Reviewerly/exHarmony huggingface dataset:
DeNet_SVM: Product Based Recommendation System using Deep Learning and Web Usage Mining

The world is abundant with digital products in which selecting, searching and buying products for the users is harder. Recommendation system helps the users to overcome this drawback and improves the users’ experience. Recommendation systems increase the outcomes of the companies by providing a list of favourite products to the users. This paper focuses on providing efficient recommendation to the users according to the session constructed for every user based on web usage. This paper proposed product-based recommendation system on image dataset using deep learning and web usage mining. In this paper, MobileNetV2, DenseNet201 are used as feature extraction and SVM, NB, Ensemble, KNN are used as classifiers. DeNet_SVM achieved 92.59% accuracy and 77.76% F-Measure. DenseNet201 and SVM i.e., DeNet_SVM gives better results in terms of accuracy, Precision, Recall and F-Measure in comparison to other techniques. The experiment is performed on standard dataset myntra.dataset which contains 600 images classified into 6 different categories.
MBCT-SR Coupled Cloud Model for Wind-photovoltaic-hydro Hybrid Power Generation System

In the high proportion of wind-photovoltaic (PV-hydro hybrid power system, the fluctuation and the dispersion make it difficult to accurately quantify the output characteristics. Therefore, this paper constructs a cloud model for a hybrid power generation system based on the output data. The multiple backward cloud transformation based on sampling with replacement (MRCT-SR) method is proposed to calculate the improved entropy and hyper entropy to analyze the fluctuation range and the dispersion degree quantitatively. The similarity index is proposed to evaluate the correlation between the wind power, PV power and hydro power. The effectiveness and applicability of this method is proofed by using a typical example in Qinghai.
The comparison of CNN based networks on infiltrating ductal carcinoma images classification in the medical application field

Breast cancer is common in women, ranking first in the incidence of cancer in women and occupying first place in the mortality rate of cancer in women. Because of the seriousness of breast cancer, researchers and institutions worldwide are making unremitting efforts to find the perfect diagnostic and therapeutic solutions. The increasing maturity of image processing technology has led to the growing use of computer-based pathological diagnosis in diagnosing various diseases, and researchers have done much research on this. This paper presents some studies on breast cancer histopathological images based on hematoxylin-eosin staining. Currently, the diagnosis of breast cancer is based on hematoxylin-eosinstained histopathological images. First, the surgeon will take a piece of tissue from the patient's lesion and make a histological section. Next, the pathologist will observe the histological section and diagnose the results. In this way of diagnosis, the patient's diagnosis depends more on the subjective judgment of the pathologist, which requires a high degree of professionalism and is not very efficient. Therefore, for hematoxylin-eosin-stained breast cancer histopathology images, there is a need for a computer-assisted automatic diagnosis method that can reduce the pathologist's burden and make the patient's diagnosis objective and efficient with the help of image processing technology. To this end, this paper compares the performance of three standard machine learning algorithms for comparing hematoxylin-eosin-stained breast cancer histopathology images.
The influence of human resource management practices on employee performance: A case study of Al-Balqa Applied University

Human resources management (HRM) has become critical in modern empirical studies. This study examines the impact of several critical HRM practices, including workforce planning, recruitment and selection, training and development, compensation, and employee appraisal, on employee performance. The study selected employees of Al-Balqa Applied University in Jordan to conduct a survey totaling 150 respondents. The paper adopted a quantitative research approach to achieve the study objectives using self-administrated questionnaires for data collection. The study used the SPSS software to conduct the primary analyses. The results showed that the university’s application of HRM practices was at a high level, where training and development took first place in priority. However, the study suggested a change in HRM practices in the pursuit of excellence to improve the productivity of employees at the university. Therefore, personnel management should always strives to implement one or another strategy of the university, based on which appropriate human resource management practices are chosen.
Heterogeneous models ensemble for Chinese grammatical error correction

