Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximation Algorithms for Probabilistic Decoding

ApproximationAlgorithmsforProbabilisticDeco dingIrinaRish,KalevKaskandRinaDechterDepartmentofInformationandComputerScienceUniversityofCalifornia,Irvinefirinar,kkask,dechterg@ics . uc i.eduAbstractIt was recently shown that the problem of deco ding messages transmitted throughanoisychannelcanb eformulatedasb eliefup datingtaskover aprobabilisticnet-work[13]. Moreover,itasobservedthatiterativeapplicationofthe(lineartime)b eliefpropagation algorithm designedfor p olytrees [14] outp erformed state of the artdeco dingalgorithms, even though the corresp ondingnetworks may have many cycles.Much of recent research in co ding fo cuses on explainingthis phenomenon.This pap er demonstrates empiricallythat an approximation algorithmapprox-mpeforsolvingthemostprobableexplanation(MPE)problem,develop edwithinre-centlyprop osedmini-bucketeliminationframework[3],outp erformsiterativeb eliefpropagationonclassesofco dingnetworksthathaveb oundedinducdwidth.Ourexp erimentssuggestthatdeco dersbasedon ndinganMPEassignment(blo ck-wisedeco ding)canb esup eriortothecommonlyusedb eliefup datingdeco ders(bit-wisedeco ders). 1Intro ductionInthispap erweevaluatethequalityofarecently prop osedapproximationscheme, calledmini-bucket, forprobabilistic deco ding.Recently, a class of parameterized approximation algorithms based on the bucket elimina-tion framework was prop osed and analyzed [3]. The approximation scheme uses as a control-ling parameter a b ound on size of probabilistic functions created during variable elimination,allowingatrade-o b etweenaccuracyandeciency [7,5]. Thealgorithmserepresentedand analyzed for several tasks, such as: nding the most probable explanation (mpe), ndingthe maximum a p osteriori hyp othesis (map), and b elief up dating.Encouraging results wereThisworkaspartiallysupp ortedbyNSFgrantIRI-9157636andAirForceOceofScienti cResearch grant, AFOSR 900136, Ro ckwell International and Amada of America.1
Diets of steers on a shrub steppe rangeland in south central Washington.

Highlight: Botanical composition of stew-s diets on a shrub-sreppe ran&and in south. upper meter of its profile. The vegetation is central Washington was examined by inspecrion of finely ground fecal samples viewed representative of the Arremisin tn’dentatathrough a microscope. Four species, Cusick bluegrass, Thurber needle~rass, hawksAgropyron spicatum association (Rickard beard and bluebunch wheatgrass, comprised 92% of the rotal diet. Grasses accountedfor et al., 1975). 73% of the diet andforbs and halfshrubs contribured 26%. Botonica, composirion of the Two pastures, each 9 ha in area, were diets changed throughout the spring grazing season with changiq availabiliry and mntuestablished within a homogenous plant ration of herbage. Preference indices in decreasing order were: Cusick bluegrass > community having a history ofno livestock Thurber needlegrass > hawksbeard > bluebunch wheatgrass, but bluebunch wheatgrass grazing since 1943 (Fig. 1). The pastures was the mo~f abundant species in thepasture. The second mmf abundant grass, Sandberg were enclosed with a four-strand barbed bluegrass, was nor . srlecred by . weers. wire fence, having a common watering tank between them. During 1971, 15 yearling Information on cattle diets is needed sea level. The precipitation averages 23 cm steers were grazed on both pastures for 58 for long-term productive use and manbetween October and May. The soil is a days. Steers were moved from one pasture aeement of raneelands. In the shrubdeep silt loam with very few stones in the to the other at weekly intervals. The same steppe region o’f south-central Washington, grazing is usually done during winter and spring months This study reports on the botanical composition of steer diets while grazing an Artemisia tridentataAgropyron spicatum pasture. Forage selectivity as influenced by species and season was also analyzed.
ROLEX: A Scalable RDMA-oriented Learned Key-Value Store for Disaggregated Memory Systems

