Miracle-dz/UniFAR-FTUs huggingface dataset:
BY STRUCTURAL ANALOGY TAKING INTO ACCOUNT THE CONTEXT FOR INTELLIGENT DECISION SUPPORT SYSTEMS

Reasoning by analogy plays a central role in many cognitive processes affecting: problem solving, creativity, basic cognitive perceptions and especially learning. However, analogy is primarily of interest as a workaday process, supporting inference in novel situations by comparison with past experience. Rather than reasoning from “first principles”, analogical reasoning uses a noted similarity between some problem domain and a well-known one to infer useful facts about that problem domain. The analogy can be used in various applications of artificial intelligence (AI) [1]. The great interest in this problem is caused by the necessity of modeling human reasoning (common sense reasoning) in AI systems and, in particular, in intelligent decision support systems (IDSS) [2]. At the present time, there are a great number of various models, schemes, and methods that describe mechanisms of reasoning by analogy [3-10]. The best known models of reasoning by analogy from the 1960’s were ARGUS and ANALOGY [3]. The analogy used for solving various problems, e.g., for solving problems of automated theorem proving [4], for generation of hypotheses about an unknown subject domain, for generalizing the experience in the form of an abstract scheme, etc. [5-10]. Starting from Winston’s work there is widely used the notion of structural analogy [7]. Unfortunately, there are not such modern systems and tools of reasoning by analogy, which are oriented to use in IDSS and, in particular, in real-time IDSS (RT IDSS). In this paper, we consider approaches and methods of reasoning by structural analogy, which are oriented towards use in RT IDSS. These systems are usually characterized by strict constraints on the duration of the solution search [1, 2]. The use of the respective methods in IDSS broadens the possibilities of IDSS and increases the efficiency of making decisions in various problem (abnormal) situations. Special attention in this paper will be paid to the most efficient inference method on the basis of structural analogy that takes into account the context.
Characterization of mobile applications according to their energy consumptions

The use of smart phones and similar devices are increasing day by day and mobile devices are moving towards becoming the mainstream computing hardware. In parallel, the number of mobile applications is increasing exponentially. Besides the many advantages of being mobile, in these applications limited battery power is a fundamental constraint. With "Energy Aware" programming techniques, the new generation of software developers are willing to spread the battery life time as long as possible. In this study, trends of energy consumption are examined for various types of mobile software in terms of software component. For this purpose, The energy profile PowerTutor software is used and without the use of additional hardware, details of energy consumption figures are reported. For selected types of mobile software, energy consumption ratio of GSM radio, processor, screen, sound system and GPS components is examined and a general characterization is sought about these applications.
Mesh Merging with Mean Value Coordinates

Merging a source mesh to a target mesh is one useful and important mesh operating technology. This paper introduces a method merging with mean value coordinates. The method firstly computes the mean value coordinates of the vertices in the source mesh with respect to its boundary. After specifying the difference between the two mesh's boundaries, it interpolates the difference of the source's inside vertices through the coordinates, and then constructs a new mesh to merge. With the same framework, smoothing the seaming line can be achieved by interpolating tangent vector variations. This method avoids the mesh parameterizations or the solving a large linear system, but does merge through computing close form expressions. Experiments show that mesh merging with this method is less artifacts and faster, comparing to the method through solving Poisson equation.
Study of Marker Array List Method for Augmented Reality Service Based Smart Home

More recently, the demand for smart home services is on the rise due to the advanced computing devices and network technology, calling for more stable and efficient visualization and simulation. This has led to a lot of research efforts on AR for virtual smart home services. AR may use a non-marker-based method or a marker-based method. In case of AR services for the indoor environment, however, powerful detection of an object cannot be done solely by a computerized perspective and thus a marker-based technique is applied to smart home services in order to detect an object more accurately. Nevertheless, the marker-based AR may detect markers of similar patterns as the same markers, which causes repeated detection of markers. In this study, therefore, we have explored a method of augmenting a large number of objects without creating a multitude of markers but by creating a list of a marker array, which can increase marker efficiency and avoid repeated detection of markers.
Generating spatiotemporal datasets on the WWW

Efficient storage, indexing and retrieval of time-evolving spatial data are some of the tasks that a Spatiotemporal Database Management System (STDBMS) must support. Aiming at designers of indexing methods and access structures, in this article we review the GSTD algorithm for generating spatiotemporal datasets according to several user-defined parameters, and introduce a WWW-based environment for generating and visualizing such datasets. The GSTD interface is available at two main sites: http://www.cti. gr/RD3/GSTD/ and http://www.cs. ualberta.ca/~mn/GSTD/.
MagPen: magnetically driven pen interactions on and around conventional smartphones

