Reviewerly/exHarmony huggingface dataset:
Analyzing Rhetorical Style: Toward Better Methods

The tools of linguistics and discourse analysis can produce an overwhelming amount of data on the language of a text. Given this richness, it is easy for analysts to select features that support a favored interpretation. How can stylistic analysis overcome selection bias, especially when examining the language of argument? Five methods for designing stylistic studies are worth considering. The first three involve making a principled selection from the text(s) under consideration: (1) If different versions or drafts of a text exist, the analyst can compare the changes the author considered critical. (2) If particular phrases or passages are widely quoted or re-circulated, they can provide evidence of noticed stylistic choices. (3) If similar texts exist, they can be examined for (in)consistent features. The final two methods triangulate using constructs from rhetorical theory: (4) The analyst selects and interprets language choices based on rhetorical features such as the line of argument used. (5) The analyst starts from a device identified in rhetorical stylistics (e.g. a figure of speech) and sees how this device is used across examples. This last option will help to ground studies conducted under the other four.
Comparison Between Block-Wise Detection and A Modular Selective Approach

On-road risk detection and alert system is a crucial and important task in our day to day life. Deep Learning approaches have got much attention in solving this noble task. In this paper, we have performed a comparative study on two recent architectures that handle the on-road risk detection task, which are Block-Wise Detection and Modular Selective Network (MS-Net). In the Block-Wise Detection, we have used the VGG19, VGG19-BN, and ResNet family as the backbone network. On the other hand, for MS-Net we have used the ResNet-44 as the router and ResNet-101 as the expert network. In this experiment, we evaluate our model on an “on-road risk detection dataset”, which was created by our research group using an RGB-D sensor mounted on a senior car. On this dataset, we can achieve an accuracy of 89.40% for MS-Net. For the Block-Wise Detection model, we can achieve an accuracy of 90.51% if we use ResNet-50 as the backbone network. However, if we choose the network models used in MS-Net, we can double the inference speed. Thus, compared with Block-Wise Detection, we think the overall performance of MS-NET is better, and is potentially more useful for driving assistance of elderly drivers.
Searching for Critical Slip Surface of Slope Based on Flower Pollination Algorithm

In order to determine the critical sliding surface of the slope, the most dangerous sliding surface was searched by flower pollination algorithm. First, the method is applied to the traditional vertical slice method for slope stability analysis. The results are very close to those of the Autobank program, indicating that the method can search the critical sliding surface and find the optimal solution. Then, the method is applied to the new horizontal slice method, and the results of the calculation examples are better than those given in the references, indicating that the application of the flower pollination algorithm to the horizontal slice method is reasonable, reliable and superior. The successful application of this method provides a new method for determining the most dangerous sliding surface of slope.
A Chaotic Attack Offering with Improving Mechanism in Economic Denial of Sustainability

EDoS (Economic Denial of Sustainability) attack is always a major threat to the cloud platform. Many researchers have developed countermeasure mechanisms against EDoS attack. The EDoS-ADS is proposed by Shawahna et al. in 2018, this mechanism can identify and drop the attack requests effectively. We attempted to discover the potential drawback of Shawahna et al. scheme and highlighted the potential risks. Inspiring by the Yo-Yo attack, we developed an approach to make EDoS-ADS scale up without dropping the attack requests. The experiments in our study show that our approach is indeed able to confuse EDoS-ADS mechanisms in efficiency.
Unconstrained Multiple Face Tracking Using Convolutional Neural Networks

We are in the digital world. In this digital world, computer vision technology plays major role. Computer vision is used as key technology in various fields like safety and security applications. When we speak about computer vision, face detection and tracking play one of the major roles. While detecting human face, it is very important to analyze and collect human facial features. When we detect face, it is important to detect multiple faces. When we try to detect multiple faces, it is necessary to collect each face features differently. Then using these face feature, we need to detect face region. After face detection, facial features are given as an input to face tracking. There are numerous approaches for detecting and tracking human faces. Still there is need of an efficient approach which can detect and track human faces accurately. So in our proposed face detecting and tracking approach, we use cascaded convolutional network model. Using this network model, we will be able to detect multiple faces in various background environments. At the end of this paper, we will compare the results of our proposed approach with various other approaches. Proposed approach delivers best results in various complicated environments.
DatAR: An Immersive Literature Exploration Environment for Neuroscientists

