Reviewerly/exHarmony huggingface dataset:
Development of Optimized Linguistic Technique Using Similarity Score on BERT Model in Summarizing Hindi Text Documents

Growth of natural language processing (NLP) in recent past has been exponential. Various use cases like chatbots, information retrieval system, spam filtering, etc., has been used widely in industries and is in demand. Technology giants like Google, Facebook, Amazon, etc., is contributing a lot in the field of NLP and state-of-the-art technology being used industry-vide. Considering geographical market, industries has started coming with multilingual systems to cater wider audience. Various solutions are available for document and information retrieval in English language. In 2018, researchers at Google AI language open-sourced a new technique for NLP called BERT. It is designed to help computers understand the meaning of ambiguous language in text by using surrounding text to establish context. Limited solutions or models are proposed that are multilingual and focuses mainly on Hindi language. In this paper, BERT model is trained with Hindi Wikipedia dataset in order to create an information retrieval system. System is capable of understanding Hindi queries and gives results on the basis of similarity score.
Drug response prediction using deep neural network trained by adaptive resampling of histopathological images

Recent advances in programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) immune checkpoint inhibitor (ICI) revolutionized the clinical practice in lung cancer treatment. The PD-L1 immunohistochemistry (IHC) test is a widely used biomarker for ICI responder in lung cancer. However, the accuracy of PD-L1 IHC test for selecting ICI responder is unsatisfactory, especially due to its low specificity. Therefore, methods which could effectively predict the efficacy of ICI are crucial for patient selection. In this article, we apply a deep neural network (DNN) to predict responders for anti-PD1 blockade on the basis of histopathological images of hematoxylin and eosin (H&E) stained tissue. The main difficulty to train DNN for responder prediction is the inability to accurately label at the image patch level. We employed a semi-supervised multi-instance learning (MIL) framework with adaptive positive patch selection in each region-of-interest (ROI). Using a dataset of 250 whole slide images (WSIs) of non-small cell lung cancer (111 responders and 139 nonresponders), we train a DNN-based MIL classifier on a case-level partition of the dataset (150 WSIs) and obtain an area under curve (AUC) of 0.773 for the test dataset (50 WSIs), which outperforms the PD-L1 IHC test (AUC=0.636). These results suggest that a DNN model can be used for assisting clinicians to make decision for treatment plan. We also confirm that the locations of adaptively selected positive patches can give valuable insights into the histological features associated with drug response.
Smart Contracts in the New Era of Contract Law

This essay examines whether the smart contract innovation is capable of displacing the orthodox adherence to traditional contracts. This examination is underpinned by an analysis of the legality of smart contracts in which it is exemplified that smart contracts ought to be considered legally binding instruments. The essay proceeds to explore the superiority of smart contracting in a technical and theoretical basis. The advantages generated through smart contract automaticity and enforceability present a concrete basis for undermining reliance on traditional contracts. Blockchain Technology also enhances the benefits of smart contract by acting as a smart contract enabler through guaranteed performance and enforceability. Nevertheless, such novel technologies inevitably suffer from several shortcomings. This essay considers examples illustrating the inflexibility of smart contracting. Apart from being susceptible to hacking and code exploitation, smart contracting is unable to deal with ambiguities and potential modifications. Overall, this suggests that the advantages of smart contract practice are currently confined to some specified limited scenarios. Smart contracts perform a different function to traditional contracting by merely guaranteeing technical enforceability as opposed to legal enforceability. This essay thus concludes that, for the time being, it is prone to regard smart contracting as a supplement to traditional contract rather than an outright displacement.
THE MARKERS OF QUALITY OF THE EDUCATIONAL PROCESS OF HIGHER EDUCATION INSTITUTIONS IN MODERN REALITIES: THE ‘IDEOLOGICAL TRIAD’ OF THE STUDY OF THE VALUE EXPERIENCE FORMATION

