Reviewerly/exHarmony huggingface dataset:
11 Tera-FLOP/s photonic convolutional accelerator and deep learning optical neural networks

Abstract Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-FLOPS (floating point operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.
Synchronization of Chaotic System Using a Brain-Imitated Neural Network Controller and Its Applications for Secure Communications

This paper proposes a new hybrid algorithm for secure communication applications. The proposed algorithm includes a fuzzy brain emotional learning controller (FBELC), a recurrent cerebellar model articulation controller (RCMAC), and a robust compensator (RC). The main brain-imitated neural network controller is a combination of the RCMAC and the FBELC, which is a mathematical model that approximates the decision and emotional activity of a human brain. A fuzzy inference system is also merged into the FBELC to produce an efficient hybrid structure, then it is used for secure communication applications. The 3-dimensional (3D) Genesio chaotic system is used for audio and image secure communication systems to show the potency and performance of the proposed algorithm. In the first application, a new image encryption algorithm is proposed to enhance security for information transmission, then several standard images are applied for the chaotic synchronization of image secure communication. In the second application, the audio signal is embedded in a 3D chaotic trajectory, which is used as an encryption carrier signal, after using the proposed method for the decryption, the source signal can be retrieved. The comparisons of simulation results using security analyses and root mean square error for recent algorithms are performed to validate the performance and efficiency of the proposed hybrid algorithm. The simulation results point out that our algorithm can attain better synchronization performance, and achieve more efficient audio and image secure communications.
X-Class: Text Classification with Extremely Weak Supervision

In this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names.This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class.We opt to attack this problem from a representation learning perspective-ideal document representations should lead to nearly the same results between clustering and the desired classification.In particular, one can classify the same corpus differently (e.g., based on topics and locations), so document representations should be adaptive to the given class names.We propose a novel framework X-Class to realize the adaptive representations.Specifically, we first estimate class representations by incrementally adding the most similar word to each class until inconsistency arises.Following a tailored mixture of class attention mechanisms, we obtain the document representation via a weighted average of contextualized word representations.With the prior of each document assigned to its nearest class, we then cluster and align the documents to classes.Finally, we pick the most confident documents from each cluster to train a text classifier.Extensive experiments demonstrate that X-Class can rival and even outperform seed-driven weakly supervised methods on 7 benchmark datasets.
To Sit or Not to Sit in VR: Analyzing Influences and (Dis)Advantages of Posture and Embodied Interaction

Virtual Reality (VR) users typically either sit or stand/walk when using VR; however, the impact of this is little researched, and there is a lack of any broad or systematic analysis of how this difference in physical posture might affect user experience and behavior. To address this gap, we propose such a systematic analysis that was refined through discussions and iterations during a dedicated workshop with VR experts. This analysis was complemented by an online survey to integrate the perspectives of a larger and more diverse group of VR experts, including developers and power users. The result is a validated expert assessment of the impact of posture and degree of embodiment on the most relevant aspects of VR experience and behavior. In particular, we posit potential strong effects of posture on user comfort, safety, self-motion perception, engagement, and accessibility. We further argue that the degree of embodiment can strongly impact cybersickness, locomotion precision, safety, self-motion perception, engagement, technical complexity, and accessibility. We provide a compact visualization of key findings and discuss areas where posture and embodiment do or do not have a known influence, as well as highlight open questions that could guide future research and VR design efforts.
Clustering-based Inference for Biomedical Entity Linking

Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data.This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities.Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions.These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form.In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions.In experiments we improve the state-of-the-art entity linking accuracy on two biomedical entity linking datasets including on the largest publicly available dataset.
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check

Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters.Statistics reveal that most Chinese spelling errors belong to phonological or visual errors.However, previous methods rarely utilize phonological and morphological knowledge of Chinese characters or heavily rely on external resources to model their similarities.To address the above issues, we propose a novel end-to-end trainable model called PHMOSpell, which promotes the performance of CSC with multi-modal information.Specifically, we derive pinyin and glyph representations for Chinese characters from audio and visual modalities respectively, which are integrated into a pre-trained language model by a well-designed adaptive gating mechanism.To verify its effectiveness, we conduct comprehensive experiments and ablation tests.Experimental results on three shared benchmarks demonstrate that our model consistently outperforms previous state-of-the-art models.
CoviChain: A Blockchain Based Framework for Nonrepudiable Contact Tracing in Healthcare Cyber-Physical Systems During Pandemic Outbreaks

With the world facing the new virus SARS-CoV-2, many countries have introduced instant Internet applications to identify people carrying the infection. Internet-of-Medical-Things (IoMT) have proven useful in collecting medical data as well in tracing an individual carrying the virus. The data collected or traced belongs to an individual and should be revealed to themselves and hospital providers, but not to any third-party unauthorized agencies. In this paper we use an off-chain distributed storage solution for loading large medical data sets and a blockchain implementation to securely transfer the data from the infected person to the hospital system using the edge infrastructure, and call it CoviChain. The Coronavirus Disease (COVID-19) statistics are loaded on to the edge, and moved to InterPlanetary File Systems (IPFS) storage to retrieve the hash of the data file. Once the hash is obtained, it is moved to the blockchain by means of smart contracts. As the information is being hashed twice, CoviChain addresses the security and privacy issues and avoid exposing individuals’ data while achieving larger data storage on the blockchain with reduced cost and time.
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification

