Reviewerly/exHarmony huggingface dataset:
Comparison of different approaches for modeling of heavy metal estimations

Soil contamination by heavy metals is very important for environmental scientists due to the greater mobilization of metals and the possibility of contamination of groundwater. One of the most effective tools for evaluating the risk is the combination of experimentation with computer modeling. Modeling techniques are important in assessing the potential risks associated with heavy metals in the environment. Determination of models that can precisely evaluate the heavy metals in soils is an important need of agricultural researches, which could eradicate the weaknesses in the measurement of heavy metals in soils. The purpose of the present study is to test and compare different models according to their suitability for describing the estimation of heavy metals. The models used in this study were multilayer perceptron neural network (MLP), M5 model tree (M5) and bagging approach (BM5P). The data from 164 sampling sites from Neyshabur and Mashhad plains were taken in this study. The inputs combination according to feature selection-based correlation was used to feed the models. To model soil heavy metals, soil attributes, namely sand, silt, clay (as texture fractions), organic carbon, pH and available phosphorus, were entered in some models. To evaluate the performance of various techniques used in this study, several statistical indexes, including the correlation coefficient, root-mean-square error, Nash–Sutcliffe coefficient, Willmott’s index (d) and mean absolute error, were assessed. Comparison of different models for Fe, Cu, Mn and Zn indicated that MLP is the most suitable method for estimations of Fe and Mn, whereas BM5P and M5P are the most suitable models for determinations of Cu and Zn, respectively. This study concluded that machine learning models can be successfully applied to the rapid prediction of soil heavy metals using soil variables.
Client-Side QoE Management for SVC Video Streaming: An FSM Supported Design Approach

HTTP adaptive streaming (HAS) which provides the flexibility of video bit-rate adjustments, is becoming the de facto framework for live and on-demand video streaming services. The flexibility of HAS is further empowered by the scalable video coding (SVC) technique which allows low overhead quality upgradations of transmitted video segments. In this paper, we present a client-side finite-state machine (FSM) supported quality of experience (QoE) centric video bit-rate adaptation and management mechanism. The proposed strategy simultaneously manages three important QoE verticals: 1) providing stutter-free video viewing experience; 2) minimizing flickers in video outputs by controlling and smoothing the rates of encoding quality switches over time; and 3) maximizing aggregate video quality over a video playout session. Based on careful consideration of these QoE verticals, the management policy dynamically decides whether to download a new segment or upgrade the quality of an already downloaded segment in the playout buffer. We have implemented and evaluated the performance of our proposed framework in a multi-client SVC video steaming test-bed. Conducted experiments using real-world network traces reveal that the proposed strategy is able to outperform other state-of-the-art adaptive streaming techniques and deliver satisfactory QoE even in the face of highly varying channel conditions.
An Analysis of DCM-Based True Random Number Generator

A true random number generator (TRNG) using Digital clock manager (DCM) blocks of a Xilinx FPGA has advantages of minimal usage of logic elements and tunability of clock frequencies to search for better randomness. Its operating principle is beat frequency detection, which uses the least significant bits (LSBs) of a counter that counts the number of successive logic-ones captured by a D flip-flop. In this brief, we evaluate the DCM-based TRNG with 100 frequency pairs. The findings of this brief include: 1) only 12 frequency pairs passed the diehard statistical tests when three LSBs were extracted as directed in the previous work; 2) the value of the counter exhibits biphasic histograms; 3) small counter values show less uncertainty; and 4) the number of frequency pairs that passed the tests was doubled to 24 by extracting only one LSB from small counter values, with a 16.9% reduction in the generation rate.
An Efficient Selective Miner Consensus Protocol in Blockchain Oriented IoT Smart Monitoring

Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
Teaching GP to program like a human software developer

Program synthesis is one of the relevant applications of GP with a strong impact on new fields such as genetic improvement. In order for synthesized code to be used in real-world software, the structure of the programs created by GP must be maintainable. We can teach GP how real-world software is built by learning the relevant properties of mined human-coded software - which can be easily accessed through repository hosting services such as GitHub. So combining program synthesis and repository mining is a logical step. In this paper, we analyze if GP can write programs with properties similar to code produced by human software developers. First, we compare the structure of functions generated by different GP initialization methods to a mined corpus containing real-world software. The results show that the studied GP initialization methods produce a totally different combination of programming language elements in comparison to real-world software. Second, we propose perplexity pressure and analyze how its use changes the properties of code produced by GP. The results are very promising and show that we can guide the search to the desired program structure. Thus, we recommend using perplexity pressure as it can be easily integrated in various search-based algorithms.
Weighted Tensor Nuclear Norm Minimization for Color Image Restoration

Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method.In particular, a low-rankness-based prior has a significant effect on performance.On the other hand, a number of color extensions of NLSS-based grayscale image restoration methods have been developed.These extensions focus on the pixel-wise correlation among color channels.However, a natural color image also has a complex dependency, known as an inter-channel dependency, among local regions from different color channels.As a result, color artifacts appear in a denoised image obtained by using the existing methods.In this paper, we propose a novel non-local and inter-channel dependency-aware prior called the weighted tensor nuclear norm (WTNN).The proposed prior is derived by incorporating inter-channel dependency to low-rank-based NLSS prior.The WTNN is a low-rankness-of-the-third-order patch tensor, and we apply it to the tensors constructed with non-local similar patches.It enables us to naturally represent the higher-order dependencies among similar color patches.We propose an image denoising algorithm using the WTNN and image restoration algorithm by using a non-trivial generalization of this algorithm.The experimental results clearly show that the proposed WTNN-based color image denoising and restoration algorithms outperform state-of-the-art methods.
Adaptive multi-subswarm optimisation for feature selection on high-dimensional classification

Feature space is an important factor influencing the performance of any machine learning algorithm including classification methods. Feature selection aims to remove irrelevant and redundant features that may negatively affect the learning process especially on high-dimensional data, which usually suffers from the curse of dimensionality. Feature ranking is one of the most scalable feature selection approaches to high-dimensional problems, but most of them fail to automatically determine the number of selected features as well as detect redundancy between features. Particle swarm optimisation (PSO) is a population-based algorithm which has shown to be effective in addressing these limitations. However, its performance on high-dimensional data is still limited due to the large search space and high computation cost. This study proposes the first adaptive multi-swarm optimisation (AMSO) method for feature selection that can automatically select a feature subset of high-dimensional data more effectively and efficiently than the compared methods. The subswarms are automatically and dynamically changed based on their performance during the evolutionary process. Experiments on ten high-dimensional datasets of varying difficulties have shown that AMSO is more effective and more efficient than the compared PSO-based and traditional feature selection methods in most cases.
Masked and dynamic Siamese network for robust visual tracking

Visual object tracking is as a critical function for many computer vision tasks such as motion analysis, event detection and action recognition. Recently, Siamese network based trackers gained enormous popularity in the tracking field due to their favorable accuracy and efficiency. However, the distraction problem caused by semantic backgrounds and the simple modeling strategy of target templates often lead to performance degradation. In this study, we propose two modules, namely the target objectness model and the target template model, based on existing Siamese network based trackers to solve these issues. The target objectness model computes the possibility of each pixel in the search area pertaining to the tracked target based on color distributions of the foreground and background areas. The computed target likelihood map is masked on the previous response map, and subsequently adjusts the final response map to focus on the target. This practice enlarges the discrimination between the tracked target and surrounding backgrounds, thus alleviating the distraction problem. The target template model proposes a Gaussian mixed model to encode target appearance variations, where each component of the model represents a different aspect of the target, and the component weights are learned and dynamically updated. The proposed Gaussian model enhances diversity and simultaneously reduces redundancy between target samples. To validate the effectiveness of our proposed method, we perform extensive experiments on four widely used benchmarks, namely OTB100, VOT2016, TC128, and UAV123. The experimental results demonstrate that our proposed algorithm achieves favorable performance compared to many state-of-the-art trackers while maintaining real-time tracking speed.
Towards certified separate compilation for concurrent programs

Certified separate compilation is important for establishing end-to-end guarantees for certified systems consisting of multiple program modules. There has been much work building certified compilers for sequential programs. In this paper, we propose a language-independent framework consisting of the key semantics components and lemmas that bridge the verification gap between the compilers for sequential programs and those for (race-free) concurrent programs, so that the existing verification work for the former can be reused. One of the key contributions of the framework is a novel footprint-preserving compositional simulation as the compilation correctness criterion. The framework also provides a new mechanism to support confined benign races which are usually found in efficient implementations of synchronization primitives.
JPEG Steganography With Estimated Side-Information

