Reviewerly/exHarmony huggingface dataset:
Multi-Scale Memetic Image Registration

Many technological applications of our time rely on images captured by multiple cameras. Such applications include the detection and recognition of objects in captured images, the tracking of objects and analysis of their motion, and the detection of changes in appearance. The alignment of images captured at different times and/or from different angles is a key processing step in these applications. One of the most challenging tasks is to develop fast algorithms to accurately align images perturbed by various types of transformations. The paper reports a new method used to register images in the case of geometric perturbations that include rotations, translations, and non-uniform scaling. The input images can be monochrome or colored, and they are preprocessed by a noise-insensitive edge detector to obtain binarized versions. Isotropic scaling transformations are used to compute multi-scale representations of the binarized inputs. The algorithm is of memetic type and exploits the fact that the computation carried out in reduced representations usually produces promising initial solutions very fast. The proposed method combines bio-inspired and evolutionary computation techniques with clustered search and implements a procedure specially tailored to address the premature convergence issue in various scaled representations. A long series of tests on perturbed images were performed, evidencing the efficiency of our memetic multi-scale approach. In addition, a comparative analysis has proved that the proposed algorithm outperforms some well-known registration procedures both in terms of accuracy and runtime.
A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.
Finally in the spotlight: How contemporary learning theory is saving education online during COVID

A surge of literature documenting myriad challenges being faced online during the COVID pandemic strongly suggests that e-learning scholarship has fallen short of conveying an understanding of how to build highly effective e-learning spaces. Recent stories from practitioners abound with reports of absenteeism, cameras and microphones turned off, inaction in forums and a general reticence on the part of learners to engage online. Where have we missed the mark in our efforts to have contemporary e-learning theory affect online practice? Scholarship is indicating that the root of the disconnect often lies in the conventional instructional designs being used in these spaces and the teaching, learning and assessment practices they support. In response to such issues, we conducted a qualitative action research initiative to apply an instructional design (ID) model, based on contemporary learning theories and goals, in a teacher education program in Chile. The study took place in 2020 over 2 academic semesters. In this study, we focussed on the impact of these changes on a small group of first-year Pre-service Teachers (PSTs, n=17), experiencing online learning for the first time. Pre and post interviews, an open-ended questionnaire, field notes from self-assessment portfolios and observations of the digital environment were used to collect data. We also draw on two other data sources in the same context: 1) an earlier report of this initiative that focussed on the Teacher Educators (TEs) in the same program (n=4), and 2) survey data collected in a preparatory stage of the action research on the experiences of the greater university student body (n=1,054). Evidence revealed that initially learners’ epistemological views were heavily influenced by the teacher-centric and content-driven pedagogies of earlier schooling. Yet, results also showed that the contemporary learning design framework had positive implications for many students’ social, cognitive, and metacognitive competencies. Clear signs of more active investment in social interactive learning online on the part of the PSTs and of flexible, self-directed behaviours were evidenced. The results of this study provide an empirically based practical solution for connecting current learning theory to practice in online contexts, solutions that could endure even once the challenges of the pandemic crisis are behind us.
Quantum steganography over noisy channels: Achievability and bounds

Characterizing secret communication over noisy quantum channels is an interesting problem from both a practical and theoretical perspective. Suppose Alice and Bob wish to communicate secret information so that an eavesdropper Eve will not suspect any type of encoded communication between the two. Classical or quantum cryptography will not suffice since it is always clear secret communication is taking place. Therefore Alice and Bob must execute what is known as a quantum steganographic protocol. Assuming Eve only has partial knowledge of the channel connecting Alice and Bob, we show that for the bit-flip and depolarizing channels Alice can use Eve's lack of knowledge of the channel parameter to encode quantum information steganographically. We give an explicit encoding procedure and calculate the rate at which Alice and Bob can communicate secretly. We also show that our encoding is optimal for nondegenerate quantum codes. We calculate the rate at which a secret key must be consumed. Finally, we discuss the possibility of steganographic communication over more general quantum channels and conjecture a general formula for the steganographic rate.
Graph coloring using the reduced quantum genetic algorithm