Multi-label text classification is one of the fundamental tasks in natural language processing.Previous studies have difficulties to distinguish similar labels well because they learn the same document representations for different labels, that is they do not explicitly extract label-specific semantic components from documents.Moreover, they do not fully explore the high-order interactions among these semantic components, which is very helpful to predict tail labels.In this paper, we propose a novel label-specific dual graph neural network (LDGN), which incorporates category information to learn label-specific components from documents, and employs dual Graph Convolution Network (GCN) to model complete and adaptive interactions among these components based on the statistical label cooccurrence and dynamic reconstruction graph in a joint way.Experimental results on three benchmark datasets demonstrate that LDGN significantly outperforms the state-of-the-art models, and also achieves better performance with respect to tail labels.
Adaptive Convolutions for Structure-Aware Style Transfer

Style transfer between images is an artistic application of CNNs, where the ‘style’ of one image is transferred onto another image while preserving the latter’s content. The state of the art in neural style transfer is based on Adaptive Instance Normalization (AdaIN), a technique that transfers the statistical properties of style features to a content image, and can transfer a large number of styles in real time. However, AdaIN is a global operation; thus local geometric structures in the style image are often ignored during the transfer. We propose Adaptive Convolutions (AdaConv), a generic extension of AdaIN, to allow for the simultaneous transfer of both statistical and structural styles in real time. Apart from style transfer, our method can also be readily extended to style-based image generation, and other tasks where AdaIN has already been adopted.
Towards protecting cyber-physical and IoT systems from single- and multi-order voice spoofing attacks

Voice-controlled systems (VCSs), a new class of cyber-physical systems (CPS), and Internet of Things (IoT) devices are increasingly employing smart speakers such as Google Home and Amazon Alexa, and other voice assistants to enable management of various remote operations at home and offices. However, these smart speakers and hence VCSs are susceptible to various voice spoofing attacks i.e. replay, cloning, etc., in a non-network environment as well as in a multi-hop network setup. These diverse spoofing threats on VCSs require an urgent need to develop a robust spoofing countermeasure for VCSs capable of detecting a variety of voice spoofing attacks. This paper presents a spoofing countermeasure that uses novel acoustic ternary patterns (ATP) with Gammatone cepstral coefficients (GTCC) features to counter the voice spoofing attacks on VCSs in single- and multi-hop network environments. Our experimental analysis demonstrates that the proposed ATP features when combined with GTCC can effectively detect the distortions in replayed samples, unnatural prosody present in the cloned samples, and both distortions and unnatural patterns of stress and intonation in cloned-replay samples. The proposed ATP-GTCC features are used to train the SVM for development of a spoofing countermeasure to cater all possible forgeries. Experimental results based on highly diversified ASVspoof 2019 and VSDC datasets signify the effectiveness of the proposed countermeasure for reliable detection of 1st- and 2nd-order replay, cloning, and cloned-replay attacks.
Chaotic RNA and DNA for security OFDM-WDM-PON and dynamic key agreement

A chaotic ribonucleic acid (RNA) and deoxyribonucleic acid (DNA) encryption scheme is firstly proposed for security OFDM-WDM-PON in this paper. We adopt a dynamic key agreement based on the messenger RNA (mRNA) codebook to distribute the key, and the security and randomness of this key are enhanced by a pre-sharing key parameter set instead of transmission of a key directly. Also, the security key can be dynamically updated in real-time according to the needs of the users. The real (I) and imaginary (Q) parts of the QAM symbol matrix after modulation are encrypted by the correspondence between transfer RNA (tRNA) and amino acids and the selection mapping of DNA base complementary rules. Also, we add cubic permutation to ensure all data security encryption. The encrypted signals of 35.29 Gb/s on different wavelength channels are successfully demonstrated over a 25-km standard single-mode fiber (SSMF) and a back-to-back (BTB) system. It is proved that the proposed security OFDM-WDM-PON encryption scheme is compatible with the traditional WDM system, which can make full use of bandwidth resources and enhance the security with a large key space.
Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map

Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques.
Hamiltonian simulation algorithms for near-term quantum hardware

