Miracle-dz/UniFAR-FTUs huggingface dataset:
Local structure and determinism in probabilistic databases

While extensive work has been done on evaluating queries over tuple-independent probabilistic databases, query evaluation over correlated data has received much less attention even though the support for correlations is essential for many natural applications of probabilistic databases, e.g., information extraction, data integration, computer vision, etc. In this paper, we develop a novel approach for efficiently evaluating probabilistic queries over correlated databases where correlations are represented using a factor graph, a class of graphical models widely used for capturing correlations and performing statistical inference. Our approach exploits the specific values of the factor parameters and the determinism in the correlations, collectively called local structure, to reduce the complexity of query evaluation. Our framework is based on arithmetic circuits, factorized representations of probability distributions that can exploit such local structure. Traditionally, arithmetic circuits are generated following a compilation process and can not be updated directly. We introduce a generalization of arithmetic circuits, called annotated arithmetic circuits, and a novel algorithm for updating them, which enables us to answer probabilistic queries efficiently. We present a comprehensive experimental analysis and show speed-ups of at least one order of magnitude in many cases.
Supporting seamless mobility in named data networking

Information-Centric Networking (ICN) architectures aim to replace current host-centric IP architecture with an information-centric one for efficient, secure, and reliable dissemination of information. ICN is built on several salient principles such as publish and subscribe paradigm, named content, innetwork caching, and security over atomic information objects. These features allow a data chunk to be cached and retrieved from multiple nodes in the network, and can be validated without building a connection with its host. Though these tenets simplify the mobility problem in ICN, seamless mobility for real-time applications still demands a control plane. We propose three cross-layer network-assisted seamless mobility schemes: (1) point of attachment based, (2) rendezvous point based, and (3) multicast based, with the overall design objective of minimizing the loss of interests and data during a handoff scenario. This paper describes these schemes in named data networking (NDN) framework and discusses their trade-offs in terms of control and forwarding plane requirements.
A Novel Noise Robust Front-End Using First Order VTS in Construction of Mel-Warped Wiener Filter

In this paper, we first review two approaches in the context of robust recognition, e.g. speech enhancement based two-stage mel-warp Wiener filtering (MWF) (A. Agarwal and Y.M. Cheng, 1999) and first-order vector Taylor series (VTS) (P.J. Moreno et al., 1996) compensation in log power spectrum, which are widely used. A new noise robust front-end is proposed, in which VTS compensation derived statistics are used to construct the mel-warped Wiener filter. We will show that this noise robust front end is superior. The experiments results prove that our proposed method does show significant improvement over VTS and MWF
A scalable Web cache consistency architecture

The rapid increase in web usage has led to dramatically increased loads on the network infrastructure and on individual web servers. To ameliorate these mounting burdens, there has been much recent interest in web caching architectures and algorithms. Web caching reduces network load, server load, and the latency of responses. However, web caching has the disadvantage that the pages returned to clients by caches may be stale, in that they may not be consistent with the version currently on the server. In this paper we describe a scalable web cache consistency architecture that provides fairly tight bounds on the staleness of pages. Our architecture borrows heavily from the literature, and can best be described as an invalidation approach made scalable by using a caching hierarchy and application-level multicast routing to convey the invalidations. We evaluate this design with calculations and simulations, and compare it to several other approaches.
Computation capacity constrained joint transmission design for C-RANs

This paper considers the joint processing design for the cloud radio access network (C-RAN) with limited cloud computation capacity. This amounts to determine the set of remote radio heads (RRHs) serving each user and the corresponding precoding vectors whose corresponding computation effort (CE) is a non-linear function of the number of antennas pooled from all serving RRHs and the modulation bits. Toward this end, we propose a novel three-step approach to solve the underlying mixed non-linear integer program. First, we transform this problem into a group association problem (GAP) with additional association constraints where each user must be associated with exactly one particular group of RRHs. Second, we study the relaxed power minimization problem (PMP) where the group association integer variables are relaxed and the computational constraint functions are approximated by weighted linear functions of transmission powers. We prove that this relaxed PMP can be solved optimally and the obtained optimal solution satisfies all association constraints of the original GAP problem. Third, we develop an iterative procedure to update the weight parameters of the approximated computational constraint functions to drive the achieved solution to an efficient and feasible solution of the original problem. Finally, we present numerical results to demonstrate the significant gains of our proposed design compared to that due to a fast greedy algorithm.
Testing antivirus engines to determine their effectiveness as a security layer

