Miracle-dz/UniFAR-FTUs huggingface dataset:
Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references.
LORA: Learning to Optimize for Resource Allocation in Wireless Networks with Few Training Samples

Effective resource allocation plays a pivotal role for performance optimization in wireless networks. Unfortunately, typical resource allocation problems are mixed-integer nonlinear programming (MINLP) problems, which are NP-hard in general. Machine learning-based methods recently emerge as a disruptive way to obtain near-optimal performance for MINLP problems with affordable computational complexity. However, a key challenge is that these methods require huge amounts of training samples, which are difficult to obtain in practice. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and can be characterized as the task mismatch issue. In this paper, to address the sample complexity issue, instead of directly learning the input-output mapping of a particular resource allocation algorithm, we propose a Learning to Optimize framework for Resource Allocation, called LORA, that learns the pruning policy in the optimal branch-and-bound algorithm. By exploiting the algorithm structure, this framework enjoys an extremely low sample complexity, in the order of tens or hundreds, compared with millions for existing methods. To further address the task mismatch issue, we propose a transfer learning method via self-imitation, named LORA-TL, which can adapt to the new task with only a few additional unlabeled training samples. Numerical simulations demonstrate that LORA outperforms specialized state-of-art algorithms and achieves near-optimal performance. Moreover, LORA-TL, relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.
Kappa Learning: A New Method for Measuring Similarity Between Educational Items Using Performance Data

Sequencing items in adaptive learning systems typically relies on a large pool of interactive assessment items (questions) that are analyzed into a hierarchy of skills or Knowledge Components (KCs). Educational data mining techniques can be used to analyze students performance data in order to optimize the mapping of items to KCs. Standard methods that map items into KCs using item-similarity measures make the implicit assumption that students performance on items that depend on the same skill should be similar. This assumption holds if the latent trait (mastery of the underlying skill) is relatively fixed during students activity, as in the context of testing, which is the primary context in which these measures were developed and applied. However, in adaptive learning systems that aim for learning, and address subject matters such as K6 Math that consist of multiple sub-skills, this assumption does not hold. In this paper we propose a new item-similarity measure, termed Kappa Learning (KL), which aims to address this gap. KL identifies similarity between items under the assumption of learning, namely, that learners mastery of the underlying skills changes as they progress through the items. We evaluate Kappa Learning on data from a computerized tutor that teaches Fractions for 4th grade, with experts tagging as ground truth, and on simulated data. Our results show that clustering that is based on Kappa Learning outperforms clustering that is based on commonly used similarity measures (Cohen Kappa, Yule, and Pearson).
Towards Plan Transformations for Real-World Pick and Place Tasks

In this paper, we investigate the possibility of applying plan transformations to general manipulation plans in order to specialize them to the specific situation at hand. We present a framework for optimizing execution and achieving higher performance by autonomously transforming robot's behavior at runtime. We show that plans employed by robotic agents in real-world environments can be transformed, despite their control structures being very complex due to the specifics of acting in the real world. The evaluation is carried out on a plan of a PR2 robot performing pick and place tasks, to which we apply three example transformations, as well as on a large amount of experiments in a fast plan projection environment.
Control Behavior Integrity for Distributed Cyber-Physical Systems

Cyber-physical control systems, such as industrial control systems (ICS), are increasingly targeted by cyberattacks. Such attacks can potentially cause tremendous damage, affect critical infrastructure or even jeopardize human life when the system does not behave as intended. Cyberattacks, however, are not new and decades of security research have developed plenty of solutions to thwart them. Unfortunately, many of these solutions cannot be easily applied to safety-critical cyber-physical systems. Further, the attack surface of ICS is quite different from what can be commonly assumed in classical IT systems.We present Scadman, a novel control-logic aware anomaly detection system for distributed cyber-physical systems. By observing the system-wide behavior, the correctness of individual controllers (like programmable logic controllers–PLCs) in ICS can be verified. This allows Scadman to detect a wide range of attacks, including malware attacks, code-reuse and dataonly attacks, as well as sensor attacks. We implemented and evaluated Scadman based on a real-world water treatment testbed for ICS security research and training. Our results show that we can detect a wide range of attacks–including attacks that have previously been undetectable by typical state estimation techniques–while causing no false-positive warning for nominal threshold values.
Path analysis for process troubleshooting

