Reviewerly/exHarmony huggingface dataset:
Chaotic image encryption algorithm based on pseudo-random bit sequence and DNA plane

In this paper, a new chaotic image encryption algorithm based on pseudo-random bit sequence and DNA plane is proposed. The coupled map lattice (CML) is applied to design a pseudo-random bit sequence generation (PBSG) system and use the system to generate the random sequence needed in the encryption process. The initial values and parameters of the system are generated by the SHA-256 hash algorithm combined with given keys. Firstly, the plane image is decomposed into four DNA planes in combination with the DNA encoding rules, and then the four DNA planes are subjected to row circular permutation and column circular permutation. After that, the diffusion operation on each DNA plane is performed. Finally, the four DNA planes are decoded and then combined into a pixel matrix, that is, the final cipher image is obtained. Throughout the encryption process, the choice of DNA encoding and decoding rules is determined by the PBSG system. Simulation results and security analysis show that the algorithm not only has good encryption effect, but also can resist various classic attacks, and has excellent security performance.
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning

A great challenge in cooperative decentralized multi-agent reinforcement learning (MARL) is generating diversified behaviors for each individual agent when receiving only a team reward. Prior studies have paid much effort on reward shaping or designing a centralized critic that can discriminatively credit the agents. In this paper, we propose to merge the two directions and learn each agent an intrinsic reward function which diversely stimulates the agents at each time step. Specifically, the intrinsic reward for a specific agent will be involved in computing a distinct proxy critic for the agent to direct the updating of its individual policy. Meanwhile, the parameterized intrinsic reward function will be updated towards maximizing the expected accumulated team reward from the environment so that the objective is consistent with the original MARL problem. The proposed method is referred to as learning individual intrinsic reward (LIIR) in MARL. We compare LIIR with a number of state-of-the-art MARL methods on battle games in StarCraft II. The results demonstrate the effectiveness of LIIR, and we show LIIR can assign each individual agent an insightful intrinsic reward per time step.
A new Sequential Image Prediction Method Based on LSTM and DCGAN

Image recognition technology is an important field of artificial intelligence. Combined with the development of machine learning technology in recent years, it has great researches value and commercial value. As a matter of fact, a single recognition function can no longer meet people’s needs, and accurate image prediction is the trend that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep Convolution Generative Adversarial Networks (DCGAN), studies and implements a prediction model by using radar image data. We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better. This new method enables effective learning of image features and makes predictive models to have greater generalization capabilities. Experiments demonstrate that our network model is more robust and efficient in terms of timing prediction than 3DCNN and traditional ConvLSTM. The sequential image prediction model architecture proposed in this paper is theoretically applicable to all sequential images.
Structured pruning of recurrent neural networks through neuron selection

Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it difficult for their deployment on edge devices. A practically effective approach is to reduce the overall storage and computation costs of RNNs by network pruning techniques. Despite their successful applications, those pruning methods based on Lasso either produce irregular sparse patterns in weight matrices, which is not helpful in practical speedup. To address these issues, we propose a structured pruning method through neuron selection which can remove the independent neuron of RNNs. More specifically, we introduce two sets of binary random variables, which can be interpreted as gates or switches to the input neurons and the hidden neurons, respectively. We demonstrate that the corresponding optimization problem can be addressed by minimizing the L0 norm of the weight matrix. Finally, experimental results on language modeling and machine reading comprehension tasks have indicated the advantages of the proposed method in comparison with state-of-the-art pruning competitors. In particular, nearly 20× practical speedup during inference was achieved without losing performance for the language model on the Penn TreeBank dataset, indicating the promising performance of the proposed method.
Improving Random Forest Method to Detect Hatespeech and Offensive Word

Hate Speech is a problem that often occurs when someone communicates with each other using social media on the Internet. Research on hate speech is generally done by exploring datasets in the form of text comments on social media such as Twitter, Facebook and MySpace. This study aims to improve the performance of the Random Forest method in detecting hatespeech and crude speech. In this paper the researcher uses a twitter hate speech and offensive identification dataset that is classified using the Random Forest method which will be compared with the results of its accuracy with AdaBoost and Neural Network to detect hatespeech and crude speech. The detection results of hatespeech and crude speech identification resulted in an accuracy of 0.722 for the Random Forest method and 0.708 using AdaBoost and 0.596 using Neural Network method.
Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT

