Reviewerly/exHarmony huggingface dataset:
Modular software for real-time quantum control systems

Real-time control software and hardware is essential for operating quantum computers. In particular, the software plays a crucial role in bridging the gap between quantum programs and the quantum system. Unfortunately, current control software is often optimized for a specific system at the cost of flexibility and portability. We propose a systematic design strategy for modular real-time quantum control software and demonstrate that modular control software can reduce the execution time overhead of kernels by 63.3% on average while not increasing the binary size. Our analysis shows that modular control software for two distinctly different systems can share between 49.8% and 91.0% of covered code statements. To demonstrate the modularity and portability of our software architecture, we run a portable randomized benchmarking experiment on two different ion-trap quantum systems.
Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization

This paper presents a new method for designing matched digital filters with discrete valued coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been applied to obtain the optimum codebook in design of matched wavelet function. Abelian group has been used to extract the similarity present in the input voiced signal. Fuzzy particle swarm optimization (FPSO) process is used to find approximate ideal vector quantization (VQ) codebook to be carried out for compression of data. FPSOVQ scheme utilises features of fuzzy inference method (FIM) and expert particle swarm optimization (PSO). The generated codebook consists of set of highly ideal features, which are considered as the filter coefficients. These coefficients are used in the designing of the filter. All of the phonemes in the American English language were included in the 30 sentences that were chosen from the IEEE database. The sentences were originally down-sampled from 25 kHz to 8 kHz. The magnitude responses of each filter have been drawn, which indicates the characteristics of the filter. A comparison has been provided using energy compaction ratio as a parameter to judge the performance of matched designed filter with db4 filter. Experimental results show the advantage of the developed algorithm as the average value of energy compaction ratio for sampled voice signals is 2.8046 times lower for matched designed filter.
Marine debris awareness improvement using immersive virtual reality

Improving awareness of marine debris could lead to large scale benefits. However, existing marine debris awareness approaches can often be limited in engagement. A more interactive and innovative educational method is needed to increase engagement and action. In this study, we use an immersive Virtual Reality (VR) approach and assess the efficacy and effectiveness of this approach. Three marine debris-related VR modules were developed. To validate the performance VR approach, we compared VR with traditional video-based education. Efficacy measured simulation sickness, system usability, and user experience; effectiveness evaluated knowledge gained and motivation. Twenty-five students were recruited in the study and randomly allocated into two groups. Within the current sample, the developed VR educational approach led to a significantly higher motivation for action and acceptable efficacy performance. These results indicate that VR-based education has the potential to lead to large scale prevention and management of marine debris.
AdaEnlight

The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services for privacy and robustness concerns. However, the performance of these applications is constrained by the raw video streams, which tend to be taken with small-aperture cameras of ubiquitous mobile platforms in dim light. Despite extensive low-light video enhancement solutions, they are unfit for deployment to mobile devices due to their complex models and and ignorance of system dynamics like energy budgets. In this paper, we propose AdaEnlight, an energy-aware low-light video stream enhancement system on mobile devices. It achieves real-time video enhancement with competitive visual quality while allowing runtime behavior adaptation to the platform-imposed dynamic energy budgets. We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
Chiral SQUID-metamaterial waveguide for circuit-QED

Superconducting metamaterials, which are designed and fabricated with structured fundamental circuit elements, have motivated recent developments of exploring unconventional quantum phenomena in circuit quantum electrodynamics (circuit-QED). We propose a method to engineer 1D Josephson metamaterial as a chiral waveguide by considering a programmed spatiotemporal modulation on its effective impedance. The modulation currents are in the form of traveling waves which phase velocities are much slower than the propagation speed of microwave photons. Due to the Brillouin-scattering process, non-trivial spectrum regimes where photons can propagate unidirectionally emerge. Considering superconducting qubits coupling with this metamaterial waveguide, we analyze both Markovian and non-Markovian quantum dynamics, and find that superconducting qubits can dissipate photons unidirectionally. Moreover, we show that our proposal can be extended a cascaded quantum network with multiple nodes, where chiral photon transport between remote qubits can be realized. Our work might open the possibilities to exploit SQUID metamaterials for realizing unidirectional photon transport in circuit-QED platforms.
A Differentiable Perspective for Multi-View Spectral Clustering With Flexible Extension

