Miracle-dz/UniFAR-FTUs huggingface dataset:
Diametral Reachable Index (DRI) of a Vertex

Every graph has one or more diametral paths. A diametral path of a graph is a shortest path whose length is equal to the diameter of the graph. Let be a diametral vertex. There may be one or more diametral paths originating from . We want to find all the diametral paths, originating from . The total number of diametral paths reachable from a vertex is called the Diametral Reachable Index of that vertex, denoted . For any vertex , the , if there are no diametral paths reachable from , else we write , where is the total number of diametral paths reachable from vertex . An algorithm is developed to find DRI of each vertex of a graph, by modifying the DFS algorithm.
Iterative Statistical Verification of Probabilistic Plans

Artificial intelligence seeks to create intelligent agents. An agent can be anything: an autopilot, a self-driving car, a robot, a person, or even an anti-virus system. While the current state-of-the-art may not achieve intelligence (a rather dubious thing to quantify) it certainly achieves a sense of autonomy. A key aspect of an autonomous system is its ability to maintain and guarantee safety—defined as avoiding some set of undesired outcomes. The piece of software responsible for this is called a planner, which is essentially an automated problem solver. An advantage computer planners have over humans is their ability to consider and contrast far more complex plans of action. Safety may be defined probabilistically, in which case the probability of “failure” must be below some given threshold θ. The process of deciding the level of safety a plan achieves is called verification. The plans considered in this work are too complex to analyze analytically (the process would take too much time and/or memory to complete). This motivates a statistical sampling based approach, which works by generating “sample traces” of the plan—like simulating a roll of dice. An initial attempt by Younes and Musliner (2002) used the Sequential Probability Ratio Test (SPRT) (Wald, 1945) to verify probabilistic plans. If failure is rare however, verification is not fast enough using this traditional sampling technique and the compact representation of the plans (necessary to even consider them) directly conflicts with the ability of more efficient techniques such as Importance Sampling (IS) (Hammersley and Handscomb, 1964). DAGification (Potts, Krebsbach, Thayer, and Musliner, 2013)—the systematic expansion of this representation—allows IS to compute the required probabilities for safety with bounded levels of error and in a reasonable number of samples. DAGification gets its name from directed acyclic graphs (DAG). Jordan Thayer, in Potts et al. (2013), proves that this is precisely the requirement for IS to work correctly (and efficiently), and this works describes why non-DAGs work poorly. DAGification was motivated by the CIRCA agent architecture (Musliner et al., 1993), which uses a plan representation that contains numerous interconnected loops (far from a DAG). CIRCA also provides DAGification with a rigorous proving ground, although the scope of DAGification (as will be shown) is not limited to CIRCA. Following the work in Potts et al. (2013), several new DAGification schemes are provided with a detailed discussion of their correctness. These improved DAGification concepts addresses the major theoretical and experimental difficulties involved with the original DAGification.
Speech and gesture integration for a game-based command and control environment

A computer's ability to interpret speech and its ability to interpret gesture have both been researched independently in great depth. The challenge now is to combine these two modalities in a single system, taking advantage of the means of expression available in each domain. For this thesis we investigated two different approaches to combining speech and gesture into a multimodal system. In the first system, Ogg That There, we took a high-level approach; gesture was immediately represented as the indicated object, and speech was immediately encoded as words. Interpretation is done using a simple FSM, which steps through a subjectverb-object command grammar based on the speech input. Gesture is used only to dereference deictics in the speech input. This resulted in a functional, but rigid application, which serves as a standard of comparison for future approaches. For the second approach we investigated combining speech and gesture at a very low level. Our goal was to take advantage of correlations between speech and gesture data streams to recover from ambiguities in the corresponding data stream. We believe that integrating speech and gesture streams in symbolic form risks missing important correlations among the data. This experiment showed evidence of improved classification when speech and gesture are processed together, as opposed to the classification with either the speech or gesture data alone. It also provided insights about finding and representing low-level gesture primitives, using of phoneme probabilities as input, and synchronizing speech and gesture primitives. Finally, this thesis discusses potential improvements to our original system, as well as additions that could be made to the system to expand the user's freedom of expression in our multimodal environment. Thesis Supervisor: Alex P. Pentland Title: Academic Head, MIT Media Arts and Sciences Program Toshiba Professor of Media Arts and Sciences
Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)

