Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Identification of Landslide Based on Deep Semi-supervised Learning

Landslides are a frequent and widespread geological disaster. Because a large number of deadly landslide disasters are newly added every year, efficient landslide identification is of great significance in applications such as earthquake prevention and disaster reduction. Visual interpretation not only consumes a lot of manpower and material resources, but also relies heavily on the experience of professionals; while deep learning-based methods often require a large amount of labeled sample data to improve model performance. In order to solve the problem of time-consuming and labor-intensive labeling of landslide sample data, this paper uses a semi-supervised semantic segmentation method of U2PL [1] to automatically identify landslides in optical remote sensing images; Unlabeled samples are used to avoid the performance degradation of traditional supervised learning when there are insufficient training samples; deep learning is used to capture more features hidden in landslide images, and semi-supervised learning is used to use a small number of labeled samples to reduce a lot of manual labeling work. The results show that with the addition of unlabeled data, the Iou of landslide identification has been significantly improved. Even with a certain number of labeled samples, gradually increasing the unlabeled sample data, the Iou of the model landslide identification has increased by 4.93%, and the overall mIou has increased by 2.51 %. At the same time, the method is superior to the existing Yolov5 algorithm in both efficiency and accuracy.
CubeP Crowds: crowd simulation integrated into "Physiology-Psychology-Physics" factors

In this paper, we present a novel CubeP model for crowd simulation that comprehensively considers physiological, psychological, and physical factors. Inspired by the theory of "the devoted actor", our model determines the movement of each individual by modeling the physical influence from physical strength and emotion. In particular, human physical strength is efficiently computed with a physiology-based method. Inspired by James-Lange theory, the emotion is determined by means of an enhanced susceptible-infectious-recovered model that leverages the inherent relation between the physical strength and the psychological emotion. As far as we know, this is the first time that we integrate physiological, psychological, and physical factors together in a unified manner, and the relationship between each other is explicitly determined. The results and comparisons with real-world video sequences verify that the new model is capable of generating effects similar to real-world scenarios. It can also reliably predict the changes in the physical strength and emotion of individuals in an emergency situation. We evaluate and validate the performance of our model in different scenarios.
Probing features in the primordial perturbation spectrum with large-scale structure data

The form of the primordial power spectrum (PPS) of cosmological scalar (matter density) perturbations is not yet constrained satisfactorily in spite of the tremendous amount of information from the Cosmic Microwave Background (CMB) data. While a smooth power-law-like form of the PPS is consistent with the CMB data, some PPS with small non-smooth features at large scales can also fit the CMB temperature and polarization data with similar statistical evidence. Future CMB surveys cannot help distinguish all such models due to the cosmic variance at large angular scales. In this paper, we study how well we can differentiate between such featured forms of the PPS not otherwise distinguishable using CMB data. We ran 15 $N$-body DESI-like simulations of these models to explore this approach. Showing that statistics such as the halo mass function and the two-point correlation function are not able to distinguish these models in a DESI-like survey, we advocate to avoid reducing the dimensionality of the problem from 3 to 2 by demonstrating that the use of a simple three-dimensional count-in-cell density field can be much more effective for the purpose of model distinction.
Thoughts on the Relationship of Knowledge Organization to Knowledge Management

As the literature in both knowledge organization and knowledge management continues to flourish and address issues common to both fields, it becomes more difficult to delineate the boundaries of each discipline. By exploring the relationship between them, as well as the knowledge environment within which a knowledge management system must operate, it is clear that much of the functionality subsumed under the heading of knowledge management requires a strong knowledge organization foundation to support it. By understanding the differences between these two disciplines, as well as the areas of inquiry within knowledge organization that are essential to the successful implementation of knowledge management systems, a wide field of fruitful research directions becomes apparent for both knowledge organization and knowledge management researchers.
Estimativa de Desempenho Acadêmico de Estudantes: Análise da Aplicação de Técnicas de Mineração de Dados em Cursos a Distância

