Reviewerly/exHarmony huggingface dataset:
Counting Minimal Unsatisfiable Subsets

Abstract Given an unsatisfiable Boolean formula F in CNF, an unsatisfiable subset of clauses U of F is called Minimal Unsatisfiable Subset (MUS) if every proper subset of U is satisfiable. Since MUSes serve as explanations for the unsatisfiability of F , MUSes find applications in a wide variety of domains. The availability of efficient SAT solvers has aided the development of scalable techniques for finding and enumerating MUSes in the past two decades. Building on the recent developments in the design of scalable model counting techniques for SAT, Bendík and Meel initiated the study of MUS counting techniques. They succeeded in designing the first approximate MUS counter, $$\mathsf {AMUSIC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>AMUSIC</mml:mi></mml:math> , that does not rely on exhaustive MUS enumeration. $$\mathsf {AMUSIC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>AMUSIC</mml:mi></mml:math> , however, suffers from two shortcomings: the lack of exact estimates and limited scalability due to its reliance on 3-QBF solvers. In this work, we address the two shortcomings of $$\mathsf {AMUSIC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>AMUSIC</mml:mi></mml:math> by designing the first exact MUS counter, $$\mathsf {CountMUST}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CountMUST</mml:mi></mml:math> , that does not rely on exhaustive enumeration. $$\mathsf {CountMUST}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CountMUST</mml:mi></mml:math> circumvents the need for 3-QBF solvers by reducing the problem of MUS counting to projected model counting. While projected model counting is #NP-hard, the past few years have witnessed the development of scalable projected model counters. An extensive empirical evaluation demonstrates that $$\mathsf {CountMUST}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CountMUST</mml:mi></mml:math> successfully returns MUS count for 1500 instances while $$\mathsf {AMUSIC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>AMUSIC</mml:mi></mml:math> and enumeration-based techniques could only handle up to 833 instances.
SHWS: Stochastic Hybrid Workflows Dynamic Scheduling in Cloud Container Services

Cloud container services as the new norm of cloud resource provisioning are more flexible and widely used in workflows scheduling. However, it is challenging to minimize the cost for workflows scheduling in cloud container services, especially when workflows arrive time and tasks execution time are uncertain. In this article, a stochastic hybrid workflows [i.e., off-line batch workflows (DIWs) and online stream workflows (DSWs)] scheduling problem in cloud container services is solved. A stochastic hybrid workflows scheduling system (SHWS), which consists of a workflow analyzer, workflow classifier, runtime estimator, workflow scheduler, and resource manager, is designed to manage and schedule DIWs and DSWs. Based on the SHWS, a stochastic hybrid workflows scheduling algorithm (SHWSA) is proposed to minimize the cost and improve resource utilization. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed SHWSA approach. The results demonstrate the superiority of SHWSA compared with the state-of-the-art algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article investigates a stochastic hybrid workflows scheduling problem in cloud container services. We propose a stochastic hybrid workflows scheduling algorithm, which is named SHWSA. The SHWSA is designed to jointly schedule off-line batch workflows (DIWs) and online stream workflows (DSWs) for minimizing the cost and improving resource utilization in cloud container services. The basic idea is to assign tasks subdeadlines and prioritize tasks for guaranteeing workflows deadlines constraints and the processing dependence requirements of tasks. Experiments show that SHWSA outperforms some state-of-the-art algorithms.
Speech Recognition Employing MFCC and Dynamic Time Warping Algorithm

Speech has been an integral part of human life acting as one of the five primitive senses of the human body. As such any software or application based upon speech recognition has a high degree of acceptance and a wide range of applications in defense, security, health care, and home automation. Speech is a waffling signal with varying characteristics at a high rate. When examined over a very short scale of time, it can be considered as a stationary signal with very small variations. In this paper, authors have worked upon the detection of a single user using multiple isolated words as speech signals. For designing the system, feature extraction using Mel-frequency cepstral coefficients (MFCCs) and feature matching using dynamic time warping (DTW) are considered as the designing of the system because of its simplicity and efficiency. Short-time spectral analysis is adopted which is the main part of the MFCC algorithm used in feature extraction. To compare any two signals varying in speed or having phase difference between them, DTW is used. Since two spoken words can never be the same, the DTW algorithm is best suited to compare two words.
FaiR-N: Fair and Robust Neural Networks for Structured Data

