Agora University Editing House: Journals
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ANALYSIS OF THE FRENCH DOCTRINE REGARDING THE NORMATIVE POWER OF THE OPINIONS OF THE COURT OF CASSATION
Full text linkIn this article I continue to research the decisions of the supreme courts, which have the constitutional role of unifying the interpretation of the law at the national level, and implicitly of the judicial practice, by studying the French legal doctrine regarding the legal nature of the notices for appeals of the Court of Cassation
THE EXERCISE OF POLICE POWERS AND ENFORCEMENT OF INTERNATIONAL HUMAN RIGHTS NORMS IN NIGERIA: AN APPRAISAL
Full text linkThe police have the duty of prevention, investigation and prosecution of crime in the society. In the course of their duties, conflicts arise between the law officers and the civilians. In Nigeria, the police are known for the abuse of legal processes and the right of the citizens often culminating at excessive use of force, unlawful detention and in extreme cases which is rampant extra-judicial killing. This paper gave an exposition of some international legal instruments and standards applicable to law enforcement and how they help curtail abuse of the citizens’ right. The paper found that there is gross abuse of human rights in the course of the duty of the police in Nigeria. It was noted that the police have an obligation to observe, ensure compliance and implementation of these international human rights norms. It was recommended that there should be training and retraining of the police in Nigeria especially in forensic and technologically driven investigation and prevention of crime. Furthermore, corruption in the Nigerian police should be curbed by giving more incentives like salary increase to the police. The paper concluded that implementation of these recommendations will improve police human right observance in Nigeria
Fused LISS IV Image Classification using Deep Convolution Neural Networks
Full text linkThese days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively
An Enhanced Neural Graph based Collaborative Filtering with Item Knowledge Graph
Full text linkRecommendation system is a process of filtering information to retain buyers on e-commerce sites or applications. It is used on all e-commerce sites, social media platform and multimedia platform. This recommendation is based on their own experience or experience between users. In recent days, the graph-based filtering techniques are used for the recommendation to improve the suggestions and for easy analysing. Neural graph based collaborative filtering is also one of the techniques used for recommendation system. It is implemented on the benchmark datasets like Yelp, Gowalla and Amazon books. This technique can suggest better recommendations as compared to the existing graph based or convolutional based networks. However, it requires higher processing time for convolutional neural network for performing limited suggestions. Hence, in this paper, an improved neural graph collaborative filtering is proposed. Here, the content-based filtering is performed before the collaborative filtering process. Then, the embedding layer will process on both the recommendations to provide a higher order relation between the users and items. As the suggestion is based on hybrid recommendation, the processing time of Convolutional neural network is reduced by reducing the number of epochs. Due to this, the final recommendation is not affected by the smaller number of epochs and also able to reduce its computational time. The whole process is realized in Python 3.6 under windows 10 environment on benchmark datasets Go Walla and Amazon books. Based on the comparison of recall and NDCG metric, the proposed neural graph-based filtering outperforms the collaborative filtering based on graph convolution neural network
Broadcast Guidance of Multi-Agent Systems
Full text linkWe consider the emergent behavior of a group of mobile agents guided by an exogenous broadcast signal. The agents’ dynamics is modelled by single integrators and they are assumed oblivious to their own position, however they share a common orientation (i.e. they have compasses). The broadcast control, a desired velocity vector, is detected by arbitrary subgroups of agents,that upon receipt of the guidance signal become "ad-hoc" leaders. The control signal and the set of leaders are assumed to be constant over some considerable intervals in time. A system without "ad-hoc" leaders is referred to as autonomous. The autonomous rule of motion is identical for all agents and is a gathering process ensuring a cohesive group. The agents that become leaders upon receipt of the exogenous control add the detected broadcast velocity to the velocity vector dictated by the autonomous rule of motion. This paradigm was considered in conjunction with several models of cohesive dynamics, linear and non-linear, with fixed inter-agent interaction topology, as well as systems with neighborhood based topology determined by the inter-agent distances. The autonomous dynamics of the models considered provides cohesion to the swarm, while, upon detection of a broadcast velocity vector, the leaders guide the group of agents in the direction of the control. For each local cohesion interaction model we analyse the effect of the broadcast velocity and of the set of leaders on the emergent behavior of the system. We show that in all cases considered the swarm moves in the direction of the broadcast velocity signal with speed set by the number of agents receiving the control and in a constellation determined by the model and the subset of "ad-hoc" leaders. All results are illustrated by simulations
A Multi-objective Location Decision Making Model for Emergency Shelters Giving Priority to Subjective Evaluation of Residents
