Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic generation of digital terrain models from Cartosat-1 stereo images

This paper proposes a novel algorithm for automatic Digital Terrain Model (DTM) generation from high resolution CARTOSAT-1 satellite images generating accurate and reliable results. It consists of two major steps: Generation of Digital Surface Models (DSM) from CARTOSAT-1 stereo scenes and hierarchical image filtering for DTM generation. High resolution stereo satellite imagery is well suited for the creation of DSM. A system for automated and operational DSM and orthoimage generation based on CARTOSAT-1 imagery is presented, with emphasis on fully automated georeferencing. It processes level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. A novel, automatic georeferencing method is used to derive a high quality RPC correction from lower resolution reference dataset, such as Landsat ETM+ Geocover and SRTM C Band DSM. Digital surface models (DSM) are derived from dense stereo matching and forward intersection and subsequent interpolation into a regular grid. In the second step which is dedicated to DSM filtering, the DSM pixels are classified into ground and non-ground using the algorithm motivated from the gray-scale image reconstruction to suppress unwanted elevated pixels. In this method, non-ground regions, i.e., 3D objects as well as outliers (very low or very high elevated regions) are hierarchically separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Profiles in the image as well as a comparison of the derived DTM to the original data and ground truth data are presented. The ground truth data consist of a high quality DTM produced from aerial images which is generated by the Institut Cartografic de Catalunya (ICC) in Barcelona, Spain. The evaluation result indicates that almost all non-ground objects regardless of their size are eliminated and the iterative approach gives good results in hilly as well as smooth residential areas.
On latent fingerprint enhancement

Automatic feature extraction in latent fingerprints is a challenging problem due to poor quality of most latents, such as unclear ridge structures, overlapped lines and letters, and overlapped fingerprints. We proposed a latent fingerprint enhancement algorithm which requires manually marked region of interest (ROI) and singular points. The core of the proposed enhancement algorithm is a novel orientation field estimation algorithm, which fits orientation field model to coarse orientation field estimated from skeleton outputted by a commercial fingerprint SDK. Experimental results on NIST SD27 latent fingerprint database indicate that by incorporating the proposed enhancement algorithm, the matching accuracy of the commercial matcher was significantly improved.
Brown v. Board of Education and the Interest-Convergence Dilemma.

After Brown v. Board of Education was decided, Professor Herbert Wechsler questioned whether the Supreme Court’s decision could be justified on the basis of “neutral” principles. To him Brown arbitrarily traded the rights of whites not to associate with blacks in favor of the rights of blacks to associate with whites. In this Comment, Prof. Derrick Bell suggests that no conflict of interest actually existed; for a brief period, the interests of the races converged to make the Brown decision inevitable. More recent Supreme Court decisions, however, suggest to Professor Bell a growing divergence of interests that makes integration less feasible. He suggests the interest of blacks in quality education might now be better served by concentration on improving the quality of existing schools, whether desegregated or all-black.
HEigen: Spectral Analysis for Billion-Scale Graphs

Given a graph with billions of nodes and edges, how can we find patterns and anomalies? Are there nodes that participate in too many or too few triangles? Are there close-knit near-cliques? These questions are expensive to answer unless we have the first several eigenvalues and eigenvectors of the graph adjacency matrix. However, eigensolvers suffer from subtle problems (e.g., convergence) for large sparse matrices, let alone for billion-scale ones. We address this problem with the proposed HEIGEN algorithm, which we carefully design to be accurate, efficient, and able to run on the highly scalable MAPREDUCE (HADOOP) environment. This enables HEIGEN to handle matrices more than 1;000 × larger than those which can be analyzed by existing algorithms. We implement HEIGEN and run it on the M45 cluster, one of the top 50 supercomputers in the world. We report important discoveries about nearcliques and triangles on several real-world graphs, including a snapshot of the Twitter social network (56 Gb, 2 billion edges) and the “YahooWeb” data set, one of the largest publicly available graphs (120 Gb, 1.4 billion nodes, 6.6 billion edges).
Linear planning logic: An efficient language and theorem prover for robotic task planning

In this paper, we introduce a novel logic language and theorem prover for robotic task planning. Our language, which we call Linear Planning Logic (LPL), is a fragment of linear logic whose resource-conscious semantics are well suited for reasoning with dynamic state, while its structure admits efficient theorem provers for automatic plan construction. LPL can be considered as an extension of Linear Hereditary Harrop Formulas (LHHF), whose careful design allows the minimization of nondeterminism in proof search, providing a sufficient basis for the design of linear logic programming languages such as Lolli. Our new language extends on the expressivity of LHHF, while keeping the resulting nondeterminism in proof search to a minimum for efficiency. This paper introduces the LPL language, presents the main ideas behind our theorem prover on a smaller fragment of this language and finally provides an experimental illustration of its operation on the problem of task planning for the hexapod robot RHex.
INTIMATE: a Web-based movie recommender using text categorization