Grammatical error correction (GEC) aims to automatically identify and correct grammatical errors in a sentence. Neural machine translation (NMT) models are the mainstream approaches for the GEC task. However, the models require a large amount of data to be adequately trained, the variety of grammatical errors and the dependencies between errors in a sentence make it difficult for a single NMT model to correct multiple errors at once. In the work, we propose an ensemble approach for heterogeneous models, which integrates rule-based, NMT, and pre-trained language model-based GEC models through the recurrent generation approach, the approach can exploit the strengths of each model and cover a wider range of errors in a sentence. We also mitigate the scarcity of task-specific data for the GEC task through the data augmentation approach. We conduct extensive experiments on the NLPCC2018 shared task dataset to demonstrate the effectiveness of our proposed methods, and reaches the <i>F</i>0.5 value of 37.26, outperforming the best model in the shared task.
Sentiment analysis of 2021 Canadian election tweets

Sentiment analysis is the technique of automatically evaluating and classifying emotions (often positive, negative, or neutral) from textual data, such as written comments and social media posts. Sentiment analysis is a subfield of natural language processing (NLP) that employs machine learning to classify the emotional tone of textual input. The fundamental model concentrates on positive, negative, and neutral categories, but it can also include the speaker's underlying emotions (pleasure, anger, insult, etc.) and purchase intents. Complexity is added to sentiment analysis by context. For example, consider the exclamation "Nothing!" Depending on whether or not the speaker enjoys the product, the meaning can vary significantly. In order for a machine to comprehend "I like it," it must be able to decipher the context and determine what "it" refers to. In addition, sarcasm and sarcasm can be tricky because the speaker may express a favorable sentiment while intending the opposite.
Frame semantic grammars: Where frame analysis meets linguistics to study collective action frames

After navigating conceptual and empirical developments in frame analysis research, I reflect on cornerstones and weaknesses in its elaboration of a rigorous analytical prism. In the reflection, I discuss how combining the frame analysis conceptual toolkit with linguistics work on semantic grammars can perhaps help heal some of these weaknesses.
Obstacles to effective model deployment in healthcare

Despite an exponential increase in publications on clinical prediction models over recent years, the number of models deployed in clinical practice remains fairly limited. In this paper, we identify common obstacles that impede effective deployment of prediction models in healthcare, and investigate their underlying causes. We observe a key underlying cause behind most obstacles - the improper development and evaluation of prediction models. Inherent heterogeneities in clinical data complicate the development and evaluation of clinical prediction models. Many of these heterogeneities in clinical data are unreported because they are deemed to be irrelevant, or due to privacy concerns. We provide real-life examples where failure to handle heterogeneities in clinical data, or sources of biases, led to the development of erroneous models. The purpose of this paper is to familiarize modeling practitioners with common sources of biases and heterogeneities in clinical data, both of which have to be dealt with to ensure proper development and evaluation of clinical prediction models. Proper model development and evaluation, together with complete and thorough reporting, are important prerequisites for a prediction model to be effectively deployed in healthcare.
Top-down effect of dialogue coherence on perceived speaker identity

A key mechanism in the comprehension of conversation is the ability for listeners to recognize who is speaking and when a speaker switch occurs. Some authors suggest that speaker change detection is accomplished through bottom-up mechanisms in which listeners draw on changes in the acoustic features of the auditory signal. Other accounts propose that speaker change detection involves drawing on top-down linguistic representations to identify who is speaking. The present study investigates these hypotheses experimentally by manipulating the pragmatic coherence of conversational utterances. In experiment 1, participants listened to pairs of utterances and had to indicate whether they heard the same or different speakers. Even though all utterances were spoken by the same speaker, our results show that when two segments of conversation are spoken by the same speaker but make sense for different speakers to say, listeners report hearing different speakers. In experiment 2 we removed pragmatic information from the same stimuli by scrambling word order while leaving acoustic information intact. In contrast to experiment 1, results from the second experiment indicate no difference between our experimental conditions. We interpret these results as a top-down effect of pragmatic expectations: knowledge of conversational structure at least partially determines a listener's perception of speaker changes in conversation.
MP‐HTLC: Enabling blockchain interoperability through a multiparty implementation of the hash time‐lock contract

Summary The idea of hash time‐lock contracts (HTLCs) has been around from 2013. Nowadays these contracts power the majority of atomic swaps making decentralized exchange of tokens possible. On the other hand, HTLCs also have some flaws. For example they can only be instantiated between two parties. This is highly inefficient when many participants want to exchange tokens between the same pair of blockchains at the same time, because the number of transactions increases linearly in the number of participants. To solve this problem, in this article, we present MP‐HTLC. MP‐HTLC lets multiple users exchange tokens on different blockchains in a single instantiation of the protocol without any leader election. We prove that in case of a UTXO‐based blockchain the number of transactions remains constant regardless the number of participants. We are able to maintain the security assumptions of HTLCs using multiparty computation in the creation of the secret preimage and threshold signatures to manage transaction signing. We also present an implementation for each of the aspects of the protocol.
Improving active participation during enterprise operations modeling with an extended story-card-method and participative modeling software

