Reviewerly/exHarmony huggingface dataset:
High Performance Big Data Graph Analytics Leveraging Near Memory System

Big data graph analytics is the future of high performance computing and key to many current and future applications. There is a growing demand for high performance graph computing for real-world social network graphs. Real-world graph algorithms are memory-intensive and generate a high percentage of accesses to the memory subsystem due to low cache locality. Near memory or 3D die-stacked memory, known for its low latency, high bandwidth communication has the potential to improve the performance of big data graph analytics.In this paper, we analyse, evaluate and compare the performance of a near memory system for big data graph analytics. Real-world graphs associated with social networks and the web are processed with graph analytics algorithms in a simulated near memory system. The performance advantage of near memory with a large number of simple in-order processor cores for graph analysis is presented.The proposed system provides a performance per Watt improvement of 3.55 - 8.55× for Breadth-First Search algorithm for big data graphs over computing systems with fat cores and traditional Double Data Rate (DDR) memory. The proposed near memory computing system provides a considerable improvement in computational performance of graph analytics algorithms with an average improvement in Instructions Per Cycle (IPC) of 5× and in performance per Watt of 7×.
Optimum Kernel Particle Filter for Asymmetric Laplace Noise in Multivariate Models

In this paper we present on-line Bayesian filtering methods for non-linear multivariate time series models corrupted by generalised asymmetric Laplace noise. We derive the optimum kernel for a particle filter applied to multivariate non-linear state-space models with scalar observations, where the observation noise is additive and asymmetric Laplacian. We show that sampling from this multivariate kernel is tractable using commonly available methods for use in particle filters, and that its associated likelihood can be evaluated. A particle filter is implemented for a test case using the developed kernel, and its performance is compared to that of a traditional bootstrap filter. The proposed methods show potential for application to systems with heavy-tailed skew noise.
A Two-Way Quantum Key Distribution Based on Two Different States

In this paper, we propose a two-way quantum key distribution protocol based on two different states (Single photon and Bell-states). It is a two-way communication protocol. There is a correspondence between the Single photon and the Bell-states. The participants both are honest. Through this protocol participants can obtain the secret key. The secret key is generated by half of participants’ key, the reason for the secret key is generated by this way is to prevent the outside attackers attacking just once that can obtain the secret key that the participant wants to distribute. And so the can establish communication by the key. We take into account the diversity of attack patterns, so the participants in our protocol are both have ability to detect attackers. In the security analysis, for the security of the whole protocol, we introduce twice eavesdropping detection in the protocol process, the analyse results show that the protocol can resistant to several well-known attacks.
Absentee ballots to be mailed soon without Green Party on them

If you requested an absentee ballot for this fall's election, it could be in your mailbox soon.
Adversarial Examples Generation System Based on Gradient Shielding of Restricted Region

In recent years, deep neural networks have greatly facilitated machine learning tasks. However, emergence of adversarial examples revealed the vulnerability of neural networks. As a result, security of neural networks is drawing more research attention than before and a large number of attack methods have been proposed to generate adversarial examples to evaluate the robustness of neural networks. Furthermore, adversarial examples can be widely adopted in machine vision, natural language processing, and video recognition applications. In this paper, we study adversarial examples against image classification networks. Inspired by the method of detecting key regions in object detection tasks, we built a restrict region-based adversarial example generation system for image classification. We proposed a novel method called gradient mask to generate adversarial examples that have a high attack success rate and small disturbances. The experimental results also validated the method’s performance.
Improved Deep Learning Techniques for Better Cancer Diagnosis

