Reviewerly/exHarmony huggingface dataset:
A multivariate cryptosystem inspired by random linear codes

We introduce a new multivariate encryption scheme inspired by random linear codes. The construction is similar to that of UOV, one of the oldest and most trusted multivariate signature schemes, but with a parameterization nothing like that of UOV. The structure of the scheme admits many generic modifications providing an array of security and performance properties. The scheme also supports an embedding modifier which allows any efficiently invertible multivariate system to be incorporated into the scheme. The product of this methodology is the fastest secure multivariate encryption scheme targeting CCA security at the 128-bit level.
Research on an improved algorithm of Apriori based on Hadoop

With the development of mobile Internet, association rules data mining is still a research hotspot. In this paper, the traditional mining algorithm of association rules for Apriori is analyzed, which has low efficiency and poor expansibility, because it scans the database many times and produces a large number of redundant frequent itemsets when dealing with big data. Therefore, it is proposed that the Apriori algorithm is improved by using the MapReduce model of Hadoop platform to parallelize processing, and the experimental results show that the improved Apriori algorithm has high efficiency and good stability in big data processing, it has great potential to excavate. Finally, the algorithm is applied to the data mining of student scores, which verifies its effectiveness and can provide services for education management.
Real-Time Data Compression for Data Acquisition Systems Applied to the ITER Radial Neutron Camera

To achieve the aim of the ITER Radial Neutron Camera Diagnostic, the data acquisition prototype must be compliant with a sustained 2 MHz peak event for each channel with 128 samples of 16 bits per event. The data is acquired and processed using an IPFN FPGA Mezzanine Card (FMC-AD2-1600) with 2 digitizer channels of 12-bit resolution and a sampling rate up to 1.6 GSamples/s mounted in a PCIe evaluation board from Xilinx (KC705) installed in the host PC. The acquired data in the event-based data-path is streamed to the host through the PCIe x8 Direct Memory Access (DMA) with a maximum data throughput per channel is 0.5 GB/s of raw data (event base), 1 GB/s per digitizer and up to 1.6 GB/s in continuous mode. The prototype architecture comprises an host PC with two KC705 modules and four channels, producing up to 2 GB/s in event mode and up to 3.2 GB/s in continuous mode. To reduce the produced data throughput from host to ITER databases, the real-time data compression was evaluated using the LZ4 lossless compression algorithm, which provides compression speed up to 400 MB/s per core. This paper presents the architecture, implementation and test of the parallel real-time data compression system running in multiple isolated cores. The average space saving and the performance results for long term acquisitions up to 30 minutes, using different data block size and different number of CPUs, is also presented.
Non-parallel hyperplanes ordinal regression machine

This paper proposes a method to solve ordinal regression problems, namely the non-parallel hyperplanes ordinal regression machine (NPHORM). The goal of this approach is to find K different hyperplanes for the K classes with ordinal information, so that each class is as close as possible to the corresponding hyperplane while as far as possible from the adjacent to the left and right classes. The more flexible separate hyperplanes are preferred using the order information of the data. As a result, this approach only needs to solve K quadratic programming problems independently. Our approach NPHORM is validated on 2 artificial datasets, 16 discretized regression datasets and 17 real ordinal regression datasets and compared with 8 outstanding SVM-based ordinal regression approaches. The results show that our approach NPHORM is comparable with the other SVM-based approaches, especially in real ordinal regression datasets. In addition, our NPHORM is also carried out on the historical color image dataset to compare the performance of deep learning method. Experimental results demonstrate that the performance of our NPHORM outperforms the deep learning methods on MAE.
Distributive laws for Lawvere theories

Distributive laws give a way of combining two algebraic structures expressed as monads; in this paper we propose a theory of distributive laws for combining algebraic structures expressed as Lawvere theories. We propose four approaches, involving profunctors, monoidal profunctors, an extension of the free finite-product category 2-monad from Cat to Prof, and factorisation systems respectively. We exhibit comparison functors between CAT and each of these new frameworks to show that the distributive laws between the Lawvere theories correspond in a suitable way to distributive laws between their associated finitary monads. The different but equivalent formulations then provide, between them, a framework conducive to generalisation, but also an explicit description of the composite theories arising from distributive laws.
Interactive Secure Function Computation

We consider interactive computation of randomized functions between two users with the following privacy requirement: the interaction should not reveal to either user any extra information about the other user's input and output other than what can be inferred from the user's own input and output. We also consider the case where privacy is required against only one of the users. For both cases, we give single-letter expressions for feasibility and optimal rates of communication. Then we discuss the role of common randomness and interaction in both privacy settings. We also study perfectly secure non-interactive computation when only one of the users computes a randomized function based on a single transmission from the other user. We characterize randomized functions which can be perfectly securely computed in this model and obtain tight bounds on the optimal message lengths in all the privacy settings.
Investigation of Agile Mindset Elements by Using Literature Review for a Better Understanding of Agility