Maintaining an overview of publications in the neuroscientific field is challenging, especially with an eye to finding relations at scale; for example, between brain regions and diseases. This is true for well-studied as well as nascent relationships. To support neuroscientists in this challenge, we developed an Immersive Analytics (IA) prototype for the analysis of relationships in large collections of scientific papers. In our video demonstration we showcase the system's design and capabilities using a walkthrough and mock user scenario. This companion paper relates our prototype to previous IA work and offers implementation details.
The Effects of Machine-powered Platform Governance: An Empirical Study of Content Moderation

With increasing participation in social media and online communities, content moderation has become an important part of the online experience. Since of beginning of these digital platforms, volunteer moderators have been the essential workforce for platform governance. However, human moderation suffers from a limited capacity in moderating massive and undesirable content. Recently, many online platforms and communities have started to adopt algorithm-based moderation tools (bot moderators) to enable machine-powered platform governance to cope with the increasing need for content moderation. As platforms move toward the technical and automated mode of governance, there is a growing concern over de-humanization and whether machines would lead volunteer moderators to reduce their contributions. To understand the role of these increasingly popular bot moderators, we conduct an empirical study to examine the impact of machine-powered regulations on volunteer moderators’ behaviors. With data collected from 156 subreddits on Reddit, a large global online community, we found that delegating moderation to machines augments volunteer moderators’ role as community managers. Human moderators engage in more moderation-related activities, including 20.2% more corrective and 14.9% supportive activities with their community members. Importantly, the effect manifests primarily among communities with large user bases and detailed guidelines, suggesting that community needs for moderation are the key factors driving more voluntary contributions in the presence of bot moderators.
A Comparison of Two Database Partitioning Approaches that Support Taxonomy-Based Query Answering

In this paper we address the topic of identification of cohorts of similar patients in a database of electronic health records. We follow the conjecture that retrieval of similar patients can be supported by an underlying distributed database design. Hence we propose a fragmentation based on partitioning the health records and present a benchmark of two implementation variants in comparison to an off-the-shelf data distribution approach provided by Apache Ignite. While our main use case in this paper is cohort identification, our approach has advantages for taxonomy-based query answering in other (non-medical) domains.
Fine-Grained Image-Text Retrieval via Complementary Feature Learning

Fine-grained image-text retrieval task aims to search the sample of same fine-grained subcategory from one modal (e.g., image) to another (e.g., text). The key is to learn an effective feature representation and accomplish the alignment between images and texts. This paper proposes a novel Complementary Feature Learning (CFL) method for fine-grained image-text retrieval. Firstly CFL encodes images and texts by Convolutional Neural Network and Bidirectional Encoder Representations from Transformers. Further, with the help of Frequent Pattern Mining technique (for images) and special classification token of Bidirectional Encoder Representations from Transformers (for texts), a stronger fine-grained feature is learned. Secondly the image information and text information are aligned in a common latent space by pairwise dictionary learning. Finally, a score function can be learned to measure the relevance between image-text pairs. Further, we verify our method on two specific fine-grained image-text retrieval tasks. Extensive experiments demonstrate the effectiveness of our CFL.
Causality and Uncertainty of Information for Content Understanding

Tasks require a clear picture of the context or the backdrop that frames the circumstances. Additionally tasks require a clear understanding of the content, the information available that will be used for completion of the task. Often the task involves a single or a set of decisions along the way. However, obtaining that content is not a perfect one. Understanding the content with is possible constraints, limitations, uncertainties adds to the challenge. To attempt to generate and express this the idea of an uncertainty of information concept that includes key aspects of causal reasoning is presented in this paper. In the paper the uncertainty of information (UoI) idea is discussed and how causality can be infused into this concept to not just provide another value for uncertainty be the causes. Moreover, can a causal UoI concept expand the idea so that a computational expression can capture the nuances of causal reasoning? This paper presents a possible vision.
Reward Function Using Inverse Reinforcement Learning and Fuzzy Reasoning

A reward function estimated with inverse reinforcement learning has been used to determine a method for controlling a robot. Inverse reinforcement learning requires observed sequences of actions to estimate a reward function. Few models of the sequences give the optimal motion of the robot; therefore, a suboptimal one may be given. However, the suboptimal sequences include some errors and ambiguities. In this paper, we propose a method for quantifying the ambiguity of the reward function, which is designed with inverse reinforcement learning using fuzzy reasoning.
Technique for storing and automated processing of weather station data in cloud platforms

