Reviewerly/exHarmony huggingface dataset:
Connected filters on generalized shape-Spaces

Classical hierarchical image representations and connected filters work on sets of connected components (CC). These approaches can be defective to describe the relations between disjoint objects or partitions of images. In practice, objects can be made of several connected components in images (due to occlusions for example), therefore it can be interesting to be able to take into account the relationship between these components to be able to detect the whole object. In Mathematical Morphology, second-generation connectivity (SGC) and tree-based shape-spaces study this relation between the connected components of an image. However, they have limitations. For this reason, we propose in this paper an extension of the usual shape-space paradigm into what we call a Generalized Shape-Space (GSS). This new paradigm allows us to analyze any graph of connected components hierarchically and to filter them thanks to connected operators.
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines

A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and being immediately ready for production deployment. In this paper, we continue the journey to explore specifically how to empower full-text search engines with fast and exact NNS in Hamming space (i.e., the set of binary codes). By revisiting three techniques (bit operation, subs-code filtering and data preprocessing with permutation) in information retrieval literature, we develop a novel engineering solution for full-text search engines to efficiently accomplish this special but important NNS task. In the experiment, we show that our proposed approach enables full-text search engines to achieve significant speed-ups over its state-of-the-art term match approach for NNS within binary codes.
Assistive Smart Cane (ASCane) for Fall Detection: First Advances

The development of fall detection systems with the capability of real-time monitoring is necessary considering that a large amount of people die and suffer severe consequences from falls. Due to their advantages, daily life accessories can be a solution to embed fall-related systems, and canes are no exception. In this paper, it is presented a cane with fall detection abilities. The ASCane is instrumented with an inertial sensor which data will be tested with three different fixed multi-threshold fall detection algorithms, one dynamic multi-threshold and machine learning methods from the literature. They were tested and modified to account the use of a cane. The best performance resulted in a sensitivity and specificity of 96.90% and 98.98%, respectively.
Green Mining: toward a less energetic impact of cryptocurrencies

While cryptocurrencies and blockchain applications continue to gain popularity, their energy cost is evidently becoming unsustainable. In most instances, the main cost comes from the required amount of energy for the Proof-of-Work, and this cost is inherent to the design. In addition, useless costs from discarded work (e.g., the so-called Forks) and lack of scalability (in number of users and in rapid transactions) limit their practical effectiveness. In this paper, we present an innovative scheme which eliminates the nonce and thus the burden of the Proof-of-Work which is the main cause of the energy waste in cryptocurrencies such as Bitcoin. We prove that our scheme guarantees a tunable and bounded average number of simultaneous mining whatever the size of the population in competition, thus by making the use of nonce-based techniques unnecessary, achieves scalability without the cost of consuming a large volume of energy. The technique used in the proof of our scheme is based on the analogy of the analysis of a green leader election. The additional difference with Proof-of-Work schemes (beyond the suppression of the nonce field that is triggering most of the waste), is the introduction of “empty blocks” which aim are to call regular blocks following a staircase set of values. Our scheme reduces the risk of Forks and provides tunable scalability for the number of users and the speed of block generation.
Evaluation of Pooling Layers in Convolutional Neural Network for Script Recognition

This paper investigates the suitable position and number of pooling layers in Convolutional Neural Network (CNN) for script recognition from scene images. A common practice of CNN for object recognition is to position a convolve layer alternately with a pooling layer followed by a few layers of fully connected layers. We re-evaluate this basic principle by examining the position of pooling layer after every convolve layer, reducing and increasing its numbers. Experimental results on MLe2e dataset for script recognition show that a CNN with less number of pooling layers and non-overlapping pooling stride can reach excellent percentage of accuracy compared to alternating convolve layer with pooling layer.
Dynamic Guidance for Depth Map Restoration

The guidance of color images greatly improves the restoration accuracy of the depth map. However, due to the incomplete texture structure consistency between the color image and the depth map, and the limitations of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L^{2}$</tex> based model, static guidance tends to cause texture copy artifacts and blurring depth discontinuities, which seriously deteriorates the quality of result. To tackle those problems, we propose a dynamic guidance model which can adaptively adjustment itself based on local smooth characteristics and continuously optimize in iteration. The proposed method can significantly alleviate the negative impact of the incomplete consistency and makes full use of the guidance information to restore fine texture at the same time. Moreover, a novel edge correction mechanism is designed to filter out incorrect depth information around boundaries, ensuring the correction of edges. In experiments, visual comparison proves that our method can alleviate texture copy artifacts and blurring depth discontinuities effectively. Quantitative results show that the proposed method restores high quality depth map with the lowest mean absolute error.
Analog quantum simulation of superradiance and subradiance in trapped ions