Multi-view clustering aims to discover common patterns from multi-source data, whose generality is remarkable. Compared with traditional methods, deep learning methods are data-driven and have a larger search space for solutions, which may find a better solution to the problem. In addition, more considerations can be introduced by loss functions, so deep models are highly reusable. However, compared with deep learning methods, traditional methods have better interpretability, whose optimization is relatively stable. In this paper, we propose a multi-view spectral clustering model, combining the advantages of traditional methods and deep learning methods. Specifically, we start with the objective function of traditional spectral clustering, perform multi-view extension, and then obtain the traditional optimization process. By partially parameterizing this process, we further design corresponding differentiable modules, and finally construct a complete network structure. The model is interpretable and extensible to a certain extent. Experiments show that the model performs better than other multi-view clustering algorithms, and its semi-supervised classification extension also has excellent performance compared to other algorithms. Further experiments also show the stability and fewer iterations of the model training.
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline

This paper introduces a high-quality open-source text-to-speech (TTS) synthesis dataset for Mongolian, a low-resource language spoken by over 10 million people worldwide. The dataset, named MnTTS, consists of about 8 hours of transcribed audio recordings spoken by a 22-year-old professional female Mongolian announcer. It is the first publicly available dataset developed to promote Mongolian TTS applications in both academia and industry. In this paper, we share our experience by describing the dataset development procedures and faced challenges. To demonstrate the reliability of our dataset, we built a powerful non-autoregressive baseline system based on FastSpeech2 model and HiFi-GAN vocoder, and evaluated it using the subjective mean opinion score (MOS) and real time factor (RTF) metrics. Evaluation results show that the powerful baseline system trained on our dataset achieves MOS above 4 and RTF about 3.30×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , which makes it applicable for practical use. The dataset, training recipe, and pretrained TTS models are freely available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/walker-hyf/MnTTS.
Tik tok as a teaching tool: The motivation of university students in Ecuador

The present study was carried out in the Faculty of Architecture, Arts and Design of the Indoamerica Technological University, Ambato-Ecuador, in the period 2021–2022, in which the motivation of university students on the use of the Tiktok social network in the learning process evidenced in their academic performance. 126 students from four training projects participated, two from the basic training axis (group A: I use Tiktok) and two from the professional training axis (group B: I do not use Tiktok), as well as four teachers. In the training projects of group A, Tiktok has been used as the main channel for the development of activities, while in group B the use of Tiktok has not been carried out. A quantitative-qualitative methodology has been used. A pre-test and a post-test are carried out to check the motivation of the students at the end of their study period. As a result, it is observed that the students who have used Tiktok as a learning tool feel more motivated and show a dedication to the content notably greater than the students who did not, which is evidenced in their academic record. On the other hand, the different perceptions on the part of the teachers are evident, those who did use Tiktok are younger and recognize its benefits, unlike the teachers who have not used it, who are older and have a harder time adapting to the use of Tiktok. technology and social networks.
An Efficient Planning Method for Autonomous Navigation of a Wheeled-Robot based on Deep Reinforcement Learning

In this paper, a planning method to autonomously navigate a wheeled-robot based on deep reinforcement learning is proposed. The planning algorithm aims at reaching a predetermined target configuration while avoiding obstacles. It is assumed that the robot chassis is differential-drive and it is equipped with multiple distance sensors to perceive the environment and Global Positioning System (GPS) for localization. A Deterministic Actor-Critic model is designed to navigate the robot in an environment including multiple obstacles. Since navigation is a multi-objective optimization problem, a novel reward function considering different contradicted criteria including reaching the target and avoiding the obstacles is proposed in this paper. To improve the performance and convergence of the learning method, a gradual learning method is applied to first learn target reaching and next obstacle avoidance. The effect of the proposed reward function is compared with some other reward functions to show its effectiveness.
State-Aware Compositional Learning Toward Unbiased Training for Scene Graph Generation

