Reviewerly/exHarmony huggingface dataset:
A consensus-based elastic matching algorithm for mapping recall fixations onto encoding fixations in the looking-at-nothing paradigm

We present an algorithmic method for aligning recall fixations with encoding fixations, to be used in looking-at-nothing paradigms that either record recall eye movements during silence or want to speed up data analysis with recordings of recall data during speech. The algorithm utilizes a novel consensus-based elastic matching algorithm to estimate which encoding fixations correspond to later recall fixations. This is not a scanpath comparison method, as fixation sequence order is ignored and only position configurations are used. The algorithm has three internal parameters and is reasonable stable over a wide range of parameter values. We then evaluate the performance of our algorithm by investigating whether the recalled objects identified by the algorithm correspond with independent assessments of what objects in the image are marked as subjectively important. Our results show that the mapped recall fixations align well with important regions of the images. This result is exemplified in four groups of use cases: to investigate the roles of low-level visual features, faces, signs and text, and people of different sizes, in recall of encoded scenes. The plots from these examples corroborate the finding that the algorithm aligns recall fixations with the most likely important regions in the images. Examples also illustrate how the algorithm can differentiate between image objects that have been fixated during silent recall vs those objects that have not been visually attended, even though they were fixated during encoding.
Blind inpainting and outlier detection using logarithmic transformation and total variation : Stent applications

In this chapter, we address the problem of image reconstruction with missing pixels or outliers, when the locations of the corrupted pixels are not known and when the noise is multiplicative, which is the algebraic model for ultrasound and synthetic aperture radar imaging.
The Emergency Next Time

This Article offers a new conceptual framework to understand the connection between law and violence in national emergencies. It is by now well-established that governments often commit state violence in times of national security crisis by implementing excessive emergency measures. The Article calls this type of legal violence “Emergency-Affirming Violence.” But Emergency Violence can also be committed through governmental non-action. This type of violence, which this Article calls, “Emergency-Denying Violence,” has come to stark light in the crisis of the COVID-19 pandemic. The Article offers a new taxonomy to better understand the phenomenon of Emergency Violence. Using 9/11 and COVID-19 as examples, the Article proposes that there are two types of Emergency Violence: Emergency-Affirming Violence and Emergency-Denying Violence. Emergency-Affirming Violence occurs when the government (1) declares and emphasizes the magnitude of an emergency; (2) calls for robust deference to experts; and (3) aggressively pursues emergency measures. Emergency-Denying Violence, by contrast, occurs when the government (1) denies or minimizes the existence of an emergency; (2) ignores or undermines experts; and (3) declines to take significant emergency measures. The Article demonstrates how the three branches of government can engage in both types of Emergency Violence. Analyzing the legal responses to the two national crises of 9/11 and COVID-19 side-by-side, the Article underscores the vulnerable individuals and communities against whom Emergency Violence is unleashed via both Emergency-Affirming and Emergency-Denying Violence. The Article proposes a principle of Emergency Non-Violence to guide public response to the next emergency.
Mapping of flood-prone-zone and puddle-prone-zone in Cimahi city, West Java province

Abstract Climate change and advanced urbanization level followed by the changing function of an area affect to the amount of water absorption area. It causes puddle and flood in city, include Cimahi, West Java, which is in the last 10 years has been surrounded by flood and puddle. To avoid the flood case in Cimahi, the re-identification and flood-factor inventory are needed. The identification and inventory are using the simplest method (literary study from any source), start from discussing session with Cimahi government, interviewing and filling questioner to the citizen who live in flood and puddle area, measuring river and drainage profile, counting the capacity of river and drainage, until simulating the 2-dimension and 3-dimension of flood and puddle. The result of the identification and inventory is shown in Flood-Prone-Zone Map and Puddle-Prone-Zone Map. According to Cimahi’s Flood-Prone-Zone Map, it shows that the flood-safe area is about 110,747 ha (2,70%), the not-flood-prone area is about 494,233 ha (12,05%), the flood-prone area is about 2016,474ha (49,18%), and the very-flood-prone area is about 1478,799 ha (36,07%). According to Cimahi’s Puddle-Prone-Zone Map, it shows that the puddle-safe area is about 128,616 ha (3,14%), the not-puddle-prone area is about 551,246 ha (13,44%), the puddle prone area is about 2702,826 ha (65,92%), and the very-puddle prone area is about 717,556 ha (17,50%). The Flood-Prone-Zone Map and the Puddle-Prone-Zone Map can be used as a reference for the Cimahi Government in formulating the policy of regional development.
Dynamic Selection of Variables When Analyzing the State of Technological Processes