The removal of non-brain regions in neuroimaging is a critical task to perform a favorable preprocessing. The skull-stripping depends on different factors including the noise level in the image, the anatomy of the subject being scanned and the acquisition sequence. For these and other reasons, an ideal brain extraction method should be fast, accurate, user friendly, open-source and knowledge based (to allow for the interaction with the algorithm in case the expected outcome is not being obtained), producing stable results and making it possible to automate the process for large datasets. There are already a large number of validated tools to perform this task but none of them meets the desired characteristics. In this paper we introduced an open source brain extraction tool (OSBET), composed of four steps using simple well-known operations such as: optimal thresholding, binary morphology, labeling and geometrical analysis that aims to assemble all the desired features. We present an experiment comparing OSBET with other six state-of-the-art techniques against a publicly available dataset consisting of 40 T1-weighted 3D scans and their corresponding manually segmented images. OSBET gave both: a short duration with an excellent accuracy, getting the best Dice Coefficient metric. Further validation should be performed, for instance, in unhealthy population, to generalize its usage for clinical purposes.
A Review on Matching For Sketch Technique

In the area of digital image processing content based image retrieval (CBIR) is one of the most popular, researches raising area. Many tools like Google, Yahoo & other used textual annotation of images. The principle work that was done by CBIR is to extract visual content of image intuitively like color, shape & texture. Additionally sketch based image retrieval system (SBIR) is also plays very imperative role within the content based image retrieval system (CBIR). The system including problem & challenges concerned with design and constitution of content based image retrieval system. The vital objective that holds the information gap between a sketch & color image we have to design & appliance task specific descriptor. The system states that a description about a possible solution for the implementation of descriptor to carry a special sequence of altering the steps of the reconstruction of full color image sketch can be compared. For the preparation of gallery can take a shop images with possible application for forensic sketch identical images. Keywords: Sketch-based image retrieval, Image databases, Image descriptors, CBIR, SBIR.
SPARQL distributed query processing over linked data

Driven by the Semantic Web standards, an increasing number of RDF data sources are published and connected over the Web by data providers, leading to a large distributed linked data network. However, exploiting the wealth of these data sources is very challenging for data consumers considering the data distribution, their volume growth and data sources autonomy. In the Linked Data context, federation engines allow querying these distributed data sources by relying on Distributed Query Processing (DQP) techniques. Nevertheless, a naive implementation of the DQP approach may generate a tremendous number of remote requests towards data sources and numerous intermediate results, thus leading to costly network communications. Furthermore, the distributed query semantics is often overlooked. Query expressiveness, data partitioning, and data replication are other challenges to be taken into account. To address these challenges, we first proposed in this thesis a SPARQL and RDF compliant Distributed Query Processing semantics which preserves the SPARQL language expressiveness. Afterwards, we presented several strategies for a federated query engine that transparently addresses distributed data sources, while managing data partitioning, query results completeness, data replication, and query processing performance. We implemented and evaluated our approach and optimization strategies in a federated query engine to prove their effectiveness.
FASTDial: Abstracting Dialogue Policies for Fast Development of Task Oriented Agents

We present a novel abstraction framework called FASTDial for designing task oriented dialogue agents, built on top of the OpenDial toolkit. This framework is meant to facilitate prototyping and development of dialogue systems from scratch also by non tech savvy especially when limited training data is available. To this end, we use a generic and simple frame-slots data-structure with pre-defined dialogue policies that allows for fast design and implementation at the price of some flexibility reduction. Moreover, it allows for minimizing programming effort and domain expert training time, by hiding away many implementation details. We provide a system demonstration screencast video in the following link: https://vimeo.com/329840716
Transparent object detection and location based on RGB-D camera

