Reviewerly/exHarmony huggingface dataset:
Research on Disaster Prevention Technology of Remote Sensing Image Segmentation of Ocean Big Data Platform Based on Internet of Things

Abstract In order to further improve the ability of disaster prevention and mitigation informatization, this article starts from the idea of comprehensive planning and construction, draws on the physiological principles of modern division of labor, and clearly coordinates the various components of the Internet of Things. The Internet of Things, big data, artificial intelligence and other information technologies, based on the Internet of Things-based ocean big data platform remote sensing image segmentation disaster prevention technology research theory, proposed a smart marine engineering construction plan, from the construction of the marine environment 3D monitoring system Ocean construction data platform, development of marine application systems, information standards and specifications, plan design and many other aspects. The platform’s remote sensing image segmentation disaster prevention technology covers marine environmental protection, disaster prevention and mitigation, islands, marine law enforcement, marine economy, marine fisheries and many other commercial fields. The research results have been practiced in the construction of smart oceans in many coastal provinces and cities in China, and have important reference significance for the construction of smart oceans in other coastal provinces and cities.
A Fast Algorithm for Mining Closed Inter-transaction Patterns

One of the important tasks of data mining is frequent pattern mining (FPM) in which frequently closed pattern mining (FCPM) is also a very interesting topic. So far, many algorithms have been proposed for mining frequent closed patterns (FCPs) of items occurring within transactions, Frequent Closed Intra-Transaction Patterns (FCITraPs), but only a few methods have been proposed for mining FCPs across transactions, Frequent Closed Inter-Transaction Patterns (FCITPs). In this study, we propose an N-list based algorithm, named NCITPs-Miner, for efficient mining FCITPs. The proposed method creates Frequent Inter-Transaction Patterns (FITPs) at 1-pattern level along with their N-lists, and then quickly generates all FCITPs by applying our proposed theorems, the closure properties, and subsumption check. Experiments show that NCITP-Miner outperforms state-of-the-art algorithms.
Sulphur and lead isotope geochemistry of the Dursunbey (Balıkesir) lead–zinc deposit

The Biga Peninsula (northwest Anatolia) hosts several Pb–Zn occurrences, and the Dursunbey Pb–Zn deposit is one such important Pb–Zn province of Turkey. The basement rocks in the study area comprise metamorphic rocks of the Menderes Massif (Simav Metamorphites, Sarıcasu Formation, and Budağan Limestone) and ophiolitic units of the İzmir-Ankara Suture Zone, known as the Dağardı Mélange. These units were intruded by the Miocene Alaçam granite. Cover units start with the Early Miocene Yeniköy Formation (comprising alternating sandstone, claystone, marl, and clayey limestone, and tuff additives) and are terminated by Civanadağ tuffs, Akdağ volcanics, and the Toklargölü Formation, consisting of unattached grains and alluvium deposits. Mineralisation that is trending along N50°W 30–35°NE occurs between the Alaçam Granite and the carbonate rocks of the Menderes Massif Metamorphites. Microscopic analysis revealed that the mineralisation contains chalcopyrite, pyrite, sphalerite, galena, fahlerz (tetrahedrite, tennantite), pyrrhotite, pyrargyrite-proustite, magnetite, and haematite as major ore minerals. The δ34S values range from 2.4 to 4.1‰ in pyrite (n = 2, mean = 3.25‰), from 1.7 to 3.4‰ in chalcopyrite (n = 4, mean = 2.8‰), 2.2–3.5‰ in sphalerite (n = 6, mean = 3.05‰), but from only 0.2–1.8‰ (n = 8, mean = 1.12‰) in galena. The low δ34S values of the sulphide minerals (varying over a narrow range) may indicate that sulphur in the ore-depositing solutions had a magmatic origin. The Pb isotope ratios of galena ranged from 18.729 to 18.742 for 206Pb/204Pb, 15.589 to 15.606 for 207Pb/204Pb, and 38.589 to 38.647 for 208Pb/204Pb. In one sphalerite sample, these ratios were measured as 18.738, 15.614, and 38.648, respectively. The Pb isotope model ages are between −90 and −45, which are consistent with those of J-type lead. In 207Pb/204Pb–206Pb/204Pb and 208Pb/204Pb–206Pb/204Pb diagrams, such ages plot below the average Pb-isotope crustal growth curve, indicating that lead in the studied mineralisation had an orogenic source.
Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program optimization

