Reviewerly/exHarmony huggingface dataset:
Four-agent bidirectional quantum controlled teleportation via quantum entanglement swapping

A total of seven qubits are in a maximally entangled state. Using such an entangled state as quantum channel is based on the construction requirements of quantum long-distance communication [Pan et al., Nature 488, 185 (2012)]. Multi-party quantum channel (QC) should be studied. We put forward three deterministic bidirectional quantum controlled teleportation (BQCT) schemes. To be specific, BQCT can be realized between any two parties in a deterministic manner with another as the control. Alternatively, the BQCT capacity of such state in the given qubit distribution is thus essentially revealed by virtue of the schemes.
Deep Attributed Network Embedding Based on the PPMI

The attributed network embedding aims to learn the latent low-dimensional representations of nodes, while preserving the neighborhood relationship of nodes in the network topology as well as the similarities of attribute features. In this paper, we propose a deep model based on the positive point-wise mutual information (PPMI) for attributed network embedding. In our model, attribute features are transformed into an attribute graph, such that attribute features and network topology can be handled in the same way. Then, we perform the random surfing and calculate the PPMI on the attribute/topology graph to effectively maintain the structural characteristics and the high-order proximity information. The node representations are learned by a shared Auto-Encoder. Besides, the local pairwise constraint is used in the shared Auto-Encoder to improve the quality of node representations. Extensive experimental results on four real-world networks show the superior performance of the proposed model over the 10 baselines.
Multi-Model and Multi-Expert Correlation Filter for High-Speed Tracking

Tracking algorithm based on correlation filter have been extensively investigated due to their powerful performance in benchmark datasets and competitions. However, the periodic assumption has contributed boundary effects and the complex scenarios will give rise to model drift, which have an extremely negative effect on both tracking precision and success rate. To mitigate these challenges, a novel multi-model and multi-expert correlation filter (MMCF) approach is proposed in this paper. The key innovation of the proposed method is to employ multiple models and experts for tracking. Multiple models can excavate diverse a large quantity of feature information, and the target information between different models can complement each other. Multiple experts provide several possible predictions, while an evaluation mechanism selects the most reliable prediction utilizing their assessment scores. To further improve the performance, an adaption strategy is utilized to update the multiple models, which can reduce the weight of the bad samples to prevent model drift. Experiments performed on three recent benchmark datasets OTB50, OTB2013, OTB100, TC-128 and UAV123@10fps, have demonstrated the superiority of our approach in comparison to the state-of-the-art trackers. Our MMCF tracker operates a speed of about 58 frames per second (FPS) running on a single central processing unit(CPU).
Hyperparameter experiments on end-to-end automatic speech recognition*

End-to-end (E2E) automatic speech recognition (ASR) has achieved promising performance gains with the introduced self-attention network, Transformer.However, due to training time and the number of hyperparameters, finding the optimal hyperparameter set is computationally expensive.This paper investigates the impact of hyperparameters in the Transformer network to answer two questions: which hyperparameter plays a critical role in the task performance and training speed.The Transformer network for training has two encoder and decoder networks combined with Connectionist Temporal Classification (CTC).We have trained the model with Wall Street Journal (WSJ) SI-284 and tested on devl93 and eval92.Seventeen hyperparameters were selected from the ESPnet training configuration, and varying ranges of values were used for experiments.The result shows that "num blocks" and "linear units" hyperparameters in the encoder and decoder networks reduce Word Error Rate (WER) significantly.However, performance gain is more prominent when they are altered in the encoder network.Training duration also linearly increased as "num blocks" and "linear units" hyperparameters' values grow.Based on the experimental results, we collected the optimal values from each hyperparameter and reduced the WER up to 2.9/1.9 from dev93 and eval93 respectively.
Development of Duplex Eye Contact Framework for Human-Robot Inter Communication