Abstract The quantum circuit model is the de-facto way of designing quantum algorithms. Yet any level of abstraction away from the underlying hardware incurs overhead. In this work, we develop quantum algorithms for Hamiltonian simulation "one level below” the circuit model, exploiting the underlying control over qubit interactions available in most quantum hardware and deriving analytic circuit identities for synthesising multi-qubit evolutions from two-qubit interactions. We then analyse the impact of these techniques under the standard error model where errors occur per gate, and an error model with a constant error rate per unit time. To quantify the benefits of this approach, we apply it to time-dynamics simulation of the 2D spin Fermi-Hubbard model. Combined with new error bounds for Trotter product formulas tailored to the non-asymptotic regime and an analysis of error propagation, we find that e.g. for a 5 × 5 Fermi-Hubbard lattice we reduce the circuit depth from 1, 243, 586 using the best previous fermion encoding and error bounds in the literature, to 3, 209 in the per-gate error model, or the circuit-depth-equivalent to 259 in the per-time error model. This brings Hamiltonian simulation, previously beyond reach of current hardware for non-trivial examples, significantly closer to being feasible in the NISQ era.
Adaptive Spiral Flying Sparrow Search Algorithm

The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.
Equation-of-Motion Theory to Calculate Accurate Band Structures with a Quantum Computer

Band structure is a cornerstone to understand electronic properties of materials. Accurate band structure calculations using a high-level quantum-chemistry theory can be computationally very expensive. It is promising to speed up such calculations with a quantum computer. In this study, we present a quantum algorithm for band structure calculation based on the equation-of-motion (EOM) theory. First, we introduce a new variational quantum eigensolver algorithm named ADAPT-C for ground-state quantum simulation of solids, where the wave function is built adaptively from a complete set of anti-Hermitian operators. Then, on top of the ADAPT-C ground state, quasiparticle energies and the band structure can be calculated using the EOM theory in a quantum-subspace-expansion (QSE) style, where the projected excitation operators guarantee that the killer condition is satisfied. As a proof of principle, such an EOM-ADAPT-C protocol is used to calculate the band structures of silicon and diamond using a quantum computer simulator.
Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces

Encoding classical data into quantum states is considered a quantum feature map to map classical data into a quantum Hilbert space. This feature map provides opportunities to incorporate quantum advantages into machine learning algorithms to be performed on near-term intermediate-scale quantum computers. The crucial idea is using the quantum Hilbert space as a quantum-enhanced feature space in machine learning models. Although the quantum feature map has demonstrated its capability when combined with linear classification models in some specific applications, its expressive power from the theoretical perspective remains unknown. We prove that the machine learning models induced from the quantum-enhanced feature space are universal approximators of continuous functions under typical quantum feature maps. We also study the capability of quantum feature maps in the classification of disjoint regions. Our work enables an important theoretical analysis to ensure that machine learning algorithms based on quantum feature maps can handle a broad class of machine learning tasks. In light of this, one can design a quantum machine learning model with more powerful expressivity.
Handwritten Mathematical Expression Recognition via Attention Aggregation Based Bi-directional Mutual Learning

Handwritten mathematical expression recognition aims to automatically generate LaTeX sequences from given images. Currently, attention-based encoder-decoder models are widely used in this task. They typically generate target sequences in a left-to-right (L2R) manner, leaving the right-to-left (R2L) contexts unexploited. In this paper, we propose an Attention aggregation based Bi-directional Mutual learning Network (ABM) which consists of one shared encoder and two parallel inverse decoders (L2R and R2L). The two decoders are enhanced via mutual distillation, which involves one-to-one knowledge transfer at each training step, making full use of the complementary information from two inverse directions. Moreover, in order to deal with mathematical symbols in diverse scales, an Attention Aggregation Module (AAM) is proposed to effectively integrate multi-scale coverage attentions. Notably, in the inference phase, given that the model already learns knowledge from two inverse directions, we only use the L2R branch for inference, keeping the original parameter size and inference speed. Extensive experiments demonstrate that our proposed approach achieves the recognition accuracy of 56.85 % on CROHME 2014, 52.92 % on CROHME 2016, and 53.96 % on CROHME 2019 without data augmentation and model ensembling, substantially outperforming the state-of-the-art methods. The source code is available in https://github.com/XH-B/ABM.
PRISED tangle: a privacy-aware framework for smart healthcare data sharing using IOTA tangle

Abstract Healthcare has evolved significantly in recent years primarily due to the advancements in and increasing adoption of technology in healthcare processes such as data collection, storage, diagnostics, and treatment. The emergence of the industrial internet of things (IIoT) has further evolved e-Health by facilitating the development of connected healthcare systems which can significantly improve data connectivity, visibility, and interoperability leading to improved quality of service delivered to patients. However, such technological advancements come with their perils—there are growing concerns with regards to the security and privacy of healthcare data especially when collected, shared, and processed using cutting-edge connected sensor devices affecting the adoption of next-generation e-healthcare systems. In particular, during the front-end and back-end data transfer in health information exchange (HIE) there exist a security risk in term of confidentiality, integrity, authentication and access control of the data due to the limited capabilities of IoT devices involved. In this paper, we investigate the use of distributed ledger technologies (DLT) to address such security concerns for emerging healthcare systems. In particular, we use masked authenticated messaging (MAM) over the Tangle to achieve secure data sharing within a healthcare system and provide a proof-of-concept of applying the proposed approach for securing healthcare data in a connected IIoT environment. Further, we have performed the evaluation and analysis of data communication against the metrics of encryption and efficiency in transaction time.