The efficiency of tensor operators is key to implement fast deep learning models. However, identifying the fastest implementation of a tensor operator for a target hardware is challenging. A wide range of different configurations have to be considered, and the evaluation of a configuration is time consuming as it requires compilation and execution of the operator. A common approach to address these issues is to boost traditional optimization algorithms with a surrogate modet, i.e., a machine learning model that approximates the objective function and is cheap to query compared to the target hardware. However, as the surrogate model grows in complexity, so does the time needed to train and maintain it. In this work, we propose to use an evolutionary optimizer and augment it with a non-parametric surrogate model (a weighted k-Nearest-Neighbor regression). We evaluate our approach on the convolution layers of a ResNetl8, and show a convergence speedup of up to 1.4×; when compared to baseline operator tuners.
Heuristic Space Diversity Measures for Population-based Hyper-heuristics

A hyper-heuristic is an optimization approach that continually selects the most appropriate heuristic(s) to apply to an optimization problem. Hyper-heuristics conduct a search in the space of heuristics, or heuristic space, for the most suitable heuristic to apply to candidate solutions in problem space. Traditionally, hyper-heuristics manage relatively simple low-level heuristics, which are often based on human domain intuition. Increasingly, hyper-heuristics are being used in conjunction with population-based meta-heuristics as the low-level heuristics. A heuristic space diversity measure helps practitioners understand the behavior of hyper-heuristics that manage population-based heuristics. This paper discusses existing measures to quantity heuristic space diversity, highlights shortcomings of these existing measures, and proposes a new heuristic space diversity entropy-based measure. Spatial and temporal volatility measures that characterize entity-to-heuristic assignments are also proposed.
LSHADE with S-shape Constraint-handling Technique in Push and Pull Search for Constrained optimization Problems

Constrained optimization problem is a common issue in science and engineering. The key to solve this problem is to balance the relationship between constraints and objectives. Therefore, this paper proposes an s-shape constraint-handling method in push and pull search (SLSHADE-PPS), which divides the whole evolutionary process into two phases, called push search phase and pull search phase respectively. The push search phase mainly focuses on the value of the objective function and uses LSHADE to push the population into the optimal region of the objective function. In the pull search phase, the s-shape constraint handling technique is combined with LSHADE to pull the infeasible individuals back to feasible region. The s-shape function makes the violation tolerance maintain high level at the start stage and strictly limits the solutions to reside in the feasible region at the end stage of pull phase. SLSHADE-PPS shows significant advantages over the state-of-the-art constraint algorithms on the 28 benchmark test functions from IEEE CEC2017.
Edge Assembly Crossover with Tabu for Traveling Salesman Problem

This paper proposes a new tabu technique to enhance the search performance of edge assembly crossover (EAX). EAX is known as the most promising recombination operator for the traveling salesman problem (TSP) among populationbased evolutional approaches such as genetic algorithm (GA). It succeeds in generating a sophisticated combination of parents’ traits and makes GA comparable to state-of-the-art heuristics for the TSP. EAX can find the optimal in very large instances, however, it sometimes does not work well on instances involving a lattice pattern. This is because, in the instance including cities that are arranged in a lattice pattern, it is difficult to find the best tour due to the existence of many alternative candidate tours. In instances that include such patterns, the diversity of edges in the population is decreased because the EAX concentrates and repeats exchanging edges in this local pattern, and pays less attention to combinations of edge globally. In this paper, we improve EAX itself by introducing a tabu scheme to inhibit repeating the exchange of the same edges in order to reduce the bias of edge to be exchanged between parents and enhance the diversity in offspring. Numerical experiments show the search performance of our new method in the instances selected from TSPLIB and VLSI TSP.
Adaptive Multi-scale Quantum Harmonic Oscillator Algorithm Based on Evolutionary Strategy

