A Novel Secure Occupancy Monitoring Scheme Based on Multi-Chaos Mapping

Smart building control, managing queues for instant points of service, security systems, and customer support can benefit from the number of occupants information known as occupancy. Due to interrupted real-time continuous monitoring capabilities of state-of-the-art cameras, a vision-based system can be easily deployed for occupancy monitoring. However, processing of images or videos over insecure channels can raise several privacy concerns due to constant recording of an image or video footage. In this context, occupancy monitoring along with privacy protection is a challenging task. This paper presents a novel chaos-based lightweight privacy preserved occupancy monitoring scheme. Persons’ movements were detected using a Gaussian mixture model and Kalman filtering. A specific region of interest, i.e., persons’ faces and bodies, was encrypted using multi-chaos mapping. For pixel encryption, Intertwining and Chebyshev maps were employed in confusion and diffusion processes, respectively. The number of people was counted and the occupancy information was sent to the ThingSpeak cloud platform. The proposed chaos-based lightweight occupancy monitoring system is tested against numerous security metrics such as correlation, entropy, Number of Pixel Changing Rate (NPCR), Normalized Cross Correlation (NCC), Structural Content (SC), Mean Absolute Error (MAE), Mean Square Error (MSE), Peak to Signal Noise Ratio (PSNR), and Time Complexity (TC). All security metrics confirm the strength of the proposed scheme

A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map

Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme

Stock market efficiency: Behavioral or traditional paradigm?Evidence from Karachi Stock Exchange (KSE) and investors community of Pakistan

Traditional finance explains the investment process on rational and logical grounds based on the assumption of rationality of average investor. This paper attempts to understand why traditional finance models fail to capture stock market movements and how behavioral finance explains that failure in the context of Pakistan’s financial market. Beginning with the basics of behavioral finance, the discussion unfolds to explain any association that investor’s decision making process has with the behavioral biases like overconfidence, regret, pyramid and risk. Primary data based on questionnaire and interviews of investors trading at Karachi Stock Exchange of Pakistan was used. The study concluded that behavioral traits have significant association with investment decision. The study will also open up the doors to further analyze the deviated scenarios which cause the market to create the loss spiral for one group and unbounded gain for the other

Antioxidant, antimicrobial and antiproliferative activities of peel and pulp extracts of red and white varieties of Ipomoea batatas (L) Lam

Purpose: To investigate the antioxidant, antibacterial and anticancer potentials of methanol and ethanol extracts of the peel and pulp of red and white species of Ipomoea batatas (L.) fruit.Methods: Total phenolic contents and flavonoids were determined using chemical assays. Antioxidant studies were carried out using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay, inhibition of linoleic acid peroxidation assay and reducing power assay. Antibacterial and antiproliferative activities of extracts were determined using disc diffusion and MDBK cancer cell line inhibition methods, respectively.Results: The extract of peels of red specie (PERS) showed total phenolic contents (TPC) 8.9 mg gallic acid equivalent (GAE)/g dry extract and flavonoids 6.5 mg catechin equivalent (CE)/g dry extract. The extract of PERS also showed promising DPPH free radical scavenging activity, inhibition of linoleic acid peroxidation and reducing power activity. However, mild antibacterial and anti-proliferative activities were noted except that the extract showed significant inhibition of Bacillus subtilis growth.Conclusion: The results indicate that the peel and the pulp of red sweet potato (SP) specie are rich in antioxidants and can potentually be processed as antioxidant food supplements.Keywords: Ipomoea batatas (L.) Lam, Sweet potato, Phenolic content, Antioxidants, Antibacterial activity, Antiproliferative activit

Une Nouvelle Mesure de Co-Similarité : Applications aux Données Textuelles et Génomique

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and there exist a multitude of different clustering algorithms for different settings. As datasets become larger and more varied, adaptations of existing algorithms are required to maintain the quality of clusters. In this regard, high-dimensional data poses some problems for traditional clustering algorithms known as 'the curse of dimensionality'. This thesis proposes a co-similarity based algorithm that is based on the concept of distributional semantics using higher-order co-occurrences, which are extracted from the given data. As opposed to co-clustering, where both instance and feature sets are hard clustered, co-similarity may be defined as a more 'soft' approach. The output of the algorithm is two similarity matrices - one for the objects and one for their features. Each of these similarity matrices exploits the similarity of the other, thereby implicitly taking advantage of a co-clustering style approach. Hence, with our method, it becomes possible to use any classical clustering method (k-means, Hierarchical clustering ...) to co-cluster data. We explore two applications of our co-similarity measure. In the case of text mining, document similarity is calculated based on word similarity, which in turn is calculated on the basis of document similarity. In this way, not only do we capture the similarity between documents coming from their common words but also the similarity coming from words that are not directly shared by the two documents but that can be considered to be similar. The second application is on gene expression datasets and is an example of co-clustering. We use our proposed method to extract gene clusters that show similar expression levels under a given condition from several cancer datasets (colon cancer, lung cancer, etc). The approach can also be extended to incorporate prior knowledge from a training dataset for the task of text categorization. Prior category labels coming from data in the training set can be used to influence similarity measures between features (words) to better classify incoming test datasets among the different categories. Thus, the same framework can be used for both clustering and categorization task depending on the amount of prior information available.La classification de données (ou apprentissage non-supervisé) vise à regrouper un ensemble d'observations sous la forme de classes homogènes et contrastées. Lorsque les données sont caractérisées par un grand nombre de variables, il devient nécessaire d'adapter les méthodes classiques, notamment au niveau des métriques, afin de maintenir des classes pertinentes ; ce phénomène est connu sous le nom de "malédiction de la dimension". Dans cette thèse, nous proposons une mesure de co-similarité basée sur la notion de co-occurrences d'ordre supérieur, directement extraites à partir des données. Dans le cas de l'analyse de texte, par exemple, les similarités entre documents sont calculées en prenant en compte les similarités entre mots, qui simultanément prennent en compte les similarités entre documents. Par cette approche " circulaire ", nous parvenons à mettre en correspondance des documents sans mots communs mais ayant juste des mots similaires. Cette approche s'effectue de manière purement numérique sans nécessiter de thesaurus externe. En outre, notre méthode peut également être étendue pour tirer parti de connaissances "a priori" afin de réaliser des tâches de catégorisation de textes : l'étiquette des documents est utilisée pour influencer les mesures de similarité entre les mots afin de classer de nouvelles données. Ainsi, le même cadre conceptuel, exprimable en terme de théorie des graphes, peut être utilisé à la fois pour les tâches de classification et de catégorisation en fonction de la quantité d'information initiale. Nos résultats montrent une amélioration significative de la précision, par rapport à l'état de l'art, à la fois pour le co-clustering et la catégorisation sur les jeux de données qui ont été testés

Text categorization using word similarities based on higher order co-occurrences

Abstract 12 In this paper, we propose an extension of the χ-Sim coclustering algorithm to deal with the text categorization task. The idea behind χ-Sim method [1] is to iteratively learn the similarity matrix between documents using similarity matrix between words and vice-versa. Thus, two documents are said to be similar if they share similar (but not necessary identical) words and two words are similar if they occur in similar documents. The algorithm has been shown to work well for unsupervised document clustering. By introducing some “a priori ” knowledge about the class labels of documents in the initialization step of χ-Sim, we are able to extend the method to deal for the supervised task. The proposed approach is tested on different classical textual datasets and our experiments show that the proposed algorithm compares favorably or surpass both traditional and state-of-the-art algorithms like k-NN, supervised LSI and SVM. Keywords: Text categorization, clustering, Higher-order co-occurrences, supervised learning