Reviewerly/exHarmony huggingface dataset:
Multi-level storage based auditing scheme for 5G and beyond defined edge computing

Aim: Edge computing has become one of the most essential approaches for processing user-side data in the future, thanks to the portability of storage and computing devices. 5G and beyond defined edge computing has become one of the most urgently required technologies. However, there are still several issues that must be resolved. Edge computing's present storage structure is incapable of adapting to new and flexible application scenarios. Simultaneously, data stored on edge devices are more vulnerable to attacks, and the integrity of the data needs to be protected. Method: For 5G and beyond defined edge computing, a novel auditing scheme based on multi-level storage is proposed in this paper. To reduce the response time of data queries, we first present a storage system with neighborhood servers. Then, a scheme for third-party-assisted auditing is described. Users can audit their data using self-defined file names. Results: The scheme is secure and efficient, according to the security analysis and performance simulation. Our scheme cost less computation overhead than the compared related work. Conclusion: The result shows that the novel proposal is efficient for the application in 5G and beyond defined edge computing.
Technique for Balanced Load Balancing in Cloud Computing Environment

Resource sharing by means of load balancing in cloud computing environments helps for efficient utilization of cloud resources and higher overall throughput.However, implementation of poor load balancing algorithms may cause some virtual machines starving for additional cloud resources.Employing meagre crafted mechanism for priority-oriented load balancing may leave low-level priority virtual machines starving.We suggest an improved resource sharing mechanism for load balancing in the cloud computing environments.The suggested mechanism helps to provide efficient load balancing by avoiding starvation.In order to cater efficient load balancing, the proposed resource sharing technique takes respective virtual machines' priority levels into consideration.An implementation of the suggested load balancing algorithm in cloud environment provides reduction in waiting time of the starving virtual machines which are looking for additional resources in cloud platform.The implementation of our proposed algorithm has been deployed on a prototype cloud computing infrastructure testbed established with open source software OpenStack.The prototype cloud testbed is supported in backend by the open source CentOS Linux operating system's minimal setup.Experimental results of proposed load balancing mechanism in the prototype cloud computing infrastructure setup designate reduction in the waiting time of overloaded starving virtual machines.The proposed mechanism is beneficial to accomplish priority-oriented and starvation free resource sharing for load balancing in cloud computing environments.In future, the proposed technique can be further enhanced for implementing load balancing in collaborated cloud computing environments.
Inferring Rewards from Language in Context

In classic instruction following, language like "I'd like the JetBlue flight" maps to actions (e.g., selecting that flight).However, language also conveys information about a user's underlying reward function (e.g., a general preference for JetBlue), which can allow a model to carry out desirable actions in new contexts.We present a model that infers rewards from language pragmatically: reasoning about how speakers choose utterances not only to elicit desired actions, but also to reveal information about their preferences.On a new interactive flight-booking task with natural language, our model more accurately infers rewards and predicts optimal actions in unseen environments, in comparison to past work that first maps language to actions (instruction following) and then maps actions to rewards (inverse reinforcement learning).
Which can Accelerate Distributed Machine Learning Faster: Hybrid Optical/Electrical or Optical Reconfigurable DCN?

We run various distributed machine learning (DML) architectures in a hybrid optical/electrical DCN and an optical DCN based on Hyper-FleX-LION. Experimental results show that Hyper-FleX-LION gains faster DML acceleration and improves acceleration ratio by up to 22.3%.
On the suitability of current augmented reality head-mounted devices

Simulation is a recognized and much-appreciated tool in healthcare and education.Advances in simulation have led to the burgeoning of various technologies.In recent years, one such technological advancement has been Augmented Reality (AR).Augmented Reality simulations have been implemented in healthcare on various fronts with the help of a plethora of devices including cellphones, tablets, and wearable AR headsets.AR headsets offer the most immersive experience of the AR simulation as they are head-mounted and offer a stereoscopic view of the superimposed 3D models through the attached goggles overlaid on real-world surfaces.To this effect, it is important to understand the performance capabilities of the AR headsets based on workload.In this paper, our objective is to compare the performances of two prominent AR headsets of today, the Microsoft Hololens and the Magic Leap One.We use surgical AR software that allows the surgeons to show internal structures, such as the rib cage, to assist in the surgery as a reference application to obtain performance numbers for those AR devices.Based on our research, there are no performance measurements and recommendations available for these types of devices in general yet. AR in Nursing Education
Agribuzz- Agriculture Management System

