Reviewerly/exHarmony huggingface dataset:
Quantum dynamics simulations beyond the coherence time on noisy intermediate-scale quantum hardware by variational Trotter compression

We demonstrate a postquench dynamics simulation of a Heisenberg model on present-day IBM quantum hardware that extends beyond the coherence time of the device. This is achieved using a hybrid quantum-classical algorithm that propagates a state using Trotter evolution and then performs a classical optimization that effectively compresses the time-evolved state into a variational form. When iterated, this procedure enables simulations to arbitrary times with an error controlled by the compression fidelity and a fixed Trotter step size. We show how to measure the required cost function, the overlap between the time-evolved and variational states, on present-day hardware, making use of several error mitigation methods. In addition to carrying out simulations on real hardware, we investigate the performance and scaling behavior of the algorithm with noiseless and noisy classical simulations. We find the main bottleneck in going to larger system sizes to be the difficulty of carrying out the optimization of the noisy cost function.
Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry

Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory.
Distributed optimization of heterogeneous UAV cluster PID controller based on machine learning

To resolve the problem of membership value and the control effect reliant on expert knowledge, a PID control approach based on an adaptive fuzzy PID-UAV attitude controller is proposed. The experimental findings demonstrate that the Unmanned Aerial Vehicle (UAV) is stable at the desired height of 2 m within 6 s after takeoff, and that altitude control can be accomplished with a single PID controller. It has been established that the control system has the advantages of high precision and ease of implementation, but the fuzzy controller has the advantages of minimal overshoot and transient response, as well as the ability to achieve precise and rapid behaviour control. When obtaining continuous square wave instructions, fuzzy PID has more obvious advantages than regular PID. The system has superior tracking and antiinterference, can achieve stable control faster, and has great dynamic and static properties.
High-speed multi-camera 3D DIC measurement of the deformation of cassette structure with large shaking table

Vision-based deformation measurement methods have been proposed to overcome the disadvantages of laborious, time-consuming measurement data that are difficult to accurately obtain using contact sensors in three directions. However, such methods remain a challenge for large-scale applications owing to difficulties in full-field imaging, stereo calibration, and measurement resolution, among other factors. To the best of our knowledge, there are no reports on the three-dimensional (3D) full-field measurement of a large shaking table structure with multiple cameras. In this paper, a multi-camera 3D DIC method using four high-speed cameras to measure the multi-layer structure model in a shaking table is proposed. To calibrate the stereo cameras with a large field of view, a speckle-based calibration method and a coordinate system unification method are proposed. The implementation of the proposed method is illustrated with a shaking table test for a 1/8 scaled 15-story high-rise cassette concrete structure with several different intensity levels of ground motions. The results of displacement, velocity, and acceleration of the structure measured using the proposed method correspond well with those of a laser displacement meter and accelerometer. The experiment results show that this method can guarantee full-field measurements in the range of tens of square meters and realize a sub-millimeter displacement measurement accuracy. They also indicate that the proposed multi-camera 3D DIC method has high potential for understanding the failure mechanism of structures and improving the structural design.
Stacking Model for Photovoltaic-Power-Generation Prediction

Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the photovoltaic power generation. As a widely used prediction method, the stacking model has been applied in many fields. However, few studies have used stacking models to predict photovoltaic power generation. In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets. The results show that the prediction accuracy of the stacking model is higher than that of the single ensemble-learning model, and that the prediction accuracy of the Stacking-GBDT model is higher than the other stacking models. The stacking model that is proposed in this research provides a reference for the accurate prediction of photovoltaic power generation.
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision

Learning RAW-to-sRGB mapping has drawn increasing attention in recent years, wherein an input raw image is trained to imitate the target sRGB image captured by another camera. However, the severe color inconsistency makes it very challenging to generate well-aligned training pairs of input raw and target sRGB images. While learning with inaccurately aligned supervision is prone to causing pixel shift and producing blurry results. In this paper, we circumvent such issue by presenting a joint learning model for image alignment and RAW-to-sRGB mapping. To diminish the effect of color inconsistency in image alignment, we introduce to use a global color mapping (GCM) module to generate an initial sRGB image given the input raw image, which can keep the spatial location of the pixels unchanged, and the target sRGB image is utilized to guide GCM for converting the color towards it. Then a pre-trained optical flow estimation network (e.g., PWC-Net) is deployed to warp the target sRGB image to align with the GCM output. To alleviate the effect of inaccurately aligned supervision, the warped target sRGB image is leveraged to learn RAW-to-sRGB mapping. When training is done, the GCM module and optical flow network can be detached, thereby bringing no extra computation cost for inference. Experiments show that our method performs favorably against state-of-the-arts on ZRR and SR-RAW datasets. With our joint learning model, a light-weight backbone can achieve better quantitative and qualitative performance on ZRR dataset. Codes are available at https://github.com/cszhilu1998/RAW-to-sRGB.
A New Paradigm for Public-Key Functional Encryption for Degree-2 Polynomials