Genetic algorithms (GA) are computational methods for solving optimization problems inspired by natural selection. Because we can simulate the quantum circuits that implement GA in different highly configurable noise models and even run GA on actual quantum computers, we can analyze this class of heuristic methods in the quantum context for NP-hard problems. This paper proposes an instantiation of the Reduced Quantum Genetic Algorithm (RQGA) that solves the NP-hard graph coloring problem in O(N1/2). The proposed implementation solves both vertex and edge coloring and can also determine the chromatic number (i.e., the minimum number of colors required to color the graph). We examine the results, analyze the algorithm convergence, and measure the algorithm's performance using the Qiskit simulation environment. Our Reduced Quantum Genetic Algorithm (RQGA) circuit implementation and the graph coloring results show that quantum heuristics can tackle complex computational problems more efficiently than their conventional counterparts.
Long-Term Secure Distributed Storage Using Quantum Key Distribution Network With Third-Party Verification

The quantum key distribution network with Vernam's One Time Pad encryption and secret sharing are powerful security tools to realize an information theoretically secure distributed storage system. In our previous work, a single-password-authenticated secret sharing scheme based on the QKD network and Shamir's secret sharing was experimentally demonstrated; it confirmed ITS data transmission, storage, authentication, and integrity. To achieve data integrity, an ITS message authentication code tag is employed and a data owner of the secret sharing performs both the MAC tag generation and verification. However, for a scenario in which the data owner and end users are different entities, the above approach may not work since the data owner can cheat the end users. In this paper, we resolve this problem by proposing an ITS integrity protection scheme employing a third-party verification with time-stamp.
Two-Layer Encryption based on Paillier and ElGamal Cryptosystem for Privacy Violation

Our life nowadays relies much on technologies and online services net banking, e-voting and so on.So, there is a necessity to secure the data that is transmitted through the internet.However, while performing decryption, it sometimes led to privacy violation so there is need to operate on users encrypted data without knowing the original plaintext.This paper represents the implementation of two-layer cryptosystem using paillier and elgamal algorithm both following asymmetric encryption.It is mainly focusing the challenges of privacy protection and secure utilization of information, where homomorphy encryption is gaining attention.Additive homomorphism is used in paillier cryptosystem which is used in fields like secure biometrics and electronic voting.Elgamal ensures that paillier encrypted data is secured that ensures two-layer encryption.
Decoder for the Triangular Color Code by Matching on a Möbius Strip

The color code is remarkable for its ability to perform fault-tolerant logic gates. This motivates the design of practical decoders that minimize the resource cost of color-code quantum computation. Here we propose a decoder for the planar color code with a triangular boundary where we match syndrome defects on a nontrivial manifold that has the topology of a Möbius strip. A basic implementation of our decoder used on the color code with hexagonal lattice geometry demonstrates a logical failure rate that is competitive with the optimal performance of the surface code. The logical failure rate scales approximately like pαn, with α≈6/73≈0.5, error rate p, and n the code length. Furthermore, by exhaustively testing over five billion error configurations, we find that a modification of our decoder that manually compares inequivalent recovery operators can correct all errors of weight ≤(d−1)/2 for codes with distance d≤13. Our decoder is derived using relations among the stabilizers that preserve global conservation laws at the lattice boundary. We present generalizations of our method to depolarizing noise and fault-tolerant error correction, as well as to Majorana surface codes, higher-dimensional color codes, and single-shot error correction.20 MoreReceived 4 October 2021Accepted 9 December 2021DOI:https://doi.org/10.1103/PRXQuantum.3.010310Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionSurface code quantum computingTopological quantum computingQuantum Information
Central Bank digital currencies

Central Bank digital currencies are a digital challenge to the international monetary and financial system. Since the development of cryptocurrency, such as bitcoin, the modern world has faced the possibility of digital technological transformation and providing a digital form of payment for the economy and the household. In addition, the announcement of a digital currency that would have a global reach, such as the Libre issued by the social network Facebook, raised questions about legal and regulatory safeguards, financial stability, and the role of the digital currency in society. All this influenced the leading central banks to recognize the need to conduct a detailed analysis of the possibilities of issuing digital currency of the central bank, which would be a supplement to the cash and non-cash form of payment. These analyzes include considering the advantages and disadvantages of that currency, determining its design and technological solution, as well as the necessary regulatory adjustments. In the coming period, we will witness a technological transformation in the operations of central banks, which, as before, should take care of preserving price and financial stability as its main goals, but also respond to new challenges of digital business.
Pivoting Open? Pandemic Pedagogy and the Search for Openness in the Viral Learning Environment