How to avoid biased predictions is an important and active research question in scene graph generation (SGG). Current state-of-the-art methods employ debiasing techniques such as resampling and causality analysis. However, the role of intrinsic cues in the features causing biased training has remained under-explored. In this paper, for the first time, we make the surprising observation that object identity information, in the form of object label embeddings (e.g. GLOVE), is principally responsible for biased predictions. We empirically observe that, even without any visual features, a number of recent SGG models can produce comparable or even better results solely from object label embeddings. Motivated by this insight, we propose to leverage a conditional variational auto-encoder to decouple the entangled visual features into two meaningful components: the object's intrinsic identity features and the extrinsic, relation-dependent state feature. We further develop two compositional learning strategies on the relation and object levels to mitigate the data scarcity issue of rare relations. On the two benchmark datasets Visual Genome and GQA, we conduct extensive experiments on the three scenarios, i.e., conventional, few-shot and zero-shot SGG. Results consistently demonstrate that our proposed Decomposition and Composition (DeC) method effectively alleviates the biases in the relation prediction. Moreover, DeC is model-free, and it significantly improves the performance of recent SGG models, establishing new state-of-the-art performance.
Generative adversarial networks for modelling clinical biomarker profiles with race/ethnicity

Modelling biomarker profiles for under-represented race/ethnicity groups are challenging because the underlying studies frequently do not have sufficient participants from these groups. The aim was to investigate generative adversarial networks (GANs), an artificial intelligence technology that enables realistic simulations of complex patterns, for modelling clinical biomarker profiles of under-represented groups.GANs consist of generator and discriminator neural networks that operate in tandem. GAN architectures were developed for modelling univariate and joint distributions of a panel of 16 diabetes-relevant biomarkers from the National Health and Nutrition Examination Survey, which contains laboratory and clinical biomarker data from a population-based sample of individuals of all ages, racial groups and ethnicities. Conditional GANs were used to model biomarker profiles for race/ethnicity categories. GAN performance was assessed by comparing GAN outputs to test data.The biomarkers exhibited non-normal distributions and varied in their bivariate correlation patterns. Univariate distributions were modelled with generator and discriminator neural networks consisting of 2 dense layers with rectified linear unit-activation. The distributions of GAN-generated biomarkers were similar to the test data distributions. The joint distributions of the biomarker panel in the GAN-generated data were dispersed and overlapped with the joint distribution of the test data as assessed by 3 multidimensional projection methods. Conditional GANs satisfactorily modelled the joint distribution of the biomarker panel in the Black, Hispanic, White and Other race/ethnicity categories.GAN is a promising artificial intelligence approach for generating virtual patient data with realistic biomarker distributions for under-represented race/ethnicity groups.
Variability-Controlled HfZrO<sub>2</sub> Ferroelectric Tunnel Junctions for Reservoir Computing

We propose reservoir computing (RC), a framework for constructing recurrent neural networks (RNNs) with simple training rule, using ferroelectric tunnel junction (FTJ) crossbar array. Not only the weights of connection between reservoir layer and output layer need to be trained, but also random fixed weights within reservoir layer can be realized by the conductance of FTJs. Random weights within reservoir layers have optimal variability, which is dependent not on the type of the distribution but on the reservoir size. Optimal variability can be attained by device-to-device variability in FTJs together with analog converters on the wordlines and the bitlines. Analog resistive switching characteristics in FTJs can also realize the weights connected to output layer, which need to be trained. Furthermore, device-to-device variability in Hf <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{{1}- {x}}$ </tex-math></inline-formula> ZrxO2 (HZO) FTJ is extensively investigated. Taking into account the low current operation in FTJs, RC with FTJ crossbar array is a potential candidate for neuromorphic computing system with low power consumption.
Analysis of influencing factors on excellent teachers' professional growth based on DB-Kmeans method

Abstract The Kmeans clustering algorithm is widely used for the advantages of simplicity and efficient operation. However, the lack of clustering centers in the algorithm usually causes incorrect category of some discrete points. Therefore, in order to obtain more accurate clustering results when studying the factors affecting the professional growth of outstanding teachers, this paper proposes an improved algorithm of Kmeans combined with DBSCAN. Observing the clustering results of the influencing factors and calculating the evaluation standard values of the clustering results, it is found that the optimized DB-Kmeans algorithm has obvious improvements in the accuracy of the clustering results, and the clustering effect of the algorithm on edge points is more advantageous than the original algorithms according to the scatter diagram.
Heuristic Evaluation of Microsoft Teams as an Online Teaching Platform: An Educators’ Perspective