Educational environments have incorporated the use of software to support learning activities. A Learning Management System (LMS) is essential to dispatch distance learning courses to students. A LMS typically stores large volumes of data, recording in detail the activities performed by students. These data can be used to discover relevant information that can help teachers in the management of the teaching-learning process. In this study, by using data mining techniques, we investigate how to obtain inferences about the performance of students in distance learning courses based on data obtained from a Learning Management System. Some experiments conducted in this research indicate the viability of this proposal, achieving accuracy rates between 73% and 80% in estimates students’ academic performance.
Hedge Scope Detection Based on Syntactic Structural Constraints

Hedge scope detection is used to distinguish factual information and uncertain information,which could improve the authenticity and reliability in information extraction.Hedge scope detection is a difficult task because of its dependency of the semantic and syntactic structures.In this paper,we propose a hedge scope detection method based on syntactic structural constraints.First,two decision trees are constructed on dependency structure and phrase structure respectively to build the syntactic constraint set.And then the hedge scope detection results based on the syntactic constraint set are used as the syntactic constraint features for Conditional Random Fields(CRF)models.Experiments on the CoNLL-2010corpus achieve the 70.28% F-score on the golden standard hedge cues,which is 4.22% higher than the system with the common syntactic construction features.
Commentary on Canonne (2018): Listening to Improvisation

The target study explores whether evaluations of the same piece of music differ under two distinct listening conditions: listening to a piece described either as an improvisation or as a preexisting composition. Participants (N = 16) in the two conditions listened to the same musical piece and provided verbal evaluative judgements. The author used a grounded theory approach to analyze listeners' responses, reporting different listening experiences in the two groups. The findings provide unique insights to enable a greater understanding of the nature of the aesthetics of improvisation. In this commentary, I first discuss the strengths of the article, followed by methodological considerations and suggestions for future research. I then present a short literature review and discussion of what I consider the most relevant topic in relation to this study, namely, the effects of contextual information on subjective evaluations.
Global Scholarly Collaboration

The development of recent research information systems allows a transformation of citations in the full text of research papers into interactive elements. Such interactivity in some cases works as an instrument of direct scholarly communications between citing and cited authors. We discuss this challenge for research e-infrastructure development including opportunities for improvements in research cooperation and in collaboration mechanisms for the global research community.
Diagnostic evaluation of needs in an environmental education center through data multiple triangulation

In this paper we try to show the importance involved implement needs assessment processes in order to establish prioritization of claims collected from the implementation of data multiple analytical triangulation processes. The experience has taken as a case study an environmental education center in the province of Granada (Spain) where it will develop an intervention program with people with sensory disabilities. The results obtained and validated with the multiple analytical triangulation strategy has favored the clustering and distribution of needs in large dimensions by the degree of agreement established through percentiles as cut-off marks and groupings of cluster analysis. In this way, we have inferred a first group of high priority corresponding to the Adaptation of Socio-environmental actions for Sensory Disabled. Secondly, we found a group of moderate priority and that could be identified with the Training and Sensitization of Sensory Disabled through Socio-environmental Actions. Finally, we contemplate a third group of low priority that we could call Design and Planning of Socio-Environmental Actions for Sensory Disabled. On the other hand, the fundamental contribution of this work has been to present results and conclusions truly combined with the objective of validating the previously exposed hierarchy of Socio-environmental needs.
Development of the Curriculum for the Introduction to Computer Science Course

