Reviewerly/exHarmony huggingface dataset:
Novel FDP mechanisms for releasing bipartite graph data on fixed and infinite intervals

As data collection increases, more and more sensitive data is being used to publish query results. This creates a significant risk of privacy disclosure. As a mathematically provable privacy theory, differential privacy (DP) provides a tool to resist background knowledge attacks. Fuzzy differential privacy (FDP) generalizes differential privacy by employing smaller sensitivity and supporting multiple similarity measures. Thus the output error can be reduced under FDP. Existing FDP mechanisms employ sliding window strategy, which perturb the true query value to an interval with this value as the midpoint to maintain similarity of outputs from neighboring datasets. It is still possible for an attacker to infer some sensitive information based on the difference between the left and right endpoints of the output range. To address this issue, this article present two solutions: fixed interval perturbation and infinite interval perturbation. These strategies perturb the true query values of two neighboring datasets to the same interval and provide fuzzy differential privacy protection for the dataset. We apply the proposed method to the privacy-preserving problem of bipartite graph subgraph counting and verify the effectiveness by experiments.
A genetic algorithm based image steganography scheme with high embedding capacity and low distortion

Since the inception of mankind, information has been an important asset; therefore, many researchers have been trying to devise different techniques which can keep messages secret from unauthorized users. Imperceptibility has been another dimension that needs to be taken into consideration and visible distortion may lead to data breach. Therefore, the data-hiding domain has witnessed many improvements but most of them have considered a static key/pattern to embed the data. Nonetheless these schemes pose security but using static keys may not guarantee the best imperceptibility and security. Therefore, a genetic algorithm-based data-hiding scheme is proposed in this paper, which uses chromosomes of genetic algorithms as key. The results have been compared against imperceptibility measures, i.e. PSNR, NC and SSIM. The result shows that the proposed scheme provides a great improvement in PSNR till 46.25 (more than 2%) in the imperceptibility.
Communication-efficient decentralized elastic-net broad learning system based on quantized and censored communications

As an efficient learning model, the broad learning system (BLS) has achieved great success in machine learning and various applications due to its outstanding performance. Differing from typical deep learning based models, the BLS has a more straightforward structure and few parameters. In this study, we focus on developing a decentralized version of the elastic-net BLS (D-ENBLS) with communication efficiency. The scenario we considered is that the training data is distributed throughout a network of interconnected agents, which prohibits sharing the raw data owing to resource limitations or privacy concerns. In such a distributed paradigm, the communication between agents is an important issue to investigate. From the perspective of saving communication resources, we introduce quantization and communication censoring strategies on the D-ENBLS algorithm to improve the communication procedure and minimize the communication cost with minimal performance degradation. The novel algorithm refers to as DQC-ENBLS, in which quantization reduces the number of bits for each transmission, whereas communication censoring reduces the total number of transmissions. By formulating the training problem as a finite-sum minimization, the alternating multiplication method (ADMM) is employed to solve the optimization problem in a decentralized manner. The experimental results verify that the DQC-ENBLS algorithm can reduce communication cost while maintaining similar performance on the test datasets.
A Novel Electromagnetic Positioning Prototype System With Simplified Receiver for Interventional Surgery Application

To meet the need of 3-D positioning of surgical instrument in interventional surgery and the requirement of smaller size sensor for the narrow blood vessels, a new electromagnetic positioning model is proposed with a simplified receiver. Based on the electromagnetic theory and geometry principle, the electromagnetic field transmitter with groups of three orthogonal coils and the simplified receiver with a smaller size than existing sensors are designed. Then, using the Biot–Savart law, the distance between receiving end and geometric center of orthogonal coils is calculated, and the spatial coordinate of receiving end is computed with the spherical intersection formula. To further reduce the positioning error, a two-round accuracy improvement algorithm is designed for selecting the optimal topology of coil groups at the transmitter end and fitting correction of the calculated distance. We implement the prototype system and perform both simulation experiments and actual experiments. The results show that our proposed new electromagnetic positioning method with a simplified receiver has higher accuracy and stronger stability, compared with the traditional positioning approach of three-coil transmitter and three-coil receiver.
Efficient Representation of Large-Alphabet Probability Distributions