The Agile Software Development movement itself emerged from practice just like most of the works in the Agile Software Development is evolved through practice. Thus, the creators and consultants of the Agile world may evangelize it with commercial concerns, resulting in "selling agility" to organizations as an object in a form of packaged practices (of methods/models/frameworks). As a result, agile mindset may easily lag behind the "sold" practices. However, effective agile individuals, teams and organizations require a particular attitude, way of thinking and behavior so called as agile mindset, beyond the given set of procedures, techniques and rituals, which makes it essential and primary before any practice (of any methods or frameworks). Despite its importance, the number of studies aiming to cover the elements of the agile mindset is surprisingly few, which calls for more study on this subject. By considering this gap and as an alternative to the limitations and monopoly of Agile Manifesto in being regarded as the dominant source of the mindset and to "selling agility" with a set of practices, this study provides the elements/factors/items of the agile mindset for a proper agility, by reviewing the literature to reach most comprehensive and up-to-date list, along with their categorization, relationships and discussions Thus, this study can serve as a basis to identify in what ways a particular entity (team, organization, or method) differs from the mindset.
A General Framework for Bounding Approximate Dynamic Programming Schemes

For years, there has been interest in approximation methods for solving dynamic programming problems, because of the inherent complexity in computing optimal solutions characterized by Bellman's principle of optimality. A wide range of approximate dynamic programming (ADP) methods now exists. It is of great interest to guarantee that the performance of an ADP scheme be at least some known fraction, say $\beta$, of optimal. This paper introduces a general approach to bounding the performance of ADP methods, in this sense, in the stochastic setting. The approach is based on new results for bounding greedy solutions in string optimization problems, where one has to choose a string (ordered set) of actions to maximize an objective function. This bounding technique is inspired by submodularity theory, but submodularity is not required for establishing bounds. Instead, the bounding is based on quantifying certain notions of curvature of string functions; the smaller the curvatures the better the bound. The key insight is that any ADP scheme is a greedy scheme for some surrogate string objective function that coincides in its optimal solution and value with those of the original optimal control problem. The ADP scheme then yields to the bounding technique mentioned above, and the curvatures of the surrogate objective determine the value $\beta$ of the bound. The surrogate objective and its curvatures depend on the specific ADP.
Recovery map for fermionic Gaussian channels

A recovery map effectively cancels the action of a quantum operation to a partial or full extent. We study the Petz recovery map in the case where the quantum channel and input states are fermionic and Gaussian. Gaussian states are convenient because they are totally determined by their covariance matrix and because they form a closed set under so-called Gaussian channels. Using a Grassmann representation of fermionic Gaussian maps, we show that the Petz recovery map is also Gaussian and determine it explicitly in terms of the covariance matrix of the reference state and the data of the channel. As a by-product, we obtain a formula for the fidelity between two fermionic Gaussian states. We also discuss subtleties arising from the singularities of the involved matrices.
RoboSeg: Real-Time Semantic Segmentation on Computationally Constrained Robots

Real-time and high-performance segmentation is a crucial but challenging perception task for computationally constrained robots, such as the humanoid NAO robot used in the RoboCup Soccer Standard Platform League. However, most existing convolutional neural network (CNN)-based models for semantic segmentation suffer from massive computational costs, which prevents them from being applied to performing real-time inference with a NAO. In this article, we first publish meticulously annotated datasets for training and evaluating semantic segmentation models. Then, we propose a fast downsampling module that downsamples the image while maintaining the spatial information and a novel dense learning module that learns high-level semantic information while recovering the spatial details. Based on these operations, by using a multiscale fusion method to recover the resolution, we propose a more efficient and real-time segmentation model called RoboSeg primarily aimed at offering better speed and accuracy tradeoffs. Finally, to accommodate practical engineering applications, we offer a promising deployment guideline for the CNN model describing how to deploy it on computational resource-limited robots and achieve real-time performance. The experimental results show that the RoboSeg exceeds the state-of-the-art networks in RoboCup scene segmentation: we attain a mean IoU of 87.35% and a pixel accuracy of 96.88% on our dataset using a model that contains only 0.29M parameters and performs just 0.73 GFLOPs. Under the proposed deployment strategies, the network can run at above 30 FPS on NAO robots with downsampled frames.
Improved ElGamal Cryptosystem for Secure Data Transfer in IoT Networks