Abstract In this paper we present an approach for design of cloud-based systems for storing and processing of sensor data from weather stations. In recent years there are abundant IoT devices that are capable of fairly precise data acquisition of local atmospheric parameters and reliable transmission over wireless network. The used dataset is acquired from a modern weather station model from a superior European manufacturer. 17 parameters are used for training neural network based LSTM model. In our research we have also compared several models and approaches for modeling this type of data. We propose a design of container based system for automated processing of the received data, visualization and cloud storage technique. We are comparing two types of open-source container technologies for implementing data processing. We have evaluated the performance and provide the set of criteria for choosing a container platform. Storing the data in the cloud is an important part and we provide our consideration for implementing a particular open source platform that could reduce costs fulfil the requirements for weather data storage. The designed system is expected to be cloud-provider agnostic. The proposed features make the system especially suitable for purposes such as local weather forecast; disaster prediction: hurricane, severe storms, floods, tornadoes, etc.; severe droughts with fire dangers; prediction of landslides. Depending on the availability of additional sensors the future development of the system may be extended with parameters of the soil. Training the model on a freely available data set is in our future plans for system development.
WITHDRAWN: Application of CNN and gated recurrent network for visual improvement of dehazing

Various restoration model depending on the prior knowledge had consider an arduous vision task. We present in this paper an effective algorithm for restoring a recognizable picture from a hazy image. Instead of using conventional methods i.e.; image priors as the restoration process, in form of color attenuation, dark channels, or color line contrast, we use a point-to-point gated background aggregation structure to reestablish the final image free from haze. A hybrid dehazing algorithm of CNN framework with features learning recurrent modeling is applied. The transmission map or its depth of scene, structural attributes is obtained in form of ranking layer is applied with the CNN network model so that hazy images can be captured at same instant. Post processing is carried out by Fast guided filter. Hence this hybrid model basically gives a inter connection idea between coarse layer and fine layer by extensive results obtained on experimental, simulated and real-world images prove that the proposed algorithm operates against state-of-the-art algorithms in a beneficial way.
Network-on-Chip for Low Power MAP Decoder Using Folded Technique and CORDIC Algorithm for 5G Network

With different constraint length (K), time scale, and code rate, modified MAP (maximum a posteriori) decoder architecture using folding technique, which has a linear life time chart, is developed, and dedicated turbo codes will be placed in a network-on-chip for various wireless applications. Folded techniques mitigated the number of latches used in interleaving and deinterleaving unit by adopting forward and backward resource utilizing method to M-2, where M is the number of rows and end-to-end delay get reduced to 2M. By replacing conventional full adder by high speed adder using 2 x 1 multiplexer to calculate the forward state metrics and reverse state metrics will minimize the power consumption utilization in an effective manner. In s similar way, CORDIC (Coordinated ROtation DIgital Computer) algorithm is used to calculate the LLR value and confer a highly precise value with less computational complexity by means of only shifting and adding methods.
Performance Investigation of Cloud Computing Applications Using Steady-State Queuing Models

Cloud computing is the technology that was gaining the attention of most of the companies in market and utilization also increasing day to day by almost from companies to ordinary people. The working of these cloud models is effortless. A considerable number of servers are used to store the data and a vast amount of data and the service of providing data to the customers staying at remote locations too. Almost all cloud-based models are not free, and users need to pay a reasonable amount to use the services of these clouds. As the vast data is stored in these servers and the usage of this data by a vast number of customers, there is a chance of overcrowded at servers. Essential data or the hot data like the new movies, exam results or bank transactions, etc., can have the most of the crowds at various time intervals. Hence, it is required to analyse the number of customers is using the current cloud models at different intervals of time. Based on the results, the adjustments or the changes in the network model can be completed. In the current article, an attempt has been made to analyse a cloud model by considering the model working in study state and the performance was analysed for two queuing models. Several queuing models are available in research to analyse the performance of a queuing model. In the current article, the queuing models considered are M/M/1 and M/M/c models. The performance of the queuing models is analysed with various performance metrics of a network, or the cloud model is arrival rates to the model, service rates to the model, traffic density, throughput, etc. The results are displayed in the results section.
After the Lips: Acoustic Resonances and Radiation