Abstract Segmentation is fundamental to medical image analysis. Recent advances in fully convolutional networks has enabled automatic segmentation; however, high labeling efforts and difficulty in acquiring sufficient and high-quality training data is still a challenge. In this study, a cascaded 3D U-Net with active learning to increase training efficiency with exceedingly limited data and reduce labeling efforts is proposed. Abdominal computed tomography images of 50 kidneys were used for training. In stage I, 20 kidneys with renal cell carcinoma and four substructures were used for training by manually labelling ground truths. In stage II, 20 kidneys from the previous stage and 20 newly added kidneys were used with convolutional neural net (CNN)-corrected labelling for the newly added data. Similarly, in stage III, 50 kidneys were used. The Dice similarity coefficient was increased with the completion of each stage, and shows superior performance when compared with a recent segmentation network based on 3D U-Net. The labeling time for CNN-corrected segmentation was reduced by more than half compared to that in manual segmentation. Active learning was therefore concluded to be capable of reducing labeling efforts through CNN-corrected segmentation and increase training efficiency by iterative learning with limited data.
Known-plaintext attack to optical encryption systems with space and polarization encoding

Space-based optical encryption (SBOE) and double random polarization encoding (DRPO) are previously considered to be more secure than common random-phase-encoding-based optical cryptosystems. The known-plaintext attack (KPA) to SBOE and DRPO was seldomly investigated in the past. A matrix regression approach based on training samples is proposed in this paper to crack these two optical cryptosystems. The relationship between plaintexts and ciphertexts is directly modeled by a complex-amplitude weighting matrix, which is optimized by a gradient descent algorithm. This approach has a simple model compared with deep learning and the KPA can be implemented without recovering the exact key. Our proposed KPA schemes reveal the security flaws of SBOE and DRPO, as well as other linear optical cryptosystems.
Demonstration of simultaneous quantum steering by multiple observers via sequential weak measurements

Quantum correlation is a fundamental resource for various quantum information tasks. It is thus of importance to share the correlation to utilize it for many parties, but sharing quantum correlation among multiple parties is strictly restricted by the well-known monogamy relations. Nonetheless, this restriction can be relaxed when weak measurements are employed. Here, we experimentally demonstrate multiple-observer quantum steering by exploiting sequential weak measurements. Specifically, we observe simultaneous triple violations of the quantum steering inequality among four observers for a bipartite entangled system. Our results not only provide fundamental insights into the relation between quantum steering and measurement disturbance, but also suggest that quantum steering might be repeatably exploited to find applications to, for example, unbounded randomness certification and sharing secret keys among multiple parties simultaneously.
Semi-Supervised Learning with Normalizing Flows

Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an end-to-end approach to generative semi supervised learning with normalizing flows, using a latent Gaussian mixture model. FlowGMM is distinct in its simplicity, unified treatment of labelled and unlabelled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data, tabular data, and semi-supervised image classification. We also show that FlowGMM can discover interpretable structure, provide real-time optimization-free feature visualizations, and specify well calibrated predictive distributions.
Is Local SGD Better than Minibatch SGD

We study local SGD (also known as parallel SGD and federated averaging), a natural and frequently used stochastic distributed optimization method. Its theoretical foundations are currently lacking and we highlight how all existing error guarantees in the convex setting are dominated by a simple baseline, minibatch SGD. (1) For quadratic objectives we prove that local SGD strictly dominates minibatch SGD and that accelerated local SGD is minimax optimal for quadratics; (2) For general convex objectives we provide the first guarantee that at least sometimes improves over minibatch SGD; (3) We show that indeed local SGD does not dominate minibatch SGD by presenting a lower bound on the performance of local SGD that is worse than the minibatch SGD guarantee.
Fair hierarchical secret sharing scheme based on smart contract

