Reviewerly/exHarmony huggingface dataset:
$ \newcommand{\e}{{\rm e}} \ell_{0}$ -minimization methods for image restoration problems based on wavelet frames

In this paper we consider a class of $\ell_{0}$-minimization and wavelet frame based models for image deblurring and denoising. Mathematically, they can be formulated as minimizing the sum of a data fidelity term and the $l_{0}$-`norm' of the framelet coefficients of the underlying image, and we are particularly interested in three different types of data fidelity forms for image restoration problems. We first study the first-order optimality conditions for these models. We then propose a penalty decomposition (PD) method for solving these problems in which a sequence of penalty subproblems are solved by a block coordinate descent (BCD) method. Under some suitable assumptions, we establish that any accumulation point of the sequence generated by the PD method satisfies the first-order optimality conditions of these problems. Moreover, for the problems in which the data fidelity term is convex, we show that such an accumulation point is a local minimizer of the problems.&#13; %{\textcolor{blue}{ $\ell_0$ regularization term is the only nonconvex part, under some assumptions }}&#13; In addition, we show that any accumulation point of the sequence generated by the BCD method is a block coordinate minimizer of the penalty subproblem. Furthermore, under some convexity assumptions on the data fidelity term, we prove that such an accumulation point is a local minimizer of the penalty subproblem. Numerical simulations show that the proposed $\ell_{0}$-minimization methods enjoy great potential for image deblurring and denoising in terms of solution quality and/or speed.
Anonymity Reduction Attacks to Monero

Monero is one of the most valuable cryptocurrencies in the market, focusing on users' privacy. The built-in features in Monero help users to obfuscate the information of the senders and the receivers, hence achieve a better privacy compared to other cryptocurrencies such as Bitcoin. Previous studies discovered multiple problems within Monero systems, and based on these findings, Monero system has been improved. Although improvements have been made, we discovered that new attacks targeting the anonymity reduction can still be conducted in Monero system. In this paper we propose two attacks. The first is an extension of a known attack called Monero Ring Attack. The second one exploits Payment ID to discover the real output of a mixin. We then propose countermeasures to these attacks.
Creating Virtual Reality in a Business and Technology Educational Context

Virtual reality applications have enabled students to experience the world through visually immersive experiences in business and educational contexts. This paper shares the results of a collaborative project in which students from universities in the United States and Romania work together to study uses for virtual reality in a business context, and then create their own VR scenes for a selected business or industry. In doing so, students follow a virtual mobility learning scenario in which they explore the capabilities of virtual reality in both business and technology educational contexts.
Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors

In this paper, we tackle the problem of random forests for regression expressed as weighted sums of datapoints.We study the theoretical behavior of k-potential nearest neighbors (k-PNNs) under bagging and obtain an upper bound on the weights of a datapoint for random forests with any type of splitting criterion, provided that we use unpruned trees that stop growing only when there are k or less datapoints at their leaves.Moreover, we use the previous bound together with the concept of b-terms (i.e., bootstrap terms) introduced in this paper, to derive the explicit expression of weights for datapoints in a random (k-PNNs) selection setting, a datapoint selection strategy that we also introduce and to build a framework to derive other bagged estimators using a similar procedure.Finally, we derive from our framework the explicit expression of weights of a regression estimate equivalent to a random forest regression estimate with the random splitting criterion and demonstrate its equivalence both theoretically and practically.
An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals

With the rapid development of communication and information technology, it is difficult for traditional signal detection and recognition methods to accurately acquire and identify the intelligence under complex environments. In order to solve this problem, this paper proposes a subtle feature extraction and recognition algorithm for radiation source individual signals based on multidimensional hybrid features. Firstly, Hilbert transform was performed on the radiation source signals from 10 identical radio devices, and the subtle features of different radiation sources’ signals were extracted. Then, traditional principal component analysis (PCA) algorithm was used to extract and reduce the principal components of the extracted feature data sets. Aiming at the insufficiency of traditional PCA algorithm, an improved principal component analysis algorithm was proposed. At last, a gray relation algorithm was used to classify and identify the radiation source individual signals, and the recognition rate was calculated. Experimental results show that Hilbert transform combined with the improved PCA algorithm can achieve a recognition rate of 99.67% for the "fingerprint" features of radiation source individual signals under the signal-to-noise ratio (SNR) of 20dB. Compared with the traditional algorithms, the recognition rate increased by 5.67%. Therefore, it provides a powerful theoretical basis for extracting subtle features of radiation source devices under complex electromagnetic environments.
Path-Balanced Logic Design to Realize Block Ciphers Resistant to Power and Timing Attacks

