Reviewerly/exHarmony huggingface dataset:
A Novel Oppositional Chaotic Flower Pollination Optimization Algorithm for Automatic Tuning of Hadoop Configuration Parameters

At present, due to the introduction of the big data era, numerous numbers of data are generated consistently. Many applications utilize big data platforms, namely Spark, Hadoop, Amazon web services, and so on, since these platforms use several parameters for tuning that further enhance the operating performances. It requires a long duration of time to tune the parameters because of the complex relationship and large quantity of parameters. As a result, the building of such parameters and performance optimization at a particular duration of time becomes a challenging task. Several auto-tuning approaches are developed to achieve an optimal design. However, these approaches increase the computation time and minimize the efficiency of the cluster. It is necessary to tune the parameters automatically with low computational and processing time as well as to improve the performance of the system. In this proposed approach, a novel automatic parameter tuning system named as Opt. Tuner is proposed to select the Hadoop configuration parameters with less computational time. The optimization of the proposed approach is achieved by the Flower Pollination Algorithm. Here, a chaotic mapping along with Opposition-Based Learning is introduced for population initialization to form a novel Oppositional Chaotic Flower Pollination Algorithm. The main motive of this initialization phase involves in generating better individuals and to guide the search agent more quickly. In this novel approach, 15 configuration parameters are considered for auto-tuning. Finally, the performance of the proposed approach utilizes the wordcount and sort application to investigate the exhibition and proficiency of diverse databases.
Design and Implementation of Data Acquisition System Based on Scrapy Technology

In this paper, a data acquisition system based on the Scrapy crawler framework was designed and implemented, which can not only obtain data according to the user's own needs, but also manage its own collection tasks simply. The Django MTV mode is used for development, and the underlying data collection framework applies Scrapy, an asynchronous crawler application framework implemented by Python. The web page analysis uses the method in combination of XPath and regular expression. The jQuery tree plug-in zTree is utilized to realize tree management of tasks, the bootstrap to achieve the effect of task name with the keyword combination query and page.
"I Feel Like This is a Bad Thing": Investigating Disassembly in Action for Novices

Materials are dynamic-they can be shaped and changed. Often however, our tools and technologies appear to fix materials in place. Disassembly is one practice that provides openings to explore and understand the dynamic nature of material. In this research, we investigate possibilities that emerge from disassembly. Specifically, we studied how novices disassembled a common digital artifact-desktop printers. We worked with 21 young people and family members across two evening workshops at a middle school. We report on the workshop interactions, categories of actions of disassembly, and four in-depth vignettes showcasing disassembly in action. In the discussion, we reflect on disassembly and permission, sustainability, the joy of disassembling, and design considerations in support of disassembly. Our contributions include: (1) extending existing theoretical framings about artifacts and materials; (2) an empirical study documenting the process by which novices disassemble; and (3) preliminary design and policy considerations that enable disassembly.
THE DESIGN OF WEB-BASED TRAINING MANAGEMENT INFORMATION SYSTEMS AT PT. SINTECH BERKAH ABADI

ABSTRACT&#x0D; Basically, companies always want to provide the best service, including in providing training in various types of programming languages ​​that are owned. A manual system and very slow dissemination of information make the training often very few people want to follow. A computerized training management information system will make it easier for companies to collect data quickly, and also make it easy for prospective applicants to quickly get information about existing training. The management has the duty to manage all matters related to the training, from managing the data of the trainees to the organization of the training. The method used is Extreme Programming (XP) an approach or software development model that tries to simplify the various stages in the development process so that it becomes more adaptive and flexible. The results obtained from this study are a computerized, user friendly training management system.&#x0D; Keywords: Training, Systems, Courses
A Low Latency Montgomery Modular Exponentiation

