Reviewerly/exHarmony huggingface dataset:
Quantum Computing for Artificial Intelligence Based Mobile Network Optimization

In this paper, we discuss how certain radio access network optimization problems can be modelled using the concept of constraint satisfaction problems in artificial intelligence, and solved at scale using a quantum computer. As a case study, we discuss root sequence index (RSI) assignment problem — an important LTE/NR physical random access channel configuration related automation use-case. We formulate RSI assignment as quadratic unconstrained binary optimization (QUBO) problem constructed using data ingested from a commercial mobile network, and solve it using a cloud-based commercially available quantum computing platform. Results show that quantum annealing solver can successfully assign conflict-free RSIs. Comparison with well-known heuristics reveals that some classic algorithms are even more effective in terms of solution quality and computation time. The non-quantum advantage is due to the fact that current implementation is a semi-quantum proof-of-concept algorithm. Also, the results depend on the type of quantum computer used. Nevertheless, the proposed framework is highly flexible and holds tremendous potential for harnessing the power of quantum computing in mobile network automation.
Deep Learning-Based Estimator for Fast HARQ Feedback in URLLC

Autonomous systems and mission-critical applications demand ultra-reliable low-latency communication (URLLC). To build wireless communication networks capable of accommodating such applications, optimization of the airinterface characteristics is vital. This paper leverages recent advancements in the field of Artificial Intelligence (AI) technologies to optimize specific aspects of the air interface design to satisfy these stringent link reliability and latency requirements. The precise aim of this research is to reduce the link latency caused by the presence of the Hybrid Automatic Repeat reQuest (HARQ) mechanism. To this end, we propose a novel deep learning-based algorithm (Deep-HARQ), employing a deep neural network (DNN) with fully connected layers to estimate the decodability of the coded-received in-phase and quadrature (I/Q) signals prior to accomplishing the majority of the complex reception tasks. This enables the receiver to respond faster, allowing for the reduction of the signal round-trip time (RTT). To evaluate Deep-HARQ with a realistic dataset, we collected training and validation samples from a waveform compatible with 3GPP 5G NR Release 15 standards. The simulation results reveal a faster estimation response, with an accuracy enhancement of 12% compared to relevant algorithms in the literature.
Security Analysis of Deterministic Re-keying with Masking and Shuffling: Application to ISAP

Single-trace side-channel attacks are important attack vectors against the security of authenticated encryption schemes relying on an internal re-keying process, such as the NIST Lightweight Cryptography finalist ISAP. In a recent work of Kannwischer et al., it was suggested to mitigate such single-trace attacks with masking and shuffling. In this work, we first show that combining masking and re-keying is conceptually useless since this combination can always be attacked with a complexity that is just the sum of the complexities to attack a masked implementation (without re-keying) and a re-keyed implementation (without masking). We then show that combining shuffling and re-keying is theoretically founded but can be practically challenging: in low-cost embedded devices (e.g., ARM Cortex-M0) that are the typical targets of single-trace attacks, the noise level of the leakages is such that multivariate attacks can be powerful enough to recover the shuffling permutation in one trace. This second result does not prevent the shuffling + re-keying combination to be effective in more noisy contexts, but it suggests that the best use cases for leakage-resilient PRFs as used by ISAP remain the ones where no additional countermeasures are needed.
A One-Class Classification Method for Human Gait Authentication Using Micro-Doppler Signatures

In this letter, a radar-based gait authentication method is proposed. We focus on the overfitting problem on the target category caused by limited training data in authentication models and propose a one-class classification model to alleviate this problem. The effectiveness of such model is verified by establishing a radar-based gait dataset, which is composed of gait micro-Doppler spectrograms derived from nine human subjects. The experimental results demonstrate that, under the condition of limited training data, the performances of an authentication model degrade because misclassification of the non-target samples easily occurs. The proposed method effectively avoids this risk, performing the other existing authentication and one-class classification methods on the metric Equal Error Rate.
Flow analysis of a front opening unified pod (FOUP) subjected to different fan filter unit (FFU) coverage area in mini-environment