Secret sharing has a number of practical applications in network-based scenarios, such as key transfer protocols, attribute-based encryption and secure multiparty computation. However, existing secret sharing schemes cannot efficiently achieve fairness. They either rely on a trusted third party or require multiple rounds of communication. In this paper, we propose the first decentralized and fair hierarchical threshold secret sharing (HTSS) scheme using blockchain. In the scheme, secret shares are distributed to different levels of parties, and any authorized subset of parties can obtain the secret. We leverage a smart contract to force all participants to commit to the secret shares; otherwise, the committer either reveals his secret share within a certain time frame or pays a fine. Thus, unlike previous HTSS schemes, the participants can reconstruct the secret fairly using Birkhoff interpolation without a trusted party and complete the computation in one round. We formally prove that our scheme is secure. We evaluate the performance of the scheme by implementing our scheme on Ethereum’s official test network. Our experiments show that our scheme can run reasonably fast and is practical.
Which *BERT? A Survey Organizing Contextualized Encoders

Pretrained contextualized text encoders are now a staple of the NLP community.We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts.While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes.Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.
Electronic wallet technology and the enabling environment of smallholder farmers in Nigeria

Purpose The purpose of this paper is to critically examine the impact of a growth enhancement support scheme (GESS) on the enabling environment of smallholder farmers in sub-Saharan Africa. The main aim is to investigate the impact of the GESS on access to rural farm credit and the transport cost of smallholder farmers in the agricultural transformation agenda (ATA) in Nigeria. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 1,200 were sampled across the six geopolitical zones of Nigeria. Findings The results from the use of a double-hurdle model indicate that the GESS has a significant impact on farmers’ access to credit, but does not significantly impact on rural farm transport cost, which subsequently influences the price of food in the country. Practical implications This implies that if the Federal Government of Nigeria is to work toward an ideal agricultural transformation agenda, transport networks should be closely aligned with the GESS priorities to provide connectivity to rural areas that provide most of the country’s agricultural output. Originality/value This research adds to the literature on the agricultural and rural development debate in developing countries. It concludes that embracing a rural finance and transportation infrastructure should form the foundation of the ATA in Nigeria, which, in turn, would provide a conducive environment for a more widespread rural economy in sub-Saharan Africa.
Applying the Transformer to Character-level Transduction

The transformer (Vaswani et al., 2017) has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks.Yet for character-level transduction tasks, e.g.morphological inflection generation and historical text normalization, there are few works that outperform recurrent models using the transformer.In an empirical study, we uncover that, in contrast to recurrent sequenceto-sequence models, the batch size plays a crucial role in the performance of the transformer on character-level tasks, and we show that with a large enough batch size, the transformer does indeed outperform recurrent models.We also introduce a simple technique to handle feature-guided character-level transduction that further improves performance.With these insights, we achieve state-of-the-art performance on morphological inflection and historical text normalization.We also show that the transformer outperforms a strong baseline on two other character-level transduction tasks: grapheme-to-phoneme conversion and transliteration.1 This claim is also based on the authors' personal communication with other researchers in morphology in the corridors of conferences and through email.
The applicability of blockchain technology in healthcare contexts to contain COVID-19 challenges

Purpose Blockchain technology, a key feature of the fourth industrial revolution, is receiving widespread attention and exploration around the world. Taking the coronavirus pandemic as an example, the purpose of this study to examine the application of blockchain technology from the perspective of epidemic prevention and control. Design/methodology/approach Exploring multiple case studies in the Chinese context at various stages of deployment, this study documents a framework about how some of the major challenges associated with COVID-19 can be alleviated by leveraging blockchain technology. Findings The case studies and framework presented herein show that utilization of blockchain acts as an enabler to facilitate the containment of several COVID-19 challenges. These challenges include the following: complications associated with medical data sharing; breaches of patients' data privacy; absence of real-time monitoring tools; counterfeit medical products and non-credible suppliers; fallacious insurance claims; overly long insurance claim processes; misappropriations of funds; and misinformation, rumors and fake news. Originality/value Blockchain is ushering in a new era of innovation that will lay the foundation for a new paradigm in health care. As there are currently insufficient studies pertaining to real-life case studies of blockchain and COVID-19 interaction, this study adds to the literature on the role of blockchain technology in epidemic control and prevention.
Robotic involvement in the service encounter: a value-centric experience framework and empirical validation

