Reviewerly/exHarmony huggingface dataset:
Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.
Tuning photon statistics with coherent fields

Photon correlations, as measured by Glauber's $n$-th order coherence functions $g^{(n)}$, are highly sought to be minimized and/or maximized. In systems that are coherently driven, so-called blockades can give rise to strong correlations according to two scenarios based on level-repulsion (conventional blockade) or interferences (unconventional blockade). Here we show how these two approaches relate to the admixing of a coherent state with a quantum state such as a squeezed state for the simplest and most recurrent case. The emission from a variety of systems, such as resonance fluorescence, the Jaynes-Cummings model or microcavity polaritons, as a few examples of a large family of quantum optical sources, are shown to be particular cases of such admixtures, that can further be doctored-up externally by adding an amplitude- and phase-controlled coherent field with the effect of tuning the photon statistics from exactly zero to infinity. We show how such an understanding also allows to classify photon statistics throughout platforms according to conventional and unconventional features, with the effect of optimizing the correlations and with possible spectroscopic applications. In particular, we show how configurations that can realize simultaneously conventional and unconventional antibunching bring the best of both worlds: huge antibunching (unconventional) with large populations and being robust to dephasing (conventional).
Residual whiteness principle for parameter-free image restoration

Selecting the regularization parameter in the image restoration variational framework is of crucial importance, since it can highly influence the quality of the final restoration.In this paper, we propose a parameter-free approach for automatically selecting the regularization parameter when the blur is space-invariant and known and the noise is additive white Gaussian with unknown standard deviation, based on the so-called residual whiteness principle.More precisely, the regularization parameter is required to minimize the residual whiteness function, namely the normalized auto-correlation of the residual image of the restoration.The proposed method can be applied to a wide class of variational models, such as those including in their formulation regularizers of Tikhonov and Total Variation type.For non-quadratic regularizers, the residual whiteness principle is nested in an iterative optimization scheme based on the alternating direction method of multipliers.The effectiveness of the proposed approach is verified by solving some test examples and performing a comparison with other parameter estimation state-of-the-art strategies, such as the discrepancy principle.
Application Ontology for Multi-Agent and Web-Services’ Co-Simulation in Power and Energy Systems

Power and energy systems are very complex, and several tools are available to assist operators in their planning and operation. However, these tools do not allow a sensitive analysis of the impact of the interaction between the different sub-domains and, consequently, in obtaining more realistic and reliable results. One of the key challenges in this area is the development of decision support tools to address the problem as a whole. Tools Control Center - TOOCC - proposed and developed by the authors, enables the co-simulation of heterogeneous systems to study the electricity markets, the operation of the smart grids, and the energy management of the final consumer, among others. To this end, it uses an application ontology that supports the definition of scenarios and results comparison, while easing the interoperability among the several systems. This paper presents the application ontology developed. The paper addresses the methodology used for its development, its purpose and requirements, and its concepts, relations, facets and instances. The ontology application is illustrated through a case study, where different requirements are tested and demonstrated. It is concluded that the proposed application ontology accomplishes its goals, as it is suitable to represent the required knowledge to support the interoperability among the different considered systems.
Promoting Trustless Computation Through Blockchain Technology

Records, irrespective of their nature (whether electronic or paper-based), are vulnerable to fraud. People's hard-earned money, their personal information, identity, and health are at a higher risk than ever due to the misuse of technology in doing forgery. However, the technology can also be used as an answer to counteracting against fraudulence prevalent in affairs from every walk of life. This short paper attempts to present the blockchain technology as a solution to overcome the menace of forgery by promoting trustless computing in business transactions. The paper explains the blockchain technology and a variety of its implementation through five different use cases in the field of drug supply chain, health insurance, land record management, courier services, and immigration records. The immigration blockchain is also proposed as a solution to check pandemic like the coronavirus (COVID-19) effectively. The implementation of the Blockchain is performed using a locally built IBM's hyper-ledger fabric-based platform, and Ethereum public platform. The results are encouraging enough to substitute existing business operations using Blockchain-based solutions.
Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph

