Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Proof of Retrievability Scheme

In the internet of things, user information is usually collected by all kinds of smart devices. The collected user information is stored in the cloud storage, and there is a risk of information leakage. In order to protect the security and the privacy of user information, the user and cloud provider will periodically execute a protocol called proof of retrievability scheme. A proof of retrievability scheme ensures the security of the data by generating proof to convince the user that the cloud provider does correctly store the user information. In this paper, we construct a proof of retrievability scheme using the blockchain technology. Using the advantage that the stored data cannot be tampered with in blockchain, this ensures the integrity of the data. Specifically, some related definitions, security models, and a blockchain-based construction of a proof of retrievability scheme are given. Then the validity and security of the scheme are proved later. As a result, user information can be protected by our scheme.
DESSB-TRNG: A novel true random number generator using data encryption standard substitution box as post-processing

In addition to providing hardware-dependent costly and slow solutions, statistical weakness is an important disadvantage of a cryptographic TRNG that is tried to be removed with post-processing techniques. In this study, the use of Data Encryption Standard (DES) substitution-box (s-box)-based post-processing method is proposed as an alternative solution to the problem of a physical TRNG's inability to generate cryptographic randomness discussed in detail. The proposed s-box-based post-processing method is applied in real time to a TRNG architecture based on two different digital noise source scenarios such as jitter and metastability on the Altera Cyclone GX FPGA chip. In both scenarios, the statistical quality of raw random numbers that s-box based transformations of 48x32 (48-bit input 32-bit output) are applied in the post-process stage is improved to meet the cryptographic competencies. This contribution of the proposed method has been analyzed and confirmed by statistical test techniques such as bias, entropy, correlation, and uniformity as well as National Institute of Standards and Technology (NIST) SP 800-22 randomness evaluation tests. In addition to the statistical analysis, the security analysis of the proposed method has been made and a detailed literature comparison of TRNG is presented by measuring the hardware performance parameters for both scenarios. All these analysis results confirm that the proposed method produces functional results for TRNG by providing a better trade-off in terms of design parameters such as security, area-energy consumption, compression rate, simplicity-applicability and hardware cost. The study is significant to showing that s-boxes with strong mathematical encryption features can be used for different cryptographic aims, such as random number generation, outside of the confusion function.
Simulated Hough Transform Model Optimized for Straight-Line Recognition Using Frontier FPGA Devices

The use of the Hough transforms to identify shapes or images has been extensively studied in the past using software for artificial intelligence applications. In this article, we present a generalization of the goal of shape recognition using the Hough transform, applied to a broader range of real problems. A software simulator was developed to generate input patterns (straight-lines) and test the ability of a generic low-latency system to identify these lines: first in a clean environment with no other inputs and then looking for the same lines as ambient background noise increases. In particular, the paper presents a study to optimize the implementation of the Hough transform algorithm in programmable digital devices, such as FPGAs. We investigated the ability of the Hough transform to discriminate straight-lines within a vast bundle of random lines, emulating a noisy environment. In more detail, the study follows an extensive investigation we recently conducted to recognize tracks of ionizing particles in high-energy physics. In this field, the lines can represent the trajectories of particles that must be immediately recognized as they are created in a particle detector. The main advantage of using FPGAs over any other component is their speed and low latency to investigate pattern recognition problems in a noisy environment. In fact, FPGAs guarantee a latency that increases linearly with the incoming data, while other solutions increase latency times more quickly. Furthermore, HT inherently adapts to incomplete input data sets, especially if resolutions are limited. Hence, an FPGA system that implements HT is inefficient for small sets of input data but becomes more cost-effective as the size of the input data increases. The document first presents an example that uses a large Accumulator consisting of 1100 × 600 Bins and several sets of input data to validate the Hough transform algorithm as random noise increases to 80% of input data. Then, a more specifically dedicated input set was chosen to emulate a real situation where a Xilinx UltraScale+ was to be used as the final target device. Thus, we have reduced the Accumulator to 280 × 280 Bins using a clock signal at 250 MHz and a few tens input points. Under these conditions, the behavior of the firmware matched the software simulations, confirming the feasibility of the HT implementation on FPGA.
Authentication of variable length messages in quantum key distribution

