Reviewerly/exHarmony huggingface dataset:
Efficient Cryptographic Hardware for Safety Message Verification in Internet of Connected Vehicles

An important security requirement in automotive networks is to authenticate, sign, and verify thousands of short messages per second by each vehicle. This requirement mandates the use of a high speed Elliptic Curve Cryptography (ECC) hardware. The Residue Number Systems (RNS) provide a natural parallelism and carry-free operations that could speed-up long integer arithmetics of cryptographic algorithms. In this article, we propose a high-speed RNS Montgomery modular reduction units with parallel computing to reduce the latency of the field modular operations. We propose a fully RNS-based ECC scalar multiplication co-processor for NIST-P256r1 and Brainpool256r1 standard curves and improved the scalar multiplication speed using NAF and DBC numbering systems. Compared to the literature, our scheme provides faster computation without compromising the security level. The performance of our fully RNS-ECC point multiplication meets the requirements of the automotive industry.
Densely-Accumulated Convolutional Network for Accurate LPI Radar Waveform Recognition

This paper presents a deep learning-based method to automatically recognize low probability of intercept (LPI) radar waveforms against diversified jamming attacks. Concretely, an efficient convolutional neural network (CNN) architecture, namely Densely-Accumulated Network (DANet), is introduced to learn the time-frequency representation transformed by the Wigner-Ville distribution. Such an architecture has several novel densely-accumulated connection modules specified by various symmetric and asymmetric convolutional layers to enrich diversified features at multiple representational maps. Besides, the skip-connection and dense-connection are leveraged to improve feature learning efficiency and prevent the vanishing gradient when the network goes deeper. Some image processing techniques (e.g., global thresholding and digital filtering) are adopted to enhance the quality of time-frequency image. Relying on simulations, we benchmark the proposed method on a synthetic 13-waveform dataset and also investigate the influence of hyper-parameters (such as image size, number of modules, training data size) on the overall recognition performance. Remarkably, with average accuracy of 98.2&#x0025; at 0 dB signal-to-noise ratio (SNR), DANet outperforms several backbone CNNs and state-of-the-art networks of LPI waveform recognition while keeping a cost-efficient model.
Oblivious Sensor Fusion via Secure Multi-Party Combinatorial Filter Evaluation

Given sensor units distributed throughout an environment, we consider the problem of consolidating readings into a single coherent view when sensors wish to limit knowledge of their specific readings. Standard fusion methods make no guarantees about what curious participants may learn. For applications where privacy guarantees are required, we introduce a fusion approach that limits what can be inferred. First, it forms an aggregate stream, oblivious to the underlying sensor data, and then evaluates that stream on a combinatorial filter. This is achieved via secure multi-party computation techniques built on cryptographic primitives, which we extend and apply to the problem of fusing discrete sensor signals. We prove that the extensions preserve security under the model of semi-honest adversaries. Also, for a simple target tracking case study, we examine a proof-of-concept implementation: analyzing the (empirical) running times for components in the architecture and suggesting directions for future improvement.
Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing

Predicting ICU readmission risk will help physicians make decisions regarding discharge. We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III). We used Natural Language Processing (NLP) and the Bag-of-Words approach on discharge summaries to build a Document-Term-Matrix with 3000 features. We compared the performance of support vector machines with the radial basis function kernel (SVM-RBF), adaptive boosting (AdaBoost), quadratic discriminant analysis (QDA), least absolute shrinkage and selection operator (LASSO), and Ridge Regression. A total of 4000 patients were used for model training and 6000 were used for validation. Using the bag-of-words determined by NLP, the area under the receiver operating characteristic (AUROC) curve was 0.71, 0.68, 0.65, 0.69, and 0.65 correspondingly for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. We then used the SVM-RBF model for feature selection by incrementally adding features to the model from 1 to 3000 bag-of-words. Through this exhaustive search approach, only 825 features (words) were dominant. Using those selected features, we trained and validated all ML models. The AUROC curve was 0.74, 0.69, 0.67, 0.70, and 0.71 respectively for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. Overall, this technique could predict ICU readmission relatively well.
Creating a Modeling Language Based on a New Metamodel for Adaptive Normative Software Agents

