Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection

The advance of smartphones and cellular networks boosts the need of mobile advertising and targeted marketing. However, it also triggers the unseen security threats. We found that the phone scams with fake calling numbers of very short lifetime are increasingly popular and have been used to trick the users. The harm is worldwide. On the other hand, deceptive advertising (deceptive ads), the fake ads that tricks users to install unnecessary apps via either alluring or daunting texts and pictures, is an emerging threat that seriously harms the reputation of the advertiser. To counter against these two new threats, the conventional blacklist (or whitelist) approach and the machine learning approach with predefined features have been proven useless. Nevertheless, due to the success of deep learning in developing the highly intelligent program, our system can efficiently and effectively detect phone scams and deceptive ads by taking advantage of our unified framework on deep neural network (DNN) and convolutional neural network (CNN). The proposed system has been deployed for operational use and the experimental results proved the effectiveness of our proposed system. Furthermore, we keep our research results and release experiment material on http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any update.Comment: 6 pages, TAAI 2017 versio

The Role Of The CEO, Executive Team, And Workforce Metrics Of A Small University In The U.S.

The Role of the CEO, Executive Team, and Workforce Metrics of a small University in the U.S. is not just a question of academic standards. The 21st century requirements of education involve a wider set of attributes, equipping the young with social and organizational skills to cope with adult life inside and outside the workplace (Barber, 2001). It is necessary to describe the importance of aligning clear strategic priorities with workforce metric of a small university in the U.S. In addition, effective strategy execution requires a new partnership between the CEO, the workforce, and a small university’s HR function. Therefore, providing insights into the execution challenge, with examples of how a small university has developed workforce and HR strategies to drive strategy execution efforts, and offering suggestions about workforce metrics might enhance the success in strategy execution

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively

RegRNA: an integrated web server for identifying regulatory RNA motifs and elements

Numerous regulatory structural motifs have been identified as playing essential roles in transcriptional and post-transcriptional regulation of gene expression. RegRNA is an integrated web server for identifying the homologs of regulatory RNA motifs and elements against an input mRNA sequence. Both sequence homologs and structural homologs of regulatory RNA motifs can be recognized. The regulatory RNA motifs supported in RegRNA are categorized into several classes: (i) motifs in mRNA 5′-untranslated region (5′-UTR) and 3′-UTR; (ii) motifs involved in mRNA splicing; (iii) motifs involved in transcriptional regulation; (iv) riboswitches; (v) splicing donor/acceptor sites; (vi) inverted repeats; and (vii) miRNA target sites. The experimentally validated regulatory RNA motifs are extracted from literature survey and several regulatory RNA motif databases, such as UTRdb, TRANSFAC, alternative splicing database (ASD) and miRBase. A variety of computational programs are integrated for identifying the homologs of the regulatory RNA motifs. An intuitive user interface is designed to facilitate the comprehensive annotation of user-submitted mRNA sequences. The RegRNA web server is now available at