Disaggregated memory systems separate monolithic servers into different components, including compute and memory nodes, to enjoy the beneﬁts of high resource utilization, ﬂexi-ble hardware scalability, and efﬁcient data sharing. By exploiting the high-performance RDMA (Remote Direct Memory Access), the compute nodes directly access the remote memory pool without involving remote CPUs. Hence, the ordered key-value (KV) stores (e.g., B-trees and learned indexes) keep all data sorted to provide rang query service via the high-performance network. However, existing ordered KVs fail to work well on the disaggregated memory systems, due to either consuming multiple network roundtrips to search the remote data or heavily relying on the memory nodes equipped with in-sufﬁcient computing resources to process data modiﬁcations. In this paper, we propose a scalable RDMA-oriented KV store with learned indexes, called ROLEX, to coalesce the ordered KV store in the disaggregated systems for efﬁcient data storage and retrieval. ROLEX leverages a retraining-decoupled learned index scheme to dissociate the model retraining from data modiﬁcation operations via adding a bias and some data-movement constraints to learned models. Based on the operation decoupling, data modiﬁcations are directly executed in compute nodes via one-sided RDMA verbs with high scalability. The model retraining is hence removed from the critical path of data modiﬁcation and asynchronously executed in memory nodes by using dedicated computing resources. Our experimental results on YCSB and real-world workloads demonstrate that ROLEX achieves competitive performance
MetropoGIS: A City Modeling System

The launch of new high resolution image sensors, the availability of reasonable cheap, high performance PCs and new technologies developed by the computer vision community are the basic requirements for a new way of modeling virtual habitats. Through the use of digital sensors redundancy is available at almost no additional cost. This redundancy is the key to solve one of the main problems in photogrammetry and computer vision, namely the correspondence problem. The main goal in all our R&D work is targeted to minimize the human interaction during the modeling process. All the results throughout this paper are obtained fully automatically if not stated otherwise. This paper is organized as follows. Section 2 describes the estimation of a 3D city block model using airborne images. In Section 3 we explain the orientation of terrestrial acquired images and their registration towards the 3D block model in more detail. Section 4 outlines our approach on how important objects are modeled very precisely. Section 5 concludes this paper and describes some ongoing and future work.
Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network Paper ID : ? ? ?

We present a new part-of-speechtagger that demonstratesthe following ideas: (i) explicit use of both precedingand following tag contexts via a dependenc y network representation,(ii) broad useof lexical features,includingjointly conditioning on multiple consecuti ve words, (iii) effective use of priors in loglinear models, and (iv) finegrainedmodelingof linguistic andunknown word features.Using theseideastogether , the resulting taggergivesa 97.21%accuracy on thePennTreebankWSJ,anerrorreductionof 4%onthebestprevioussingleautomaticallylearnedtaggingresult.
Automatic generation of tests from domain and multimedia ontologies

The aim of this article is to present an approach for generating tests in an automatic way. Although other methods have been already reported in the literature, the proposed approach is based on ontologies, representing both domain and multimedia knowledge. The article also reports on a prototype implementation of this approach, which automatically creates tests using the Semantic Web standard technology OWL (Ontology Web Language) as well as proper annotations of images. The proposed approach is independent of specific domain characteristics, since question items are generated according to generic ontology-based strategies. In the presented prototype implementation, simple natural language generation techniques are used to project the items in the tests.
An algorithm for computing minimal bidirectional linear recurrence relations

We consider the problem of computing a linear recurrence relation (or equivalently a Linear Feedback Shift Register) of minimum order for a finite sequence over a field, with the additional requirement that not only the highest but also the lowest coefficient of the recurrence is non-zero. Such a recurrence relation can then be used to generate the sequence in both directions (increasing or decreasing order of indices), so we will call it bidirectional. If the field is finite, a sequence is periodic if and only if it admits a bidirectional linear recurrence relation. For solving the above problem we propose an algorithm similar to the Berlekamp-Massey algorithm and prove its correctness. We also describe the set of all solutions to this problem and prove some properties of the minimal polynomials of initial segments of the sequence.
Performance Analysis of IEEE 802.15.4 Transceiver System under Adaptive White Gaussian Channel

Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15. 4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15. 4. The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique.
Discovering Human Interactions with Novel Objects via Zero-Shot Learning: Supplementary Material

HO-RPN Given the feature pyramid {P3, P4, P5, P6} from the backbone network, we use two separate branches to generate human and object region proposals. The human branch uses the original region proposal network (RPN) [10] with FPN. At each sliding window position, specifically, 3 anchor boxes with different aspect ratios {1:2, 1:1, 2:1} are generated. At each level in the feature pyramid, we perform a 3× 3 convolution on Pi to obtain a feature map Hi (with 256 channels), followed by two sibling 1× 1 convolutions for classification and regression respectively. Here the classification is to predict the score of human anchor boxes and the regression is to adjust its shape. Over all levels in the feature pyramid, the top K (K = 8 in our experiments) human anchor boxes after the non-maximum suppression (NMS) are kept. In the object branch, similar to RPN, the first step is to perform a 3 × 3 convolution on Pi to get a feature map Oi (with 256 channels). Here we still use a 1 × 1 convolution to regress the anchor boxes, while a relational network is used to predict its score. Specifically, at each sliding window position, the relational network takes as input its 256-D feature in Oi and the 256-D feature of top K human anchor boxes from {Hi}, and outputs the score of 3 anchor boxes. In Eq.(3), g(·) : R 7→ R and f(·) : R 7→ R are both multi-layer perceptrons (MLPs). Over all levels in the feature pyramid, the top 100 object region proposals after NMS are kept.
AIS Electronic Library (AISeL)

