Reviewerly/exHarmony huggingface dataset:
An Overview of Grayscale Image Colorization Methods

Conversion of grayscale images to color images is a process of adding color to gray, monochrome images in a convincing, visually acceptable way. Nowadays, automated conversion is a challenging area that links machine and deep learning methods with art. Although many experts claim that grayscale images contain a special artistic value, lack of color can be considered as a loss of information. This paper presents an overview of methods and techniques that have been developed for grayscale image colorization. The paper provides a classification of relevant methods, explains the principles on which they are based and emphasizes their advantages and disadvantages. Special focus is put on methods that involve deep learning algorithms. The results show that deep learning colorization methods provide automated conversion and outperform other methods both in terms of quality and speed.
Privacy-Preserving and Efficient Public Key Encryption with Keyword Search Based on CP-ABE in Cloud

In the area of searchable encryption, public key encryption with keyword search (PEKS) has been a critically important and promising technique which provides secure search over encrypted data in cloud computing. PEKS can protect user data privacy without affecting the usage of the data stored in the untrusted cloud server environment. However, most of the existing PEKS schemes concentrate on data users’ rich search functionalities, regardless of their search permission. Attribute-based encryption technology is a good method to solve the security issues, which provides fine-grained access control to the encrypted data. In this paper, we propose a privacy-preserving and efficient public key encryption with keyword search scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique to support both fine-grained access control and keyword search over encrypted data simultaneously. We formalize the security definition, and prove that our scheme achieves selective indistinguishability security against an adaptive chosen keyword attack. Finally, we present the performance analysis in terms of theoretical analysis and experimental analysis, and demonstrate the efficiency of our scheme.
Inertial Neural Networks with Unpredictable Oscillations

In this paper, inertial neural networks are under investigation, that is, the second order differential equations. The recently introduced new type of motions, unpredictable oscillations, are considered for the models. The motions continue a line of periodic and almost periodic oscillations. The research is of very strong importance for neuroscience, since the existence of unpredictable solutions proves Poincaré chaos. Sufficient conditions have been determined for the existence, uniqueness, and exponential stability of unpredictable solutions. The results can significantly extend the role of oscillations for artificial neural networks exploitation, since they provide strong new theoretical and practical opportunities for implementation of methods of chaos extension, synchronization, stabilization, and control of periodic motions in various types of neural networks. Numerical simulations are presented to demonstrate the validity of the theoretical results.
Transfer learning based countermeasure against label flipping poisoning attack

Recent studies indicate that a classifier is vulnerable in an adversarial environment. The label flipping attack aims to mislead the training process. Some countermeasures have been proposed, but are usually designed for a particular classifier only, or may cause information loss. This study aims to investigate a generic model which fully utilizes the contaminated samples in learning. We assume a small untainted dataset is obtained from an application in addition to a contaminated dataset. The adversarial learning problem is formulated as transfer learning in which the influence of contaminated samples is reduced by only extracting the information similar to the untainted samples from the contaminated set using transfer learning. Our study considers a popular method, TrAdaBoost, and indicates that its performance is closely related to the initialized weights of samples. A initialization method is devised specifically for an adversarial setting to avoid assigning a large weight to the contaminated samples. The experimental results confirm that TrAdaBoost extracts only the benign knowledge from the contaminated set successfully. Moreover, our proposed initialization method significantly enhances the robustness of the model. This study presents a promising direction using transfer learning to defend against poisoning attacks.
Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification

This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times.
Provably Quantum-Secure Tweakable Block Ciphers

Recent results on quantum cryptanalysis show that some symmetric key schemes can be broken in polynomial time even if they are proven to be secure in the classical setting. Liskov, Rivest, and Wagner showed that secure tweakable block ciphers can be constructed from secure block ciphers in the classical setting. However, Kaplan et al. showed that their scheme can be broken by polynomial time quantum superposition attacks, even if underlying block ciphers are quantum-secure. Since then, it remains open if there exists a mode of block ciphers to build quantum-secure tweakable block ciphers. This paper settles the problem in the reduction-based provable security paradigm. We show the first design of quantum-secure tweakable block ciphers based on quantum-secure block ciphers, and present a provable security bound. Our construction is simple, and when instantiated with a quantum-secure n-bit block cipher, it is secure against attacks that query arbitrary quantum superpositions of plaintexts and tweaks up to O(2n/6) quantum queries. Our security proofs use the compressed oracle technique introduced by Zhandry. More precisely, we use an alternative formalization of the technique introduced by Hosoyamada and Iwata.
Digital Competence in Spanish University Education and Its Use by Students

Technologies have massively burst into all fields, including Higher Education. The current students have grown up surrounded by technologies, which is reflected in their behavior. For this reason, universities have adapted by integrating digital competence into their training offer, improving learning processes and adjusting to the university profile. The objective of this work is to ascertain how digital skills are used by Spanish higher education (bachelor’s and master’s degree) students, thus verifying whether so-called digital competence is being actively used in higher education. A survey was applied to 324 individuals, highlighting among its results that the university panorama is in a situation where digital tools are very useful for its improvement. These data were collected before the global pandemic, after which the use of online tools intensified. However, the students are still not aware of all of them, or they do not use them.
Multi-person Absolute 3D Human Pose Estimation with Weak Depth Supervision