ElGamal cryptosystem possess many technical challenges such as encryption of large messages, integrity and authentication of data, non-malleability, semantic security etc. This paper proposes an efficient ElGamal based public-key cryptosystem (PKEIE) to achieve the encryption of large messages along with confidentiality, integrity and authentication. The algorithm is built on DDH assumption (Decisional Diffie- Hellman assumption) which is quite hard to break. Apart from the normal key generation, it uses a secret key for the secured hash function to provide integrity and authentication. The algorithm is capable enough to encrypt data of any size which is not possible with the ElGamal cryptosystem. The message to be encrypted is split into blocks of equal size and each block is assigned with a block index. The block indexing makes the scheme a probabilistic one. The algorithm holds semantic security because it is built on the DDH assumption. It assures non-malleability against attacks. The algorithm is compared with the ElGamal variant schemes based on the attained level of security and throughput and suitable for IoT networks by in cooperating integrity, authentication and encryption using a single algorithm.
Comparison of sort algorithms in Hadoop and PCJ

Abstract Sorting algorithms are among the most commonly used algorithms in computer science and modern software. Having efficient implementation of sorting is necessary for a wide spectrum of scientific applications. This paper describes the sorting algorithm written using the partitioned global address space (PGAS) model, implemented using the Parallel Computing in Java (PCJ) library. The iterative implementation description is used to outline the possible performance issues and provide means to resolve them. The key idea of the implementation is to have an efficient building block that can be easily integrated into many application codes. This paper also presents the performance comparison of the PCJ implementation with the MapReduce approach, using Apache Hadoop TeraSort implementation. The comparison serves to show that the performance of the implementation is good enough, as the PCJ implementation shows similar efficiency to the Hadoop implementation.
Scheduling coflows of multi-stage jobs under network resource constraints

As an emerging network abstraction, coflow greatly improves the communication performance of data-parallel computing jobs. Many existing studies have focused on the design of coflow scheduling to minimize the completion time of jobs. However, they treat the underlying network as a large non-blocking switch without considering the constraints of network resources, which may increase network bottlenecks, reduce link capacity utilization, and extend job completion time. In this paper, we take network resource constraints into account and study how to schedule coflows in multi-stage jobs with the objective of minimizing the total weighted job completion time. We first formalize this multi-stage job scheduling problem as nonlinear programming and prove its NP-hardness. By introducing a priority order of jobs using a linear programming relaxation-based approach, we propose a polynomial-time algorithm with a performance guarantee, which can achieve a constant approximation ratio in many typical scenarios. Simulation results based on a Facebook trace show that, compared with a state-of-the-art approach, our algorithm can shorten the average weighted job completion time by up to 48.46% and run faster by up to 19.12×.
Low-Dose CT Image Blind Denoising with Graph Convolutional Networks

Convolutional Neural Networks (CNNs) have been widely applied to the Low-Dose Computed Tomography (LDCT) image denoising problem. While most existing methods aim to explore the local self-similarity of the synthetic noisy CT image by injecting Poisson noise to the clean data, we argue that it may not be optimal as the noise of real-world LDCT image can be quite different compared with synthetic noise (e.g., Poisson noise). To address these issues, instead of manually distorting the clean CT to construct paired training set, we estimate the noise distribution over the real-world LDCT images firstly and then generate noise samples through Generative Adversarial Network (GAN) such that a paired LDCT image dataset can be constructed. To explore the non-local self-similarity of LDCT images, Graph Convolutional Layers (GCLs) is utilized to obtain the non-local patterns of LDCT images. Experiments were performed using real-world LDCT image dataset and the proposed method achieves much better performance compared with other approaches with respect to both quantitative and visual results.
Geometric fluid approximation for general continuous-time Markov chains

Fluid approximations have seen great success in approximating the macro-scale behaviour of Markov systems with a large number of discrete states. However, these methods rely on the continuous-time Markov chain (CTMC) having a particular population structure which suggests a natural continuous state-space endowed with a dynamics for the approximating process. We construct here a general method based on spectral analysis of the transition matrix of the CTMC, without the need for a population structure. Specifically, we use the popular manifold learning method of diffusion maps to analyse the transition matrix as the operator of a hidden continuous process. An embedding of states in a continuous space is recovered, and the space is endowed with a drift vector field inferred via Gaussian process regression. In this manner, we construct an ordinary differential equation whose solution approximates the evolution of the CTMC mean, mapped onto the continuous space (known as the fluid limit).
Deep learning regularization in imbalanced data

Deep neural networks are known to have a large number of parameters which can lead to overfitting. As a result various regularization methods designed to mitigate the model overfitting have become an indispensable part of many neural network architectures. However, it remains unclear which regularization methods are the most effective. In this paper, we examine the impact of regularization on neural network performance in the context of imbalanced data. We consider three main regularization approaches: L1, L2, and dropout regularization. Numerical experiments reveal that the L1 regularization method can be an effective tool to prevent overfitting in neural network models for imbalanced data. Index Terms-regularization, neural networks, imbalanced data.
Intrusion-Tolerant and Confidentiality-Preserving Publish/Subscribe Messaging

