Reviewerly/exHarmony huggingface dataset:
Autonomic Computing in Cloud: Model and Applications

Autonomic computing could be defined as the methodology using which computing systems can manage themselves. The main objective of autonomic computing is to develop systems that can self-manage the management complexities arising out of rapidly growing technologies. Autonomic systems consist of autonomic elements that automatically employ policies. Many researchers have focused their research on this topic with their aim being improved application performance, improved platform efficiency, and optimizing resource allocation. The performance of any system depends on the effective management of resources. This is particularly significant in cloud computing systems that involve management of large number of virtual machines and physical machines. Cloud computing has brought a revolution in the way computing and storage devices are acquired and utilized. Organizations are no longer needed to invest for these resources but can use the services of cloud service providers who rent these resources. The fact that the resources can be obtained for rent from cloud service providers shows that these resources are not part of the local physical infrastructure and hence results in lesser tasks that need specific hardware expertise. But still, the onus is on the system administrators to estimate the required computing and storage resources, acquire them, and maintain them. Autonomic provisioning of resources in cloud computing provides a healthy alternative to this scenario. Virtual provisioning is a virtual technology in which resources are allocated on demand to devices. Virtual provisioning enables virtualized environments to control the allocation and management of physical resources connected with virtual machines (VM). Virtual provisioning is used more in a virtual environment. Virtual provisioning is used to simplify the administration of resources by allowing the administrators to meet requests for capacity on demand. Virtual provisioning provides an illusion to a host that it has more resources than is physically needed. Physical resources are allocated only when the resource is needed and not during the initial application configuration. Virtual provisioning reduces power and cooling costs by cutting down on the number of idle resources. The only condition for virtual provisioning is that the administrators are required to carefully monitor the usage of virtually provisioned resources to ensure that no virtual device is idle, resulting in errors for mission-critical applications.
Signal Estimation &amp;amp; Reproduction in Frequency Domain using Tunable Q-Factor Wavelet Transform

The present patterns and development in communication and digitization builds the importance of signal and image processing concepts. This paper concentrates on former one i.e., signal processing, and mainly concentrates on receiver section in planning and actualizing the channels to recuperate the information with less measure of noise or with high SNR. In this paper, rather than processing the time domain data, spectral data nothing but frequency domain processing is executed on the approximate coefficients produced with the backing of Tunable Q-Factor Wavelet Transform [TQWT].
Landsat 9: ready for launch

Landsat 9 is in its final preparations for launch from Vandenberg Space Force Base on 16 September 2021. It has completed its environmental testing at Northrop Grumman Space (NGSP) in Gilbert, Arizona and has been transported to its California launch site. It will be launched into a 705 km orbit replacing Landsat 7 to provide 8-day Earth land mass coverage in concert with Landsat 8. Landsat 8 carries the first Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS); Landsat 9 carries the second of each: OLI-2 and TIRS-2. Once launched it will undergo a 90-day activation, checkout, characterization and calibration, a.k.a. commissioning phase before transitioning to operations. For a several-day period during this commissioning phase, Landsat 9 will under-fly Landsat 8, allowing near simultaneous data collection by both sensors of common Earth targets. These data will be used to compare the radiometric calibrations of the instruments and allow for adjustments of processing parameters to provide more consistent data products.
A Robust Lossless Steganography Method Based on H.264/AVC

This paper presents a robust lossless steganography H.264/AVC method. First, the embedded message is distributed by a polynomial and obtains a series of sub- messages and BCH is used to encode each sub- message. Then, the message is embedded into the frames of the video which meet the specific prediction modes. Last, the hidden message will be recovered by the t sub- messages of the video frames. This method can recover the original video completely while extracting the embedded message, and have a good robustness in frame and bit error. The experiment results show that the method can reach a good effect and have a good visual quality.
Construction and Performance Applications of an Augmented Violin: TRAVIS II

