Reviewerly/exHarmony huggingface dataset:
E-commerce Service Equilibrium Prediction Simulation Based on Ontology

Accurately predicting the needs of users in the process of e-commerce services can expand the scope of e-commerce services and improve the quality of e-commerce services. When forecasting demand, it is necessary to establish different forecasting indicators for different factors that affect e-commerce services. The traditional dual-neighbor selection strategy can only select recommended behaviors based on a single indicator, which reduces the demand for e-commerce services. Accuracy. This paper proposes an ontology-based method for predicting user demand balance in the process of e-commerce services. Firstly, construct an e-commerce service balanced recommendation user ontology, calculate the semantic similarity between the ontology, and obtain a set of similar users. On this basis, predict the neighbors of the target user as the prediction group, and calculate the trust subgroups with higher prediction probability in the group. Through the dynamic measurement of uncertain neighbors for balanced prediction, effectively completed the balanced prediction of user demand in e-commerce services. Simulation experiments prove that the proposed method can effectively improve the accuracy of user demand prediction in e-commerce services, and it is valuable.
Standardization Procedure to Provide a Unified Multi-Method Elemental Compositional Dataset, Application to Ferruginous Colouring Matters from Namibia

Curation of archaeological materials often leads to carrying out multi-analytical methodologies that combine e non-invasive and invasive elemental analyses. Such materials are often analysed with different techniques. It results in the production of complementary but apparently non-compatible compositional datasets that cannot be easily compared directly. In the present paper, we propose to compare results acquired on geological ferruginous colouring matters from Namibia with analytical techniques (X-Ray Fluorescence spectrometry (XRF), Proton-Induced-X-ray Emission spectrometry (PIXE), Inductively Coupled Plasma coupled to Optical Emission spectrometry (ICP-OES) and Inductively Coupled Plasma coupled to Mass Spectrometry (ICP-MS)). We aim to provide a unified elemental dataset about these ferruginous colouring matters, usually referred to as “ochre” in archaeology. Analysed geological samples come from three distinct tectonostratigraphic zones of Namibia surveyed in the frame of rock art research. When compared directly, three of the four datasets obtained from these measurements appear as non-compatible because of the inter-equipment variability. However, through a simple standardization procedure, we demonstrate that it is possible to unify these datasets. This procedure minimizes the inter-equipment variability, making the inter-zones of provenance predominant and allowing distinctions of the samples according to their sole origin. Beyond shedding new light on the possibility to compare different elemental analytical techniques, this procedure paves the way for robust statistical provenance studies of ferruginous colouring matter.
Multiple aggregation model using Ensembles Classifier for feature selection: A Homogeneous Approach

A device's substantial success is highly dependent on its users and their interaction with it. The characteristics of the user's database and a thorough understanding of their expectations are critical for developing a device that works. This entails developing a tool that utilizes a homogeneous ensemble feature selector to achieve the same or improved performance on datasets with fewer features. This feature is in support of improving the device performance. In this article, we used the Ensemble classification model for feature selection in the context of machine learning. One of the ML supervised classification models, bagging and boosting, is utilized to work on bootstrap training set samples. This generates a device and runs the user's dataset through feature selectors to get feature rankings. These ranks are then combined using various rank aggregation algorithms to determine the most important attributes in the dataset. This device implements a device that runs the user's dataset through feature selectors and returns feature ranks. These ranks are then merged with the help of various rank aggregation techniques to discover the most important qualities in the supplied dataset.
Determining Optimal Blocking Sizes for Large Exam Groups When Data Matching

Computer literacy courses in the University of Botswana have a large number of students registered for them every year. Exams are multiple-choice, involving shading of identification details and answers on answer forms. This creates a data matching problem, since sometimes identification details are incorrectly shaded. In this paper, we explore the best way to structure such exams such that data matching can be used to match answer forms with students in the master registration list, in such a way that data matching accuracy is very good, due to blocking by exam group, and at the same time maximizing the class room size to save exam administration costs. Our results indicate the effectiveness of an optimal exam room group.
Overview of Basic Latent Structure Models

Latent structure analysis is classified into four analyses according to types of latent and manifest variables. Basic latent structure models, that is, latent class model, latent trait model, latent profile model, and factor analysis model are reviewed. A discussion of the models in a framework of generalized linear models is also given. In order to estimate the parameters of the latent structure models in the succeeding chapters, the Expectation–Maximization (EM) algorithm is also reviewed.
Remote Sensing Image Retrieval Based on Key Region Detection

