Category Theory as Interpretation Law Model in Artificial Intelligence Era

Nowadays, artificial intelligence entities operate autonomously and they actively participate in everyday social activities. At a macro-perspective, they play the role of mediator between people and their actions, as components of the fundamental structure of every social activity. At a micro-perspective, they can be considered as fixed critical points whose hypostasis is not subject to established legal framework. A key point is that embedding artificial intelligence entities in everyday activities may maximize legal uncertainty both at the macro and micro-level, as well as at the interim phase, i.e., the switch between the two levels. In this paper, we adapt a well-known concept from Category Theory, namely that of the pushout, in order to approximate the core interpretation legal framework of such activities, by considering each level as an open system. The purpose of using Systems Theory in combination with Category Theory is to introduce a mathematical approach to uniquely interpret complex legal social activities and to show that this novel area of artificially enhanced activities is of prime and practical importance and significance to law and computer science practitioners.

Automated Multimodal image fusion for brain tumor detection

In medical domain, various multimodalities such as Computer tomography (CT) and Magnetic resonance imaging (MRI) are integrated into a resultant fused image. Image fusion (IF) is a method by which vital information can be preserved by extracting all important information from the multiple images into the resultant fused image. The analytical and visual image quality can be enhanced by the integration of different images. In this paper, a new algorithm has been proposed on the basis of guided filter with new fusion rule for the fusion of different imaging modalities such as MRI and Fluorodeoxyglucose images of brain for the detection of tumor. The performance of the proposed method has been evaluated and compared with state-of-the-art image fusion techniques using various qualitative as well as quantitative evaluation metrics. From the results, it has been observed that more information has achieved on edges and content visibility is also high as compared to the other techniques which makes it more suitable for real applications. The experimental results are evaluated on the basis of with-reference and without-references metric such as standard deviation, entropy, peak signal to noise ratio, mutual information etc.

The Legalhood of Artificial Intelligence: AI Applications as Energy Services

The importance of data has increased in the last century and these days it is an essential resource for any human activity as well as a vital component for our society. The use of AI is a major improvement in handling these data, the amount of which is becoming enormous. In a regulatory perspective, AI applications have an impact on the social and economic structure and the rights and values on which it is based upon. This paper is a crucial step on the path of building a consensus on the legal hypostasis of AI. It is our belief that unforeseeable and ground-breaking AI applications can be regulatorily tackled with respect to energy law.

State Aviation Risk Assessment Level Determination Using Hierarchical Fuzzy Inference System Based on Cognitive Maps

During 2018, ICAO (International Civil Aviation Organization, a specialized UN organization) made available the results of its USOAP (Universal Safety Oversight Audit Program): the ratio of compliance for each ICAO member State to 1047 aviation safety-related protocol questions, divided into eight audit areas. Numbers itself has little meaning, even for aviation personnel. Using Cognitive Mapping (CogMap), a Problem Structuring Method tool, this paper develops a framework to extract and organize information from aviation specialists, allowing define Risk Assessment Level for each State, and for each Aviation Safety Branch defined. Using Fuzzy Inference Systems (FIS), helpful supporting decision making, Big Data available from ICAO is converted to Risk Levels for each State and audit area, what may be used to make informed better Safety decisions on the World Aviation Market. Up to the moment, there’s no evidence on the literature of using CogMap to establish a FIS.

Intelligent Virtual Reality Therapy Systems for Motor and Cognitive Rehabilitation: A Survey based on Clinical Trial Studies

Rehabilitation is the process related to the recovery, maintenance or improvement of physical mental and / or cognitive skills necessary to carry out daily activities. Virtual reality therapy, virtual reality (VR) immersion therapy, simulation therapy or virtual reality exposure therapy is an intervention method of using virtual reality technology for psychological or occupational therapy. The possibility of simulating situations necessary for the treatment, controlling variables and reducing the patient’s exposure to risks are popular factors for this tool. Many studies indicate that therapy with the aid of virtual reality brings great benefits to the patient. In this article, we present, through a review of 117 articles, the feasibility of applying VR in treatments with clinical trial methodology, identifying through the "Patient, Intervention, Comparison and Outcomes" the characteristics, population, treatment time, forms of comparison and if the results obtained are effective. The characteristics identified during the process show that virtual reality applied to therapies can be used without negative interference in the treatment. In addition, the results show that VR in rehabilitation treatments are motivating and show better results than traditional treatments.

