Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR

Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task

Lucene4IR: Developing information retrieval evaluation resources using Lucene

The workshop and hackathon on developing Information Retrieval Evaluation Resources using Lucene (L4IR) was held on the 8th and 9th of September, 2016 at the University of Strathclyde in Glasgow, UK and funded by the ESF Elias Network. The event featured three main elements: (i) a series of keynote and invited talks on industry, teaching and evaluation; (ii) planning, coding and hacking where a number of groups created modules and infrastructure to use Lucene to undertake TREC based evaluations; and (iii) a number of breakout groups discussing challenges, opportunities and problems in bridging the divide between academia and industry, and how we can use Lucene for teaching and learning Information Retrieval (IR). The event was composed of a mix and blend of academics, experts and students wanting to learn, share and create evaluation resources for the community. The hacking was intense and the discussions lively creating the basis of many useful tools but also raising numerous issues. It was clear that by adopting and contributing to most widely used and supported Open Source IR toolkit, there were many benefits for academics, students, researchers, developers and practitioners - providing a basis for stronger evaluation practices, increased reproducibility, more efficient knowledge transfer, greater collaboration between academia and industry, and shared teaching and training resources

Depression and anxiety in social media: Jordan case study

The expression "social media" refers to a software-based platform developed for users’ benefit. People use it to gain social power, market their products, conduct online business, and share information and ideas. This digital ecosystem has become helpful in various ways, but research indicates that it does not come for free. Addiction, depression, and anxiety are some of the adverse conditions discussed in many studies. The purpose of this study is to mark if there is a relationship between using social media networks and the numbering of people with anxiety or depression. Also, by addressing the need to learn more about what makes people use social networks and how that use affects anxiety and depression in Arabic-speaking users in Jordan, we can help people from different cultures understand each other better. This research uses TAM, telepresence, and survey data from 1050 people, mainly from Jordan. The research looks at how the usage of social media is related to supposed usefulness, supposed ease of use, trust, social influence, age, gender, level of education, marital status, the time spent on the internet, preferred social media network, and perceived usefulness of SNS. AMOS 20 methods of confirmatory factor analysis (CFA), structural equation modeling (SEM), and machine learning (ML), such as SMO, ANN, random forest, and the bagging reduced error pruning tree (RepTree), were used to test the proposed model hypotheses. According to the results, the researchers found high correlations between social network usage and depression and anxiety. The use of social networking sites is also affected by how useful they are seen to be, how easy they are to use, trust, social influence, and telepresence. Also, the moderator's age, gender, level of education, marital status, amount of time spent on the internet, experience with the internet, and favorite social networks all affect how they plan to use social networks

Evaluating the influence of security considerations on information dissemination via social networks

This study investigates the factors that influence the sharing of information on social media platforms and examines the effects of perceived security, perceived privacy, and user awareness on users' trust in social media platforms, as well as the moderating effects of age, gender, educational attainment, and internet proficiency on information sharing. The study collected data from 837 social media users in Jordan and analyzed them using structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods. The findings of the study indicate that perceived security, perceived privacy, and user awareness all have a significant impact on users' trust in social media platforms. Trust, in turn, has a significant impact on the amount of information shared on these platforms. Also, the findings of this study provide valuable insights into the dynamics of information sharing on social networks. This knowledge will be of interest to managers, policymakers, and developers of social media platforms. In addition, the findings of the study also have implications for the privacy and security of social media users. For example, social media users can be more careful about the information they share on social media platforms, and they can take steps to protect their privacy

DGR: Gender Recognition of Human Speech Using One-Dimensional Conventional Neural Network

The speech entailed in human voice comprises essentially paralinguistic information used in many voice-recognition applications. Gender voice is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e., male or female) from a voice signal. The contributions are three-fold including (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. The experimental results show the importance of subfeatures over others, which are vital for enhancing the efficiency of classification models’ performance. Experimentation reveals that the best recall value is equal to 99.97%; the best recall value is 99.7% for two models of deep learning (DL) and support vector machine (SVM), and with feature selection, the best recall value is 100% for SVM techniques

Experimental study on semi-structured peer-to-peer information retrieval network

In the recent decades, retrieval systems deployed over peer-to-peer (P2P) overlay networks have been investigated as an alternative to centralised search engines. Although modern search engines provide efficient document retrieval, they possess several drawbacks. In order to alleviate their problems, P2P Information Retrieval (P2PIR) systems provide an alternative architecture to the traditional centralised search engine. Users and creators of web content in such networks have full control over what information they wish to share as well as how they share it. The semi-structured P2P architecture has been proposed where the underlying approach organises similar document in a peer, often using clustering techniques, and promotes willing peers as super peers (or hubs) to traffic queries to appropriate peers with relevant content. However, no systematic evaluation study has been performed on such architectures. In this paper, we study the performance of three cluster-based semi-structured P2PIR models and explain the effectiveness of several important design considerations and parameters on retrieval performance, as well as the robustness of these types of network

EMT: Ensemble Meta-Based Tree Model for Predicting Student Performance

In recent decades, predicting the performance of students in the academic field has revealed the attention by researchers for enhancing the weaknesses and provides support for future students. In order to facilitate the task, educational data mining (EDM) techniques are utilized for constructing prediction models built from student academic historical records. These models present the embedded knowledge that is more readable and interpretable by humans. Hence, in this paper, the contributions are presented in three folds that include the following: (i) providing a thorough analysis about the selected features and their effects on the performance value using statistical analysis techniques, (ii) building and studying the performance of several classifiers from different families of machine learning (ML) techniques, (iii) proposing an ensemble meta-based tree model (EMT) classifier technique for predicting the student performance. The experimental results show that the EMT as the ensemble technique gained a high accuracy performance reaching 98.5% (or 0.985). In addition, the proposed EMT technique obtains a high performance, which is a superior result compared to the other techniques

Artificial Neural Network for Classifying Financial Performance in Jordanian Insurance Sector

Over the past few decades, financial performance has attracted researchers’ attention, especially in the insurance sector. Insurance is a tool for the growth and sustainability of both rising and developing economies. It promotes economic stability for people, organizations, and governments by taking on risk and spreading it across the market. We intend to classify insurance companies’ financial performance in Jordan’s Amman Stock Exchange (ASE). The sample size is 15 out of 22 selected insurance firms from 2008 to 2020. We apply the Multi-Layer Perceptron (MLP) model for the detection of (high/low) total asset turnover (TAT) as output, while we select the subrogation (SB), claims paid (CP), market capitalization (MC), and total shareholders’ equity (SE) as input to the MLP model. The performance of the MLP model is evaluated using different criteria, namely the false positive rate (FP rate), false negative rate (FN rate), F-measure, precision, and accuracy (ACC). The results show that MLP is efficient and performs well in multiple criterion tests through iteration growth. Based on our knowledge, the paper assesses the financial performance of Jordanian insurance firms, which has not been investigated previously. Furthermore, this study gives valuable information to regulators and policymakers to improve asset management efficiency in the insurance sector

Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks

In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.Peer reviewe