Reviewerly/exHarmony huggingface dataset:
Simplify Your Law: Using Information Theory to Deduplicate Legal Documents

Textual redundancy is one of the main challenges to ensuring that legal texts remain comprehensible and maintainable. Drawing inspiration from the refactoring literature in software engineering, which has developed methods to expose and eliminate duplicated code, we introduce the duplicated phrase detection problem for legal texts and propose the Dupex algorithm to solve it. Leveraging the Minimum Description Length principle from information theory, Dupex identifies a set of duplicated phrases, called patterns, that together best compress a given input text. Through an extensive set of experiments on the Titles of the United States Code, we confirm that our algorithm works well in practice: Dupex will help you simplify your law.
Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model

AbstractThere has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes. An R package with an efficient c++ implementation of the proposed algorithms is provided.KEYWORDS: Bayesian inferenceSparsityStochastic optimizationVariational methods Supplementary MaterialsSvilf_supplementary.pdf Supplemental materials containing additional simulation studies with varying latent dimensionality H, parameter γ and with the probit link function.R-package: R-package svilf implementing the methods described in the article in c++, using convenient R wrappers. The package provides a common interface calling different implementations of SVILF with probit or logit link function, using uniform or adaptive sub-sampling. A tutorial illustrating the main functionalities of the package is also provided.AcknowledgmentsThe authors would like to thank Bruno Scarpa, Peter Hoff and Daniele Durante for their comments and suggestions on the main idea of this work. This research used the HPC cluster system of the VERA center at Ca’ Foscari University of Venice.Additional informationFundingThe work of Emanuele Aliverti was partially funded by miur-prin 2017 project 20177BRJXS, as well as grant no. bird-188753/18 of the University of Padova, Italy.
A Identification Method of Logging Mud Ratio is Based on Deep Learning

During the logging, the sand ratio of rock debris is an important index to describe the oil content of the well at a certain depth, which can reflect the well depth of the bottom of the drill rod in different kinds of rock layers. The traditional calculation method of ratio sandstone to mudstone is artificial recognition, which takes a long time and has a low accuracy. This text applies the deep learning method to realize the automatic recognition calculation of ratio sandstone to mudstone, which has low cost, fast speed and high accuracy, and is suitable for practical field application. This text first uses the morphological watershed algorithm to divide the rock chips and obtain the rock particles, and then uses the convolutional neural network to calculate ratio sandstone to mudstone.
Moving Object Detection for Airport Scene Using Patterns of Motion and Appearance

This paper presents a novel method for localization and recognition of moving objects in a real airport surface scene. Different from the traditional applications, moving object detection (MOD) in the airport surface is more challenging because the background is an open outdoor environment, which means that the target objects are usually low in resolution and the MOD task is vulnerable to many undesired changes, such as cloud movement and illumination variations. To address these issues, this paper proposes a unified and effective deep-learning-based MOD architecture, which combines both appearance and motion cues. Specifically, a novel moving region proposal generation module is first designed, which can effectively locate the regions of moving object based on the motion information. Meanwhile, a novel cascade multilayer feature fusion module with transposed convolution is applied to produce both enriched-semantics and fine-resolution convolutional feature maps for category recognition. Finally, a large-scale dataset acquired by the daily surveillance videos of a real airport surface is manually constructed. Results show that the proposed methods outperform state-of-the-art solutions in extracting moving objects from airport surface scenes.
Straddling-gates problem in multipartite quantum systems

We study a variant of quantum circuit complexity, the binding complexity: Consider an $n$-qubit system divided into two sets of ${k}_{1}, {k}_{2}$ qubits each (${k}_{1}\ensuremath{\le}{k}_{2}$) and gates within each set are free; what is the least cost of two-qubit gates ``straddling'' the sets for preparing an arbitrary quantum state, assuming no ancilla qubits allowed? First, our work suggests that, without making assumptions on the entanglement spectrum, $\mathrm{\ensuremath{\Theta}}({2}^{{k}_{1}})$ straddling gates always suffice. We then prove any $\text{U}({2}^{n})$ unitary synthesis can be accomplished with $\mathrm{\ensuremath{\Theta}}({4}^{{k}_{1}})$ straddling gates. Furthermore, we extend our results to multipartite systems, and show that any $m$-partite Schmidt decomposable state has binding complexity linear in $m$, which hints its multiseparable property. This result not only resolves an open problem posed by Vijay Balasubramanian, who was initially motivated by the complexity = volume conjecture in quantum gravity, but also offers realistic applications in distributed quantum computation in the near future.
AN ANALYSIS OF TEACHING WRITING PROBLEMS AND STRATEGIES AT THE ELEVENTH GRADERS

