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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

The aim of the innovative Physical Internet (PI) concept is to reverse the unsustainability situation existing in current logistic systems, to meet the increasingly demanding services required by the users and to increase the global logistic performance. In the PI approach, the goods are encapsulated in modularly dimensioned, reusable or recyclable, and smart containers, called PI-containers. This paper focuses on the informational context of such containers and more particularly on their associated activeness. This capability allows the PI-container to have an active role in achieving its mission and in the PI management and operation. After a presentation of the physical and informational aspects associated to PI-containers in hyperconnected logistics, a state-of-the-art review of the field of smart containers is provided. The limitations of actual solutions for fulfilling the informational requirements are highlighted. The notion of activeness is detailed and a descriptive framework is introduced. Finally, a grouping application of containers in a PI-hub is used to illustrate the proposed framework.

On the activeness of intelligent Physical Internet containers

Traffic congestion, dominated by private mobility, reveals not only negative impacts on road safety and the environment, but also on community cohesion. With the global COVID-19/SARS-CoV-2COVID-19 (COVID-19) epidemic, there is an urgent need for social isolation and the use of individual private transport as per the approved health guidelines. Urban transport, especially public transportation (PT), is among the primary sectors affected due to the COVID-19 pandemic. Thus, novel alternatives for competitive PT services still have to be provided to remain meeting the socio-economic and ecological PT challenges. In this respect, sharing PT vehicles carrying passengers (shared freight-PT) could exploit a significant residual capacity as absorptive capacity is actually reduced. However, such use is based on a large-scale mutualization. The idea of integrating freight in passenger transit networks could be efficient within a Physical Internet (PI or π) framework for improving system monitoring, operational performance and, user comfort. This paper explores the major trends in the theory and practice of shared transport systems, in terms of passengers and freight, and suggests a PI conceptual framework to check if we could promote such logistics. In exploring the PI approach, a number of proposals appear providing answers and advance researches towards shared freight-PT.

Shared public transport within a physical internet framework: Reviews, conceptualization and expected challenges under COVID-19 pandemic

Predicting car park availability for a better delivery bay management

Finding a parking space in big cities is becoming more and more impossible. In addition, the emergence of car has created several problems relating to urban mobility for the city. But with the development of technology, these problems can be solved. In this paper, the problem of parking has been addressed by proposing an architecture to automate the parking process using the internet of things, artificial intelligence and multi agent systems.

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Smart Parking Architecture based on Multi Agent System

An Unmanned Aerial Vehicle (UAV) is a pilotless aerial vehicle which can be controlled either autonomously by onboard computers or remotely controlled by a pilot at the Ground Control Station (GCS). UAV and Ground Control Station (GCS) define a new form network, this kind of network persuade some specific characteristics as sufficient energy, network connectivity, mobility and network security. These specifications persuade difficult challenges for building a trustworthy and secure communication architecture solution. In this paper we present our new secure communication protocol taking into account the specifications of UAV network. This new architecture depends mostly on the definition of a secure protocol which provides authentication, confidentiality and integrity, in preserving network resources for effective data exchanged between UAV and GCS.

A New Adaptative Security Protocol for UAV Network

The Intrusion Detection System architectures used in commercial and research systems have a number of problems that limit their configurability. An important problem of agents: learning is not used. The concept of learning in existing IDSs used in general to learn the normal behavior of the system to secure. Thus, the IDS does not have the ability to detect new attacks. We propose in this paper a new architecture for intrusion detection based in multi agent systems adding a learning feature abnormal behaviors that correspond to new attack patterns. We present the motivation and description of the approach, for the detection step, the approach adopted is based on the technique of Case-Based Reasoning (CBR). The proposed architecture is based on a hierarchical and distributed strategy separated into three layers. We focus after on the modeling of our Multi agent systems Architecture, for reasons of simplicity, we use the methodology O-MaSE.

Siham Benhadou

Papers

Shared public transport within a physical internet framework: Reviews, conceptualization and expected challenges under COVID-19 pandemic

Predicting car park availability for a better delivery bay management

Smart Parking Architecture based on Multi Agent System

A New Adaptative Security Protocol for UAV Network

New collaborative intrusion detection architecture based on multi agent systems