Interactivity Features of Online Newspapers:Use and Effect on Gratification Among Zambian Readers

Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sought)for use of interactivity features(“process motivation”)and how widely they are used.It also attempts to ascertain the gratification obtained from their use among readers.The probable relationships between use of the interactivity features(“audience interactivity”)and gratification obtained from them(“process gratification”)and the impact of the perceived credibility of the online newspapers on gratification are also examined.Past studies present mixed results on use of interactivity and gratification obtained from it.This study finds that use of interactivity in Zambian online newspapers is at a low level,although among the three broad categorisations of features of online newspapers,interactivity attracts greater use than hyper-textuality and multi-mediality.Human interactivity features-“knowing what others think about an issue”,“chat on the Facebook page of the newspaper”,“ability to navigate on the Facebook page of the newspaper”,and“posting own comments on stories”-are the main motivations for use of online newspapers,the most frequently used,and the most gratifying to the readers.While readers express an interest in interacting with other readers via online newspapers,they seem less interested in posting their own stories as“citizen journalists”and linking up with the publishers and editors.This finding challenges the notion that all new media are catalysts of participatory and cyclic communication.

AFExplorer:Visual analysis and interactive selection of audio features

Acoustic quality detection is vital in the manufactured products quality control field since it represents the conditions of machines or products.Recent work employed machine learning models in manufactured audio data to detect anomalous patterns.A major challenge is how to select applicable audio features to meliorate model’s accuracy and precision.To relax this challenge,we extract and analyze three audio feature types including Time Domain Feature,Frequency Domain Feature,and Cepstrum Feature to help identify the potential linear and non-linear relationships.In addition,we design a visual analysis system,namely AFExplorer,to assist data scientists in extracting audio features and selecting potential feature combinations.AFExplorer integrates four main views to present detailed distribution and relevance of the audio features,which helps users observe the impact of features visually in the feature selection.We perform the case study with AFExplore according to the ToyADMOS and MIMII Dataset to demonstrate the usability and effectiveness of the proposed system.

Click-Through Rate Prediction Network Based on User Behavior Sequences and Feature Interactions

In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.

Modal Interactive Feature Encoder for Multimodal Sentiment Analysis

Multimodal Sentiment analysis refers to analyzing emotions in infor-mation carriers containing multiple modalities.To better analyze the features within and between modalities and solve the problem of incomplete multimodal feature fusion,this paper proposes a multimodal sentiment analysis model MIF(Modal Interactive Feature Encoder For Multimodal Sentiment Analysis).First,the global features of three modalities are obtained through unimodal feature extraction networks.Second,the inter-modal interactive feature encoder and the intra-modal interactive feature encoder extract similarity features between modal-ities and intra-modal special features separately.Finally,unimodal special features and the interaction information between modalities are decoded to get the fusion features and predict sentimental polarity results.We conduct extensive experi-ments on three public multimodal datasets,including one in Chinese and two in English.The results show that the performance of our approach is significantly improved compared with benchmark models.

Feature Setup Determination in Integrated CAD/CAM System For Concurrent Engineering

This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining sequence can be determined automatically in this system. The set of knowledge-based rules for process planning and manufacturability evaluation is provided and can be shared by all stages of full product life-cycle. An approach for MTAD (Multiple Tool Axis Direction) feature setup generation is presented and the appropriate Tool Axis Direction(TAD) is chosen to minimize the total setup numbers of a part. The classification and process planning of interacting feature are discussed and the knowledge-based rules are used to solve the feature interaction problem.

Feature-based Integrated CAD/CAPP/CAM System For Concurrent Engineering

This paper presents methodologies and technologies of feature_based integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The product information is represented on the basis of hierarchical and dynamic structure of feature representation. The Object_Oriented feature modeling method is adopted to represent the feature classification, feature relationship and feature interaction. The set of knowledge_based rule for process planing and manufacturiability evaluation is provided and can be shared by all stages of full product life_cycle. The feature_based machining operation and machining sequence can be determined automatically. The machining process of the machining feature can be determined according to the set of knowledge_based rule.