Abstract Remote sensing (RS) images usually describe large-scale natural geographical scenes with complex and rich background information, which will affect the retrieval performance of image features. How to reduce the background interference and improve the reliability of remote sensing image retrieval (RSIR) features is a problem that needs to be solved. In this paper, a RSIR method based on key region detection was proposed. Firstly, the ground objects of the image are extracted by a famous deep learning object detection model, a YOLO v5 model. Next, we extract the key region of the image according to these ground objects. Then, the image content in the key region is used to extract the retrieval feature by the convolutional neural networks (CNN) model, Resnet. Moreover, the weighted distance based on class probability is used to further improve retrieval performance. Our method utilizes the object detection capability of the YOLO model and the feature extraction capability of RESNET. Our method uses the target detection ability of the YOLO model and the feature extraction ability of RESNET to extract the retrieval feature of RS images. The experimental results on UCMD show that this method can improve the performance of RSIR.
Exploring Implicit and Explicit Relations with the Dual Relation-Aware Network for Image Captioning

Recently, Transformer based architectures using object region features and graph convolutional networks using scene graphs have made significant progress in the image captioning task. However, previous works paid little attention to discovering the high-level semantic relations in visual space. Specifically, they typically neglected the problem of relation mismatching between sentences and images, which may result in generating a pale list of image objects. From the perspective of alignment, there are elements such as objects, attributes, and relations in a sentence, but in visual space, there are only objects and their attributes that can be directly detected. Previous works merely focused on aligning objects and attributes between sentences and images while ignoring the relations that just appeared in sentences but cannot be visually observed in images. In this paper, we introduce a novel dual relation-aware network (DRAN) for image captioning which composes of a dual-path relation encoder and an adaptive context relation decoder to alleviate this problem. Concretely, the dual-path relation encoder in DRAN learns to encode implicit relations and explicit relations between objects into relation-aware features. Then the contextual gated fusion module in the decoder fuses adaptively two types of relation-aware features to help the decoder generate semantically richer captions. Experimental results on the MSCOCO dataset demonstrate the superiority of DRAN in relation encoding and learning, which indicates that the proposed DRAN can capture more semantic relations and details. These conclusions are reflected by the best performance of SPICE score and also by the visual examples illustrated qualitatively.
Split-Based Sequential Sampling for Realtime Security Assessment

Machine learning (ML) for real-time security assessment requires a diverse training database to be accurate for scenarios beyond historic records. This work proposes a novel split-based sequential sampling approach to generate diverse operation scenarios for training ML models. This work also proposes a volume-based coverage metric, the convex hull volume (CHV), to quantify the quality of samplers based on the coverage of generated data. This metric accounts for the distribution of samples across multidimensional space to measure coverage within the physical network limits. Studies on IEEE test cases with 6, 68 and 118 buses demonstrate the efficiency of the approach. Samples generated using the proposed split-based sampling cover 37.5% more volume than random sampling in the IEEE 68-bus system. The proposed CHV metric can assess the quality of generated samples (standard deviation of 0.74) better than a distance-based coverage metric which shows always the same value (standard deviation of <0.001) for very different data distributions in the IEEE 68-bus system. The proposed split-based sampling provides an essential pre-step for training ML models for critical tasks such as security assessment.
The Martini-Henry Rifle and the Origin of<i>Martini</i>as the Name of the Cocktail

Other| February 01 2022 The Martini-Henry Rifle and the Origin of Martini as the Name of the Cocktail Lowell Edmunds Lowell Edmunds Rutgers, the State University of New Jersey, Emeritus lowell edmunds is professor of classics emeritus at Rutgers, the State University of New Jersey. His primary areas of research are Greek myth and Greek literature. He has also published on Roman poetry. His long-standing interest in the Martini cocktail dates back to the first edition of his book The Silver Bullet: The Martini in American Civilization (Greenwood, 1981). The second edition was modified and translated into Italian by Guido Largomarsino and published, with a preface by Umberto Eco, as Ed è subito Martini (Archinto, 2000). Email: . Search for other works by this author on: This Site Google American Speech (2022) 97 (1): 109–119. https://doi.org/10.1215/00031283-9767654 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Lowell Edmunds; The Martini-Henry Rifle and the Origin of Martini as the Name of the Cocktail. American Speech 1 February 2022; 97 (1): 109–119. doi: https://doi.org/10.1215/00031283-9767654 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsAmerican Dialect SocietyAmerican Speech Search Advanced Search The text of this article is only available as a PDF. Copyright 2022 by the American Dialect Society2022 Article PDF first page preview Close Modal You do not currently have access to this content.
A Classifier Ensemble Method for Breast Tumor Classification Based on the BI-RADS Lexicon for Masses in Mammography