This paper introduces MagPen, a magnetically driven pen interface that works both on and around mobile devices. The proposed device is accompanied by a new vocabulary of gestures and techniques that increase the expressiveness of the standard capacitive stylus. These techniques are: 1) detecting the orientation that the stylus is pointing to, 2) selecting colors using locations beyond screen boundaries, 3) recognizing different spinning gestures associated with different actions, 4) inferring the pressure being applied to the pen, and 5) identifying various pens associated with different operational modes. These techniques are achieved using commonly available smartphones that sense and analyze the magnetic field produced by a permanent magnet embedded in a standard capacitive stylus. This paper explores how magnets can be used to expand the design space of current pen interaction, and proposes a new technology to achieve such results.
Influence of contrast enhancement methods in brain tumor detection

In image processing, contrast enhancement is an important area as it improves the quality of an image. This paper presents different contrast enhancement techniques for brain tumor detection using MRI and assesses their performance on the basis of factors like tumor size, area and volume calculations. The proposed methodology was applied on datasets of different patients and the subsequent results were evaluated. The results facilitated in gauging overall performance of the tumor detection process. Statistical measures like Dice Similarity Coefficient, Jaccard Similarity Index and Structural Similarity Index are used to compute the accuracy of contrast enhancement techniques. After detecting tumor in 2-Dimensional (2D) slices, 3-Dimensional (3D) reconstruction was performed to anticipate type of tumor along with 5 year survival rate by looking at its shape in 3D. The work is expected to aid in the treatment of the disease in a better way.
Altered Cervical Vestibular-Evoked Myogenic Potential in Children with Attention Deficit and Hyperactivity Disorder

Objective Emerging evidence suggests that children with attention deficit and hyperactivity disorder (ADHD) present more difficulties in standing and walking balance than typically developing children. Most of previous studies have assessed these functions using postural and sensory organization tests showing differences in balance performance between control and ADHD children. However, to date, it is unknown whether these balance alterations are accompanied with vestibular dysfunction. The principal aim of this study is to evaluate vestibular otolith function in ADHD and matched control children. Methods We assessed vestibular otolith function in children with ADHD and controls using the subjective visual vertical (SVV) bucket test and cervical vestibular-evoked myogenic potentials (cVEMPs). In addition, gait and balance were evaluated using the dynamic gait index (DGI) and computerized posturography. Results Non-significant differences between groups were obtained in SVV evaluation. DGI results show lower scores for overall test performance in children with ADHD (p < 0.001), while computerized postural recordings showed significant differences for the limit of stability between groups (p = 0.02). cVEMPs in response to 500 Hz tone bursts presented at 100 dB were absent or reduced in children with ADHD, as revealed by differences in P1 and N1 peak-to-peak amplitudes between groups (p < 0.01). Conclusion These findings suggest that vestibular brainstem reflexes are altered in a subset of children with ADHD. We propose to include cVEMP reflexes in the clinical evaluation of ADHD patients.
Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity

Collaborative filtering, one of the most widely used approach in recommender system, predicts a user's rating towards an item by aggregating ratings given by users having similar preference to that user. In existing approaches, user similarity is often computed on the whole set of items. However, because the number of items is often very large, and so is the diversity among items, users who have similar preference in one category of items may have totally different judgement on items of another kind. In order to deal with this problem, we propose a method of clustering items, so that inside a cluster, similarity between users does not change significantly. After that, when predicting rating of a user towards an item, we only aggregate ratings of users who have high similarity degree with that user inside the cluster to which that item belongs. Experiments evaluating our approach are carried out on the real dataset taken from movies recommendation system of MovieLens web site. Preliminary results suggest that our approach can improve prediction accuracy compared to existing approaches.
JeCo: Combining Program Visualization and Story Weaving

We present a collaborative learning tool for programming, Jeliot Collaboratively or JeCo. Jeliot Collaboratively is a combination of a program visualization tool for Java programs, called Jeliot 3, and a collaborative authoring tool, Woven Stories. We introduce these systems and explain how they can be used in learning. Furthermore, we present future directions in order to support a wider range of use cases with JeCo.
A New Efficient Simulation Equivalence Algorithm