This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search scale, the searching step decreases too fast at the later stage of the evolution and is more likely to suffer premature convergence and stagnation. To improve the convergence performance, an adaptive attenuation mechanism of scaling is proposed to dynamically adjust the exploration and exploitation properties. Evolutionary strategies such as selection, crossover and DE/rand/1 mutation are implemented in the proposed algorithm to enhance the exploration and exploitation abilities. Experimental results evaluated on several unimodal and multimodal benchmark functions indicate the significant improvement of the proposed algorithm to the original MQHOA. Meanwhile, the experimental results compared with several state-of-the-art optimizers show the superiority or competitiveness of the proposed algorithm.
World library policy in web analytics: Current state and trends

The libraries’ web-outposts open up new opportunities, enable libraries to interact efficiently with their users and to promote their information products in the virtual space. The systematic assessment is integral to high performance of libraries’ web-divisions. The author explores the intensity of applying evaluation methods to the libraries’ web-sites. She examines the current state and popular trends in libraries’ representation in the virtual space using the relevant methods of web-analytics. The findings of the 2019 survey “Modern trends in the development of library resources in the web space”, comprising 387 Russian and foreign libraries of various organizational and legal status, are discussed. Particular attention is paid to the presence of libraries in the web space and resource management using web-analytic instruments. Based on her research findings, the author concludes that the major accomplishment for the libraries today is the very fact of their presence in the web space in the form of official websites and accounts in social media. Though the respondents confirmed that they used web-based analytical tools to evaluate their own web-based representation, the survey demonstrates that just a small part of the analytical tools potentiality is actually used.
Preparing America's Cyber Intelligence Workforce

Cyber intelligence requires analytic and technical skills. It is a pillar of U.S. national security and is needed in the private sector. We outline a framework for cyber intelligence education, including a dual-track model to balance the technical and analytic requirements.
Energy and Complexity in Evolving Collective Robot Bodies and Brains

The impact of the environment on evolving increasingly complex morphologies (bodies) and controllers (brains) remains an open question in evolutionary biology and has important implications for the evolutionary design of robots. This study uses evolutionary robotics as an experimental platform to evaluate relationships between environment complexity and evolving body-brain complexity given energy costs on evolving complexity. We evolve robot body-brain designs for increasingly complex environments (difficult cooperative transport tasks) in a collective robotic gathering simulation. The impact of complexity costs on body-brain evolution is evaluated across such increasingly complex environments. Results indicate that complexity costs enable the evolution of simpler body-brain designs that are effective in simple environments but yield negligible behavior (task performance) differences in more complex environments.
A Training Difficulty Schedule for Effective Search of Meta-Heuristic Design

In the context of optimization problems, the performance of an algorithm depends on the problem. It is difficult to know a priori what algorithm (and what parameters) will perform best on a new problem. For this reason, we previously proposed a framework that uses grammatical evolution to automatically generate swarm intelligence algorithms given a training problem. However, we observed two issues that affected the results of the framework. The first issue was that sometimes the training problems are too easy, and any candidate algorithm could solve it or too difficult that no candidate algorithm could solve them. The second issue was the presence of parameters in the grammar, which causes a significant increase in the search space. In this work, we addressed those issues by investigating three training schedules in which the problems start easy and get harder over time. We also investigated whether numerical parameters should be part of the grammar. We compared these training schedules to the previous one and compared the performance of the generated algorithms against the traditional algorithms, which are DE, PSO, and CS. We found that gradually increasing the difficulty of the training problem produced algorithms that could solve more testing instances than training only in 10-D. The results suggest that a step-by-step increase in difficulty is a better approach overall. We also found that including parameters in the grammar resulted in algorithms on par with the traditional meta-heuristics. Besides, as expected, our results show that removing parameters from the grammar exhibit the worst overall performance. However, interestingly it could solve most of the testing instances within the given testing budget.
Integrated Metric Learning Based Multiple Object Tracking Method under Occlusion in Substations