This project proposes to reformat the curriculum for an Introduction to Computer Science course for high school students, currently taught as a one semester course. Several issues with the current course are addressed with recommendations for changes intended for the benefit of students at their school. In the past five years, enrollment in the school's Advanced Placement (AP) Computer Science course has decreased from 50 students to 25 students despite no significant change in overall enrollment or student demographics. For the portion of those students enrolled in the course who have taken the Advanced Placement exam during the past four years, the passing rate was 50% to 100%. When students were encouraged to take the AP Computer Science A exam, a less rigorous exam, the passing rate increased. The school has been known to develop a curriculum that best meets the needs of its students. The current Introduction to Computer Science course is not meeting their needs. This project addresses several key aspects of the course that could be changed to better prepare the students for the Advanced Placement (AP) Computer Science course and increase enrollment in both the introductory and advanced courses, particularly (this would be an added bonus) with respect to female students. The key aspects with the Introduction to Computer Science course that this paper will address are as follows: 1. The effectiveness of the curriculum as an introduction to the Advanced Placement (AP) Computer Science, following the curriculum as outlined by the College Board. iii Computer Science Curriculum 2. The course objectives such as the educational philosophy of the course, how the students will be introduced to object-oriented programming using java, the programming language used in the AP Computer Science course, and choosing the software, textbook and supplemental materials that would best meet the needs of the students and support the course objectives. 3. The classroom teaching methodology. This would include, but not be limited to, the expectations of the students both in the classroom and as it relates to homework beyond class times, the nature of homework assignments, when and how much would be assigned on a daily basis, the types of assessments that would determine the students grades, and how these assessment would be graded. 4. Building student interest in the computer science field and demonstrating that every student is capable of basic programming skills. iv Computer Science Curriculum
Algorithm of Moving Target Tracking Based on SIFT Feature Optical Flow

The available feature-optical-flow algorithms have great shortages of computing complexity and anti-noise performance.Concerning this problem,a moving target tracking algorithm based on scale invariant feature transform and Kalman filter algorithm was proposed.First,the SIFT features are extracted in images.Then,the feature points of moving target are matched according to the minimum absolute error criterion and the optical flow vectors of SIFT features are estimated by Kalman filter algorithm.Finally,recognition and tracking of moving target are achieved using the clustering algorithm based on optical features.The experimental results suggest that the algorithm performs well on the feature points tracking in natural scene.The algorithm is easy to calculate and achieve.
Finding near-replicas of documents on the Web

We consider how to efficiently compute the overlap between all pairs of web documents. This information can be used to improve web crawlers, web archivers and in the presentation of search results, among others. We report statistics on how common replication is on the web, and on the cost of computing the above information for a relatively large subset of the web - about 24 million web pages which corresponds to about 150 Gigabytes of textual information.
Word Power Made Easy

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Mothers’ Experiences with their Own Caregivers and Child’s Behaviors Attachment

Early experiences of mothers with their own caregivers tend to influence later relationships, particularly with future children. Thus, the objective of this study was to investigate the mothers’ experiences with their own caregivers and the relation with the child’s current attachment behaviors. Participants were 48 mothers and their children (M = 28.1m; SD = 4.3). The mothers’ experience with their own caregivers was investigated through their perceptions of the care received from their caregivers (Parental Bonding Instrument), and by the representations of attachment (Attachment Script Assessment). The attachment behaviors of the child were investigated by Attachment Q-Sort. Analyzes revealed a reasonable consistency between the mothers’ perceptions about received care, their representations of attachment, and the attachment behaviors of their children. The results support the literature on the relation between mother’ experiences with their own caregivers and the attachment behaviors of the child and bring contributions when presenting evidences in a Brazilian sample.
A COMPUTER SYSTEM FOR DECISION ANALYSIS IN HODGKINS DISEASE

This report describes the development of an interactive computer system to assist physicians in evaluating and managing patients with Hodgkins disease. The system employs the techniques of decision analysis to select a sequence of testing and treatment which optimizes the patient''s chance of long-term survival. The system additionally presents the physician user with information about patient discomfort and dollar cost to facilitate choice between alternatives with approximately equal survival potential. The statistical data used in the decision analysis is derived from a data base representing approximately 1200 Hodgkins disease patients. The validity of the approach and the underlying assumptions was tested statistically. The methodology of threshold analysis and applications to general management dilemmas in Hodgkins disease are presented. Details of the system design and implementation in the Lisp programming language are shown. Much of the statistical data used by the program is included as an appendix.
Recognition of Humans and Their Activities for Video Surveillance