Moisture contamination control has been adopted in many wafer handling and storage processes. In portable wafer carriers called front opening unified pods (FOUPs), the current industry-accepted methods of moisture control are gas purging and laminar air curtain (LAC). Both typically use purified clean dry air (CDA). In this study, we investigated five different methods of FOUP moisture control: conventional and diffuser-based purging, LAC, and combinations of each purging method with LAC. Two different scenarios of 100% and 75% FFU coverage area were examined. The FOUP was empty and its internal volume was exposed to a mock-up mini-environment (ME)–a process confinement device used in semiconductor fabs. Also, the LAC was mounted inside the ME above the FOUP opening. Smoke flow visualization, particle image velocimetry (PIV) and humidity measurement were performed in the FOUP median plane. Streamline flow patterns were obtained from the PIV results that can significantly enhance the evaluation of the moisture removal performance of different case studies. The findings showed that the combination of diffuser purge and LAC was the most resistant against external lateral moist air flow wherein it yielded the lowest average humidity. The findings could be beneficial for reducing waste and increasing product quality in semiconductor manufacturing. This can lead to significant reductions in energy usage and drastically reduce manufacturing costs.
Digital Filter Architecture With Calculations in the Residue Number System by Winograd Method F (2 × 2, 2 × 2)

Improving the technical characteristics of digital signal processing devices is an important problem in many practical tasks. The paper proposes the architecture of a device for two-dimensional filtering in a residue number system (RNS) with moduli of a special type according to the Winograd method. The work carried out the technical parameters theoretical analysis of the proposed filter architecture for different RNS moduli sets by the “unit-gate”-model. In addition, the proposed architecture is compared with known digital filter implementations. The theoretical analysis results showed that the use of the proposed filter architecture makes it possible to increase the signal processing speed by 1.33 – 6.90 times, in comparison with the known device implementations. Also, in the work, the hardware simulation of the proposed filter architecture was performed on FPGA, which showed that the performance of the proposed device is 1.31 – 4.12 times higher than known digital filter architectures. The research results can be used in digital signal processing systems to increase their performance and reduce hardware costs. In addition, the developed architectures can be applied in the development of hardware accelerators for complex digital signals analysis systems.
CS-REP: Making Speaker Verification Networks Embracing Re-Parameterization

Automatic speaker verification (ASV) systems, which determine whether two speeches are from the same speaker, mainly focus on verification accuracy while ignoring inference speed. However, in real applications, both inference speed and verification accuracy are essential. This study proposes cross-sequential re-parameterization (CS-Rep), a novel topology re-parameterization strategy for multi-type networks, to increase the inference speed and verification accuracy of models. CS-Rep solves the problem that existing re-parameterization methods are not suitable for typical ASV backbones. When a model applies CS-Rep, the training-period network utilizes a multi-branch topology to capture speaker information, whereas the inference-period model converts to a time-delay neural network (TDNN)-like plain backbone with stacked TDNN layers to achieve the fast inference speed. Based on CS-Rep, an improved TDNN with friendly test and deployment called Rep-TDNN is proposed. Compared with the state-of-the-art model ECAPA-TDNN, Rep-TDNN increases the actual inference speed by about 50% and reduces the EER by 10%. The code and trained models are available at https://github.com/zrtlemontree/CS-Rep.
Bidirectional teleportation through an entangled coherent quantum network

A bidirectional quantum teleportation protocol is introduced over a quantum network consisting of more than four members sharing a coherent entangled state, where it is implemented in a perfect or noisy environment. The results show that the amplitude of the coherent state and the decoherence parameter play important roles in maximizing or minimizing this probability. At fixed values of the amplitude and decoherence parameters, the success of probability increases as the number of users increases. The fidelity of the teleported coherent state depends on the type of encoded information, whether entangled/partial or classical information, namely, it depends on the weight parameter. The stable behavior of the fidelity is displayed when users teleport classical information.
Dynamic Automated Infrastructure for Efficient Cloud Data Centre

We propose a dynamic automated infrastructure model for the cloud data centre which is aimed as an efficient service stipulation for the enormous number of users. The data center and cloud computing technologies have been at the moment rendering attention to major research and development efforts by companies, governments, and academic and other research institutions. In that, the difficult task is to facilitate the infrastructure to construct the information available to application-driven services and make business-smart decisions. On the other hand, the challenges that remain are the provision of dynamic infrastructure for applications and information anywhere. Further, developing technologies to handle private cloud computing infrastructure and operations in a completely automated and secure way has been critical. As a result, the focus of this article is on service and infrastructure life cycle management. We also show how cloud users interact with the cloud, how they request services from the cloud, how they select cloud strategies to deliver the desired service, and how they analyze their cloud consumption.
Characterization of a Quantum Random Number Generator Based on Vacuum Fluctuations

