Trusted third parties (TTPs) are frequently used for serving as an authority to issue and verify transactions in applications. Although the TTP-based paradigm provides customers with convenience, it causes a whole set of inevitable problems such as security threats, privacy vulnerabilities, and censorship. The TTP-based paradigm is not suitable for all modern networks, e.g., 5G and beyond networks, which are been evolving to support ubiquitous, decentralized, and autonomous services. Driven by the vision of blockchain technologies, there has been a paradigm shift in applications, from TTP-based to decentralized-trust-based. Decentralized applications (DApps) with blockchains promise no trust on authorities, tackling the key challenges of security and privacy problems. A main thrust of blockchain research is to explore frameworks and paradigms for decentralizing applications, fostering a number of new designs ranging from network architectures to business models. Therefore, this paper provides a compact and concise survey on the state-of-the-art research of decentralizing applications with blockchain in the 5G and beyond perspective. We provide four burning 5G and beyond challenges and discuss five aspects of motivation for decentralizing applications with blockchain. Then, we define nine fundamental modules of blockchains and explain the potential influence of these modules on decentralization in depth. We also discuss the interrelation between decentralization and some desired blockchain properties. Particularly, we present the capabilities of blockchain for decentralizing applications through reviewing DApps for 5G and beyond. We clearly distinguish three blockchain paradigms and discuss how developers to make right choices for 5G and beyond. Finally, we highlight important learned lessons and open issues in applying blockchain for decentralizing applications. Lessons learned and open issues from this survey will facilitate the transformation of centralized applications to DApps.

A Survey of Decentralizing Applications via Blockchain: The 5G and Beyond Perspective

The efficiency in breast cancer from imaging-based computer-aided diagnosis (CAD) has been revealed in recent years. As a fact, the methods grounded on a single modality constantly lack behind multimodal CAD imaging. However, owing to the restrictions of imaging devices, expressly in rural hospitals, single-modal imaging becomes a favorite in clinical practice for diagnosis. A fresh learning model trending nowadays known as learning using privileged information (LUPI) adopts additional privileged information (PI) modality to help during the training stage, but PI does not contribute in the testing stage. Meanwhile, the link exists between PI and training samples; the same is then reassigned to the learned model. We propose a LUPI-based CAD framework for breast cancer using privileged information in this work. The work offers both a classifier- or feature-level LUPI, in which the information is shifted from the additional PI modality to the diagnosis modality. A thorough comparison has been made among six classifier-level algorithms and six feature-level LUPI algorithms. The experimental results on both the acquired primary datasets show that all classifier-level and deep learning-based feature-level LUPI algorithms can enhance the performance of a single-modal imaging-based CAD for breast cancer by relocating PI.

Transfer learning privileged information fuels CAD diagnosis of breast cancer

We conducted a review to collect the validated basketball-specific physical field tests and to provide practical advice for their appropriate selection and application. A comprehensive electronic literature search was performed via three electronic databases (PubMed, GoogleScholar, and SportDiscuss). Results of 93 studies provided recommendations for seven test packages and eighteen individual tests that have already been validated for basketball players. Although there is a lack of standardized, widely, and systematically used test protocols for testing the fitness levels of basketball players, standardized, normative data from NBA Combine Testing and other basketball-specific tests have the potential to help coaches compare their players with elite basketball players. Our review indicated that agility and reactive agility are fundamental skills in basketball; however, linear sprinting ability should not be considered a determining factor of success for basketball players. Finally, the countermovement jump test can help experts monitor fatigue, loss of explosive force, and interlimb asymmetries. In general, we found that identifying and developing a talented player is a complex task and requires experts from different fields, including trainers, coaches, performance- and movement analyzers, and physiotherapists. We found that during the testing of basketball players, experts always have to normalize their data with anthropometric measures for valid results. Most importantly, although experts always need to define an aim of testing and should follow the protocol of the chosen test, they also have to be open to making adjustments if the actual circumstances require it.

Collection and Advice on Basketball Field Tests—A Literature Review

Verbal encouragement (VE) and listening to preferred music (PM) are considered effective ergogenic methods in strength and conditioning, but studies examining the effectiveness of these two methods simultaneously are lacking. This study analyzed the influence of PM and VE on repeated change-of-direction performance (R-CoD) in elite young basketball players. On alternate days, 18 elite young basketball players (17.2 ± 0.61 years; 189.8 ± 7 cm; 71.6 ± 6.7 kg; body fat: 12.3 ± 2.5%) were assessed on R-CoD under three randomized conditions: team-selected PM, VE, and control condition. Total time (TT), peak time (PT), and fatigue index were registered and compared across conditions. Significant differences across conditions were evidenced for TT and PT (F-test = 6.96 and 4.15, p < 0.05; large effect size), with better results in VE and PM than in the control condition and no significant differences between VE and PM. No correlations were evidenced between changes that occurred as a result of VE and those which occurred as a result of PM, indicating individual responsiveness of the players to VE and PM. The results evidenced positive acute effects of VE and PM on R-CoD performance, indicating the usefulness of these training methods in the conditioning of youth basketball players. Future studies should evaluate the applicability of VE and PM in the training of other conditioning capacities and the individual responsiveness of players toward VE and PM.