This paper presents a method of dynamic variable selection in the analysis of production processes, as part of the condition monitoring system of an industrial facility. The existing methods of analyzing the state of technological processes are considered. The most effective methods were machine learning, which showed a high percentage of accuracy. Based on the known models of technological processes, a new model of the technological process, previously not found in open sources, is proposed as an infinite data stream with changing properties with a large number of parameters. An experiment is described in which the effectiveness of the proposed method is studied and its results are presented. Its consistency is shown, and it is noted that it can be used in modern systems for monitoring the state of technological processes to identify malfunctions and threats to information security.
A Simple Character Recognition Algorithm on the Image Based on FPGA

As we know, the characters recognition uses neural network algorithms typically. However, calculation of neural network algorithms is complex. This paper proposes a new threading algorithm which is suitable for FPGA implementation for the character recognition on an image. The algorithm can identify the characters on an image quickly and accurately. The algorithm selects six lines of image data located the region on the image where the character is located after the image is binarized, and counts the number of “1”s therein, then compares with the parameters gotten by software to obtain the determination result. The accuracy of this algorithm is close to 100%. The algorithm is written on Quartus II software (Version 15.0) of Intel company and implemented on Terasic DE1_SOC. It has the advantages of high reliability and high speed through board level verification and signaltap verification.
Multispectral Image Denoising by Multi-scale Spatial-spectral Residual Network

Image denoising algorithm has made impressive progress in the last decades. However, most of them can not be directly applied to noisy multispectral images (MSI) because of complex spectral noise patterns which always result in spectral distortion. For the purpose of solving this issue, we propose a multi-scale spatial-spectral residual network (MSSRN) for MSI denoising task. Firstly, a multi-scale feature discovery strategy is used for extracting rich spectral structure information. Secondly, we design a spatial-spectral decomposing block, which performs convolution from the spatial and spectral dimensions to maximize the use of both spatial and spectral information. Finally, the obtained multiscale spatial-spectral information leverages the denoising performance stably and efficiently. Whether subjective or objective image qualities, experimental results over the CAVE dataset outperform outstanding MSI denoising algorithms.
On the Structure Property of PCR’s Adjacency Graph with a Prime Order and Its Application of Constructing M-Sequences

In this paper we find a deterministic structure respecting the adjacency graph of a pure circulating shift register (PCR) with a prime order, and give a theoretical method for constructing all floor weight class of M-sequences. As a special case of this method, we calculate a lower bound of the number of floor weight class of M-sequences and give their corresponding feedback functions.
Ancient Chinese Lexicon Construction Based on Unsupervised Algorithm of Minimum Entropy and CBDB Optimization

Ancient Chinese text segmentation is the basic work of the intelligentization of ancient books. In this paper, an unsupervised lexicon construction algorithm based on the minimum entropy model is applied to a large-scale ancient text corpus, and a dictionary composed of high-frequency co-occurring neighbor characters is extracted. Two experiments were performed on this lexicon. Firstly, the experimental results of ancient text segmentation are compared before and after the lexicon is imported into the word segmentation tool. Secondly, the words such as person’s name, place name, official name and person relationship in CDBD are added to the lexicon, and then the experimental results of ancient text segmentation before and after the optimized lexicon is imported into the word segmentation tool are compared. The above two experimental results show that the lexicon has different enhancement effects on the segmentation effect of ancient texts in different periods, and the optimization effect of CDBD data is not obvious. This article is one of the few works that applies monolingual word segmentation to ancient Chinese word segmentation. The work of this paper enriches the research in related fields.
Multimode Generative Adversarial Networks for Sequence Data Generation

Abstract As a new type of artificial intelligence technology, generative adversarial network (GAN) has good data understanding and generation capabilities, and has a wide range of application prospects in the fields of image and speech. However, due to the lack of prior knowledge, its training process is less robust and prone to occur the pattern ignore. Its development is restricted to a certain extent, and its application scope still needs to be expanded. To solve the above problems, this paper introduces a knowledge confidence multimode GAN (KC-MGAN) algorithm, calculates the confidence of the input data through the reasoning method, and then puts the confidence and the input data into the GAN system to generate new sample data. During the training process, the confidence of the input data is continuously calculated, while the generated data samples are continuously evaluated. The training process will end until the GAN system reaches a stable condition. Finally, this paper takes the generation of UAV flight trajectory data as an example to verify the effectiveness of the proposed method. Some explorations have been made for the application of data generation and GAN’s training mode with the prior knowledge.
Character Recognition on Time Series Data collected from Smartphone Sensors

