Reviewerly/exHarmony huggingface dataset:
Web Server Part 1: Apache/Nginx Basics

The web server is the software that makes the website(s) accessible. The web server does this by listening on the ports 80 and 443 and serving the files in certain directories as responses to requests received on those ports. Port 80 is the default port for HTTP (Hypertext Transfer Protocol), and port 443 is the port for the encrypted HTTPS variant (the S meaning Secure). Even though web servers can usually be configured to listen on other ports, a client like a web browser or a web crawler will always send HTTP and HTTPS requests without an explicit port indication to ports 80 and 443, respectively; if the user does not specify a protocol, clients will usually fall back to HTTP.
Research on the Application of NLP Artificial Intelligence Tools in University Natural Language Processing

Abstract Natural language formal analysis theory has created brilliant achievements on the basis of previous studies. However, with the development of computing power and the advent of the deep learning boom, some people believe that the rule-based rationalist method is outdated, and deep learning that relies on massive data can truly realize artificial intelligence. When traditional natural language is directly transplanted to text language, the short content of natural language will cause data sparseness and result in deviation of calculation results. This paper proposes a new natural language similarity measurement method by using NLP artificial intelligence tools. This method first preprocesses short texts, then builds a complex network model for natural language, calculates the complex network feature values of natural language words, and then uses NLP artificial intelligence tools to calculate the semantic similarity between natural language words, and then combines natural language Semantic similarity is defined to calculate the similarity between natural languages.
COMPARATIVE STUDY OF WAVELET TRANSFORM AND WAVELET PACKET TRANSFORMATION IN IMAGE DENOISING USING THRESHOLDING TECHNIQUE

In this modern world where images are considered to be the mode of communication among people and the presence of noises in images can affect the information, which is present in that images, to retain back the original information wavelet transformation and wavelet packet transformation can be used.This paper deals with the comparative result of image denoising using Wavelet Transformation and Wavelet Packet transformation with gaussian noise using thresholding technique, in order to have look on the transformation that can provide the better result to get back the original information of images, the comparative result is measured by Peak signal to noise ratio (PSNR) and mean square value (MSE).
TEAM HUB@LT-EDI-EACL2021: Hope Speech Detection Based On Pre-trained Language Model

This article introduces the system description of TEAM_HUB team participating in LT-EDI 2021: Hope Speech Detection. This shared task is the first task related to the desired voice detection. The data set in the shared task consists of three different languages (English, Tamil, and Malayalam). The task type is text classification. Based on the analysis and understanding of the task description and data set, we designed a system based on a pre-trained language model to complete this shared task. In this system, we use methods and models that combine the XLM-RoBERTa pre-trained language model and the Tf-Idf algorithm. In the final result ranking announced by the task organizer, our system obtained F1 scores of 0.93, 0.84, 0.59 on the English dataset, Malayalam dataset, and Tamil dataset. Our submission results are ranked 1, 2, and 3 respectively.
INCOMPLETE NATURE AND SELF - BODY PROBLEM: EMERGENTIST TRANSCENDENCE OF THE DUALISM OF SPIRIT AND MATTER

The article presents an overview of the key arguments of Terrence Deacon's theory of how mind emerged from matter. Deacon’s emergentism is analyzed as a way of refocusing the «hard problem» of consciousness. He suggests considering the phenomenon of consciousness as a dynamic coupling of mutually constraining processes. Such coupling is the defining feature of the subjective self and other teleodynamic phenomena. So self cannot be found as something embodied in existing material substrates. Consciousness is not present in such substrates themselves, but in the way different processes unfolding in these substrates constrain each other. Deacon shows that even looking at the simplest forms of life (autogens) one can observe that in them each part, interacting with other parts, creates the whole, and the whole as a synergetic complex makes possible the reproduction of its parts. The same principle underlies the organization of subjective consciousness, as subjective consciousness is hierarchically entangled with other levels of sentience. Thus, Deacon's emergentism is an attempt to take seriously the problem of the interrelation of spirit and matter by not simply to disregarding explanations that refer to the spiritual substance, but by offering the models of consciousness, sentience and purposiveness that could convincingly solve fundamental questions about the nature of consciousness in an alternative way. It is also an attempt to avoid the «naturalistic dualism» of David Chalmers, which involves splitting material information into physical and phenomenal aspects. According to Deacon, in explaining subjective self, one can do without both Cartesian spiritual substance and Chalmers' phenomenal information, but then what is necessary is to acknowledge the significance of absential phenomena (the phenomena that are intrinsically existing in relation to something missing, separate or nonexistent).
Variable metric proximal stochastic variance reduced gradient methods for nonconvex nonsmooth optimization