The demand for creating increasingly dynamic, autonomous and proactive software systems is challenging for the traditional Multi-agent Systems (MASs) approaches. Such requirement has given rise to adaptive software agents approaches. At the same time, norm is an essential and challenging feature that still tends to be addressed in adaptive MAS. In fact, norms to regulate agent behavior is still a vague concept that has not been properly investigated in terms of modeling and implementation. Even though many researchers have proposed modeling languages to deal with different abstractions, these languages fail to support the modeling of abstractions, such as adaptation and norms. Even more severe is the fact that little has been done to support the systematic design of Adaptive Normative Multi-Agent Systems (ANMASs). To facilitate the design and development of ANMASs, this paper presents a new metamodel, as well as language support, as means to provide tools to enable software developers. The proposed metamodel fosters a better understanding of the way agents are able to change their behaviors to deal with norms and captures interactions between agent’s norms and adaptation. To this end, our research is organized into five steps: (i) a literature review to identify the limitations of existing approaches related to ANMAS modeling; (ii) propose a new metamodel to support adaptative and normative concepts; (iii) propose a new language for modeling ANMASs; (iv) perform a qualitative and quantitative evaluation of the proposed language using a real case scenario, and (v) an empirical evaluation. The proposed metamodel and its associated modeling language advances the state of the art in modeling MASs and the approach is assessed in terms of correctness, time and difficulty. Our initial results revealed that our approach can be feasibly applied in a real world application, and is less difficult to apply and requires less time in comparison with a traditional approach. As software applications become more dynamic and adaptive, we believe it is essential to support developers to model MASs with abstractions such as adaptive agents, norms and their relationships. Such information can be foundational to steer future research on modeling adaptive agents capable of understanding and dealing with norms and adaptation.
Homomorphic Encryption

Homomorphic Encryption is a class of encryption methods envisioned by Rivest, Adleman, and Dertouzos already in 1978, and first constructed by Craig Gentry in 2009. It differs from typical encryption methods in the sense that it allows computation operations to be performed directly on encrypted data without requiring access to a secret key (A Few Thoughts on Cryptographic Engineering). The result of such a computation remains in encrypted form, and can at a later point be revealed by the owner of the secret key. This form of encryption allows computation on ciphertexts, generating an encrypted result which, when decrypted, matches the result of the operations as if they had been performed on the plaintext. The purpose of Homomorphic Encryption is to allow computation on encrypted data. Usually, it is used for large-scale statistical analysis and mostly used in data encryption and decryption. Thus, it is used programs that rely mainly on information security and high-security documents in many governmental segments. The challenging aspect is performing statistical analysis on encrypted data and getting an accurate result, without putting the data through the risk of being stolen or having a backdoor copy for it.
Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Abstract This work intends to introduce an optimization‐based privacy preservation model via selecting the optimal key matrix. Here, privacy preservation is carried out under two processes, namely, “data sanitization and restoration.” In fact, data sanitization is the data preservation method, where the data (message) are preserved using the optimal key. Similarly, data restoration is the inverse procedure of sanitization. Here, the key matrix is optimally chosen using a novel hybrid algorithm. For optimization purpose, this work deploys a hybrid optimization approach known as random‐based grey dragon algorithm (R‐GDA) that involves the concepts of both “dragonfly algorithm (DA) and grey wolf optimization (GWO) algorithm.” The novelty of the work is introduced in hybrid optimization approach R‐GDA. Eventually, the supremacy of the adopted method is validated over other existing approaches in terms of various measures such as privacy, utility, and so on. The privacy preservation in the cloud is achievable in the field of education, banking sector, military, and the research community.
Unified Graph-Based Missing Label Propagation Method for Multilabel Text Classification

In multilabel classification, each sample can be allocated to multiple class labels at the same time. However, one of the prominent problems of multilabel classification is missing labels (incomplete labels) in multilabel text. The multilabel classification performance is reduced significantly with the presence of missing labels. In order to address the incomplete or missing label problem, this study proposes two methods: an aggregated feature and label graph-based missing label handling method (GB-AS), and a unified graph-based missing label propagation method (UG-MLP). GB-AS is used to obtain an initial label matrix based on the similarity of both document levels: feature-based weighting representation and label-based weighting representation. On the other hand, UG-MLP is introduced to construct a mixed graph that combines GB-AS and label correlations into a single groundwork. A high-order label correlation is learned from the incomplete training data and applied to supplement the missing label matrix, which guides the creation of multilabel classification models. The combination of the mixed graphs by UG-MLP is aimed to obtain the benefits of both graphs to increase the classification performance. To evaluate UG-MLP, the metrics of precision, recall and F-measure were used on three benchmark datasets, namely, the Reuters-21578, Bibtex and Enron datasets. The experimental results show that UG-MLP outperformed GB-AS as well as other state-of-the-art approaches. Therefore, we can infer from the findings that by plotting a unified graph based on joining aggregated feature and label weightings together with the label correlation, the performance of multilabel classification can be improved.
Regularized Pooling