Abstract Authentication plays a critical role in the security of quantum key distribution (QKD) protocols. We propose using Polynomial Hash and its variants for authentication of variable length messages in QKD protocols. Since universal hashing is used not only for authentication in QKD but also in other steps in QKD like error correction and privacy amplification, and also in several other areas of quantum cryptography, Polynomial Hash and its variants as the most efficient universal hash function families can be used in these important steps and areas, as well. We introduce and analyze several efficient variants of Polynomial Hash and, using deep results from number theory, prove that each variant gives an ε -almost-Δ-universal family of hash functions. We also give a general method for transforming any such family to an ε -almost-strongly universal family of hash functions. The latter families can then, among other applications, be used in the Wegman–Carter MAC construction which has been shown to provide a universally composable authentication method in QKD protocols. As Polynomial Hash has found many applications, our constructions and results are potentially of interest in various areas.
TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants

Wake-up word spotting in noisy environments is a critical task for an excellent user experience with voice assistants. Unwanted activation of the device is often due to the presence of noises coming from background conversations, TVs, or other domestic appliances. In this work, we propose the use of a speech enhancement convolutional autoencoder, coupled with on-device keyword spotting, aimed at improving the trigger word detection in noisy environments. The end-to-end system learns by optimizing a linear combination of losses: a reconstruction-based loss, both at the log-mel spectrogram and at the waveform level, as well as a specific task loss that accounts for the cross-entropy error reported along the keyword spotting detection. We experiment with several neural network classifiers and report that deeply coupling the speech enhancement together with a wake-up word detector, e.g., by jointly training them, significantly improves the performance in the noisiest conditions. Additionally, we introduce a new publicly available speech database recorded for the Telefónica’s voice assistant, Aura. The OK Aura Wake-up Word Dataset incorporates rich metadata, such as speaker demographics or room conditions, and comprises hard negative examples that were studiously selected to present different levels of phonetic similarity with respect to the trigger words “OK Aura”.
Self-Supervised Human Activity Representation for Embodied Cognition Assessment

Physical activities, according to the embodied cognition theory, are an important manifestation of cognitive functions. As a result, in this paper, the Activate Test of Embodied Cognition (ATEC) system is proposed to assess various cognitive measures. It consists of physical exercises with different variations and difficulty levels designed to provide assessment of executive and motor functions. This work focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. A self-supervised approach is employed in this work that can exploit a small set of annotated data to obtain an effective human activity representation. The performance of different self-supervised approaches along with a supervised method are investigated for automated cognitive assessment of children performing ATEC tasks. The results show that the supervised learning approach performance decreases as the training set becomes smaller, whereas the self-supervised methods maintain their performance by taking advantage of unlabeled data.
Rethinking 360° Image Visual Attention Modelling with Unsupervised Learning

Despite the success of self-supervised representation learning on planar data, to date it has not been studied on 360° images. In this paper, we extend recent advances in contrastive learning to learn latent representations that are sufficiently invariant to be highly effective for spherical saliency prediction as a downstream task. We argue that omni-directional images are particularly suited to such an approach due to the geometry of the data domain. To verify this hypothesis, we design an unsupervised framework that effectively maximizes the mutual information between the different views from both the equator and the poles. We show that the decoder is able to learn good quality saliency distributions from the encoder embeddings. Our model compares favorably with fully-supervised learning methods on the Salient360!, VR-EyeTracking and Sitzman datasets. This performance is achieved using an encoder that is trained in a completely unsupervised way and a relatively lightweight supervised decoder (3.8 × fewer parameters in the case of the ResNet50 encoder). We believe that this combination of supervised and unsupervised learning is an important step toward flexible formulations of human visual attention. The results can be reproduced on GitHub
Exploit Feature and Relation Hierarchy for Relation Extraction

Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical features extracted from the successive word sequence may introduce some noise that has little or no meaningful content. Meanwhile, the syntactic features are usually encoded via graph convolutional networks which have restricted receptive field. In addition, the relation between lexical and syntactic features in the representation space has been largely neglected. To address the above limitations, we propose a multi-scale representation and metric learning framework to exploit the feature and relation hierarchy for RE tasks. Methodologically, we <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">build a lexical and syntactic feature and relation hierarchy</i> in text data. Technically, we first develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a multi-scale convolutional neural network</i> to aggregate the non-successive lexical patterns in the word sequence. We also design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a multi-scale graph convolutional network</i> to increase the receptive field via the coarsened syntactic graph. Moreover, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a multi-scale metric learning</i> paradigm to exploit both the feature-level relation between lexical and syntactic features and the sample-level relation between instances with the same or different classes. Extensive experiments on three public datasets for two RE tasks prove that our model achieves a new state-of-the-art performance.
Event prediction from news text using subgraph embedding and graph sequence mining