Abstract Nowadays most of background subtraction based algorithms lack the robustness to handle tracking multiple objects with specific situations such as heavy occlusion in intelligent video surveillance system. This paper proposed an integrated metric learning based multiple object tracking method in smart substations. First tracks personnel obtaining bounding box, then integrates obtained mahalanobis distance and cosine distance of personnel. When occlusion occurs, proposed method compares the integrated metric vale with threshold so as to track personnel in different frames. Experiments in the smart substation confirm that our method can track multiple personnel under occlusion effectively and reliably.
Hierarchical Mixed Signal Detection and Modulation Classification

Signal detection and modulation classification plays an important role in civil and military. In this paper, some challenging problems in signal detection and modulation classification have been discussed. We employed signal cyclostationary theory and Support Vector Machine (SVM) method to blindly detect and classify mixed signals, which have significant overlap in both time domain and frequency domain. Compared with other similar studies, our analysis and experiments are more practical and challenge. In addition, the simulation results demonstrates that our proposed approach shows great performance in different conditions and environment.
DiLeNA

This paper describes the Distributed Ledger Network Analyzer (DiLeNA), a new software tool for the analysis of the transactions network recorded in Distributed Ledger Technologies (DLTs). The set of transactions in a DLT forms a complex network. Studying its characteristics and peculiarities is of paramount importance, in order to understand how users interact in the distributed ledger system. The tool design and implementation is introduced and some results are provided. In particular, the Bitcoin and Ethereum blockchains, i.e. the most famous and used DLTs at the time of writing, have been analyzed and compared.
A Chaotic Inertia Weight TLBO Applied to Troubleshooting optimization Problems

When a failure event occurs in a complex system with multiple interconnected components, identifying the faulty component can be a challenging task. A troubleshooting optimization problem consists in finding the sequence of activities that must be followed to find the faulty component and fix the system with minimum expected cost of repair (ECR). Troubleshooting optimization can be modeled as a combinatorial optimization problem, and different algorithms have been proposed to solve it. This paper proposes a chaotic inertia weight Teaching-Learning Based optimization (TLBO) algorithm for the troubleshooting optimization problem. A chaotic sequence is used to update the inertia weight used in each iteration of TLBO. Numerical experiments using three troubleshooting models and nine chaotic maps are conducted to evaluate the performance of the proposed algorithm. The standard TLBO algorithm is also considered in the experiments to establish a reference baseline. The results showed that the proposed model presented a better performance in terms of average ECR, when compared with the standard TLBO algorithm.
Multi Depth-Velocity Filters for Enhancing Multiple Moving Objects in 5-D Light Field Videos

A moving object in a five-dimensional (5-D) light field video (LFV) can be selectively enhanced using the depth and the velocity of the object. In this paper, a 5-D depth-velocity (DV) filter is proposed to enhance multiple moving objects at different depths and with different velocities in an LFV. The 5-D DV filter is designed as a cascade of an infinite-extent impulse response multi-depth filter and a finite-extent impulse response multi-velocity filter. Experimental results obtained with numerically generated and real LFVs indicate that more than 15 dB improvement in signal-to-interference ratio can be achieved with the proposed 5-D multi DV filter compared to previously proposed multi depth-only filters.
Can Hardware Performance Counters Detect Adversarial Inputs?