: Wireless value added services (VAS) have drawn managerial and academic attention in recent years. Even though these services have been offered for a decade, Short Messaging Services (SMS) is the only VAS that managed to capture a significant market share. Validating a model that explains the diffusion of SMS is necessary for understanding potential adoption of other VAS. The extant literature provides little insight into technology adoption in pay-per-use contexts, such as SMS. This study taps into this void and suggests a convergent marketing and information systems view by including perceived value (PV) as a key multidimensional determinant of behavioral intentions to use VAS. A sample of 171 SMS users is utilized for hypothesis testing and model validation using PLS. Overall, this study introduces the concept of PV into information systems research and forms the foundations for subsequent studies of technology adoption of wireless VAS and other pay-per-use information services.
Automatic Extraction of Roads from High Resolution Aerial and Satellite Images with Heavy Noise

are also complex to analyze. For GIS systems, many features require fast and reliable extraction of roads and intersections. In this paper, we study efficient and reliable automatic extraction algorithms to address some difficult issues that are commonly seen in high resolution aerial and satellite images, nonetheless not well addressed in existing solutions, such as blurring, broken or missing road boundaries, lack of road profiles, heavy shadows, and interfering surrounding objects. The new scheme is based on a new method, namely reference circle, to properly identify the pixels that belong to the same road and use this information to recover the whole road network. This feature is invariable to the shape and direction of roads and tolerates heavy noise and disturbances. Road extraction based on reference circles is much more noise tolerant and flexible than the previous edge-detection based algorithms. The scheme is able to extract roads reliably from images with complex contents and heavy obstructions, such as the high resolution aerial/satellite images available from Google maps.
Security challenges for e-learning ecosystems

E-learning systems are used by almost every educational institution and the system is upgrading day-by-day (e.g. traditional monolithic e-learning system to modern elearning ecosystem or cloud-based system). E-learning system provides a lot of benefits in the educational process, at the same time security is one of the important concern for e-learning systems. To prevent loss of user’s data and to prevent from any kind of damage, a secured e-learning system is a must. Every system has some security issues and challenges. To ensure the security of such system existing security issues and problems are investigated. The study is interested to know stakeholder’s view on the security of e-learning system as well as how the user of the system deals with the security problem. The e-learning system has faced a lot of security issues before, which has been solved now. This kind of experienced issues will be discussed in this study with current security issues. This study is also investigated the security benefits and increased threats of shifting from traditional monolithic system to modern elearning ecosystem or cloud-based system. The study has used a combination of two research methodologies (i.e. survey and interview). To find out stakeholder’s view on the security issues and challenges of existing e-learning system a survey is used as a method. To find out the increased threats and benefits of security of modern e-learning ecosystem the interview is used. The answer of the survey is collected from the global student and the interview is conducted among some technical stuff of Norwegian University of Science and Technology, as they are able to provide some inside knowledge about this subject. This study is one of the first that has tested the link between trust, privacy, and security in the e-learning context. The result suggested that perceived trust can lead the e-learning system to a new direction as the user willingly provide more information to the system. The result also shows that the chance of violation of confidentiality, integrity, and availability is higher in the asset assignment. Thus more attention should be given to protect the assets. E-learning system has already experienced some security issues like phishing attacks, identity theft, unauthorized access etc. Still, the system has some other minor security issues and challenges. The shifting from traditional monolithic system to modern elearning ecosystem has a lot of other benefits, yet does not provide security benefits. Rather, this shifting increases the security threats. This research shows the findings of the increased threats of shifting to modern e-learning ecosystem. The findings of this study have various implications for research as well as practice. This finding of security challenges and issues will help to improve the security of e-learning system.
Distribution network reconfiguration for load balancing with a coloured Petri net algorithm

A rule-based expert system with a coloured Petri net (CPN) algorithm is developed for load balancing of distribution systems. CPN models of the distribution components such as four-way line switches are proposed to derive the proper switching operations. In this way, the loads on transformers and feeders will become more balanced. Heuristic rules based on the standard load transfer operation procedures of the Taipower distribution system are included in the best first search of the CPN to obtain the desired switching operations for load balancing. For the overload contingency during the summer peak season, load shedding is also considered in the CPN models, to provide service to as many key customers and loads as possible. The priority indices of each feeder and service zone are determined in advance according to the key customers within the service territory. A Taipower distribution system with 68 feeders is selected for computer simulation to demonstrate the effectiveness of the proposed methodology. It is found that load balancing of the distribution system can be obtained very efficiently and the risk of overloads can be reduced by applying the proposed CPN model to determine the optimal switching operation.
Using Active Learning for Activity Recognition on Smartwatches