Abstract: Farming is the Prime Occupation in India in spite of this, today the people involved in farming belong to the lower class and are in deep poverty. The Advanced techniques and the Automated machines which are leading the world to new heights are been lagging when it is concerned to Farming, either the lack of awareness of the advanced facilities or the unavailability leads to poverty in Farming. Even after all the hard work and the production done by the farmers, in today’s market, the farmers are cheated by the Agents, leading to poverty. Agro-marketing would make all things automatic which makes it easier to serve as the best solution to all the problems. Agribuzz farming will serve as a way for the farmers to sell their products across the country just with some basic knowledge about how to use the website. The site will guide the farmers in all aspects, including the current market rate of different products, the total sale and the earned profit for the sold products, access to the new farming techniques through learning, and a centralized approach to view different governments' agriculture schemes including the compensation schemes for farming. Getting availed of the required information related to the markets and different products can be made possible through the SMS facility provided by the system. Keywords: Website, farm-marketing, market rate, bill, eLearning, SMS facility.
Rimigs: the impact of gamification on students’ motivation and performance in programming class

Gamification is described as the art of changing human activity to a game-like environment to gain user engagement. Rimigs is a developed gamification system discussed in this research and was built based on Octalysis framework. This research aims to design and identify the impact of using a gamification system on students’ motivation and performance, especially in programming classes. Through Rimigs, students’ engagement and performance can be enhanced by freely choosing tasks or quests according to the rank points, completing these tasks or quests, getting experience points, getting rewards, avoiding punishment at certain times, and competing with others friends. The proposed research was done by using the iterative methodologies and consists of four phases, starting from i) literature review, which identifies the Octalysis’ core, ii) design, which identifies the game elements used in the system and designing the unified modeling language (UML) diagram of the system, iii) testing, which involve the user in the using of the system, and iv) implementation, which is the analysis step for the impact of Rimigs in students’ performance and engagement. This research finds that Rimigs as a gamification system can positively impact both students’ motivation and performance.
Effects of Thai Chi exercises, and laughter therapy on active aging in community-dwelling older adults: A systematic review.

Background: Community-dwelling older adults lack opportunities for health care, health-related knowledge, participation in health care activities, and a sense of security. These all are some of the identified hindrances on Active-aging elderly living in the community. This study explores the fundamental elements of long-distance Happy-age education in terms of Active-aging in older adults living in the community. Methodology: An extensive literature search returned 443 studies. Of these, only 10 studies met the inclusion criteria of a scientific investigation of the effectiveness of long-distance Happy age education programs, including fun exercise and laughter that contain appropriate elaboration on theoretical constructs and methods. Results: The qualitative assessment resulted that programs containing more fun-based interventions are effective regardless of the type of intervention. The effectiveness is also modulated by other variables such as the participant's education level, gender, current health status, and financial constraints. Conclusion: This review suggests that the level of the community-dwelling older adults have seemed to benefit from Thai Chi Rhythmic exercises and laughter exercises. More studies of this topic with older adult populations are needed to select the most convenient Happy-age education program that can improve the level of Active-aging and prolong life with happiness can be fulfilled.
A Blockchain Application Prototype for the Internet of Things

The emergence of the Internet of things (IoT), associated with the explosion in the number of connected objects, and the growth in user needs, makes the Internet network very complex. IoT objects are diverse and heterogeneous, which requires establishing interoperability and efficient identity management on the one hand. On the other hand, centralized architectures such as cloud-based ones can have overhead and high latency, with a potential risk of failure. Facing these challenges, Blockchain technology, with its decentralized architecture based on a distributed peer-to-peer network, offers a new infrastructure that allows IoT objects to interact reliably and securely. In this paper, a new approach is proposed with a three-layer architecture: layer of sensing and collection of data made up of the IoT network, layer of processing and saving of data exchanges at the Blockchain level, and access and visualization layer via a web interface. The prototype implemented in this study allows all transactions (data exchanges) generated by IoT devices to be recorded and stored on a dedicated Blockchain, assuring the security of IoT objects' communications. This prototype also enables access to and visualization of all data and information, thus enhancing the IoT network's transparency.
A Cognitive Semantic Analysis of the Preposition at