Full text linkEarthquake is regarded as the most destructive and terrible disaster among all-natural disasters [1]. Experts agree that immediate emergency evacuation is the safest and most effective response to the earthquake disaster [2]. In the research of emergency evacuation planning, the influence of human subjectivity has gradually attracted researchers’ attention. In this paper, we take the human subjectivity as one of the most important factors for emergency evacuation planning. Based on the preferences of the residents at each demand point for the attributes of every candidate emergency shelter, the subjective score of each candidate emergency shelter is obtained. The preferences of residents will change with the refuge time, so do the weights of residents’ subjective scores of all attributes of candidate emergency shelters. Therefore, we use the subjective score function to describe the change of residents’ evaluations for the emergency shelter over time, and take the average value of subjective scores at all refuge times as the primary basis for location decision making. On these bases, we build a multi-objective location decision making model for emergency shelters giving priority to subjective evaluation of residents. In the model, we consider transfer distance, the efficiency of construction funds and the distribution of people among emergency shelters. Considering fairness, we minimize the standard deviation of the scores and the standard deviation of the transfer distances in the model. This model is applied to a case, which verifies its feasibility and shows that human subjectivity plays an important role in emergency evacuation planning
Opportunity Based Energy Efficient Routing Algorithm for Underwater Wireless Sensor Network for Submarine Detection
Full text linkThe submarine detection is the most significant research area of Under Water Acoustic (UWA) environment with extensive application in commercial and navy domains. The environmental complexity and variable nature of the UWA makes Underwater Wireless Sensor Network (UWSN) to exhibit fluidity, sparse deployment, time unpredictability, frequency selectivity, limited accessible bandwidth and energy constraints pose problems in the underwater positioning technology. Thus, an adaptable, scalable, and highly efficient UWA is required for the submarine routing systems. The depth-based routing has received lots of interest as it is capable of delivering effective operation without requiring full-dimensional position information of nodes. However, it has issues of vacant regions and detouring forwards. To delineate the aforementioned problems, this paper proposes an Opportunity-based Distance Vector Routing (ODVR) technique. The distance vectors, which have lowest hop counts in the direction of sink for underwater sensor nodes are determined by ODVR through a query method. Depending on the distance vectors, a dynamic routing is created to manage the packet forwarding. In the opportunistic forwarding, the ODVR has a minimal signaling cost and minimum energy consumption with the potential of eliminating the long detours issues. The outcomes of simulations demonstrate that the ODVR outperforms the conventional routing algorithms
Distributed Adaptive Control for Nonlinear Heterogeneous Multi-agent Systems with Different Dimensions and Time Delay
Full text linkA distributed neural network adaptive feedback control system is designed for a class of nonlinear multi-agent systems with time delay and nonidentical dimensions. In contrast to previous works on nonlinear heterogeneous multi-agent with the same dimension, particular features are proposed for each agent with different dimensions, and similar parameters are defined, which will be combined parameters of the controller. Second, a novel distributed control based on similarity parameters is proposed using linear matrix inequality (LMI) and Lyapunov stability theory, establishing that all signals in a closed loop system are eventually ultimately bounded. The consistency tracking error steadily decreases to a field with a small number of zeros. Finally, simulated examples with different time delays are utilized to test the effectiveness of the proposed control technique
Fuzzy Logic-Based System for the Estimation of the Usability Level in User Tests
Full text linkStarting from the challenge of obtaining the usability level in numerical and linguistic terms within a user test based on the three attributes that define usability, the development of a system based on fuzzy logic is proposed for estimating the level of output usability in a test with users based on the ISO 9241-11 standard. The attributes are effectiveness, efficiency, and satisfaction, which have different metrics and numerical scales. For the development of the system, five methodological stages were defined: characterization of the structure of a usability test, definition of membership functions for the inputs and outputs of the system, design of the inference rules that relate the inputs and outputs, design and implementation of the fuzzy system, and development of the case study. The proposed system was implemented using the FCL (Fuzzy Control Language) and the jFuzzyLogic API that takes as inputs the values calculated for the attributes of effectiveness, efficiency, and satisfaction, and obtains the usability level as output considering the membership functions of the inputs and outputs, as well as a set of inference rules defined by a set of experts. As a case study, the proposed fuzzy system was validated from the results obtained in a usability test with 5 users which was developed on the Sigma Electrónica website. From the results obtained in the case study, it could be concluded that the implemented system is adequate in terms of obtaining a level of usability in numerical and linguistic terms in conventional usability tests developed in a usability laboratory considering the attributes of ISO 9241-11
Segmentation Method of Magnetic Tile Surface Defects Based on Deep Learning
Full text linkMagnet tile is an essential part of various industrial motors, and its performance significantly affects the use of the motor. Various defects such as blowholes, break, cracks, fray, uneven, etc., may appear on the surface of the magnet tile. At present, most of these defects rely on manual visual inspection. To solve the problems of slow speed and low accuracy of segmentation of different defects on the magnetic tile surface, in this paper, we propose a segmentation method of the weighted YOLACT model. The proposed model uses the resnet101 network as the backbone, obtains multi-scale features through the weighted feature pyramid network, and performs two parallel subtasks simultaneously: generating a set of prototype masks and predicting the mask coefficients of each target. In the prediction mask coefficient branch, the residual structure and weights are introduced. Then, masks are generated by linearly combining the prototypes and the mask coefficients to complete the final target segmentation. The experimental results show that the proposed method achieves 43.44/53.44 mask and box mAP on the magnetic tile surface defect dataset, and the segmentation speed reaches 24.40 fps, achieving good segmentation results