Cryptocurrencies are the new form of trade that has revolutionized how we look into our financial institutions. Bitcoin dominates the industry with the highest market share among the hundreds of other cryptocurrencies. However, high energy consumption leading to increasing carbon emission, prioritizing high-value transactions, and long waiting times are some of the flaws preventing it from reaching its full potential. Owing to the block rewards getting halved every four years, miners and researchers are fearful that this would be the breaking point of Bitcoin’s success. This article proposes an Industry-4.0-compliant next-generation Bitcoin architecture by introducing a dynamic and sustainable block concept. Along with our modified knapsack algorithms, i.e., priority-based 0/1 knapsack and advanced-priority-based 0/1 knapsack, we can ensure a balanced transaction selection, quicker verification, higher transaction throughput, reduced carbon emission, and increased earnings for the miners. Moreover, with the addition of only one of our proposed sustainable blocks, we can cut down verification times by 50% and increase throughput by 39%. We can also reduce carbon emissions per transaction by 61.3%, which would help reduce Bitcoins’ large carbon footprint, enabling us to approach greener digital transactions.

Currently available local feature descriptors used in facial expression recognition at times suffer from unstable feature descriptions, especially in the presence of weak and distorted edges due to noise, limiting their performances. We propose a novel local descriptor named Neighborhood-aware Edge Directional Pattern (NEDP) to overcome such limitations. Instead of relying solely on the local neighborhood to describe the feature around a pixel, as done by the existing local descriptors, NEDP examines the gradients at the target (center) pixel as well as its neighboring pixels to explore a wider neighborhood for the consistency of the feature in spite of the presence of subtle distortion and noise in local region. We introduce template-orientations for the neighboring pixels, which give importance to the gradients in consistent edge directions, prioritizing the specific neighbors falling in the direction of the local edge to represent the shape of the local textures, unambiguously. Moreover, due to the effective management of the featureless regions, no such region is erroneously encoded as a feature by NEDP. Experiments of the performances for person-independent recognition on benchmark expression datasets also show that NEDP performs better than other existing descriptors, and thereby, improves the overall performance of facial expression recognition.

Detection of Betacam dropout defects that can occur in the digitisation process of old archived media has importance in the restoration of degraded data to a higher quality. Most of the existing methods rely on the temporal information of multiple consecutive frames to detect Betacam dropouts, which sometimes may not work well as several successive frames may contain a Betacam error at the same position. In this study, an automatic method of Betacam dropout error detection is proposed based on vertical patterns in a single frame. Hence, it is also applicable when temporal information‐based detectors fail. The results of performance tests done in real working environments demonstrate that the proposed Betacam dropout detection method performs much better than the existing methods.

Summary Securing user identity from data breach in a web server is one of the major concerns for the users of the web applications. Similarly, protecting user access pattern from unauthorized access should be taken seriously, because the potential threats such attacks may pose, are huge. However, these security measures should not be adopted at the expense of user experience and convenience. Nevertheless, any extra overhead in the form of security measures introduced in a distributed system results in significant performance declination. The target of a secured framework for a distributed system like web application should be a reasonable trade‐off between security and user experience. Thus, in this work, we present a framework that ensures security for the user identity along with keeping the online activities of the users anonymous while ensuring scalability of the system. Our framework is designed in a scalable form that can work with other distributed architectures that provide security to user data and identities. To ensure all these measures, our proposal includes the implementation of Forward Secrecy using Diffie‐Hellman Key exchange protocol where the server cannot remember a user's history after a session ends. In addition, we present our own mechanism to hide logical data sharing strategies to protect users against selective DoS attacks. Moreover, we implemented a modified version of bloom filter to safeguard user access pattern in a compromised server. Our proposed implementation of bloom filter also ensures that the scalability of distributed system is preserved even with little infrequent overhead in the server because of security measures proposed in this work. Finally, we implemented different modules of our framework using both Web Socket and Long Polling transport protocols and recorded the time required to perform various tasks. Web socket protocol took less time to perform each task than the long polling protocol, which is convincing enough to suggest that web socket performs better than long polling in the given scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

An appropriate aging description from face image is the prime influential factor in human age recognition, but still there is an absence of a specially engineered aging descriptor, which can characterize discernible facial aging cues (e.g., craniofacial growth, skin aging) from a detailed and more finer point of view. To address this issue, we propose a local face descriptor, directional age-primitive pattern (DAPP), which inherits discernible aging cue information and is functionally more robust and discriminative than existing local descriptors. We introduce three attributes for coding the DAPP description. First, we introduce Age-Primitives encoding aging related to the most crucial texture primitives, yielding a reasonable and clear aging definition. Second, we introduce an encoding concept dubbed as Latent Secondary Direction, which preserves compact structural information in the code avoiding uncertain codes. Third, a globally adaptive thresholding mechanism is initiated to facilitate more discrimination in a flat and textured region. We apply DAPP on separate age group recognition and age estimation tasks. Applying the same approach to both of these tasks is seldom explored in the literature. Carefully conducted experiments show that the proposed DAPP description outperforms the existing approaches by an acceptable margin.

Summary This paper addresses the problem of efficient federation formation by the cloud providers (CPs) with an aim to fulfill the dynamic resource demands of users for supporting data‐intensive workloads. Existing works only focus on forming federations based on the highest profit gained by each of the CPs in a federation. Therefore, these approaches often suffer from the risk of selecting unreliable CPs in the federation resulting in additional penalty cost and loss of CPs's reputation due to service level agreement violation between the users and the federation. In contrast, we argue that a trust model is necessary to find the most promising cloud collaborators. Accordingly, we propose a novel cloud federation formation mechanism by utilizing a trust‐based cooperative game theory, which enables the CPs to dynamically form a federation based on profit maximization and penalty cost minimization as a result of selecting the trustworthy CPs. Simulation results show that the cloud federation formed by the proposed mechanism is stable, satisfies the fairness property, and yields higher profit for the participating CPs in the long run without incurring penalty cost as compared with the state‐of‐the‐art approaches. Copyright © 2015 John Wiley & Sons, Ltd.

A federation of cloud providers (CPs) consists of a set of self-interested CPs that cooperate in order to provide virtual machine (VM) resources requested by users. The CPs by virtue of being part of a federation can make some profit by selling their unused VM capacity. This paper presents an efficient mechanism of resource and revenue sharing in a cloud federation that motivates the CPs to cooperate. The proposed mechanism models the interactions among the CPs in a federation as a coalition game. In contrast to existing approaches, the game model aims at maximizing the social welfare or total profit of the CPs in a federation to promote their long-term individual profit. In addition, we present a comprehensive analysis of the related costs and revenue associated with the various decisions of the CPs related to their joining in a federation. Various simulations were carried out to validate and verify the effectiveness of the proposed cooperative capacity sharing mechanism. Simulation results demonstrated that the proposed mechanism can satisfy the fairness and stability properties, maximize the social welfare the CPs in a federation and achieve cost effective resource sharing.

We propose a user-specified histogram equalization (UHE) approach, which provides user with a single variable to have the control over the enhancement procedure. By tuning this single parameter, user can achieve the desired enhanced look of the intended objects in an image.

A new approach for skin region segmentation is proposed. It uses color distance map (CDM) and an algorithm based on the property of flow of water. The CDM itself is a grayscale image, which makes the algorithm very simple. However, it is still capable of providing color information based on which some skin and non-skin seed regions can be determined reliably. Then a water-flow based procedure determines skin and non-skin segments completely. The color distance map is robust against variations in imaging conditions and the water-flow procedure efficiently uses the region information to extract solid skin segments without generating much noisy segments.