Fairness and robustness in machine learning are crucial when individuals are subject to automated decisions made by models in high-stake domains. To promote ethical artificial intelligence, fairness metrics that rely on comparing model error rates across subpopulations have been widely investigated for the detection and mitigation of bias. However, fairness measures that rely on comparing the ability to achieve recourse have been relatively unexplored. In this paper, we present a novel formulation for training neural networks that considers the distance of data observations to the decision boundary such that the new objective: (1) reduces the disparity in the average ability of recourse between individuals in each protected group, and (2) increases the average distance of data points to the boundary to promote adversarial robustness. We demonstrate that models trained with this new objective are more fair and adversarially robust neural networks, with similar accuracies, when compared to models without it. We also investigate a trade-off between the recourse-based fairness and robustness objectives. Moreover, we qualitatively motivate and empirically show that reducing recourse disparity across protected groups also improves fairness measures that rely on error rates. To the best of our knowledge, this is the first time that recourse disparity across groups are considered to train fairer neural networks.
Improving Software Maintainability Prediction Using Hyperparameter Tuning of Baseline Machine Learning Algorithms

Software maintainability is a prime trait of software, measured as the ease with which new code lines can be added, obsolete ones can be deleted, and those having errors can be corrected. The significance of software maintenance is increasing in today’s digital era leading to the use of advanced machine learning (ML) algorithms for building efficient models to predict maintainability, although several baseline ML algorithms are already in use for software maintainability prediction (SMP). However, in the current study, an effort has been made to improve the existing baseline models using hyperparameter tuning. Hyperparameter tuning chooses the best set of hyperparameters for an algorithm, where a hyperparameter is that parameter that uses its value for controlling the training process. This study employs default hyperparameter tuning as well as the grid search-based hyperparameter tuning. Five regression-based ML algorithms, i.e., Random Forest, Ridge Regression, Support Vector Regression, Stochastic Gradient Descent, and Gaussian Process Regression, have been implemented using two commercial object-oriented datasets, namely QUES and UIMS for SMP. To evaluate the performance, a comparison has been made between the baseline models and the models developed after hyperparameter tuning based on the three accuracy measures, viz., R-Squared, Mean Absolute Error (MAE), and Root Mean Squared Logarithmic Error (RMSLE). The results depict that the performance of all the five baseline ML algorithms improved after applying hyperparameter tuning. This conclusion is supported by the improved R-squared, MAE, and RMSLE values obtained in this study. Best results are obtained when the grid search method is used for the tuning purpose. On average, the values of R-squared, MAE, and RMSLE measures improved by 20.24%, 12.26%, and 30.28%, respectively, for the QUES dataset. On the other hand, in the case of the UIMS dataset, an average improvement of 6.27%, 15.71%, and 16.39% has been achieved in terms of R-squared, MAE, and RMSLE, respectively.
Linear MIMO Precoders With Finite Alphabet Inputs via Stochastic Optimization and Deep Neural Networks (DNNs)

In this paper, we investigate designs of linear precoders for vector <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gaussian</i> channels via stochastic optimizations and deep neural networks (DNNs). We assume that the channel inputs are drawn from practical finite alphabets, and we search for precoders maximizing the mutual information between channel inputs and outputs. Though the problem is generally non-convex, we prove that when the right singular matrix of precoder is fixed, any local optima of this problem is a global optima. Based on this fact, an efficient projected stochastic gradient descent (PSGD) algorithm is designed to search the optimal precoders. Moreover, to reduce the complexity of calculating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posterior</i> means involved in gradients calculation, K-best algorithm is adopted to make approximations of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posterior</i> means with negligible loss of accuracy. Furthermore, to avoid explicit calculation of mutual information and its gradients, DNN-based autoencoders (AEs) are constructed for this precoding task, and an efficient training algorithm is proposed. We also prove that the AEs, with ‘softmax’ activation function and ‘categorical cross entropy’ loss, maximize the mutual information under reasonable assumptions. Then, in order to extend the AE methods to large scale systems, ‘sigmoid’ activation function and ‘binary cross entropy’ loss are used such that the size of AEs will not grow prohibitively large. We prove that this maximizes a lower bound of the mutual information under reasonable assumptions. Finally, to make the precoders practical for high speed wireless scenarios, we propose an offline training paradigm which trains DNNs to infer optimal precoders given channel state information instead of training online for every different channel. Simulation results show that all the proposed methods work well in maximizing mutual information and improving bit error rate (BER) performance.
HEVC VMAF-oriented Perceptual Rate Distortion Optimization using CNN