We present Chios, an intrusion-tolerant publish/subscribe system which protects against Byzantine failures. Chios is the first publish/subscribe system achieving decentralized confidentiality with fine-grained access control and strong publication order guarantees. This is in contrast to existing publish/subscribe systems achieving much weaker security and reliability properties. Chios is flexible and modular, consisting of four fully-fledged publish/subscribe configurations (each designed to meet different goals). We have deployed and evaluated our system on Amazon EC2. We compare Chios with various publish/subscribe systems. Chios is as efficient as an unreplicated, single-broker publish/subscribe implementation, only marginally slower than Kafka and Kafka with passive replication, and at least an order of magnitude faster than all Hyperledger Fabric modules and publish/subscribe systems using Fabric.
Siamese High-Level Feature Refine Network for Visual Object Tracking

Siamese network-based trackers are broadly applied to solve visual tracking problems due to its balanced performance in terms of speed and accuracy. Tracking desired objects in challenging scenarios is still one of the fundamental concerns during visual tracking. This research paper proposes a feature refined end-to-end tracking framework with real-time tracking speed and considerable performance. The feature refine network has been incorporated to enhance the target feature representation power, utilizing high-level semantic information. Besides, it allows the network to capture the salient information to locate the target and learns to represent the target feature in a more generalized way advancing the overall tracking performance, particularly in the challenging sequences. But, only the feature refine module is unable to handle such challenges because of its less discriminative ability. To overcome this difficulty, we employ an attention module inside the feature refine network that strengths the tracker discrimination ability between the target and background. Furthermore, we conduct extensive experiments to ensure the proposed tracker’s effectiveness using several popular tracking benchmarks, demonstrating that our proposed model achieves state-of-the-art performance over other trackers.
PhyCoVIS: A visual analytic tool of physical coordination for cheer and dance training

Abstract Processing and analyzing dance videos are important in the application of online cheer leading and dance training for physical coordination measurement. However, it is challenging for users to evaluate a massive amount of uploaded video, to precisely quantize and compare dance moves, and to visualize training results. To overcome these challenges, we propose a visualization‐driven approach for analyzing dance videos. We first encode extracted video frames into a set of heat maps via neural network, which calculates a skeleton structure for pose estimation with enhanced post‐processing to help capture dance moves. A subsequent pose similarity method allows users to quantize differences between student training videos and the standard one. Finally, an interactive visualization tool enables users and domain experts to interactively analyze the quality of dance moves along the time line. We demonstrate the applicability and effectiveness of our proposed tool using case studies involving physical coordination research.
Detection of Smart Ponzi Schemes Using Opcode

Blockchain is becoming an important infrastructure of the next generation of information technology. But now, the fraud on blockchain is serious which has affected the development of blockchain ecology. Smart Ponzi scheme which realized by smart contract is a new type of Ponzi scheme and running on Ethereum. It would cause more serious damage to society in less time than other Ponzi schemes. Timely and comprehensive detection of all smart Ponzi schemes is the key to constructed an automatic detection model of smart Ponzi scheme. A model that effectively detect smart Ponzi scheme in its full lifecycle is proposed in this paper. The model only uses features based on operation codes (i.e., opcodes) of smart contract on Ethereum. The systematic modeling strategy realizes the efficient automatic detection model of smart Ponzi scheme step by step. Precision, Recall and F1-score of the model are 0.98, 0.93 and 0.95 respectively by experiments. Smart Ponzi schemes hidden on Ethereum are detected effectively by the model. More importantly, the performance of model is guaranteed at any moment in the lifecycle, even at the birth of a smart Ponzi scheme.
Stable Training of Bellman Error in Reinforcement Learning

The optimization of Bellman error is the key to value function learning in principle. However, it always suffers from unstable training and slow convergence. In this paper, we investigate the problem of optimizing Bellman error distribution, aiming at stabilizing the process of Bellman error training. Then, we propose a framework that the Bellman error distribution at the current time approximates the previous one, under the hypothesis that the Bellman error follows a stationary random process if the training process is convergent, which can stabilize the value function learning. Next, we minimize the distance of two distributions with the Stein Variational Gradient Descend (SVGD) method, which benefits the balance of exploration and exploitation in parameter space. Then, we incorporate this framework in the advantage actor-critic (A2C) algorithms. Experimental results on discrete control problems, show our algorithm getting better average returns and smaller Bellman errors than both A2C algorithms and anchor method. Besides, it would stabilize the training process.