In recent years, the research of combining a knowledge graph with recommendation systems has caused widespread concern. By studying the interconnections in knowledge graphs, potential connections between users and items can be discovered, which provides abundant and complementary information for recommendation of items. However, most existing studies have not effectively established the relation between entities and users. Therefore, the recommendation results may be affected by some unrelated entities. In this paper, we propose a neighborhood aggregation collaborative filtering (NACF) based on knowledge graph. It uses the knowledge graph to spread and extract the user’s potential interest, and iteratively injects them into the user features with attentional deviation. We conducted a large number of experiments on three public datasets; we verifyied that NACF is ahead of the most advanced models in top-k recommendation and click-through rate (CTR) prediction.
Sentiment-guided Sequential Recommendation

The existing sequential recommendation methods focus on modeling the temporal relationships of user behaviors and are good at using additional item information to improve performance. However, these methods rarely consider the influences of users' sequential subjective sentiments on their behaviors---and sometimes the temporal changes in human sentiment patterns plays a decisive role in users' final preferences. To investigate the influence of temporal sentiments on user preferences, we propose generating preferences by guiding user behavior through sequential sentiments. Specifically, we design a dual-channel fusion mechanism. The main channel consists of sentiment-guided attention to match and guide sequential user behavior, and the secondary channel consists of sparse sentiment attention to assist in preference generation. In the experiments, we demonstrate the effectiveness of these two sentiment modeling mechanisms through ablation studies. Our approach outperforms current state-of-the-art sequential recommendation methods that incorporate sentiment factors.
NDNetGaming - development of a no-reference deep CNN for gaming video quality prediction

Abstract Gaming video streaming services are growing rapidly due to new services such as passive video streaming of gaming content, e.g. Twitch.tv, as well as cloud gaming, e.g. Nvidia GeForce NOW and Google Stadia. In contrast to traditional video content, gaming content has special characteristics such as extremely high and special motion patterns, synthetic content and repetitive content, which poses new opportunities for the design of machine learning-based models to outperform the state-of-the-art video and image quality approaches for this special computer generated content. In this paper, we train a Convolutional Neural Network (CNN) based on an objective quality model, VMAF, as ground truth and fine-tuned it based on subjective image quality ratings. In addition, we propose a new temporal pooling method to predict gaming video quality based on frame-level predictions. Finally, the paper also describes how an appropriate CNN architecture can be chosen and how well the model performs on different contents. Our result shows that among four popular network architectures that we investigated, DenseNet performs best for image quality assessment based on the training dataset. By training the last 57 convolutional layers of DenseNet based on VMAF values, we obtained a high performance model to predict VMAF of distorted frames of video games with a Spearman’s Rank correlation (SRCC) of 0.945 and Root Mean Score Error (RMSE) of 7.07 on the image level, while achieving a higher performance on the video level leading to a SRCC of 0.967 and RMSE of 5.47 for the KUGVD dataset. Furthermore, we fine-tuned the model based on subjective quality ratings of images from gaming content which resulted in a SRCC of 0.93 and RMSE of 0.46 using one-hold-out cross validation. Finally, on the video level, using the proposed pooling method, the model achieves a very good performance indicated by a SRCC of 0.968 and RMSE of 0.30 for the used gaming video dataset.
Projected Augmented Reality to Drive Osteotomy Surgery: Implementation and Comparison With Video See-Through Technology

In recent years, the spreading of visual augmented reality as an effective tool in image-guided surgery, has stimulated the research community to investigate the use of commercial augmented reality headsets a broad range of potential applications. This aroused enthusiasm among clinicians for the potential of augmented reality, but also revealed some technological and human-factor limitations that still hinder its routine adoption in the operating room. In this work, we propose an alternative to head-mounted displays, based on projected augmented reality. Projected augmented reality completely preserves the surgeon's natural view of the operating field, because it requires no perspective conversion and/or optical mediation. We selected a cranio-maxillofacial surgery application as a benchmark to test the proposed system and compare its accuracy with the one obtained with a video see-through system. The augmented reality overlay accuracy was evaluated by measuring the distance between a virtual osteotomy line and its real counterpart. The experimental tests showed that the accuracy of the two augmented reality modes is similar, with a median error discrepancy of about 0.3 mm for the projected augmented reality mode. Results suggest that projected augmented reality can be a valuable alternative to standard see-through head-mounted displays to support in-situ visualization of medical imaging data as surgical guidance.
Justifications for Goal-Directed Constraint Answer Set Programming