A computer-aided diagnosis (CAD) system is a tool to assist clinicians in interpreting medical images. In mammography, CADs provide a classification of tumors to distinguish between benign and malignant cases, aiming to support the clinical conduct. Nevertheless, CADs disregard informing about the internal criteria utilized to classify breast tumors, particularly, compatible with the Breast Imaging-Reporting and Data System (BI-RADS). In this context, we propose a new scheme of tumor classification based on the BI-RADS lexicon for masses. The terms of shape, margin, and density are modeled using specific feature sets to provide different perspectives of the tumor in terms of benign and malignant findings. The outcomes of the three models are further used for the final histopathological classification of the tumor. The proposed method is compared with two conventional CAD systems that classify tumors using a single feature set. The results show that the proposed method obtains 90% accuracy, whereas the two conventional CADs reach an accuracy of 89% and 76%. Therefore, the proposed method is suitable for the histopathological classification of tumors by using the information provided by the three models of the BI-RADS lexicon for masses.
A Joint Training Framework Based on Adversarial Perturbation for Video Semantic Tags Classification

In this paper, we present a solution of “Knowledge Enhanced Video Semantic Understanding” of 2021 CCKS Track 14th task [1]. We separate video semantic understanding framework into two related tasks, namely the multi-classes video cate classification (VCC) task and the multi-label video tag classification (VTC) task. Meanwhile we propose a joint training framework for VCC task and VTC task based on adversarial perturbations strategy. In the final leaderboard, we achieved 3rd place in the competition. The source code has been at Github ( https://github.com/stone-yzx/2021-CCKS-Trace14-3rd-semantic-tag-classification ).
FDataCollector: A Blockchain Based Friendly Web Data Collection System

In the last decade, a growing number of people use web crawlers to collect the data from the Internet for data analysis. The web crawlers greatly increase the workload of web servers and hence hinder normal accesses of the websites located in the servers. The accesses from web crawlers also affect the effectiveness of web mining, which assumes that the accesses are all from normal users. Moreover, the un-licensed collection of data from websites are often prohibited by laws and regulations of government and commercial organizations. To restrict the data collection from web crawlers, currently anti-crawler technique is applied to the websites. The behaviors of web crawler are recognized and their accesses are denied. This overcome the aforementioned problem, however, become a big obstacle for data exchange, considering that the large volume of data in the Internet could be useful for many data analysis applications. The dilemma of data collection using web crawlers and anti-crawler techniques demand a better solution. In this study, we propose to a friendly data sharing system FDataCollector to allow the data collection and also alleviate the workload of web servers by using blockchain techniques. We first make the data uploaded to the data sharing system by a few trustful users and then sell to public users in a traceable and P2P sharing way. The other accesses of web web crawlers are prohibited. On the user side, this design not only enable a convenient search of data but also improve the download efficiency. On the data holder side, this traceable and benefit way encourages them to share the data. We implement the system to demonstrate our idea. The results show that the system has high efficiency even when many transactions occur at the same time.
Criteria and Indicators of Efficiency of Cryptographic Protection Mechanisms

Solutions to build monitoring systems to prevent the spread of infectious diseases already exist and are developing rapidly in many countries around the world. In this regard, in the means of tracking contacts as the main subsystem of population monitoring, there are practical and theoretical tasks for the mechanisms of cryptographic protection of personal data of users, providing: efficiency; objectivity of decision-making; depersonalization; generalized processing, reliability, availability of decentralized storage. This section substantiates the criteria and indicators of the effectiveness of cryptographic protection mechanisms. In particular, general theoretical information on criteria and indicators for assessing the security of symmetric cryptocurrencies, hash functions, generation of pseudo-random sequences, asymmetric cryptographic transformations, etc. is presented. This section discusses general approaches that can be used to determine the criteria and performance indicators of cryptographic protection mechanisms for users’ personal data and interoperability protocols that provide various security services when tracking contacts with system users.
Synthesizing Programs from Program Pieces Using Genetic Programming and Refinement Type Checking

