Reviewerly/exHarmony huggingface dataset:
An ensemble dynamic optimization based inverse adaptive heuristic critic in IaaS cloud computing for resource allocation

In the recent years increase in computer and mobile user’s, data storage has become a priority in all fields. Large- and Small-Scale businesses today thrive on their data and they spent a huge amount of money to maintain this data. Cloud Storage provides on– demand availability of IT services via Large Distributed Data Centers over High Speed Networks. Network Virtualization is been considered as a recent proliferation in cloud computing which emerges as a Multifaceted method towards future internet by facilitating shared resources. Provisioning of the Virtual Network is considered to be a major challenge in terms of creating NP hard problems, minimization of workflow processing time under control resource etc. In order to cope up with the challenges our work has proposed an Ensemble Dynamic Optimization based on Inverse Adaptive Heuristic Critic (IAHC) for overcoming the virtual network provisioning in cloud computing. Our approach gets observed from Expert Observation and provides an approximate solution when various workflows arrives online at various Window Time (WT). It also provides an Optimal Policy for predicting the effect of Resource Allocation of one task for Present as well as Future time Windows. In order to the above approaches it also avoids the high sample complexity and maintains the cost while scaling up to provide Resource Provision. Therefore, our work achieves an adequate policy towards Resource Allocation, reduces the Cost as well as Energy Consumption and deals with real time uncertainties to avoid the Virtual Network provisioning.
Metric learning by similarity network for deep semi-supervised learning

Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed input sets. Although these methods may achieve positive results, they ignore the relationship information between data instances. To solve this problem, we propose a novel method named Metric Learning by Similarity Network (MLSN), which aims to learn a distance metric adaptively on different domains. By co-training with the classification network, similarity network can learn more information about pairwise relationships and performs better on some empirical tasks than state-of-art methods.
Software Development Life Cycle Models-A Comparative Study

Software Development is one of the most powerful, vital, and the need for an hour in today's generation. Every organization, industries, small firms, institutes, etc. require the software for the functionality of their system and reducing the manual work or the traditional work, which used to be insecure and had more errors. SDLC is all about the minimization of the risk and failure and maximization of the quality of the product. To make the development works in a step by step procedure and precisely SDLC came into existence. The SDLC defines the framework that includes different activities and tasks to be carried out during the software development process. There are many types of SDLC models, which have their advantages and disadvantages and will work as per their needs.
Mobile technologies for contact tracing and prevention of COVID-19 positive cases: a cross- sectional study

Purpose The purpose of this paper is to review the techniques for versatile advancements in contact tracing for the coronavirus disease 2019 (COVID-19) positive cases in this pandemic and to introduce the way of using the mobile location information collected within the country India. As the method, an exploratory review of current measures was conducted for confirmed COVID-19 contact tracing after understanding the current situation of the world. This paper has examined the way of using free locational information in an innovative way to reduce the spread of COVID-19 spread. Design/methodology/approach COVID-19 pandemic is the utmost global economic and health challenge of the century. One powerful and consistent procedure to slow down the spread and decrease the effect of COVID-19 is to track the essential and auxiliary contacts of confirmed COVID-19 positive cases by using contact-tracing innovation. Findings Although it takes the information from various clients, there are numerous odds in the information. The sincere measures were taken by the authors to avoid the abuse of information by any kind. A portion of the tips for keeping information from getting abused is on the whole, the information ought to be with just higher specialists, and they ought not to have the authorization to impart information to anybody. Originality/value This paper helps to track the COVID-19 positive cases as of now by using the field information assortment and outbreak examination stages. At the same time, mobile location information used inside the current guideline, rules for information handlers must incorporate measures to reduce the abusing of information.
Two-stream spatial-temporal neural networks for pose-based action recognition