We discuss a protocol for the analog quantum simulation of superradiance and subradiance using a linear chain of $N$ trapped qubit ions with a single sympathetic cooling ion. We develop a simple analytic model that shows the dynamics of the qubit subspace converge to those of a cloud undergoing Dicke superradiance and subradiance. We provide numerical simulations of the full ion chain and show that they converge to the dynamics predicted by our analytic model with no fitting parameters. We also map out the parameter regime needed to reach this convergence.
Research on the Disciplinary Evolution of Deep Learning and the Educational Revelation

The significant growing popularity of Artificial Intelligence education is coincidence with the aggressive application of AI technology in modern society. Taking articles titled with “deep learning” and their citations from Web of Science core collections as an example, the disciplinary evolution and interdisciplinary progress of this field are analyzed and visualized. The interdisciplinary characteristics of different countries represent different directions and tendencies of scientific research and technique development on deep learning. Following the rule of theoretical and technological development of AI techniques, especially the interdisciplinary, reform of AI education need to focus on interdisciplinary in both education mode and curriculum content, deep integration of research and education, and close cooperation of industry, universities, and research institutes.
Concurrency Strategies for Attack Graph Generation

The network attack graph is a powerful tool for analyzing network security, but the generation of a large-scale graph is non-trivial. The main challenge is from the explosion of network state space, which greatly increases time and storage costs. In this paper, three parallel algorithms are proposed to generate scalable attack graphs. An OpenMP-based programming implementation is used to test their performance. Compared with the serial algorithm, the best performance from the proposed algorithms provides a 10X speedup.
Question-Answer Selection in User to User Marketplace Conversations

Sellers in userKumar, Girish toHenderson, Matthew userChan, Shannon marketplacesNguyen, Hoang can be inundated with questions from potential buyers. Answers are oftenNgoo, Lucas already available in the product description. We collected a dataset of around 590 K such questions and answers from conversations in an online marketplace. We propose a question answering system that selects a sentence from the product description using a neural-network ranking model. We explore multiple encoding strategies, with recurrent neural networks and feed-forward attention layers yielding good results. This paper presents a demo to interactively pose buyer questions and visualize the ranking scores of product description sentences from live online listings.
AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks

The power budget for embedded hardware implementations of Deep Learning algorithms can be extremely tight. To address implementation challenges in such domains, new design paradigms, like Approximate Computing, have drawn significant attention. Approximate Computing exploits the innate error-resilience of Deep Learning algorithms, a property that makes them amenable for deployment on low-power computing platforms. This paper describes an Approximate Computing design methodology, AX-DBN, for an architecture belonging to the class of stochastic Deep Learning algorithms known as Deep Belief Networks (DBNs). Specifically, we consider procedures for efficiently implementing the Discriminative Deep Belief Network (DDBN), a stochastic neural network which is used for classification tasks, extending Approximation Computing from the analysis of deterministic to stochastic neural networks. For the purpose of optimizing the DDBN for hardware implementations, we explore the use of: (a) Limited precision of neurons and functional approximations of activation functions; (b) Criticality analysis to identify the nodes in the network which can operate at reduced precision while allowing the network to maintain target accuracy levels; and (c) A greedy search methodology with incremental retraining to determine the optimal reduction in precision for all neurons to maximize power savings. Using the AX-DBN methodology proposed in this paper, we present experimental results across several network architectures that show significant power savings under a user-specified accuracy loss constraint with respect to ideal full precision implementations.
An Integer Programming Approach for the 2-class Single-group Classification Problem

Two sets XB,XR⊆Rd are linearly separable if their convex hulls are disjoint, implying that a hyperplane separating XB from XR exists. Such a hyperplane provides a method for classifying new points, according to the side of the hyperplane in which the new points lie. In this work we consider a particular case of the 2-class classification problem, which asks to select the maximum number of points from XB and XR in such a way that the selected points are linearly separable. We present an integer programming formulation for this problem, explore valid inequalities for the associated polytope, and develop a cutting plane approach coupled with a lazy-constraints scheme.
Cloud Computing: A Framework for Balancing Accountability and Privacy Based on Multi-Agent System