This research has been undertaken to empirically test the assumption that it is trivial to bypass an antivirus application and to gauge the effectiveness of antivirus engines when faced with a number of known evasion techniques. A known malicious binary was combined with evasion techniques and deployed against several antivirus engines to test their detection ability. The research also documents the process of setting up an environment for testing antivirus engines as well as building the evasion techniques used in the tests. This environment facilitated the empirical testing that was needed to determine if the assumption that antivirus security controls could easily be bypassed. The results of the empirical tests are also presented in this research and demonstrate that it is indeed within reason that an attacker can evade multiple antivirus engines without much effort. As such while an antivirus application is useful for protecting against known threats, it does not work as effectively against unknown threats.
Categorization of common coupling and its application to the maintainability of the Linux kernel

Data coupling between modules, especially common coupling, has long been considered a source of concern in software design, but the issue is somewhat more complicated for products that are comprised of kernel modules together with optional nonkernel modules. This paper presents a refined categorization of common coupling based on definitions and uses between kernel and nonkernel modules and applies the categorization to a case study. Common coupling is usually avoided when possible because of the potential for introducing risky dependencies among software modules. The relative risk of these dependencies is strongly related to the specific definition-use relationships. In a previous paper, we presented results from a longitudinal analysis of multiple versions of the open-source operating system Linux. This paper applies the new common coupling categorization to version 2.4. 20 of Linux, counting the number of instances of common coupling between each of the 26 kernel modules and all the other nonkernel modules. We also categorize each coupling in terms of the definition-use relationships. Results show that the Linux kernel contains a large number of common couplings of all types, raising a concern about the long-term maintainability of Linux.
Eurographics Symposium on Geometry Processing 2015 Analysis and Synthesis of 3d Shape Families via Deep-learned Generative Models of Surfaces

Figure 1: Given a collection of 3D shapes, we train a probabilistic model that performs joint shape analysis and synthesis. (Left) Semantic parts and corresponding points on shapes inferred by our model. (Right) New shapes synthesized by our model. Abstract We present a method for joint analysis and synthesis of geometrically diverse 3D shape families. Our method first learns part-based templates such that an optimal set of fuzzy point and part correspondences is computed between the shapes of an input collection based on a probabilistic deformation model. In contrast to previous template-based approaches, the geometry and deformation parameters of our part-based templates are learned from scratch. Based on the estimated shape correspondence, our method also learns a probabilistic generative model that hierarchically captures statistical relationships of corresponding surface point positions and parts as well as their existence in the input shapes. A deep learning procedure is used to capture these hierarchical relationships. The resulting generative model is used to produce control point arrangements that drive shape synthesis by combining and deforming parts from the input collection. The generative model also yields compact shape de-scriptors that are used to perform fine-grained classification. Finally, it can be also coupled with the probabilistic deformation model to further improve shape correspondence. We provide qualitative and quantitative evaluations of our method for shape correspondence, segmentation, fine-grained classification and synthesis. Our experiments demonstrate superior correspondence and segmentation results than previous state-of-the-art approaches. 1. Introduction Discovering geometric and semantic relationships of 3D shapes is fundamental to several computer graphics applications. In particular, several tools for geometric modeling, manufacturing, shape retrieval and exploration can benefit from algorithms that automatically extract part, region and point correspondences within large shape collections. During the recent years, due to the growing number of online
A color image segmentation approach based on fuzzy similarity measure

We propose an image segmentation scheme based on a fuzzy color similarity measure to segment out meaningful objects in an image according to human perception. The proposed method first defines a set of fuzzy colors based on the HLS color coordinate space. Each pixel in an image is represented by a set of fuzzy colors that are the most similar colors in the color palette selected by humans. Then, a fuzzy similarity measure is developed for evaluating the similarity of fuzzy colors between two pixels. We recursively merge adjacent pixels to form meaningful objects by the fuzzy similarity measure until there is no similar color between adjacent pixels. Experiments demonstrate that the proposed method can extract meaningful objects from images effectively.
Repairing Incomplete Measured Data with Haptic Interaction

In this paper, a novel incomplete measured data repair method based on haptic interaction is proposed. The proposed method is implemented with PHANTOM Desktop haptic device, which is used to provide force and torque feed back to the user and to provide accessibility to the surface of the input digitized model. The user can feel the surface of the model, and select the area that needs to be repaired. Through a case study, this paper highlights the capability of haptic modeling and haptic interaction based on SensAble Phantom/ FreeForm system when used in data repair.
Discrimination power weighted subword-based speaker verification