In this paper, a model-free data-driven approach to process troubleshooting is proposed. The method is simple and can handle both univariate and multivariate processes. The only information needed for such an analysis is the data. The objective is to identify possible source of variability/oscillation from all interacting variables. To achieve this objective, a model-free method known as path analysis is used. In this paper, we will summarize the theory and algorithms developed for such an analysis. An industrial case study is presented to demonstrate the feasibility of the proposed method.
Automated CFI Policy Assessment with Reckon

Protecting programs against control-flow hijacking attacks recently has become an arms race between defenders and attackers. While certain defenses, e.g., \textit{Control Flow Integrity} (CFI), restrict the targets of indirect control-flow transfers through static and dynamic analysis, attackers could search the program for available gadgets that fall into the legitimate target sets to bypass the defenses. There are several tools helping both attackers in developing exploits and analysts in strengthening their defenses. Yet, these tools fail to adequately (1) model the deployed defenses, (2) compare them in a head-to-head way, and (3) use program semantic information to help craft the attack and the countermeasures. Control Flow Integrity (CFI) has proved to be one of the promising defenses against control flow hijacks and tons of efforts have been made to improve CFI in various ways in the past decade. However, there is a lack of a systematic assessment of the existing CFI defenses. In this paper, we present Reckon, a static source code analysis tool for assessing state-of-the-art static CFI defenses, by first precisely modeling them and then evaluating them in a unified framework. Reckon helps determine the level of security offered by different CFI defenses, and find usable code gadgets even after the CFI defenses were applied, thus providing an important step towards successful exploits and stronger defenses. We have used Reckon to assess eight state-of-the-art static CFI defenses on real-world programs such as Google's Chrome and Apache Httpd. Reckon provides precise measurements of the residual attack surfaces, and accordingly ranks CFI policies against each other. It also successfully paves the way to construct code reuse attacks and to eliminate the remaining attack surface, by disclosing calltargets under one of the most restrictive CFI defenses.
A Unified Convolutional Beamformer for Simultaneous Denoising and Dereverberation

This letter proposes a method for estimating a convolutional beamformer that can perform denoising and dereverberation simultaneously in an optimal way. The application of dereverberation based on a weighted prediction error (WPE) method followed by denoising based on a minimum variance distortionless response (MVDR) beamformer has conventionally been considered a promising approach, however, the optimality of this approach cannot be guaranteed. To realize the optimal integration of denoising and dereverberation, we present a method that unifies the WPE dereverberation method and a variant of the MVDR beamformer, namely a minimum power distortionless response beamformer, into a single convolutional beamformer, and we optimize it based on a single unified optimization criterion. The proposed beamformer is referred to as a weighted power minimization distortionless response beamformer. Experiments show that the proposed method substantially improves the speech enhancement performance in terms of both objective speech enhancement measures and automatic speech recognition performance.
A Review Paper on Continuous Neighbors Discovery in Asynchronous Sensor Networks

It can be possible that a large number of sensor network comprising a number of nodes to be deployed in the simple wireless sensor nodes. In a large area, such networks usually have a network structure. My sensor network nodes are static, but the node even acceptable, as the loss between the wireless communication, the transmission power changes, or synchronous interruption of neighbouring nodes changes. Therefore, even if the sensor aware of its neighbours, it must continuously maintain its view, which we call continuous neighbour discovery process. Here, we discuss the next neighbour discovery in wireless sensor network solutions to more power. Each sensor coordinate efforts by reducing the time required to detect hidden sensors to reduce power consumption.
AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING

One of the most influential factors on human health is air pollution, such as the concentration of PM10 and PM2.5 is a damage to a human. Despite the growing interest in air pollution in Korea, it is difficult to obtain accurate information due to the lack of air pollution measuring stations at the place where the user is located. Deep learning is a type of machine learning method has drawn a lot of academic and industrial interest. In this paper, we proposed a deep learning approach for the air pollution prediction in South Korea. We use Stacked Autoencoders model for learning and training data. The experiment results show the performance of the air pollution prediction system and model that proposed.
Survival Analysis, Overview