In convolutional neural networks (CNNs), pooling operations play important roles such as dimensionality reduction and deformation compensation. In general, max pooling, which is the most widely used operation for local pooling, is performed independently for each kernel. However, the deformation may be spatially smooth over the neighboring kernels. This means that max pooling is too flexible to compensate for actual deformations. In other words, its excessive flexibility risks canceling the essential spatial differences between classes. In this paper, we propose regularized pooling, which enables the value selection direction in the pooling operation to be spatially smooth across adjacent kernels so as to compensate only for actual deformations. The results of experiments on handwritten character images and texture images showed that regularized pooling not only improves recognition accuracy but also accelerates the convergence of learning compared with conventional pooling operations.
Resampling and Network Theory

Particle filtering provides an approximate representation of a tracked posterior density which converges asymptotically to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational complexity. This complexity can be implemented by operating the particle filter in parallel architectures. However, the resampling step in the particle filter requires a high level of synchronization and extensive information interchange between the particles, which impedes the use of parallel hardware systems. This paper establishes a new perspective for understanding particle filtering — that particle filtering can be achieved by adopting the principles of information exchange within a network, the nodes of which are now the particles in the particle filter. We propose to connect particles via a minimally connected network and resample each locally. This strategy facilitates full information exchange among the particles, but with each particle communicating with only a small fixed set of other particles, thus leading to minimal communication overhead. The key benefit is that this approach facilitates the use of many particles for accurate posterior approximation and tracking accuracy.
A Privacy-Preserving Vehicular Data Sharing Framework atop Multi-Sharding Blockchain

Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons and infrastructures, and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and brakes) with different entities nearby. However, sharing these vehicular data over the air raises concerns about identity privacy leakage. Besides, the centralized architecture adopted in existing IoV systems is fragile to single point of failure and malicious attacks. With the emergence of blockchain technology, it has the chance to solve these problems due to its features of tamper-proof, traceability and decentralization. In this paper, we propose a privacy-preserving vehicular data sharing framework based on blockchain. In particular, we design an anonymous and auditable data sharing scheme using Zero-Knowledge Proof (ZKP) technol-ogy so as to protect the identity privacy of vehicles while preserving the vehicular data auditability for Trusted Authorities (TAs). In response to high mobility of vehicles, we design an efficient multi-sharding protocol to decrease blockchain communication costs without compromising the blockchain security. We implement a prototype of our framework and conduct extensive experiments and simulations on it. Evaluation and analysis results indicate that our framework can not only strengthen system security and data privacy, but also increase the data authenticity verification efficiency by 5x comparing to existing privacy-preserving schemes.
Improving hand gestures recognition capabilities by ensembling convolutional networks

Abstract Hand gestures provide humans a convenient way to interact with computers and many applications. However, factors such as the complexity of hand gesture models, differences in hand size and position, and other factors can affect the performance of the recognition and classification algorithms. Some developments of deep learning such as convolutional neural networks (CNNs) and capsule networks (CapsNets) have been proposed to improve the performance of image recognition systems in this particular field. While CNNs are undoubtedly the most widely used networks for object detection and image classification, CapsNets emerged to solve part of the limitations of the former. For this reason, in this work a particular ensemble of both networks is proposed to solve the American Sign Language recognition problem very effectively. The method is based on increasing diversity in both the model and the dataset. The results obtained show that the proposed ensemble model together with a simple data augmentation process produces a very competitive accuracy performance with the all considered datasets.
A Novel Compression Scheme Based on Hybrid Tucker-Vector Quantization Via Tensor Sketching for Dynamic Light Fields Acquired Through Coded Aperture Camera

Emerging computational light field displays are a suitable choice for realistic presentation of 3D scenes on autostereoscopic glasses-free platforms. However, the enormous size of light field limits their utilization for streaming and 3D display applications. In this paper, we propose a novel representation, coding and streaming scheme for dynamic light fields based on a novel Hybrid Tucker TensorSketch Vector Quantization (HTTSVQ) algorithm. A dynamic light field can be generated from a static light field to capture a moving 3D scene. We acquire images through different coded aperture patterns for a dynamic light field and perform their low-rank approximation using our HTTSVQ scheme, followed by encoding with High Efficiency Video Coding (HEVC). The proposed single pass coding scheme can incrementally handle tensor elements and thus enables to stream and compress light field data without the need to store it in full. Additional encoding of low-rank approximated acquired images by HEVC eliminates intra-frame, inter-frame and intrinsic redundancies in light field data. Comparison with state-of-the-art coders HEVC and its multi-view extension (MV-HEVC) exhibits superior compression performance of the proposed scheme for real-world light fields.
Quantum Neural Network model for Token allocation for Course Bidding