Since the discriminative powers of different phones vary, weighting techniques can be applied to bias the log-likelihood ratio of different phone segments to emphasize the more discriminating phones. First, we derive a Kullback-Leibler distance-based phone log-likelihood weighting scheme that incorporates the duration information. Second, we propose a posterior probability transformation method such that contributions from different phones are weighted implicitly according to their discrimination power. Using the two proposed approaches reduces speaker verification equal error rate by more than 10% on the YOHO database.
Deep Distance Metric Learning with Data Summarization

We present Deep Stochastic Neighbor Compression (DSNC), a framework to compress training data for instance-based methods (such as k-nearest neighbors). We accomplish this by inferring a smaller set of pseudo-inputs in a new feature space learned by a deep neural network. Our framework can equivalently be seen as jointly learning a nonlinear distance metric (induced by the deep feature space) and learning a compressed version of the training data. In particular, compressing the data in a deep feature space makes DSNC robust against label noise and issues such as within-class multi-modal distributions. This leads to DSNC yielding better accuracies and faster predictions at test time, as compared to other competing methods. We conduct comprehensive empirical evaluations, on both quantitative and qualitative tasks, and on several benchmark datasets, to show its effectiveness as compared to several baselines.
Fano resonances in a three-terminal nanodevice

Electron transport through a quantum sphere with three one-dimensional wires attached to it is investigated. An explicit form for the transmission coefficient as a function of the electron energy is found from first principles. The asymmetric Fano resonances are detected in the transmission of the system. The collapse of the resonances is shown to appear under certain conditions. A two-terminal nanodevice with an additional gate lead is studied using the developed approach. Additional resonances and minima of transmission are indicated in the device.
MARS: a MultilAnguage Recommender System

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more complex language-independent representation based on word meanings. As a consequence, the recommender system is able to suggest items represented in a language different from the one used in the content-based user profile. Experiments conducted in a movie recommendation scenario show the effectiveness of the approach.
Sliding Window Object Detection without Spatial Clustering of Raw Detection Responses

Abstract Sliding window object detection has received remarkable attention in recent years due to great versatility and extraordinary detection performance. However, straightforward applications of the concept fail to meet criteria of real applications due to insufficient precision and inaccurate localization. Localization accuracy is especially important when the detection needs to be followed by recognition. In this paper, we present a detection approach which completely obviates the need for blind spatial clustering of nearby detection responses, which is known as a major factor of localization inaccuracy. The approach has been evaluated on traffic sign detection, where we consider the superclass of triangular warning signs. The obtained results confirm the viability of the approach and provide useful directions for future work.
A Bidirectional Process Algorithm for Mining Probabilistic Frequent Itemsets

Nowadays, frequent item set mining is the major task in association rule mining. With the observation that the support plays an important role in mining frequent item sets, in this paper, we review the previous efficient algorithms and study the effect of different order of support in the performance of frequent item sets mining algorithms and propose our improved schedule. In our new algorithm, items are sorted in descending order according to the frequencies in transaction cache while item sets use ascending order of support during support count. Compared with other algorithms, the results of experiments show that the new algorithm gains better performance on on well-known benchmark data sets.
Modeling and analysis of TCP dynamics over IEEE 802.11 WLAN

Today, IEEE 802.11 wireless LAN (WLAN) is a prevailing solution for the wireless Internet access while transport control protocol (TCP) is the dominant transport protocol in the Internet. It is known that, in the infrastructure WLAN with multiple long-lived TCP flow stations, the actual number of contending TCP stations is limited to a small number, and the aggregated TCP throughput is basically independent of the total number of TCP stations. It is due to the behavior of the TCP flow control in the infrastructure WLAN, where the downlink (i.e., access point-to-stations) is the bottleneck link. In this paper, we develop an accurate and realistic analytical model of long-lived TCP over WLAN based on a well-known p-persistent model of the 802.11. We calculate the average number of the active TCP stations as well as the aggregated TCP throughput using our model for a given number of TCP stations and the maximum TCP receive window size. We verify our model via ns-2 simulations. Moreover, we find from our work that the minimum contention window sizes of the standards (i.e., 31 and 15 for IEEE 802.11b and 802.11a, respectively) are not optimal in the sense of the TCP throughput maximization
Small Molecules from Nature Targeting G-Protein Coupled Cannabinoid Receptors: Potential Leads for Drug Discovery and Development