In 3D human pose estimation one of the biggest problems is the lack of large, diverse datasets. This is especially true for multi-person 3D pose estimation, where, to our knowledge, there are only machine generated annotations available for training. To mitigate this issue, we introduce a network that can be trained with additional RGB-D images in a weakly supervised fashion. Due to the existence of cheap sensors, videos with depth maps are widely available, and our method can exploit a large, unannotated dataset. Our algorithm is a monocular, multi-person, absolute pose estimator. We evaluate the algorithm on several benchmarks, showing a consistent improvement in error rates. Also, our model achieves state-of-the-art results on the MuPoTS-3D dataset by a considerable margin. Our code will be publicly available (https://github.com/vegesm/wdspose).
Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.
Webinar Technology: Developing Teacher Training Programs for Emergency Remote Teaching amid COVID-19

In the light of the COVID-19 pandemic, schools worldwide are compelled to find new ways of providing education to their students. Educational institutions are shifting to emergency E-learning, while their underprepared teachers find themselves confronted with numerous unexpected challenges of emergency remote teaching (ERT). Teachers are not fully prepared and professionally trained to teach through ERT, while they also struggle with the challenges that arise from the lack of readiness for an abrupt transition from conventional curriculum to online-based curriculum. To mitigate the dilemma that the teachers experience in delivering instruction amid COVID-19, educational institutions switched to digital technologies to empower the teachers as frontline workers during the pandemic. As a result, the teachers in most countries throughout the world are undergoing training through webinar technology. Holding webinars to train the teachers can pave the way to address the challenges in remote teaching while simultaneously preventing the spread of the novel coronavirus.
Leveraging application classes to save power in highly-utilized data centers

Data center energy consumption has become an increasingly significant contributor both to greenhouse emissions and costs. To increase utilization of individual hosts and improve efficiency, most modern data centers co-locate workloads belonging to different application classes, some being latency-sensitive (LS) and others best-effort (BE) which are more tolerant to performance variation. It is therefore necessary to design mechanisms that reduce power consumption even in the resulting high-utilization environment, while preserving LS task performance. Moreover, the abundance of different workloads and the security implications of public cloud make mechanisms that rely on extensive knowledge of workload characteristics or on application-exported metrics challenging to deploy.
Specific Emitter Identification Against Unreliable Features Interference Based on Time-Series Classification Network Structure

Specific emitter identification (SEI) aims to distinguish different emitter individuals based on the subtle differences in the signals. The technology can be widely applied to various wireless communication systems. Existing individual identification methods often ignore the radio frequency (RF) fingerprint information carried by the waveforms and thus can be interfered by unreliable RF features. To alleviate these issues, we devise the deep bidirectional long short-term memory (DBi-LSTM) and the one-dimensional residual convolution network with dilated convolution and squeeze-and-excitation block (Conv-OrdsNet). We exploit the combination of DBi-LSTM and Conv-OrdsNet (CoBONet) to extract temporal structure features directly from baseband in-phase and quadrature (I/Q) samples. The proposed network is able to capture the fine-grained details of signals and combine different information extracted by two networks. Moreover, we propose a data augmentation method to solve the interference of unreliable features by randomly changing the values of noise, power, frequency offset (FO), and phase offset (PO). It is worth noting that our method only needs a short slice of a steady-state signal without complex preprocessing, which reduces the cost of acquisition and calculation. Extensive experiments show that our method can effectively extract reliable RF fingerprinting features from I/Q samples and the classification results are better than most existing methods.
Machine Translation in Foreign Language Learning Classroom-Learners’ Indiscriminate Use or Instructors’ Discriminate Stance

The use of machine translation (MT) tools in language learning classroom is now omnipresent, which raises a dilemma for instructors because of two issues, language proficiency and academic integrity, caused by that fact. However, with the unstoppable development and irresistible use of MT in language learning, rather than entangling with using it or banning it, it is more significant to figure out why learners turn to MT in spite of the prohibition from their instructors and how can instructors guide learners to use it appropriately. Consequently, this paper reviews articles with regard to the reason why learners turn to MT, the practical use of MT in learners’ writing, and some pedagogical solutions for making peace with MT in language learning classroom respectively. Implications can be garnered like that a course for learners of how to use MT tools properly should be included in the curriculum design, and simultaneously, the holistic understanding of these overwhelmingly fast-developed technology tools for instructors should be a part of teachers’ self-development, since instructors without knowledge said technology tools can not fully motivate language learners and implement the pedagogical solutions offered.
Significance and Causality in Continuous Wavelet and Wavelet Coherence Spectra Applied to Hydrological Time Series

