Reviewerly/exHarmony huggingface dataset:
An Optimized Approach to Pipelined Architecture for Fast 2D Normalized Cross-Correlation

An optimized hardware architecture for fast normalized cross-correlation (NCC) is essential in real-time high-speed applications. Typical applications of NCC are in object localization, as one of the best motion estimators and as a similarity measure in the field of image processing. However, high computational cost is a significant drawback of NCC. To reduce computation time and hardware resource usages, this paper presents a feed-forward single-path architecture with parallel computation of numerator and denominator components of NCC. The cross-correlation of two signals is implemented in the frequency domain using pipelined architecture of 2D FFT with polyphase sequential subband decomposition technique. The FFT can be determined for any even length of signal when compared to the traditional method involving a power of two-signal length. The proposed pipelined architecture of NCC is more efficient in terms of computational complexity and memory requirement. This proposed architecture is implemented in Verilog HDL with fixed-point data type which helps to devise simple and efficient architecture, which gives excellent SQNR of more than 90 dB with 22-bit output word length. It can operate at the maximum clock frequency of 220.4[Formula: see text]MHz, takes a total NCC time of 4.36[Formula: see text][Formula: see text]s and has a latency of 996 cycles, giving a throughput of 220 Msamples/s for a block size of [Formula: see text] pixels using Virtex-7 FPGA. This pipelined architecture not only offers an attractive solution for different sizes of image block, but also improves the speed of the system.
A New Image Similarity Metric for Improving Deformation Consistency in Graph-Based Groupwise Image Registration

Graph-based groupwise image registration (G-GIR) aims to register a group of input images accurately without any bias. In G-GIR, an image similarity metric (ISM) is used to construct a graph that links similar images with graph edges. From the graph, a group center image and the shortest paths linking it to all other images can be determined. The deformation field aligning each image to the group center image can be obtained by composing sub-deformation fields that come from registration of adjacent images along the corresponding shortest path. The majority of ISMs used in G-GIR are based on image intensity. Since image intensity can be ambiguous and is not directly related to deformation directions, inconsistency problem in the sub-deformation fields along the shortest paths can occur. The word ``inconsistency'' mentioned here refers to the directions of deformation vectors in the sub-deformation fields along each shortest path are significantly different or even opposite at corresponding locations. Such problem can make G-GIR inefficient and easily to be trapped in local minimum. In this paper, we propose a new ISM for G-GIR, by which the consistency in the sub-deformation fields along the shortest paths can be significantly improved. We evaluate our method in comparison with three state-of-the-art ISMs using a common G-GIR framework. The experimental results with both toy and real images show that our method significantly improves registration accuracy.
Leap Motion Performance in an Augmented Reality Workspace: Integrating Devices with an Interactive Platform

Advances in mobile technology have enabled virtual reality (VR) and augmented reality (AR) systems to become more accessible and affordable. There are several devices that can be integrated with the mobile platform to make the applications more interactive, such as Leap Motion (LM). In this article, an AR environment has been designed that uses an Android smartphone with the LM. It has been evaluated for usability and accuracy by designing 15 sphere-targeting tasks that require the participants to use the LM to place the tip of a virtual index finger within the sphere. The task completion time and fingertip location were recorded, and the accuracy of the task was evaluated by calculating the distance between the fingertip location and the center of the sphere in three dimensions and each individual direction. Participants were the most accurate in the width and height directions, but there was a significant decrease in accuracy in the depth direction. Several participants experienced a decrease in task completion time as they progressed through the tasks, but half of the participants experienced tracking problems that increased their task completion times. Overall, the participants reported that the system was very intuitive and performed as designed; however, further improvements are needed.
Detecting Suspicious File Migration or Replication in the Cloud

There has been a prolific rise in the popularity of cloud storage in recent years. While cloud storage offers many advantages such as flexibility and convenience, users are typically unable to tell or control the actual locations of their data. This limitation may affect users' confidence and trust in the storage provider, or even render cloud unsuitable for storing data with strict location requirements. To address this issue, we propose a system called LAST-HDFS which integrates Location-Aware Storage Technique (LAST) into the open source Hadoop Distributed File System (HDFS). The LAST-HDFS system enforces location-aware file allocations and continuously monitors file transfers to detect potentially illegal transfers in the cloud. Illegal transfers here refer to attempts to move sensitive data outside the (“legal”) boundaries specified by the file owner and its policies. Our underlying algorithms model file transfers among nodes as a weighted graph, and maximize the probability of storing data items of similar privacy preferences in the same region. We equip each cloud node with a socket monitor that is capable of monitoring the real-time communication among cloud nodes. Based on the real-time data transfer information captured by the socket monitors, our system calculates the probability of a given transfer to be illegal. We have implemented our proposed framework and carried out an extensive experimental evaluation in a large-scale real cloud environment to demonstrate the effectiveness and efficiency of our proposed system.
Robustness Speaker Recognition Based on Feature Space in Clean and Noisy Condition