Quantum computer has shown the advantage over the classical computer to solve some problems using the laws of quantum mechanics. With a combination of knowledge of machine learning and quantum computing, Quantum neural networks adapted the concept from classical neural networks and apply parameterized quantum gates as neural network weights. In this paper, we present an application of quantum neural networks with real-world data to predict token price used in a course bidding system. The experiments were carried out on the Qiskit quantum simulator. The result shows that quantum neural networks can achieve a good prediction result compared to the classical neural network. The best model configuration has the lowest RMSE 6.38%. This approach opens an opportunity to explore the benefit of quantum machine learning in many research fields in the future.
Denoising 3D magnetic resonance images based on weighted tensor nuclear norm minimization using balanced nonlocal patch tensors

The denoising of magnetic resonance (MR) images is important to improve the accuracy of organ tissue information recognition in the process of medical diagnosis. This paper proposes a 3D MR image denoising method based on weighted tensor nuclear norm minimization using balanced nonlocal patch tensors. In order to make utilization of the nonlocal self-similarity in high-dimensional images and the correlation among different dimensions, a high-dimensional adaptive clustering technology is developed to construct highly correlated 3D MR image patches into nonlocal patch tensors. Since the generated tensor exhibits low-rank characteristics, a low-rank tensor approximation technique based on the tensor singular value decomposition framework can be used to denoise the MR data. To improve the denoising accuracy, the local noise level information of different nonlocal patch tensors is considered. Besides, a method for constructing more balanced 3D nonlocal patch tensors is proposed to exploit the effectiveness of the tensor singular value decomposition. Experimental results show that the proposed method has a greater improvement over the existing MR image denoising methods in terms of objective measurement and subjective visual quality on simulated and real 3D MR images with different categories and noise levels.
Private Linear Computation for Noncolluding Coded Databases

Private computation in a distributed storage system (DSS) is a generalization of the private information retrieval (PIR) problem. In such a setting, a user wishes to compute a function of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> messages stored in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> noncolluding coded databases, i.e., databases storing data encoded with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> linear storage code, while revealing no information about the desired function to the databases. We consider the problem of private linear computation (PLC) for coded databases. In PLC, a user wishes to compute a linear combination over the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> messages while keeping the coefficients of the desired linear combination hidden from the databases. For a DSS setup where data is stored using a code from a particular family of linear storage codes, we derive an outer bound on the PLC rate, which is defined as the ratio of the desired amount of information and the total amount of downloaded information. In particular, the proposed converse is valid for any number of messages and linear combinations, and depends on the rank of the coefficient matrix obtained from all linear combinations. Further, we present a PLC scheme with rate equal to the outer bound and hence settle the PLC capacity for the considered class of linear storage codes. Interestingly, the PLC capacity matches the maximum distance separable coded capacity of PIR for the considered class of linear storage codes.
An IGGM-Based Poisson Multi-Bernoulli Filter and its Application to Distributed Multisensor Fusion

This article proposes a joint estimate approach in terms of target states and target cardinality as well as detection probability based on the Poisson multi-Bernoulli (PMB) filter. For the situations, where the detection probability is unknown or unreliable, the usual tracking methods often fail to work, so online estimation of the detection probability is indispensable.To depict the unknown detection probability, an inverse gamma Gaussian mixture (IGGM) implementation is adopted to propagate nonnegative features, including signal amplitude and signal-to-noise ratio (SNR). The IGGM-based PMB filter, abbreviated as IGGM-PMB, is proposed to solve the multitarget tracking (MTT) along with the unknown and time-varying detection profile. Specifically, the Poisson random finite set (RFS) intensity is approximated as an IGGM, while the density of Bernoulli RFS is approximated as a single inverse gamma Gaussian component (IGGC). Then, the proposed IGGM-PMB filter is applied to distributed multisensor fusion, wherein the estimated detection probabilities are used as a choice of the fusion ordering. Simulation results demonstrate the effectiveness and superiority of the proposed approach via comparisons with state-of-the-art approaches.