Abstract We present the second iteration of a Touch-Responsive Augmented Violin Interface System, called TRAVIS II, and two compositions that demonstrate its expressivity. TRAVIS II is an augmented acoustic violin with touch sensors integrated into its 3-D printed fingerboard that track left-hand finger gestures in real time. The fingerboard has four strips of conductive PLA filament that produce an electric signal when fingers press down on each string. Although these sensors are physically robust, they are mechanically assembled and thus easy to replace if damaged. The performer can also trigger presets via four sensors attached to the body of the violin. The instrument is completely wireless, giving the performer the freedom to move throughout the performance space. Although the sensing fingerboard is installed in place of the traditional fingerboard, all other electronics can be removed from the augmented instrument, maintaining the aesthetics of a traditional violin. Our design allows violinists to naturally create music for interactive performance and improvisation without requiring new instrumental techniques. The first author composed two compositions to highlight TRAVIS II: “Dream State” and “Kindred Dichotomy.” Both of these compositions involve improvisation in their creative process and include interactive visuals. In this article we describe the design of the instrument, experiments leading to the sensing fingerboard, performative applications of the instrument, and compositional considerations for the resultant pieces.
Sentiment classification method of hierarchical retrieval based on comment data

In order to help enterprises and the news industry liberate human resources, quickly understand the public opinion guidance of news reviews, and improve the quality of product content, this paper, aiming at multi-source digital news reviews, combined with crawler technology and Bert pre training model, carries out experiments on DNN, CNN, LSTM and other deep learning models. According to the particularity of the samples, a hierarchical retrieval emotion classification method is designed and proposed, which is applied to the field of news.
Learning Through Immersion: Assessing the Learning Effectiveness

A new generation has grown up with computers. The use of information technologies has become a part of their daily routine. Integrating technologies into students’ teaching and learning activities is widely recognized as a fundamental strategy for the next generation of learners. Despite the growing interest in advanced learning technologies and the application of immersive technologies in tertiary education, research investigating the perceived learning effectiveness (particularly in the form of learning satisfaction) has been very scarce. Thus, the objective of this study is to gain a better understanding of factors influencing learners’ perceived learning effectiveness of using one type of immersive visualization system known as cave automatic virtual environment (CAVE). Data collection consisted of quantitative and qualitative measures on a sample of 199 university students. The implications of the results and improvement proposals are also discussed in this study.
A Guidelines for Controlled Experimental Design to Evaluate the Metrics of Software Component Reusability

This research aims is to develop an approach in supporting component developers measure the reusability of developed software components. The approach could be used by software developers to select the reusable components during software development. Thus, this paper presents a controlled experimental design using human subjects. The overall method includes a detail explanation of the procedures and guidelines for performing the experimental activities. The experiment consists of numerous activities: designing the experimental method and procedure, designing the experimental tasks, running the controlled experiment, collecting data, analyzing the results, and recommending improvements based on the implications of earlier studies. The findings of the study can be applied to evaluate the metrics for the Component Reusability Evaluation Approach (CREA), namely documentation, observability, customisability, and the external dependency of the component.
Global and Graph Encoded Local Discriminative Region Representation for Scene Recognition

Scene recognition is a fundamental task in computer vision, which generally includes three vital stages, namely feature extraction, feature transformation and classification. Early research mainly focuses on feature extraction, but with the rise of Convolutional Neural Networks (CNNs), more and more feature transformation methods are proposed based on CNN features. In this work, a novel feature transformation algorithm called Graph Encoded Local Discriminative Region Representation (GEDRR) is proposed to find discriminative local representations for scene images and explore the relationship between the discriminative regions. In addition, we propose a method using the multi-head attention module to enhance and fuse convolutional feature maps. Combining the two methods and the global representation, a scene recognition framework called Global and Graph Encoded Local Discriminative Region Representation (G2ELDR2) is proposed. The experimental results on three scene datasets demonstrate the effectiveness of our model, which outperforms many state-of-the-arts.
Development of SOA-Based Hydrological Data Compilation Management System

Abstract As the results of hydrological data compilation are important fundamental information resources in China, how to efficiently manage them and provide data support for water science research is a major problem that needs to be addressed at present. In this paper, a hydrological database of Haihe River Basin was preliminarily established based on the compilation of the complete electronic data files, and a SOA-based management system of hydrological data compilation results was developed, featuring functions related to the compilation results. The research shows that the system boasts strong practicability and operability, and will be popularized in the later period, in order to improve the capacity and level of hydrological service, support water conservancy information and drive the modernization of hydrological service.
A QoS Model to Identify Required QoS for Guaranteeing Quality of Internet Video Streaming Services

Understanding the required quality of service (QoS) for guaranteeing the necessary video quality for Internet video streaming services is important for enabling network operators to provide high-quality networks. Recent trace-based approaches that infer video quality through machine-learning based traffic-feature analysis have a drawback in terms of the maintenance cost for updating their analysis model due to the massive number of videos uploaded daily. In this paper, we construct a QoS analysis model that calculates the required throughput for delivering video with a certain resolution by using encoding information instead of traffic tracing. For accurate modeling, we consider and formulate communication overhead, i.e. streaming behavior, retransmission, and headers. Through experiments, we demonstrate that our model can perform in two use cases: (1) estimating effective resolution for arbitrary QoS, and (2) calculating the required QoS for guaranteeing the necessary video quality.
Dynamic phase measurements employing a pixelated polarizing camera and temporal phase unwrapping algorithms