In this paper, binary decision diagram (BDD)-based dual-rail precharge logic circuit schemes have been developed to counter differential power analysis attacks, timing attacks, and early propagation attacks. Different precharge logic schemes (top precharging, top-bottom precharging, bottom precharging, and symmetric nMOS bottom precharging) are presented and evaluated. The hallmark of our circuit schemes is that an identical number of switchings is ensured on each circuit path. The transistors are interconnected to create pull-up and pull-down paths to outputs by way of binary decisions based on the input variables, so as to realize the required Boolean function. A simple synthesis algorithm for mapping a given Boolean function to such a BDD-based circuit is also presented. Experimentation has been carried out on two 4-, 16-, 32-, and 64-bits S-boxes to establish resistance of our circuits to power analysis attack and to highlight the low-power characteristics with the help of attributes such as peak power variance, average power, propagation delay, normalized standard deviation of power, and normalized energy deviation. Differential power attacks such as difference of mean and correlation power analysis have been carried out. Resilience to the early propagation effect is also demonstrated.
Breaking the Trust Dependence on Third Party Processes for Reconfigurable Secure Hardware

Modern CPU designs are beginning to incorporate secure hardware features, but leave developers with little control over both the set of features and when and whether updates are available. Reconfigurable logic (e.g., FPGAs) has been proposed as an alternative as it is both hardware, so can have similar capabilities at a reasonable performance degradation, and programmable, allowing customization of the secure hardware. This programmability, however, opens new attack vectors that allow an adversary to re-program the FPGA. Past attempts to solve this rely on a party maintaining a shared key with the FPGA, but these business processes to keep that key secret have been shown to be quite vulnerable. In this paper, we propose a new mechanism which eliminates the trust dependence on third party processes. This new mechanism consists of a self-provisioning stage, where keys are generated internal to the FPGA and never exposed externally, coupled with a secure update mechanism which allows updates to be governed by a policy defined by the secure hardware application. To demonstrate, we fully implemented these mechanisms on a Xilinx Zynq UltraScale+ FPGA along with an example secure co-processor with remote attestation with a flexible root of trust (in contrast to Intel SGX which fixes the root of trust to be Intel). Our performance evaluation of two applications, a password manager and a contact matching application, illustrates using FPGAs is practical.
A Self Controlled Simulated Annealing Algorithm using Hidden Markov Model State Classification

The Simulated Annealing (SA) is a stochastic local search algorithm. Its efficiency involves the adaptation of the cooling law. In this paper, we integrate Hidden Markov Model (HMM) in SA to adapt the geometric cooling law at each iteration, based on the history of the search. This approach allows to controls the cooling of SA during the run, based on sequence of state generated from a set of rules. The HMM parameters are calculated and updated at each cooling step. The Viterbi algorithm is then used to classify the observed sequence as an exploration or exploitation or an escape from the local minimum. An experiments was performed on many benchmark functions and compared with others SA variants.
Labelled multi‐Bernoulli filter with amplitude information for tracking marine weak targets

In marine radar detection applications, the threshold is generally lowered to detect weak targets with low signal to clutter ratios. Consequently, however, a large number of sea clutters can exceed the threshold, which leads to overwhelming spurious detections and makes the subsequent multi-target tracking (MTT) difficult. In order to improve the tracking performance for marine weak targets, here, an approach to exploit the amplitude information of neighbouring cells is introduced. First, the joint amplitude likelihood of the neighbouring cells in sea clutter is derived, and then it is incorporated into the labelled multi-Bernoulli filter to enhance the discrimination between targets and clutters. The simulation results of Swerling type 1 targets tracking in K-distributed sea clutter show that the proposed method is superior in state estimation and cardinality estimation accuracy compared to the labelled multi-Bernoulli filter utilising amplitude information of single cells. In addition, experiment results with real data prove its applicability to marine target tracking scenarios.
A Novel Scoring Method Based on Distance Calculation for Similarity Measurement in Text-Independent Speaker Verification

Nowadays, stochastic models are state-of-the-art for text-independent speaker verification. However, they are costly in terms of time-consuming and may need much more data in the training phase. This paper proposes a novel scoring method based on distance calculation for similarity measurement in text-independent speaker verification. The basic idea of our approach aims to propose a new similarity measurement method using, directly, the speaker’s feature vectors (MFCC), in order to preserve and take advantage of the speaker’s specific features. Experiments on two open source speaker recognition corpora confirm our idea. Results demonstrate that our approach largely outperforms state-of-the-art approaches, GMM-UBM and i-vector/PLDA.
Capturing Emotion Distribution for Multimedia Emotion Tagging