Establishing eye contact is the fundamental key to begin any interaction between human-human and robot-human. Two approaches are available to develop an eye contact mechanism for robot-human interaction, such as simplex and duplex. The two most critical tasks: gaze crossing and gaze awareness, are prerequisite to implementing an active eye contact mechanism in any approach. However, most past robot-human interaction studies implemented a gaze crossing function to develop eye contact in the simplex mode where a robot holds for the human to initiate the communication. However, implementing gaze crossing alone is inadequate to create an active eye contact episode; the gaze awareness function also essential to achieve. This paper aims to develop a mechanism of duplex eye contact for robot-human inter-communication satisfying both functions. This work proposes a conceptual model of a duplex eye contact mechanism considering two cases: human initiative (where the human starts communication with the robot) and robot initiative (where the robot starts the communication with the participant) to achieve a duplex eye contact mechanism. Moreover, a simple robotic system is developed consisting of four software constituents: face detection module, gaze detection and tracking module, gaze awareness module, and robot response and control module to implement the conceptual model of duplex eye contact. Several preliminary experiments are performed to extract necessary cues for designing the duplex eye contact mechanism’s behavioural protocol and present their results to show the usefulness of extracted cues. Moreover, the robotic framework results in a scenario ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g., reading the book</i> ) with the proposed duplex eye contact mechanism are presented. The results show that the proposed scheme achieved 92% and 86% accuracy for human initiative case and robot initiative case, respectively in making eye contact.
On the Properties of Some Adaptive Morphological Filters for Salt and Pepper Noise Removal

Mathematical Morphology (MM) is a tool that can be applied to many digital image processing tasks that include the reduction of impulsive or salt and pepper noise in grayscale images. The morphological filters used for this task are filters resulting from two basic operators: erosion and dilation. However, when the level of contamination of the image is higher, these filters tend to distort the image. In this work we propose a pair of operators with properties, that better adapt to impulsive noise than other classical morphological filters, it is demonstrated to be increasing idempotent morphological filters. Furthermore, the proposed pair turns out to be a ?-filter and a ?-filter which allow to build morphological openings and closings. Finally, they are compared with other filters of the state-of-the-art such as: SMF, DBAIN, AMF and NAFSM, and have shown a better performance in time-quality ratio when the noise level is above 50%.
Exponential stabilization of spin-1/2 systems based on switching state feedback

The exponential stabilization of eigenstates by using switching state feedback strategy for quantum spin-$\frac{1}{2}$ systems is considered in this paper. In order to obtain faster state exponential convergence, we divide the state space into two subspaces, and use two different continuous state feedback controls in the corresponding subspace. The two continuous state feedback controls form the switching state feedback, under which the state convergence is faster than that under continuous state feedback. The exponential convergence and the superiority of switching state feedback are proved in theory and verified in numerical simulations. Besides, the influence of the control parameter on the state convergence rate is also studied.
Dynamic Network Embedding Survey

Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of evolving graphs but not only the latest network, for preserving both structural and temporal information from the dynamic networks. Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper. Our survey inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them. Specifically, we present two basic data models, namely, discrete model and continuous model for dynamic networks. Correspondingly, we summarize two major categories of dynamic network embedding techniques, namely, structural-first and temporal-first that are adopted by most related works. Then we build a taxonomy that refines the category hierarchy by typical learning models. The popular experimental data sets and applications are also summarized. Lastly, we have a discussion of several distinct research topics in dynamic network embedding.
Liability in the context of blockchain-smart contract nexus: Introductory considerations

Blockchain technology becomes relevant in economic exchange as it lowers costs and contributes to cost-efficiency and effectiveness of economic transactions. The key quality of Blockchain lies in ensuring the authenticity of digital data: trust in the traditional legal relationship has been replaced by digital verification of data in blocks. As an important phenomenon, Blockchain calls for legal answers on the issues arising from its application. An example of this development is the legal regime of smart contracts. A smart contract is a transaction in which any rights and obligations of the contracting parties are programmed in a code. Being the result of Blockchain technology application, such a contract implies the need for trust between the contracting parties. As a legal phenomenon, Blockchain (smart contract) technology raises the issue of liability for performing contractual obligations. Smart contracts can minimize certain contract risks and additionally simplify contract execution. They are immediately put into effect, without the need for any further interaction between the parties. The essential components of smart contracts are the digitally verifiable data and the automatic performance of legally relevant actions based on digitally received and processed information. All of the enlisted issues are important for proper understanding of liability of Blockchain actors.
A Spectrum Regeneration and Demodulation Method for Multiple Direct Undersampled Real Signals

We propose a spectrum regeneration and demodulation method for multiple direct RF undersampled real signals by using a new algorithm. Many methods have been proposed to regenerate the RF spectrum by using undersampling because of its simple circuit architecture. However, it is difficult to regenerate the spectrum from a real signal that has a band wider than a half of the sampling frequency, because it is difficult to include complex conjugate relation of the folded spectrum into the linear algebraic equation in this case. We propose a new spectrum regeneration method from direct undersampled real signals that uses multiple clocks and an extended algorithm considering the complex conjugate relation. Simulations are used to verify the potential of this method. The validity of the proposed method is verified by using the simulation data and the measured data. We also apply this algorithm to the demodulation system.
Mojo Vision: Designing Anytime, Anywhere AR Contact Lenses with Mojo Lens