Wavelet transform, wavelet spectra, and coherence are popular tools for studying fluctuations in time series in the form of a bidimensional time and scale representation. We discuss two aspects of wavelet analysis—namely the significance and stochastic/deterministic character of the wavelet spectra. Real-time series of discharge, sodium, and sulfate concentrations in the alpine Rhône River, Switzerland, are used to illustrate these issues. First, the consequences of using an arbitrary stochastic process (usually, AR (1)) instead of the best-fitted general ARMA process in the evaluation of the significance of wavelet spectra are analyzed. Using a general ARMA instead of AR (1) decreases the significance level of the differences in wavelet power spectra (WPS) of ARMA and AR (1) compared to the WPS of the time series in all cases studied and points to a possible systematic overestimation of significance in many published studies. Besides, the significance of particular patches in the spectra is affected by multiple testing. A (conservative) way to circumvent this problem, using global wavelet spectra and global coherence spectra, is evaluated. Finally, we discuss the issue of causality and investigated it in the three measured time series mentioned above. Even if the use of the best fitted ARMA pointed to no deterministic features being present in the corrected series studied (i.e., stochastic processes are dominant in the three data series), coherence spectra between variables allowed to reveal cause-effect relationships between two “coherent” variables and/or the existence of a common effect on both variables. Therefore, such type of analysis provides a useful tool to better understand data causal relationships.
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-supervised Learning and Dual-UNet

Catheter segmentation in 3D ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep convolutional neural network (CNN) to segment the catheter, which is expensive and time-consuming. In this paper, we propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method, but nevertheless achieves better performance. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation and which can efficiently localize the target catheter. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data. To train the Dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions. Experiments show the proposed scheme achieves a higher performance than state-of-the-art semi-supervised methods, while it demonstrates that our method is able to learn from large-scale unlabeled images.
Satellite Quantum Communications When Man-in-the-Middle Attacks Are Excluded

An application of quantum communications is the transmission of qubits to create shared symmetric encryption keys in a process called quantum key distribution (QKD). Contrary to public-private key encryption, symmetric encryption is considered safe from (quantum) computing attacks, i.e. it provides forward security and is thus attractive for secure communications. In this paper we argue that for free-space quantum communications, especially with satellites, if one assumes that man-in-the-middle attacks can be detected by classical channel monitoring techniques, simplified quantum communications protocols and hardware systems can be implemented that offer improved key rates. We term these protocols photon key distribution (PKD) to differentiate them from the standard QKD protocols. We identify three types of photon sources and calculate asymptotic secret key rates for PKD protocols and compare them to their QKD counterparts. PKD protocols use only one measurement basis which we show roughly doubles the key rates. Furthermore, with the relaxed security assumptions one can establish keys at very high losses, in contrast to QKD where at the same losses privacy amplification would make key generation impossible.
Kidney Segmentation in 3-D Ultrasound Images Using a Fast Phase-Based Approach

Renal ultrasound (US) imaging is the primary imaging modality for the assessment of the kidney's condition and is essential for diagnosis, treatment and surgical intervention planning, and follow-up. In this regard, kidney delineation in 3-D US images represents a relevant and challenging task in clinical practice. In this article, a novel framework is proposed to accurately segment the kidney in 3-D US images. The proposed framework can be divided into two stages: 1) initialization of the segmentation method and 2) kidney segmentation. Within the initialization stage, a phase-based feature detection method is used to detect edge points at kidney boundaries, from which the segmentation is automatically initialized. In the segmentation stage, the B-spline explicit active surface framework is adapted to obtain the final kidney contour. Here, a novel hybrid energy functional that combines localized region- and edge-based terms is used during segmentation. For the edge term, a fast-signed phase-based detection approach is applied. The proposed framework was validated in two distinct data sets: 1) 15 3-D challenging poor-quality US images used for experimental development, parameters assessment, and evaluation and 2) 42 3-D US images (both healthy and pathologic kidneys) used to unbiasedly assess its accuracy. Overall, the proposed method achieved a Dice overlap around 81% and an average point-to-surface error of ~2.8 mm. These results demonstrate the potential of the proposed method for clinical usage.
Fast re-optimization via structural diversity

When a problem instance is perturbed by a small modification, one would hope to find a good solution for the new instance by building on a known good solution for the previous one. Via a rigorous mathematical analysis, we show that evolutionary algorithms, despite usually being robust problem solvers, can have unexpected difficulties to solve such re-optimization problems. When started with a random Hamming neighbor of the optimum, the (1+1) evolutionary algorithm takes Ω(n2) time to optimize the LeadingOnes benchmark function, which is the same asymptotic optimization time when started in a randomly chosen solution. There is hence no significant advantage from re-optimizing a structurally good solution.
TNT: Text Normalization based Pre-training of Transformers for Content Moderation

In this work, we present a new language pre-training model TNT (Text Normalization based pre-training of Transformers) for content moderation.Inspired by the masking strategy and text normalization, TNT is developed to learn language representation by training transformers to reconstruct text from four operation types typically seen in text manipulation: substitution, transposition, deletion, and insertion.Furthermore, the normalization involves the prediction of both operation types and token labels, enabling TNT to learn from more challenging tasks than the standard task of masked word recovery.As a result, the experiments demonstrate that TNT outperforms strong baselines on the hate speech classification task.Additional text normalization experiments and case studies show that TNT is a new potential approach to misspelling correction.