RSA (Rivest-Shamir-Adleman) is a widely known and popular public-key cryptosystem used in many security applications. The main mathematical function in RSA is Modular Exponentiation, which is demanding in terms of speed and area. Due to repetitive Modular Multiplications involved in Exponentiation function, it becomes time-consuming, especially for large operands. To accelerate the RSA Encryption/Decryption operation, a reduction in number of Modular Multiplications as well as reduction in clock-cycles to perform one Modular Multiplication operation is required. The main objective of this paper is to develop high-speed, area-efficient hardware Montgomery Modular Exponentiation architecture for RSA Encryption/Decryption operations by using Lookup-Table (LUT) based high-radix Montgomery Modular Multiplier. In this paper, we present a hardware architecture designed to implement Right-to-Left (R-L) Montgomery Modular Exponentiation using a lookup-table based Radix-16 Modular Multiplication with Montgomery reduction. The proposed architecture provides tremendous improvement by reducing the number of clock cycles for one Modular Multiplication from n (operand bit-length) to n/4 cycles. With our implementation results, for Montgomery Modular Exponentiation with Radix-16 lookup-table based Montgomery Multiplier architecture, we are able to achieve computational delay of 6.35ms, 43.16ms, 320.4ms for computing 1024/ 2048/ 4096 bits of Modular Exponentiation, respectively. We were able to achieve significant improvement in terms of time and Time-Area (TA).
Composite fuzzy-wavelet-based active contour for medical image segmentation

Purpose In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images. Design/methodology/approach The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach. Findings The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%. Originality/value The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.
Design of Computer-based Music Distance Education Learning System

Abstract With the rapid development of Internet technology, colleges and universities have designed a music distance teaching system with the help of computer. It is not only convenient for students to study actively, but also uses modern information technology and Internet technology to carry out teaching activities. Moreover, the network-based autonomous learning can provide more free space for students. Therefore, the design of music distance teaching system has very important practical significance. In this paper, the principle of system design and the structure and function of the system are analyzed comprehensively, and the practicability and feasibility of the system are proved by the practice of the system, so it is worth popularizing and applying.
Survey of denoising and segmentation techniques for MRI images of prostate for improving diagnostic tools in medical applications

Prostate cancer (PCa) is one of the deadliest cancers in men after lung cancer, thus its early detection remains a great concern to reduce the rate of mortality. The traditional approaches used for detection were manual, time consuming and prone to subjective errors. Magnetic resonance imaging (MRI) is the commonly used imaging modality for PCa, as it produces detailed and smooth images of the prostate gland in comparison to other modalities using a large magnet and radio waves in the MRI scanner. Computer aided diagnosis (CAD) systems can increase the overall accuracy of traditional approaches by detecting the cancer in the prostate gland using MRI. CADs mainly consist of three stages i.e. denoising, segmentation, and classification. Denoising is an approach to remove the noise present in MRI for better performance of segmentation and classification. Whereas, segmentation is the extraction of desired regions of interest i.e. prostate for further classification as malignant or benign. This paper presents a survey of various denoising and segmentation approaches used for detection of prostate cancer using MRI and the analysis presented in this study could be used for improving performance of diagnostic tools for medical purposes.
Who Filters the Filters: Understanding the Growth, Usefulness and Efficiency of Crowdsourced Ad Blocking

Ad and tracking blocking extensions are popular tools for improving web performance, privacy and aesthetics. Content blocking extensions generally rely on filter lists to decide whether a web request is associated with tracking or advertising, and so should be blocked. Millions of web users rely on filter lists to protect their privacy and improve their browsing experience.
mHealth Communication Framework using Blockchain and IoT Technologies

The mHealth is a term that is used for mobile health supported by smart devices such as mobile phones, tablets, and wearable smart devices, etc. The smart devices strengthen the efficiency and effectiveness of interaction with patients, physicians, and specialists. Patients nowadays would like to be intimately involved in their diagnosis as well as to make more informed decisions concerning their care. It has begun to measure the success of the quality of treatment. This was a reason that patients trust mHealth to provide them with consistency in their communications with the physicians. Most wireless strategies do not measure up to this standard so that patient engagement ultimately ended up decreasing. The blockchain can boost mHealth through storing and sharing electronic data securely and transparently. It can enhance the accessibility of patient information in real-time. The Internet of Things (IoT) provides a unique identification number to every connected device such as mobile devices, medical devices, and wearable devices. This framework uses the blockchain and IoT technologies together to provide quick help to the patients, monitor remotely, reduce the cost and unnecessarily hospitalization physically and find the real diagnosis. In order to increase patient involvement, mHealth framework with blockchain and IoT technologies has built with the key objective of providing patients with full information on their treatment and diagnosis.
Scalable Differential Privacy with Certified Robustness in Adversarial Learning.