A number of engineering and scientific problems require representing and manipulating probability distributions over large alphabets, which we may think of as long vectors of reals summing to 1. In some cases it is required to represent such a vector with only b bits per entry. A natural choice is to partition the interval 0,1 into 2b uniform bins and quantize entries to each bin independently. We show that a minor modification of this procedure – applying an entrywise non-linear function (compander) f(x) prior to quantization – yields an extremely effective quantization method. For example, for b=8(16) and 105-sized alphabets, the quality of representation improves from a loss (under KL divergence) of 0.5(0.1) bits/entry to 10-4(10-9) bits/entry. Compared to floating point representations, our compander method improves the loss from 10-1(10-6) to 10-4(10-9) bits/entry. These numbers hold for both real-world data (word frequencies in books and DNA k-mer counts) and for synthetic randomly generated distributions. Theoretically, we analyze a minimax optimality criterion and show that the closed-form compander f(x)ArcSinh(cK(KlogK)x) is (asymptotically as b∞) optimal for quantizing probability distributions over a -letter alphabet. Non-asymptotically, such a compander (substituting 1/2 for cfor simplicity) has KL-quantization loss bounded by ≤8∙2-2blog2. Interestingly, a similar minimax criterion for the quadratic loss on the hypercube shows optimality of the standard uniform quantizer. This suggests that the ArcSinh quantizer is as fundamental for KL-distortion as the uniform quantizer for quadratic distortion.
Lightweight Fine-Grained Multiowner Search over Encrypted Data in Cloud-Edge Computing

Edge computing, as an extension of cloud computing, outsources encrypted sensitive data to edge nodes to decrease latency and improve broadband efficiency. Although attribute-based keyword search (ABKS) can ensure the security of outsourced data and promote fine-grained access control of data, deficiencies still exist under the general ABKS scheme in the cloud, as follows: shared files owned by a single data owner, inflexible access control, key escrow problem, and high computational overhead. We propose an attribute-based multidata owner searchable encryption scheme in cloud-edge computing with effective policy update to address these problems. The two-level access control is used to realize the common management and fine-grained sharing of data by the multidata owner. All user keys are jointly generated by the central authority and attribute authority, and a key escrow is no longer required. Moreover, partial encryption and decryption calculations are shifted from resource-constrained data owners and users to edge nodes. Furthermore, our scheme not only supports policy update but also realizes the dynamic updating of the index. The security proof and performance analysis show that the scheme has strong security and practicality in the edge computing environment.
Masked unbiased principles for parameter selection in variational image restoration under Poisson noise

Abstract In this paper we address the problem of automatically selecting the regularization parameter in variational models for the restoration of images corrupted by Poisson noise. More specifically, we first review relevant existing unmasked selection criteria which fully exploit the acquired data by considering all pixels in the selection procedure. Then, based on an idea originally proposed by Carlavan and Blanc-Feraud to effectively deal with dark backgrounds and/or low photon-counting regimes, we introduce and discuss the masked versions—some of them already existing—of the considered unmasked selection principles formulated by simply discarding the pixels measuring zero photons. However, we prove that such a blind masking strategy yields a bias in the resulting principles that can be overcome by introducing a novel positive Poisson distribution correctly modeling the statistical properties of the undiscarded noisy data. Such distribution is at the core of newly proposed masked unbiased counterparts of the discussed strategies. All the unmasked, masked biased and masked unbiased principles are extensively compared on the restoration of different images in a wide range of photon-counting regimes. Our tests allow to conclude that the novel masked unbiased selection strategies, on average, compare favorably with unmasked and masked biased counterparts.
Faster isogeny computation on twisted Hessian curves

Isogenies between elliptic curves have been the subject of recent study due to their significance in the post quantum cryptosystem based on isogeny. Various methods for faster isogeny computation have been proposed on different elliptic curve models. In this paper, we generalize a w-coordinate system to twisted Hessian curves. Based on the w-coordinate system, the computation of scalar multiplication and isogeny on twisted Hessian curves could be notably simplified.
Synergies between resource sustainability and energy performance of cloud servers: The role of virtual machine repacking approach

Dynamic virtual machine (VM) consolidation to combine the workload of active devices and switch idle nodes into low-power states while preserving the quality of service (QoS) guarantee is the primary proposed solution’s goal to maintain dependable services between providers and customers. We propose a multi-step resource provision algorithm to address this issue efficiently. The forwarding step unites the iterative search for critical paths and the priority selection for task layers to reduce the makespan and reach an optimal critical task placement. The backward step trains processing servers against different loads using eclectic utilization levels to guarantee those servers function at their most efficient utilization values. Furthermore, this step optimizes energy consumption and resource diffusion by reallocating the existent VMs and mitigating an attempted overload of machines. Different evaluation metrics are assessed with respect to the number of tasks and VMs. The results show that the proposed paradigm optimizes energy consumption and resource utilization efficiently, lowering the makespan, overhead time, and network utilization and maximizing the throughput productivity and QoS satisfaction.
Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