This paper is based on the authors’ experiences and reflections working in educational technology and design support roles in higher education during the COVID-19 pandemic. We retrace our lived experience from the beginning of the pandemic in the spring (from our vantage points in the UK and Canada) and the associated ‘pivot online’ enacted in education around the world, through to the autumn of 2020, when we appeared to be transitioning into a so-called ‘new normal’ of the mid-pandemic. As digital education practitioners, who are also educators, researchers, and also simply as humans and friends living through a global pandemic, we had turned to each other initially for support in terms of work, wellness, and sharing news, information and sense-making, during which we began to consider researching under-examined dimensions of the evolving situation. The experiences and issues we reference are drawn from our own work, as well as from our responses to popular narratives advanced by key voices who have encouraged certain interpretations of the pandemic and its educational effects. Using Schön’s (1983) reflection-in-action lens, we examine these experiences and narratives of pandemic pedagogy through the frame of our multiple identities. In particular, from our perspective as researchers and advocates of open education, we noted calls for openness (such as the use of open educational resources) in response to the online pivot, which did not appear to be cutting through the noise of the sudden deluge of information, advice and broadly negative coverage of online teaching. However, through our reflective narrative and synthesis, we offer an alternative interpretation, which is that openness was nonetheless flourishing, but that the ‘pivot open’ was to practices rather than resources. Open exchange, community building and support amongst educators were apparent in multiple contexts. While pandemic profiteering has highlighted the need for open resources and infrastructures, and we anticipate this case continuing to be made more strongly as we emerge from the emergency, it is the turn to open practices which has met the immediate needs of educators and learners through community, interactions, sharing and care.
Pruning Ratio Optimization with Layer-Wise Pruning Method for Accelerating Convolutional Neural Networks

Pruning is an effective technique to reduce computational complexity of Convolutional Neural Networks (CNNs) by removing redundant neurons (or weights). There are two types of pruning methods: holistic pruning and layer-wise pruning. The former selects the least important neuron from the entire model and prunes it. The latter conducts pruning layer by layer. Recently, it has turned out that some layer-wise methods are effective for reducing computational complexity of pruned models while preserving their accuracy. The difficulty of layer-wise pruning is how to adjust pruning ratio (the ratio of neurons to be pruned) in each layer. Because CNNs typically have lots of layers composed of lots of neurons, it is inefficient to tune pruning ratios by human hands. In this paper, we present Pruning Ratio Optimizer (PRO), a method that can be combined with layer-wise pruning methods for optimizing pruning ratios. The idea of PRO is to adjust pruning ratios based on how much pruning in each layer has an impact on the outputs in the final layer. In the experiments, we could verify the effectiveness of PRO.
An Algorithm for Classifying Handwritten Signatures Using Convolutional Networks

In this study, a model based on convolutional neural networks is proposed to quickly and efficiently classify and identify a person's signature with over 90% accuracy. For this purpose, two signature datasets were used. The first, called CEDAR, is publicly available. The second set, called GC-DB, was collected by the researchers using uncontrolled environments (different signing positions). This dataset has 121 local signatories from the Republic of Ecuador, who submitted 45 copies of signatures each. In this set of signatures, the implicit noise produced by the capture device and by the paper of different thicknesses used in the collection made noise removal a relatively complex operation. The effectiveness of the proposed algorithm was compared with two other algorithms that were implemented and validated using the two data sets. The results show that it is possible to perform an efficient classification of handwritten signatures with the developed algorithm. Additionally, the developed algorithm is lightweight and easy to implement and can be installed on portable devices such as cell phones or tablets.
A deadline-based elastic approach for balanced task scheduling in computing cloud environment