Video coding standards like HEVC and VVC have achieved significant coding performance. However, the RDO module in coding framework ignores the characteristics of human visual system (HVS), which leads to insufficiency for perceptual video coding. Recently, learning-based objective assessment metric VMAF is developed and has been demonstrated higher quality assessment accuracy than conventional metrics. To incorporate VMAF into RDO aiming at improving perceptual coding efficiency, in this paper, a perceptual RDO scheme is proposed. A CNN-based on-line training method is first explored to determine the VMAF-related distortion estimation coefficient. Based on the VMAF-related coefficient and R-D model, a VMAF-based Lagrangian multiplier is proposed to adjust the R-D performance of each coding block. Experiments demonstrate that the proposed method can achieve an average -2.80% VMAF-based BD-Rate compared with the original HEVC, which effectively improves the coding performance.
A Novel Blockchain Secure to Routing Protocol in WSN

A blockchain is a database that contains the history of all exchanges made between its users since its creation. A blockchain is a distributed and secure ledger of all transactions made since the start of the distributed system. It is a technology for storing and transmitting information, transparent, secure, and operating without a central control body. Our paper studies a new device authentication for mobile sensor node to prove its authenticity to unknown network manager. To solve the authentication of sensor among multiple networks, our proposal a blockchain scheme where the transaction specifies the authentication of given cluster head CH. As we claim, a blockchain guarantees an integrity and availability of message under assumption that every node has some public and private key pairs. However, it does not provide any authentication mechanism for symmetric key. Hence, the proposed scheme is secure as we claim. More concretely, in the protocol proposed Blockchain Security IoT (BSI) is to provide authentication between the mobile base station (BS), the cluster head and the member nodes for wireless sensor networks (WSN). Our protocol BSI protocol makes it possible to put the necessary keys of the networks at the level of each sensor for the different scenarios carried out, BS mobile node and migration node. We evaluate the performance of our protocol with simulations using MATLAB. The results confirm that the BSI protocol is robust and efficient, provides lower power consumption and fast computing time.
4D computed tomography super-resolution reconstruction based on tensor product and nuclear norm optimization

Four-dimensional computed tomography (4D-CT) has been widely used in preoperative evaluation and radiotherapy planning of lung tumors. To reduce the damage to healthy tissue, it is a better way to limit the scan time and the number of CT slices. Yet, it leads to the reduction of CT image resolution in the superior-inferior direction. To improve the resolution of the 4D-CT image, we propose a super-resolution (SR) algorithm based on tensor product and nuclear norm optimization. The proposed cost function includes a tensor fidelity term and a nuclear norm regularization term. The tensor fidelity term consists of low-resolution (LR) and high-resolution (HR) image tensors, as well as SR operators. The nuclear norm regularization term is used to preserve the operators’ low-rank. The optimization problem can be effectively solved by an alternative direction method of the multipliers (ADMM) technique. The SR operators can extract useful information from each dimension of LR image tensors to enhance the equality of 4D-CT SR reconstruction. Experimental results show that the proposed method can preserve the edge details of the 4D-CT image. Moreover, quantitative comparisons show that the proposed method increases peak signal-to-noise ratio from 1.5 dB to 5.5 dB, structural similarity index from 2% to 11%, visual information fidelity from 6% to 20%, edge model-based blur metric from 5% to 15%, and decreases the spatial-spectral entropy-based quality index from 1% to 5%, compared with conventional 4D-CT SR algorithms.
Secure efficient signature for internet of things over near-ring

Abstract Prediction of air quality is a topic of great interest in air quality research due to direct With rapid progression in remote innovation and inescapable computerized innovation have given in expanding prevalence and enthusiasm of Internet of Things (IoT) procedure, universally giving comfort and knowledge to our day by day exercises. In IoT based framework situation, shrewd parts are associated wherever as all inclusive things connected in an inescapable model. Guaranteeing security for intersection among brilliant items is fundamentally progressively significant, in this paper, we propose a novel signature scheme which is utilized for carrying communication amongst devices in IoT environment. Moreover we revealed different scheme that are vulnerable. The significance of the proposed scheme over other existing scheme are analyzed in terms of the summary which is illustrated using performance and security comparison.
Imbalanced Adversarial Training with Reweighting

Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks. However, the majority of existing studies are focused on balanced datasets, where each class has a similar amount of training examples. Research on adversarial training with imbalanced training datasets is rather limited. As the initial effort to investigate this problem, we reveal the facts that adversarially trained models present two distinguished behaviors from naturally trained models in imbalanced datasets: (1) Compared to natural training, adversarially trained models can suffer much worse performance on under-represented classes, when the training dataset is extremely imbalanced. (2) Traditional reweighting strategies which assign large weights to underrepresented classes will drastically hurt the model’s performance on well-represented classes. In this paper, to further understand our observations, we theoretically show that the poor data separability is one key reason causing this strong tension between under-represented and well-represented classes. Motivated by this finding, we propose the Separable Reweighted Adversarial Training (SRAT) framework to facilitate adversarial training under imbalanced scenarios, by learning more separable features for different classes. Extensive experiments on various datasets verify the effectiveness of the proposed framework.
Unintended Features of APIs: Cryptanalysis of Incremental HMAC

Many cryptographic APIs provide extra functionality that was not intended by the designers. In this paper we discuss such an unintended functionality in the API of HMAC, and study the security implications of it’s use by applications. HMAC authenticates a single message at a time with a single authentication tag. However, most HMAC implementations do not complain when extra data is added to the stream after that tag is computed, nor they undo the side effects of the tag computation. Think of it as an API of a new authentication primitive, that provides tags to prefixes, rather than just to the full message. We call such primitives Incremental MACs (IncMACs). IncMACs may be used by applications to efficiently authenticate long messages, broken into fragments, which need their own individual authentication tag for performing an early abort or to retransmit only bad fragments, while each tag (strongly) authenticates the message prefix so far, and the last tag fully authenticates the full message. It appears that some applications (e.g., Siemens S7 protocol) use the standard HMAC API to provide an incremental MAC, allowing to identify transmission errors as soon as the first error occurs, while also directly authenticating the full message. We discuss two common implementations, used by cryptographic libraries and programs, whose APIs do not forbid using them incrementally, continuing with extra data after computing the tag. The most common one, which Siemens uses, uses a naive implementation (as natively coded from the RFCs). The other is the implementation of the OpenSSL library. We discuss these implementations, and show that they are not as secure as HMAC. Moreover, some of them may even be highly insecure when used incrementally, where in the particular case of OpenSSL it is possible to instantly find collisions and multi-collisions, which are also colliding under any key. We also discuss the fine details of the definition of IncMACs, and propose secure versions of such a primitive.
Democratizing Information Visualization. A Study to Map the Value of Graphic Design

Visual representations are a consistent component of human evolution. They are forms of concept designs using combinations of colors, patterns, and geometrical figures. As history displays, graphical visualization has been used since early ages as a mean to transfer knowledge between human beings across generations. Recently, visual representations became recognized by various scientists as tools to ease cognitive comprehension in various scientific fields. Therefore, visual representations are becoming important in science communication and education. Notwithstanding the general relevance of visual representations, there is not extensive recognition of the value of graphical representation to improve communication of scientific results and the level of awareness of visual design principles among non-design-centered communities is low. Considering these aspects, this explorative study investigates the perception of STEM researchers, without any specific visual design background, and the value of visual representations as tools to support the communication of technical and scientific knowledge among academics and a wider non-design-centered community. To measure the user perception and the value of visual representations in daily working practice, an interpretive approach was used, and a pilot study was conducted with a group of voluntary participants recruited among research members from a well-known American institution. Results emphasize how researchers, who included professionally executed graphical representations in their publications, reports, and grant proposals, perceived their work as more easily readable. Early findings show that visual representations can positively support scientists to share research outcomes in a more compelling, visually clear, and impactful manner, reaching a wider audience across different disciplines.
Privacy Concerns, IoT Devices and Attacks in Smart Cities

The advancement of technology and wide adoption of the Internet of Things (IoT) across the board will soon make the smart city a reality. Many governments are currently planning on rapidly transforming their cities into smart cities, while others are currently building new smart cities. These smart cities demand the installation of millions and in some cases billions of devices across the cities, homes and other institutions and different embedded systems and networks. Combined, these systems produce tons of data that can be used in different contexts. For the most part, proponents of smart cities reference the benefits of this implementation such as improved user experiences and security associated with improved monitoring capabilities. However, there have been concerns regarding privacy and how these capabilities can be misused by governments, private corporations, and malicious attackers. A potential solution to improve privacy while not infringing upon security is to implement blockchain technology which uses a combination of consensus and encryption algorithms. This paper will discuss the use of a fully homomorphic algorithm to encrypt data in such a way that maintains the user’s privacy and allows them complete control over their data.