The purpose of the paper is to specify the quality markers of the educational process of higher education on the basis of scientific theoretical, methodological and practical research as the most important element of the internal quality assurance system of higher education. The results obtained: The paper proves that the urgent task in terms of improving the quality of higher education is necessity to study and specify the markers of assessing the quality of higher education in the modern realities of professional development of research and teaching staff. Within the framework of the Centre for Pedagogical Skills of a Higher School Teacher in the context of the COVID-19 pandemic, markers of the quality of the higher school educational process in modern realities have been identified and specified from the positions of student, teacher and quality expert. The result of this work was an understanding of the need for a methodological audit of educational components. A questionnaire was compiled, which helps to verify specific data for analysis and further evaluation, providing recommendations. Evaluating the effectiveness of the educational process is a comprehensive procedure in which the teacher must be clear about the relationship between the predicted learning outcomes and ways to set goals to achieve them at the time of course development.
Autoencoders and Embeddings: How Unsupervised Structural Learning Enables Fast and Efficient Goal-Directed Learning

Feature engineering is crucial towards advancements in the field of artificial intelligence as understanding underlying hidden components and correlation of data is an essential part of traditional machine learning algorithms. However, it is challenging to disentangle features invariant to irrelevant features but still relevant to the task. Autoencoders and embeddings can efficiently accomplish this challenge of extracting relevant features and simplifying most of the unsupervised problems. Among many of the techniques in representation learning, this chapter focuses on autoencoders and embeddings as these techniques assure preserving as much information as possible and learning a good representation of data.
Secure Forensic Data Using Blockchain and Encryption Technique

Blockchain is the most recent technology, and it is based on decentralization. All data are recorded in a chain of blocks due to the decentralized nature of the system. To protect the data, it generates the unbreakable hash values. The preferable solution for securing and maintaining the integrity of forensic data is blockchain. However, the data can be retrieved if the forensic data have been tampered with by a third party. This paper implements a few security mechanisms to the forensic data by giving the e-mail notification for each data added by using SMTP protocol and also an access key is sent to the authorized person in order to view the recorded forensic evidence. Also a simple scenario has been added to recover the forensic data if the data got tampered and the hash value in tampered and untampered report in the blockchain are compared.
Simulating Neural Network Processors

Deep learning has achieved competing results compared with human beings in many fields. Traditionally, deep learning networks are executed on CPUs and GPUs. In recent years, more and more neural network accelerators have been introduced in both academia and industry to improve the performance and energy efficiency for deep learning networks. In this paper, we introduce a flexible and configurable functional NN accelerator simulator, which could be configured to simulate u-architectures for different NN accelerators. The extensible and configurable simulator is helpful for system-level exploration of u-architecture, as well as operator optimization algorithm developments. The simulator is a functional simulator that simulates the latencies of calculation and memory access and the concurrent process between modules, and it gives the number of program execution cycles after the simulation is completed. We also integrated the simulator into the TVM compilation stack as an optional backend. Users can use TVM to write operators and execute them on the simulator.
Accelerating MRI with efficient generative neural network as prior

Image reconstruction is a key task in various applications related to medical scanning, geological prospecting, etc. Usually measured data for reconstruction is limited due to physical constraints in these practices. Consequently image reconstruction with limited data becomes an underdetermined inverse problem. Recently, using un-trained Deep Neural Networks (DNN) such as Deep Decoder and ConvDecoder has been proved as a plausible approach for image reconstruction. By leveraging the DNN structure itself as a prior, the un-trained neural networks have shown great advantages over the traditional reconstruction methods like total-variation (TV) in many aspects. Comparing with the stateof-art trained neural networks like U-net, the un-trained neural networks also alleviate the cost of robustness and scalability in out-of-distribution samples. In this work, we propose a new un-trained image reconstruction neural network called EffiDecoder. Our contribution is two-fold. First we introduce mobile inverted bottleneck (MBconv) at each layer with computational-friendly depthwise separable convolutions. Second, the feature channels are adaptively reduced according to the output. In accelerated MRI reconstruction tasks the experimental results demonstrate that the proposed method can achieve competitive performance with reduced computational cost. Therefore, with the notable improvements of EffiDecoder, we believe the un-trained neural networks have great potentials to be discovered in extensive reconstruction applications.
A Brief Survey of Cloud Data Auditing Mechanism

