Prioritizing policy tools to support development of IoT technologies in Iran

The Internet of Things is a new perspective on the information technology industry that encompasses all technical, social and economic concepts. Identifying priority application areas for this technology is one of the key points for its effective use. Governments also have a variety of tools for policy-making to support the development of this technology. Therefore, knowing which tool has a higher priority for support is a very important point that can not only prevent the loss of resources but also improve the speed of development. In this research, using the opinion of experts and using the TOPSIS method, an attempt has been made to identify the priority of IoT application areas as well as the priority of government support policy tools in these areas. The results of this research have shown that the important areas in this field respectively are Smart cities, Factories and industries, Shipping, Healthcare, Supply chain management, Buildings and houses and finally Agriculture and animal husbandry. Also Government policy tools respectively, in order of priority, are Financial and Investment Incentives, Flexible regulatory, Tax Exemption, Deploying IOT applications in E-government, Standards and Accreditation, Technology Infrastructure, Macro Policies, Application Infrastructure, Cybersecurity Regulation, Privacy Regulation

A Novel Theory in Risk-Management by Numerical Pattern Analysis in Data-Mining

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The Effect of Different Ratios of Malonic Acid to Plyvinylalcohol on Electrochemical and Mechanical Properties of Polyacrylonitrile Electrospun Separators in Lithium-Ion Batteries

The present study aimed to investigate the mechanical, thermal, and electrochemical properties of Polyacrylonitrile (PAN) electrospun separators in the presence of Polyvinylalcohol (PVA) hydrophilic materials and Malonic Acid (MA) crosslinker inside the lithium-ion batteries. The results showed that the M3 modified separator with the MA to PVA+MA (wt./wt.) optimum ratio of 37.5 % had the best performance in all tests. This separator had a value of 3.16 mS/cm in the ion conductivity test. Additionally, it had an electrolyte uptake of 1172 % (2.39 times more than the neat PAN separator) and thermal shrinkage of 7.4 % at 180 °C, where this value was 14.5 % for neat PAN separator at the same experimental condition. Furthermore, the acceptable performance in the battery performance tests was compared with other separators

MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks

Implementations of SGD on distributed systems create new vulnerabilities, which can be identified and misused by one or more adversarial agents. Recently, it has been shown that well-known Byzantine-resilient gradient aggregation schemes are indeed vulnerable to informed attackers that can tailor the attacks (Fang et al., 2020; Xie et al., 2020b). We introduce MixTailor, a scheme based on randomization of the aggregation strategies that makes it impossible for the attacker to be fully informed. Deterministic schemes can be integrated into MixTailor on the fly without introducing any additional hyperparameters. Randomization decreases the capability of a powerful adversary to tailor its attacks, while the resulting randomized aggregation scheme is still competitive in terms of performance. For both iid and non-iid settings, we establish almost sure convergence guarantees that are both stronger and more general than those available in the literature. Our empirical studies across various datasets, attacks, and settings, validate our hypothesis and show that MixTailor successfully defends when well-known Byzantine-tolerant schemes fail.Comment: To appear at the Transactions on Machine Learning Research (TMLR

APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations

Recent advances in learning aligned multimodal representations have been primarily driven by training large neural networks on massive, noisy paired-modality datasets. In this work, we ask whether it is possible to achieve similar results with substantially less training time and data. We achieve this by taking advantage of existing pretrained unimodal encoders and careful curation of alignment data relevant to the downstream task of interest. We study a natural approach to aligning existing encoders via small auxiliary functions, and we find that this method is competitive with (or outperforms) state of the art in many settings while being less prone to overfitting, less costly to train, and more robust to distribution shift. With a properly chosen alignment distribution, our method surpasses prior state of the art for ImageNet zero-shot classification on public data while using two orders of magnitude less time and data and training 77% fewer parameters