We propose a polarizing Michelson interferometer coupled to a pixelated polarizing camera to visualize dynamic phase objects. Considering the capabilities of the polarizing camera, we employed a temporal phase unwrapping algorithm to process information. Experimental results are presented showing the capabilities of our proposal.
Achieving Cinematic Motion With High-Dynamic Range

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This paper explores how the appearance of cinematic motion (the characteristic balance of smoothness and realism of motion commonly used in cinema) is affected by high-dynamic range (HDR) presentation. We report on a psychovisual experiment that investigated how observers rated the comparative smoothness of motion among scenes of different motion types (pans, action, slow movement), frame rates, and luminance levels. We found that to match the level of cinematic motion in today’s non-HDR presentation, the frame rate may need to either remain at 24 frames/sec or be increased to 30 frames/sec, depending on the type of motion and the luminance level of the scene. We conclude that by selecting the appropriate frame rate depending on the motion type in each scene, content creators will be able to achieve a similar cinematic motion without compromising their use of the HDR color palette</i> .
MEMANFAATKAN HASIL ASESMEN KOMPETENSI MINIMUM (AKM) UNTUK MENDESAIN MULTIMODAL LEARNING

The method used in this discussion is a literature review from various sources. In general, this study aims to describe the multimodl learning design by utilizing the AKM results. The AKM result captures the real condition of student in mastering basic competence, namely reading literacy and numeracy literacy. The result can be used to improve the learning quality in classroom. To carry out teaching and learning in classroom, teachers need a certain approach. One being effective to improve learning outcomes is a multimodal approach that leads to multimodal learning. The multimodal learning design is constructed by utilizing the AKM results, intervention need students, basic, competent, and proficient. Meanwhile, multimodal learning design includes educational games, think-pair-share, case-based learning, personal journal entries, and multimodal research projects.
Frequency multiplexed optical extreme learning machine

We propose an optical implementation of an Extreme Learning Machine (ELM) inspired by frequency-multiplexing techniques previously employed for Reservoir Computing. The input layer of the ELM is encoded in the lines of a frequency comb and the hidden layer is generated by making comb lines interfere. Multiplication by output weights can be performed optically. This approach combines the potential high-speed, low-power and paral- lelization advantages of Optical Neural Networks with the cheap training (both in terms of speed and power) of ELMs, which do not require slow gradient descent and error backpropagation algorithms. We present preliminary experimental results compared with simulations.
Rational Contracts: Data-driven Service Provisioning in Blockchain-powered Systems

Smart Contracts (SCs), which are software programs that run on blockchain platforms, provide appealing security guarantees characterized by decentralized, autonomous, and verifiable execution. On the other hand, Service Provisioning (SP) systems (i.e., systems that assign users to service providers in a way that maximizes the global utility) have been leveraging SCs to provide trust and transparency features. Such features are obtained by deploying the SP’s assignment criteria as an SC on the blockchain. However, deploying optimal assignment criteria as SCs does not guarantee the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, potentially deeming the initial assignment sub-optimal. Furthermore, modifying the criteria manually by a third party at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, in this paper, we propose the use of online learning SCs that leverage the chained data to continuously self-tune their assignment criteria and maintain maximum utility. We show that the proposed data-driven method can achieve high performance on the multi-stage assignment problem. We also compare the proposed approach to multiple assignment algorithms as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks compared to planning techniques.
Biometric data prediction based on deep learning and piezoelectric sensors

Biometric data prediction is an important research direction of portable sleep monitoring devices such as piezoelectric ceramics. The current sleep monitoring sensors have limited anti-interference ability and accuracy, which limits the accuracy of prediction results. In order to improve the accuracy of sign data measurement of piezoelectric sensors, this paper proposes a depth measurement method based on LSTM based on the analysis of existing prediction models. The experimental results are compared with other time series prediction models. The results show that the predicted value of the LSTM model is close to the real value, and the mean absolute error and root mean square error of the LSTM model are lower than those of RNN and ARIMA models, which verifies the prediction performance of the LSTM model and can provide effective sign data information for sleep monitoring equipment.rs, Footnotes, or Math in Paper Title or Abstract.
Research on Enterprise Competitive Information Management Based on Co-Occurrence Analysis