In today’s digital world, the demand for cloud storage is increasing. Cloud has attracted a vast number of users in today’s environment because it delivers a wide variety of services. Users can access data from the cloud server whenever they need it, and data owners can store any type of data on it. It provides a cost-effective solution for medium and small businesses, releasing them from the burdens of data storage. The term “cloud storage” refers to data storage on a centralized server that is part of another organization’s infrastructure. The corporation owns and operates the data storage, while the user is charged with the storage space used. Since the data is processed on a cloud server, the accuracy of data storage is jeopardized. Users have a low level of trust in cloud service providers because they can be deceiving at times. These programs often come with a critical concern, and due to newly introduced policies such as GDPR, the organizations can face legal and financial consequences. Since the cloud infrastructure is regulated by a cloud service provider, a separate operating agency, data privacy is critical. As a result, several auditing schemes have been proposed to protect the integrity and confidentiality of cloud records effectively. In this article, we looked at a variety of existing data integrity auditing systems and their outcomes.
Simulation and Implementation of Robot Obstacle Avoidance Algorithm on ROS

Abstract Robots often collide in the process of simulation soccer match. The collision will block the robot’s own motion trajectory, which is not conducive to the robot’s ball grasping and defense. Considering that the obstacles have the characteristics of dynamics, antagonism and uncertainty, the robot’s motion trajectory, path selection and obstacle avoidance are very important. Taking ROS as the research platform, the study focuses on the path planning of simulated soccer robot. Meanwhile, we have applied some obstacle avoidance algorithms to the Middle Size Simulation League of soccer.
Mobile Application for Tour Planning Using Augmented Reality

Over past years, there have been many advancements in the digital world, and augmented reality is one of the fastest emerging fields. Augmented reality is a section of mixed reality that superimposes the virtual world in the real world. Tourism, a tertiary sector industry, can make use of AR for their own benefits. Our paper proposes an application for tourists to make their tour experience more interactive. ART, augmented reality in tourism, intends to scan a food menu, display 3D food models and description about it so tourists get better idea about the food item and food ingredients which they are not familiar with; the application also proposes the idea of digital brochure for hotels which superimposes 3D view of the hotel rooms and the amenities provided by the hotel on the available brochure making the search for hotels more user-friendly; further the application also allows the AR camera to scan the map of a particular area, and user can navigate through the map to seek information about the locations via texts or video. This application focuses on three different areas which are considered the major concerns for tourists while travelling to non-familiar places.
Human Intelligence and Artificial Intelligence: Divergent or Complementary Intelligences?

Artificial intelligence (AI) is a relatively new phenomenon. The general consensus holds that its field of exploration began in 1956 at a summer conference at Dartmouth College sponsored by the Defense Advanced Research Projects Agency. Somewhat earlier, Alan Turing sparked interest in AI in a paper published in Mind in 1950 by wondering if a machine could think and how that could be determined. AI has progressed significantly since then. Meanwhile, human intelligence (HI) has been studied by philosophers and researchers for centuries but has remained relatively unchanged in terms of its fundamental nature (e.g., logic and problem solving). In this chapter, we examine how AI and HI have converged in certain contexts but how they remain distinct in other aspects. Two areas will be highlighted in the discussion – namely, expertise and embodiment.
Flat Chance! Using Stochastic Gradient Estimators to Assess Plausible Optimality for Convex Functions

This paper studies methods that identify plausibly near-optimal solutions based on simulation results obtained from only a small subset of feasible solutions. We do so by making use of both noisy estimates of performance and their gradients. Under a convexity assumption on the performance function, these inference methods involve checking only a system of inequalities. We find that these methods can yield more powerful inference at less computational expense compared to methodological predecessors that do not leverage stochastic gradient estimators.
A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm

Abstract Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently.
Image Retrieval via Canonical Correlation Analysis and Binary Hypothesis Testing

Canonical Correlation Analysis (CCA) is a classic multivariate statistical technique, which can be used to find a projection pair that maximally captures the correlation between two sets of random variables. The present paper introduces a CCA-based approach for image retrieval. It capitalizes on feature maps induced by two images under comparison through a pre-trained Convolutional Neural Network (CNN) and leverages basis vectors identified through CCA, together with an element-wise selection method based on a Chernoff-information-related criterion, to produce compact transformed image features; a binary hypothesis test regarding the joint distribution of transformed feature pair is then employed to measure the similarity between two images. The proposed approach is benchmarked against two alternative statistical methods, Linear Discriminant Analysis (LDA) and Principal Component Analysis with whitening (PCAw). Our CCA-based approach is shown to achieve highly competitive retrieval performances on standard datasets, which include, among others, Oxford5k and Paris6k.
Common Core 2022