Deep learning has become an integral part of modern-day applications. Recent research has shown how inputs for a Neural Network can be perturbed to disrupt its detection accuracy and lead to fatal consequences. In this paper, we investigate whether hardware performance counters available in most modern microprocessors uncover adversarial inputs constructed using perturbations to the clean input images. Our experiments on three different datasets, having real-life DNN applications ranging from traffic sign to melanoma detectors, paints an interesting picture- while the hardware performance counters show a difference (approximately 1% for some performance counters) between clean and adversarial inputs on individual samples, they do not show any significant trend to distinguish between clean and modified samples.
PRBN: A Pipelined Implementation of RBN for CNN Training

Recently, training CNNs (Convolutional Neural Networks) on-chip has attracted much attention. With the development of the CNNs, the proportion of the BN (Batch Normalization) layer’s execution time is increasing and even exceeds the convolutional layer. The BN layer can accelerate the convergence of training. However, little work focus on the efficient hardware implementation of BN layer computation in training. In this work, we propose an accelerator, PRBN, which supports the BN and convolution computation in training. In our design, a systolic array is used for accelerating the convolution and matrix multiplication in training, and RBN (Range Batch Normalization) array based on hardware-friendly RBN algorithm is implemented for computation of BN layers. We implement PRBN on FPGA PYNQ-Z1. The working frequency of it is 50 MHz and the power of it is 0.346 W. The experimental results show that when compared with CPU i5-7500, PRBN can achieve 3.3 $$\times $$ speedup in performance and 8.9 $$\times $$ improvement in energy efficiency.
MSB-First Distributed Arithmetic Circuit for Convolution Neural Network Computation

This paper presents a novel circuit implementation for convolution filters and rectified linear activation function used in deep neural networks. By conducting computation in an MSB-first bit-serial manner, it can predict earlier if the outcomes of filter computations will be negative and subsequently terminate the remaining computations to save power. It also stores pre-computed partial products into look-up tables to eliminate the need for multiplier circuits, leading to hardware efficient implementation. The proposed circuit is implemented on an FPGA and it shows significant power and performance improvements compared to a conventional design implemented on the same FPGA.
Efficient and Trusted Detection of Rootkit in IoT Devices via Offline Profiling and Online Monitoring

We present LKRDet: a framework based on a Trusted Execution Environment to detect Kernel rootkits in IoT devices. LKRDet checks the consistency of hardware events, occurring in specific system call routines, to detect abnormalities caused by the kernel rootkits. LKRDet relies on Hardware Performance Counters to efficiently and safely count the hardware events occurring in the system. We implement a prototype of LKRDet for the ARM TrustZone architecture, on top of the Open Portable Trusted Execution Environment and evaluate our prototype with four popular rootkits. Our evaluation reveals that LKRDet can accurately detect the presence of all the rootkits in the device.
Adversarial Perturbation with ResNet

Most of security issues in deep learning are based on human-imperceptible adversarial perturbation, which can fool image recognition models of deep learning and bring a serious security threats to many practical applications. However, how to construct a universal adversarial perturbation for images is still an open question. In this paper, we make fully use of a residual network to get a universal perturbation, and then utilize a loss network to perform the similarity measure of images to carry out the adversarial attack. Experiment results on the CIFAR-10 dataset show that our scheme can get an 89% attack success rate.
Face Recognition Technique in Transform Domains

In a recently published article, a new discriminative sparse representation method for robust face identification via ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization (SRFI) was presented. In our previous works, coefficients from Two-Dimensional Discrete Cosine Transform (2D DCT), 2D Discrete Wavelet Transform (2D DWT), were employed individually or combined to implement face identification systems. In this paper, a mixed SRFI (MSRFI) system is proposed by utilizing weight-based selected coefficients from the two non-orthogonal domains, i.e., 2D DCT and 2D DWT. The use of such mix as an input to the MSRFI maintains the high recognition accuracy of the SRFI while remarkably reducing the storage requirements, and the computational complexity. By referring to our previous works results and to prove the improved features of the MSRFI, extensive simulations were implemented on two face datasets, namely, ORL, and YALE.