We present INTIMATE, a Web-based movie recommender that makes suggestions by using text categorization to learn from movie synopses. The performance of various feature representations, feature selectors, feature weighting mechanisms and classifiers is evaluated and discussed. INTIMATE was also compared with a feature-based movie recommender. The results show that the text-based approach outperforms the feature-based if the ratio of the number of user ratings to the vocabulary size is high.
DeLight: A Peer-to-Peer Storage and Processing System

The change in data types from few and small files with textual content to enormous amounts of unstructured data like images and videos provides new challenges for the systems storing and processing the data. In addition, the wide range of digital devices we surround us with, like mobile phones, laptops, and music players, all provide some amount of storage and processing capacity. We propose DeLight, an integrated storage and processing system based on a Peer-to-Peer storage network of heterogeneous nodes. The network can adapt to the runtime environment and individual user requirements, it utilizes existing storage and processing resources, and it is easy to extend on demand. Initial testing shows that this system can answer the new requirements we envision for these systems.
Thematically Reinforced Explicit Semantic Analysis

We present an extended, thematically reinforced version of Gabrilovich and Markovitch’s Explicit Semantic Analysis (ESA), where we obtain thematic information through the category structure of Wikipedia. For this we first define a notion of categorical tfidf which measures the relevance of terms in categories. Using this measure as a weight we calculate a maximal spanning tree of the Wikipedia corpus considered as a directed graph of pages and categories. This tree provides us with a unique path of “most related categories” between each page and the top of the hierarchy. We reinforce tfidf of words in a page by aggregating it with categorical tfidfs of the nodes of these paths, and define a thematically reinforced ESA semantic relatedness measure which is more robust than standard ESA and less sensitive to noise caused by outof-context words. We apply our method to the French Wikipedia corpus, evaluate it through a text classification on a 37.5 MB corpus of 20 French newsgroups and obtain a precision increase of 9‐10% compared with standard ESA.
Subjective Status, Depression, and Neuroticism

Subjective status refers to the way in which individuals perceive themselves in relation to others in society, in terms of social status. Lower subjective status has been linked to poorer health outcomes, particularly depression. Individuals with lower subjective status report more symptoms of depression, even when their objective socio-economic status (i.e., education, occupation, and income) is taken into account. Previous research has largely ignored individuals' personality differences, particularly individuals' level of neuroticism, when looking at the relation between subjective status and depression. Neuroticism is a core personality characteristic that negatively impacts many different areas of life. Individuals with higher neuroticism are more likely to report symptoms of depression. In addition, individuals with higher neuroticism have lower socio-economic status and poorer health, factors which may influence their subjective status. In light of such observations, the aim of the present research was to determine whether neuroticism plays a role in the relation between subjective status and depression. Participants were 371 retirees participating in a longitudinal study on life adjustment after retirement. Cross-sectional analysis of the data indicated that individuals with lower subjective status reported more symptoms of depression, even when their objective socio-economic status was taken into account. When neuroticism was taken into account, however, individuals with lower status did not report more symptoms of depression. Further analyses indicated that negative affect was not a sufficient proxy for neuroticism in the relation between subjective status and depression. In other words, when negative affect was taken into account, subjective status continued to play a statistically significant role in predicting symptoms of depression. Keywords: subjective status, socio-economic status, depression, neuroticism, negative affect
Wireless Motion Control of an Intelligent Wheelchair Using Hand Gesture Recognition Technology

A ‘hand gesture’ based easy to operate navigation mechanism using the wireless technology in the form of wheelchair control system is presented in this work. This work proposes an integrated approach for detection, tracking and recognition of hand gestures in real time. The approach uses acceleration technology to establish a reliable medium of human-machine interaction for the movement control of an intelligent wheelchair. The remote control facility up to 60 meters and obstacle avoidance technology provided for additional comfort during navigation task for elderly or disabled people. The wheelchair motion is controlled by employing Arduino microcontroller interfaces with MEMS accelerometer sensor, motor driver unit, proximity and edge detection sensors. The designed system was tested with five experiments consisting of 50 trials each by two participants independently. An average accuracy of 98 to 99% was found for all modules. Overall system performed well and result shows encouraging outcomes.
Determinants of the Utilization of Diversified Types of Professionals for Mental Health Reasons in a Montreal (Canadian) Catchment Area