Ethical and legal concerns make it necessary for programs that may directly influence the life of people (via, e.g., legal or health counseling) to justify in human-understandable terms the advice given. Answer Set Programming has a rich semantics that makes it possible to very concisely express complex knowledge. However, justifying why an answer is a consequence from an ASP program may be non-trivial -- even more so when the user is an expert in a given domain, but not necessarily knowledgeable in ASP. Most ASP systems generate answers using SAT-solving procedures on ground rules that do not match how humans perceive reasoning. We propose using s(CASP), a query-driven, top-down execution model for predicate ASP with constraints to generate justification trees of (constrained) answer sets. The operational semantics of s(CASP) relies on backward chaining, which is intuitive to follow and lends itself to generating explanations that are easier to translate into natural language. We show how s(CASP) provides minimal justifications for, among others, relevant examples proposed in the literature, both as search trees but, more importantly, as explanations in natural language. We validate our design with real ASP applications and evaluate the cost of generating s(CASP) justification trees.
Efficient CNN Accelerator on FPGA

Convolutional neural networks (CNNs) are classical models for computer vision and machine learning applications such as video surveillance, pattern recognition, weather forecasting, traffic, and safety. CNNs involve computationally intensive operations and require huge off-chip memory bandwidth, which makes it a challenging task to deploy on real-time embedded systems. Compared to central processing units and graphic processing units, field programmable gate arrays (FPGA)-based CNNs are gaining popularity owing to their flexibility and efficiency. In this work, we present an efficient CNN accelerator based on blocked Winograd-GEMM architecture with high performance. We implement ResNet-18 CNN model on XC7VX690T FPGA using proposed architecture. This implementation operates at a clock frequency of 200 MHz and gives average throughput of 383 GOPS which is comparable to other state-of-art implementations. This manuscript is an extended version of [S. Kala, J. Mathew, B. R. Jose, and S. Nalesh, “UniWiG: Unified Winograd-GEMM Architecture for Accelerating CNN on FPGAs,” in 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID), Delhi, NCR, India, 2019, pp. 209–214. DOI: 10.1109/VLSID.2019.00055.].
U(1)-symmetric recurrent neural networks for quantum state reconstruction

Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experimental measurements, and can aid in the calculation of physical observables. In this paper, we employ a recurrent neural network (RNN) to reconstruct the ground state of the spin-1/2 XY model, a prototypical Hamiltonian explored in trapped ion simulators. We explore its performance after enforcing a U(1) symmetry, which was recently shown by Hibat-Allah et al. [Phys. Rev. Research 2, 023358 (2020)] to preserve the autoregressive nature of the RNN. By studying the reconstruction of the XY model ground state from projective measurement data, we show that imposing U(1) symmetry on the RNN significantly increases the efficiency of learning, particularly in the early epoch regime. We argue that this performance increase may result from the tendency of the enforced symmetry to alleviate vanishing and exploding gradients, which helps stabilize the training process. Thus, symmetry-enforced RNNs may be particularly useful for applications of quantum simulators where a rapid feedback between optimization and circuit preparation is necessary, such as in hybrid classical-quantum algorithms.
Neural Network Training Using a Biogeography-Based Learning Strategy

The performance of multi-layer feed-forward neural networks is closely related to the success of training algorithms in finding optimal weights in the network. Although conventional algorithms such as back-propagation are popular in this regard, they suffer from drawbacks such as a tendency to get stuck in local optima. In this paper, we propose an effective hybrid algorithm, BLPSO-GBS, for neural network training based on particle swarm optimisation (PSO), biogeography-based optimisation (BBO), and a global-best strategy. BLPSO-GBS updates each particle based on neighbouring particles and a biogeography-based learning strategy is used to generate the neighbouring particles using the migration operator in BBO. Our experiments on different benchmark datasets and comparison to various algorithms clearly show the competitive performance of BLPSO-GBS.
Experimental demonstration of quantum walks with initial superposition states