The COVID-19 pandemic emphasized the need for process automation, using agile software development practices. However, when agile methods are used in scaled contexts, many software development efforts fail, mainly due to lacking requirements engineering practices. When business-oriented software needs to be developed within a scaled context, the story-card method (SCM), developed as part of a previous study, assists in structuring emerging software requirements within a taxonomy that represents enterprise operation. The SCM helps agile team members to develop a common understanding about enterprise operation when they construct the enterprise operation taxonomy. Digital participatory enterprise modeling (PEM) may increase collaboration and understanding among team members, especially when team members are geographically dispersed, when they co-model their understanding of enterprise operations. Using design science research to further evolve the existing SCM, we identified two concerns regarding the existing SCM: (1) The modeling software did not encourage active participation during modeling, and (2) Low quality of the resulting cooperation structure diagram (CSD) that is used to derive an enterprise operation taxonomy, i.e., the need to further extend the existing SCM. As main contribution of this article, we addressed previous deficiencies of the SCM, developing an extended SCM (eSCM), based on principles and guidelines that would encourage online participation during PEM, also providing a comprehensive case to demonstrate the eSCM. As a second contribution, we used survey-feedback from research participants, as well as activity tracking to evaluate whether the modeling tool encouraged active PEM. Our third contribution is to evaluate the quality of the resulting CSDs with suggestions for future improvement.
Spatial Differentiation Characteristics of Rural Areas Based on Machine Learning and GIS Statistical Analysis—A Case Study of Yongtai County, Fuzhou City

With the development of machine learning and GIS (geographic information systems) technology, it is possible to combine them to mine the knowledge rules behind massive spatial data. GIS, also known as geographic information systems, is a comprehensive discipline, which combines geography and cartography and has been widely used in different fields. It is a computer system for inputting, storing, querying, analyzing, and displaying geographic data. This paper mainly studies the spatial differentiation characteristics of rural areas based on machine learning (ML) and GIS statistical analysis. This paper studies 21 township units in Yongtai County. In this paper, ENVI remote sensing image processing software is used to carry out the geometric correction of Landsat-8 remote sensing data. ML is multidisciplinary and interdisciplinary, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and other disciplines. It is specialized in studying how computers simulate or realize human learning behavior to obtain new knowledge or skills, and reorganize existing knowledge structures to continuously improve its own performance. The purpose of using band fusion is to provide more data information for the study and improve the accuracy of land classification results. Through the extraction of evaluation elements, this paper preliminarily confirms the evaluation index object of a rural human settlement environment evaluation system from the perspective of spatial layout rationality. This paper uses a VMD-GWO-ELM-based three-stage evolutionary extreme learning machine evaluation method to simulate the model. In the same way, when the model is trained again, extra weight is given to extract the feature points to reduce the similarity. Experimental data show that GWO-SVM has good classification performance, with the cross-validation rate reaching 91.66% and the recognition rate of test samples reaching 82.41%. The results show that GIS statistics can provide a reference for environmental protection, which is conducive to land-use planning, implementation of environmental impact assessment of land-use planning, and ultimately achieving sustainable development.
Global Context Attention for Robust Visual Tracking

Although there have been recent advances in Siamese-network-based visual tracking methods where they show high performance metrics on numerous large-scale visual tracking benchmarks, persistent challenges regarding the distractor objects with similar appearances to the target object still remain. To address these aforementioned issues, we propose a novel global context attention module for visual tracking, where the proposed module can extract and summarize the holistic global scene information to modulate the target embedding for improved discriminability and robustness. Our global context attention module receives a global feature correlation map to elicit the contextual information from a given scene and generates the channel and spatial attention weights to modulate the target embedding to focus on the relevant feature channels and spatial parts of the target object. Our proposed tracking algorithm is tested on large-scale visual tracking datasets, where we show improved performance compared to the baseline tracking algorithm while achieving competitive performance with real-time speed. Additional ablation experiments also validate the effectiveness of the proposed module, where our tracking algorithm shows improvements in various challenging attributes of visual tracking.
A New Construction of Weightwise Perfectly Balanced Functions with High Weightwise Nonlinearity