It is well known that simulation equivalence is an appropriate abstraction to be used in model checking because it strongly preserves ACTL* and provides a better space reduction than bisimulation equivalence. However, computing simulation equivalence is harder than computing bisimulation equivalence. A number of algorithms for computing simulation equivalence exist. Let Sigma denote the state space, rarr the transition relation and Psim the partition of Sigma induced by simulation equivalence. The algorithms by Henzinger, Henzinger, Kopke and by Bloom and Paige run in O(|Sigma||rarr|)-time and, as far as time-complexity is concerned, they are the best available algorithms. However, these algorithms have the drawback of a quadratic space complexity that is bounded from below by Omega(|Sigma|2). The algorithm by Gentilini, Piazza, Policriti appears to be the best algorithm when both time and space complexities are taken into account. Gentilini et al.'s algorithm runs in O(|Psim|2|rarr|)-time while the space complexity is in O(|Psim|2 + |Sigma| log(|Psim|)). We present here a new efficient simulation equivalence algorithm that is obtained as a modification of Henzinger et al.'s algorithm and whose correctness is based on some techniques used in recent applications of abstract interpretation to model checking. Our algorithm runs in O(|Psim||rarr|)-time and O(|Psim||Sigma|)-space. Thus, while retaining a space complexity which is lower than quadratic, our algorithm improves the best known time bound.
A Precise Magnetic Walking Mechanism

Inspired by magnetically actuated precision motion devices based on magnetic levitation and those based on elastic suspension, we devised a novel magnetic actuation mechanism, namely, a magnetic walking mechanism. This biped precision mechanism is a legged precision mechanism that consists of two elastically connected rigid legs. We designed the mechanism to be driven by one horizontal actuator and two vertical actuators, each comprising a permanent magnet and an air-core coil, and to produce both fine motion and walking motion. We discuss the underlying principles of force generation, leg actuation, fine motion, and walking motion. To demonstrate that both fine motion and walking motion can be created with this mechanism, we built and experimentally tested a two-legged walker. We successfully created a legged precision device capable of long-range fine motion and long-step walking motion based on a magnetic walking mechanism.
On the FCLT Version of L = λW

The functional central limit theorem (FCLT) version of Little’s law (L = λW ) shows that the fundamental relation between cumulative processes underlying L = λW leads to a corresponding relation among the limits for the FCLT-scaled stochastic processes. It supports statistical analysis, e.g., estimating confidence intervals. Here, this statistical motivation is reviewed and then the FCLT in Glynn and Whitt (1986) is extended to show that a bivariate FCLT for the number in system and the waiting times implies a FCLT for the arrival process, and thus the joint FCLT for all processes. The new result is based on a converse to the preservation of convergence by the composition map with centering, exploiting monotonicity, which should have other applications.
Evaluation of Anomaly Based Character Distribution Models in the Detection of SQL Injection Attacks

The ubiquity of Web applications has led to an increased focus on the development of attacks targeting these applications. One particular type of attack that has recently become prominent is the SQL injection attack. SQL injection attacks can potentially result in unauthorized access to confidential information stored in a backend database. In this paper we describe an anomaly based approach which utilizes the character distribution of certain sections of HTTP requests to detect previously unseen SQL injection attacks. Our approach requires no user interaction, and no modification of or access to, either the backend database or the source code of the web application itself. Our practical results suggest that the model proposed in this paper is superior to existing models at detecting SQL injection attacks. We also evaluate the effectiveness of our model at detecting different types of SQL injection attacks.
SPAM Control Using CPU Timestamps

—Email spam has reached alarming proportions because it costs the sender very little to send; almost all of the costs are paid by the recipient, carrier and the email servers. Email spam's inadvertently affect the performance of email servers which are kept busy in processing huge number of emails sent by the spammers. It also affects the productivity of the recipients who have to skim through lots of irrelevant emails to find the ones that actually require their attention. We propose a solution to control spam at the ingress points on the email servers by throttling the sender's CPU i.e., making an email client pay a stamp fee for every email sent. The stamp fee is collected in terms of resource usage, in our case CPU cycles. The solution is based on the use of Discrete Logarithm Problem (DLP), which is considered to be one of the hardest mathematical problems to solve and is the basis for many cryptosystems. DLP is used to generate CPU stamps as a proof of the CPU cycles expended by the sender's system. A separate stamp has to be calculated for each email and for each recipient which restricts the total number of emails that an email client can send in a time interval. We further claim that a normal user will not notice this cost in his day to day operations and a spammer will not be able to get past this mechanism.
Manipulating atoms with photons