In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples. By leveraging the sequential composition theory in DP, we randomize both input and latent spaces to strengthen our certified robustness bounds. To address the trade-off among model utility, privacy loss, and robustness, we design an original adversarial objective function, based on the post-processing property in DP, to tighten the sensitivity of our model. A new stochastic batch training is proposed to apply our mechanism on large DNNs and datasets, by bypassing the vanilla iterative batch-by-batch training in DP DNNs. An end-to-end theoretical analysis and evaluations show that our mechanism notably improves the robustness and scalability of DP DNNs.
Blockchain Code as Antitrust

In this article, the authors show that blockchain can help in reaching the goals of antitrust law in situations where the rule of law does not (fully) apply. They detail what needs to be done to this end, from both a technical and legal standpoint.
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Neural processes (NPs) constitute a family of variational approximate models for stochastic processes with promising properties in computational efficiency and uncertainty quantification. These processes use neural networks with latent variable inputs to induce a predictive distribution. However, the expressiveness of vanilla NPs is limited as they only use a global latent variable, while target-specific local variation may be crucial sometimes. To address this challenge, we investigate NPs systematically and present a new variant of NP model that we call Doubly Stochastic Variational Neural Process (DSVNP). This model combines the global latent variable and local latent variables for prediction. We evaluate this model in several experiments, and our results demonstrate competitive prediction performance in multi-output regression and uncertainty estimation in classification.
Multi Layer Neural Networks as Replacement for Pooling Operations.

Pooling operations, which can be calculated at low cost and serve as a linear or nonlinear transfer function for data reduction, are found in almost every modern neural network. Countless modern approaches have already tackled replacing the common maximum value selection and mean value operations, not to mention providing a function that allows different functions to be selected through changing parameters. Additional neural networks are used to estimate the parameters of these pooling functions.Consequently, pooling layers may require supplementary parameters to increase the complexity of the whole model. In this work, we show that one perceptron can already be used effectively as a pooling operation without increasing the complexity of the model. This kind of pooling allows for the integration of multi-layer neural networks directly into a model as a pooling operation by restructuring the data and, as a result, learnin complex pooling operations. We compare our approach to tensor convolution with strides as a pooling operation and show that our approach is both effective and reduces complexity. The restructuring of the data in combination with multiple perceptrons allows for our approach to be used for upscaling, which can then be utilized for transposed convolutions in semantic segmentation.
IMMERSIVE VISUALISATIONS IN DESIGN: USING AUGMENTED REALITY (AR) FOR INFORMATION PRESENTATION

Abstract Immersive visualisations introduce new possibilities for experiencing design, and as such for presenting information. To date, studies in design have focused mostly on immersive visualisations supporting product decisions. However, little attention has been paid to information presentation, e.g. in design reviews, for decisions in the boardroom, and/or for client presentations. This study with industry practitioners identifies information presentation practices and challenges, develops an immersive visualisation prototype, and explores opportunities for the use of immersive visualisations.
Space-borne quantum memories for global quantum communication

Global scale quantum communication links will form the backbone of the quantum internet. However, exponential loss in optical fibres precludes any realistic application beyond few hundred kilometres. Quantum repeaters and space-based systems offer to overcome this limitation. Here, we analyse the use of quantum memory (QM)-equipped satellites for quantum communication focussing on global range repeaters and Measurement-Device-Independent (MDI) QKD. We demonstrate that satellites equipped with QMs provide three orders of magnitude faster entanglement distribution rates than existing protocols where QMs are located in ground stations. We analyse how entangle- ment distribution performance depends on memory characteristics, determine benchmarks to assess performance of different tasks, and propose various architectures for light-matter interfaces. Our work provides a practical roadmap to realise unconditionally secure quantum communications over global distances with current technologies.
The Development of Proxy Re-Encryption