In order to improve the accuracy and efficiency of robot grasping, we propose a new method for transparent object detection and location that utilize depth image, RGB image and IR image. In detection process, an active depth sensor (RealSense) is firstly employed to retrieve the transparent candidates from the depth image and the corresponding candidates in the RGB image and IR image are then extracted separately. A transparent candidate classification algorithm is subsequently presented that uses SIFT features to recognize the transparent ones from the candidates. In location process, we obtain a new group of RGB images and IR images by adjusting camera orientation to make its optical axis perpendicular to the normal direction of the plane on which the object is placed. The object contours in RGB image and IR image are then extracted, respectively. The three-dimensional object is finally reconstructed by means of stereo matching of the two contours, and the current pose information of the object is calculated in the end. In order to verify the feasibility of the method, we built a hand-eye test system with a movable industrial robot to detect and capture transparent objects at different locations. The final test results demonstrate that the method is more general and effective than the traditional one.
PTB Graph Parsing with Tree Approximation

The Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies. This paper proposes a method that approximates PTB graph-structured representations by trees. By our approximation method, we can reduce nonlocal dependency identification and constituency parsing into single tree-based parsing. An experimental result demonstrates that our approximation method with an off-the-shelf tree-based constituency parser significantly outperforms the previous methods in nonlocal dependency identification.
Effects of long-range dependence of VBR video traffic on queueing performances

Previous works relating to the self-similarity of VBR video traffic have mainly focused on whether the queueing performances largely depend on long range dependence (LRD) or short range dependence (SRD) characteristics. In this paper, by correlation matching of input traffic, we show that there is a knee point of buffer size below which the buffer distribution is dominated by short-range correlations and above which by long-range correlations. It is also shown that the knee point is a decreasing function of utilization parameter. We conclude that if the queue operates under the conditions of low utilization and practical buffer size, the input traffic can be modeled by short-range dependent model which is generally Markovian although the input traffic hits long-range dependence.
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems

An arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions. This work presents a novel approach, Quantity Tagger, that automatically discovers such hidden relations by tagging each quantity with a sign corresponding to one type of mathematical operation. For each quantity, we assume there exists a latent, variable-sized quantity span surrounding the quantity token in the text, which conveys information useful for determining its sign. Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches.
Assessing teachers' acceptance of educational technologies: beware for the congruency between user acceptance and actual use

In this study, we investigated the extent to which self-reported measures for user acceptance (attitude, behavioral intention, self-reported frequency of use), added to the prediction of several aspects of use of a portal for education. Data from 835 teachers was collected: questionnaire "acceptance" data on one occasion and five parameters for observed use (number of logins, downloads, uploads, page views, and reactions) were extracted on two occasions from the portal database. We found that the self-reported measures for acceptance primarily predicted search behavior (monthly number of logins, downloads and page views), and not share behavior (monthly number of uploads and reactions). So, researchers aiming to assess teachers' acceptance of a technology to contribute information, should adjust their measures for user acceptance so that these correspond with the targeted actual behavior.
Mixed Integer Multi-Objective Goal Programming Model For Green Capacitated Vehicle Routing Problem

Nowadays, environmental effects of logistics and extensive consumption of natural resources have been given more attention by governments and industries. Â Conventionally, these issues and their impact on distribution logistics were not emphasized specifically or addressed directly when solving the Vehicle Routing Problem (VRP). Hence, Green VRP (GVRP) has been introduced to take into consideration both the economic and environmental costs when determining effective routes for distribution services. GVRP is a branch of green logistics in which the externalities of using vehicles, enhancement of transportation effectiveness at operational level, ensuring optimal energy consumption of vehicles, and minimizing fuel consumption are taken into account in the routing and scheduling. This paper presents our study which concerns with formulating a mathematical programming model of Green Capacitated VRP (GCVRP), which focuses are on minimization of the greenhouse gas emissions and fuel consumption, to assuage the resulting effects of transportation on the environment. Â The formulated Mixed Integer Goal Programming (MIGP) model has multiple objective functions as its goal, which are minimizing the total distance travelled, minimizing the total fuel consumption and minimizing the total Carbon Dioxide emissions. Two set of benchmark instances have been used to test the proposed model. The MIGP model is solved by the preemptive GP approach and using the MATLAB intlinprog solver. Â Based on the computational results, the model has been proven to be able to produce optimal solutions, thus indicating that it has the potential to be applied to real-world VRPs.
Inventing Literate Identities: The Influence of Texts and Contexts