Advancements in wearable technologies have equipped smartwatches with various sensors. Motion and direction sensors, namely accelerometers, gyroscopes, and magnetometers, have broad applications in human activity recognition. While many activity recognition algorithms have been proposed in recent years, most studies emphasize the use of inertial sensors in smartphone devices or other body-worn sensors. By comparison, very few works have evaluated the application of smartwatches for activity monitoring applications. In this study, we present a system to detect five daily activities using smartwatches. Our system identifies activities with 91.9% accuracy based on over 540 minutes of data collected from twelve subjects. We also demonstrate that the opportunity to deploy active learning for activity recognition can be a significant advantage of smartwatches over other devices. By collecting personal data from subjects online, active learning improves the accuracy of classifier predictions, while lowering variance between different users.
A CORF Computational Model of a Simple Cell

Contribution • CORF is a computational model of a simple cell. • The proposed CORF model shares more properties with a simple cell than the Gabor Function (GF) model. Some properties of a real simple cell CORF [1] GF [2] 1. Orientation selectivity 2. Cross orientation suppression 3. Contrast invariant orientation tuning 4. Response saturation • CORF is more effective than GF in contour detection, which is assumed to be the primary biological role of simple cells. Motivation
Texture Resampling While Raytracing : Approximating the Convolution Region Using Caching MS-CIS-94-03 HUMAN MODELING & SIMULATION LAB

We present a cache-based approach to handling the difficult problem of performing visually acceptable texture resampling/filtering while ray-tracing. While many good methods have been proposed to handle the error introduced by the ray-tracing algorithm when sampling in screen space, handling this error in texture space has been less adequately addressed. Our solution is to introduce the Convolution Mask Approximation Module (CMAM). The CMAM locally approximates the convolution region in texture space as a set of overlapping texture triangles by using a texture sample caching system and ray tagging. Since the caching mechanism is hidden within the CMAM, the ray-tracing algorithm itself is unchanged while achieving an adequate level of texture filtering (area sampling as opposed to point sampling/interpolation in texture space). The CMAM is easily adapted to incorporate prefiltering methods such as MIP mapping and summed-area tables as well as direct convolution methods such as elliptical weighted average filtering.
Enhanced movement tracking with Kinect supported by high-precision sensors

The paper presents a proposal of sensor fusion combining data derived from a Kinect device and highprecision sensors. The main idea was to enhance the tracking of a human arm in order to obtain precise coordinates. The Kinect plays the role of a calibration device and sensors' data are used in kinematics equations for enhanced tracking of the arm. This way the resulting information has less uncertainty and can be directly transmitted to another system, e.g. robotics one. The system has been implemented using kinematics-based approach with transformation operators as well as quaternions. In terms of accuracy the main result is that the Kinect performs very similar with sensors.
Cooperative relative positioning for intelligent transportation system

In an intelligent transportation system, preventing collision accidents from happening requires to maintain a safe distance between vehicles, which is usually computed from position information. In urban areas, however, non-correlated errors due to multipath propagation in absolute positions degrade the accuracy of relative position. On the other hand, simply removing reflected signals might lead to a shortage of satellites in fixing positions. In this paper, we suggest a cooperative relative positioning (CoRelPos) scheme. Vehicles learn measured information of nearby vehicles by inter-vehicle communications. Correlated information, including that of reflected signals, is used to compute relative position. Statistical analysis of experimental logs verifies that information measured by nearby vehicles is highly correlated under most cases. But exceptions do exist, which require correlation detection. Simulation evaluation and initial experimental results confirm that the proposed scheme can effectively improve the accuracy of relative position compared with the state-of-the-art schemes.
J UX M EM : An Adaptive Supportive Platform for Data Sharing on the Grid

: We address the challenge of managing large amounts of numerical data within computing grids consisting of a federation of clusters. We claim that storing, accessing, updating and sharing such data should be considered by applications as an external service . We propose a hierarchical architecture for this service, based on a peer-to-peer approach. This architecture is illustrated through a software platform called J UX M EM (for Juxtaposed Memory), which provides transparent access to mutable data, while enhancing data persistence in a dynamic environment. Managing the volatility of storage resources is specially emphasized. As a proof of concept, we describe a prototype implementation on top of the JXTA peer-to-peer framework, and we report on a preliminary experimental evaluation.