The present work is a pre-step toward large scale research. It aims at achieving two objectives: First, developing a cognitive diagram that covers the semantic range of the preposition at; second, examining EFL learners' semantic range on the preposition at within three broad categories: sentences, expressions and phrasal verbs. Accordingly, the researchers designed a test that covers the overall usages of the preposition at. Such a test helps examining the different cognitive domains of at and analyzing qualitatively and quantitatively the subjects' answers. The model that is used in the analysis of the first objective is Domain Highlighting by William Croft (1993). The main conclusions arrived at are: First, the cognitive theories can be applied to many language-related studies. The model used for instance managed to provide a miniature image to the different usages of the preposition at. Second, the diagram provided cognitively might help EFL learners recall the various semantic domains of the preposition at. Third, the subjects have a high lack of knowledge regarding the semantics of the preposition at in sentences, expressions and phrasal verbs. Fourth, the subjects' wrong choice of prepositions might be attributed to the semantic overlapping between the correct and incorrect ones; lack of knowledge, or to the limited frequency of usage;
Exploring Incentives and Challenges for Cybersecurity Intelligence Sharing (CIS) across Organizations: A Systematic Review

Cybersecurity intelligence sharing (CIS) has gained significance as an organizational function to protect critical information assets, manage cybersecurity risks, and improve cybersecurity operations. However, few studies have synthesized accumulated scholarly knowledge on CIS practices across disciplines. Synthesizing the pertinent literature through a structured literature review, we investigated the incentives and challenges that influence organizations around adopting CIS practices. We used the overarching TOE framework to categorize these factors and propose a theoretical framework to establish common ground for future studies. We also developed a holistic and inclusive definition for cybersecurity intelligence that we present in the paper. We found 46 papers on CIS in different disciplines and analyzed them to answer our research questions. We identified 35 factors that we classified according to the TOE framework. With this paper, we facilitate further theory development by overviewing theories that researchers can use as a basis for CIS studies, suggesting future directions, providing a reference source, and developing a reference CIS framework for IS scholars.
An efficient human activity recognition model based on deep learning approaches

Human Activity Recognition (HAR) has gained traction in recent years in diverse areas such as observation, entertainment, teaching and healthcare, using wearable and smartphone sensors. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Different developed models for HAR have been explained in literature. Deep learning systems and algorithms were shown to perform highly in HAR in recent years, but these algorithms need lots of computerization to be deployed efficiently in applications. This paper presents a HAR lightweight, low computing capacity, deep learning model, which is ideal for use in real-time applications. The generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains and standard Convolutional Neural Network (CNN) used for classification. The findings demonstrate that many of the deployed deep learning and machine learning techniques are surpassed by the proposed model. TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back // ORIGINAL: "; langMenu.appendChild(origLangDiv); LanguageMenu.Init('LanguageMenu', LanguageMenu_keys, LanguageMenu_values, LanguageMenu_callback, LanguageMenu_popupid); window["LanguageMenu"] = LanguageMenu; clearInterval(intervalId); } }, 1); // ]]>
An Authorization Framework for Preserving Privacy of Big Medical Data via Blockchain in Cloud Server

In recent years, cloud-based medical record sharing has greatly improved the process of researching the disease and patient diagnosis.However, since cloud systems are centralized, there is serious concern about data security and privacy.Blockchain technology is viewed as a promising method of dealing with privacy issues and data security because of its exclusive features of distributed ledgers, secrecy, verifiability, and enhanced security.The literature review has shown significant works on integrating blockchain technology with cloud system for managing and sharing healthcare data.It has been analyzed that previous works are primarily dependent on the centralized data storage approach, which raises privacy concerns.The previous works also do not emphasize handling big medical data and lack the reliability of the end-to-end security features system.This paper has presented an authorization framework for ensuring data security and privacy preservation using blockchain technology with IPFS as decentralized file storage and sharing system.The proposed study devises a proof of replication algorithm using smart contracts to provide a better access control mechanism.The implementation of the proposed framework is based on the symmetric encryption and Ethereum blockchain platform.The study outcome illustrates the efficiency and availability of the proposed scheme compared to the typical cloud-based blockchain method.
Wavebender GAN: An Architecture for Phonetically Meaningful Speech Manipulation