Survival analysis is the generic name for analysis of time-to-event data. The distinguishing feature of such data is the presence of right-censored observations. Thus, for some subjects, instead of observing the real time to the event, only a lower limit for this time, the right-censoring time, is known. This kind of incomplete data calls for special statistical techniques for the analysis. This article reviews such methods with cross-references to other articles dealing with special techniques. Keywords: censored data; time-to-event data; life table; proportional hazards; Cox model
The description of Bunraku puppet's motion in computer based on Labanotation

The authors are researching human and humanoid movement. They discuss the transformation system among the movement score, the movement description in computer and the body movement data which are used to generate computer graphics animation of humanoid movement. The proposed humanoid movement description method can describe not only the movement information which are described by Labanotation but also more detailed movement information. This movement description is called "movement MIDI based on Labanotaton" or "continuous Labanotation", and it exists between the Labanoation (which the choreographer can read but the computer cannot) and the humanoid movement data (which the computer can read but the choreographer cannot). It can be read by both the choreographer and the computer, and it is useful to modify the humanoid movement data in order to modify the CG animation, since the information of Labanotation is not sufficient to generate natural display of CG animation. We report the results of transformation from the humanoid motion captured data into the proposed movement description in computer.
Study of Lithospheric Structure , Rheology and Heterogeneity in Eastern Indian Shield and Adjoining Regions View project

The 2001 Mw7.7 Bhuj mainshock seismic sequence in the Kachchh area, occurring during 2001 to 2012, has been analyzed usingmono-fractal andmulti-fractal dimension spectrum analysis technique. This region was characterized by frequent moderate shocks of Mw ≥ 5.0 for more than a decade since the occurrence of 2001 Bhuj earthquake. The present study is therefore important for precursory analysis using this sequence. The selected longsequence has been investigated first time for completeness magnitude Mc 3.0 using the maximum curvature method. Multi-fractal Dq spectrum (Dq ∼ q) analysis was carried out using effective window-length of 200 earthquakes with amoving window of 20 events overlapped by 180 events. The robustness of the analysis has been tested by considering the magnitude completeness correction term of 0.2 to Mc 3.0 as Mc 3.2 and we have tested the error in the calculus of Dq for each magnitude threshold. On the other hand, the stability of the analysis has been investigated down to the minimum magnitude of Mw ≥ 2.6 in the sequence. The analysis shows the multi-fractal dimension spectrum Dq decreases with increasing of clustering of events with time before a moderate magnitude earthquake in the sequence, which alternatively accounts for non-randomness in the spatial distribution of epicenters and its self-organized criticality. Similar behavior is ubiquitous elsewhere around the globe, and warns for proximity of a damaging seismic event in an area. OS: Please confirm math roman or italics in abs. © 2017 Elsevier B.V. All rights reserved.
The Shape and Frequency of Edgeworth Price Cycles in an Australian Retail Gasoline Market

Gasoline prices in many markets follow a saw-toothed pattern known as an Edgeworth Cycle. Lewis (2009) introduces a novel way of measuring the shape of the cycle, the median change in price, and regresses this against a number of explanatory variables in US markets. Here, we undertake a similar regression analysis, but using data from Perth, Australia, and with a novel measure of market structure as a regressor. We also explore a novel measure, based on spectral analysis, of the use of cycles in a mixed strategy, and the factors which drive this use.
An Efficient System for Material Based Image Retrievals with Re-ranking Using Graph Based Ranking Algorithm