With recent advances in human pose estimation and human skeleton capture systems, pose-based action recognition has drawn lots of attention among researchers. Although most existing action recognition methods are based on convolutional neural network and long short-term memory, which present outstanding performance, one of the shortcomings of these methods is that they lack the ability to explicitly exploit the rich spatial-temporal information between the skeletons in the behavior, so they are not conducive to improving the accuracy of action recognition. To better address this issue, the two-stream spatial-temporal neural networks for pose-based action recognition is introduced. First, the pose features that are extracted from the raw video are processed by an action modeling module. Then, the temporal information and the spatial information, in the form of relative speed and relative distance, are fed into the temporal neural network and the spatial neural network, respectively. Afterward, the outputs of two-stream networks are fused for better action recognition. Finally, we perform comprehensive experiments on the SUB-JHMDB, SYSU, MPII-Cooking, and NTU RGB+D datasets, the results of which demonstrate the effectiveness of the proposed model.
Innovations in the Architectural Environment: the Impact of Society on the Positive Perception and Conflict of the Incomprehensible

Abstract The article is focused on issue of human perception of new objects of architecture and design, overcoming people’s commitment to the old and familiar. New ideas often face a negative reaction from society. Modern architecture, any projects of modern style, cause not only curiosity in its viewers but irritation, stress, negativity, psychological aversion as well. Original and common information of perception of the architectural environment is characterized by a dialectical unity. With complete originality there is a conflict of the incomprehensible. With complete commonness – the conflict of oversaturation. Unapprehensiveness generates the transfer of functions, the transfer of an object to another functional group. The more developed the society and the specific observer are, the easier it is to perceive new forms and technologies for them. There is a larger amplitude scale at the stage of information filtering. The development of society brings perception to a new stage, new forms and styles transform into typical ones, a psychological adaptation occurs. Crisis situations in a country and the general perception of life as critical, non-positive notion forms a negative reaction to new proposals. The situation is similar to “Seligman’s dogs”, the so-called learned helplessness. The psychological impossibility of non-action is the impossibility of thought, the unwillingness to look for analogies and images that would lead to metaphorical matches. Often the perception of a new object can be influenced in advance by technologies and mass media. In order to resist these technologies, to have his own, personal opinion, a person needs a high level of knowledge, world outlook, synthesis of information about the object. The formation of new human values, raising living standards lead to an adequate perception of the new architectural environment.
Employing Blockchain in Rice Supply Chain Management

Blockchain is considered to be the next paradigm in information technology after mainframes, computer, Internet, and smartphones. With so much hype around cryptocurrencies the mechanism on which these were built, “The Blockchain” has drawn attention of many scientists and developers around the world. With time, other than cryptocurrency, Blockchain had impact on several other industries such as logistics, health care, real estate, legal industries, etc. One such application leads to employing Blockchain technology into food supply chain. Rice is the major food consumed by people in India. Rice supply chains play a vital role in supplying rice from manufacturer to consumers. Hence, for attaining a corruption-free, transparent, and efficient rice supply chain, Blockchain is to be employed in the functioning of these supply chains such that safety of rice can be monitored at different stages involved in the supply chain. In this paper, a theoretical study is shown on how Blockchain can be integrated into regular rice supply chain.
The TPOA Telecentre: A Community Sustainable Telecentre Architecture

This paper presented the telecentre implementation for the Orang Asli villages in remote rural areas under the Telecentre Program for Orang Asli (TPOA).TPOA telecentre architecture aims to assist the achievement of a rural community sustainable telecentre through innovation and strategic adoption of ICT technology.Lessons learned from our past telecentre experience have outlined various challenges in the technical aspects of the telecentre implementation and operation.The TPOA telecentre ICT architecture has been designed to address the outlined issues hence producing a smoother telecentre operation that enables the rural communities to selfsustain their own telecentres.The technical support for the remote rural telecentre can be very expensive and impractical due to the extreme physical access condition.Hence, the rural communities themselves have to carry out the support and maintenance to sustain the operation of the telecentre.The TPOA telecentre architecture has enabled a relatively friendly to operate ICT platform in order to assist and make it possible for the Orang Asli to sustain, support, and maintain the telecentre operation.
Symmetric–asymmetric hybrid encryption and decryption system based on chaotic iris phase mask and computer-generated holography

