Reviewerly/exHarmony huggingface dataset:
Cross-Identity Motion Transfer for Arbitrary Objects Through Pose-Attentive Video Reassembling

We propose an attention-based networks for transferring motions between arbitrary objects. Given a source image(s) and a driving video, our networks animate the subject in the source images according to the motion in the driving video. In our attention mechanism, dense similarities between the learned keypoints in the source and the driving images are computed in order to retrieve the appearance information from the source images. Taking a different approach from the well-studied warping based models, our attention-based model has several advantages. By reassembling non-locally searched pieces from the source contents, our approach can produce more realistic outputs. Furthermore, our system can make use of multiple observations of the source appearance (e.g. front and sides of faces) to make the results more accurate. To reduce the training-testing discrepancy of the self-supervised learning, a novel cross-identity training scheme is additionally introduced. With the training scheme, our networks is trained to transfer motions between different subjects, as in the real testing scenario. Experimental results validate that our method produces visually pleasing results in various object domains, showing better performances compared to previous works.
BPMN Extensions and Semantic Annotation in Public Administration Service Design

Internet of Things (IoT), and in general the Internet of Everything (IoE), are deeply influencing the business processes Digital Transformation, also in the Public Administration context, introducing new actors and interactions among People, Process, Data and Things. Therefore, a re-design of the processes or the creation of new ones is necessary, in a way that the design of a public service meets needs and skills of different end users (citizens, business managers, experts, etc.). This paper presents a framework for the design of public service user interfaces that, on the basis of domain ontologies and BPMn extensions, support the modeling of new interactions with IoT and bot services, in particular Telegram Bots, in the context of public administration processes. A service semantic annotation model can be shared with and reused by other organizations, thus reducing the user interface design and implementation time, and consequently the overall service development time.
Proof-of-Reputation Blockchain with Nakamoto Fallback

Reputation is a major component of trustworthy systems. However, the subjective nature of reputation, makes it tricky to base a system’s security on it. In this work, we describe how to leverage reputation to establish a highly scalable and efficient blockchain. Our treatment puts emphasis on reputation fairness as a key feature of reputation-based protocols. We devise a definition of reputation fairness that ensures fair participation while giving chances to newly joining parties to participate and potentially build reputation. We also describe a concrete lottery in the random oracle model which achieves this definition of fairness. Our treatment of reputation-fairness can be of independent interest. To avoid potential safety and/or liveness concerns stemming from the subjective and volatile nature of reputation, we propose a hybrid design that uses a Nakamoto-style ledger as a fallback. To our knowledge, our proposal is the first cryptographically secure design of a proof-of-reputation-based (in short PoR-based) blockchain that fortifies its PoR-based security by optimized Nakamoto-style consensus. This results in a ledger protocol which is provably secure if the reputation system is accurate, and preserves its basic safety properties even if it is not, as long as the fallback blockchain does not fail.
A framework for reducing the overhead of the quantum oracle for use with Grover’s algorithm with applications to cryptanalysis of SIKE

Abstract In this paper we provide a framework for applying classical search and preprocessing to quantum oracles for use with Grover’s quantum search algorithm in order to lower the quantum circuit-complexity of Grover’s algorithm for single-target search problems. This has the effect (for certain problems) of reducing a portion of the polynomial overhead contributed by the implementation cost of quantum oracles and can be used to provide either strict improvements or advantageous trade-offs in circuit-complexity. Our results indicate that it is possible for quantum oracles for certain single-target preimage search problems to reduce the quantum circuit-size from <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>O</m:mi> <m:mfenced open="(" close=")"> <m:mrow> <m:msup> <m:mn>2</m:mn> <m:mrow class="MJX-TeXAtom-ORD"> <m:mi>n</m:mi> <m:mrow class="MJX-TeXAtom-ORD"> <m:mo>/</m:mo> </m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> <m:mo>⋅</m:mo> <m:mi>m</m:mi> <m:mi>C</m:mi> </m:mrow> </m:mfenced> </m:math> $O\left(2^{n/2}\cdot mC\right)$ (where C originates from the cost of implementing the quantum oracle) to <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>O</m:mi> <m:mo stretchy="false">(</m:mo> <m:msup> <m:mn>2</m:mn> <m:mrow class="MJX-TeXAtom-ORD"> <m:mi>n</m:mi> <m:mrow class="MJX-TeXAtom-ORD"> <m:mo>/</m:mo> </m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> <m:mo>⋅</m:mo> <m:mi>m</m:mi> <m:msqrt> <m:mi>C</m:mi> </m:msqrt> <m:mo stretchy="false">)</m:mo> </m:math> $O(2^{n/2} \cdot m\sqrt{C})$ without the use of quantum ram, whilst also slightly reducing the number of required qubits. This framework captures a previous optimisation of Grover’s algorithm using preprocessing [21] applied to cryptanalysis, providing new asymptotic analysis. We additionally provide insights and asymptotic improvements on recent cryptanalysis [16] of SIKE [14] via Grover’s algorithm, demonstrating that the speedup applies to this attack and impacting upon quantum security estimates [16] incorporated into the SIKE specification [14].
An upgraded fruit fly optimisation algorithm for solving task scheduling and resource management problem in cloud infrastructure

