Reviewerly/exHarmony huggingface dataset:
Low-Overhead Fault-Tolerant Quantum Error Correction with the Surface-GKP Code

Fault-tolerant quantum error correction is essential for implementing quantum algorithms of significant practical importance. In this work, we propose a highly effective use of the surface-GKP code, i.e., the surface code consisting of bosonic GKP qubits instead of bare two-dimensional qubits. In our proposal, we use error-corrected two-qubit gates between GKP qubits and introduce a maximum likelihood decoding strategy for correcting shift errors in the two-GKP-qubit gates. Our proposed decoding reduces the total CNOT failure rate of the GKP qubits, e.g., from $0.87\%$ to $0.36\%$ at a GKP squeezing of $12$dB, compared to the case where the simple closest-integer decoding is used. Then, by concatenating the GKP code with the surface code, we find that the threshold GKP squeezing is given by $9.9$dB under the the assumption that finite-squeezing of the GKP states is the dominant noise source. More importantly, we show that a low logical failure rate $p_{L} < 10^{-7}$ can be achieved with moderate hardware requirements, e.g., $291$ modes and $97$ qubits at a GKP squeezing of $12$dB as opposed to $1457$ bare qubits for the standard rotated surface code at an equivalent noise level (i.e., $p=0.36\%$). Such a low failure rate of our surface-GKP code is possible through the use of space-time correlated edges in the matching graphs of the surface code decoder. Further, all edge weights in the matching graphs are computed dynamically based on analog information from the GKP error correction using the full history of all syndrome measurement rounds. We also show that a highly-squeezed GKP state of GKP squeezing $\gtrsim 12$dB can be experimentally realized by using a dissipative stabilization method, namely, the Big-small-Big method, with fairly conservative experimental parameters. Lastly, we introduce a three-level ancilla scheme to mitigate ancilla decay errors during a GKP state preparation.
Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review

Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require high quality and near-zero error rates to ensure the demands and safety of end-users, AR can also equip operators with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. However, there is currently no systematic review paper about AR technology enhancing the quality sector. The purpose of this paper is to conduct a systematic literature review (SLR) to conclude about the emerging interest in using AR as an assisting technology for the quality sector in an industry 4.0 context. Five research questions (RQs), with a set of selection criteria, are predefined to support the objectives of this SLR. In addition, different research databases are used for the paper identification phase following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to find the answers for the predefined RQs. It is found that, in spite of staying behind the assembly and maintenance sector in terms of AR-based solutions, there is a tendency towards interest in developing and implementing AR-assisted quality applications. There are three main categories of current AR-based solutions for quality sector, which are AR-based apps as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. In this SLR, an AR architecture layer framework has been improved to classify articles into different layers which are finally integrated into a systematic design and development methodology for the development of long-term AR-based solutions for the quality sector in the future.
Semantically Coherent Out-of-Distribution Detection

Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortunately introduce some unwanted and impractical goals, e.g., to perfectly distinguish CIFAR dogs from ImageNet dogs, even though they have the same semantics and negligible covariate shifts. These unrealistic goals will result in an extremely narrow range of model capabilities, greatly limiting their use in real applications. To overcome these drawbacks, we re-design the benchmarks and propose the semantically coherent out-of-distribution detection (SC-OOD). On the SC-OOD benchmarks, existing methods suffer from large performance degradation, suggesting that they are extremely sensitive to low-level discrepancy between data sources while ignoring their inherent semantics. To develop an effective SC-OOD detection approach, we leverage an external unlabeled set and design a concise framework featured by unsupervised dual grouping (UDG) for the joint modeling of ID and OOD data. The proposed UDG can not only enrich the semantic knowledge of the model by exploiting unlabeled data in an unsupervised manner, but also distinguish ID/OOD samples to enhance ID classification and OOD detection tasks simultaneously. Extensive experiments demonstrate that our approach achieves the state-of-the-art performance on SC-OOD benchmarks. Code and benchmarks are provided on our project page: https://jingkang50.github.io/projects/scood.
MLOps - Definitions, Tools and Challenges

This paper is an concentrated overview of the Machine Learning Operations (MLOps) area. Our aim is to define the operation and the components of such systems by highlighting the current problems and trends. In this context we present the different tools and their usefulness in order to provide the corresponding guidelines. Moreover, the connection between MLOps and AutoML (Automated Machine Learning) is identified and how this combination could work is proposed. The novelty of our approach relies on the combination of state-of-the-art topics such as AutoML, exlainability and sustain-ability in order to overcome the current challenges in MLOps identifying them not only as the answer for the incorporation of ML models in production but also as a possible tool for efficient, robust and accurate machine learning models.
College music education and teaching based on AI techniques