This article tackles the problem of finding similarity between web time-sensitive news articles, which can be a challenge. This challenge was approached with a novel methodology that uses supervised learning algorithms with carefully selected features (Semantic, Lexical and Temporal features (content and contextual features)). The proposed approach considers not only the textual content, which is a well-studied approach that may yield misleading results, but also the context, community engagement, and community-deduced importance of that news article. This paper details the major procedures of title pair pre-processing, analysis of lexical units, feature engineering, and similarity measures. Thousands of web articles are being published every second, and therefore, it is essential to determine the similarity of these articles efficiently without wasting time on unnecessary text processing of the bodies. Hence, the proposed approach focuses on short contents (titles) and context. The conducted experiment showed high precision and accuracy on a Really Simple Syndication (RSS) dataset of 8000 Arabic news article pairs collected automatically from 10 different news sources. The proposed approach achieved an accuracy of 0.81. Contextual features increased the accuracy and the precision. The proposed algorithm achieved a 0.89 correlation with the evaluations of two human judges based on Pearson’s Correlation Coefficient. The results outperform the state-of-the-art systems on Arabic news articles.
“SMART” cytology: The next generation cytology for precision diagnosis

Cytology plays an important role in diagnosing and managing human diseases, especially cancer, as it is often a simple, low cost yet effective, and non-invasive or minimally invasive diagnostic tool. However, traditional morphology-based cytology practice has limitations, especially in the era of precision diagnosis. Recently there have been tremendous efforts devoted to apply computational tools and to perform molecular analysis on cytological samples for a variety of clinical purposes. Now is probably the appropriate juncture to integrate morphology, machine learning, and molecular analysis together and transform cytology from a morphology-driven practice to the next level - "SMART" Cytology. In this article we will provide a rather brief review of the relevant works for computational analysis on cytology samples, focusing on single-cell-based multiplex quantitative analysis of biomarkers, and introduce the conceptual framework of "SMART (Single cell, Multiplex, AI-driven, and Real Time)" Cytology.
Sistem Informasi Aplikasi Kasir Pada Koperasi RSCM Jakarta Pusat Berbasis Java

The progress of an agency or company can be seen from how to use computer technology as a tool in solving problems. This is very supportive in the operation of an agency or company, both small, medium and large companies in increasingly fierce business competition. In this case the application in question is the Cashier Application System at the Indonesian Employee Cooperative, RSCM Central Jakarta, which is located at Jln. Diponegoro no. 71 Central Jakarta. This cashier and report application was built based on needs analysis from KPRI RSCM. This application was built using the JAVA programming language (Desktop) using Netbeans as its IDE, and using the MySql database. The methodology used by the researcher is the Agile software development method. The data collection methods used in this study were closed interviews, observation, and review of decisions from stakeholders so that the data obtained were able to accommodate the needs and initiation of the application to be built. This application is tested using blackbox as a test of application functionality.
A Static Reduced-Order Multiple-Model Adaptive Estimator for Noise Identification

A reduced-order multiple-model (MM) estimator for noise identification in dynamic stochastic systems is developed. The unknown noise statistics are assumed to be static. While a standard static MM estimator does not grow exponentially over time, its computational complexity grows exponentially with the number of modes. The proposed algorithm reduces the computational complexity of MM estimation from exponential to polynomial by constructing a significantly smaller set of mode models, which are updated every time step. It is assumed that the constructed mode models do not change significantly between time steps, which in turn holds if the smoothness of the mode probabilities is guaranteed. It is shown that in the case where there is “enough” statistical distinction between the noise modes, the proposed reduced-order MM estimator converges to the standard MM estimator. The proposed reduced-order MM estimator is evaluated using Monte Carlo simulations, showing that it performs nearly similar to the standard MM estimator with a fraction of its complexity. The numerical example shows less than a 2% increase in the root mean-squared errors of the reduced-order MM estimator from the standard one, while the reduction in the number of filters in the reduced-order MM estimator is 300%. To further validate the proposed filter, experimental results are presented of an unmanned aerial vehicle (UAV) navigating with terrestrial signals of opportunity. Opportunistic navigation serves a relevant application for MM-based estimation, as system parameters, namely the statistics of the clock error dynamics of opportunistic sources, are unknown and must be adaptively estimated. The experimental results show a UAV navigating for more than 5 min over a trajectory of more than 3 km, achieving a final position error of 6.21 m obtained using the standard MM estimator versus a final position error of 6.25 m obtained using the proposed reduced-order MM estimator. A standard extended Kalman filter was implemented for comparative analysis, showing a final error of 40.03 m. In the experiments, the reduced-order MM estimator was implemented with 16 filters, while the standard MM was implemented with 256 filters.
The Juice Is Worth the Squeeze: Analysis of Autonomous System Provider Authorization in Partial Deployment