Event detection from textual content by using text mining concepts is a well-researched field in the literature. On the other hand, graph modeling and graph embedding techniques in recent years provide an opportunity to represent textual contents as graphs. Text can be enriched with additional attributes in graphs, and the complex relationships can be captured within the graphs. In this paper, we focus on news prediction and model the problem as subgraph prediction. More specifically, we aim to predict the news skeleton in the form of a subgraph. To this aim, graph-based representations of news articles are constructed and a graph mining based pattern extraction method is proposed. The proposed method consists of three main steps. Initially, graph representation of the news text is constructed. Afterwards, frequent subgraph mining and sequential rule mining algorithms are adapted for pattern prediction on graph sequences. We consider that a subgraph captures the main story of the contents, and the sequential rules indicate the subgraph patterns’ temporal relationships. Finally, extracted sequential patterns are used for predicting the future news skeleton (i.e. main features of the news). In order to measure the similarity, graph embedding techniques are also employed. The proposed method is analyzed on both a collection of news from an online newspaper and on a benchmark news dataset against baseline methods.
Quantum steering with vector vortex photon states with the detection loophole closed

Abstract Violating a nonlocality inequality enables the most powerful remote quantum information tasks and fundamental tests of quantum physics. Loophole-free photonic verification of nonlocality has been achieved with polarization-entangled photon pairs, but not with states entangled in other degrees of freedom. Here we demonstrate completion of the quantum steering nonlocality task, with the detection loophole closed, when entanglement is distributed by transmitting a photon in an optical vector vortex state, formed by optical orbital angular momentum (OAM) and polarization. As well as opening up a high-efficiency encoding beyond polarization, the critically important demonstration of vector vortex steering opens the door to new free-space and satellite-based secure quantum communication devices and device-independent protocols.
A Novel Joint Dataset and Incentive Management Mechanism for Federated Learning Over MEC

In this study, to reduce the energy consumption for the federated learning (FL) participation of mobile devices (MDs), we design a novel joint dataset and incentive management mechanism for FL over mobile edge computing (MEC) systems. We formulate a Stackelberg game to model and analyze the behaviors of FL participants, referred to as MDs, and FL service providers, referred to as MECs. In the proposed game, each MEC is the leader, whereas the MDs are followers. As the leader, to maximize its own revenue by considering the trade-off between the cost of providing incentives and the estimated accuracy attained from an FL operation, each MEC provides full incentives to the MDs for the participation of each FL task, as well as the target accuracy level for each MD. The suggested total incentives are allocated over MDs’ proportion to the amount of dataset applied for local training, which indirectly affects the global accuracy of the FL. Based on the suggested incentives, the MDs determine the amount of dataset used for the local training of each FL task to maximize their own payoffs, which is defined as the energy consumed from FL participation and the expected incentives. We study the economic benefits of the joint dataset and incentive management mechanism by analyzing its hierarchical decision-making scheme as a multi-leader multi-follower Stackelberg game. Using backward induction, we prove the existence and uniqueness of the Nash equilibrium among MDs, and then examine the Stackelberg equilibrium by analyzing the leader game. We also discuss extensions of the proposed mechanism where the MDs are unaware of explicit information of other MD profiles, such as the weights of the revenue as a practical concern, which can be redesigned into the Stackelberg Bayesian game. Finally, we reveal that the Stackelberg equilibrium solution maximizes the utility of all MDs and the MECs.
Underwater Image Restoration via DCP and Yin–Yang Pair Optimization

Underwater image restoration is a challenging problem because light is attenuated by absorption and scattering in water, which can degrade the underwater image. To restore the underwater image and improve its contrast and color saturation, a novel algorithm based on the underwater dark channel prior is proposed in this paper. First of all, in order to reconstruct the transmission maps of the underwater image, the transmission maps of the blue and green channels are optimized by the proposed first-order and second-order total variational regularization. Then, an adaptive model is proposed to improve the first-order and second-order total variation. Finally, to solve the problem of the excessive attenuation of the red channel, the transmission map of the red channel is compensated by Yin–Yang pair optimization. The simulation results show that the proposed restored algorithm outperforms other approaches in terms of the visual effects, average gradient, spatial frequency, percentage of saturated pixels, underwater color image quality evaluation and evaluation metric.
TECHNICAL IT SOLUTIONS IN TEACHING