The way that education is delivered changed significantly during the COVID-19 pandemic to be completely online in many countries for many institutions. Despite the fact that they are not online teaching platforms, virtual meeting platforms were utilized to deal with this transformation. One of the platforms Philadelphia University utilized for the unplanned shift to online teaching was Microsoft Teams. This paper examines how heuristic evaluation may be used to guide the evaluation of online meeting platforms for teaching and focuses on the use of heuristic evaluation to assess the level of usability of Microsoft Teams. The level of Zoom’s usability is also evaluated using heuristic evaluation in order to compare it to that of Microsoft Teams and to assess Microsoft Teams’ overall usability in comparison to other platforms being used for the same purpose. Microsoft Teams was identified as having a few issues that need to be addressed. Additionally, strengths, weaknesses, opportunities, and threats to Microsoft Teams’ usability were assessed.
A Revolutionary Paradigm Shift in Supply Chain Management: The Blockchain Mechanism

Blockchain, in general, diversifies supply chain management in record-keeping and maintains authenticity. In addition, traditional issues and challenges like overflow and information overload press down mysteriously whenever the blockchain steering wheel of the supply chain turns. Factually, the miracle and twists in supply chain resilience have not been incorporated under systematic review homogeneously. As a result, this study reviews the potential impact of blockchain on logistics and supply chain (LSC) efficiency. The methodology of this study provides a subjective assessment of the utility of blockchain-based LSC performance.On the other hand, the review reveals new insights on its current acceptance and applications, with a particular emphasis on the Limit Redundancy Mechanism and Core Information-based Direct Comparison. Prospectively, the identified facts under the research paradigm and extensive literature survey will subsidize the practices of blockchain technology and possible areas of extension in supply chain resilience in luminous fashion in the future. After all, this study materializes new solid magnitudes, adaptability, and a realistic overview of blockchain-based LSC movements.
Natural and Artificial Dynamics in Graphs: Concept, Progress, and Future

Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of graph research, i.e., graph mining, graph representations, and graph neural networks (GNNs). Then, we introduce the definitions of natural dynamics and artificial dynamics in graphs, and the related works of natural and artificial dynamics about how they boost the aforementioned graph research topics, where we also discuss the current limitation and future opportunities.
The Role of Legal Governance Framework in the Metaverse World

The book chapter analyses how the current legal framework of self-governance is weak in regulating the metaverse world. The rule of law, as a discourse that emphasizes the legitimacy of governance and appropriate limits on the exercise of power, provides a useful framework as a first step to reconceptualizing and evaluating these tensions in communities at the intersection of the real and the virtual, the social and the economic, and the public and the private. Technologists, practitioners, and regulators must be open to these tensions to appropriately develop the correct legal governance through a mix of user control, industry practice, and regulatory oversight for a just and equitable metaverse world.
How robots will affect the future of retailing

To predict how robots will affect the future of retailing, the authors begin this article with a brief review of recent deployments of robots in the retail and service sectors, along with relevant research pertaining to their uses. Next, they focus on two crucial dimensions, reflecting whether the robots (1) are customer facing or not and (2) intend to augment or substitute for retail associates. These two dimensions suggest four strategic quadrants relating to future robot deployments; some quadrants are attractive in the near term, and others attractive in the years beyond. In closing, the authors also suggest an agenda for continued research.
A simple and effective multi-person pose estimation model for low power embedded system

In recent years, algorithms based on human pose estimation have been applied more and more in low power embedded system. However, the keypoints detection under occlusion is not well solved, resulting in poor effect in practical application on embedded devices. In this paper, we propose a novel Simple and Effective Network (SEN) to deal with the multi-person pose estimation problem of low power embedded system via detecting occlusion keypoints quite well to a certain extent. This model is easy to apply to embedded devices, and has the characteristics of simplicity, strong expansibility and wide application, which are very important in the world of Internet of things. Our model contains three novel modules: Feature Fusion Module (FFM), Channel Enhancement Attention Module (CEAM), and Feature Enhancement Module (FEM). The FFM fuses the shallow and deep feature maps, bringing rich context information to the model. At the same time, it can alleviate the problem of information loss caused by downsampling operations and locate the keypoints more accurately. The FEM and the CEAM act on the deep feature maps of the network, which helps to infer the keypoints of occlusion or invisibility. Related experiments explain that the raised means is effective and achieves the superior performance over two benchmark datasets: the COCO keypoints detection dataset and the MPII Human Pose dataset.