Quantum random number generators (QRNGs) are currently in high demand across a large number of cryptographic applications as secure sources of true randomness. In this work, we characterize the conditions from which randomness can be extracted in a QRNG based on homodyne measurements of vacuum fluctuations by assessing the impact of experimental limitations, such as the digitizer resolution or the presence of excess local oscillator (LO) noise due to an unbalanced detection. Moreover, we propose an estimation method to quantify the excess entropy contribution introduced by an unbalanced detection and analyze the implementation of the post-processing algorithm. Finally, we submitted the generated numbers to a set of statistical tests to assess the quality of its output randomness and verified that it passes the standard libraries.
Translationally Invariant Universal Quantum Hamiltonians in 1D

Recent work has characterised rigorously what it means for one quantum system to simulate another, and demonstrated the existence of universal Hamiltonians -- simple spin lattice Hamiltonians that can replicate the entire physics of any other quantum many body system. Previous universality results have required proofs involving complicated `chains' of perturbative `gadgets'. In this paper, we derive a significantly simpler and more powerful method of proving universality of Hamiltonians, directly leveraging the ability to encode quantum computation into ground states. This provides new insight into the origins of universal models, and suggests a deep connection between universality and complexity. We apply this new approach to show that there are universal models even in translationally invariant spin chains in 1D. This gives as a corollary a new Hamiltonian complexity result, that the local Hamiltonian problem for translationally-invariant spin chains in one dimension with an exponentially-small promise gap is PSPACE-complete. Finally, we use these new universal models to construct the first known toy model of 2D--1D holographic duality between local Hamiltonians.
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning

Safety is essential for reinforcement learning (RL) applied in real-world tasks like autonomous driving. Imposing chance constraints (or probabilistic constraints) is a suitable way to enhance RL safety under model uncertainty. Existing chance constrained RL methods like the penalty methods and the Lagrangian methods either exhibit periodic oscillations or learn an over-conservative or unsafe policy. In this paper, we address these shortcomings by elegantly combining these two methods and propose a separated proportional-integral Lagrangian (SPIL) algorithm. We first rewrite penalty methods as optimizing safe probability according to the proportional value of constraint violation, and Lagrangian methods as optimizing according to the integral value of the violation. Then we propose to add up both the integral and proportion values to optimize the policy, with an integral separation technique to limit the integral value within a reasonable range. Besides, the gradient of policy is computed in a model-based paradigm to accelerate training. The proposed method is proved to reduce oscillations and conservatism while ensuring safety by a car-following experiment.
Viewing Vision Videos Online: Opportunities for Distributed Stakeholders

Creating shared understanding between stakeholders is essential for the success of software projects. Conflicting requirements originating from misaligned mental models can hinder the development process. The use of videos to present abstract system visions is one approach to counteract this problem. These videos are usually shown in in-person meetings. However, face-to-face meetings are not suited to every situation and every stakeholder, for example due to scheduling constraints. Methods for the use of vision videos in online settings are necessary. Furthermore, methods enabling an asynchronous use of vision videos are needed for cases when conjoined meetings are impossible even in an online setting.In this paper, we compare synchronous and asynchronous viewings of vision videos in online settings. The two methods are piloted in a preliminary experiment. The results show a difference in the amount of arguments regarding the presented visions. On average, participants who took part in asynchronous meetings stated more arguments. Our results point to multiple advantages and disadvantages as well as use cases for each type. For example, a synchronous meeting could be chosen when all involved stakeholders can attend the appointment to discuss the vision and to quickly resolve ambiguities. An asynchronous meeting could be held if a joint meeting is not feasible due to time constraints. We also discuss how our findings can be applied to the elicitation of requirements from a crowd of stakeholders.
HAR Using Bi-directional LSTM with RNN

It is hard to monitor human activities in various contexts like security surveillance, healthcare, and human-computer interaction. Human Activity Recognition is the process of predicting what a person is doing based on the traces of their movement. We propose using deep recurrent neural networks (DRNNs) for building recognition models capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM), DRNNs and evaluate their effectiveness on various benchmark datasets. Long shortterm memory (LSTM) is an artificial deep recurrent neural network (DRNN) architecture used in deep learning, especially for time series prediction. It can process single data points (such as images) and entire data sequences (such as speech or video). LSTM networks are well-suited for classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between essential events in a time series in real-time. We proposed Human Activity Recognition (HAR) using a smartphone dataset and LSTM. Compared to a classical approach, using Deep Recurrent Neural Networks (DRNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network, which acts as a black box, modeling the problem correctly. This means that the neural networks are almost always able to identify the movement type correctly. We used jupyter notebook with python 3.7+.
Measuring true stress–strain curves of cylindrical bar samples with mirror‐assisted multi‐view digital image correlation