Multimedia collections usually induce multiple emotions in audiences. The data distribution of multiple emotions can be leveraged to facilitate the learning process of emotion tagging, yet has not been thoroughly explored. To address this, we propose adversarial learning to fully capture emotion distributions for emotion tagging of multimedia data. The proposed multimedia emotion tagging approach includes an emotion classifier and a discriminator. The emotion classifier predicts emotion labels of multimedia data from their content. The discriminator distinguishes the predicted emotion labels from the ground truth labels. The emotion classifier and the discriminator are trained simultaneously in competition with each other. By jointly minimizing the traditional supervised loss and maximizing the distribution similarity between the predicted emotion labels and the ground truth emotion labels, the proposed multimedia emotion tagging approach successfully captures both the mapping function between multimedia content and emotion labels as well as prior distribution in emotion labels, and thus achieves state-of-the-art performance for multiple emotion tagging, as demonstrated by the experimental results on four benchmark databases.
Quantum‐resistant authentication algorithms for satellite‐based augmentation systems

NAVIGATION is a quarterly journal published by the Institute of Navigation. The journal publishes original, peer-reviewed articles on all aspects of positioning, navigation, and timing. The journal also publishes selected technical notes and survey articles, as well as papers of exceptional quality drawn from the Institute's conference proceedings.
Fast and accurate determination of earthquake moment, rupture length and stress release for the 2016–2017 Central Italy seismic sequence

In this study, a straightforward and rapid methodology is proposed and tested to determine the seismic moment, the earthquake rupture length/duration and the static stress drop. To this purpose, three ground motion parameters, that is, P-wave peak acceleration (⁠|${P_a}$|⁠), velocity (⁠|${P_v}$|⁠) and displacement (⁠|${P_d}$|⁠) are evaluated as a function of time from the first P arrival. The average of the logarithm of the P-wave amplitude (LPDT curves), corrected for the distance-attenuation effect, is calculated using all the available stations in expanded P-wave time windows. The LPDT curves show an exponential growth shape and increase with time until they reach a constant value (plateau), which is related to the magnitude of the earthquake. From the obtained observations, we demonstrate that the corner time of the plateau level on the weighted-fit curve to the LPDT curves is related to the half-duration of the rupture. Thus, using the theoretical scaling, the source radius and stress drop can be obtained from the measured half-duration of the source. This method has been applied and tested to the records of the 2016–2017 Central Italy seismic sequence, with moment magnitude ranging between 3.4 and 6.5. Our study shows that source parameters match a self-similar, constant-stress-drop scaling with a relatively low average stress drop of about |$1.1 \pm 0.5\ \mathrm{ MPa}$|⁠, except for the largest event of the sequence showing a relatively higher stress release, which is associated with the dominant radiation from a localized high slip patch on the fracture surface. The proposed approach based on a simple time domain signal analysis is innovative and may complement longer spectral technique for fast estimating earthquake source properties.
On Reliable Neural Network Sensorimotor Control in Autonomous Vehicles

This paper deals with (deep) neural network implementations of sensorimotor control for automated driving. We show how to construct complex behaviors by re-using elementary neural network building blocks that can be trained and tested extensively; one of our goals is to mitigate the “black box” and verifiability issues that affect end-to-end trained networks. By structuring complex behaviors within a subsumption architecture, we retain the ability to learn (mostly at motor primitives level) with the ability to create complex behaviors by subsuming the (well-known) learned elementary perception-action loops. The learning process itself is simplified, since the agent needs only to learn elementary behaviors. At the same time, the structure imposed with the subsumption architecture ensures that the agent behaves in predictable ways (e.g., treating all obstacles uniformly). We demonstrate these ideas for longitudinal obstacle avoidance behavior, but the proposed approach can also be adapted to other situations.
Brain Tissue Segmentation and Bias Field Correction of MR Image Based on Spatially Coherent FCM with Nonlocal Constraints

Influenced by poor radio frequency field uniformity and gradient-driven eddy currents, intensity inhomogeneity (or bias field) and noise appear in brain magnetic resonance (MR) image. However, some traditional fuzzy c-means clustering algorithms with local spatial constraints often cannot obtain satisfactory segmentation performance. Therefore, an objective function based on spatial coherence for brain MR image segmentation and intensity inhomogeneity correction simultaneously is constructed in this paper. First, a novel similarity measure including local neighboring information is designed to improve the separability of MR data in Gaussian kernel mapping space without image smoothing, and the similarity measure incorporates the spatial distance and grayscale difference between cluster centroid and its neighborhood pixels. Second, the objective function with an adaptive nonlocal spatial regularization term is drawn upon to compensate the drawback of the local spatial information. Meanwhile, bias field information is also embedded into the similarity measure of clustering algorithm. From the comparison between the proposed algorithm and the state-of-the-art methods, our model is more robust to noise in the brain magnetic resonance image, and the bias field is also effectively estimated.
Will Library E-Books Help Solve the Textbook Affordability Issue? Using Textbook Adoption Lists to Target Collection Development