In this manuscript, an upgraded Fruit Fly Optimization Algorithm (FFOA) is proposed for optimising task scheduling and resource management processes. The proposed FOA algorithm is utilized to address the issues. In the proposed algorithm, every solution is represented by fruit fly. Every fruit fly upgrades their status through well-organized smell search process. First, a basic approach is put forward to allocate every task for numerous resources and execution time is measured for every task. Second, the overloaded virtual machines (VMs) are identified and the load is balanced to obtain optimal system resource utilisation. The ability of Fly Task Scheduling Algorithm is to schedule the VMs execution time of tasks is minimal. The results of the Fruit Fly-based algorithms such as Fruit Fly Task Scheduling Algorithm, Modified Fruit Fly Task Scheduling Algorithm, Improved Fruit Fly Task Scheduling Algorithm and Multi-Swarm Fruit Fly Task Scheduling Algorithm (MSFFTSA) are proposed and analysed. The upgraded Fruit Fly Optimization algorithm of MSFFTSA is compared with different Fruit Fly algorithms. Finally, the proposed algorithm is compared with other algorithms and the experimental results shows that the proposed MSFFTSA technique is better than the other algorithms of the fruit fly algorithms.
Performance of dense coding and teleportation for random states: Augmentation via preprocessing

In order to understand the resourcefulness of a natural quantum system in quantum communication tasks, we study the dense coding capacity (DCC) and teleportation fidelity of Haar uniformly generated random multipartite states of various ranks. We prove that when a rank-2 two-qubit state, a Werner state, and a pure state possess the same amount of entanglement, the DCC of a rank-2 state belongs to the envelope made by pure and Werner states. In a similar way, we obtain an upper bound via the generalized Greenberger-Horne-Zeilinger state for rank-2 three-qubit states when the dense coding with two senders and a single receiver is performed and entanglement is measured in the senders:receiver bipartition. The normalized frequency distributions of DCC for randomly generated two-, three-, and four-qubit density matrices with global as well as local decodings at the receiver's end are reported. The estimation of mean DCC for two-qubit states is found to be in good agreement with the numerical simulations. Universally, we observe that the performance of random states for dense coding as well as teleportation decreases with the increase of the rank of states, which we have shown to be surmounted by the local preprocessing operations performed on the shared states before starting the protocols, irrespective of the rank of the states. The local preprocessing employed here is based on positive operator valued measurements along with classical communication and we show that unlike dense coding with two-qubit random states, the senders' operations are always helpful to probabilistically enhance the capabilities of implementing dense coding as well as teleportation.
Atoms in separated resonators can jointly absorb a single photon

Abstract The coherent nonlinear process where a single photon simultaneously excites two or more two-level systems (qubits) in a single-mode resonator has recently been theoretically predicted. Here we explore the case where the two qubits are placed in different resonators in an array of two or three weakly coupled resonators. Investigating different setups and excitation schemes, we show that this process can still occur with a probability approaching one under specific conditions. The obtained results provide interesting insights into subtle causality issues underlying the simultaneous excitation processes of qubits placed in different resonators.
Efficiency analysis of polynomial filtering algorithms in navigation data processing for a class of nonlinear discrete dynamical systems

A polynomial filtering algorithm is designed for estimating a Markov sequence based on linear measurements. The feature of the estimation problem is that the Markov sequence is described by a nonlinear shaping filter, which is a second-order polynomial with respect to the state vector components. The algorithm efficiency is illustrated by three examples of navigation data processing. It is shown that the polynomial filter provides accuracy close to the best potential one calculated using the particle filter. At the same time, the amount of computation required to implement this algorithm is much smaller than that for the particle filter. In addition, the polynomial filter provides a consistent calculated accuracy characteristic.
Protection of the Rights and Freedoms of Minors in the Digital Space