Abstract In the modern era, smartphones have become part and parcel of life. The exponential increase in the number of smartphone users has given rise to a copious amount of data. Although the data produced by smartphones are of various types, this paper mainly focuses on the usage of sensory data produced by smartphones. This paper explores the field of recognizing characters with the aid of time-series sensor data. The primary focus of the research is to utilize recurrent neural networks to predict the digits 0 - 9 and characters A - Z . The objective achieved was by the help of sensor data that included the readings of Accelerometer, Magnetometer, Gyroscope, and Linear Accelerometer sensors providing information with respect to three-axis x , y , and z , having an interval of 0.01 seconds between two corresponding values. We succeeded in achieving the accuracy of 93.60% on the training data and 89.51% on the testing data.
A Discretization Method for Industrial Data Based on Big Data Technology

A parallel improvement of the traditional K-Means clustering algorithm is achieved based on the Mapreduce architecture, and the new parallelized clustering algorithm is used to realize the discretization of industrial big data in this paper. The new algorithm streamlines the calculation process, meanwhile, saves the computational overhead caused by data analysis and communication consumption caused by information transfer.
Research on Technical Architecture and Overall Scheme of Railway Block Chain Service Platform

The block chain has many unique characteristics, like decentralization, stability, openness and transparency. We can apply block chain in many fields of railway to improve the comprehensive efficiency and benefit, such as railway passenger and freight service, engineering construction, big data sharing and so on. With the gradual completion of data service platform, big data lake and cloud computing in China Railway, these foundations can support the railway block chain application. The paper analyzed the development status and existing problems of block chain at home and abroad. Through the multidimensional analysis for the mainstream block chain platform architecture, the thesis gets the architecture selection of the railway block chain platform. Based on the hyperledger fabric, we propose the technical architecture and overall scheme for the railway block chain service platform respectively. The paper also designs the implementation path for the platform application construction. This research plays an important role and significance in promoting the block chain technology applying and developing in the railway field.
다문서 이해에 대한 텍스트 구조적 접근 -고등학생 독자들의 다문서 구조화 양상 분석을 중심으로-

The purpose of this study is to reexamine the integrated comprehension of multiple documents (multiple texts) through the discussion of “multiple-documents structuring” and to present empirical examples of the discussion through an analysis of multiple-documents structuring patterns of high school student readers. Research on the comprehension of multiple-documents has been conducted from various angles from the late 20th century to the present, but there are not many cases where it has been viewed from the perspective of “structure.” This study is part of an attempt to expand the essential characteristics of “multiple-documents comprehension” into a chapter of “text comprehension” based on the “multiple-documents structure.”The research data and methods are as follows: Six printed texts were distributed by the researcher, and two texts were found by the participants themselves, comprising eight total tests that the participant referred to to create a multi-document structure diagram. The reader symbolized the eight texts in the form of a mind map. Depending on the participant, there were cases where an accidental dictation was made while preparing a rescue diagram, but if not, the person was allowed to explain his or her rescue diagram at the post-intervention interview. The data that were analyzed are one sheet of multi-document structure diagram (a total of eight chapters) written by the participants on a blank paper and the participants’ thought dictation or post-intervention interview speech performed regarding the construction of the structure diagram.As a result of the analysis, it was possible to distinguish between the readers’ group (hereinafter, the proficient group), who created a skillful multiple-documents structure, and the inexperienced group (the immature group). Skilled readers considered whether the text could be trusted in the construction of multiple documents, whether a given text was related to other texts, and whether a text was useful for solving the task. On the other hand, readers who carried out immature structuring seemed to have a lack of comprehesion of the higher structure of each text, and no analysis was made on the cross-relevance of each text. Finally, the students who showed immature structuralization did not show any references or schematics related to task relevance and information evaluation compared to the students who performed skillful structuralization.
Advanced Numerical Methods Based on Optimization

In this chapter the unconstrainedUnconstrained and constrainedConstrained optimizationOptimization algorithms for numerical methodsNumerical methods are envisaged. The numerical solutions to the fundamental problems in energy systemsEnergy systems are provided. The recent heuristicHeuristic methods used in power systemsPower systems are highlighted, and the specific algorithms are proven by simulations. The selection of the best solution is one of the authors’ concern. Therefore, the optimization algorithms along with numerical examples are delivered in this chapter.
The Performance of Indonesia’s President: A Sentiment Analysis in Social Media