Acute Effects of Verbal Encouragement and Listening to Preferred Music on Maximal Repeated Change-of-Direction Performance in Adolescent Elite Basketball Players—Preliminary Report

Medical data is shared with a wide range for various research purposes and an extensive amount of research has been developed in the data privacy community for anonymization. Unfortunately, Data anonymization techniques do not provide data privacy guarantees and synthetic data generation is an alternative approach in data anonymization. Deep learning has recently achieved more reputation for its high accuracy and privacy concern. Nowadays, deep learning is extensively applied in the medical field for classification, segmentation and privacy-preserving. Using Deep learning, synthetic data can be generated to improve the privacy of the original medical data and also to prevent attacks. Deep learning models capture the relationship between multiple features in medical data. In this research, Healthcare Cramer Generative Adversarial Network (HCGAN) is proposed, where (i) the Quasi Identifiers (QI) are identified in medical data and separated as QI attributes and the remaining attributes are considered as Sensitive Attributes (SA) (ii) f–differential privacy anonymization technique is applied only to the identified QI and the final result is combined with the SA attribute (iii) The anonymized medical data is used as real data for training Cramer Generative Adversarial Network (GAN) where Cramer distance is used to improve the efficiency of the model. (iv) Finally, Privacy is checked by overcoming the attacks. The result shows that the HCGAN method effectively prevents attacks during the training and testing phase compared to Wasserstein GAN. The result demonstrates that health care GAN generates synthetic data that can provide high privacy and overcome various attacks.

Healthcare Cramér Generative Adversarial Network (HCGAN)

The eye disease is prominent in many nations including India and is said to affect up to 80% patients having diabetes. Diabetic Retinopathy is the medical term for denoting the damages to retina caused due to diabetes mellitus. Implying K means Clustering algorithm for coarse segmentation, hard distils are identified with better accuracy than the classical approaches. The variance based methods for segmenting hard distils are reviewed in the surveys and had to be improved. To remove the background features from the picture and conserve computational costs, a mathematical morphological method is used to reconstruct the image features for better segmentation. The results obtained for 96.4% sensitivity and 97.2% specificity. Along with this advantage, a graphical user interface is developed which will simplify the usage of this system. This model will divide the fragments into regions of interests having lesions and normal regions carrying normal features. After this segmentation, ophthalmologists will utilize the results to grade diabetic retinopathy and devise a treatment plan.

An Effective Detection Mechanism for Localizing Macular Region and Grading Maculopathy.

Current agility tests assess one or multiple components of agility, including unidirectional, bidirectional, and multidirectional movements, which may or may not include retropulsion and rapid acceleration and deceleration transitions along with performing basketball-specific activities such as jumping, turning around 180o and 360o, along with picking a ball and throwing it when changing direction Hence, there is a requirement for a test which is specific to a Basketball game and also possesses the intricate movements associated with the game in a single test Objectives: To develop a new sport-specific agility test for assessing agility in Basketball players and determine its reliability by establishing the interrater and interrater reliability Materials and methods: In this explorative study 54 male Basketball players were taken with the age group 20±3 years Subjects having acute injuries of lower limb injuries, disabled sportsmen, peripheral nerve injuries of upper & lower extremities, history of recurrent ankle sprains/ other injuries of lower limbs were excluded The subjects had to perform the Basketball Multicomponent Agility Test (BMAT) which is a special test that has been designed specifically for Basketball players after an intricate and thorough analysis of the game through various videos of time-motion analysis The final test hence includes all the components occurring most commonly in the game Results: The Intraclass correlation coefficient (ICC) across the trials for the Interrater reliability was 997 and p= < 001 with 95% CI For the Intrarater reliability the ICC across the trials was 946 at 95% CI, with p= <001 Conclusion: The BMAT is a reliable assessment tool for sport specific agility assessment in Basketball players It can be routinely used by sports scientists, strength and conditioning practitioners, for monitoring the training programs for Basketball players

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Reliability of a New Multicomponent Agility Test for Assessing Agility in Basketball Players: The Basketball Multicomponent Agility Test (BMAT)

Internet of Things (IoT) is a network of the devices which are capable of receiving the data from the environment and communicating through the internet. The IoT devices generate a significant amount of sensitive information which may be prone to the several security attacks. The evolution of the IoT demands a high degree of security in terms of confidentiality, integrity, reliability and other factors. Because of the hardware restrictions, the security mechanisms used for the normal networks cannot be applied. For such reasons, instead of a centralized approach, the distributed record based technique called the blockchain (BC) approach is used. This produces better results in terms of security consideration such as security, privacy etc. when compared with the existing security mechanisms.

Blockchain based Security System for the Devices in Internet of Things

A Comparative Study of Cardiovascular Fitness in Normal Versus Obese Children

1375 Efficacy of Breathing Retrainer on Retraining Breathing Pattern, Dyspnea and Lung Function in Patients with Copd Dr. M. Vijayakumar 1, Dr. Megha V. Malu2, Dr. Aishwarya Dehadrai2, Dr. Adnan Ezzy2, Dr. Tushar J. Palekar3 1 Associate Professor, Physiotherapy Department, Dr. D. Y. Patil Vidyapeeth, Pune, India 2 Resident, Physiotherapy Department, Dr. D. Y. Patil Vidyapeeth, Pune, India 3Principal, Dr. D. Y. Patil Vidyapeeth, Pune, India ABSTRACT

M. Vijayakumar

Papers

An Effective Detection Mechanism for Localizing Macular Region and Grading Maculopathy.

Reliability of a New Multicomponent Agility Test for Assessing Agility in Basketball Players: The Basketball Multicomponent Agility Test (BMAT)

Blockchain based Security System for the Devices in Internet of Things

A Comparative Study of Cardiovascular Fitness in Normal Versus Obese Children

Efficacy of Breathing Retrainer on Retraining Breathing Pattern, Dyspnea and Lung Function in Patients with Copd