Background &amp; Objective: Speaker Recognition (SR) techniques have been developed into a relatively mature status over the past few decades through development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security and forensics, robustness and reliability of the system are crucial. Methods: The background noise and reverberation as often occur in many real-world applications are known to compromise recognition performance. To improve the performance of speaker verification systems, an effective and robust technique is proposed to extract features for speech processing, capable of operating in the clean and noisy condition. Mel Frequency Cepstrum Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GFCC) are the mature techniques and the most common features, which are used for speaker recognition. MFCCs are calculated from the log energies in frequency bands distributed over a mel scale. While GFCC has been acquired from a bank of Gammatone filters, which was originally suggested to model human cochlear filtering. This paper investigates the performance of GFCC and the conventional MFCC feature in clean and noisy conditions. The effects of the Signal-to-Noise Ratio (SNR) and language mismatch on the system performance have been taken into account in this work. Conclusion: Experimental results have shown significant improvement in system performance in terms of reduced equal error rate and detection error trade-off. Performance in terms of recognition rates under various types of noise, various Signal-to-Noise Ratios (SNRs) was quantified via simulation. Results of the study are also presented and discussed.
Edge based enhancement of retinal images using an efficient JPEG-compressed domain technique

With substantial usage of Imaging Technology in the medical field for the diagnosis and treatment of illnesses, a huge volume of medical images are being generated which provide a bigger challenge in terms of storage, transmission and processing. The high resolution medical images thus generated oc cupy large storage space, and hence they are subjected to compression to make them storage and transmission efficient. Though compression overcomes the issues of storage and transmission to some extent, but the problem of processing compressed images still remains as a challenge. This is because; the usual way of processing the compressed medical images is through the operations of decompression and recompression, which consume lots of computing resources. Therefore, it would be novel, if the compressed medical images are processed and analysed directly in the compressed formats without involving the expensive operations like decompression and recompression. In this direction, the current research paper demonstrates a novel technique of edge based enhancement of retinal images, which is a very critical operation from disease diagnosis perspective, directly in the JPEG compressed domain. The developed algorithm is validated with publicly available retinal datasets of DRIVE and DIARETDB1, and the performance reported is compared with the state-of-the-art techniques in the uncompressed (spatial) domain in terms of both quality of enhancement and computation time.
Privacy Preserving Data Utility Mining Architecture

Since the creation of the cloud computing (CC) paradigm, there has been a rising interest in the adoption of CC from municipalities and city governments toward their effort to address complex civilian issues. CC is a prolific, service-based delivery of enormous computer processing power and data storage across connected communications channels, and is the most significant technological advancement in the IT industry since the inception of the Internet (web), and more so in the evolution of smart cities. It became feasible to share confidential data records through CC for further analysis and mining. However, data sharing prompted various security threats and concerns that have been imposed via ever-escalating phishing attacks using advanced deception. Supreme cyberspace security solutions and mitigation against such phishing attacks became mandatory due to the momentous impacts on global security and the economy. Intensifying the protection performance, precision, and effectiveness of data mining against such cyber-forgery by lowering computational costs appears challenging. Thus, privacy-preserving data mining has emerged as a new research avenue, where various algorithms are developed to anonymize the data to be mined. The K-anonymizing privacy-preserving approach, being the most prospective one, is widely used to secure data. It preserves published data from being linked back to an individual. Yet, the protection and truthfulness potency of this generalized technique is limited to a tiny output space, and often leads to unacceptable utility loss in cases of strict privacy requirements. To surmount this limitation, we propose a hybrid K-anonymity data relocation algorithm. The data relocation, which is a tradeoff between trustworthiness and utility, acts as a control input parameter. The performance of each K-anonymity's iteration is measured to decide the feasibility of data relocation, where data rows are changed into small groups of tuples. These tuples, being indistinguishable to each other, create anonymizations of finer granularity with an assured privacy standard. Furthermore, this approach is designed for L-diversity, and (a, K) anonymity. Experimental results demonstrate considerable utility enhancement as a function of a relatively small number of group relocations.
An Asymptotically Optimal Set Approach for Simulation Optimization