Today’s augmented reality systems deliver immersive experiences, but can manufacturers better address consumer needs through design? In addition to the social taxes that larger glasses create, heavy and bulky headsets prevent consumers from using them in everyday life. Hear Mike Wiemer discuss Mojo Lens, an augmented reality smart contact lens system with a unique design to address these challenges.
A blockchain model proposal for the decentralized management of academic credentials in Ecuadorian universities

Blockchain (BC) technology is a big bet for the new technological era, however, there is a big challenge in introducing this technology and the benefits it offers in different areas. This article develops and applies a framework for the application of BC technology in the field of higher education. The frame comprises a robust architecture that can be used for different decentralized applications (Dapps). The framework is validated in the case of academic certification, which includes beneficiaries, advantages offered by technology, disadvantages and risks. The framework provides valuable help in understanding in a timely and coherent manner the functioning of BC technology and its contribution to higher education. In addition, it serves to create a standard of how Dapps work in this area.
Visual display for surgical targeting: concepts and usability study

Abstract Purpose Interactive image-guided surgery technologies enable accurate target localization while preserving critical nearby structures in many surgical interventions. Current state-of-the-art interfaces largely employ traditional anatomical cross-sectional views or augmented reality environments to present the actual spatial location of the surgical instrument in preoperatively acquired images. This work proposes an alternative, simple, minimalistic visual interface intended to assist during real-time surgical target localization. Methods The estimated 3D pose of the interventional instruments and their positional uncertainty are intuitively presented in a visual interface with respect to the target point. A usability study with multidisciplinary participants evaluates the proposed interface projected in surgical microscope oculars against cross-sectional views. The latter was presented on a screen both stand-alone and combined with the proposed interface. The instruments were electromagnetically navigated in phantoms. Results The usability study demonstrated that the participants were able to detect invisible targets marked in phantom imagery with significant enhancements for localization accuracy and duration time. Clinically experienced users reached the targets with shorter trajectories. The stand-alone and multi-modal versions of the proposed interface outperformed cross-sectional views-only navigation in both quantitative and qualitative evaluations. Conclusion The results and participants’ feedback indicate potential to accurately navigate users toward the target with less distraction and workload. An ongoing study evaluates the proposed system in a preclinical setting for auditory brainstem implantation.
A Data Provenance System for Myanmar Rice Cycle Based on Ethereum Blockchain

The Myanmar rice cycle's existing system is still relying on a third party to manage every rice data information from several organizations.It is inconvenient to supervise simultaneously due to the unreliability of information provided by organizations.Thus, the rice cycle's original data is challenging to be utterly trusted since irresponsible parties can manipulate the current state of information.Moreover, the applied system does not preserve a proper incentive for the involved parties.In this paper, we leverage the Ethereum blockchain to be adopted to tackle the aforementioned issues.The main objective is to build trust between parties in the Myanmar rice cycle system.Our proposed scheme allows customers to check and trace information about the rice cycle information without worrying about the integrity of the data.Furthermore, the authorized parties are also rewarded by the government through Ethereum smart contract features.Eventually, our scheme achieves traceability in the rice chain system and leads to the complete digitization and automation of the rice cycle information.
User Query-Based Automatic Text Summarization of Web Documents Using Ontology

Web document summarization is a very important function for knowledge management when documents are very huge and dynamic. This helps the document readers to easily read and understand. In general, summarization is done based on the extraction of sentences from the retrieved documents and assembled with the needed proposition. Text content plays a major role in the Web, and extraction of text content is a challenging task. Recently, one of the problems that arise with the rapid growth of the Web and general information availability (sometimes referred to as an information overloading) is the increased need for effective and powerful text summarization. In this paper, a system is built for automatic text summarization from the source documents which is retrieved from the Web. The proposed method considers the ontology approach to extract the text summary according to query terms, which are existing in the ontology graph based on their depth. Also, attributes are used to improve the semantic representation of a sentence’s information from contents.
Network model of predictive coding based on reservoir computing for multi-modal processing of visual and auditory signals

