Reviewerly/exHarmony huggingface dataset:
Improved image clustering with deep semantic embedding

Dimensionality reduction has found extensive use in the area of high dimensional data clustering. But unfortunately some valuable discriminative information might be lost in the process of dimensionality reduction, including feature extraction and feature selection. This issue inevitably degrades the performance of clustering algorithms. Recently, the semantic space embedding is emerging as a promising technique for state-of-the-art clustering methods, which can provide extra discriminative information and reasonably improve the clustering performance. In this paper, we plan to improve the performance of high dimensional image clustering by embedding semantic information into the original visual space. Inspired by the great success of deep learning, we employed a multi-layer autoencoder based on deep neural networks (DNNs) to undertake the semantic feature embedding and dimensionality reduction. By this way, the final image clustering task is carried out in the lower-dimensional feature space with deep semantic embedding. A series of experiments on acknowledged benchmark image datasets demonstrate that the proposed approaches can achieve superior performance over several existing clustering methods.
Active authentication using file system decoys and user behavior modeling: results of a large scale study

Data theft is a growing threat to consumers and organizations which existing security safeguards do not sufficiently address. In particular, existing authentication mechanisms are frequently bypassed or circumvented although in situations where attacks are launched by malicious insiders who already possess valid credentials. We propose methods to enhance existing authentication paradigms with continuous active authentication. Our system adds additional levels of security without burdening the user with more credentials to manage. We utilize two complementary authentication modalities to validate user-identity: (1) behavior profiling for user-system interaction, and (2) baiting adversaries using automatically distributed file-decoy tripwires. We present the results from a 160-subject user study used to validate our system. Our results show that the presence of decoy documents on a system does not interfere with normal user activities, and that, with 95% accuracy, our system will detect an intrusion within 15 minutes with at most one false-positive for 40 hours of user activity.
Service-oriented architecture of environmental information systems to forecast the impacts of natural disasters in South Korea

Purpose While high-quality information is critical in decision-making for environmental issues and assessing the impact of natural disasters, there is a significant lack of research on how environmental information systems (EISs) can be integrated through service-oriented architecture (SOA). The purpose of this paper is to investigate the EISs in South Korea and to propose an SOA to improve the quality of EISs. Design/methodology/approach This study follows the design science research methodology proposed by Peffers et al. (2007). A pilot test was conducted with Environmental Impact Assessment Support System (EIASS) end users based on the modified DeLone and McLean’s IS success model to discern the moderating effect of SOA readiness. Findings This study proposed a new SOA for the South Korea EIASS; the work processes among EIASS, nationwide EISs and local EISs are also analyzed. Conceptual and hardware SOA for the EIASS were presented. The finding shows that SOA readiness has a significant moderating effect for EIASS end user benefits. Practical implications Supply chain managers can exploit GIS-based environmental information, which can be provided by the new EIASS architecture, to estimate the risk of a natural disaster within the geographical region of their supply chain. Originality/value This is one of the few studies in the EIS literature that propose an SOA to improve the quality of EIS. In addition, the study remodeled DeLone and McLean’s IS success model to assess the proposed SOA for EAISS and found that the new SOA would be useful for end users.
Resilience in information stewardship

Information security is concerned with protecting the confidentiality, integrity, and availability of information systems. System managers deploy their resources with the aim of maintaining target levels of these attributes in the presence of reactive threats. Information stewardship is the challenge of maintaining the sustainability and resilience of the security attributes of (complex, interconnected, multi-agent) information ecosystems. In this paper, we present, in the tradition of public economics, a model of stewardship which addresses directly the question of resilience. We model attacker-target-steward behaviour in a fully endogenous Nash equilibrium setting. We analyse the occurrence of externalities across targets and assess the steward's ability to internalise these externalities under varying informational assumptions. We apply and simulate this model in the case of a critical national infrastructure example.
Robust CPHD algorithm for maneuvering targets tracking via airborne pulsed Doppler radar