The problem of ensuring information security is currently urgent for the Russian Federation as well as for the whole world. The number of cyber-threats is increasing at a great speed, and they concern not only private citizens, but also organizations, the community and the state. Special attention should be paid to the information security of children and teenagers. Minors are most susceptible to negative influences on the Internet, they risk becoming victims of cyberbullying, fraud and illegal access to personal data. Common threats to the information security of minors include a constant increase in the number of sites with aggressive or illegal content, including those inciting to suicide or abuse of drugs and psychoactive substances, as well as cyber-stalking or virtual sexual harassment. The information security of minors in the digital space is a complex issue, whose successful solution requires a consolidation of legal and information resources. The article analyzes Russian and foreign experience of ensuring the information security of minors. The authors summarize research approaches to solving the problem of protecting minors on the Internet. They present a classification of the most urgent cyber-threats: software-technical (intentional dissemination of viruses and Trojan software), economic (theft and sale of credit card details, phishing-attacks, hacking of payment accounts), and content (public dissemination of any materials, including illegal ones, on the Internet). The authors also examine legal, social and technical measures of ensuring the information security of minors and suggest changes to the current legislation which regulates the information security of minors. The authors also study specific methods of solving this task and outline a number of measures aimed at protecting the rights and freedoms of minors in the digital space (thematic prevention classes for minors, development of special information protection software).
An Xgboost based system for financial fraud detection

Credit card fraud leads to billions of losses in online transaction. Many corporations like Alibaba, Amazon and Paypal invest billions of dollars to build a safe transaction system. There are some studies in this area having tried to use machine learning or data mining to solve these problems. This paper proposed our fraud detection system for e- commerce merchant. Unlike many other works, this system combines manual and automatic classifications. This paper can inspire researchers and engineers to design and deploy online transaction systems.
TensorMoG: A Tensor-Driven Gaussian Mixture Model with Dynamic Scene Adaptation for Background Modelling

Decades of ongoing research have shown that background modelling is a very powerful technique, which is used in intelligent surveillance systems, in order to extract features of interest, known as foregrounds. In order to work with the dynamic nature of different scenes, many techniques of background modelling adopted the unsupervised approach of Gaussian Mixture Model with an iterative paradigm. Although the technique has had much success, a problem occurs in cases of sudden scene changes with high variation (e.g., illumination changes, camera jittering) that the model unknowingly and unnecessarily takes into account those effects and distorts the results. Therefore, this paper proposes an unsupervised, parallelized, and tensor-based approach that algorithmically works with entropy estimations. These entropy estimations are used in order to assess the uncertainty level of a constructed background, which predicts both the present and future variations from the inputs, thereby opting to use either the incoming frames to update the background or simply discard them. Our experiments suggest that this method is highly integrable into a surveillance system that consists of other functions and can be competitive with state-of-the-art methods in terms of processing speed.
Learning to Track Aircraft in Infrared Imagery

Airborne target tracking in infrared imagery remains a challenging task. The airborne target usually has a low signal-to-noise ratio and shows different visual patterns. The features adopted in the visual tracking algorithm are usually deep features pre-trained on ImageNet, which are not tightly coupled with the current video domain and therefore might not be optimal for infrared target tracking. To this end, we propose a new approach to learn the domain-specific features, which can be adapted to the current video online without pre-training on a large datasets. Considering that only a few samples of the initial frame can be used for online training, general feature representations are encoded to the network for a better initialization. The feature learning module is flexible and can be integrated into tracking frameworks based on correlation filters to improve the baseline method. Experiments on airborne infrared imagery are conducted to demonstrate the effectiveness of our tracking algorithm.
Smart Grid Management Using Blockchain: Future Scenarios and Challenges

Decentralized management and coordination of energy systems are emerging trends facilitated by the uptake of the Internet of Things and Blockchain offering new opportunities for more secure, resilient, and efficient energy distribution. Even though the use of distributed ledger technology in the energy domain is promising, the development of decentralized smart grid management solutions is in the early stages. In this paper, we define a layered architecture of a blockchain-based smart grid management platform featuring energy data metering and tamper-proof registration, business enforcement via smart contracts, and Oracle-based integration of high computational services supporting the implementation of future grid management scenarios. Three such scenarios are discussed from the perspective of their implementation using the proposed blockchain platform and associated challenges: peer to peer energy trading, decentralized management, and aggregation of energy flexibility and operation of community oriented Virtual Power Plants.
Iterative group-based and difference ranking method for online rating systems with spamming attacks