The study was designed to identify factors associated with the diversity of professionals consulted by 212 individuals affected by at least one mental disorder in the past 12 months in a Montreal catchment area. For inclusion in the study, participants had to be aged 15 to 65 and reside in the study zone. A comprehensive set of variables were analyzed in accordance with the Andersen’s behavioural model of health service use. General practitioners, psychiatrists, and psychologists were the main professionals consulted in this study. Having post-secondary education, more than a single mental disorder, excellent relationships with neighbours, and (marginally) being a lifelong victim of violence were associated with higher numbers of professionals consulted. As this study highlights the large number of diversified professionals consulted for reason of mental disorders, shared care initiatives may prove beneficial. Greater effort could also be made in increasing services toward those deemed more vulnerable.
The Effects of Semantic Annotations on Precision Parse Ranking

We investigate the effects of adding semantic annotations including word sense hypernyms to the source text for use as an extra source of information in HPSG parse ranking for the English Resource Grammar. The semantic annotations are coarse semantic categories or entries from a distributional thesaurus, assigned either heuristically or by a pre-trained tagger. We test this using two test corpora in different domains with various sources of training data. The best reduces error rate in dependency F-score by 1% on average, while some methods produce substantial decreases in performance.
IL-10 Deficiency Reveals a Role for TLR2-Dependent Bystander Activation of T Cells in Lyme Arthritis

T cells predominate the immune responses in the synovial fluid of patients with persistent Lyme arthritis; however, their role in Lyme disease remains poorly defined. Using a murine model of persistent Lyme arthritis, we observed that bystander activation of CD4+ and CD8+ T cells leads to arthritis-promoting IFN-γ, similar to the inflammatory environment seen in the synovial tissue of patients with posttreatment Lyme disease. TCR transgenic mice containing monoclonal specificity toward non–Borrelia epitopes confirmed that bystander T cell activation was responsible for disease development. The microbial pattern recognition receptor TLR2 was upregulated on T cells following infection, implicating it as marker of bystander T cell activation. In fact, T cell–intrinsic expression of TLR2 contributed to IFN-γ production and arthritis, providing a mechanism for microbial-induced bystander T cell activation during infection. The IL-10–deficient mouse reveals a novel TLR2-intrinsic role for T cells in Lyme arthritis, with potentially broad application to immune pathogenesis.
Continuous circular scribble arts

Scribble art is a kind of illustrative drawing. Artists use continuous lines to convey the impression of an image or concept of a design. Unlike conventional line drawings such as sketching and hatching that commonly comprise of short and straight line segments, scribble artists aim at depicting the image with long and continuous curves. In this work, we study a typical curve pattern, circular scribble that appears most frequently in the artworks. Circular lines are drawn in either clockwise or counter-clockwise direction with varying radius in the circular scribble arts. The artists delicately trace along a seemingly random path and control the size and orientation of circular line pattern to depict a subject of their artwork. The main challenges lie in producing smooth transition between grayscale levels and preserving dominant image features using continuous loops and intersections of a circular scribble. Thus, the creation of circular scribble art is skill-demanding and time-consuming. In order to facilitate such process, we introduce a systematic approach to automatically synthesize circular scribble arts from images by tracing along a virtual path using solely a single continuous circular scribble with varying radius and orientation. We have tested our approach using a wide range of images and generate visually pleasing circular scribble arts (see Figure 1).
Application Performance Isolation in Virtualization

Modern data centers use virtual machine based implementation for numerous advantages like resource isolation, hardware utilization, security and easy management. Applications are generally hosted on different virtual machines on a same physical machine. Virtual machine monitor like Xen is a popular tool to manage virtual machines by scheduling them to use resources such as CPU, memory and network. Performance isolation is the desirable thing in virtual machine based infrastructure to meet Service Level Objectives. Many experiments in this area measure the performance of applications while running the applications in different domains, which gives an insight into the problem of isolation. In this paper we run different kind of benchmarks simultaneously in Xen environment to evaluate the isolation strategy provided by Xen. Results are presented and discussed for different combinations and a case of I/O intensive applications with low response latency has been presented.
Feature Space of Deep Learning and its Importance: Comparison of Clustering Techniques on the Extended Space of ML-ELM

Based on the architecture of deep learning, Multilayer Extreme Learning Machine (ML-ELM) has many good characteristics which make it distinct and widespread classifier in the domain of text mining. Some of its salient features include non-linear mapping of features into a high dimensional space, high level of data abstraction, no backpropagation, higher rate of learning etc. This paper studies the importance of ML-ELM feature space and tested the performance of various traditional clustering techniques on this feature space. Empirical results show the efficiency and effectiveness of the feature space of ML-ELM compared to TF-IDF vector space which justifies the prominence of deep learning.
Towards Linked Data Extraction From Tweets