In this chapter describes the resonant properties of the air column contained within a brass instrument and the nature of the sound radiated from it. The wave equation is presented. Lumped and distributed resonators are defined; travelling and standing waves are explained, and the relationship between standing waves and acoustic resonances is discussed. Time domain and frequency domain descriptions of acoustic processes are compared, and the related concepts of impulse response and input impedance are introduced. Experimental techniques for measuring the input impedance of a brass instrument are described, and the technique of pulse reflectometry is outlined. The concepts of equivalent cone length and equivalent fundamental pitch are explained. The different types of bore profile which are found in brass instruments (cylindrical, conical and flaring) are reviewed, and the Bessel horn model of a flaring tube is examined. The ‘horn function’ is defined and its properties discussed. The effects of the mouthpiece and flaring bell on intonation and timbre are outlined, and the modifications introduced by opening toneholes, muting and hand stopping on horns are explained. The nature of sound radiation from brass instruments is reviewed. In a Going Further section, mathematical techniques for calculating input impedance are discussed, including transfer matrix methods taking into account the effects of losses and bends.
Identification of Key Parameters Contributing to Technical Debt in Software Using Rank-Based Optimization

In classical terms, the amount borrowed from the issuer causes interest. It is not usual that, interest rises above the principal amount. However, in the case of technical debt, the accumulated debt in the form of interest can rise to the sum, which is more than the efforts required to repay it than the initial technical debt. In this paper, the estimation of the breakpoint to find the reasons for the abrupt growth in the technical debt is tried. For the analysis of the breakpoint, the highest contributor factors towards the technical debt are identified. Further, the extent to which these attributes contribute to technical debt is also explored. The rank-based optimization mechanism (RF-based approach) is proposed to predict the technical debt. The mechanism obtains the dominating factors that contribute towards the technical debt. The practitioners indulged in coding also get benefited in reducing the impact of the technical debt, if they understand and determine the insight of these contributing factors in advance.
HelpCloud: Hierarchy Health Evaluation for Private Cloud

Abstract Although the private cloud technology is developing rapidly, the operation and maintenance of private cloud are still too complicated for many non-IT enterprises. System health evaluation for private clouds is beneficial to reduce operation and maintenance complexity and workload. This paper aims to provide an easy-to-use method to evaluate a private cloud's system health. Firstly, we propose the Private Cloud Management Object Model (PCMOM). PCMOM uses a hierarchical architecture to organize a variety of entities in the private cloud. Secondly, based on PCMOM, Hierarchy health evaluation for the private Cloud (HelpCloud) Model is generated using the Analytic Hierarchy Process and D-S evidence theory. In the end, a set of tests verified the effectiveness of the HelpCloud model. Moreover, the management portal for the private cloud which integrates the HelpCloud model is designed and implemented. After several enterprises' trials, these enterprises recognized that using the HelpCloud model could simplify the private cloud's operation and maintenance.
Corpus-Based Semantic Prosody Study of English-Chinese Translation: Taking Trump’s Popular Saying “It Is What It Is” as an Example

US President Trump once again uttered a “popular sentence”, “It is what it is”, causing widespread complaints. Although its frequently reporting by major media abroad, Chinese English learners are so unfamiliar with the expression. Besides, the official translation given by the “China Daily” is “情况就是这样”, which is unlikely to be a satisfied translation. To find empirical support for the argument, this paper focused on the frequency, diachronic trend and semantic prosody tendency of it and its official translation in two corpora (COCA and BCC). The research result shows that major differences exist between “It is what it is” and “情况就是这样”. In contrast, “事已至此” and “It is what it is” are more comparable in all dimensions. Thus, “事已至此” is a better version of “It is what it is”. This study regards semantic prosody as an indispensable part of translation, providing new perspectives for corpus-assisted translation studies.
A Efficient Approach for Securing Images Using RSA and Arnold Technique Metamorphose

People across the globe exchange information but security remains primary concern while sharing information (data or images). This paper highlights the encryption technique using Arnold’s cat mapping and RSA algorithm in which first images are jostled using Arnold technique and then encrypted these jostled images using RSA algorithm. This concludes that the suggested new method will improve the security and encrypted and decrypted quality of images.
Ecosystem evolution analysis and trend prediction of projects in Android application framework

The application framework layer in the Android system consists of numerous project repositories, which rely on each other to form a co-evolving software ecosystem. Android's application framework layer provides many useful APIs to millions of Android Apps, so its evolution will affect the robustness and stability of Android Apps. Code dependency analysis technology is a common way to analyze software ecosystems. However, the code size of projects in the Android application framework layer is so huge that ordinary analysis methods are unacceptable due to the excessive resources required.