&lt;p style='text-indent:20px;'&gt;We study the problem of minimizing the sum of two functions. The first function is the average of a large number of nonconvex component functions and the second function is a convex (possibly nonsmooth) function that admits a simple proximal mapping. With a diagonal Barzilai-Borwein stepsize for updating the metric, we propose a variable metric proximal stochastic variance reduced gradient method in the mini-batch setting, named VM-SVRG. It is proved that VM-SVRG converges sublinearly to a stationary point in expectation. We further suggest a variant of VM-SVRG to achieve linear convergence rate in expectation for nonconvex problems satisfying the proximal Polyak-Łojasiewicz inequality. The complexity of VM-SVRG is lower than that of the proximal gradient method and proximal stochastic gradient method, and is the same as the proximal stochastic variance reduced gradient method. Numerical experiments are conducted on standard data sets. Comparisons with other advanced proximal stochastic gradient methods show the efficiency of the proposed method.&lt;/p&gt;
DevOps as a culture of interaction and deployment in an insurance company.

This study provides the contributions of the implementation and development of DevOps in the integration and deployment of software in the organization due to problems previously detected regarding the speed and quality of reports. The arguments assumed from a DevOps culture, respond to: techniques and practices of continuous integration, continuous deployment, test automation, collaboration, automation, measurement and monitoring; these facts, was thanks to the implementation of the pre-experimental research design of applied type, in a sample of 48 weekly sprints, with a total of 96 user stories of the project taken from the organization. The study allowed to demonstrate the decrease of the Cycletime of days on average, increase the frequency of code release and increase the percentage of attention ratio, based on these results, it was possible to demonstrate the improvement of the speed during the deployment and quality of software processes, these results were thanks to the adoption of DevOps as a culture of interaction and deployment in an organization in the insurance sector.
Self-Regulation and its Relationship to Social Intelligence among College of Education Female Students at Prince Sattam University

&lt;p style="text-align:justify"&gt;The current study aimed to identify the level of self-regulation and social intelligence among students of the College of Education at Prince Sattam University in Wadi Al-Dawasir, and to reveal the relationship between self-regulation and social intelligence, as well as the possibility of predicting social intelligence through self-regulation. A sample consisting of (204) female students at the College of Education, Prince Sattam bin Abdulaziz University in Wadi Ad-Dawasir participated in this study. The measurement instruments included two researcher-made scales of self-regulation and social intelligence, and the SPSS program was used for statistical treatments. Results revealed that female students of the College of Education in Wadi Ad-Dawasir, Prince Sattam University, have a high level of self-regulation as well as social intelligence, and that there is a statistically significant positive correlation between the scores of self-regulation and social intelligence among the study sample. In addition, it was found that social intelligence can be predicted through self-regulation. It is possible to benefit from the results of this study in directing university planning officials to the importance of transforming the self-regulation and social intelligence of students into a culture and systematic practice through courses, symposiums and workshops, or integrating them into academic courses.&lt;/p&gt;
Real-time object detection in 360-degree videos

