Smart TV face monitoring for children privacy

© 2018 Taiwan Academic Network Management Committee. All Rights Reserved. Many of the modern Television (TV) sets and digital TV set-top boxes are endowed with Smart TV capabilities. Those include computing and connectivity to online services such as video on demand, online games and even sports and healthcare. A lot of Smart TV devices also have built-in cameras, microphones and other sensors that provide for environmental monitoring and consequent context dependent feedback. Such Smart TV capabilities, however, can lead to privacy violations through unwanted tracking and user profiling by broadcasters and other service providers. There is a concern when underage users such as children who may not fully understand the concept of privacy are involved in using the Smart TV services. To address this issue, face recognition experiments were conducted with the IBM\u27s Watson and the Microsoft\u27s Face Application Programming Interface to reveal the potential of integrating facial recognition in future privacy aware Smart TV services

Automatically accelerating non-numerical programs by architecture-compiler co-design

Because of the high cost of communication between processors, compilers that parallelize loops automatically have been forced to skip a large class of loops that are both critical to performance and rich in latent parallelism. HELIX-RC is a compiler/microprocessor co-design that opens those loops to parallelization by decoupling communication from thread execution in conventional multicore architecures. Simulations of HELIX-RC, applied to a processor with 16 Intel Atom-like cores, show an average of 6.85× performance speedup for six SPEC CINT2000 benchmarks.</jats:p

Description of Ways to Combat Recruitment to Terrorist Structures in Social Networks on the Example of ISIS*

В статье рассматриваются способы борьбы с вербовкой в террористические структуры в сети интернет. В качестве примера авторы описывают Исламское государство. Подробно разбираются нормативно-правовые акты в сфере борьбы с терроризмом.This article discusses ways to combat recruitment to terrorist structures on the Internet. As an example, the author describes the Islamic State. Regulatory legal acts in the field of combating terrorism are examined in detail

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Visiting Professor presso Shizuoka University

Ottobre 201