Human Activity Recognition is an active area of research in computer vision with wide-scale applications in video surveillance, motion analysis, virtual reality interfaces, robot navigation and recognition, video indexing, browsing, HCI, choreography, sports video analysis, etc. The analysis of vision-based human activities in videos is an area with increasingly important consequences from security and surveillance to public place and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at mid-level processing, and semantic representation of human activities at higher-level processing make this problem hard to solve. The task is challenging due to variations in motion performance, recording settings, and interpersonal differences. In this chapter, the authors explicitly address these challenges. They present a survey of existing work and describe some of the more well-known methods in these areas. They also describe their own research and outline future possibilities. Detailed overviews of current advances in the field are provided. Image representations and the subsequent classification processes are discussed separately to focus on the novelties of recent research. Moreover, the authors discuss the limitations of the state of the art and outline promising directions of research. Alok Kumar Singh Kushwaha Indian Institute of Technology (BHU), India Rajeev Srivastava Indian Institute of Technology (BHU), India
Discovery of core episodes from sequences using generalization for defragmentation of rule sets

We consider the problem of knowledge induction from sequential or temporal data. Patterns and rules in such data can be detected using methods adopted from association rule mining. The resulting set of rules is usually too large to be inspected manually. We show that (amongst other reasons) the inadequacy of the pattern space is often responsible for many of these patterns: If the true relationship in the data is fragmented by the pattern space, it cannot show up as a peak of high pattern density, but the data is divided among many different patterns, often difficult to distinguish from incidental patterns. To overcome this fragmentation, we identify core patterns that are shared among specialized patterns. The core patterns are then generalized by selecting a subset of specialized patterns and combining them disjunctively. The generalized patterns can be used to reduce the size of the set of patterns. We show some experiments for the case of labeled interval sequences, where patterns consist of a set of labeled intervals and their temporal relationships expressed via Allen's interval logic.
Intra compression efficiency in VP9 and HEVC

The amount of video data stored on local devices or transmitted over the networks is permanently increased. The emerging of a more efficient next generation video coding standard is of a high demand at the moment. There seem to be two main contenders for the position of the next state-of-the-art video compression standard: JCT-VC H.265/HEVC and Google VP9. The announced aim of HEVC is to achieve twice more efficient compression compared to H.264/AVC, and VP9 was developed to get half the bit-rate of VP8 with royalty-free video 6804 M.P. Sharabayko, O.G. Ponomarev and R.I. Chernyak codec. Intra compression is one of the main features that determines the compression efficiency of the whole codec. In this paper we get into detailed overview of intra compression data-flow in HEVC and VP9. We describe common and unique stages of both standards. Then we carry out experiments with JCT-VC HM and WebM VP9 encoders on intra compression efficiency. We also turn some of the HEVC features off to get its dataflow as close to VP9 as possible. Finally we get into discussion of the efficiency of both codecs, the corresponding standards and their intra compression algorithms. Mathematics Subject Classification: 94A08
Based on principal component analysis and D-S evidence theory and application of sensor fault diagnosis

Because the type sensor for underground complex,the measured parameters of the data were huge,used principal component analysis to reduce the dimensions of the data.Used RBF neural network to carry out the feature level data fusion,and established distribution function of basic trust,further used the evidence theory advantage of representation and reasoning to inaccurate information,realized the fault detection and isolation capabilities effectively.The simulation shows that the use of principal component analysis and D-S theory can be correctly located and accurately isolate the failure of sensors.
Adaptive hypermedia on World Wide Web

Because of stateless characteristics of HTTP, is not possible for HTTP servers to adapt responses on the basis of previous interactions with the same user. Although efficient, this is a limiting aspect of the protocol, particularly for Web documents that are directed toward a broad audience, including experts and novices and an adaptive response would be helpful in providing information with the right style. We present here the key concepts of CHEOPS, a system that is being developed as a tool that can be used by hyperdocument designers to provide their document with adaptivity and navigational aids. Version 0.9 of CHEOPS, actually available, is implemented by several CGI-BIN scripts that enables an HTTP server to interact with a user and follow her on the Path to Knowledge. Solution is transparent to the user and to the client, and has a moderate impact on server performances. Further directions of the work toward version 1.0 is also presented.