Aiming at the problems of small value range of a single chaotic parameter, low sequence chaos, and transient effects, a composite chaotic system of cascaded Sine-Sine mapping, Logistic chaos and generalized third-order Fibonacci is proposed (S-L-F). The new system is highly sensitive to initial values, the maximum spectral entropy of the generated sequence can reach 0.95, and the value range of the parameter x is expanded to [0,4] compared with the traditional Logistic, indicating that the new system is suitable for generating pseudo-random sequences for image encryption. For the problem that the traditional multi-image encryption scheme can only encrypt images of the same type and size, the practicability is poor, and a multi-image encryption scheme based on image reorganization and biting is proposed. The algorithm recombines any number, different sizes and different types of images into a three-dimensional matrix, converts it into a binary matrix, performs bit-level scrambling and surface cyclic scrambling, and then restores the scrambling matrix to decimal, and the chaotic sequence performs exclusive-or diffusion, and completes simultaneous encryption at one time, which greatly improves the encryption efficiency and scope of application. The NPCR of the ciphertext image is 0.9961, and the UACI is 0.3345, which proves that the ciphertext image can effectively resist the difference attack. The information entropy is greater than 7.999, which can effectively resist attacks. It has certain application value in image information security. Experimental analysis shows that the algorithm has high security and strong practicability.

Due to the recent COVID-19 outbreak the world has experienced many challenges. Limit and control the virus spread rate is one of them. This letter focuses on limiting the speed of virus spreading by monitoring the use of facial mask in crowded public environments such as tourism places, commercial centres, etc. The proposed method first accurately localizes faces using a state-of-the-art approach and, segments facial mask in a second step. The facial mask segmentation allows to distinguish whether the current subject is wearing a facial mask or not but also if it is properly covering the human face. Indeed, most recent face detection algorithms provide as output a set of facial features such as nose tip and mouth corners. By combining these facial features with facial mask segmentation, the proposed method detects real-time subjects that indirectly encourage virus spread in crowded environments. The proposed facial mask segmentation model is trained with pairs of RGB images and its corresponding alpha image created by extending the publicly available real-world masked face dataset. Further, the proposed model is pruned and optimized using the TensorRt library to be usable for real-world applications.

This article addresses the adoption and use of Hypertext Transfer Protocol Secure (HTTPS) in the entry pages of the official websites of all (308) Portuguese municipalities. This is relevant because such websites are typically used to provide transactional services to citizens, and citizens need to trust that websites are authentic and that confidentiality and integrity of the information exchanged is assured in the communication process. Automated and, whenever needed, manual analyses were used to investigate the entry pages. Specifically, we checked for the existence of an HTTPS site; the correctness of website certificates and their certification chain; coherence between contents of the HTTP and HTTPS versions of websites; redirection from the HTTP version of a website to its HTTPS version; the existence of resources fetched using HTTP in HTTPS versions of websites; and exploitation of HSTS. A Quality Indicator was then defined and a classification of the municipalities into quality groups was produced. Possible determinants for the results obtained by the municipalities were also investigated. The general conclusion is that there is still much to be done to assure that citizens can communicate securely with the websites of all Portuguese municipalities, since only 3.6% of the municipalities were considered good, while 46.1% do not guarantee the minimum conditions. We argue that these results are associated with the fact that most Portuguese municipalities do not have the critical technical and managerial mass to correctly implement and maintain their websites. To mitigate this limitation, we propose the dissemination of technical instructions on how to correctly configure and deploy municipal HTTPS websites and the creation of shared services between the smaller municipalities.

The prevalence of Location-Based Social Networks (LBSNs) significantly improves the location-aware capability of services by providing Geo-tagged information. Relied on a great number of user check-in data in the location-based social networks, their essential mobility modes are able to be comprehensively studied, which is basic for forecasting the next venue where a specific user is going to visit considering his relevant historical check-in data. Since there exist different kinds of nodes and interactions between nodes, these information could be look upon as a network that is made up of heterogeneous information. In this network a few of different semantic meta paths could be obtained. Enlightened from the competitive advantage of embedding method relied upon meta-path contexts in the heterogeneous information network, we study a joint deep learning scheme exploring different meta-path context information to forecast fine-grained location. In order to capture different semantics in a user-location interaction, we adopt a simple but high-efficient attention method to learn the meta-path importance or weights. In the terms of model optimization, considering we have only positive sample data and there exists intrinsically latent feedback in check-in information, herein a pairwise learning method is utilized for maximizing the margin between visited and invisible venues. Experiment in different data-sets validate the competitive performance of the suggested approach under different assessment criterion.

