Reviewerly/exHarmony huggingface dataset:
Adjectives in Arabic

The main aim of this paper is to describe the main adjectival constructions in Modern Standard Arabic (MSA). It contributes a new analysis of an adjectival construction in MSA that was analysed in traditional grammar as a special postnominal adjectival construction, called the unreal adjectival construction. We argue that it is an example of prenominal adjectival constructions in MSA. We also provide a new analysis of the prenominal construction, suggesting that the adjective modifies the following noun, and this noun is the subject of the adjective. Moreover, we provide an analysis of predicate adjectives in MSA, whether with or without a copula, arguing that the single-tier analysis in Lexical Functional Grammar (LFG) is the appropriate analysis for predicate adjectival constructions in MSA. Additionally, this paper shows the similarities and differences between adjectival words and participial words, which are both used as modifiers in MSA.
Intelligent Quality Strategic Management Platform with Flow Data Mining and PHP Coding for Smart Higher Education Guiding Optimization

This manuscript studies the intelligent quality strategic management platform with flow data mining and PHP coding for smart higher education guiding optimization. The previous examples illustrate important similarities in the data patterns and basic operations in different application contexts. At the same time, there are some differences in the workload characteristics, such as flow arrival rate or the amount of the historical data required, as listed below some basic continuous query operations on the data stream. By connecting the nonlinear editing network, the hard disk broadcasting system, production and broadcasting digital integration system, audio production network and TV Internet webcast system with other application systems and database systems in the form of data flow can build a TV station data flow network system. Then, the integration of the guiding optimization is studied. After that, the simulation is conducted for robustness testing.
A Deep Learning Approach for Interior Designing of Apartment Building Architecture using U2 Net

The introduction of Deep Learning in computer technology has mastered various fields of study like medical, Banking, Finance etc. but the field of construction is lagging behind by still requiring some manual work. Apartment building designing is a complex task which requires professional architects and various tools. It would be easier and more fascinating if one can get the interior design just by having the shape of the land area. By considering this, a model has been proposed to serve the purpose of designing apartment architecture using generative adversarial networks. This study describes a system consisting of two sequential U<sup>2</sup>Net [13] based GAN models in which the first model has been trained for printing footprint of a building whereas second one for showing segmented sections for various areas of apartment.
Fake Review Detection using Unsupervised Learning

People rely on online reviews to help them make the proper decisions in a world fueled by the internet. The impact of online reviews is important but also manipulates the people trust. The feedback from consumers helps to identify aspects that influence people decision towards the business. Having the right online reviews is important for a consumer to have trust towards the business. This venture proposes a clever methodology for this assignment, where it is inferred that key highlights characterize validity of an audit and join these elements into a grouping model. Our methodology is exceptionally generalizable and above all doesn&#x0027;t need express marking (Fake/Non Fake) of the review. Evaluation of our model on true client surveys for caf&#x00E9;s taken from Yelp.
Big Data Analysis for knowledge based on Machine Learning using Classification Algorithm

From ancient days machines do obey the commands that are prepared by a human or a user, according to the program the machine does operate in its style, and this way of controlling a machine is said as Machine learning. Not only in the development of IT companies but also the rise of education system it takes a major part. Normally a human can able to store his memories. Using such kinds of stored memories he is learning new things which makes him feel better than before. Machines are quite different from human knowledge, instead of using memory power, it uses statistical comparison to analyze data. Here the number of data is stored in a database and according to the reaction received from the user, it gets additional data to create new data. For example let us have a music application, once a person hears music using the application then for this further entry he will get repeated music which he/she heard before. In this case, the application is working based on the machine learning algorithm, at first, it collects the information from the user then it uses the same information (data) to make efficient of the user&#x0027;s work when he returns. This paper approaches the necessity and development made in higher education system using Machine learning algorithm, most commonly the author have involved the concepts and algorithm based on Support Vector Machines and Learning Management systems. The prediction model is the most important thing which is created by the machine, while seeing about the statistical model with music the tempo and medium tempo is received as data from the user. Based on this concept the author has compared it with the educational system with a large amount of data consumption and that is similar to Deep learning.
A Novel System for Prediction of Facemask and maintaining Social Distance in COVID