Abstract The role of a leader is critical, especially when a person leads a country. Indonesia, as the biggest archipelago country, led by Jokowi since 2014. From his first period in 2014 until his second period started in 2019, many opinions, suggestions, and criticisms addressed to the president, Jokowi. This research aims to measure the performance of Jokowi as Indonesia’s president by collecting public opinions on social media, peculiarly in an interview video published on YouTube. All comments are scrapped and processed using machine learning tools, which are Python and TextBlob. The results of this research are positive and negative sentiments related to Jokowi. From the research conducted, the most influential positive sentiments of Jokowi are his personality. The negative sentiments gained in the video are about the poor quality of translation and the English skill of Jokowi.
Implement of the transformation for markerless facial motion capture in 3D animation

Abstract Motion capture is a motion assisting technology that can resemble the motion of objects being captured and is now widely used for film development, especially in visual effects or animation. From that, the author has the idea, namely to use a Blender application to implement a markerless facial motion capture on motion of object 3D modeling based on webcam because it didn’t have a menu or feature for markerless motion capture. Markerless is meant to process the assignment of annotation points to objects with a computer system so that this method is very simple and saves time in its operation, especially in facial motion capture. In this research, the face detection method uses haar cascade and to provide facial landmark annotation using detector landmark lbf model. We are using an example of 3D modeling a human face that has a bone on the neck, head, mouth, eyes, and eyebrows that will follow the movement of the human face. The combination of face landmark detection and rigging in 3D modeling a human face uses the transformation geometry method to produce real-time motion. From this research study, 3D animated face objects can follow motion on real face targets but there is still noise.
Information retrieval and its sister disciplines

This article presents a summary graph to show the relationships between Information Retrieval (IR) and other related disciplines. The figure tells the key differences between them and the conditions under which one would transition into another.
Fuzzy cognitive map based vulnerability analysis of agricultural products cold chain logistics: a perspective of carbon footprint

Abstract As an important part of the development of a low-carbon economy, developing low-carbon logistics is an important measure to implement transform the economic development mode and cope with global warming. In order to effectively realize the low-carbon operation of agricultural cold chain logistics, a fuzzy cognitive map is employed to analyze the vulnerability of agricultural cold chain logistics on the perspective of carbon footprint. With the analysis above, the influencing factors of carbon emissions in cold chain logistics operations can be identified. Therefore, the carbon reduction strategy of the cold chain logistics of agricultural products can be obtained. Our findings can be as decision-making reference for relevant departments and enterprises to better formulate cold chain low carbon emission strategies and promote green logistics to achieve high-quality development.
Toward more accurate developer recommendation via inference of development activities from interaction with bug repair process

Abstract Software projects usually receive a large number of submitted bug reports every day. Manually triaging the bug reports is often time‐consuming and error‐prone; thus, it is necessary to automatically assign the bug reports to the suitable developers for bug repair, with the help of bug tracking systems. Aiming to reducing the time consumption and mismatch of bug report assignments, we present a developer recommendation model for bug repair based on weighted recurrent neural network, namely, DTPM, which contains two parts: One obtains multisource semantic information of bug reports and fuses them into high‐dimensional semantic feature vectors, and the other combines a penalty matrix into a single hidden layer neural network to obtain more reasonable developer recommendations. We conduct experiments on five datasets of open bug repositories (NetBeans, OpenOffice, GCC, Mozilla, and Eclipse), and the experimental results show that DTPM can achieve better performance than state‐of‐the‐art models LDA_KL, LDA_KL, LDA_SVM, DERTOM, DREX, and DeepTriage.
On non-parametric density estimation on linear and non-linear manifolds using generalized Radon transforms

Here we present a new non-parametric approach to density estimation and classification derived from theory in Radon transforms and image reconstruction. We start by constructing a “forward problem” in which the unknown density is mapped to a set of one dimensional empirical distribution functions computed from the raw input data. Interpreting this mapping in terms of Radon-type projections provides an analytical connection between the data and the density with many very useful properties including stable invertibility, fast computation, and significant theoretical grounding. Using results from the literature in geometric inverse problems we give uniqueness results and stability estimates for our methods. We subsequently extend the ideas to address problems in manifold learning and density estimation on manifolds. We introduce two new algorithms which can be readily applied to implement density estimation using Radon transforms in low dimensions or on low dimensional manifolds embedded in Rd. The code for our algorithms can be found here https://github.com/jameswebber1/On-nonparametric-density-estimation-on-linear-and-nonlinear-manifolds. We test our algorithms performance on a range of synthetic 2-D density estimation problems, designed with a mixture of sharp edges and smooth features. We show that our algorithm can offer a consistently competitive performance when compared to the state–of–the–art density estimation methods from the literature.