Primary or secondary music directors, college or collaborative music directors, and music educators are trained in music education. Students in music education research and develop new methods for teaching and learning music. Through peer-reviewed publications and instruction at the University of Music for music teachers, students in the field of music conduct their research. Using artificial intelligence in the classroom is a challenge because music is an available domain that requires students and teachers to innovate and solve problems. In this paper, Music Education and Teaching based on AI (MET-AI) techniques are increasingly comprehensive with modern science and technology, enhancing music education. The use of artificial intelligence in music education breached the conventional paradigm of music education, particularly electronic music and innovative music software in private colleges, which has significantly enhanced the standard of teaching music and the teaching model for music education. The experimental results show us that the network teaching platform Music Major should be continuously improved based on artificial intelligence technologies. AI can make more optimized environments and professional music classes so that teachers and students can make the most of this and ensure smooth improvement in the network's teaching model. The MET-AI scores students’ learning outcome rate of 95.2%, an efficiency ratio of 98.1%, the mean square error rate of 17.9%, accuracy ratio of 95.3%, teaching performance analysis ratio of 90.7%, the false-positive rate of 18.6%, true positive rate 95.7% and flexibility ratio of 92,1%.
Attention-based BiLSTM models for personality recognition from user-generated content

Emojis have been widely used in social media as a new way to express various emotions and personalities. However, most previous research only focused on limited features from textual information while neglecting rich emoji information in user-generated content. This study presents two novel attention-based Bi-LSTM architectures to incorporate emoji and textual information at different semantic levels , and investigate how the emoji information contributes to the performance of personality recognition tasks. Specifically, we first extract emoji information from online user-generated content, and concatenate word embedding and emoji embedding based on word and sentence perspectives. We then obtain the document representations of all users from the word and sentence levels during the training process and feed them into the attention-based Bi-LSTM architecture to predict the Big Five personality traits . Experimental results show that the proposed methods achieve state-of-the-art performance over the baseline models on the real dataset, demonstrating the usefulness and contribution of emoji information in personality recognition tasks. The findings could help researchers and practitioners better understand the rich semantics of emoji information and provide a new way to introduce emoji information into personality recognition tasks.
Influence of blockchain technology in SME internationalization: Evidence from high-tech SMEs in India

This study examines the impact of blockchain technology (BCT) on small and medium-sized enterprises’ (SMEs) internationalization. Further, it examines how Amazon as a platform reframes the global partnership environment via the BCT network that enables Indian SMEs to operate globally. Data were collected from 291 employees at 43 high-tech SMEs in India. They revealed untapped interest in how Indian SMEs will use Amazon for global operations. Several factors, such as processes to strategize, synergize, and standardize, are described as needing to be addressed before SMEs can boost integrated business efficiency, especially with regards to marketing capacity, scale, scope, and financial performance. The study represents a critical theoretical contribution to Amazon-based global operations by developing the 3S Triangle Model, which could provide a basis for future business practice. Furthermore, the study defines and addresses the evolution of BCT that Amazon has used—and continues to use—as a global operations platform. At the convergence of BCT, Amazon, and global retail activities, we propose and advance an integrated model of BCT-driven global operations among SMEs that provides a holistic view of state-of-the-art practices and exciting avenues for future study.
A Survey on Convolutional Neural Network Accelerators: GPU, FPGA and ASIC

In recent years, artificial intelligence (AI) has been under rapid development, applied in various areas. Among a vast number of neural network (NN) models, the convolutional neural network (CNN) has a mainstream status in application such as image and sound recognition and machine decision. The convolution operation is the most complex and requires acceleration. A practical method is to optimize the architecture of the deep learning processor (DLP). The traditional CPU architecture lacks parallelism and memory bandwidth and is not suitable for CNN operations. Current researches are focused on graphic processing unit (GPU), field programmable gate array (FPGA) and application specific integrated circuit (ASIC). GPU is the maturest and the most widely applied, however it is not flexible and has high cost and energy consumption. Even though FPGA possesses high flexibility and low energy consumption, it is inferior in performance. ASIC, due to targeted design, is advanced in performance and energy consumption. However, it is highly inflexible. This article reviews the research outcomes of the three classic types of processors applied to CNN, and put forward the future research trend. In particular, this paper analyzes and compares the experimental performance of several processors of different types, and then summarizes the respective advantageous application fields. Hence, the novelty of this article is in the summary of practical DLPs, which is expected to provide helps for the AI researchers, and guide the selection of CNN-supporting hardware in industrial application.
Weight-Quantized SqueezeNet for Resource-Constrained Robot Vacuums for Indoor Obstacle Classification