Previous studies have exhibited that incorporating side-information, e.g., a high-quality precover image, can significantly improve steganographic security for JPEG images. This motivates us to estimate the side-information for traditional steganographic scenario in which only a JPEG image is available. It is expected to achieve high-level security by utilizing the estimated side-information similar to side-informed steganography, even though the estimated side-information is not perfectly precise. In this paper, a general framework of side-information estimated (SIE) JPEG steganography is proposed, under which the core problems are how to better estimate the precover and modulate the distortion function correspondingly. To address the two problems, we test several denoising filters and a deblocking filter to obtain the estimated precover, and we introduce two implementation models for modulating the costs. We finally recommend the combination of the deblocking filter and the modulation model using the polarity of the estimated rounding error. The experimental results show that the proposed method dramatically improves the existing additive distortions for images of an arbitrary quality factor and outperforms the state-of-the-art methods based on estimating side-information when resisting modern steganalysis.
ProcessExplorer: Intelligent Process Mining Guidance

Large amount of data is collected in event logs from information systems, reflecting the actual execution of business processes. Due to the highly competitive pressure in the market, organizations are particularly interested in optimizing their processes. Process mining enables the extraction of valuable knowledge from event logs, such as deviations, bottlenecks, and anomalies. Due to the increase of process complexity in flexible environments, visual exploration is increasingly becoming more challenging. In this paper, we propose ProcessExplorer, an interactive process mining approach to enable fast data analysis and exploration. ProcessExplorer takes an event log as input to automatically suggest subsets of similar process behavior, evaluate each subset, generate interesting insights, and suggest the subsets with the most interesting characteristics. We implemented our approach into an interactive visual exploration system, which we use as part of a user study conducted to evaluate our approach. Our results show that ProcessExplorer can be successfully applied to analyze and explore real-life data sets efficiently.
Building Sankie: An AI Platform for DevOps

There has been a fundamental shift amongst software developers and engineers in the past few years. The software development life cycle (SDLC) for a developer has increased in complexity and scale. Changes that were developed and deployed over a matter of days or weeks are now deployed in a matter of hours. Due to greater availability of compute, storage, better tooling, and the necessity to react, developers are constantly looking to increase their velocity and throughput of developing and deploying changes. Consequently, there is a great need for more intelligent and context sensitive DevOps tools and services that help developers increase their efficiency while developing and debugging. Given the vast amounts of heterogeneous data available from the SDLC, such intelligent tools and services can now be built and deployed at a large scale to help developers achieve their goals and be more productive. In this paper, we present Sankie, a scalable and general service that has been developed to assist and impact all stages of the modern SDLC. Sankie provides all the necessary infrastructure (back-end and front-end bots) to ingest data from repositories and services, train models based on the data, and eventually perform decorations or provide information to engineers to help increase the velocity and throughput of changes, bug fixes etc. This paper discusses the architecture as well as some of the key observations we have made from wide scale deployment of Sankie within Microsoft.
Standardising smart contracts: Automatically inferring ERC standards

Ethereum smart contracts have become common enough to warrant the need for standards to ensure ease of use. The most well known standard was created for the emerging token ecosystem and the exchanges serving it: the ERC20 standard. In this work we use the function selectors present in Ethereum smart contract bytecode to define contract purpose. Contracts are clustered according to the selectors they have. A Reverse look-up from selectors to function names is used to label clusters. We use the function names in clusters to suggest candidates for ERC standardisation.
Enhancement of Brain MR-T1/T2 Images Using Mathematical Morphology

Brain tumor is a life-threatening disease with a fast growth rate, which makes its detection a critical task. However, low contrast and high noise content in brain MR images hamper the screening of tumor. Enhancement is therefore done to improve the perceivable features of these images. This paper presents an improved enhancement technique of brain MR-T1/T2 images by employing morphological filters. In this method, a disk-shaped flat structuring element along with top-hat and bottom-hat morphological operators is used. The performance of the filter is validated by incrementing values of contrast improvement index (CII) and peak signal-to-noise ratio (PSNR) parameters indicating a successful enhancement without noise amplification.
Electromagnetic Signal Classification Based on Deep Sparse Capsule Networks