SARS stands for Severe Acute Respiratory Syndrome, which is also known as COVID-19. Covid is an uncommonly overwhelming contamination that spreads through breathing dots from corrupted individuals talking, sneezing, or hacking. At whatever point you come into near contact with a polluted person or contact a contaminated surface or thing, the disease can spread rapidly. There&#x0027;s clearly no checking pro open to battle COVID-19, and the foremost sublime strategy for ensuring oneself against an sickness is to remain lost from it. Wearing a facemask that covers the nose and mouth in an open reach and washing hands routinely utilizing something like 70&#x0025; alcohol based sanitizers are prescribed to diminish viral degradation. Significant Learning advancement has shown its capacity to see and arrange things by analyzing photographs. A collected picture data includes 20,000 photos that were consistently cropped in 224&#x00D7;224 pixels and had a 97 percent accuracy rate during the model&#x0027;s training. Utilizing Python and Open CV utilizing Tensor Stream, the developed system sees individuals wearing or not wearing a facemask and chooses the genuine separate between them. The detected informational stored in cloud. It raises an alert when it recognizes persons who aren&#x0027;t wearing a mask and snaps images of their faces. It is unconcerned with physical distance. This study will aid in preventing the virus from spreading and in preventing people from contracting it.
A Comparative Study on Performance Improvement for Camouflaged Object Detection

Due to high intrinsic similarities in the foreground and background of the image in Camouflaged objects it is hard for even humans to distinguish them from the background, let alone computers which perceive all visual media as numerical data. As a consequence of this approach, they require image processing algorithms to inspect the contents of images, especially ones that can differentiate the differences in foreground and background of such images. In this paper, two primary research contributions are presented. First, this research study attempts to find the most advantageous parameters to create a model to detect COD. Second, a comparative study has been presented on the performance of different sets of parameters to perform object detection. Experimental analysis provided in this paper can be used to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.
Construction of a Visualized Automatic Entry System based on Character Recognition of Foreign Trade English Specific Vocabulary

This article uses the information visualization technology of CiteSpace to analyze the knowledge map of 1106 documents related to international trade semantics in the Web of Science-SSCI core collection from 2008 to 2021, and uses eye tracking technology to restore the dynamic process of vocabulary processing and depth-based information. The learned semantic recognition performs feature recognition and marking on the graph, and the recognition efficiency is increased by 7.6&#x0025;, and a fully automatic visual input system for international trade proprietary vocabulary is proposed. In the future, it will become a new research method in combination with ERP and other technologies.
Machine Learning based Real-Time Face Mask Detection System

In the pandemic situation, people are quickly affected in our day-to-day life. Wearing the mask is being normal nowadays for controlling the spread of COVID-19. The government and public sectors will ask the public/customers to wear masks to control the spread of COVID-19. Mask detection has become an essential task to help society&#x0027;s well-being to protect our life. This paper provides a simplified approach to detect face masks using basic ML packages in PYTHON like tensor Flow, Keras, OpenCV. This paper helps to analyze an image to detect the face correctly and then identifies whether there is a mask on the face or not. It is a surveillance task to perform the security to create awareness among the people. This method attains the accuracy of scanning face up to 96.88&#x0025; and 92.39&#x0025; respectively. This detection is based on two datasets, one is about without wearing a mask and with wearing a mask. This mechanism helps to detect the mask on people&#x0027;s faces in real-time scenarios.
Economic development: Structures and values

Discusses the range of economic development, definition of six stages of development, diversity of economic structures, variation in national values and relationship to business creation, the global distribution of national economies by stage of development, global variation in participation in business creation, and challenges for analysis.
Bypass Container Overlay Networks with Transparent BPF-driven Socket Replacement

Containerization on the cloud offers several crucial benefits. However, these benefits are negated by the effects of virtual network stack and address encapsulation, especially for workloads that require intense communication. Socket replacement is a promising approach to breach this wall without changing the underlay infrastructure by replacing a nested network stack with a simple host network stack. Current state-of-the-art approaches perform this replacement by preloading the overridden socket library in a containerized process. However, the preloading approach requires user effort to modify the deploying manifests and a compromised security policy configuration of privileged containers to access the host namespace. This paper introduces a new replacement framework where a secured control plane agent performs the replacement by utilizing low-overhead BPF kernel tracing technology. As a result, containers can obtain host-native network performance and neither modification nor escalated privileges are required for user containers. Experiments on multiple benchmarks including iPerf, MPI, memslap, and GROMACS have been conducted to confirm efficacy.
Layered Contention Mitigation for Cloud Storage