Millions of Twitter users post messages every day to communicate with other users in real time information about events that occur in their environment. Most of the studies on the content of tweets have focused on the detection of emerging topics. However, to the best of our knowledge, no approach has been proposed to create a knowledge base and enrich it automatically with information coming from tweets. The solution that we propose is composed of four main phases: topic identification, tweets classification, automatic summariza-tion and creation of an RDF triplestore. The proposed approach is implemented in a system covering the entire sequence of processing steps from the collection of tweets written in English language (based on both trusted and crowd sources) to the creation of an RDF dataset anchored in DBpedia's namespace.
Experiential Learning and Discipline-Based Art Education

This article examines how our understanding of the concepts of experiential and theoreti cal learning has evolved within the context of current Discipline-based Art Education (DBAE) theory and practice. It also explores how these two types of learning are related to each of the four DBAE disciplines, and it argues that each discipline is a locus for both types of learning experiences. Finally, the ar ticle proposes that excellence in aesthetic education can be achieved through educa tional practices that emphasize the interac tion of experiential and theoretical learning both within and across the four art disci
One-year follow-up in patients with brainstem infarction due to large-artery atherothrombosis

Background Posterior circulation infarction accounts for approximately 25% of the ischemic strokes. A number of different conditions may be associated with the development of brainstem infarction. Prognosis and recurrence rate of brainstem infarction due to large-artery atherothrombosis is still controversial. Methods A total of 826 patients with ischemic stroke were admitted to our clinic during a 15-month period. Patients with clinical and radiological evidence of brainstem infarction were comprehensively assessed with appropriate vascular imaging modalities and for cardiological causes. Subjects with an established diagnosis of large-artery atherothrombosis were followed up for 1 year in terms of prognosis and recurrence rates. Results Of the 101 patients with an established brainstem infarction, the diagnostic work-up indicated the presence of large-artery atherothrombosis as the causative factor in 53. A recurrent stroke was detected in the posterior circulation within the first 3 months in 5.8% and within 1 year in 9.8% of these individuals. The 1-year mortality rate was 11.7%. All patients with recurrent stroke had intracranial vascular narrowing at baseline. Conclusion Our findings demonstrate a particularly high rate of recurrence and mortality at 1 year among patients who have a brainstem infarction due to intracranial arterial stenosis.
Summarization of Related Work through Citations

Automatically generating the related work section for a writing paper is useful for researchers as it can save a lot of time and avoid missing related works. The related work section of scientific paper usually introduces other researchers' work published before and makes comparisons with the current author's work. This paper proposes an approach to automatically generating related work by comparing the main text of the writing paper with the citations to the references from other papers. Our approach firstly collects the papers that cite the reference papers of the writing paper and extracts the corresponding citation sentences to form a citation document. It then makes a summarization that makes a comparison between the citation document and the writing paper's abstract, introduction and conclusion. It extracts the representative keywords from the citation document and the writing paper, and constructs a graph of the keywords. The discriminated nodes in the graph are figured out, and then the minimum Steiner tree that covers the nodes is extracted. A summary is generated by extracting the sentences covering the Steiner tree. The experiments show that our approach outperforms three baselines MEAD, ReWoS and ARWG according to ROUGE evaluation results.
Developing Pervasive e-Heatth for Moving Experts from Hospital to Home

This paper presents a project on treating diabetic patients with foot ulcers at home. There is a great potential for a better treatment of patients with ulcers at home while obtaining substantial cost-savings. The project investigates whether pervasive computing has something to add in a telemedical setting, where the patient and the home care nurse is communicating with the expert at the hospital. Methods of user-centered design is applied and presented. The overall design principles for enabling cooperation between patients and residential nurses at home and ulcer experts at the hospital are presented, and the current status of the technical implementation is outlined.
ARIES-RRH: restricted repeating of history in the ARIES transaction recovery method

A method called ARIES-RRH (algorithm for recovery and isolation exploiting semantics with restricted repeating of history) is presented which is a modified version of the ARIES transaction recovery and concurrency control method implemented to varying degrees in Starburst, QuickSilver, the OS/2 Extended Edition Database Manager, DB2 V2, Workstation Data Save Facility/VM and the Gamma database machine. The repeating history paradigm of ARIES is analyzed to propose a more efficient handling of redos, especially when the smallest granularity of locking is not less than a page, by combining the paradigm of selective redo from DB2 V1. Even with fine-granularity locking, it is not always the case that all the unapplied but logged changes needed to be redone. ARIES-RRH, which incorporates these changes, still retains all the good properties of ARIES-avoiding undo of undos, single-pass media recovery, nested top actions, etc. The fundamentals behind why DB2 V1's selective redo works, in spite of failures during restart recovery, are also explained. <<ETX>>