The cloud servicing environment allows sharing of parallelised virtual resource among tasks based on quality of service requirements. Proficient resource management and scheduling of tasks to execute within deadline is a challenging issue. Several approaches available at present for scheduling and workload balancing among the virtual machines (VM) in cloud, but most of them are not conforming to emerging features like elasticity for dynamic provisioning or deprovisioning of VM's while allocating workload. The proposed deadline based elastic approach for load balancing and scheduling (DL_ELBalTSch) considers the percentage of VM's overloaded or underloaded as a supporting threshold at that movement and takes decision either to raise or cut the VM's. This approach is competent enough to meet established deadline by raising successful execution rate of tasks on variable number of resources. The extensive simulations performed on CloudSim obtained higher task execution-ratio, and lower makespan-time and execution-cost when compared with existing approaches.
The Effect of Electronic Marketing Tools on Customer Satisfaction with Electronic Services in Jordanian Islamic banks (A case study of the Islamic International Arab Bank in Amman City – Jordan)

This study aims to identify the effect of modern electronic marketing tools on customer satisfaction with electronic services in Jordanian Islamic banks. These tools were applied to the Islamic Arab Bank as a model for Jordanian Islamic banks, and the study sample consisted of (385) individuals. To achieve the objectives of the study, the descriptive and analytical approach was used for its suitability for the purposes of the study, and the questionnaire was the tool for data collection. The study included independent variables (social media, website, SMS, smartphone application, internet banking, and finally the phone bank) and the dependent variable is (customer satisfaction). The results of the study showed that there is a statistically significant effect of banking e-marketing tools. In its dimensions (social media, website, SMS, mobile, internet banking and phone bank) on customer satisfaction with electronic services in the Islamic International Arab Bank. The study recommended the necessity of greater interaction on social media between customers and the bank in order to encourage customers to subscribe to the services provided. Allocate more space for customers to listen to their ideas and suggestions on social media.
Implementations Impact on Iterative Image Processing for Embedded GPU

The emergence of low-power embedded Graphical Processing Units (GPUs) with high computation capabilities has enabled the integration of image processing chains in a wide variety of embedded systems. Various optimisation techniques are however needed in order to get the most out of an embedded GPU. This paper explores several optimisation methods for iterative stencil-like image processing algorithms on embedded NVIDIA GPUs using the Compute Unified Device Architecture (CUDA) API. We chose to focus our architectural optimisations on the TV-L1 algorithm, an optical flow estimation method based on total variation (TV) regularisation and the L1 norm. It is widely used as a model for more complex optical flow estimations and is used in many recent video processing applications. In this work we evaluate the impact of architecture-oriented optimisations on both execution time and energy consumption on several Nvidia Jetson GPU embedded boards. Results show a speedup up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times$</tex> compared to State-of-the-Art versions as well as a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.6\times$</tex> decrease in energy consumption.
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit

Changhan Wang, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Ann Lee, Peng-Jen Chen, Jiatao Gu, Juan Pino. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2021.
Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss

A thermal camera can robustly capture thermal radiation images under harsh light conditions such as night scenes, tunnels, and disaster scenarios. However, despite this advantage, neither depth nor ego-motion estimation research for the thermal camera have not been actively explored so far. In this paper, we propose a self-supervised learning method for depth and ego-motion estimation from thermal images. The proposed method exploits multi-spectral consistency that consists of temperature and photometric consistency loss. The temperature consistency loss provides a fundamental self-supervisory signal by reconstructing clipped and colorized thermal images. Additionally, we design a differentiable forward warping module that can transform the coordinate system of the estimated depth map and relative pose from thermal camera to visible camera. Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks. Networks trained with the proposed method robustly estimate the depth and pose from monocular thermal video under low-light and even zero-light conditions. To the best of our knowledge, this is the first work to simultaneously estimate both depth and ego-motion from monocular thermal video in a self-supervised manner.
AUVANA: An Automated Video Analysis Tool for Visual Complexity

Visual complexity is widely considered to be an important variable underlying visual perception. While videos have become versatile in their use of visual imagery, surprisingly, little research has been devoted to understanding the impact of visual complexity. In this paper, we present Automated Video Analysis (AUVANA) software, an open-source tool for extracting, computing, and visualizing visual complexity in digital videos. Through leveraging more sophisticated computer vision and video processing algorithms, AUVANA automatically extracts and computes 78 video visual complexity indices. Results of explanatory analyses demonstrated that rather than a unitary construct video visual complexity is more likely a multidimensional and multifaceted phenomenon. We conclude the paper with a discussion about the potential applications of the software