Generalized Embedding Machines for Recommender Systems

Factorization machine (FM) is an effective model for feature-based recommendation that utilizes inner products to capture second-order feature interactions. However, one of the major drawbacks of FM is that it cannot capture complex high-order interaction signals. A common solution is to change the interaction function, such as stacking deep neural networks on the top level of FM. In this work, we propose an alternative approach to model high-order interaction signals at the embedding level, namely generalized embedding machine (GEM). The embedding used in GEM encodes not only the information from the feature itself but also the information from other correlated features. Under such a situation, the embedding becomes high-order. Then we can incorporate GEM with FM and even its advanced variants to perform feature interactions. More specifically, in this paper, we utilize graph convolution networks (GCN) to generate high-order embeddings. We integrate GEM with several FM-based models and conduct extensive experiments on two real-world datasets. The results demonstrate significant improvement of GEM over the corresponding baselines.

CAN:Effective Cross Features by Global Attention Mechanism and Neural Network for Ad Click Prediction

Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction.Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors.However,real-world data present a complex and nonlinear structure.Hence,second-order feature interactions are unable to represent cross information adequately.This drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature interactions.However,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features.In this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN models.The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions.The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.

Online approach to feature interaction problems in middleware based system

As a popular infrastructure for distributed systems running on the Internet, middleware has to support much more diverse and complex interactions for coping with the drastically increasing demand on information technology and the extremely open and dynamic nature of the Internet. These supporting mechanisms facilitate the development, deployment, and integration of distributed systems, as well as increase the occasions for distributed systems to interact in an undesired way. The undesired interactions may cause serious problems, such as quality violation, function loss, and even system crash. In this paper, the problem is studied from the perspective of the feature interaction problem （FIP） in telecom, and an online approach to the detection and solution on runtime systems is proposed. Based on a classification of middleware enabled interactions, the existence of FIP in middleware based systems is illustrated by four real cases and a conceptual comparison between middleware based systems and telecom systems. After that, runtime software architecture is employed to facilitate the online detection and solution of FIP. The approach is demonstrated on J2EE （Java 2 Platform Enterprise Edition） and applied to detect and resolve all of the four real cases.

Detecting feature interactions in Web services with model checking techniques

As a platform-independent software system, a Web service is designed to offer interoperability among diverse and heterogeneous applications. With the introduction of service composition in the Web service creation, various message interactions among the atomic services result in a problem resembling the feature interaction problem in the telecommunication area. This article defines the problem as feature interaction in Web services and proposes a model checking-based detection method. In the method, the Web service description is translated to the Promela language - the input language of the model checker simple promela interpreter （SPIN）, and the specific properties, expressed as linear temporal logic（LTL） formulas, are formulated according to our classification of feature interaction. Then, SPIN is used to check these specific properties to detect the feature interaction in Web services.

A Feature Selection Method for Prediction Essential Protein

Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.

Scenario-based verification in presence of variability using a synchronous approach

This paper presents a new model of scenarios, dedicated to the specification and verification of system be- haviours in the context of software product lines （SPL）. We draw our inspiration from some techniques that are mostly used in the hardware community, and we show how they could be applied to the verification of software components. We point out the benefits of synchronous languages and mod- els to bridge the gap between both worlds.

A machine learning-based analytical framework for employee turnover prediction

Employee turnover(ET)can cause severe consequences to a company,which are hard to be replaced or rebuilt.It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET,allowing the human resource management team to take pro-active action for retention or plan for succession.However,building such a system faces challenges due to the variety of influential human factors,the lack of training data,and the large pool of candidate models to choose from.Solutions offered by existing studies only adopt essential learning strategies.To fill this methodological gap,we propose a machine learning-based analytical framework that adopts a streamlined approach to feature engineering,model training and validation,and ensemble learning towards building an accurate and robust predictive model.The proposed framework is evaluated on two representative datasets with different sizes and feature settings.Results demonstrate the superior performance of the final model produced by our framework.