Link data source lack has seriously hampered the research development of competitive information for enterprises based on co-link analysis in recent years, and exploring application and innovation of the new method of URL co-occurrence analysis in enterprises competitive information research is very important to make up for current inadequacy. And exploration on problems such as whether Jaccard handling needs to be conducted on collected data and how the effect is to conduct information research in different methods for data in different handling ways is meaningful to theoretical perfection of competitive information research of enterprises. Non-Jaccard data and Jaccard data are compared with examples of Chinese representative banks based on new methods of URL co-occurrence analysis, and competitive information research of enterprises is conducted in the multidimensional scaling method and social network analysis method; and comprehensive exploration and analysis are conducted from multiperspectives such as gradient grading analysis, recognition of main competitors, and competitive landscape analysis of fine grit. Innovative research competitive information for enterprises based on URL co-occurrence analysis in this thesis effectively makes up for current inadequacy due to link data source lack, and it expands new spaces for competitive information research of enterprises; it is also in research in this thesis that analysis result based on non-Jaccard data is far better than analysis result based on Jaccard data when multidimensional scaling analysis is conducted; analysis result based on non-Jaccard data and analysis result based on Jaccard data uncover competitive information of related enterprises when social network analysis is conducted, and above conclusions in this thesis are all verified and analyzed through example data.
Bearing Wave Resistant Pictorial Secret Code Validation Conceive for Web Supporting Functions

From the time when plan and improvement of primary pictorial validation spearheaded through Blonder around 1996, various investigates have been led at this space to be utilized in various situations particularly at Internet. Out of it one significant spark is the image predominance which is shown at studies, indicates that photos / images give elevated memorability rather than Text supporting validation. Be that as it may, pictorial validation is as yet confronted for certain difficulties. The paper bestow a bearing riding safe pictorial validation plot is proposed to handle a significant issue identified with the pictorial validation plans generated. The projected conspirator gives a significant degree of protection from bearing riding assaults, alleviating the need to transfer photos and helps in discovering picked objects in the plan.
Coherence and Cohesion for the Assessment of Text Readability by Coh-Metrix

The readability of text depends on many variables. In many studies, lexical and grammatical factors have been analyzed a lot. However, there are not many quantitative analyses on the impact of coherence and cohesion on text readability. Coh-Metrix is a text analysis tool based on computer technology. After continuous development and improvement, it can accurately analyze the vocabulary, phrase, sentence frequency and character length in the text, and can analyze the grammar and semantics of the text. This article uses the Coh-Metrix tool to analyze the readability of the two texts, and compares the scores of readers' understanding of the text to illustrate the impact of coherence and cohesion on the readability of the text.
Decoding Linear Codes over Chain Rings Given by Parity Check Matrices

We design a decoding algorithm for linear codes over finite chain rings given by their parity check matrices. It is assumed that decoding algorithms over the residue field are known at each degree of the adic decomposition.
Method for Body Pose Recognition based on Two-Finger Touch Bezel on Wearable Device

Nowadays, wearable devices are becoming more and more widely used. The development of interfaces for wearable devices is a challenge due to their small size on the one hand and on the other hand - their increasing capabilities (respectively functions) that must be managed by their interface. The purpose of the presented study is to propose a method for recognizing the pose of the user (lying, sitting, standing) when waking a wearable device from sleep mode. The experimental study and results are given.
Captionbot for Assistive Vision

Visually challenged people find it difficult to lead their life as they cannot see things and scenes around them like normal people. Blind people are always in need of a person to help them know about what is happening around. Technologies like mobile, camera has become a part of our daily life. So, these technologies can be used to make visually challenged people be aware of what is happening around them and enjoy life like normal people. The system aims to recognize the context of an image and describe them in natural language like English and convert it to voice so that visually challenged people can know about their surroundings. The features from the image are extracted with the help of Convolutional Neural Network (CNN). Long Short-Term Memory (LSTM), which is well suited for predicting the sequence is fed with the word embeddings of partial sentence. This approach makes use of merge architecture for generating image captions. The encoding of linguistic and perceptual features is kept separate which are then merged at the multimodal layer, at which point predictions are made. Bilingual Evaluation Understudy (BLEU) is used as measure to evaluate the generated caption. The caption generated is converted to audio using the Google Text To Speech (GTTS) module in python.