Nowadays, the vast majority of the devices around us are based on electronics, contain a processor, and are controlled by software. With sensors, they are able to convert the physical, chemical and biological signals of the outside world into electronic quantities and then into information, which can be processed with the help of software. Modern electronics allow devices smaller and smaller to handle more and more signals, whether running on batteries or accumulators, to run operating systems and a variety of applications. These electronic tools can be used effectively to raise the quality of teaching programming in secondary school and university education. In particular, it has a major impact on the development of technical and computational thinking, without which students will find it difficult to succeed in today’s job market.The market trends that are determining the electronics industry today point to a sharp increase in the use of IoT devices, sensors are collecting data around us, using wireless data transmission technologies to transmit the measured values to cloud-based databases, which are processed with various software. Low-power microcontrollers developed for battery power, which are widely used today, provide sensor data collection and data transfer control.We show with examples how technical IT solutions can provide an appropriate experiential learning opportunity and background in programming education. We focus on teaching methods that use microcontrollers and various sensors to develop programming skills and acquire programming knowledge. We try to develop computer thinking as well as algorithmic thinking.In our article, we present a literature search on the technical IT teaching tools in use today, some of which are inherently educational and popular with students and teachers. We pay attention to the educational principles of technical IT methods.
Examining students’ attitudes towards online education during COVID-19: evidence from Turkey (<i>Análisis de las actitudes de los estudiantes hacia la educación en línea durante la pandemia de COVID-19. Evidencia de un estudio realizado en Turquía</i>)

The novel coronavirus (COVID-19) has affected the education system at all levels around the world. Coping with the essentialities of online teaching and learning throughout the COVID-19 pandemic, this study aims to give insights into the interpretation of online teaching during the lockdown in Turkey. We applied an ‘opinions regarding online education’ survey (n = 1,007) to examine students’ attitudes towards online education. The participants were students having online classes due to COVID-19 lockdown at different universities in Turkey. Results showed that COVID-19 quarantine has influenced the academic performance of most participants to fluctuating degrees. Yet, most participants believed that online classes could not replace actual classes (n = 725; 72.0%), while only 10.1% of them believed online classes could replace face-to-face classes. Besides, students regularly attending the online classes have positive online education attitudes higher than those of the students who sometimes or never attend the lessons. Thus, this study presents a general illustration of ongoing online education activities during the lockdown and subsequently gives recommendations for future digital learning activities.
Accurate training of the Cox proportional hazards model on vertically-partitioned data while preserving privacy

Abstract Background Analysing distributed medical data is challenging because of data sensitivity and various regulations to access and combine data. Some privacy-preserving methods are known for analyzing horizontally-partitioned data, where different organisations have similar data on disjoint sets of people. Technically more challenging is the case of vertically-partitioned data, dealing with data on overlapping sets of people. We use an emerging technology based on cryptographic techniques called secure multi-party computation (MPC), and apply it to perform privacy-preserving survival analysis on vertically-distributed data by means of the Cox proportional hazards (CPH) model. Both MPC and CPH are explained. Methods We use a Newton-Raphson solver to securely train the CPH model with MPC, jointly with all data holders, without revealing any sensitive data. In order to securely compute the log-partial likelihood in each iteration, we run into several technical challenges to preserve the efficiency and security of our solution. To tackle these technical challenges, we generalize a cryptographic protocol for securely computing the inverse of the Hessian matrix and develop a new method for securely computing exponentiations. A theoretical complexity estimate is given to get insight into the computational and communication effort that is needed. Results Our secure solution is implemented in a setting with three different machines, each presenting a different data holder, which can communicate through the internet. The MPyC platform is used for implementing this privacy-preserving solution to obtain the CPH model. We test the accuracy and computation time of our methods on three standard benchmark survival datasets. We identify future work to make our solution more efficient. Conclusions Our secure solution is comparable with the standard, non-secure solver in terms of accuracy and convergence speed. The computation time is considerably larger, although the theoretical complexity is still cubic in the number of covariates and quadratic in the number of subjects. We conclude that this is a promising way of performing parametric survival analysis on vertically-distributed medical data, while realising high level of security and privacy.