Abstract This paper proposes to measure true stress–strain curves at the necking region of cylindrical bar samples by using an advanced mirror‐assisted multi‐view digital image correlation (MV–DIC) technique. The mirror‐assisted MV–DIC can measure the panoramic shape and deformation fields of a cylindrical bar sample with only a single two‐camera stereo‐DIC system and two planar mirrors. The measured panoramic shape enables accurate determination of the cross‐section area and radius of curvature at the necking region by using cylindrical or torus fitting. The retrieved cross‐section area and radius of curvature at the necking region further enable the measurement of the true stress–strain curves and even the Bridgman's correction of the true stress.
Multimodal Graph Meta Contrastive Learning

In recent years, graph contrastive learning has achieved promising node classification accuracy using graph neural networks (GNNs), which can learn representations in an unsupervised manner. However, such representations cannot be generalized to unseen novel classes with only few-shot labeled samples in spite of exhibiting good performance on seen classes. In order to assign generalization capability to graph contrastive learning, we propose multimodal graph meta contrastive learning (MGMC) in this paper, which integrates multimodal meta learning into graph contrastive learning. On one hand, MGMC accomplishes effectively fast adapation on unseen novel classes by the aid of bilevel meta optimization to solve few-shot problems. On the other hand, MGMC can generalize quickly to a generic dataset with multimodal distribution by inducing the FiLM-based modulation module. In addition, MGMC incorporates the lastest graph contrastive learning method that does not rely on the onstruction of augmentations and negative examples. To our best knowledge, this is the first work to investigate graph contrastive learning for few-shot problems. Extensieve experimental results on three graph-structure datasets demonstrate the effectiveness of our proposed MGMC in few-shot node classification tasks.
Speech-Driven Conversational Agents using Conditional Flow-VAEs

Automatic control of conversational agents has applications from animation, through human-computer interaction, to robotics. In interactive communication, an agent must move to express its own discourse, and also react naturally to incoming speech. In this paper we propose a Flow Variational Autoencoder (Flow-VAE) deep learning architecture for transforming conversational speech to body gesture, during both speaking and listening. The model uses a normalising flow to perform variational inference in an autoencoder framework and is a more expressive distribution than the Gaussian approximation of conventional variational autoencoders. Our model is non-deterministic, so can produce variations of plausible gestures for the same speech. Our evaluation demonstrates that our approach produces expressive body motion that is close to the ground truth using a fraction of the trainable parameters compared with previous state of the art.
Early prediction for merged vs abandoned code changes in modern code reviews

The modern code review process is an integral part of the current software development practice. Considerable effort is given here to inspect code changes, find defects, suggest an improvement, and address the suggestions of the reviewers. In a code review process, usually, several iterations take place where an author submits code changes and a reviewer gives feedback until is happy to accept the change. In around 12% cases, the changes are abandoned, eventually wasting all the efforts. In this research, our objective is to design a tool that can predict whether a code change would be merged or abandoned at an early stage to reduce the waste of efforts of all stakeholders (e.g., program author, reviewer, project management, etc.) involved. The real-world demand for such a tool was formally identified by a study by Fan et al. [1]. We have mined 146,612 code changes from the code reviews of three large and popular open-source software and trained and tested a suite of supervised machine learning classifiers, both shallow and deep learning based. We consider a total of 25 features in each code change during the training and testing of the models. The best performing model named PredCR (Predicting Code Review), a LightGBM-based classifier achieves around 85% AUC score on average and relatively improves the state-of-the-art [1] by 14-23%. In our empirical study on the 146,612 code changes from the three software projects, we find that (1) The new features like reviewer dimensions that are introduced in PredCR are the most informative. (2) Compared to the baseline, PredCR is more effective towards reducing bias against new developers. (3) PredCR uses historical data in the code review repository and as such the performance of PredCR improves as a software system evolves with new and more data.
Reactive navigation in partially familiar planar environments using semantic perceptual feedback

This article solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in simultaneous localization and mapping (SLAM) and visual object recognition to recast prior geometric knowledge in terms of an offline catalog of familiar objects. The resulting vector field planner guarantees convergence to an arbitrarily specified goal, avoiding collisions along the way with fixed but arbitrarily placed instances from the catalog as well as completely unknown fixed obstacles so long as they are strongly convex and well separated. We illustrate the generic robustness properties of such deterministic reactive planners as well as the relatively modest computational cost of this algorithm by supplementing an extensive numerical study with physical implementation on both a wheeled and legged platform in different settings.