Journal of Adolescent & Adult LiteracyVolume 65, Issue 5 p. 447-450 COLUMN Common Core 2022 Don Zancanella, Corresponding Author Don Zancanella zanc@unm.edu Search for more papers by this author Don Zancanella, Corresponding Author Don Zancanella zanc@unm.edu Search for more papers by this author First published: 24 February 2022 https://doi.org/10.1002/jaal.1221Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume65, Issue5March/April 2022Pages 447-450 RelatedInformation
Sparse Enhancement Fuzzy Broad Learning System Based on Multiple Clustering Methods

Abstract The fuzzy broad learning system (FBLS) is a novel, neuro-fuzzy model. Different from other neuro-fuzzy models with low efficiency, FBLS can obtain better performance using less computation time. However, the clustering-based fuzzy rule generation approach makes the performance of FBLS limited. Meanwhile, it is unknown how the enhancement layers from FBLS contribute to the model performance, which hinders the further extension of the model structure. To solve these problems, we propose a sparse enhancement fuzzy broad learning system (SEFBLS). It uses only a sparse set of enhancement nodes to replace the original enhancement node groups. To obtain a better representation, the designed principal component-based sparse autoencoder is used for feature reconstruction and information removal. In addition, to explore the optimal model structure and performance, multiple clustering methods (fuzzy and non-fuzzy) are used to improve SEFBLS. The results on 10 UCI classification datasets show that the proposed SEFBLS obtains competitive accuracy using fewer fuzzy rules.
A Perfect Knob to Scale Thread Pool on Runtime

Scalability is one of the utmost nonfunctional requirement of server applications, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) has been used extensively as a middleware service in server applications. The size of thread pool is the most significant factor, that affects the overall performance of servers. Determining the optimal size of thread pool dynamically on runtime is a challenging problem. The most widely used and simple method to tackle this problem is to keep the size of thread pool equal to the request rate, i.e., the frequency-oriented thread pool (FOTP). The FOTPs are the most widely used TPSs in the industry, because of the implementation simplicity, the negligible overhead and the capability to use in any system. However, the frequency-based schemes only focused on one aspect of changes in the load, and that is the fluctuations in request rate. The request rate alone is an imperfect knob to scale thread pool. Thus, this paper presents a workload profiling based FOTP, that focuses on request size (service time of request) besides the request rate as a knob to scale thread pool on runtime, because we argue that the combination of both truly represents the load fluctuation in server-side applications. We evaluated the results of the proposed system against state of the art TPS of Oracle Corporation (by a client-server-based simulator) and concluded that our system outperformed in terms of both; the response times and throughput.
Online Learning of Logic Based Neural Network Structures

In this paper, we present two online structure learning algorithms for NeuralLog, NeuralLog+OSLR and NeuralLog+OMIL. NeuralLog is a system that compiles first-order logic programs into neural networks. Both learning algorithms are based on Online Structure Learner by Revision (OSLR). NeuralLog+OSLR is a port of OSLR to use NeuralLog as inference engine; while NeuralLog+OMIL uses the underlying mechanism from OSLR, but with a revision operator based on Meta-Interpretive Learning. We compared both systems with OSLR and RDN-Boost on link prediction in three different datasets: Cora, UMLS and UWCSE. Our experiments showed that NeuralLog+OMIL outperforms both the compared systems on three of the four target relations from the Cora dataset and in the UMLS dataset, while both NeuralLog+OSLR and NeuralLog+OMIL outperform OSLR and RDN-Boost on the UWCSE, assuming a good initial theory is provided.
Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

Internet of Things (IoT) with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications. At the same time, machine learning (ML) and data mining approaches are presented for accomplishing prediction and classification processes. With this motivation, this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection (IML-ELNVBD) in IoT environment. The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors, cameras, etc. which are then connected to the cloud server for further processing. In addition, the modelling and extraction of behaviour take place. Moreover, extreme learning machine sparse autoencoder (ELM-SAE) model is employed for the detection and classification of non-verbal behaviour. Finally, the Ant Colony Optimization (ACO) algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE model. In order to ensure the improved performance of the IML-ELNVBD model, a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.