Based on chaotic biological cryptography and computer-generated holography, a symmetric–asymmetric hybrid encryption and decryption system was proposed. This method has multiple keys, in which two decryption keys are different from the encryption keys. The encryption and decryption system possesses both symmetric and asymmetric key cryptography features. The biometric code is unique and unchangeable, not easy to be stolen or lost, which further enhances the security of the system. Chaos technology can reduce the amount of information, when combined with the advantages of the digitization of computer-generated holography, which can be conducive to the preservation and the transmission of encrypted information. The numerical simulation results show that this scheme is feasible and has better security and higher robustness.
Efficient AGCD-Based Homomorphic Encryption for Matrix and Vector Arithmetic

We propose a leveled homomorphic encryption scheme based on the Approximate Greatest Common Divisor (AGCD) problem that operates natively on vectors and matrices. To overcome the limitation of large ciphertext expansion that is typical in AGCD-based schemes, we randomize the ciphertexts with a hidden matrix, which allows us to choose smaller parameters. To be able to efficiently evaluate circuits with large multiplicative depth, we use a decomposition technique à la GSW. The running times and ciphertext sizes are practical: for instance, for 100 bits of security, we can perform a sequence of 128 homomorphic products between 128-dimensional vectors and $$128\times 128$$ matrices in less than one second. We show how to use our scheme to homomorphically evaluate nondeterministic finite automata and also a Naïve Bayes Classifier.
Bifixer and Bicleaner: two open-source tools to clean your parallel data

This paper shows the utility of two open-source tools designed for parallel data cleaning: Bifixer and Bicleaner. Already used to clean highly noisy parallel content from crawled multilingual websites, we evaluate their performance in a different scenario: cleaning publicly available corpora commonly used to train machine translation systems. We choose four English–Portuguese corpora which we plan to use internally to compute paraphrases at a later stage. We clean the four corpora using both tools, which are described in detail, and analyse the effect of some of the cleaning steps on them. We then compare machine translation training times and quality before and after cleaning these corpora, showing a positive impact particularly for the noisiest ones.
Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.
Improved Helmet Wearing Detection Method Based on YOLOv3

In order to monitor the wearing of safety helmet in dangerous workplace in real-time and ensure the safety of production, this paper proposes a method based on the yolov3 algorithm to detect the wearing of safety helmet. Firstly, K-means algorithm is used to cluster the target boxes on the self-made data set, so that the prediction boxes can fit the target better that in the data set. At the same time, the network is pre-trained on the voc2007 data set to make the model parameters more accurate and reduce the training time. Secondly, multi-scale feature extraction and multi anchor box mechanism are used to improve the accuracy of small object detection. Finally, optimizing the non-maximum suppression (NMS) algorithm with Gaussian function that can improve the detection accuracy of the occluded target. Experimental results show that the algorithm has better detection effect while meeting the real-time helmet wearing detection.
Nonclassicality of photon-modulated atomic coherent states in the Schwinger bosonic realization*

We theoretically introduce two new photon-modulated atomic coherent states (ACSs) via using the Schwinger bosonic representation of the angular momentum operators (the sequential operations <?CDATA ${J}_{\pm }^{n}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mi>J</mml:mi> <mml:mo>±</mml:mo> <mml:mi>n</mml:mi> </mml:msubsup> </mml:mrow> </mml:math> ) on an ACS, and investigate their nonclassicality using the Wigner distribution, photon number distribution, and entanglement entropy. It is found that photon-modulated ACSs possess more stronger nonclassicality than the original ACS in certain regions of τ , the nonclassicality enhances with increasing number n of the operations J ± and the operation <?CDATA ${J}_{+(-)}^{n}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mi>J</mml:mi> <mml:mrow> <mml:mo>+</mml:mo> <mml:mo stretchy="false">(</mml:mo> <mml:mo>−</mml:mo> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mi>n</mml:mi> </mml:msubsup> </mml:mrow> </mml:math> enhances the entanglement in the region of small (large) τ .
Bring your own device to work: how serious is the risk?