By using quasi-resonant exchanges of energy, linear and angular momentum between atoms and photons, it is possible to polarize atoms, to displace their energy levels and to control their position and their velocity. A few physical mechanisms allowing one to trap atoms and to cool them in the microkelvin, and even in the nanokelvin range, are briefly described and classified with the help of a few simple guidelines. Various possible applications of such ultracold atoms are also reviewed. They take advantage of the long interaction times and long de Broglie wavelengths which are now available with laser cooling and trapping techniques. New quantum situations can also be achieved in these experiments, calling for new theoretical approaches. The last part of this paper is devoted to a brief discussion of these problems.
Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos

We implemented an automatic open-source processing pipeline for neonatal brain tissue segmentation. The framework makes use of N4 to correct bias field, the deformable registration SyN to warp a public neonatal template to the test image, and multivariate MRF-based segmentation Atropos to perform segmentation. The pipeline does not need training and runs efficiently. It can achieve satisfactory results for most classes of a neonatal brain MR image. 1 A Prior-Based Segmentation Algorithm We contribute a template-based method for the challenging problem of tissue segmentation in the neonatal brain. The task is difficult because of intensity inversion of unmyelinated white matter and low resolution (or large slice thickness) in standard neonatal T1 or T2-weighted MRI. Our framework employs SyN diffeomorphic registration and Atropos prior-based multivariate segmentation methods available within the open-source, multi-platform Advanced Normalization Tools (ANTs). SyN and Atropos are general-purpose tools, which were not designed specifically for neonatal brain processing. However, with the assistance of a neonatal brain template, the general purpose methods are adapted for a task-specific algorithm. There are four components in the proposed framework: registration, bias correction, segmentation and post-processing (see Fig. 1). 1.1 Registration Assuming there is a proper template available for each test image to be segmented, we aim to align the template with the test image and thus transform the priors associated with the template to the subject space. The registration is performed via the diffeomorphic mapping methods available within ANTs software [1], which rank among top performers in terms of registration accuracy [2]. ANTs is freely available at http://picsl.upenn. edu/ANTS/download.php. We use the neighborhood cross correlation as similarity measure for the mono-modal registration and mutual information for intra-subject affine registration between T1 and T2 weighted MR images. Registration between T1 and T2 weighted modalities enables us to employ a multivariate data-term in the segmentation 2 Jue Wu and Brian Avants Fig. 1. Overview of the proposed algorithm formulation. The brain mask from the template was transformed to subject space and helped remove non-brain tissues. The line of code for registration between template and test image is: ANTS 3 -m CC[t2template-GA.nii.gz, test_image. nii.gz, 1, 2] -r Gauss[3,0] -t SyN[0.25] -i 90x100x80 -o output.nii.gz
User-level Process towards Exascale Systems

The process oversubscription, which binds multiple parallel processes to one CPU core, can hide the communication latency and reduce CPU idle time. However, the lightweight OS kernels for Exascale systems may no longer support OS task scheduling. Without OS task scheduling, only one parallel process per CPU core is allowed, and then the process oversubscription is impossible. Even if the OS task scheduling is supported, the overhead of the context switch between parallel processes decreases the application performance and prevents the process oversubscription from being utilized. By assigning a role of a parallel process to a user-level thread, high-performance process oversubscription can be achieved without OS task scheduling. However, the process oversubscription utilizing the user-level thread dramatically changes the programming model of the parallel application. In this paper, we propose user-level process. The user-level process is a ”process”, which can be scheduled in the user-space. The user-level process has the beneficial features of the user-level thread. Meanwhile, it has its own program code and data like a traditional process. By assigning a role of a parallel process to an user-level process, high-performance process oversubscription can be achieved without OS task scheduling. Moreover, the process oversubscription utilizing the user-level process does not change the programming model of the parallel application.
Environmental field estimation with hybrid-mobility sensor networks

The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 °C over a 16 m2 area ranging in value from 25-65 °C.
Power Aware Domain Migration in a Virtualized Cluster

Modern data centers usually have computing resources sized to handle expected peak demand, but average demand is generally much lower than peak. This leads to a high degree of underutilization. Past techniques have exploited this fact to achieve significant power savings, but these works generally focus on throughput-oriented systems that satisfy a single fine-grained request stream. We propose a more general solution: a technique to achieve power savings by dynamically migrating virtual machines and “packing” them onto fewer nodes when possible. We call this Power-Aware Domain Distribution (PADD). As a first step, we are developing a simulator to explore the design decisions presented by this problem. This paper documents a work-in-progress, discusses the simulator design, our preliminary findings, and avenues of future development.