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

Recommendation systems can help internet users to find interesting things that match more with their profile. With the development of the digital age, recommendation systems have become indispensable in our lives. On the one hand, most of recommendation systems of the actual generation are based on Collaborative Filtering (CF) and their effectiveness is proved in several real applications. The main objective of this paper is to improve the recommendations provided by collaborative filtering using clustering. Nevertheless, taking into account the intrinsic relationship between users can enhance the recommendations performances. On the other hand, cooperative game theory techniques such as Shapley Value, take into consideration the intrinsic relationship among users when creating communities. With that in mind, we have used SV for the creation of user communities. Indeed, our proposed algorithm preforms into two steps, the first one consists to generate communities user based on Shapley Value, all taking into account the intrinsic properties between users. It applies in the second step a classical collaborative filtering process on each community to provide the Top-N recommendation. Experimental results show that the proposed approach significantly enhances the recommendation compared to the classical collaborative filtering and k-means based collaborative filtering. The cooperative game theory contributes to the improvement of the clustering based CF process because the quality of the users communities obtained is better.

The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.

Crime is one of the biggest and dominating problems in today’s world and it is not only harmful to the person involved but also to the community and government. Due to escalation in crime frequency, there is a need for a system that can detect and predict crimes. This paper describes the summary of the different methods and techniques used to identify, analyze and predict upcoming and present crimes. This paper shows, how data mining techniques can be used to detect and predict crime using association mining rule, k-means clustering, decision tree, artificial neural networks and deep learning methods are also explained. Most of the researches are currently working on forecasting the occurrence of future crime. There is a need for approaches that can work on real-time crime prediction at high speed and accuracy. In this paper, a model has been proposed that can work on real-time crime prediction by recognizing human actions.

As enormous volume of electronic data increased gradually, searching as well as retrieving essential info from the internet is extremely difficult task. Normally, the Information Retrieval (IR) systems present info dependent upon the user’s query keywords. At present, it is insufficient as large volume of online data and it contains less precision as the system takes syntactic level search into consideration. Furthermore, numerous previous search engines utilize a variety of techniques for semantic based document extraction and the relevancy between the documents has been measured using page ranking methods. On the other hand, it contains certain problems with searching time. With the intention of enhancing the query searching time, the research system implemented a Modified Firefly Algorithm (MFA) adapted with Intelligent Ontology and Latent Dirichlet Allocation based Information Retrieval (IOLDAIR) model. In this recommended methodology, the set of web documents, Face book comments and tweets are taken as dataset. By means of utilizing Tokenization process, the dataset pre-processing is carried out. Strong ontology is built dependent upon a lot of info collected by means of referring via diverse websites. Find out the keywords as well as carry out semantic analysis with user query by utilizing ontology matching by means of jaccard similarity. The feature extraction is carried out dependent upon the semantic analysis. After that, by means of Modified Firefly Algorithm (MFA), the ideal features are chosen. With the help of Fuzzy C-Mean (FCM) clustering, the appropriate documents are grouped and rank them. At last by using IOLDAIR model, the appropriate information’s are extracted. The major benefit of the research technique is the raise in relevancy, capability of dealing with big data as well as fast retrieval. The experimentation outcomes prove that the presented method attains improved performance when matched up with the previous system.

Data security aims to prevent the use, modification, and spread of data by unauthorized people. In this study, our purpose was to provide data privacy and confidentiality with information flow control in distributed databases. In particular, a decentralized label model was developed that maintained confidentiality, including privacy, with data flow control. This model consists of an actor, an object, and a label. The owners of the objects are actors, and they need to share their data objects with others. Actors label the data objects and then send them out. A label contains the policy statements of data security issued by each of the owners. Each owner sets its own security and privacy policy independently of the other owners. The confidentiality of data in unsecured transport channels is ensured for all the actors in the system by means of labels while the data are in flow. Data objects are spread and shared securely among actors within unsecured environments. In addition, with the path compression, the long node chain that is formed while the data objects are passing between the source node and the destination is broken, so that the objects are retrieved fast, and the cost of access is reduced. This result was shown experimentally by modeling the distributed environment.