We introduce an ecosystem of contention mitigation supports within the operating system, runtime and library layers. This ecosystem provides an end-to-end request abstraction that enables a uniform type of contention mitigation capabilities, namely request cancellation and delay prediction, that can be stackable together across multiple resource layers. Our evaluation shows that in our ecosystem, multi-resource storage applications are faster by 5-70% starting at 90P (the 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile) compared to popular practices such as speculative execution and is only 3% slower on average compared to a best-case (no contention) scenario.
BELDaaS: Blockchain Enabled Lucky Draw as a Service

A sweepstake (also called a lucky draw) is a promotional event in which prizes are given out without requiring the participants to incur any additional cost. However, traditional sweepstakes, both offline and online, lack the important aspects of verifiability and auditability. The participants have to vest trust on the organizers that the draw is conducted fairly. In this paper, we present BELDaas - Blockchain Enabled Lucky Draw as a Service. It can be effectively used by sweepstake organizers to host their own events and draw random winners. At the same time, the proposed service also allows participants to independently verify the fairness of the draw directly from the blockchain. Experimental results show that the cost incurred for using the service is nominal.
Game difficulty prediction algorithm based on improved Monte Carlo tree

In the unreleased stage of the game, how to correctly set the game difficulty is an important task to user&#x2019;s feeling. Whether the difficulty of the game can be accurately measured will directly affect whether the difficulty setting is reasonable. As a commonly used stochastic simulation algorithm, Monte Carlo tree Search(MCTS) has been widely used in the field of game simulation. However, the traditional MCTS does not consider the specific situation of the game during the simulation, resulting in a low simulation success rate.In this paper, an improved MCTS is proposed to address above problems, and the UCB formula used for node selection is improved. The distance factor is included in the consideration of the process of Monte Carlo tree select node in the maze game. After compared the simulation success rate with the unimproved UCB, and tested the influence of the number of obstacles and the distance of path-finding on the performance of the algorithm, it is proved that in the case of less and shorter path-finding distance, the simulation success rate is greatly improved compared to the previous algorithm.
A General KPI Anomaly Detection Using Attention Models

To determine whether a Web service executes accurately, IT operation engineers must manually monitor multiple KPIs (Key Performance Indications). KPI anomaly detection is crucial to make sure undisrupted business. However, it is a burdensome task for IT operations engineers that analyze multivariate KPIs and detect the KPI exceptions. As a multiple time series, the KPIs of a service has two kinds of dependencies: temporal dependence and intermetric dependence. Existing forecasting-based models usually focus on temporal dependence and ignore intermetric dependencies between different KPI dimensions, which leads to low accuracy in the multivariate KPIs anomaly detection. Therefore, we propose a model, named GGIAnomaly, to consider both temporal dependence and inter-metric dependence in KPI anomaly detection. GGIAmomaly is composed of four parts: KPIs pre-process, intermetric dependence processing, temporal dependence processing and anomaly detection. Specially, GAT (Graph Attention Network) is used in GGIAnomaly for capturing the relationship between different KPI sequences. To better model various patterns of temporal dependence, GGIAnomaly integrates a recent attention model, named Informer, with GRU (Gated Recurrent Unit Network). The experiments on open-source datasets show that GGIAnomaly has better performance on both univariate and multivariate KPI anomaly detection compared to the existing methods.
Learning Effect of Strength Training System for Beginners Using Motion Capture Device

We aim to make strength training safe and efficient for beginners. We developed a strength training system that can judge the correctness of the posture of a bodyweight squat movement in real-time. In addition, we combined two teaching methods, “Step by Step” and “Overall”, and compared their learning effects on beginners' performances. As a result, we found it effective to estimate the correctness of the movement with features determined based on the joint angles. We also found a significant difference between the two groups, who were trained with two different approaches, “Step by Step” or “Overall”, respectively. The result shows that the learning effects of the “Overall” method outperform the “Step by Step” method. Our findings will enable to develop a strength training support system, which allows people to safely train themselves.
Effect of Hyper-Parameter Tuning on the Performance of Augmented Random Search

Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. This algorithm is imperative as it provides better results over other reinforcement learning algorithms. Finding the optimal values of hyper-parameters helps the algorithm to work up to its full potential. In this work, we have made an effort to find out the best hyper-parameter settings for the learning rate and direction, for this specific task. Our results show how a change in these values can impact the final maximum rewards obtained during the training process. We report novel findings of the impact of hyper-parameters on the performance of ARS algorithm. Finally, an effort has been made to find out by what percentage the ARS performs better than other Reinforcement Learning algorithm, TRPO.
Bangla Language Processing: Sandhi

Algorithms for Sandhi Yoga (joining) and Vichchheda (Splitting) for Bangla language processing have been proposed. These algorithms may be extended to other Indian languages, including Sanskrit. These may also find wide use in teaching-learning of Bangla grammar.
Human Activity Recognition Using Deep Learning

It is widely accepted that poor hygiene and irresponsible behavior of humans towards their health are major reasons for diseases in developing countries. The maintenance of cleanliness at public places is very much required for everyone. This paper focuses to automate the process to detect un-hygienic activities anywhere, especially at public places, organizations such as offices, schools, colleges, etc., and to detect and recognize the persons. It can further be used to arrange hygiene sanitation workshops for such persons to make them understand the importance of hygiene in life. Such automated systems may also enforce the fine to such persons to demotivate them to involve in misconduct through monetary losses. The proposed system works on Resnet50 architecture to detect and recognize such activities. This pre-trained model is trained transferred in our application and is trained on the dataset that we have prepared for human activities like spitting, throwing garbage, wearing a mask, and not wearing a mask. After the activity recognition, the haar-cascade classifier detects the human face in the activity and Principal Component Analysis (PCA) algorithm with Linear Discriminant Analysis (LDA) recognizes the person from the database available with us of that organization.
Medical service oriented blockchain data credibility evaluation method

In the study of trusted platforms for medical services, many scholars use the tamper-evident property of blockchain and consensus mechanism to ensure the trusted storage and processing of on-chain data, but they ignore the basic and important issue of trustworthiness of blockchain medical data sources, i.e., medical data to be on-chain. The traditional data trustworthiness evaluation method monitors the historical behavior of data subjects, service quality and other characteristics, and then grades the trustworthiness of the data generated by them. However, this evaluation method has great limitations for medical data subjects (patients) who are highly mobile. Therefore, this paper proposes a trustworthiness evaluation method that combines skewed distribution and entropy weight method, classifies medical data and divides multiple evaluation indicators, integrates the weights of each indicator and performs statistical analysis and trustworthiness probability calculation on the indicators, so as to obtain the trustworthiness of the data to be chained and improve the quality of medical services.
MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments

Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments.In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost.In this regard, several data-driven deep neural networks (DNNs) based schedulers have been proposed in recent years to allow scalable and efficient resource management in dynamic workload settings.However, optimal scheduling frequently relies on sophisticated DNNs with high computational needs implying higher execution costs.Further, even in non-stationary environments, sophisticated schedulers might not always be required and we could briefly rely on low-cost schedulers in the interest of cost-efficiency.Therefore, this work aims to solve the non-trivial meta problem of online dynamic selection of a scheduling policy using a surrogate model called MetaNet.Unlike traditional solutions with a fixed scheduling policy, MetaNet on-the-fly chooses a scheduler from a large set of DNN based methods to optimize task scheduling and execution costs in tandem.Compared to state-of-the-art DNN schedulers, this allows for improvement in execution costs, energy consumption, response time and service level agreement violations by up to 11, 43, 8 and 13 percent, respectively.
A Non-standardized Chinese Express Delivery Address Identification Model Based on Enhanced Representation

Intelligent recognition of express delivery address information is an important means to improve the efficiency of express list filling. At present, the confusion in user input and the inconsistent expression brings challenges to intelligent address information recognition. In addition, the recognition accuracy of the existing solutions has low identification and limited by the data format. To address the problems, this paper proposes a non-standardized Chinese express delivery address identification model based on enhanced representation, which has been improved from two aspects: entity extraction and memory network. The improved entity extraction model based on word embedding can mine the context information in the text in both positive and negative directions and consider the correlation between characters, thus outputting a more accurate prediction sequence. In order to solve the problem of information overload in existing models, a convolutional block memory network based on BERT was designed. The results of the experimental comparative analysis showed that the improved method effectively improved the identification accuracy of non-standardized Chinese express delivery address information, thus proving the effectiveness and availability of the method.