We propose a hierarchical network model based on predictive coding and reservoir computing as a model of multi-modal sensory integration in the brain. The network is composed of visual, auditory, and integration areas. In each area, the dynamical reservoir acts as a generative model that reproduces the time-varying sensory signal. The states of the visual and auditory reservoir are spatially compressed and are sent to the integration area. We evaluate the model with a dataset of time courses, including a pair of visual (hand-written characters) and auditory (read utterances) signal. We show that the model learns the association of multiple modalities of the sensory signals and that the model reconstructs the visual signal from a given corresponding auditory signal. Our approach presents a novel dynamical mechanism of the multi-modal information processing in the brain and the fundamental technology for a brain like an artificial intelligence system.
Asymptotic behavior of the number of distinct values in a sample from the geometric stick-breaking process

Discrete random probability measures are a key ingredient of Bayesian nonparametric inference. A sample generates ties with positive probability and a fundamental object of both theoretical and applied interest is the corresponding number of distinct values. The growth rate can be determined from the rate of decay of the small frequencies implying that, when the decreasingly ordered frequencies admit a tractable form, the asymptotics of the number of distinct values can be conveniently assessed. We focus on the geometric stick-breaking process and we investigate the effect of the distribution for the success probability on the asymptotic behavior of the number of distinct values. A whole range of logarithmic behaviors are obtained by appropriately tuning the prior. A two-term expansion is also derived and illustrated in a comparison with a larger family of discrete random probability measures having an additional parameter given by the scale of the negative binomial distribution.
Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions.

Training and deploying graph neural networks (GNNs) remains difficult due to their high memory consumption and inference latency. In this work we present a new type of GNN architecture that achieves state-of-the-art performance with lower memory consumption and latency, along with characteristics suited to accelerator implementation. Our proposal uses memory proportional to the number of vertices in the graph, in contrast to competing methods which require memory proportional to the number of edges; we find our efficient approach actually achieves higher accuracy than competing approaches across 5 large and varied datasets against strong baselines. We achieve our results by using a novel adaptive filtering approach inspired by signal processing; it can be interpreted as enabling each vertex to have its own weight matrix, and is not related to attention. Following our focus on efficient hardware usage, we propose aggregator fusion, a technique to enable GNNs to significantly boost their representational power, with only a small increase in latency of 19% over standard sparse matrix multiplication. Code and pretrained models can be found at this URL: this https URL.
Genetic Algorithm-Based Imperceptible Image Steganography Technique with Histogram Distortion Minimization

In image steganography, the confidential information is hidden in a cover image to camouflage the existence of covert communication. The steganography algorithm should be highly imperceptible to avoid being detected by a visual steganalysis attack. However, at the same time, the image steganography algorithm should also have the capability to resist statistical steganalysis attacks. These statistical steganalysis attacks can be histogram attack, RS analysis, chi-square attack, etc. A genetic algorithm (GA)-based imperceptible image steganography scheme that can resist histogram attack is presented in this paper. GA is used to find suitable locations in the cover image to insert the secret message so that the data embedding process causes very few distortions in the cover image. At the same time, GA has to ensure that these locations cause very minimum histogram distortion. The proposed technique can insert the secret data at 1 bit per pixel capacity (bpp). The proposed technique yields imperceptible stego images with an average PSNR value of 51.34 dB at 1 bpp data embedding capacity.
SWIRL ++ : Evaluating Performance Models to Guide Code Transformation in Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are ubiquitous in applications ranging from self-driving cars to various branches of health care. CPUs with large core counts and wide SIMD support are used in HPC clusters and supercomputers; therefore, high-performance CPU implementations of CNNs are valuable, in addition to the more prevalent GPU implementations. In this paper, we describe SWIRL ++, an optimization approach for CNNs that incorporates an analytical performance model to identify optimization strategies that minimize data movement overheads of CNN execution. We integrate the model with the SWIRL DSL compiler to automatically generate high-performance implementations of CNNs, optimized for cache hierarchies, and both thread-level and SIMD parallelism. We compare resulting performance of generated code with TensorFlow, integrated with Intel’s MKL-DNN library (TF-MKL), and PyTorch on an Intel Xeon 8280 CascadeLake platform. Performance exceeds PyTorch on average by $$2\times $$ , and is comparable on average for both TF-MKL and the SWIRL compiler, showing that an automated code optimization approach achieves performance comparable to hand-tuned libraries and DSL compiler techniques.