Image retrieval from databases or from the Internet needs an efficient and effective technique d to the explosive growth of digital images. Image retrieval is considered as an area of extensive research, especi ally in content based image retrieval (CBIR). CBIR retrieve s similar images from large image database based on image fea tures, which has been a very active research area recently . The content, that can be derived from image such as col or, texture, shape.etc., are called features. This paper will pr esent a survey and discuss the current literature of differ ent types of image retrieval (IR) systems. An overview of the im portant techniques in image retrieval will be discussed. Fi nally, some urgent challenges in IR, that have been raised rece ntly, will be presented as well as possible directions for fut ure research. The proposed model includes two separate st ges such as an offline stage for structuring of ranking model as well as an online stage form an aging of new query. With the proposed system, we can handle database by one mill ion images and perform online retrieval in a short inst ance. Keywords—Image Retrieval by Content-Basis, Graph-Based Ranking, Manifold Ranking, Data Recovery.
Scaffolding extensive reading in the beginning adult education ESL classroom

Research has showed repeatedly the value of extensive reading in the L2 classroom for improved reading comprehension (Elley & Mangubhai, 1981; Mason & Krashen, 1997; Renandya, Rajan, & Jacobs, 1999). However, additional support beyond traditional extensive reading practices may be needed for very low proficiency learners. This report reviews research on implementing an extensive reading program for beginning level adult-education English language learners. It presents arguments for supporting extensive reading through shared reading, including read-alouds, use of children’s literature, and strategy modeling techniques. It also discusses the benefits of student-generated texts for boosting vocabulary and comprehension skills. The report concludes with pedagogical implications for including a strong reading component in the adult education ESL curriculum and suggestions for evaluating the strength and utility of such a program.
Clinical role of diosmectite in the management of diarrhea

Background: Diosmectite (DS), an antidiarrheal absorbent natural clay, was proposed for treatment of acute gastroenteritis and more recently considered for diarrhea within the functional bowel disorder syndrome. Objective: To review pharmacology efficacy, mechanisms of action and safety of DS. Methods: We consulted PubMed/Medline using the keywords of smectite and diarrhea or gastroenteritis, and reviewed most recent recommendations by scientific societies. Conclusions: Solid data show that DS is effective in infectious diarrhea, mainly as an ion antisecretory agent and in preventing intestinal damage. Despite clinical data being consistent and convincing, DS is not considered as first-line treatment because of the presence of alternative therapeutic options. However, it definitely may play a role in infectious colitis, where treatment options are less convincing. Some available evidences demonstrate a role of DS in functional diarrhea.
P 5 . 12 IMPROVED PRECIPITATION FORECAST BY CORRECTING PHASE AND INTENSITY ERRORS OF A MESOSCALE NUMERICAL MODEL

Radar-based extrapolation utilizes observations in an optimal way to generate accurate precipitation forecast. Recent advance in data assimilation techniques facilitates the use of observations in numerical models and leads to the improved precipitation forecast. However, they still suffer from phase and intensity errors, possibly due to imperfect parameterization of various physics. Small phase errors can easily undermine the use of the traditional point-by-point skill scores (Ebert and McBride 2000). Thus, the separate evaluation of phase and intensity errors is extremely important to better represent the model performance. In addition, their consistency should be evaluated to better understand dynamics and parameterization in numerical models. In this work, we demonstrate the systematic evaluation of model errors (phase and intensity errors) from a meso-scale model and their consistency in space and time. Furthermore, we apply a correction algorithm of these errors to improve the accuracy of model precipitation forecast. This correction algorithm assumes the consistency of model errors in time and utilizes radar extrapolation techniques to advect model errors into the future. The applicability of this correction technique at the scale of North America is already shown (Lee and Zawadzki 2006).
Association Rule Mining for Failure Recovery Strategy Selection in Composite Web Services

Web services composition has a special position in the industry. Using the concept of combining web services, large and complex applications can be solved easily. The most important issue in web services composition is fault tolerance. Different errors may occur in web services composition, leading to a failure in the execution of web services. There are two strategies to recover failures (forward and backward recoveries) and both strategies have overhead costs. Usually in the forward recovery, alternative web services are invoked. Although these services have low failure rate, they have high costs. Further, the backward recovery done by the compensation web services imposes a rollback cost. Hence, an accurate selection for recovery strategy is very important because it has significant impact on reducing overhead costs. This research uses association rules mining technique to predict the possible combination of the executive states. The results illustrate that the extracted rules can predict success or failure in the implementation of web services with high accuracy.