A robust cardinalized probability hypothesis density based on interacting multiple model (RCPHD-IMM) is proposed for multiple maneuvering targets tracking under the Doppler blind zone of airborne pulse Doppler radar. This tracking algorithm integrates the advantages of unbiased converted measurements (UCM), interacting multiple model (IMM), and cardinalized probability hypothesis density (CPHD). Under the architecture of the proposed algorithm, an IMM deals with the model switching, and the UCM accounts for nonlinearity in the dynamic system models, whereas the CPHD estimates the number and state of the targets. The gain adjustment matrix is designed to improve the robustness of the proposed algorithm in the blind zone. Furthermore, an adaptive gating strategy is proposed to remove the irrelevant measurements and guarantee the detection of new targets, thereby effectively reduce the computational complexity of the proposed algorithm. Simulation results demonstrate the validity and feasibility of the proposed algorithm. The number and state of the targets in the loss of measurements can be estimated more accurately, and the amount of calculation needed with the RCPHD-IMM algorithm is reduced by 16.03% compared with the traditional CPHD algorithm.
Improvement of compensated closed-loop Kalman filtering using autoregressive moving average model

Data loss problems severely effect the performance of state estimation in most of communication and control systems. The normal techniques adopted for compensation in the process of state estimation are Open-loop Kalman filter and compensating closed-loop Kalman filter. The compensated closed-loop Kalman filtering scheme employs three (03) strategies namely Normal Equation, Levionson-Durbin and Leroux-Gueguen algorithms, using Autoregressive (AR) model, where only previous measurements are used. In this paper, the compensated vector is proposed based on Autoregressive Moving Average or ARMA model instead of AR model. This model contains more information than Autoregressive model, i.e. measurement and input signals, which is believed to generate more efficient results. Necessary steps including the computation of linear prediction coefficients have been taken to accommodate the input signal. Computation of this extra linear prediction coefficient however, bears an observable increase in computation time. The selection of AR and ARMA models is a trade-off between computation time and improved performance, which is the ultimate consequence of extra input signal. A standard mass-spring-damper case study is simulated to provide a comprehensive comparison in light of various parameters including state estimation, error, gain elements etc.
Conditional distributions of multivariate normal mean–variance mixtures

In this paper, we show that the conditional distribution of a multivariate normal mean–variance mixture (MNMVM) distribution is also a MNMVM distribution. We show the usefulness of the established result by deriving the conditional distributions of multivariate generalized hyperbolic distribution.
On coarser interval temporal logics

The primary characteristic of interval temporal logic is that intervals, rather than points, are taken as the primitive ontological entities. Given their generally bad computational behavior of interval temporal logics, several techniques exist to produce decidable and computationally affordable temporal logics based on intervals. In this paper we take inspiration from Golumbic and Shamir's coarser interval algebras, which generalize the classical Allen's Interval Algebra, in order to define two previously unknown variants of Halpern and Shoham's logic (HS) based on coarser relations. We prove that, perhaps surprisingly, the satisfiability problem for the coarsest of the two variants, namely HS3, not only is decidable, but PSpace-complete in the finite/discrete case, and PSpace-hard in any other case; besides proving its complexity bounds, we implement a tableau-based satisfiability checker for it and test it against a systematically generated benchmark. Our results are strengthened by showing that not all coarser-than-Allen's relations are a guarantee of decidability, as we prove that the second variant, namely HS7, remains undecidable in all interesting cases.
Bug Prediction of SystemC Models Using Machine Learning

In system-on-chip design, resources for verification is limited by time-to-market and cost. In order to allocate verification resources effectively, managers need to rely on their experience backed by design related metrics. However, often there are also other aspects of development process, such as bug history and developer information that can improve the effectiveness of verification. Software bug prediction is a machine learning (ML)-based technique which predicts whether a given software module is bug-prone by using product and process metrics of the module. Therefore, it can help direct verification effort, reduce costs, and improve the quality of software. Although there is a plethora of work in software bug prediction, no such work exists for SystemC. We propose an ML-based software bug prediction solution for verification of SystemC models used in virtual prototypes that takes into account system level design metrics and demonstrate its effectiveness on several open source system level designs. We find that 96% of modules could be correctly predicted as buggy or clean.
Geochemistry of trace elements and their relations with organic matter in Kuh-e-Sefid phosphorite mineralization, Zagros Mountain, Iran