Abstract: This study aims to analyze the teaching writing problems and strategies at the eleventh graders of MAN 2 Bojonegoro. The type of this research is qualitative research. Data collection was done by interviewing two senior English teachers of MAN 2 Bojonegoro and supported by documentation. To analyze the data, the researcher took the procedures, namely data reduction, data display, and conclusion drawing. The finding showed that each teacher had different problems and strategies in teaching writing. The problem faced by the first teacher in teaching writing was students were not used to writing whilst the second teacher problem was the low motivation of students in learning. The strategies used by the teachers were asking question technique, group discussion, small group work and snowball throwing game. The researcher concluded that eleventh grade teachers of MAN 2 Bojonegoro used various strategies to make students understand the materials given by the teacher easily.Keywords: Teaching Problem, Strategy, Writing Skill, Eleventh Graders.
Hyperspectral Image Denoising Based on Multi-Stream Denoising Network

Hyperspectral images (HSIs) have been widely applied in many fields, such as military, agriculture, and environment monitoring. Nevertheless, HSIs commonly suffer from various types of noise during acquisition. Therefore, denoising is critical for HSI analysis and applications. In this paper, we propose a novel blind denoising method for HSIs based on Multi-Stream Denoising Network (MSDNet). Our network consists of the noise estimation subnetwork and denoising subnetwork. In the noise estimation subnetwork, a multiscale fusion module is designed to capture the noise from different scales. Then, the denoising subnetwork is utilized to obtain the final denoising image. The proposed MSDNet can obtain robust noise level estimation, which is capable of improving the performance of HSI denoising. Extensive experiments on HSI dataset demonstrate that the proposed method outperforms four closely related methods.
Perspectives on Effective E-tutoring that Motivates and Enhances Students’ Learning through Learner Management Systems and Social Media*

Online teaching particularly through Open Distance and e-Learning (ODeL) has become a phenomenon in the twenty-first century. ODeL and blended approaches inevitably lead to increasing dependence on electronic communication systems. The University of South Africa (Unisa), where the author teaches, enables students through its Learner Management System to interact with lecturers and e-tutors online. The responsibilities of e-tutors are of an educative and technical nature. Their roles include guiding and assisting students, encouraging active participation, responding to their queries and grading their assignments. In addition, e-tutors provide notifications and assign tasks or activities that students are expected to complete and submit. In several cases, these forms of assistance are absent, when there is a lack of follow-up within the response period which is 24 hours – missing notifications and lack of guidance – rendering these e-tutors ineffective. The chapter provides strategies that were analyzed and implemented to motivate effective tutoring and enhance student participation learning. The author draws on her analysis as a virtual ethnographer and long-term participant observer as an e-tutor and lecturer who supervised e-tutors and taught a large number of students – 2,500. The objective of the chapter is to encourage effective tutoring that can enhance students’ success.
The Effects of Instructional and Administrative Text Messages on Academic Achievement and Student Perception of Learning in a High School Food, Nutrition, and Wellness Classroom

This study investigated the effects of instructional and administrative text messages on student academic achievement and sense of learning. Ninety-eight Food, Nutrition, and Wellness students in grades 9th through 12th were involved in this study. In this quasi-experimental study, there was an experimental group and a comparison group. Students in the experimental group received instructional and administrative text messages three times a week for a total of nine weeks. A one-way ANCOVA and independent samples t-test were used for data analyses. The study found a statistically significant difference in academic achievement and sense of learning with students in the experimental group scoring higher as compared to students in the control group. In addition, there was a statistically significant difference with females in the treatment group scoring higher than males in the treatment group.
Towards Investigation of Various Load Balancing Techniques in Cloud Computing