The rise of cloud computing gives community's advantages to an open environment. Outsourcing of data and applications to third parties can cause serious harm to the organizations. Security in cloud computing is a challenging issue. In this paper, we design a framework for balancing accountability and privacy in cloud computing through the development of intelligent techniques. The proposed solution is based on multi-agent systems.
Multi-person Collaborative Interaction Algorithm and Application Based on HoloLens

In order to solve the concurrency conflict problem generated by multiple HoloLens in the process of collaborative interaction, a multi-person collaborative interaction algorithm is proposed. The algorithm adopts a three-dimensional scene information sharing algorithm and a multi-user collaborative interaction concurrent control strategy, so that different users share the same scene information, and all the user information in the scene is updated and synchronized. Solve the concurrency conflict problem in the process of multi-person collaborative interaction. A multi-person collaborative interaction system based on HoloLens is designed, which supports multiple HoloLens users to interact interactively in the same scene. The results show that the system achieves the cooperative interaction of multiple HoloLens users in augmented reality environment. Compared with the previous human–computer interaction platform, not only the interactive environment is more realistic, but also the interaction is more natural and the user experience is higher.
FPGA Accelerated Deep Learning Radio Modulation Classification Using MATLAB System Objects &amp; PYNQ

Floating point Convolutional Neural Networks(CNNs) are computationally expensive and deeper networks can be impractical to deploy on FPGAs - consuming a large number of resources and power, as well as having lengthy development times. Previous work has shown that CNNs can be quantised heavily using fixed point arithmetic to combat this without significant loss in classification accuracy. We aim to quantize an existing CNN architecture for radio modulation classification to 2-bit weights and activations, while retaining a level of accuracy close to the original paper, for deployment on a Zynq System on Chip (SoC). To improve the development time for hardware synthesisable CNNs, we make use of MATLAB System Objects and HDL Coder. The PYNQ framework is presented as a practical means for accessing the functionality of the CNN. Our preliminary results show a high classification accuracy even with 2-bit weights and activations.
Characterizing Software Maintainability in Issue Summaries using a Fuzzy Classifier

Despite the importance of software maintainability in the life cycle of software systems, accurate measurement remains difficult to achieve. Previous work has shown how bug reports can be classified by expressed quality concerns which can give insight into maintainability across domains and over time. However, the amount of manual effort required to produce such classifications limits its usage. In this paper, we build a fuzzy classifier with linguistic patterns to automatically map issue summaries into the seven subgroup SQ classifications provided in a software maintainability ontology. We investigate how long it takes to generate a stable set of rules and evaluate the performance of the rule set on both rule generating and nonrule generating projects. The results validate the generalizability of the fuzzy classifier in correctly and automatically identifying the subgroup SQ classifications from given issue summaries. This provides a building block for analyzing project maintainability on a larger scale.
SACABench: Benchmarking Suffix Array Construction

We present a practical comparison of suffix array construction algorithms on modern hardware. The benchmark is conducted using our new benchmark framework SACABench, which allows for an easy deployment of publicly available implementations, simple plotting of the results, and straight forward support to include new construction algorithms. We use the framework to develop a construction algorithm running on the GPU that is competitive with the fastest parallel algorithm in our test environment.
Using Hardware Performance Counters to Detect Control Hijacking Attacks

Code reuse techniques can circumvent existing security measures. For example, attacks such as Return Oriented Programming (ROP) use fragments of the existing code base to create an attack. Since this code is already in the system, the Data Execution Prevention methods cannot prevent the execution of this reorganised code. Existing software-based Control Flow Integrity can prevent this attack, but the overhead is enormous. Most of the improved methods utilise reduced granularity in exchange for a small performance overhead. Hardware-based detection also faces the same performance overhead and accuracy issues. Benefit from HPC's large-area loading on modern CPU chips, we propose a detection method based on the monitoring of hardware performance counters, which is a lightweight system-level detection for malicious code execution to solve the restrictions of other software and hardware security measures, and is not as complicated as Control Flow Integrity.
A Robust Throughput Scheme for Bitcoin Network without Block Reward