Over the past several decades, Computer-Aided Diagnosis (CAD) for diagnosis of medical images has prospered due to the advancements in the digital world, advancements in software, hardware and precise and fine-tune images acquired from sensors. With the advancement in the field of medical and applications of Artificial Intelligence scaling to the height of improvement, modern state-of-the-art applications of Deep Learning for better cancer diagnosis have been incepted in recent years. CAD and computerized algorithms and solutions in diagnosing cancer obtained from different modalities, i.e., MRI, CT scans, OCT and so on plays an immense impact on disease diagnosis. Learning model based on transfer mechanisms that stored knowledge for one aspect and using it for another aspect with Deep Convolutional Neural Network paved the way for automatic diagnosis. Recently, improved deep learning algorithm has resulted in great success resulting in robust image characteristics, involving higher dimensions. Analysis of bi-cubic interpolation preprocessing technique paves way for robust obtaining of a region of interest. For an inflexible object with a higher amount of dissimilarity, a comprehensive form for detecting the region of interest and determination of actual positioning may not be robust. Robust perception and localization schemes are analyzed. By integrating Deep Learning with Neighborhood Position Search unseen cases are said to be identified and segmented accordingly via Maximum Likelihood decision rule, forming robust segmentation. The favorable result of an a better cancer diagnosis is indeed contingent on the cancer diagnosis however, an anticipating prediction should consider certain factors more than a straight forward diagnostic decision. Besides the application of different medical data analyses and image processing techniques used in the study of cancer diagnosis deeper insights of the relevant solutions in the light of higher collections of deep learning techniques are found to be vital. Hence, certain factors to be analyzed are the forecasting of risk involved, forecasting of cancer frequency and the forecasting of cancer survival. These factors are analyzed according to the diagnosis criterion, sensitivity, specificity, and accuracy.
Robust centralized fusion Kalman predictor for networked multisensor systems with packet dropouts and uncertain-variance linearly correlated white noises

In this paper, the robust centralized fusion Kalman filtering problem is studied for a class of networked multisensor systems with packet dropouts, uncertain noise variances and linearly correlated noises. The measurement noises and process noises are linearly correlated. By augmentation of the state and measurement, the original system is converted into a stochastic parameter system with uncertain noise variances. Further, using the fictitious noises approach, the original system is converted into one with constant parameter and uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case centralized fusion system with the conservative upper bounds of the noise variances, the robust centralized fusion time-varying Kalman predictor is presented. By use of the Lyapunov equation approach, and introducing a matrix, the robustness of the proposed fused predictor is proved in the sense that its actual prediction error variance is guaranteed to have the corresponding minimal upper bound for all admissible uncertainties. The accuracy relations among the robust local and fused time-varying Kalman predictors are proved. Simulation results show the effectiveness and correctness of the proposed results.
Compact Hardware Architectures of Enocoro-128v2 Stream Cipher for Constrained Embedded Devices

Lightweight cryptography is a vital and fast growing field in today’s world where billions of constrained devices interact with each other. In this paper, two novel compact architectures of the Enocoro-128v2 stream cipher are presented. The Enocoro-128v2 is part of the ISO/IEC 29192-3 standard. The first architecture has an 8-bit datapath while the second one has a 4-bit datapath. The proposed architectures were implemented on the BASYS3 board (Artix 7 XC7A35T) using the VERILOG hardware description language. The hardware implementation of the proposed 8-bit architecture runs at a 189 MHz clock and reaches a throughput equal to 302 Mbps, while at the same time, it utilizes only 254 Look-up Tables (LUTs) and 330 Flip-flops (FFs). Each round of computations requires 5 clock cycles. The 4-bit implementation has an operating frequency of 204 MHz and reaches a throughput equal to 181 Mbps, with each round requiring 9 clock cycles. The 4-bit implementation utilizes 249 LUTs and 343 FFs. To our knowledge, this is the first time that such implementations of the Enocoro-128v2 are presented. Both implementations utilize a very low number of resources (only 78 FPGA slices are required for the 8-bit architecture and only 83 for the 4-bit one) and the results demonstrate that they are sustainable for area constrained embedded devices.
Research on Anomaly Location Method for Nuclear Pump Vibration Monitoring Data Based on Correlation Coefficient

Nuclear power plants have accumulated a large amount of process monitoring data, but most of the data are not marked with specified patterns, which cannot be directly applied to the data-driven intelligent early warning and fault diagnosis. On-site alarm threshold can only locate a small number of abnormal vibration data, ignoring a large number of data that doesn't exceed alarm threshold but is obviously abnormal fluctuation of vibration phenomenon. To solve this problem, a method is proposed to locate abnormal vibration data based on correlation coefficient in this paper. This method takes the correlation coefficient of vibration data and corresponding time as the fluctuation index of measuring vibration data, and calculates the fluctuation threshold through historical data statistics, so as to locate abnormal vibration data. The vibration monitoring data of nuclear pump show that the proposed method can effectively detect the abnormal fluctuation of the data and locate the starting point of abnormal vibration.
Background Subtraction with a Hierarchical Pitman-Yor Process Mixture Model of Generalized Gaussian Distributions

This paper presents hierarchical Pitman-Yor process mixture of generalized Gaussian distributions for background subtraction. The motivation behind choosing generalized Gaussian distribution is its flexibility as compared to the widely used Gaussian. We also integrate the Pitman-Yor process into our proposed model for an infinite extension that leads to better performance in the task of background subtraction. Our model is learned via a variational Bayes approach and is applied on the challenging Change Detection dataset. Experimental results on background subtraction show the effectiveness of the proposed algorithm.
Does Randomness in the Random Visual Cryptography Protocol Depend on the Period of Pseudorandom Number Generators?