Purpose Taking a mixed-method approach, this research developed and validated a novel, value-centric experience framework delineating robotic involvement in the service encounter and its subsequent impact on customers' experiences. Design/methodology/approach Focused on robotic restaurant businesses where robots are mainly involved in food production processes, this research utilized online customer review data and a multistage, mixed-method design for empirical examination. Automated thematic analysis was first adopted to facilitate a comprehensive understanding of the empirical reality as reflected in online customer reviews. Following an analytic induction process, a theoretical framework was developed integrating prior literature with the empirical reality to uncover the relationships across robotic involvement, experiential values and evaluative outcomes. A theory-driven, dictionary-based content analysis together with path analysis further enabled empirical validation of the developed theoretical framework. Findings The current research developed and validated a value-centric experience framework to theorize robotic involvement in the service encounter and its downstream impact on customers' experiences. Specifically, this framework conceptualizes robotic involvement as a five-dimensional composition of robotic visibility, competency, performanism, co-creativity and prominence. In addition, the framework specifies seven dimensions of experiential values revolving around high-tech–high-touch robotic service encounters, namely sensorial, utilitarian, hedonic, social, agentic, epistemic and aesthetic values. Following empirical validation, this framework sheds light on robotic involvement and experience design for high-tech–high-touch service businesses aiming to incorporate robots in their service encounters. Originality/value Drawing on classic service role theory and consumption value theory, this research developed and validated a novel theoretical model connecting robotic involvement dimensions with experiential consumption values and downstream customer evaluative outcomes. This research and theoretical framework open an exciting avenue for future research in robotic services and customer experiences.
Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification

Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractical to build a large homogeneous annotated dataset for a specific pathology. Self-supervised methods offer a new way to learn a representation of the images in an unsupervised manner with a neural network. In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks. Nonetheless, this method does not take advantage of available meta-data, such as participant’s age, viewed as prior knowledge. Here, we propose to leverage continuous proxy metadata, in the contrastive learning framework, by introducing a new loss called y-Aware InfoNCE loss. Specifically, we improve the positive sampling during pre-training by adding more positive examples with similar proxy meta-data with the anchor, assuming they share similar discriminative semantic features. With our method, a 3D CNN model pre-trained on \(10^4\) multi-site healthy brain MRI scans can extract relevant features for three classification tasks: schizophrenia, bipolar diagnosis and Alzheimer’s detection. When fine-tuned, it also outperforms 3D CNN trained from scratch on these tasks, as well as state-of-the-art self-supervised methods. Our code is made publicly available here.
Scalable and effective multi-level entangled photon states: a promising tool to boost quantum technologies

Abstract Multi-level (qudit) entangled photon states are a key resource for both fundamental physics and advanced applied science, as they can significantly boost the capabilities of novel technologies such as quantum communications, cryptography, sensing, metrology, and computing. The benefits of using photons for advanced applications draw on their unique properties: photons can propagate over long distances while preserving state coherence, and they possess multiple degrees of freedom (such as time and frequency) that allow scalable access to higher dimensional state encoding, all while maintaining low platform footprint and complexity. In the context of out-of-lab use, photon generation and processing through integrated devices and off-the-shelf components are in high demand. Similarly, multi-level entanglement detection must be experimentally practical, i.e., ideally requiring feasible single-qudit projections and high noise tolerance. Here, we focus on multi-level optical Bell and cluster states as a critical resource for quantum technologies, as well as on universal witness operators for their feasible detection and entanglement characterization. Time- and frequency-entangled states are the main platform considered in this context. We review a promising approach for the scalable, cost-effective generation and processing of these states by using integrated quantum frequency combs and fiber-based devices, respectively. We finally report an experimentally practical entanglement identification and characterization technique based on witness operators that is valid for any complex photon state and provides a good compromise between experimental feasibility and noise robustness. The results reported here can pave the way toward boosting the implementation of quantum technologies in integrated and widely accessible photonic platforms.