The Kuh-e-sefid phosphorite is located in Zagros fold-thrust belt, in the Ramhormoz area, northeast of Khuzestan province. Phosphorite mineralization occurred as phosphorite lenses of Eocene age hosted by the Pabdeh Formation. The aim of this study is to investigate the role of organic matter in trace metal portioning during phosphorite mineralization in the Kuh-e-Sefid deposit. Field observations and petrographic studies indicate that the host-rock of mineralization is mainly consists of shale, marl, and limestone with textures varying from wackestone to packestone. Phosphoritic components, such as authigenic apatite, pellets, intraclasts, fish skeletal fragments, and micro-fossils are present. Moreover, non-phosphorite minerals including calcite, glauconite, pyrite, iron-oxide and to a lesser extend montmorillonite with microcrystalline quartz occurred in phosphorite lenses. NASC-normalized patterns of REEs showed the enrichment of Heavy Rare Earth elements (HREE) and negative Ce anomaly with co-existence of slightly negative and positive Eu anomalies represent a transitional change from oxic to anoxic sedimentation conditions during mineralization. Importantly, Ni/Co versus V/Cr ratios and V/(V + Ni) diagrams confirmed dysoxic to anoxic conditions for the hydrocarbon-bearing phosphorite and hosted shales, while non-hydrocarbon-bearing phosphorites exhibit oxic conditions. The FTIR studies revealed qualitative information about the component bonds and nature of the organic matter and mineral-organic bonds like OH, carboxylic OH, carboxylic acid CO, CC alkaline, group CH2, CC aromatic, CH aliphatic, and aromatic stretching associated with phosphate mineralization. It seems that there is a link between a relatively high concentration of trace metals (i.e., REEs, U, Co, Zn, Ni, V, Sr and Mo) and phosphorites enriched in organic matter especially those of aromatic hydrocarbon groups.
Software expert discovery via knowledge domain embeddings in a collaborative network

Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users' expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence.
A hybridization of extended Kalman filter and Ant Colony Optimization for state estimation of nonlinear systems

In this paper, a new nonlinear heuristic filter based on the hybridization of an extended Kalman filter and an ant colony estimator is proposed to estimate the states of a nonlinear system. In this filter, a group of virtual ants searches the state space stochastically and dynamically to find and track the best state estimation while the position of each ant is updated at the measurement time using the extended Kalman filter. The performance of the proposed filter is compared with well-known heuristic filters using a nonlinear benchmark problem. The statistical results show that this algorithm is able to provide promising and competitive results. Then, the new filter is tested on a nonlinear engineering problem with more than one state. The problem is to estimate simultaneously the states of an unmanned aerial vehicle as well as the wind disturbances, applied to the system. In this case, a processor-in-the-loop experiment is also performed to verify the implementation capability of the proposed approach. This paper also investigates the real-time implementation capability of the proposed filter in the attitude estimation of a three degrees of freedom experimental setup of a quadrotor to further investigate its effectiveness in practice.
Exploiting re-voting in the Helios election system

Election systems must ensure that representatives are chosen by voters. Moreover, each voter should have equal influence. Traditionally, this has been achieved by permitting voters to cast at most one ballot. More recently, this has been achieved by tallying the last ballot cast by each voter. We show this is not achieved by the Helios election system, because an adversary can cause a ballot other than a voter's last to be tallied. Moreover, we show how the adversary can choose the contents of such a ballot, thus the adversary can unduly influence the selection of representatives.
OptStream: Releasing Time Series Privately

Many applications of machine learning and optimization operate on data streams. While these datasets are fundamental to fuel decision-making algorithms, often they contain sensitive information about individuals and their usage poses significant privacy risks. Motivated by an application in energy systems, this paper presents OPTSTREAM, a novel algorithm for releasing differentially private data streams under the w-event model of privacy. OPTSTREAM is a 4-step procedure consisting of sampling, perturbation, reconstruction, and post-processing modules. First, the sampling module selects a small set of points to access in each period of interest. Then, the perturbation module adds noise to the sampled data points to guarantee privacy. Next, the reconstruction module reassembles non-sampled data points from the perturbed sample points. Finally, the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to improve accuracy by redistributing the added noise. OPTSTREAM is evaluated on a test case involving the release of a real data stream from the largest European transmission operator. Experimental results show that OPTSTREAM may not only improve the accuracy of state-of-the-art methods by at least one order of magnitude but also supports accurate load forecasting on the private data.
A novel edge-centric approach for graph edit similarity computation

