stood as the main barrier in the front of it. In this paper, we have found that the digital divide is the main factor responsible for the failure of digital learning in Assam. Though there are different causes of the digital divide existed, we have limited our discussion by focusing explicitly on economic causes of the digital divide in Assam. Illiteracy, lack of skills, inadequate investment in ICT, infrastructural gap, low PCI are the major factors responsible for the digital divide in Assam.
No-reference transform-based quality assessment for imaging applications

This paper aims to introduce a new transform domain no-reference (NR) image quality assessment that predicts the perceptual quality for improving imaging systems performance. The main idea is that enhancing the contrast of diverse real-world digital images by creating more high-frequency content in the improved image than the original image. The proposed measure uses different fast orthogonal transforms, such as Fourier and Fibonacci. A generalized transform-based model of local transform-based coefficients is derived and transformed the model parameters into features used for perceptual image quality score prediction. To test the performance of the proposed algorithm, we use the well-known and publicly available database TID2008. The Pearson correlation coefficient is utilized to measure and compare the proposed quality measures performance with state-of-the-art approaches.
Analisis Banjir, Faktor Penyebab Dan Prioritas Penanganan Sungai Anduonuhu

Anduonuhu Village is one of the areas in Kendari City that has been&#x0D; affected by flooding in the last 10 years. Data of fload eventsoccurred in 2013 and 2017. Based on the Data of the Kendari CityEnfironmental Status Report in 2010, the distribution of potentialareans prone to flooding in the city of Kendari, Anduonuhu subdistrictwasranked2 (two)withanareaof359.817 ha.&#x0D; The purpose of this sudy is to analyze extreme rainfall over the past10 years, to analyze the factors that cause flooding and the priorityof handling the anduonuhu river. In analyzing, the autors collectdata by conducting field surveys and collecting data on gydrology,tophography and land use at the study site. The results of data analysis show that extreme rainfall in the last 10years at the study site occurred in 2013 amounted to 191.6 mm.factors causing flooding at the study site were caused by extremerainfall inundating the Anduonuhu watershed and land use change.The priority handling is done by modeling scenarios withnormalization and river embankments.
Towards a Linked Open Code

In the last two decades, the Linked Open Data paradigm has been experiencing exponential growth. Regularly, new datasets and ontologies are made publicly available, and novel projects are initiated to stimulate their continuous development and reuse, pushing more and more actors to adhere to the Semantic Web principles. The guidelines provided by the Semantic Web community allow to (i) homogeneously represent, (ii) uniquely identify, and (iii) uniformly reference any piece of information. However, the same standards do not allow defining and referencing the methods to exploit it: functions, procedures, algorithms, and code in general, are left out of this interconnected world. In this paper, we present our vision for a Web with Linked Open Code in which functionscould be accessed and used as Linked Data, allowing logic harnessing the latter to be semantically described and FAIR-ly accessible. Hereafter, we describe the challenges presented by the implementation of our vision. We propose first insights on how to concretize it, and we provide a non-exhaustive list of communities that could benefit from such an ideal.