The preparation of initial superposition states of discrete-time quantum walks (DTQWs) are necessary for the study and applications of DTQWs. In linear optics, it is easy to prepare initial superposition states of the coin, which are always encoded by polarization states; while the preparation of superposition states of the walker is challenging. Based on a novel encoding method, we here propose a DTQW protocol in linear optics which enables the preparation of arbitrary initial superposition states of the walker and the coin. With this protocol, we report an experimental demonstration of DTQW with the walker initially in superposition states, by using only passive linear-optical elements. The effects of the walker's different initial superposition states on the spread speed of the DTQW and on the entanglement between the coin and the walker are also experimentally investigated, which have not been reported before. When the walker starts with superposition states, we show that the properties of DTQW are very different from those of DTQW starting with a single position. Our findings reveal different properties of DTQW and paves an avenue to study DTQW with arbitrary initial states. Moreover, the encoding method enables one to encode an arbitrary high-dimensional quantum state using a single physical qubit and may be adopted to implement other quantum information tasks.
A Black-box Monitoring Approach to Measure Microservices Runtime Performance

Microservices changed cloud computing by moving the applications’ complexity from one monolithic executable to thousands of network interactions between small components. Given the increasing deployment sizes, the architectural exploitation challenges, and the impact on data-centers’ power consumption, we need to efficiently track this complexity. Within this article, we propose a black-box monitoring approach to track microservices at scale, focusing on architectural metrics, power consumption, application performance, and network performance. The proposed approach is transparent w.r.t. the monitored applications, generates less overhead w.r.t. black-box approaches available in the state-of-the-art, and provides fine-grain accurate metrics.
Experimental creation of multi-photon high-dimensional layered quantum states

Abstract Quantum entanglement is one of the most important resources in quantum information. In recent years, the research of quantum entanglement mainly focused on the increase in the number of entangled qubits or the high-dimensional entanglement of two particles. Compared with qubit states, multipartite high-dimensional entangled states have beneficial properties and are powerful for constructing quantum networks. However, there are few studies on multipartite high-dimensional quantum entanglement due to the difficulty of creating such states. In this paper, we experimentally prepared a multipartite high-dimensional state $$\left|{\Psi }_{442}\right\rangle =\frac{1}{2}(\left|000\right\rangle +\left|110\right\rangle +\left|221\right\rangle +\left|331\right\rangle )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mfenced> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>Ψ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>442</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:mfenced> <mml:mo>=</mml:mo> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfrac> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mfenced> <mml:mrow> <mml:mn>000</mml:mn> </mml:mrow> </mml:mfenced> <mml:mo>+</mml:mo> <mml:mfenced> <mml:mrow> <mml:mn>110</mml:mn> </mml:mrow> </mml:mfenced> <mml:mo>+</mml:mo> <mml:mfenced> <mml:mrow> <mml:mn>221</mml:mn> </mml:mrow> </mml:mfenced> <mml:mo>+</mml:mo> <mml:mfenced> <mml:mrow> <mml:mn>331</mml:mn> </mml:mrow> </mml:mfenced> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> by using the path mode of photons. We obtain the fidelity F = 0.854 ± 0.007 of the quantum state, which proves a real multipartite high-dimensional entangled state. Finally, we use this quantum state to demonstrate a layered quantum network in principle. Our work highlights another route toward complex quantum networks.
Squeezed Very Deep Convolutional Neural Networks for Text Classification

Most of the research in convolutional neural networks has focused on increasing network depth to improve accuracy, resulting in a massive number of parameters which restricts the trained network to platforms with memory and processing constraints. We propose to modify the structure of the Very Deep Convolutional Neural Networks (VDCNN) model to fit mobile platforms constraints and keep performance. In this paper, we evaluate the impact of Temporal Depthwise Separable Convolutions and Global Average Pooling in the network parameters, storage size, and latency. The squeezed model (SVDCNN) is between 10x and 20x smaller, depending on the network depth, maintaining a maximum size of 6MB. Regarding accuracy, the network experiences a loss between 0.4% and 1.3% and obtains lower latencies compared to the baseline model.