We propose an asymptotically optimal set (AOS) approach for solving stochastic optimization problems with discrete or continuous feasible regions. Our AOS approach is a framework for designing provably convergent algorithms that are adaptive in seeking new points and in resampling or discarding already sampled points. The framework is an improvement over the adaptive search with resampling (ASR) method for stochastic optimization in that it spends less effort on inferior points and uses a more robust estimate of the optimal solution. We present conditions guaranteeing that the AOS approach is globally convergent and will eventually discard suboptimal sampled points with probability one, compare the algorithms, and analyze when (additional) resampling (beyond the minimum) is desirable. Our theoretical results show that AOS has stronger performance guarantees than ASR. Our numerical results suggest that AOS makes substantial improvements over ASR, especially for difficult problems with large numbers of local optima. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0811 .
Cascaded Stripe Memory Engines for Multi-Scale Object Detection in FPGA

Object detection in embedded systems is important for many contemporary applications that involve vision and scene analysis. In this paper, we propose a novel architecture for object detection implemented in FPGA, based on the Stripe Memory Engine (SME), and point out shortcomings of existing architectures. SME processes a stream of image data so that it stores a narrow stripe of the input image and its scaled versions and uses a detector unit which is efficiently pipelined across multiple image positions within the SME. We show how to process images with up to 4K resolution at high frame rates using cascades of SMEs. As a detector algorithm, the SMEs use boosted soft cascade with simple image features that require only pixel comparisons and look-up tables; therefore, they are well suitable for hardware implemenation. We describe the components of our architecture and compare it to several published works in several configurations. As an example, we implemented face detection and license plate detection applications that work with HD images (1280 ×720 pixels) running at over 60 frames/s on Xilinx Zynq platform. We analyzed their power consumption, evaluated the accuracy of our detectors, and compared them to Haar Cascades from OpenCV that are often used by other authors. We show that our detectors offer better accuracy as well as performance at lower power consumption.
Managing Rich Metadata in High-Performance Computing Systems Using a Graph Model

High-performance computing (HPC) systems generate huge amounts of metadata about different entities such as jobs, users, and files. Existing systems can efficiently record and manage part of these metadata, mainly the POSIX metadata of data files (e.g., file size, name, and permissions mode). But another important set of metadata, referred to as “rich” metadata in this study, which record not only wider range of entities (e.g., running processes and jobs) but also more complex relationships between them, are mostly missing in current HPC systems. Yet such rich metadata are critical for supporting many advanced data management functions such as identifying data sources and parameters behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. To uniformly and efficiently manage the rich metadata generated in HPC systems, We propose to utilize a graph model in this study. We identify the key challenges of implementing such a graph-based HPC rich metadata management system and present GraphMeta, a graph-based rich metadata management system designed and optimized for HPC platforms, to tackle these challenges. Extensive evaluations on both synthetic and real HPC metadata workloads show its advantages in both performance and scalability compared with existing solutions.
Managing Variability as a Means to Promote Composability

Complex systems usually have to deal with a huge number of potential situations and contingencies. Therefore, a mechanism is required that enables the expression of variability at design-time so that it can be efficiently resolved at run-time. As composability plays an increasingly relevant role in building systems in an economic way, variability management should also contribute to and be taken into account in terms of composability. This chapter presents a variability management workflow aimed at supporting different developer roles in an ecosystem context. Two kinds of variability are addressed: in system operation and associated with quality of service. The former provides robustness to contingencies, while the latter focuses on the quality of the application (in terms of non-functional properties) under changing situations and limited resources. The concepts introduced in this chapter conform to the structures and principles of the H2020 European Project RobMoSys, which consolidates composability in the robotics domain.
Saving the Data

Three decades ago, Bogen and Woodward ([1988]) argued against the possibility and usefulness of scientific explanations of data. They developed a picture of scientific reasoning where stable phenomena were identified via data without much input from theory. Rather than explain data, theories ‘save the phenomena’. In contrast, I argue that there are good reasons to explain data, and the practice of science reveals attempts to do so. I demonstrate that algorithms employed to address inverse problems in remote-sensing applications should be understood as attempts to identify phenomena by explaining the data. Thus, this paper furthers understanding of data-to-phenomena reasoning in science, and demonstrates theory may play a more central role in phenomena identification than previously recognized.
Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling

In latent Dirichlet allocation, the number of topics, T, is a hyperparameter of the model that must be specified before one can fit the model. The need to specify T in advance is restrictive. One way of dealing with this problem is to put a prior on T, but unfortunately the distribution on the latent variables of the model is then a mixture of distributions on spaces of different dimensions, and estimating this mixture distribution by Markov chain Monte Carlo is very difficult. We present a variant of the Metropolis–Hastings algorithm that can be used to estimate this mixture distribution, and in particular the posterior distribution of the number of topics. We evaluate our methodology on synthetic data and compare it with procedures that are currently used in the machine learning literature. We also give an illustration on two collections of articles from Wikipedia. Supplemental materials for this article are available online.
Speeding up parameter and rule learning for acyclic probabilistic logic programs

This paper introduces techniques that speed-up parameter and rule learning for acyclic probabilistic logic programs. We focus on maximum likelihood estimation of parameters, and show that significant improvements can be obtained by efficiently handling probabilistic rules. We then move to structure learning, where we learn sets of rules, by introducing an algorithm that greatly simplifies exact score-based learning. Experiments demonstrate that our methods can produce orders of magnitude speed-ups over the state-of-art in parameter and rule learning.
TT-Miner: Topology-Transaction Miner for Mining Closed Itemset

Mining frequent closed itemsets (FCIs) from transaction databases is a fundamental problem in many data mining applications. All the enumeration algorithms enumerate FCIs by adding a singleton item to an itemset and then checking whether it is closure. In order to reduce enumerations, we enumerate FCIs by adding an itemset to the existing FCI itemset. To this end, we first analyze a binary relation on the set of itemsets in transaction databases, show co-occurrence relation among items, and prove that the relation is reflexive and transitive. Next, we use the relation to construct a topology for the set of itemsets and prove that all FCIs are included in the topology. Then, we construct a topology-transaction tree (TT-tree) and provide a topology-transaction miner (TT-Miner) algorithm for enumerating FCIs in the TT-tree. Finally, an extensive experimental evaluation on a number of real and synthetic databases shows that the TT-Miner is an efficient and scalable algorithm compared with the previous methods.
The Application of AR Technology to Spatial Skills Learning in Carpentry Training

Furniture carpentry requires plentiful cognitive judgements of views and capability of spatial understanding.For beginners, the idea of spatial geometry in mortice-tenon structure is often hard to comprehend, and being restricted by traditional 2D graphic, lacking basic 3D spatial concept.Yet augmented reality (AR) has already been proven to be capable of geometry training and enhancing conceptual manifestations of 3D space.Through combination of solid controller and virtual 3D subject of AR, dual feedbacks in both senses of touch and vision, this study aims at enhancing beginners' spatial skills in the making of furniture carpentry and further the improvement of carpentry skills.After experiments of each phase, evaluations are done through questionnaires and experts' observations on furniture works.It has proven that AR technique can truly and efficiently solve the perspective problems in complicated and implicit structure and spatial obstacles in furniture carpentry learning for beginners.
Finger Displacement Sensing: FEM Simulation and Model Prediction of a Three-Layer Electrode Design

There is a growing and significant interest in home-based therapy or telerehabilitation for physiological disabilities, for example, as a result of stroke. These technologies allow more flexibility in implementing rehabilitation sessions and offer the potential to reduce the economic burden of outpatient appointments and reduce the reliance on healthcare systems. However, extra effort needs to be made to make such systems effective. This paper investigates the feasibility of a home-based device, which is capable of detecting minute movements of patients' fingers in regular training and testing sessions, addressing ease of use, motivation of practice, as well as feedback and guidance on performance. Toward this aim, the measuring techniques that are compatible with these targets were investigated. Based on a customizable three-layer electrodes design for use with an MGC3030 motion sensor IC, a finite-element method simulation in COMSOL Multiphysics and a nonlinear regression analysis using MATLAB were carried out. Four nonlinear equations were introduced to describe the motion of the index and middle fingers in the electrical field (E-field) generated. The form of the prediction models agrees with the hypothesis based on the quasi-static E-field sensing theory. In addition, these prediction models fit well with the relationship between finger distance and the voltage signals detected. With the prediction model, the targeted system is capable of detecting combined movements of two fingers at a resolution of 0.94 mm in a portable smart device for robust hand rehabilitation.