With the diversification of electronic devices, cloud-based services have become the link between different devices. As a cryptosystem with secure conversion function, proxy re-encryption enables secure sharing of data in a cloud environment. Proxy re-encryption is a public key encryption system with ciphertext security conversion function. A semi-trusted agent plays the role of ciphertext conversion, which can convert the user ciphertext into the same plaintext encrypted by the principal’s public key. Proxy re-encryption has been a hotspot in the field of information security since it was proposed by Blaze et al. [Blaze, Bleumer and Strauss (1998)]. After 20 years of development, proxy re-encryption has evolved into many forms been widely used. This paper elaborates on the definition, characteristics and development status of proxy re-encryption, and classifies proxy re-encryption from the perspectives of user identity, conversion condition, conversion hop count and conversion direction. The aspects of the existing program were compared and briefly reviewed from the aspects of features, performance, and security. Finally, this paper looks forward to the possible development direction of proxy reencryption in the future.
Blockchain-Enabled Computing Resource Trading: A Deep Reinforcement Learning Approach

Driven by the vision of the Internet of Things (IoT) under the fifth-generation (5G) wireless network, computing resource trading attracts numerous attention from both academia and industry. Prior works mainly focus on the design of auction mechanisms to implement pricing and resource allocation. However, it is still a challenging problem because of the following three aspects: 1) How to ensure that the auction mechanism runs fairly? An auction mechanism is vulnerable and questionable since the auctioneer may fail the orders matching operation or collude with a few peers. 2) It's hard to assign the computing resources of providers to customers and guarantee reasonable rewards for each participator. 3) How to make bidding strategies for each participator? Each participator has its willingness to selWuy, which are time-variant and private. To address the above issues, we build a blockchain-enabled computing resource trading system that takes both pricing and bidding strategies into consideration, on which providers and customers can trade computing resources securely, safely and willingly. Next, we formulate a decision-making problem in the continuous double auction (CDA) to maximize their payoffs. Then, we propose a universal model-free Deep Reinforcement Learning (DRL) framework for both computing resource providers and customers. We conduct extensive experiments to evaluate the performance of our DRL framework. Simulation results show that our solution outperforms others in both static and dynamic scenarios. Our DRL framework can achieve higher rewards than others by at least 35%. Furthermore, the average trading price from our DRL framework is less volatile than that from the compared methods. The DRL framework promotes trading and brings larger trading quantities, thus resulting in higher social welfare by at least 25% than the compared schemes.
Normalization Matters in Zero-Shot Learning

An ability to grasp new concepts from their descriptions is one of the key features of human intelligence, and zero-shot learning (ZSL) aims to incorporate this property into machine learning models. In this paper, we theoretically investigate two very popular tricks used in ZSL: normalize+scale trick and attributes normalization and show how they help to preserve a signal's variance in a typical model during a forward pass. Next, we demonstrate that these two tricks are not enough to normalize a deep ZSL network. We derive a new initialization scheme, which allows us to demonstrate strong state-of-the-art results on 4 out of 5 commonly used ZSL datasets: SUN, CUB, AwA1, and AwA2 while being on average 2 orders faster than the closest runner-up. Finally, we generalize ZSL to a broader problem -- Continual Zero-Shot Learning (CZSL) and test our ideas in this new setup. The source code to reproduce all the results is available at this https URL.
Rice crop management expert system with forwarding chaining method and certainty factor

Abstract Rice plants are one of the focuses on food sustainability so that an increase in rice production needs to be done. Farmers need information and knowledge to improve rice crop production by practicing good planting methods that begin with the selection of superior varieties of seeds and handling plant pests properly. The problem that arises is that assistance from agricultural specialists is not always available when farmers need it, making it difficult for farmers to consult experts according to their needs at any time. Therefore, in this study, an expert system was proposed which was able to help farmers to obtain information according to their needs. This research builds an expert system for the management of rice plants including the selection of rice seed varieties and handling pests of rice plants. The method used in this expert system is forward chaining and certainty factor. Knowledgebase data was obtained from experts in agricultural extension, experts on plant pest organisms and literature from the agricultural service. From this study, an expert system was produced that was able to provide information on the selection of seed varieties and pest of rice plants according to the conditions and needs of the users.