In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

We present the local path planning and obstacle avoidance method used on the autonomous tour-guide robot RoboX. It has proven its value during a 5 month operation of ten such robots in a real-world application, a very crowded exhibition. Three known approaches (DWA, elastic band, NF1) have been integrated into a system that performs smooth motion efficiently, in the sense of computational effort as well as goal-directedness. Apart from modifications to the DWA and the elastic band, we present the formulations that allow this fusion.

Smooth and Efficient Obstacle Avoidance for a Tour Guide Robot

Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research.

Image pattern recognition in big data: taxonomy and open challenges: survey

Depressive symptomatology is manifested in greater first-person singular pronoun use (i.e., I-talk), but when and for whom this effect is most apparent, and the extent to which it is specific to depression or part of a broader association between negative emotionality and I-talk, remains unclear. Using pooled data from N = 4,754 participants from 6 labs across 2 countries, we examined, in a preregistered analysis, how the depression–I-talk effect varied by (a) first-person singular pronoun type (i.e., subjective, objective, and possessive), (b) the communication context in which language was generated (i.e., personal, momentary thought, identity-related, and impersonal), and (c) gender. Overall, there was a small but reliable positive correlation between depression and I-talk (r = .10, 95% CI [.07, .13]). The effect was present for all first-person singular pronouns except the possessive type, in all communication contexts except the impersonal one, and for both females and males with little evidence of gender differences. Importantly, a similar pattern of results emerged for negative emotionality. Further, the depression–I-talk effect was substantially reduced when controlled for negative emotionality but this was not the case when the negative emotionality–I-talk effect was controlled for depression. These results suggest that the robust empirical link between depression and I-talk largely reflects a broader association between negative emotionality and I-talk. Self-referential language using first-person singular pronouns may therefore be better construed as a linguistic marker of general distress proneness or negative emotionality rather than as a specific marker of depression.

Depression, Negative Emotionality, and Self-Referential Language: A Multi-Lab, Multi-Measure, and Multi-Language-Task Research Synthesis_Tackman et al_Preprint

On the suitability of blockchain platforms for IoT applications: architectures, security, privacy, and performance

Multi-tenancy is an essential feature in cloud computing and is a major component to achieve scalability and energy-efficient solution to gain high level of economic benefits. As the cloud, computi...

Reinforcement learning-based controller for adaptive workflow scheduling in multi-tenant cloud computing:

In this paper, a data mining technique, Fuzzy C Means clustering (FCM) is used to locate the optic disc in colour fundus image Green plane from the RGB fundus image is considered in the proposed approach because it provides better contrast than the other red and blue planes As the intensity of the optic disc is brighter than other region, the initial optic disc is identified by the brightest points which are called Region of Interest (ROI) FCM is a method of clustering which allows one piece of data to belong to two or more clusters Before applying the FCM technique to cluster the optic disc, the initial ROI is de-noised by using median filtering technique Results show the efficiency of the FCM to localize the optic disc in order to diagnose glaucoma

Localization of optic disc using Fuzzy C Means clustering

In spite of several advancements in technologies pertaining to Optical character recognition, handwriting continues to persist as means of documenting information for day-to-day life. The process of segmentation and recognition pose quiets a lot of challenges especially in recognizing cursive handwritten scripts of different languages. The concept proposed is a solution crafted to perform character recognition of hand-written scripts in Tamil, a language having official status in India, Sri Lanka, and Singapore. The approach utilizes discrete Hidden Markov Models (HMMs) for recognizing off-line cursive handwritten Tamil characters. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database and the precision of the results demonstrates its application on commercial usage. The methodology promises to present a simple and fast scaffold to construct a full OCR system extended with suitable pre-processing.

Off-line cursive handwritten Tamil character recognition

due to several reasons such as attentions given to design features and to modification from littler to medium sized system that results in trivial enhancement in performance.In spite of the tremendous achievement of grids which run parallel jobs, there still exists demand of requesting applications to avail opportunistic computing for considering the right selection of prerogative supercomputers. The objective of this research is to build a framework for on the fly computational commencing on socially convolved terrestrial grids to facilitate the sharing of computing workload over wireless networks as virtual supercomputers of unprecedented power. This will assist computing in heterogeneously interconnected mobile assets, Internet of Things, in crowdedscenarios for variable ranges like: stadiums, shopping centers, and so forth.

Quasi Opportunistic Supercomputing for Geospatial Socially Networked Mobile Devices

It has been proved that the decisions in stock market exchange may bring influence on the investors, financial institutions, banking sectors etc. The stock market is a highly composite system in addition often concealed with mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is considers different parameters of a particular stock. Analysis is performed after obtaining the stock scores. This analysis involves visualization of stock scores in the form of various plots and prediction of the scores using a time series model known as ARIMA (auto regressive moving average). The results shows that the time series model performed a descent prediction of the market scores with considerably high accuracy. Each factor was studied independently to find out its association with market performance. Furthermore the results suggests that behavior of market can be predicted using machine learning techniques.

R. Jagadeesh Kannan

Papers

Reinforcement learning-based controller for adaptive workflow scheduling in multi-tenant cloud computing:

Localization of optic disc using Fuzzy C Means clustering

Off-line cursive handwritten Tamil character recognition

Quasi Opportunistic Supercomputing for Geospatial Socially Networked Mobile Devices

Analysis of Stock Market using Streaming data Framework