Deep learning has revolutionised synthetic speech quality. However, it has thus far delivered little value to the speech science community. The new methods do not meet the controllability demands that practitioners in this area require e.g.: in listening tests with manipulated speech stimuli. Instead, control of different speech properties in such stimuli is achieved by using legacy signal-processing methods. This limits the range, accuracy, and speech quality of the manipulations. Also, audible artefacts have a negative impact on the methodological validity of results in speech perception studies.This work introduces a system capable of manipulating speech properties through learning rather than design. The architecture learns to control arbitrary speech properties and leverages progress in neural vocoders to obtain realistic output. Experiments with copy synthesis and manipulation of a small set of core speech features (pitch, formants, and voice quality measures) illustrate the promise of the approach for producing speech stimuli that have accurate control and high perceptual quality.
Pointer over Attention: An Improved Bangla Text Summarization Approach Using Hybrid Pointer Generator Network

Despite the success of the neural sequence-to-sequence model for abstractive text summarization, it has a few shortcomings, such as repeating inaccurate factual details and tending to repeat themselves. We propose a hybrid pointer generator network to solve the shortcomings of reproducing factual details inadequately and phrase repetition. We augment the attention-based sequence-to-sequence using a hybrid pointer generator network that can generate Out-of-Vocabulary words and enhance accuracy in reproducing authentic details and a coverage mechanism that discourages repetition. It produces a reasonable-sized output text that preserves the conceptual integrity and factual information of the input article. For evaluation, we primarily employed “BANSData” <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> - a highly adopted publicly available Bengali dataset. Additionally, we prepared a large-scale dataset called “BANS-133” which consists of 133k Bangla news articles associated with human-generated summaries. Experimenting with the proposed model, we achieved ROUGE-1 and ROUGE-2 scores of 0.66, 0.41 for the BANSData” dataset and 0.67, 0.42 for the BANS-133k” dataset, respectively. We demonstrated that the proposed system surpasses previous state-of-the-art Bengali abstractive summarization techniques and its stability on a larger dataset. “BANS-133” datasets and code-base will be publicly available for research.
Cooperative Spectrum Sensing in Cognitive Radio

Abstract: As we all are aware about the rapid raise in wireless communication has huge demand. Spectrum sensing acts a key role part in (CR)network to highlight presence of the resource . This project focuses on the issues of Spectrum sensing in detection performance in process is usually compromised with the multipath. Cast of shadow over and receive uncertain errors. To alleviate the consequences of these errors Cooperative been shown that well organized methodology to intensify the detection performance. We also talk about the performance of the CR for 5th generation &amp; if possible route the frequency allocation .Taking into count many detection performance of cooperative secondary users, we have gather a completely unique reliability based decision combination program where weight is assign to each and every secondary user local conclusion supported its reliability .As the grasp of local prospect of detection and warning for every secondary detector might not be aware of the practice , we also employ a counting process to reckon these probabilities supported past global and native decisions. Keywords: (CR), spectrum sensing, false alarm, cognitive spectrum
Neural Network-Powered Nonlinear Compensation Framework for High-Speed Continuous Variable Quantum Key Distribution

In this paper, we present a neural network-based framework to compensate for the nonlinear distortion of high-speed continuous variable quantum key distribution (CV-QKD) systems. In practice, the nonlinear impairment of imperfect devices could bring interference to the measured quadrature values and thus compromise the parameter estimation procedure and the secure key rate. Specifically, for a high-speed CV-QKD system, the balanced homodyne detector (BHD) and the following analog-to-digital converter are the primary sources of nonlinear impairment. The nonlinear distortion effect will be more pronounced as the pulse repetition rate increases. We propose an autoencoder-based network to learn the inner patterns of distorted data and then compensate the quadrature measurements in real-time at the receiver’s side. The numerical simulation demonstrates that the excess noise induced by nonlinear impairment is reduced to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-3}$</tex-math></inline-formula> level in the shot noise unit. Consequently, such a software approach has the ability to significantly boost the bandwidth efficiency of the BHD and the secure key bit rate.