Feature Extraction aligned Email Classification based on Imperative Sentence Selection through Deep Learning

Most commonly used channel for communication among peoples is emails. In this era where everyone is so busy in their routine and work, it is very difficult to check all email when one receives huge amount of emails. Previous research has done work on email categorization in which they have mostly done spam filtration. The problem with spam filtration is that sometimes person mistakenly mark an important email received from high authority as spam and according to previous research, this email will be filtered as spam that can cause a great threat for job of an employee. In this research, we are introducing a methodology which classifies email text into three categories i.e. order, request and general on basis of imperative sentences. This research use Word2Wec for words conversion into vector and use two approaches of deep learning i.e. Convolutional neural network and Recurrent neural network for email classification. We conduct experiment on Dataset collected from Personal Gmail account and Enron which consists of 1000 emails. The experiment result show that RNN gives better accuracy than CNN. We also compare our methods with previously used method Fuzzy ANN results and Our proposed methods CNN and RNN gives better results than Fuzzy ANN. This research has also included different experimental result in which CNN and RNN applied on different ratios of training and testing dataset. These experiment show that increasing in the ratio of training dataset results in increasing accuracy of algorithm.

An Improved Fuzzy Inventory Model Under Two Warehouses

The objective of this work is to present an improved inventory system with fuzzy constraints dealing with two warehouses system-own and rented. In the present model, we analyze the system under the consideration of two warehouses and without shortages with the assumptions of the linear demand function (increasing function of time). Generally, in today’s business scenario for sessional products, some constraints like storage cost, deteriorating cost, and ordering cost change with their original values. Therefore, these constraints cannot be assumed to be constant in that situation. Depending on these facts that we handle these costs as a triangular fuzzy number and hence apply the signed distance technique to solve the corresponding problem. The key objective of this work is to determine the optimal inventory level, and inventory time schedule to a minimum of the whole inventory cost. The proposed model is demonstrated with two numerical examples to observe the behavior of constraints with system cost and compare their performance with and without fuzzy environment.

Intelligent Technology to Enhance Policing and Public Accountability

In view of the situation of violence faced in Brazil, several actors, from the most diverse areas of knowledge, have been dedicated to studying, analyzing and proposing solutions for public security. The great challenge of much of what is produced is to combine theory with practice. In addition, the fact that we do not have State policies, but Government policies contribute significantly to the lack of long-term studies. Discontinuities, whether due to the ineffectiveness of what is proposed, or due to cultural and organizational changes in crime, preclude a cycle of planning, execution, evaluation and correction. This brings us to the first observation of modern public security: volatility. Thus, it is impossible to imagine modern management without the use of technology for the quick and assertive analysis of the problems faced. In this sense, the use of the intelligence, strategy, and technology triad becomes essential for accurate monitoring of these changes, providing guidelines and subsidies for the modernization of public security management and security and policing matrix. Given these statements, the present study has the general objective of presenting the Policy to Combat the Mobility of Crime and its effects on the Public Security of the State of Cear[Pleaseinsert“PrerenderUnicode–˝intopreamble] (Brazil), referring to the period from 2017 to 2019. Through an empirical analysis, statistical data were collected to present a direct scenario of the implementation and the results achieved and present the theoretical relationship between the actions and the results, thus providing an exploratory depth of the facts and their impacts. In order to show the positive results achieved, a quantitative and qualitative method was used to correlate aspects and concepts in the large area of the humanities with practical policing and technological applications. As a result of the implementation of the Combating the Mobility of Crime, the State of Cear[Pleaseinsert“PrerenderUnicode–˝intopreamble] managed to place the number of robberies and homicides among the lowest rates of the decade, gaining national prominence of strategy and technology employed. Thus, the Policy to Combat the Mobility of Crime changed the policing matrix, allowing greater efficiency of the resources used and better monitoring the indicators.

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.