It is significant to assign reputation scores to users and identify spammers in the bipartite rating networks. In this paper, we propose an Iterative Group-based and Difference Ranking (IGDR) method, which is based on the original Iterative Group-based Ranking (IGR) method. The IGR method considers users grouping behaviors, but it ignores the characteristics of the individual ratings. It is discovered that individual rating characteristics could also contribute to the redistribution of reputation scores of users. The user with a smaller rating deviation will be given a higher reputation score. The proposed method outperforms IGR method ranging from 8% to 163% tested on three real datasets. It also can be applied to deal with big data in a short time.
ANNETTE: Accurate Neural Network Execution Time Estimation With Stacked Models

With new accelerator hardware for Deep Neural Networks (DNNs), the computing power for Artificial Intelligence (AI) applications has increased rapidly. However, as DNN algorithms become more complex and optimized for specific applications, latency requirements remain challenging, and it is critical to find the optimal points in the design space. To decouple the architectural search from the target hardware, we propose a time estimation framework that allows for modeling the inference latency of DNNs on hardware accelerators based on mapping and layer-wise estimation models. The proposed methodology extracts a set of models from micro-kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation. We test the mixed models on the ZCU102 SoC board with Xilinx Deep Neural Network Development Kit (DNNDK) and Intel Neural Compute Stick 2 (NCS2) on a set of 12 state-of-the-art neural networks. It shows an average estimation error of 3.47% for the DNNDK and 7.44% for the NCS2, outperforming the statistical and analytical layer models for almost all selected networks. For a randomly selected subset of 34 networks of the NASBench dataset, the mixed model reaches fidelity of 0.988 in Spearman’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> rank correlation coefficient metric.
“What is on your mind?” Automated Scoring of Mindreading in Childhood and Early Adolescence

In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence.We create MIND-CA, a new corpus of 11,311 question-answer pairs in English from 1,066 children aged 7 to 14.We perform machine learning experiments and carry out extensive quantitative and qualitative evaluation.We obtain promising results, demonstrating the applicability of state-of-the-art NLP solutions to a new domain and task.
Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization

Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption. However, current learning protocols fail to provide scalable and efficient solutions to photonic circuit optimization in practical applications. In this work, we propose a novel on-chip learning framework to release the full potential of ONNs for power-efficient in situ training. Instead of deploying implementation-costly back-propagation, we directly optimize the device configurations with computation budgets and power constraints. We are the first to model the ONN on-chip learning as a resource-constrained stochastic noisy zeroth-order optimization problem, and propose a novel mixed-training strategy with two-level sparsity and power-aware dynamic pruning to offer a scalable on-chip training solution in practical ONN deployment. Compared with previous methods, we are the first to optimize over 2,500 optical components on chip. We can achieve much better optimization stability, 3.7x-7.6x higher efficiency, and save &gt;90% power under practical device variations and thermal crosstalk.
Mining frequent approximate patterns in large networks

Abstract Frequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so far has considered the structural differences of vertices and their labels. Discerning how to identify cases that differ from the initial pattern by any number of vertices, edges, or labels has become the main challenge of recent research works. As a solution, we suggest a novel FMP algorithm named mining frequent approximate patterns (MFAPs) with two central new characteristics. First, we begin by using the inexact matching technique, which allows for structural differences in edge, vertices, and labels. Second, we follow the approximate matching with a focus on mining patterns within the directed graph, as opposed to the more commonly explored case of patterns being mined from the undirected graph. Our results illustrate the effectiveness of this new MFAP algorithm in identifying patterns within an optimized time.
Exposure Interpolation for Two Large-Exposure-Ratio Images

Existing multi-scale exposure fusion (MEF) algorithms which focus on defining weight maps to extract reliable information. However, they are difficult to preserve brightness order among over-exposed regions of a bright image and under-exposed regions of a dark image in a fused image when input images are two large-exposure-ratio images. In this paper, a new concept of exposure interpolation, which generates an appropriate bracketed sequence of virtual images from two input images, is introduced to address the problem. The possibly minimal number of virtual images is determined by using an optimization method. Then, an effective method by employing the intensity mapping function (IMF) is proposed to generate virtual images with proportional exposure times. The final image is produced by fusing both the two input images and the virtual images using multi-scale fusion. Experimental results show that the brightness order is preserved much better and the MEF-SSIM is significantly improved by the exposure interpolation.