The cannabinoid molecules are derived from Cannabis sativa plant which acts on the cannabinoid receptors types 1 and 2 (CB1 and CB2) which have been explored as potential therapeutic targets for drug discovery and development. Currently, there are numerous cannabinoid based synthetic drugs used in clinical practice like the popular ones such as nabilone, dronabinol, and Δ9-tetrahydrocannabinol mediates its action through CB1/CB2 receptors. However, these synthetic based Cannabis derived compounds are known to exert adverse psychiatric effect and have also been exploited for drug abuse. This encourages us to find out an alternative and safe drug with the least psychiatric adverse effects. In recent years, many phytocannabinoids have been isolated from plants other than Cannabis. Several studies have shown that these phytocannabinoids show affinity, potency, selectivity, and efficacy towards cannabinoid receptors and inhibit endocannabinoid metabolizing enzymes, thus reducing hyperactivity of endocannabinoid systems. Also, these naturally derived molecules possess the least adverse effects opposed to the synthetically derived cannabinoids. Therefore, the plant based cannabinoid molecules proved to be promising and emerging therapeutic alternative. The present review provides an overview of therapeutic potential of ligands and plants modulating cannabinoid receptors that may be of interest to pharmaceutical industry in search of new and safer drug discovery and development for future therapeutics.
RCS: Runtime resource and core scaling for power-constrained multi-core processors

Providing a sufficient voltage/frequency (V/F) scaling range is critical for effective power management. However, it has been fraught with decreasing nominal operating voltage and increasing manufacturing process variability that makes it harder to scale the minimum operating voltage (VMIN). In this paper, we first present a resource and core scaling (RCS) technique that jointly scales (i) the resources of a processor and (ii) the number of operating cores to maximize the performance of power-constrained multi-core processors. More specifically, we uniformly scale the resources that are both associated with each core (e.g., L1 caches and execution units (EUs)) and shared by all the cores (e.g., last-level cache (LLC)) as a means to compensate for lack of a V/F scaling range. Under the maximum power constraint, disabling some resources allows us to increase the number of operating cores, and vice versa. We demonstrate that the best RCS configuration for a given application can improve the geometric-mean performance by 21%. Second, we propose a runtime system that predicts the best RCS configuration for a given application and adapts the processor configuration accordingly at runtime. The runtime system only needs to examine a small fraction of runtime to predict the best RCS configuration with accuracy well over 90%, whereas the runtime overhead of prediction and adaptation is small. Finally, we propose to selectively scale the resources in RCS (dubbed sRCS) depending on application's characteristics and demonstrate that sRCS can offer 6% higher geometric-mean performance than RCS that uniformly scales the resources.
Utilizing a Remote LED Stimulus to Concurrently Measure Cognitive and Visual Task Demand

The ISO 17488 standard Detection Response Task (DRT) has been validated as an effective tool for measuring fluctuations in cognitive workload while driving and performing secondary tasks. This research evaluated the possibility of consolidating a dual stimulus DRT to a single remote LED stimulus to concurrently measure visual and cognitive demand. Hit rate and reaction times to a remote LED stimulus and an ISO standard vibrotactor stimulus were compared for three in-vehicles tasks: a single task baseline, a cognitively demanding task, and a visually demanding task. Analyses showed that the remote LED and vibrotactor were equally sensitive to cognitive load, while the remote LED was more sensitive to visual load. We suggest the remote LED DRT system serves as a cost-effective, practical, sensitive method to concurrently assess cognitive and visual demand.
Recognition-based word counting for reliable barge-in and early endpoint detection in continuous speech recognition

In this paper, we present a word counting method that enables speech recognition systems to perform reliable barge-in detection and also make a fast and accurate determination of end of speech. This is achieved by examining partial recognition hypotheses and imposing certain “word stability” criteria. Typically, a voice activity detector is used for both barge-in detection and end of speech determination. We propose augmenting the voice activity detector with this more reliable recognition-based method. Experimental results for a connected digit task show that this approach is more robust for supporting barge-in since it is less prone to interrupting the announcement when extraneous speech input is encountered. Also, by using the early endpoint decision criterion, average response times are sped up 75% for this connected digit task.