Program synthesis automates the process of writing code, which can be a very useful tool in allowing people to better leverage computational resources. However, a limiting factor in the scalability of current program synthesis techniques is the large size of the search space, especially for complex programs. We present a new model for synthesizing programs which reduces the search space by composing programs from program pieces, which are component functions provided by the user. Our method uses genetic programming search with a fitness function based on refinement type checking, which is a formal verification method that checks function behavior expressed through types. We evaluate our implementation of this method on a set of 3 benchmark problems, observing that our fitness function is able to find solutions in fewer generations than a fitness function that uses example test cases. These results indicate that using refinement types and other formal methods within genetic programming can improve the performance and practicality of program synthesis.
NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities and offers several advantages over focusing solely on traditional high performance computing (HPC) workloads. The integration of cloud services is especially potent for workflows that fuse data streams, modeling and simulation (“modsim”), and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is aligning optimal capability, data, and user intent on the right resources for each step in a workflow. The National Renewable Energy Laboratory (NREL) Stratus service provides a basis for this alignment. Stratus layers the capabilities needed to make cloud services accessible to a lab-based scientific community on commercial offerings, and currently supports upward of 200 projects, ranging from Internet of Things (IoT) integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements, which enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow. We highlight multiple components, including sensor data intake, cleaning and transforming (edge/cloud suitable), generation of synthetic data through modsim, computationally heavy machine learning training and hyperparameter optimization (HPC suitable), and inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis of the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined in this paper allow for better understanding of new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.
Multi-View Subspace Clustering with Local and Global Information

Multi-view clustering can mine the underlying structure of multi-view data and has attracted increasing attention. Most existing multi-view clustering methods either construct the similarity matrix from the global level through self-representation learning or construct the similarity matrix from the local level through graph learning. Spectral clustering method can be used to yield the clustering results based on the similarity matrix. However, the similarity matrix that only considers global information or local information is not robust. Moreover, separating the similarity matrix learning and clustering as two steps may lead to sub-optimal clustering results. To address these issues, we propose in this paper, a multi-view subspace clustering with local and global information (MVSCLG) method. Our method combines the self-representation learning and graph learning to learn a similarity matrix with global and local information, and simultaneously utilizes the spectral decomposition and the spectral rotation techniques to yield the clustering results. We also develop an effective optimization algorithm to solve the resulting optimization problem. The effectiveness and superiority of this method are verified on four multi-view benchmark data sets.
MuseBar: Alleviating Posterior Collapse in Recurrent VAEs Toward Music Generation

Machine learning has shown remarkable artistic values and commercial potentials in the music industry. Recurrent variational autoencoders (RVAEs) have been widely applied to this area due to the condensing, inclusive, and smooth nature of their latent space. However, RNNs are powerful auto-regressive models on their own, where the decoder in a RVAE can be strong enough to work independently from the encoder. When this happens, the model degrades from an autoencoder to a traditional RNN, which is known as posterior collapse. In this paper, we propose a cost-effective bar-wise regulation schema called MuseBar to alleviate this problem for music generation. We impose a prior on the hidden state of every music bar in the RNN encoder, instead of only on the last hidden state as in the standard RVAEs, such that the latent code is learned under stronger regulations. We further evaluate our proposed method, quantitatively and qualitatively, with extensive experiments on manually scraped musical data. The results demonstrate that the bar-wise regulation significantly improves the quality of the latent space in terms of Mutual Information and Kullback-Leibler divergence.
Multi-Objective Genetic Algorithm for Job Planning in Cloud Environment

In Cloud environments, scheduling decisions must be made in the shortest time because there are many users competing for resources and time. Optimization measures are used when making scheduling decisions and represent the objectives of the scheduling process. Better resource utilization, a higher amount of effectively accomplished jobs and minimum reply time are the key constraints for optimization. The events are stated by the cost of objective function which measures the worth of computed solution and associates it with dissimilar solution. Here objective role is defined with the help of constraints and criteria. The suggested job planning methodology centred on Multi-Objective Genetic procedures in cloud computing settings uses an enhanced cost-based planning set of rules for building resourceful mapping of jobs to existing resources in cloud computing. This scheduling algorithm achieves the minimization of both assets cost and working performance.