Bitcoin, as the spearhead of cryptocurrencies, is playing a more and more critical role in the world. With the increasing number of users in Bitcoin, the public has risen the demand for transaction throughput, and many hard forks are generating by the continuous throughput improvement. Meanwhile, the vulnerability of Bitcoin's incentive mechanism also makes it vulnerable to be attacked by generating soft forks. Without a robust throughput scheme, Bitcoin suffers from hard forks, soft forks and possible attacks like undercutting attack. In this work, we create a useful model to describe the throughput and the incentive mechanism of Nakamoto consensus protocol. Based on our model, We find that the throughput, which is far higher than user needs, will result in soft forks which reduce the consistency of Bitcoin in the future. We design a robust throughput scheme to reduce the soft forks and hard forks by automatically adjusting block size according to users' needs. In our experiments, we simulate attacks on Bitcoins and our scheme at different mining powers. We find that the soft fork block rate of Nakamoto consensus protocol without block reward is 6.0%, and our scheme can reduce the soft fork block rate to 0.6% under the attacker with the mining power of 0.25.
Adaptive geometric median prototype selection method for k-nearest neighbors classification

The k-nearest neighbors (kNN) algorithm is one of the most popular and simplest lazy learners. However, as the training dataset becomes larger, the algorithm suffers from the following drawbacks: large storage requirements, slow classification speed, and high sensitivity to noise. To overcome these drawbacks, we reduce the size of the training data by only selecting the necessary prototypes before the classification. This study proposes an extended prototype selection technique based on the geometric median (GM). We compare the proposed method with seven state-of-the-art prototype selection methods and 1NN as the baseline model. We use 25 datasets from the KEEL and UCI dataset repository website. The proposed method runs at least 3.5 times faster than the baseline model at the cost of slightly reduced accuracy. In addition, the classification accuracy and kappa value of the proposed method are comparable to those of all the state-of-the-art prototype selection methods considered.
The Latest Research on Clustering Algorithms Used for Radar Signal Sorting

As an important part of electronic intelligence (ELINT) and electronic support measurement (ESM) systems, radar signal sorting directly affects the performance of electronic reconnaissance equipment and is a key technology for decision making. This paper discusses several clustering methods which could be used for radar signal sorting. The discussion includes artificial neural networks, classical clustering algorithm and its improvement, and support vector clustering.
Performance analysis of a backlight dimming method using weighted mean-square-error based on joint edge-saliency characteristics

In this paper, a new backlight dimming method using weighted mean-square-error (MSE) based on joint edge-saliency characteristics is proposed. In contrast to conventional backlight dimming methods that cannot accurately evaluate the saturation error and determine the optimal clipping point to achieve the best trade-off between image quality and power consumption, the proposed method, with consideration of human visual characteristics, analyzes the joint edge-saliency characteristics based on spatio-temporal saliency map and edge-strength map of the input images, and weights the MSE accordingly. It can therefore evaluate the allowable saturation error and determine the optimal clipping point. Simulation results show that the proposed method outperforms other benchmark methods in the case of overall consideration of image quality and power consumption. Additionally, the proposed method maximizes the power reduction rate while successfully preserving image details in the regions of interest and maintaining high perceived image quality. Our results also show that the average computation time of the proposed algorithm was reduced by up to 30.184%, 19.162% and 8.027%, respectively, compared with I2GEC, SMVA2 and SPBD algorithms.
The Visualization-Driven Approach to the Analysis of the HVAC Data

The smart heating, ventilation and air conditioning systems (HVAC) are able to reduce energy consumption by fitting it better to the users’ behavior or building and equipment needs. Monitoring the HVAC state allows detecting the anomalous deviations in the system. The paper presents the visualization-driven approach to the analysis of the HVAC systems logs that can be applied for monitoring in the real time mode. The authors propose to define the time periods during which the state of the system remains almost unchanged and visualize the average values of the HVAC parameters using the heat map. This allows attracting the analyst attention to the changes in the HVAC system as well as detecting its life rhythm. We demonstrate our approach with an application to the VAST MiniChallenge-2 2016 data set, which describes the functioning of the three-storey building.
Improving data quality with label noise correction

Data gathered from real world often contains label noise, which is harmful to the quality of data. Moreover, any data mining process suffers a deterioration when it is applied on noisy data. In this paper, a new approach is proposed to improve data quality by correcting mislabeled data. The propose d method employs a procedure to estimate the level of the noise in the data and combines this noise estimation with a correction process. A clustering method and k nearest neighbors approach are applied in the correction process. Extensive experimental results using real-world data sets from UCI machine learning repository are provided. The empirical study shows that our approach successfully improves data quality in many cases and outperforms several correction methods.