Graph similarity is an important notion with many applications. Graph edit distance is one of the most flexible graph similarity measures available. The main problem with this measure is that in practice it can only be computed for small graphs due to its exponential time complexity. This paper addresses the high complexity of graph edit distance computations. Specifically, we present CSI_GED, a novel edge-centric approach for computing graph edit distance through common sub-structure isomorphisms enumeration. CSI_GED utilizes backtracking search combined with a number of heuristics to reduce memory requirements and quickly prune away a large portion of the mapping search space. Experiments show that CSI_GED is highly efficient for computing graph edit distance; it outperforms the state-of-the-art methods by over three orders of magnitude. It also shows that CSI_GED scales the computation gracefully to larger and distant graphs on which current methods fail to run. Moreover, we evaluated CSI_GED as a stand-alone graph edit similarity search query method. The experiments show that CSI_GED is effective and scalable, and outperforms the state-of-the-art indexing-based methods by over two orders of magnitude.
Decentralised estimation with correlation limited by optimal processing of independent data

Decentralised estimation aims at providing the best combination of multiple estimates. Since the exact solutions are expensive in terms of computation and communication requirements, the mean square error optimality is traded for the bound optimality. Fusion under unknown correlations has been inspected for two decades and the research now focuses on a partial knowledge of the correlations. This paper focuses on the assumption that the estimates to be fused were obtained by the optimal processing of local data with independent measurement errors. A generalisation of a recent solution to such a problem is proposed. In particular, non-uniqueness of the optimal fusion weights is discovered. The relation of the generalised and existing solutions is discussed and illustrative examples are given.
Optimal phase-shifting algorithm for interferograms with arbitrary steps and phase noise

Phase-shifting is a technique for phase retrieval that requires a series of intensity measurements with certain phase steps. The purpose of the present work is to study the phase extraction from phase-shifted interferograms with arbitrary steps and random phase-noise, and to deduce an algorithm that minimizes the phase-retrieval error induced by weak phase-noise. Simulations and validation experiments are presented.
A proposed multi-image compression technique

Image compression is a process that reduces memory space required to store an image. The image compression techniques are broadly classified into two categories a) Lossless technique b) Lossy technique. Lossy compression technique achieves higher result, but due to data loss it may cause spatial in consistency, blocking artifact, quantization noise in the decompressed image degrading the image quality. Most of the existing compression techniques are applicable on single image separately. In this study an image compression method is proposed where multiple images of the same size are combined together and compressed to achieve higher compression ratio while keeping image quality as close as standard image compression techniques. Luminance channel of each image is compressed separately using Vector Quantization (VQ) algorithm while two chrominance channels, Cb and Cr of all images are combined into a three dimensional matrix that forms training vector. Clustering is applied on the training vector to get the initial color representatives. Thus, for the chrominance channels of n number of images, the proposed method generates one index matrix and one centroid matrix of size 256×2× n where 256 is the number of clusters. This centroid matrix contains one 256×2 dimensional centroid matrix for each individual image. This 256×2 matrix contains centroid of each cluster. The centroids of each and every cluster of an image are updated individually using optimization technique to get a better centroid (Cb, Cr) pair. This process updates the color representative pair of Cb and Cr further. This method has been applied on standard images in literature and images collected from UCID v. 2 color image database. Experimental results are analyzed in terms of PSNR and space reduction. Experimental results show that the proposed method achieves a higher compression ratio retaining almost similar image information as other standard lossy compression algorithm.
Video semantic analysis based kernel locality-sensitive discriminative sparse representation

Kernel based locality-sensitive sparse representation is currently an active hot research topic in artificial intelligence, pattern recognition, signal processing and multimedia applications. In this paper, a new kernel based approach; named Video Semantic Analysis based Kernel Locality-Sensitive Discriminative Sparse Representation (KLSDSR) is proposed. This is to improve video semantic analysis for military intelligence systems and video surveillance. The proposed algorithm is able to learn more discriminative sparse representation (SR) coefficients based on group sparsity for video semantic analysis by incorporating both sparsity and locality-sensitive in kernel feature space with mapping of the SR features into a high dimensional space. Furthermore, an optimal dictionary is however engendered to compute the SR of video features aimed at good preservation of the locality structure among video semantic features and an improvement on computational cost. The proposed method gave promising classification results, when compared with state-of-the-art comparative approaches based on experimental results on video semantic concepts, which significantly improves the discrimination of SR features and it outperformed the other baseline methods.