BGP, the de-facto standard protocol for exchanging routes on a network-wide basis called AS employs invalid routes. Recently, a data object called Autonomous System Provider Authorization (ASPA) was proposed as a new specification for verifying PATH information in BGP security. In this paper, we shed light on the effectiveness of ASPAs in a partial deployment alongside the conventional BGP through experiments based on a real AS topology. To this end, we also present a novel simulation tool, LOTUS, for BGP route exchange, including ASPAs. We then evaluate deployments of ASPAs and their verification with LOTUS for two cases on network topology in Japan: the case in deployment from ASes whose number of connections with other ASes is large, i.e., deployment from top ASes, and the case in deployment from ASes at the end of the network topology, i.e., deployment from leaf-node ASes. As a result, we confirm that the number of victim ASes decreases in the former case, while ASPAs provide no advantage in the latter case. Notably, the number of victim ASes decreases by about 96% on average by deploying the verification with ASPAs in the top-eight ASes. Based on these results, we further conduct extensive experiments in the deployment from the top ASes, whereby ASes outside the network topology advertise malicious routes to the victim ASes. We also discuss a case whereby an adversary tries to leverage ASPAs. Our promising results show that the adversary will no longer obtain an advantage even by leveraging ASPAs.
User structural information in priority-based ranking for top-N recommendation

The recommender system is a set of data recovery tools and techniques used to recommend items to users based on their selection. To improve the accuracy of the recommendation, the use of additional information (e.g., social information, trust, item tags, etc.) in addition to user-item ranking data has been an active area of research for the past decade. In this paper, we present a new method for recommending top-N items, which uses structural information and trust among users within the social network and extracts the implicit connections between users and uses them in the item recommendation process. The proposed method has seven main steps: (1) extract items liked by neighbors, (ii) constructing item features for neighbors, (iii) extract embedding trust features for neighbors, (iv) create user-feature matrix, (v) calculate user’s priority, (vi) calculate item’s priority and finally, (vii) recommend top-N items. We implement the proposed method with three datasets for recommendations. We compare our results with some advanced ranking methods and observe that the accuracy of our method for all users and cold-start users improves. Our method can also create more items for cold-start users in the list of recommended items.
Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units

The mortality rate in the intensive care unit (ICU) is a key metric of hospital clinical quality. To enhance hospital performance, many methods have been proposed for the stratification of patients' different risk categories, such as severity scoring systems and machine learning models. However, these methods make capturing time sequence information difficult, posing challenges to the continuous assessment of a patient's severity during their hospital stay. Therefore, we built a predictive model that can make predictions throughout the patient's stay and obtain the patient's risk of death in real time. Our proposed model performed much better than other machine learning methods, including logistic regression, random forest, and XGBoost, in a full set of performance evaluation processes. Thus, the proposed model can support physicians' decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality.
Radio frequency fingerprinting feature fusion based on multi-domain discriminant kernel canonical correlation analysis

Specific emitter identification (SEI) refers to the precise identification of a specific transmitter through the extraction of hardware fingerprint information from the received signal. The majority of related research focuses on the definition and extraction of new fingerprint features, with less emphasis on the comprehensive use of existing features. Because the characteristics of different analysis domains are complementary to the description of the radio frequency fingerprint, this paper proposes a multi-domain feature fusion strategy for SEI based on multi-domain discriminant kernel canonical correlation analysis (MDKCCA), which fully exploits the complementarity between the features of different domains and combines feature tag information. MDKCCA affords multi-domain feature dimensionality reduction and fusion in a high-dimensional space.The algorithm's performance is validated on four different types of real-world data sets using eight common fingerprint features in four feature analysis domains. The results show that this method eliminates the need for manual feature optimization and can significantly reduce the dimensionality of fusion features. The recognition rate on various targets exceeds 95%, which is higher than the best single feature. It is also superior to the simple feature synthesis method based on direct cascade or PCA dimensionality reduction transformation, the neural network-based feature synthesis method, and the feature fusion method based on discriminant canonical correlation methods.