We give the first public-key functional encryption that supports the generation of functional decryption keys for degree-2 polynomials, with succinct ciphertexts, whose semi-adaptive simulation-based security is proven under standard assumptions. At the heart of our new paradigm lies a so-called partially function-hiding functional encryption scheme for inner products, which admits public-key instances, and that is sufficient to build functional encryption for degree-2 polynomials. Doing so, we improve upon prior works, such as the constructions from Lin (CRYPTO 17) or Ananth Sahai (EUROCRYPT 17), both of which rely on function-hiding inner product FE, that can only exist in the private-key setting. The simplicity of our construction yields the most efficient FE for quadratic functions from standard assumptions (even those satisfying a weaker security notion). The interest of our methodology is that the FE for quadratic functions that builds upon any partially function-hiding FE for inner products inherits the security properties of the latter. In particular, we build a partially function-hiding FE for inner products that enjoys simulation security, in the semi-adaptive setting, where the challenge sent from the adversary can be chosen adaptively after seeing the public key (but before corrupting functional decryption keys). This is in contrast from prior public-key FE for quadratic functions from Baltico et al. (CRYPTO 17), which only achieved an indistinguishability-based, selective security. As a bonus, we show that we can obtain security against Chosen-Ciphertext Attacks straightforwardly. Even though this is the de facto security notion for encryption, this was not achieved by prior functional encryption schemes for quadratic functions, where the generic Fujisaki Okamoto transformation (CRYPTO 99) does not apply.
Data protection by design: Building the foundations of trustworthy data sharing

Abstract Data trusts have been conceived as a mechanism to enable the sharing of data across entities where other formats, such as open data or commercial agreements, are not appropriate, and make data sharing both easier and more scalable. By our definition, a data trust is a legal, technical, and organizational structure for enabling the sharing of data for a variety of purposes. The concept of the “data trust” requires further disambiguation from other facilitating structures such as data collaboratives. Irrespective of the terminology used, attempting to create trust in order to facilitate data sharing, and create benefit to individuals, groups of individuals, or society at large, requires at a minimum a process-based mechanism, that is, a workflow that should have a trustworthiness-by-design approach at its core. Data protection by design should be a key component of such an approach.
Particle Swarm Optimization

Particle swarm optimization (PSO) is a swarm intelligence based algorithm and is among the most widely used nature-inspired algorithms. This chapter introduces the standard PSO, its variants and demo implementation.
Current Best Opposition-Based Learning Salp Swarm Algorithm for Global Numerical Optimization

The salp swarm algorithm is one of the novel swarm intelligence metaheuristics. The work proposed in this paper provides further improvements of the salp swarm algorithm, that have been achieved by modifications of the original approach. By analyzing solutions’ quality and convergence speed of basic salp swarm during practical simulations, it was concluded that the exploitation process can be improved. Improvements were achieved by introducing the concept of opposite solutions in the initialization phase, as well as in iterative search process, where a fine-tuned exploitation of the current best solution is performed by generating its opposite individual. Proposed improved salp swarm algorithm was tested on thirteen well-known global benchmarks. Comparative analysis was performed with seven other modern metaheuristics methods, and against the original salp swarm algorithm. Accomplished results have proven that proposed approach in a large degree outscores original algorithm and other approaches included in comparative analysis.
Semantic Communications for Speech Signals

We consider a semantic communication system for speech signals, named DeepSC-S. Motivated by the breakthroughs in deep learning (DL), we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit level or symbol level as in the traditional communication systems. Particularly, based on an attention mechanism employing squeeze-and-excitation (SE) networks, we design the transceiver as an end-to-end (E2E) system, which learns and extracts the essential speech information. Furthermore, in order to facilitate the proposed DeepSC-S to work well on dynamic practical communication scenarios, we find a model yielding good performance when coping with various channel environments without retraining process. The simulation results demonstrate that our proposed DeepSC-S is more robust to channel variations and outperforms the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder

State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop in the presence of domain differences between source and target datasets. The strong discrimination ability on the source dataset does not necessarily translate to high classification accuracy on the target dataset. In this work, we address this cross-domain few-shot learning (CDFSL) problem by boosting the generalization capability of the model. Specifically, we teach the model to capture broader variations of the feature distributions with a novel noise-enhanced supervised autoencoder (NSAE). NSAE trains the model by jointly reconstructing inputs and predicting the labels of inputs as well as their reconstructed pairs. Theoretical analysis based on intra-class correlation (ICC) shows that the feature embeddings learned from NSAE have stronger discrimination and generalization abilities in the target domain. We also take advantage of NSAE structure and propose a two-step fine-tuning procedure that achieves better adaption and improves classification performance in the target domain. Extensive experiments and ablation studies are conducted to demonstrate the effectiveness of the proposed method. Experimental results show that our proposed method consistently outperforms SOTA methods under various conditions.
Predictive privacy: towards an applied ethics of data analytics

Abstract Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.
IIoT Data Sharing Based on Blockchain: A Multileader Multifollower Stackelberg Game Approach

The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities&#x2019; distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59&#x0025;, and the total utility of data owners is maximally increased by 52&#x0025;.
Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts

Toward the goal of automatic production for sports broadcasts, a paramount task consists in understanding the high-level semantic information of the game in play.For instance, recognizing and localizing the main actions of the game would allow producers to adapt and automatize the broadcast production, focusing on the important details of the game and maximizing the spectator engagement.In this paper, we focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game.To that end, we propose a novel feature pooling method based on NetVLAD, dubbed NetVLAD++, that embeds temporally-aware knowledge.Different from previous pooling methods that consider the temporal context as a single set to pool from, we split the context before and after an action occurs.We argue that considering the contextual information around the action spot as a single entity leads to a sub-optimal learning for the pooling module.With NetVLAD++, we disentangle the context from the past and future frames and learn specific vocabularies of semantics for each subsets, avoiding to blend and blur such vocabulary in time.Injecting such prior knowledge creates more informative pooling modules and more discriminative pooled features, leading into a better understanding of the actions.We train and evaluate our methodology on the recent large-scale dataset SoccerNet-v2, reaching 53.4% Average-mAP for action spotting, a +12.7% improvement w.r.
Knowledge discovery of geochemical patterns from a data-driven perspective

We have entered the fourth research paradigm with the overwhelming availability of vast amounts of data. The processing and mining these data for a better understanding of earth systems and predicting mineral resources is challenging. This study discusses a data-driven knowledge discovery of geochemical patterns and presents a case study of geochemical data processing from a data-driven perspective. We employed local indicators of spatial association (LISA), principal component analysis (PCA), and deep autoencoder network (DAN) procedures to explore spatial association of geochemical patterns, extract elemental associations, and detect geochemical anomalies related to AuSb mineralization in the Daqiao district, Gansu Province, China. The results indicate the following: (1) both Au and Sb, and Pb and Zn have a close spatial correlation, indicating genetic connections among them; (2) the elemental association of Au, Sb, As, Hg and Ag can be adopted as a geochemical signature for the discovery of AuSb polymetallic mineralization in the study area; and (3) the geochemical anomalies identified by DAN exhibit a strong spatial relationship with locations of known mineral deposits and can provide a significant clue for further mineral exploration in this district. These findings indicate that data-driven procedures can help in the knowledge discovery of geochemical patterns in mineral exploration. Additional efforts are required for data-driven knowledge discovery in both geochemical prospecting and mineral exploration.
Automatic Detection of Cyberbullying and Abusive Language in Arabic Content on Social Networks: A Survey

As a key player in today’s world, online social networks are emerging, providing a platform for expression and content distribution. This technology enables users to communicate easily with each other and share their data instantly. However, the internet isn’t generally protected; it can be a source for abusive and harmful content and causing harm to others. There is a great need for approaches and strategies to solve these issues due to the negative effect of abusive language and cyberbullying. Arabic text is known for its challenges, complexity, and scarcity of its resources. Many languages have made many efforts to find automated solutions for detecting abusive language and cyberbullying, but not much for the Arabic language. This work analyzes 27 studies on automatic Arabic abusive language and cyberbullying and its related detection approaches. The goal of this paper is to review the findings of the previous studies about cyberbullying and abusive detection in Arabic content on online social networks and help researcher in the future to develop automatic detection systems that are effective and realistic.
Deep Graph Generators: A Survey

Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the advances in graph-based deep learning, and in particular graph representation learning, deep graph generation methods have recently emerged with new applications ranging from discovering novel molecular structures to modeling social networks. This paper conducts a comprehensive survey on deep learning-based graph generation approaches and classifies them into five broad categories, namely, autoregressive, autoencoder-based, reinforcement learning-based, adversarial, and flow-based graph generators, providing the readers a detailed description of the methods in each class. We also present publicly available source codes, commonly used datasets, and the most widely utilized evaluation metrics. Finally, we review current trends and suggest future research directions based on the existing challenges.