Actual randomness of the shares in the binary random visual cryptography protocol was tested with the NIST statistical test suite. Lack of randomness was noticed for a large secret image (shares 10K by 10K) in that the OverlappingTemplate test failed. The symptom of failure was much weaker for a twice smaller image and absent for a four times smaller one. The failure rate nonmonotonically depended on the period of the pseudorandom number generator, but was the largest for the worst generator. Four generators were used with periods from $$2^{64}$$ to $$2^{19937}$$ . Hence, the reason for the nonrandomness seemed not to be the excessively large image size versus generator period, provided that the generator is reasonably good. Using the battery of tests made it possible to detect a gap in the randomness (not in the security) of the random visual cryptography protocol.
Validating Untrained Human Annotations Using Extreme Learning Machines

We present a process for validating and improving annotations made by untrained humans using a two-step machine learning algorithm. The initial validation algorithm is trained on a high quality annotated subset of the data that the untrained humans are asked to annotate. We continue by using the machine learning algorithm to predict other samples that are also annotated by the humans and test several approaches for joining the algorithmic annotations with the human annotations, with the intention of improving the performance beyond using either approach individually. We show that combining human annotations with the algorithmic predictions can improve the accuracy of the annotations.
Towards a Continuous Model-Based Engineering Process for QoS-Aware Self-adaptive Systems

Modern information systems connecting software, physical systems, and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of service and lead to incomplete, inaccurate, and unreliable results. In this context, self-adaptation is considered as an effective approach for managing run-time uncertainty. However, classical approaches for quality engineering are not suitable to deal with run-time adaptation, as they are mainly used to derive the steady-state solutions of a system at design-time. In this paper, we envision a Continuous Model-based Engineering Process that makes use of architectural analysis in conjunction with experimentation to have a wider understanding of the system under development. These two activities are performed incrementally, and jointly used in a feedback loop to provide insights about the quality of the system-to-be.
Adaptive domain of dynamic distribution based on manifold space

Domain adaption aims to use the source domain knowledge to assist the model learning. Most of the existing methods are based on the feature representation learning model, which are achieved by aligning the data distribution between two domains. However, in the process of feature representation learning, due to the diversity of distribution differences between domains, it faces the problems of degenerated feature transformation and unevaluated distribution alignment, which bring challenges to the existing research. Therefore, the dynamic distribution alignment factors of marginal distribution and conditional distribution are introduced to reduce the spatial distortion in the process of feature representation, and the domain adaptation is realized based on the dynamic manifold regularization constraints and structural risk minimization learning. In this paper, we propose the structural risk minimization and an improved dynamic manifold regularization constraint to solve these problems. Experimental results show that compared with traditional methods and deep level methods, the algorithm in this paper has a significant improvement in classification accuracy.
Research on Online Shopping Comments Based on Text Emotional Comments

In order to draw up Sunshine company' s online sales strategy, enhance customers' satisfaction to the products, and predict Sunshine's future sales trends, we do the following work:First, during the performance of data preprocessing, we consider the data labels as variables, process abnormal data, and select meaningful variables based on data redundancy and association principles. Then we regularize the text review, calculate the frequency of emotional words, words-modifiers and negation words and analyze the emotional level of the review body qualitatively. By developing the correlation analysis model, and using the Spearman coefficient to measure the correlation between different variables, we find that there is a strong correlation between helpful votes and the total votes.Secondly, we group and rank emotional words and words modifiers, assign negation values to -1, consider the length of the text-review and the number of times that negation words appear to build the text-review emotion quantification model to calculate the emotional values of each review. And normalizing emotional values which range [0, 5]. After that, with the linear weighted sum method between the star ratings and the emotional values, we obtain a new score called 〖score(i)〗_new, and refer to the literature for a set of reasonable weight values.
Data Augmentation for Polarity Sentiment Analysis