Cloud computing is an on-demand service where customers may access any time common IT resources, information, software and other equipment. It is a web-based development that offers virtual resources over the Internet as a service. The higher the cloud use, the higher the charge. The allocation of loads to components processing is a challenging phenomenon.In a multi-node system, there is a very good possibility that some nodes will be idle while others will be overloaded. The load balancing algorithms' objective is to keep the load on each processing element constant. A technique for Random Load Balancing in Cloud Computing is devised, in which the amount of user requests is instantly assigned to the resources. This method employs a random strategy to decrease the request's waiting time. This proposed load balancing technique is simulated in Cloud Analyst tool. The performance comparison study between proposed random method and other available load balancing methods is conducted. It is found that the performance of proposed random method is better in terms of response time. Keywords: Cloud Computing, Load Balancing, Response Time, Virtual Machine, Sorting
Preferences, Financial Literacy, and Economic Development

Using data from 74 countries, we uncover important differences in the association between financial literacy and preferences by the level of economic development. We find that patience is only salient in wealthier countries, i.e. countries with their GDP per capita above the sample median. In such cases, countries with higher level of patience display higher levels of financial literacy. Importantly, this association is not driven by a multitude of institutional or cultural factors known to be related to financial literacy. In impoverished countries, we document a higher level of financial literacy in countries with higher levels of risk-taking but with lower levels of trust, positive reciprocity, and altruism. Countries' legal origin drives most of the association with risk-taking and about two fifths of the relationship with trust and positive reciprocity. At the same time, the country's religious composition drives the association between altruism and financial knowledge. Our findings underscore that financial education programs need to be tailored to the cultural aspect of group preferences and suggest what type of traits policies and programs ought to be reinforced in poorer countries.
CSFCube - A Test Collection of Computer Science Research Articles for Faceted Query by Example

Query by Example is a well-known information retrieval task in which a document is chosen by the user as the search query and the goal is to retrieve relevant documents from a large collection. However, a document often covers multiple aspects of a topic. To address this scenario we introduce the task of faceted Query by Example in which users can also specify a finer grained aspect in addition to the input query document. We focus on the application of this task in scientific literature search. We envision models which are able to retrieve scientific papers analogous to a query scientific paper along specifically chosen rhetorical structure elements as one solution to this problem. In this work, the rhetorical structure elements, which we refer to as facets, indicate backgrounds, methods, or results of a scientific paper. We introduce and describe an expert annotated test collection to evaluate models trained to perform this task. Our test collection consists of a diverse set of 50 query documents, drawn from computational linguistics and machine learning venues. We carefully followed the annotation guideline used by TREC for depth-k pooling (k = 100 or 250) and the resulting data collection consists of graded relevance scores with high annotation agreement. The data is freely available for research purposes.
Humanities students’ views on the advantages and disadvantages of distance learning during the COVID-19 pandemic in the context of post-materialistic values

The transition of universities to distance learning during the CODID-19 pandemic caused discussions about the development of the educational system in the context of digitalization of society. The question arose about the readiness of universities, students, and teachers to use digital educational tools. Studies of students’ ideas about distance learning have revealed its difficulties, positive and negative effects. The analysis of the survey data made it possible to reveal the problems of forming the basic competencies of students taking into account the requirements of the digital economy. The authors conclude, based on the analysis of the results of a survey of humanities students, that the previously discovered difficulties of distance learning persist during the «second wave» of the pandemic. They reveal the relevance of survival values for students (according to R. Inglehart and K. Welzel). The authors suggest that the decline in significance of self-expression values and the decrease in the sense of existential security may be a consequence of the pandemic crisis. The possibilities of digitalization of education to strengthen the resources of the subject and increase its mobility have come into conflict with the return of students to settledness when introducing distance learning to implement epidemic restrictions.
Learning Theory (Supervised/Unsupervised) for Signal Processing

Signal processing is a combination of applied mathematics, information theory tools, probability and statistics, vector spaces, harmonic analysis, optimization, and machine learning. It enriches the capability to transfer and share information. The discipline behind our digital lives is signal processing. Machine learning (ML) is a discipline that agrees with the improvement of algorithms that study from data. This chapter aims at introducing the fundamentals of ML techniques useful for various signal processing applications. ML is divided into three major categories and further into various subcategories, and this chapter comprehends the particulars about them. This chapter also comprises the mathematical and statistical methodologies used for signal processing along with machine learning.
Research on Volleyball Video Intelligent Description Technology Combining the Long-Term and Short-Term Memory Network and Attention Mechanism