In response to rising textbook costs, many have urged adoption of open educational resources (OER) for students. However, library-owned e-books also provide free access to students, without the potential disadvantages of OER. In this study the authors explore the suitability of e-books as textbooks by examining the social sciences textbook list in Fall 2017 at Florida Gulf Coast University (FGCU). Overall, 18% of the textbooks were available as a multi-user format and this rate increased with course level. Despite their limited availability, multi-user e-books provide a free, convenient option for students, and they may play a role in the textbook affordability solution.
Non-zero Inner Product Encryption Schemes from Various Assumptions: LWE, DDH and DCR

In non-zero inner product encryption (NIPE) schemes, ciphertexts and secret keys are associated with vectors and decryption is possible whenever the inner product of these vectors does not equal zero. So far, much effort on constructing bilinear map-based NIPE schemes have been made and this has lead to many efficient schemes. However, the constructions of NIPE schemes without bilinear maps are much less investigated. The only known other NIPE constructions are based on lattices, however, they are all highly inefficient due to the need of converting inner product operations into circuits or branching programs. To remedy our rather poor understanding regarding NIPE schemes without bilinear maps, we provide two methods for constructing NIPE schemes: a direct construction from lattices and a generic construction from schemes for inner products (LinFE). For our first direct construction, it highly departs from the traditional lattice-based constructions and we rely heavily on new tools concerning Gaussian measures over multi-dimensional lattices to prove security. For our second generic construction, using the recent constructions of LinFE schemes as building blocks, we obtain the first NIPE constructions based on the DDH and DCR assumptions. In particular, we obtain the first NIPE schemes without bilinear maps or lattices.
A new IMMU-based data glove for hand motion capture with optimized sensor layout

The number of people with hand disabilities caused by stroke is increasing every year. Developing a low-cost and easy-to-use data glove to capture the human hand motion can be used to assess the patient’s hand ability in home environment. While a majority of existing hand motion capture methods are too complex to be used for patients in residential settings. This paper proposes a new sensor layout strategy using the inertial and magnetic measurement units and designs a multi-sensor Kalman data fusion algorithm. The sensor layout strategy is optimized according to the inverse kinematics and the developed hand model, and the number of sensors can be significantly reduced from 12 in conventional systems to 6 in our system with the hand motion being completely and accurately reconstructed. Hand motion capture experiments were conducted on a healthy subject using the developed data glove. The hand motion can be restored completely and the hand gesture can be recognized with an accuracy of 85%. The results of a continuous hand movement indicate an average error under 15% compared with the common glove with full sensors. This new set with optimized sensor layout is promising for lower-cost and residential medical applications.
Searchable Encryption Scheme for Personalized Privacy in IoT-Based Big Data

The Internet of things (IoT) has become a significant part of our daily life. Composed of millions of intelligent devices, IoT can interconnect people with the physical world. With the development of IoT technology, the amount of data generated by sensors or devices is increasing dramatically. IoT-based big data has become a very active research area. One of the key issues in IoT-based big data is ensuring the utility of data while preserving privacy. In this paper, we deal with the protection of big data privacy in the data storage phase and propose a searchable encryption scheme satisfying personalized privacy needs. Our proposed scheme works for all file types including text, audio, image, video, etc., and meets different privacy needs of different individuals at the expense of high storage cost. We also show that our proposed scheme satisfies index indistinguishability and trapdoor indistinguishability.
Generic Constructions of Robustly Reusable Fuzzy Extractor

Robustly reusable Fuzzy Extractor (rrFE) considers reusability and robustness simultaneously. We present two approaches to the generic construction of rrFE. Both of approaches make use of a secure sketch and universal hash functions. The first approach also employs a special pseudo-random function (PRF), namely unique-input key-shift (ui-ks) secure PRF, and the second uses a key-shift secure auxiliary-input authenticated encryption (AIAE). The ui-ks security of PRF (resp. key-shift security of AIAE), together with the homomorphic properties of secure sketch and universal hash function, guarantees the reusability and robustness of rrFE. Meanwhile, we show two instantiations of the two approaches respectively. The first instantiation results in the first rrFE from the LWE assumption, while the second instantiation results in the first rrFE from the DDH assumption over non-pairing groups.