Purpose Currently, one of the most significant challenges organizations face is that corporate data is being delivered to mobile devices that are not managed by the information technology department. This has security implications regarding knowledge leakage, data theft, and regulatory compliance. With these unmanaged devices, companies have less control and visibility, and fewer mitigation options when protecting against the risks of cyber-attacks. Therefore, the purpose of this study is to investigate how millennials' use of personal mobile devices for work contributes to increased exposure to cyber-attacks and, consequently, security and knowledge leakage risks. Design/methodology/approach This research used a mixed-method approach by using survey questionnaires to elicit the views of millennials regarding the cybersecurity risks associated with bring your own device policies and practices. Interviews were done with security personnel. Data analysis consisted of descriptive analysis and open coding. Findings The results indicate that millennials expect to have ready access to technology and social media at all times, irrespective of security and privacy concerns. Companies also need to improve and enforce bring your own device policies and practices to mitigate against knowledge leakage and security risks. Millennials increasingly see the use of personal devices as a right and not a convenience. They are expecting security measures to be more seamless within the full user experience. Originality/value This paper can help organizations and millennials to understand the security risks entering the workforce if the threats of using privately owned devices on the job are ignored and to improve organizational performance.
Pattern-based dual learning for point-of-interest (POI) recommendation

Purpose Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit. Design/methodology/approach The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users. Findings The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F -measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin. Originality/value This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.
Polarization-multiplexed continuous-variable quantum key distribution

We first experimentally demonstrate a polarization-multiplexed continuous-variable quantum key distribution architecture eliminating the requirement for polarization controlling, where the polarization mixing and phase rotation are simultaneously compensated in a dual reference compensation scheme.
Verifiable Encrypted Search with Forward Secure Updates for Blockchain-Based System

Enabling secure and reliable search over encrypted data is essential for data owners to protect their sensitive data from a compromised server. Recent advances in cloud computing are further pushing forward the development of this technique, known as searchable encryption. However, existing encrypted search schemes mainly consider a centralized setting, where a search is conducted in a traditional client-server model. How to apply searchable encryption schemes to an untrusted distributed setting like the blockchain environment remains to be explored. Meanwhile, the advanced security property like forward security is posing new challenges that traditional technologies are no longer sufficient to cope with. In this work, we explore the potential of blockchain technique and propose a novel dual index structure for forward-secure encrypted search with dynamic file updates. We show how to synthesize this design strategy in the context of blockchain-based storage systems and achieve both optimal search and update complexity. We also propose a verification scheme to verify the correctness of search results and customize an encrypted on-chain checklist to achieve strong data protection and lower the blockchain overhead. We implement the prototype on a Redis cluster and conduct performance evaluations on Amazon Cloud. Extensive experiments demonstrate the security and efficiency of the design.
EfficientNet-eLite: Extremely Lightweight and Efficient CNN Models for Edge Devices by Network Candidate Search

Embedding Convolutional Neural Network (CNN) into edge devices for inference is a very challenging task because such lightweight hardware is not born to handle this heavyweight software, which is the common overhead from the modern state-of-the-art CNN models. In this paper, targeting at reducing the overhead with trading the accuracy as less as possible, we propose a novel of Network Candidate Search (NCS), an alternative way to study the trade-off between the resource usage and the performance through grouping concepts and elimination tournament. Besides, NCS can also be generalized across any neural network. In our experiment, we collect candidate CNN models from EfficientNet-B0 to be scaled down in varied way through width, depth, input resolution and compound scaling down, applying NCS to research the scaling-down trade-off. Meanwhile, a family of extremely lightweight EfficientNet is obtained, called EfficientNet-eLite. For further embracing the CNN edge application with Application-Specific Integrated Circuit (ASIC), we adjust the architectures of EfficientNet-eLite to build the more hardware-friendly version, EfficientNet-HF. Evaluation on ImageNet dataset, both proposed EfficientNet-eLite and EfficientNet-HF present better parameter usage and accuracy than the previous start-of-the-art CNNs. Particularly, the smallest member of EfficientNet-eLite is more lightweight than the best and smallest existing MnasNet with 1.46x less parameters and 0.56% higher accuracy. Code is available at https://github.com/Ching-Chen-Wang/EfficientNet-eLite .