With the development of computer technology, video description, which combines the key technologies in the field of natural language processing and computer vision, has attracted more and more researchers’ attention. Among them, how to objectively and efficiently describe high-speed and detailed sports videos is the key to the development of the video description field. In view of the problems of sentence errors and loss of visual information in the generation of the video description text due to the lack of language learning information in the existing video description methods, a multihead model combining the long-term and short-term memory network and attention mechanism is proposed for the intelligent description of the volleyball video. Through the introduction of the attention mechanism, the model pays much attention to the significant areas in the video when generating sentences. Through the comparative experiment with different models, the results show that the model with the attention mechanism can effectively solve the loss of visual information. Compared with the LSTM and base model, the multihead model proposed in this paper, which combines the long-term and short-term memory network and attention mechanism, has higher scores in all evaluation indexes and significantly improved the quality of the intelligent text description of the volleyball video.
Device-Free Handwritten Character Recognition Method Using Acoustic Signal

In this paper, we propose a method for recognizing handwritten characters by a finger using acoustic signals. This method is carried out using a smartphone placed on a flat surface, such as a desk. Specifically, this method uses an ultrasonic wave transmitted from the smartphone, which is reflected by the finger, and an audible sound is generated when writing a handwritten character. The proposed method does not require an additional device for handwritten character recognition because it uses the microphone/speaker built into the device. Evaluation results showed that it was able to recognize 36 types of characters with an average accuracy of 77.8% in a low noise environment for 10 subjects. In addition, it was verified that combining an audible sound and an ultrasonic wave in this method achieved higher recognition accuracy than when only an audible sound or an ultrasonic wave was used.
Constructs and Indicators: An Ontological Analysis

Constructs and indicators are central to the efforts of many researchers who seek to build and test theories and articulate rich narratives about real-world phenomena. For this reason, an extensive discourse exists about their nature. Increasingly, this discourse has become fraught with controversy. Using Bunge’s (1977, 1979) ontology, I examine the nature of constructs and indicators as they are discussed in the extant literature. I define these concepts precisely, disentangle conceptual from measurement issues, and point to ways that discourse about them could better proceed. I show that unidimensional constructs, multidimensional constructs, dimensions, and indicators are all properties in general of a class of things. I also show that only three types of indicators exist—synonyms of the focal construct and succeeding or preceding properties in a pre-order of properties that includes the focal construct. I examine ontologically the notions of content validity, convergent validity, discriminant validity, and internal-consistency reliability and show their problematic nature. I introduce two new concepts, scope validity and the level of concomitance of indicators, that have rigorous ontological foundations. Together, they provide an improved foundation for assessing the construct validity of a set of indicators.
Optimization Research of Multimedia Technology Based on Multi-Chromosome Genetic Algorithm in College Music Teaching

Objectives: In recent years, along with the rapid development of multimedia technology and network technology, such a combination can assist music teaching through the combination of computer platform and multimedia. Methods: In order to improve the level of music teaching in schools, what is needed in the current music teaching environment is to fully understand the teaching methods in the current music classroom. Results: Based on the computer platform multimedia combination assisted music teaching mode research, the current music teaching mode is optimized by genetic algorithm. Conclusion: The results prove that this study is very successful.
Migration-Based Local Event-Location Workflow for Ocean-Bottom Seismometer (OBS) Records in Subduction Zones: A Practical Approach for Addressing a Large Number of Events

ABSTRACT An efficient event-location workflow is highly desired to analyze large numbers of local earthquakes recorded by ocean-bottom seismometers (OBSs) in subduction zones. The present study proposes a migration-based event-location approach for evaluating OBS records to examine local subduction-zone earthquakes. This approach can significantly reduce the amount of manual time picks compared with conventional methods. The event-location workflow was designed to detect arrival onsets of both P and S phases. Synthetic tests have shown that the proposed migration-based event-location method is robust against different types of noise, such as environmental noise and local spike noise. This workflow was then applied to real OBS data in the off-Ibaraki region at the southern end of the Japan trench. The results show that this approach is applicable to real data from subduction-zone events: It gives reasonable agreement with manual time picks for both P and S waves and reasonable error bars, and it demonstrates a clear down-dip trend of seismicity. The results also show fair agreement with event distributions from previous studies of the off-Ibaraki region. This proposed workflow can be used to examine the seismicity of local earthquakes around the subduction zone using OBSs. This approach is especially effective when the seismicity is high and/or in cases in which long-term OBS monitoring has recorded a large number of events.