Streaming of 360-degree videos over the internet is challenging task, but it provides rich multimedia experiences by allowing viewers to navigate 360-degree contents. The 360-degree videos need larger bandwidth and less latency to be streamed over the internet than the conventional videos. Therefore, non-visible area must be discarded from the video to save bandwidth. View prediction techniques have been used to predict visible area of the 360-degree video frames to be streamed. Linear regression using viewer’s past viewing behavior data is useful to predict short-term future behavior of the viewer, which is not useful when the network delay is longer than the prediction horizon. Object detection techniques help predicting viewers’ future motion for longer prediction horizon since the viewers tend to follow the objects that draw their attention. However, conventional object detection techniques using a convolutional neural network, such as YOLO, are difficult to be applied to 360-degree videos. There are distortions in the 360-degree videos when the spherical 360-degree video is projected into equi-rectangular videos for processing and storing purposes. A same object could have different shapes in the equi-rectangular video depends on their angular position in the sphere. Therefore, in this paper, we propose a multi-directional projection (MDP) technique to detect objects in the 360-degree videos. The proposed multi- directional projection technique mitigates the distortions in the equi-rectangular videos and feeds the redirected videos to the object detection system. Therefore, the neural network trained with conventional video dataset can be used without any change. Experimental result shows that the proposed method helps detecting objects in the edges of the 360-degree videos.
Accumulating confidence for deep neural network object detections and semantic segmentations in sequential UAS imagery through spatiotemporal feature correspondences generated from SfM techniques

Unmanned aerial systems (UAS) equipped with visual sensors can be quickly deployed to map novel regions, a useful ability in GPS-denied regions, search and rescue operations, disaster response, and defense. Assisted by such a UAS, a ground vehicle could safely navigate a given region, aware of the potential hazards seen from airborne sensors. Here, we propose a pipeline for identifying and mapping maneuverable regions and objects pertinent to safe navigation (cars, barriers, etc.) in sequential imagery captured from UAS sensors. First we use a semantic labeling deep neural network for identifying roads, an object detection neural network for detecting hazards of known classes, and a model that uses linear features to detect potential road hazards in labeled road pixels. This visual evidence regarding maneuverability is collected across temporally-sequential images and is spatially fused into a single map via visual feature correspondence. Fusion of road evidence is done on a per-pixel basis while clustering techniques are used to find objects given a set of co-mapped detections. We show the use of this pipeline for the quick and automated creation of maps that contain useful information with regard to safe navigation of a region captured by UAS sensors. These techniques serve as a part in the development of a model for safe, ecient navigation of GPS-denied/rapidly changing regions through the use of UAS-enabled mapping.
LAFEAT: Piercing Through Adversarial Defenses with Latent Features

Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks. On top of this, we introduce a unified $\ell_\infty$-norm white-box attack algorithm which harnesses latent features in its gradient descent steps, namely LAFEAT. We show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent on the effective use of the defender's hidden components, and it should no longer be viewed from a holistic perspective.
Combined attention mechanism and CenterNet pedestrian detection algorithm

Traditional pedestrian detection methods can no longer be effective improvement of pedestrian detection, so this paper proposes a pedestrian detection method combined with the attention mechanism, SE-Net attention mechanism and ECA-Net attention mechanism for channel focusing on sex to suppress some not important information channel, which greatly improves the recognition accuracy. Algorithm in this paper, based on CenterNet experiment, by predicting the pedestrian center and return the size information, this kind of anchor-free algorithm greatly reduces the time complexity. By targeting some occlusion and small scale pedestrian attention, our results on the ETH dataset are a good improvement over the original network.
Emergent reinforcement learning behaviors through novel testing conditions

In adversarial multi-agent paradigms, it is often difficult to describe what is learned by reinforcement-learning agents and to measure the robustness of the learned policies. To achieve a better view, we explore the effect of manipulating agent capabilities and policies in a predator-prey pursuit task. In these experiments, we trained a single prey using multi-agent reinforcement learning with three slower predators, then tested the prey against three faster predators using fixed “interceptor” strategies (head to the closest possible intersection with the prey assuming the prey maintains its current velocity) instead of their learned policies. While the prey’s performance was impressive under these novel conditions, it varied widely. Initial locations and velocities (randomized during training and testing) were limited in explaining differences in prey’s performance across test conditions. Nevertheless, visual inspection indicates that more successful prey quickly begin a circling pattern, whereas less successful prey often become cornered and double back into predator collisions. To quantify this behavior, we computed windowed entropy measures of the prey’s angle relative to the arena origin to show when an agent transitioned in and out of this unsuccessful behavior. Ultimately, these transitions suggest that circling is triggered by coming close to the center of the arena. By varying agents’ capabilities and predator policies upon evaluation, we achieve a more comprehensive view of the prey’s learned policy, and we suggest that these windowed entropy measures, along with correlations between entropy and performance, result in a quantification of the learned policy.
Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning

Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk.
Local Interpretable Model-Agnostic Explanations of Predictive Models for Hypertension

Hypertension is one of the chronic medical conditions and the major risk factor for multiple diseases and strokes. Prevention of hypertension is significant to delay the incidence of disease progression and decrease the severe health complications. In recent years, predictive models have been developed to recognize hypertensive and normotensive individuals. However, most accurate machine learning classifiers are problematic to understand the rationale behind their predictions and utilize in medical decision support systems due to the lack of interpretation of black boxes. In this study, we suggest the model-agnostic explanation approach to enhance the interpretability for the end-user of the system. Our proposed framework consists of the main two steps. In the first step, significant features were selected harnessing the t-test and chi-square test followed by a dissimilarity feature selection using correlation analysis. Thereafter, we construct the prediction models using several machine learning classifiers in order to determine the best hypertension predictive model among the Korean population. In the second step, Local Interpretable Model-Agnostic Explanations (LIME) are utilized to give interpretation at the individual level for each prediction model. In our experimental result, the eXtreme Gradient Boosting (XGBoost) and neural networks (NN) achieved the best prediction performances. In addition, utilization of the LIME technique is presented the prediction probability in each risk factor at the individual level. This may support to healthcare experts to make personalized decisions as well as public healthcare concerns.
Cyber Security Behaviorin Online Distance Learning: Utilizing National E-Learning Policy

This present study discovers cybersecurity awareness in online distance learning (ODL) during the pandemic crisis. ODL is an excellent way of teaching and learning during this catastrophe. The Government has imposed the National E-learning Policy to support ODL procedures. Unfortunately, the policies have not been carefully planned on security elements. Therefore, by utilizing Information Security Awareness Capability Model and Situation Awareness-Oriented Cyber Security Education Model, we able to: (1) design the security elements for National e-Learning Policy (2) develop a conceptual model of security behavior that benefits ODL procedure. Principally, four factors are demanding attention in the domain of cybersecurity features. On the other hand, there are partially supported hypotheses between the dimensions of cybersecurity and behaviour. Nutshell, we believe our paper is extending the body of knowledge in cybersecurity elements, awareness, and behaviour literature. Future direction, limitation, and conclusion have been thoroughly discussed.
Facial Emotion Recognition Model

This qualitative study of facial emotion recognition aims to help people suffering from Alexithymia (by genes) in starting off their young age understanding simple human emotion through pictures. This model works on the basis of supervised learning with the help of convolutional neural network. These layers furcate the image highlighting the main feature that differentiate it from other images or identifies it to fall into a particular category. This idea widens up the scope of AI and Machine Learning in the field of Psychology.
The Strategy of Negative Politeness in Visual Information Media on Sea Transportation

This research deals with the realization of the use of negative politeness in visual information media on sea transportation. The purpose of this study is to explain the negative politeness strategies used in the visual information media. The approach applied in this study is a qualitative descriptive method. The data source utilized in this study is in the form of visual information media such as placards, banners and leaflets installed in a number of locations related to sea transportation, namely ticket sales agents, ports and ships. The data examined is written discourse from the media of visual information containing politeness in language. In collecting research data, the techniques applied were seeing, recording and noting techniques. Analysis techniques, used in the article, are Brown and Levinson’s language politeness. The result of this study showed that there are seven strategies of negative politeness in visual media information on sea transportation including conventionally indirect strategy, hedges, minimizing the imposition, giving deference, impersonalize S and H, Stating the FTA as a general rule and Nominalize.
Shortest Path Finding Using Dijkstra’s Algorithm

In the professional work, discipline is a very important to see work ethic of an employee. Discipline has also the most important element of good behavior, both as individual and social. A problem is when people on the road in finding the shortest path in order to get to the destination on time. Finding shortest path improves workers discipline and add value to employees. With the Dijkstra’s Algorithm, we can find the shortest route. The value on the edge of a graph can be expressed as the distance between nodes (roads). Through this proposed application, it easier for us to find the shortest route in a more effective time.