Twitter produces various colloquial tweets as an open communication platform. Previous research shows the frequency of negative sentences in spoken sentences is twice that of written texts. Negative items in negative sentences can shift the polarity of words with feelings, and leads to wrong classification. Therefore, negation processing is essential for the sentiment classification of tweets generated by Twitter. On the basis of considering the importance of negation which is often ignored in previous work, this paper firstly combines the technique of Conjunction Analysis (CA) with the technique of Punctuation Mark Identification (PMI) to detect the negation clue and its scope more accurately. In addition, We propose the OL-DAWE model. The model uses Data Augmentation (DA) approach to generate the opposed tweet according to the original tweet. The model extends learnable data and learns its polarity from both of positive and negative aspects of a tweet. In predicting the polarity of a tweet, the OL-DAWE model takes the positive (negative) degree of the original tweet into account, and also considers the negative (positive) degree of the opposed tweet. We conduct two experiments on two real-world data sets and analyze the experimental results from the perspectives of accuracy and robustness. We prove the effectiveness of our combined technology in negation processing and show that our OL-DAWE model in the polarity sentiment analysis of tweets is better than the baseline for its simplicity and high efficiency.
Low-latency Block-wise Object Detection Method using SSD for High Resolution Video

In recent years, in the fields such as surveillance cameras and in-vehicle camera systems, efficient deep-learning-based object detection methods, such as Single Shot MultiBox Detector (SSD), that do not require window scanning have received a significant attention. However, these methods require a lot of memory and computation. For this reason, when we applying them to higher definition video, it can be necessary to divide the video into multiple blocks for inference processing due to restrictions on memory capacity of GPUs or FPGAs. To avoid accuracy degeneration due to block division, we can use conventional overlapping block technique. However, it increases the latency of object detection because we need to process more blocks. In this paper, we propose a low-latency block-wise object detection method which assigns a different block pattern into each frame, divides each frame based on the block pattern assignment, and integrates the results of multiple frames. In the experiments, the object detection accuracy and latency were evaluated using three data from the Multiple Object Tracking Benchmark dataset 2017. When the movement of object is small, we reduced the latency of human detection by about 40% while the accuracy degeneration is 0% to 2%.
An Efficient Quantum Private Comparison Protocol Based on Conjugate Coding

Quantum private comparison (QPC) allows at least two participants who do not trust each other to compare whether their secret data are the same while maintaining data privacy. In order to improve the qubit efficiency and avoid using entangled state, we propose an efficient quantum private comparison based on conjugate coding. Our protocol only uses a set of qubits which are X-basis or Z-basis, as the information carrier. Participants use conjugate coding rules to encode their information into these qubits, which is to make the qubit efficiency of the protocol reach 100%. And these qubits transmitted through a circular path, which can effectively reduce resource consumption. Moreover, decoy photon technology and quantum key distribution (QKD) are used to ensure the security of information, making both external and internal attacks invalid.
Standard and Gaussian Particle Filters for Nonlinear System with Missing Measurements

In this paper, we propose three particle filters which are standard particle filter and two Gaussian particle filters for nonlinear system with missing measurements. For standard particle filter, we derive an explicit expression for the importance weights when the possible occurrence of measurement loss is taken into account. Based on this importance weights, a modified standard particle filtering algorithm with missing measurements is proposed. To improve sampling efficiency, we also propose two Gaussian particle filters for nonlinear system with missing measurements. For Gaussian particle filter, we derive the formulas for the importance density function when take the missing measurements into account. To fulfill the numerical computation of these formulas, we give two approximated methods based on local linearization and unscented transform. Based on these two approximated methods, the two Gaussian particle filtering algorithms with missing measurements are proposed. The effectiveness of the proposed methods are illustrated through a nonlinear simulation example.
DP2: A Highly Parallel Range Join for Genome Analysis on Distributed Computing Platform

Rapid growth of the sheer amount of genome data and intense computation become great challenges for downstream genome analytics. Efficient parallel processing and distributed computing are the two effective schemes to address the analysis of big data. Range join is a widely used, effective, yet time-consuming operation that finds the overlap between two different sets of genome features. The current widely adopted BEDTools [6] pipeline adopts single-node binary tree approach, while the distributed GenAp scheme fails to exploit the massive parallel computation on modern throughput processors, such as GPU (Graphic Processing Unit). This paper proposes a novel Distributed Parallel P-ary search (DP2) that applies novel P-ary analysis to enable high parallelism at algorithmic level, and extensively utilize multiple GPUs at system and architecture level. Efficient computation allocation is implemented to leverage the distributed computing on clusters. The proposed framework can be well integrated with current BEDTools [6] pipeline, and achieves an average of 25× speedup for the actual range-join operation when compared with Binary tree approach of GenAp and a 13× end-to-end (total execution time) speedup in comparison to ADAM.