/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called "Beyond AI". Specifically, we plan to develop an intelligent learning model called "Brain Intelligence (BI)" that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.

Brain Intelligence: Go beyond Artificial Intelligence

Prefaces Acknowledgements 1. Mountains and their climatological study 2. Geographical controls of mountain meteorological elements 3. Circulation systems related to orography 4. Climatic characteristics of mountains 5. Regional case studies 6. Mountain bioclimatology 7. Changes in mountain climates Appendix General index Author index.

Mountain Weather and Climate

Recently, the world witnessed a significant increase in the number of used drones, with a global and continuous rise in the demand for their multi-purpose applications. The pervasive aspect of these drones is due to their ability to answer people’s needs. Drones are providing users with a bird’s eye that can be activated and used almost anywhere and at any time. However, recently, the malicious use of drones began to emerge among criminals and cyber-criminals alike. The probability and frequency of these attacks are both high and their impact can be very dangerous with devastating effects. Therefore, the need for detective, protective and preventive counter-measures is highly required. The aim of this survey is to investigate the emerging threats of using drones in cyber-attacks, along the countermeasures to thwart these attacks. The different uses of drones for malicious purposes are also reviewed, along the possible detection methods. As such, this paper analyzes the exploitation of drones vulnerabilities within communication links, as well as smart devices and hardware, including smart-phones and tablets. Moreover, this paper presents a detailed review on the drone/Unmanned Aerial Vehicle (UAV) usage in multiple domains (i.e civilian, military, terrorism, etc.) and for different purposes. A realistic attack scenario is also presented, which details how the authors performed a simulated attack on a given drone following the hacking cycle. This review would greatly help ethical hackers to understand the existing vulnerabilities of UAVs in both military and civilian domains. Moreover, it allows them to adopt and come up with new techniques and technologies for enhanced UAV attack detection and protection. As a result, various civilian and military anti-drones/UAVs (detective and preventive) countermeasures will be reviewed.

Security analysis of drones systems: Attacks, limitations, and recommendations

Review on recent progress in observations, source identifications and countermeasures of PM2.5.

Deep Learning algorithms are designed in such a way that they mimic the function of the human cerebral cortex. These algorithms are representations of deep neural networks i.e. neural networks with many hidden layers. Convolutional neural networks are deep learning algorithms that can train large datasets with millions of parameters, in form of 2D images as input and convolve it with filters to produce the desired outputs. In this article, CNN models are built to evaluate its performance on image recognition and detection datasets. The algorithm is implemented on MNIST and CIFAR-10 dataset and its performance are evaluated. The accuracy of models on MNIST is 99.6 %, CIFAR-10 is using real-time data augmentation and dropout on CPU unit.

Convolutional Neural Network (CNN) for Image Detection and Recognition

Internet is the most powerful medium as on date, facilitating varied services to numerous users. It has also become the environment for cyber warfare where attacks of many types (financial, ideological, revenge) are being launched. The ecommerce transactions being carried out online are of major interest to cybercriminals. The Internet needs to be protected from these attacks and an appropriate response has to be generated to handle them to reduce the impact. Network forensics is the science that deals with capture, recording, and analysis of network traffic for investigative purpose and incident response. There are many tools which assist in capturing data transferred over the networks so that an attack or the malicious intent of the intrusions may be investigated. This paper presents a generic framework for network forensic analysis by specifically identifying the steps connected only to network forensics from the already proposed models for digital investigation. Each of the phases in the framework is elucidated. A comparison of the proposed model is done with the existing models for digital investigation. Research challenges in various phases of the model are approached with specific reference to network forensics.

/pdf/a-generic-framework-for-network-forensics-2gu6b7w768.pdf

A Generic Framework for Network Forensics

Due to the heterogeneity and geographically distribution of Grid resources, effective and efficient task scheduling algorithms are required. Resource load balancing and minimizing makespan are the fundamental goals of effective and efficient task scheduling. It becomes more complicated when various QoS demands arise from users. In this paper, we have presented two algorithms, QoS Guided Weighted Mean Time-min and QoS Guided Weighted Mean Time Min-Min Max-Min Selective, for QoS based Grid task scheduling. Both algorithms consider the resource performance and QoS demands of tasks for scheduling. The algorithms are simulated using GridSim. The results show that the proposed algorithms outperform in makespan, resource utilization and load balancing than other algorithms such as, Weighted Mean Time-min, Weighted Mean Time Min-Min Max-Min Selective, Min-Min, Max-Min and QoS Guided Min-Min.

/pdf/qos-guided-heuristic-algorithms-for-grid-task-scheduling-54h65271rw.pdf

QoS Guided Heuristic Algorithms for Grid Task Scheduling

Ambient trace gases (NH3, NO, NO2 and SO2) and black carbon (BC) were measured along with particulate matter (PM2.5) over the northwestern Himalayan region (Palampur, Kullu, Shimla, Solan and Nahan) of Himachal Pradesh (HP), India in a campaign mode during 12–22 March 2013 to evaluate the ambient air quality of the region. The average mixing ratio of ambient NH3, NO, NO2 and SO2 were recorded as 7.1 ± 2.6, 3.1 ± 1.3, 3.9 ± 1.4 and 1.7 ± 0.7 ppb respectively over the northwestern Himalayan region. The average concentration of BC was estimated as 2.2 ± 0.5 µg m−3 over the region whereas average concentration of PM2.5 mass was estimated as 41.8 ± 7.9 µg m−3. The spatial variation of ambient trace gases (NH3, NO, NO2 and SO2), BC and PM2.5 over the northwestern Himalayan region, India reveals that the region is mainly influenced by local activities, i.e., tourism activities, agricultural activities, biomass burning and vehicular emission. A significant positive linear correlation of NH3 and NH4
 + with SO4
 2−, NO3
 − and Cl− (NH4
 + vs. SO4
 2−
 , r
 2 = 0.652; NH4
 + vs. NO3
 −, r
 2 = 0.701; and NH4
 + vs. Cl−, r
 2 = 0.627) of the PM2.5 indicates the possible formation of (NH4)2SO4, NH4NO3 and NH4Cl aerosols over the region.

Measurements of Particulate (PM2.5), BC and Trace Gases Over the Northwestern Himalayan Region of India

This article analyses tourism in Sikkim, a small Himalayan state of India, in light of the most general kind of tourism process models and tourist influx trends. The tourism trends are studied to map the state of progress and prospects of tourism in the state, and for the general understanding of patterns of tourist influx. Tourism in Sikkim is predominantly domestic; foreign tourists comprise only 5.5% of the total tourist influx. The trends reveal that Sikkim tourism is in the developmental stage of increasing growth of tourist influx. Simulations based on three scenarios suggest that approximately 7.6―10.4 lakhs tourists would visit Sikkim during the year 2017, which would also have implications on the infrastructure, environment, natural resources, culture and eco-tourism of the state. The impacts are also analysed in light of projected increase in the population and trends of livestock resources and agricultural production. A proactive planning involving optimal use of management options, therefore, is required for a viable symbiosis of tourism and environment, and sustainability of tourism in the state.

R. C. Joshi

Papers

Convolutional Neural Network (CNN) for Image Detection and Recognition

A Generic Framework for Network Forensics

QoS Guided Heuristic Algorithms for Grid Task Scheduling

Measurements of Particulate (PM2.5), BC and Trace Gases Over the Northwestern Himalayan Region of India

Environmental sustainability and tourism ― implications of trend synergies of tourism in Sikkim Himalaya