This study investigated the influence of different (and sometimes conflicting) literacy contexts on the literate identities of “struggling” beginning readers. The participants were five first graders from three different classrooms in a school in a large Midwestern city. The children met with the researchers in a Reading Club twice weekly throughout the school year to explore reading and writing strategies that focused on predicting and constructing meaning. The classroom instruction of the two children reported here focused them on accuracy in reading and writing. The findings reveal that supplementary programs may not be able to overcome classroom experiences that focus on accuracy and skills.
Research on domain terminology recognition based on dependency tree-conditional random field

In view of the inconsistency of Chinese patent information in manual marking and classification, which leads to problems such as missed detection, partial detection and noise of patent search, this paper proposes a method based on the dependency tree-conditional random field(CRF) identification field terminology. The method is based on the modern grammar theory of dependency, using the existing technology to mark the dependency relationship. Finally, the corresponding technical feature words are identified in the results of the dependency labelling, and the training data is used as the training data to train the conditional random field model to identify the domain terminology. The experimental results show that the acquisition of training data through the dependency tree can improve the accuracy, recall and F value of the recognition results.
Medical image registration based on fuzzy c-means clustering segmentation approach using SURF

An approach to medical image registration using Fuzzy c-Means (FCM) clustering segmentation and Speeded-Up Robust Feature (SURF) detector is presented. This approach uses FCM to obtain reference- and floating-segmented images. Volume control points of these segmented images determine the quality of image registration. Based on these volume control points, features are extracted from reference and floating images using SURF and then matched to perform image registration. The proposed registration algorithm using FCM and SURF is faster and robust against different image transformations like standard SIFT, other recent fuzzy and neural-based methods quantitatively. Simulations for FCM clustering using SURF based on a multi-resolution approach using the image of the same size but with different scales are also shown here.
Screwing assembly oriented interactive model segmentation in HMD VR environment

Although different approaches of segmenting and assembling geometric models for 3D printing have been proposed, it is difficult to find any research studies, which investigate model segmentation and assembly in head‐mounted display (HMD) virtual reality (VR) environments for 3D printing. In this work, we propose a novel and interactive segmentation method for screwing assembly in the environments to tackle this problem. Our approach divides a large model into semantic parts with a screwing interface for repeated tight assembly. Specifically, after a user places the cutting interface, our algorithm computes the bounding box of the current part automatically for subsequent multicomponent semantic Boolean segmentations. Afterwards, the bolt is positioned with an improved K3M image thinning algorithm and is used for merging paired components with union and subtraction Boolean operations respectively. Moreover, we introduce a swept Boolean‐based rotation collision detection and location method to guarantee a collision‐free screwing assembly. Experiments show that our approach provides a new interactive multicomponent semantic segmentation tool that supports not only repeated installation and disassembly but also tight and aligned assembly.
Locally Balanced Inductive Matrix Completion for Demand-Supply Inference in Stationless Bike-Sharing Systems

Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing mode greatly solves the last mile issue of the commuters, and better meets their real bike usage demand. However, it also poses new challenges for operators to manage the system. The first primary challenge is how to accurately estimate the real bike usage demand in different areas of a city and in different time intervals, which is crucial for the system planning and operation. This paper for the first time proposes a data driven approach for bike usage demand inference in stationless bike-sharing systems. The idea is that we first estimate the demands in some regions and time intervals from a small number of observed bike check-out/in data directly, and then use them as seeds to infer the region-level bike usage demands of an entire city. Specifically, we formulate this problem as a matrix completion task by modeling the bike usage demand as a matrix whose two dimensions are time intervals of a day and regions of a city, respectively. With the observation that POI distribution of a region is an important indicator to bike demand, we propose to utilize inductive matrix factorization by considering POIs as side information. As the bike usage data are highly correlated in both spatial and temporal dimensions, we also incorporate the spatial-temporal correlations as well as the balanced bike usage constraint into a joint optimization framework. We evaluate the proposed model on a large Mobike trip dataset collected from Beijing, and the experimental results show its superior performance by comparison with various baseline methods.