The FLIP cipher was proposed at Eurocrypt 2016 for the purpose of meliorating the efficiency of fully homomorphic cryptosystems. Weightwise perfectly balanced Boolean functions meet the balancedness requirement of the filter function in FLIP ciphers, and the construction of them has attracted serious attention from researchers. Nevertheless, the literature is still thin. Modifying the supports of functions with a low degree is a general construction technique whose key problem is to find a class of available low-degree functions. We first seek out a class of quadratic functions and then, based on these functions, present the new construction of weightwise perfectly balanced Boolean functions by adopting an iterative approach. It is worth mentioning that the functions we construct have good performance in weightwise nonlinearity. In particular, some p-weight nonlinearities achieve the highest values in the literature for a small number of variables.
Acceleration of Wheel Factoring Techniques

The efficiency with which an integer may be factored into its prime factors determines several public key cryptosystems’ security in use today. Although there is a quantum-based technique with a polynomial time for integer factoring, on a traditional computer, there is no polynomial time algorithm. We investigate how to enhance the wheel factoring technique in this paper. Current wheel factorization algorithms rely on a very restricted set of prime integers as a base. In this study, we intend to adapt this notion to rely on a greater number of prime integers, resulting in a considerable improvement in the execution time. The experiments on composite numbers n reveal that the proposed algorithm improves on the existing wheel factoring algorithm by about 75%.
A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults

Older adults' independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode's feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.
Time Series Prediction Method of Metrics of Dispatching Automation System Based on AI Platform

Abstract In view of the shortcomings of manual operation mode and passive anomaly perception in the current dispatching automation system, this paper provides a time series prediction method for the metrics of the dispatching automation system based on the artificial intelligence platform. Firstly, based on the collected metrics of the dispatching automation system, the time series prediction algorithm of metrics based on Long Short-Term Memory(LSTM) and LightGBM(LGB) combined model is studied. Then, the algorithm is integrated into a one-stop graphical interactive modeling tool and the model training process is drawn. Through the task is automatic split and distributed model training is automatically carried out on a regular basis. Then, the AI model service sharing technology based on Kubernetes is used to realize the model service release and automatic update, and the platform interactive development component is used to realize automatic online prediction and rolling iterative update. Finally, the actual data of the system metrics is used for example analysis, and the results show that the proposed method can realize the advance prediction of the system metrics, and realize the whole process automatic integration model training and prediction based on the artificial intelligence platform, and effectively improve the model training efficiency, meet the real-time and security requirements of the dispatching automation system, and is more suitable for engineering application scenarios.
A Noise Rate Estimation Method for Image Classification with Label Noise

Abstract In a dataset, the misidentified labels can be assumed as the true labels flipped with a probability. In this paper, we study a general situation in which sample labels are corrupted at random. We propose a noise rate estimation method and prove that by adopting importance reweighting, the accuracy of classification with label noise problem can rise approximately 10% through any surrogate loss function. The two classification methods we choose for robustness analysis are convolutional neural network and convolutional neural network with importance reweighting. The details of these two methods are fully illustrated in this paper. We discuss the label noise problems and solutions in the introduction part and explain how the importance reweighting method and the noise rate estimation method are combined to deal with this problem. Experiments on Fashion-MNIST0.5, Fashion-MNIST0.6, and CIFAR with noise verify our approach. In the end, we also provide the transition matrix of the flip rate for each dataset.
Metric Mutation Anomaly Detection Method of the Power Grid Dispatching Automatic System Based on Machine Learning and Statistics

Abstract With the increase of the scale, function and complexity of power grid dispatching automatic system, the difficulty of system operation and maintenance increases significantly. This paper presents a metric mutation anomaly detection method based on the combination of machine learning and statistical algorithm. First, the system data is smoothed by machine learning algorithm, and the difference between the smoothed value and the real value is calculated. Then, the difference was used to detect data anomaly by ensemble of statistical method. The effectiveness and accuracy of the method is verified by the test data of the power grid dispatching automatic system. The method can quickly locate the anomaly components of the system and assist the system maintenance personnel in making decisions.