In complex electromagnetic environments, electromagnetic signal classification rates are low as long time have to be the cost to extract features.To cope with the issue, in this paper, an electromagnetic signal classification method is proposed based on deep sparse capsule networks.In the proposed method, received signals are frequency reduced and sampled processing first.Subsequently, a cross ambiguity function based on linear canonical transformation, a cross ambiguity function based on linear canonical domain, and higherorder spectrum are estimated, respectively.The maximum value of each section of the cross ambiguity function is combined with the maximum value of equally spaced cross sections of higher order amplitude spectrum to obtain the two-dimensional feature information.Finally, electromagnetic signals are classified by the deep sparse capsule networks.The simulation results show that the proposed method not only has good classification performance but also can automatically get a hierarchical feature representation by learning.Moreover, the corresponding time cost can be effectively reduced.
Partially-Independent Framework for Breast Cancer Histopathological Image Classification

The automated classification of histopathology images relives pathologists workload and, hence utilizing the resources to focus more on the most suspicious cases. More recently, inspired by the success of deep learning methods in computer vision application, such frameworks have also been applied in various medical image analysis applications. However, existing approaches showed less interest in exploring multi-layer features for improving the classification. We propose the integration of multi-layer features from a ResNet model for breast cancer histopathology image classification. Specifically, this work focuses on making a framework which considers both independent nature of layers as well as some partial dependency among them. Knowing that, not all the layers learn discriminative features, consideration of layers which learn to negative features will deteriorate the accuracy. Hence, we select the optimal subset of the layers based on an information-theoretic measure (ITS). Various experiments are performed on publicly available BreaKHis dataset, and demonstrate that the proposed multi-layer feature fusion yields better performance than the traditional way of using the highest layer features. This indicates that mid-and low-level features also carry useful discriminative information when explicitly considered. We also demonstrate improved performance, in most cases, over various state-of-the-art methods.
Transaction Clustering Using Network Traffic Analysis for Bitcoin and Derived Blockchains

Bitcoin is a decentralized digital currency introduced in 2008 and launched in 2009. Bitcoin provides a way to transact without any trusted intermediary, but its privacy guarantees are questionable, and multiple deanonymization attacks have been proposed. Cryptocurrency privacy research has been mostly focused on blockchain analysis, i.e., extracting information from the transaction graph. We focus on another vector for privacy attacks: network analysis. We describe the message propagation mechanics in Bitcoin and propose a novel technique for transaction clustering based on network traffic analysis. We show that timings of transaction messages leak information about their origin, which can be exploited by a well connected adversarial node. We implement and evaluate our method in the Bitcoin testnet with a high level of accuracy, deanonymizing our own transactions issued from a desktop wallet (Bitcoin Core) and from a mobile (Mycelium) wallet. Compared to existing approaches, we leverage the propagation information from multiple peers, which allows us to overcome an anti-deanonymization technique (“diffusion”) used in Bitcoin.
Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA

Nowadays, a growing number of web services are offered in API marketplaces browsed by service developers or third-party registries.Under this situation, API marketplaces' users greatly rely on a search engine to find suitable web services.However, due to the fact that functional attributes of web services are usually described in short texts, the search engine-based discovery approach suffers from the semantic sparsity problem, which hinders the effect of service discovery.To address this issue, we propose a novel web service discovery approach using word embedding and Gaussian latent Dirichlet allocation (Gaussian LDA).Unlike most existing service discovery approaches, our approach first uses context information generated by word embedding to enrich the semantics of service descriptions and users' queries.Then, the enriched service description is loaded into the Gaussian LDA model to acquire service description representation.Finally, the services are ranked by considering the relevance between the extended user's query and service description representation.The experiments conducted on a real-world web service dataset and the results demonstrate that the proposed approach achieves superior effectiveness on web service discovery.
Improved Multiplication Triple Generation over Rings via RLWE-Based AHE

An important characteristic of recent MPC protocols is an input-independent setup phase in which most computations are offloaded, which greatly reduces the execution overhead of the online phase where parties provide their inputs. For a very efficient evaluation of arithmetic circuits in an information-theoretic online phase, the MPC protocols consume Beaver multiplication triples generated in the setup phase. Triple generation is generally the most expensive part of the protocol, and improving its efficiency is the aim of our work. We specifically focus on computation over rings of the form $$\mathbb {Z}_{2^{\ell }}$$ in the semi-honest model and the two-party setting, for which an Oblivious Transfer (OT)-based protocol is currently the best solution. To improve upon this method, we propose a protocol based on RLWE-based Additively Homomorphic Encryption. Our experiments show that our protocol is more scalable, and it outperforms the OT-based protocol in most cases. For example, we improve communication by up to 6.9x and runtime by up to 3.6x for 64-bit triple generation.