With the rapid development of artificial intelligence (AI) theory, particularly deep learning neural networks, robot vacuums equipped with AI power can automatically clean indoor floors by using intelligent programming and vacuuming services. To date, several deep AI models have been proposed to distinguish indoor objects between cleanable litter and noncleanable hazardous obstacles. Unfortunately, these existing deep AI models focus entirely on the accuracy enhancement of object classification, and little effort has been made to minimize the memory size and implementation cost of AI models. As a result, these existing deep AI models require far more memory space than a typical robot vacuum can provide. To address this shortcoming, this paper aims to study and find an efficient deep AI model that can achieve a good balance between classification accuracy and memory usage (i.e., implementation cost). In this work, we propose a weight-quantized SqueezeNet model for robot vacuums. This model can classify indoor cleanable litters from noncleanable hazardous obstacles based on the image or video captures from robot vacuums. Furthermore, we collect videos or pictures captured by built-in cameras of robot vacuums and use them to construct a diverse dataset. The dataset contains 20,000 images with a ground-view perspective of dining rooms, kitchens and living rooms for various houses under different lighting conditions. Experimental results show that the proposed deep AI model can achieve comparable object classification accuracy of around 93% while reducing memory usage by at least 22.5 times. More importantly, the memory footprint required by our AI model is only 0.8 MB, indicating that this model can run smoothly on resource-constrained robot vacuums, where low-end processors or microcontrollers are dedicated to running AI algorithms.
Augmented reality technology in language learning: A<scp>meta‐analysis</scp>

Abstract Background As a recently emerging innovative technology, augmented reality (AR) has become a popular tool for language learning. However, to date, very few meta‐analytical studies has been conducted on AR in this field to understand its effectiveness on language learning. Objectives This meta‐analysis was conducted to systematically synthesize the findings from primary studies published between 2008 and 2020 to establish the effects of AR on language learning gains and students' motivation. Methods The 21 studies met all the inclusion criteria were included in the meta‐analysis to extract effect size statistics. The robust variance estimation (RVE) technique using the “Robumeta” R‐package was adopted to estimate the pooled effect size. Given the heterogeneity of the effect sizes, a mixed‐effects meta‐regression model was estimated to examine any association between the effectiveness of AR technologies and moderator variables. Results and conclusion The pooled effect‐size estimate was 0.93 for language gains and 0.42 for motivation, which indicates that AR applications have a large effect on learners' language gains and a small to medium effect on learners' motivation. The moderator analysis results suggested that learners' educational levels and intervention durations are significant moderating factors that impact the effect of AR on learners' motivation. In particular, elementary school students in this meta‐analysis study experienced a large measurable effect in terms of both language gains and motivation. Additionally, exposure to AR applications for up to 1 week is especially effective for enhancing learners' motivation. Major takeaways The findings of this meta‐analysis study demonstrated how AR applications can be utilized in language teaching and learning contexts, and how language educators could adopt AR technologies in classrooms to promote learners' language gains and motivation.
A novel color image encryption scheme based on a new dynamic compound chaotic map and S-box

In modern technological era image encryption has become an attractive and interesting field for researchers. They work for improving the security of image data from unauthorized sources. Chaos theory, due to its randomness and unpredictable behaviors, is considered favorite for the purpose of image encryption. This paper proposes a diffusion based image encryption algorithm by using chaotic maps. Firstly a chaotic map (piecewise linear chaotic map) is used for the generation of S-box, then it is used for the pixel values modification to generate element of non-linearity. After this these modified values are further diffused with another random sequence, generated by tent logistic chaotic map. Finally the color components of pre-encrypted image are mixed with each other so that the developed randomness uniformly distributed in them. For image data we develop non-linearity and diffusion by using S-box and then more randomness is added in the pre-encrypted image with the help of Boolean operation XOR. The use of this combination of chaotic maps along with S-box and Boolean operation XOR is a different technique, that provides satisfactory results for security aspects and also works efficiently.
High-quality indoor scene 3D reconstruction with RGB-D cameras: A brief review

Abstract High-quality 3D reconstruction is an important topic in computer graphics and computer vision with many applications, such as robotics and augmented reality. The advent of consumer RGB-D cameras has made a profound advance in indoor scene reconstruction. For the past few years, researchers have spent significant effort to develop algorithms to capture 3D models with RGB-D cameras. As depth images produced by consumer RGB-D cameras are noisy and incomplete when surfaces are shiny, bright, transparent, or far from the camera, obtaining high-quality 3D scene models is still a challenge for existing systems. We here review high-quality 3D indoor scene reconstruction methods using consumer RGB-D cameras. In this paper, we make comparisons and analyses from the following aspects: (i) depth processing methods in 3D reconstruction are reviewed in terms of enhancement and completion, (ii) ICP-based, feature-based, and hybrid methods of camera pose estimation methods are reviewed, and (iii) surface reconstruction methods are reviewed in terms of surface fusion, optimization, and completion. The performance of state-of-the-art methods is also compared and analyzed. This survey will be useful for researchers who want to follow best practices in designing new high-quality 3D reconstruction methods.
From ambivalence to trust: Using blockchain in customer loyalty programs

Global initiatives on climate protection and national sustainability policies are accelerating the replacement of fossil fuels with renewable energy sources. Many electricity suppliers are engaged in efforts to monetize this transition with ‘green’ services and products, such as Green Electricity Tariffs. These promise customers that their supply includes a specific share of green electricity, yet since electricity suppliers often fail to deliver on those promises, many customers have lost trust in their suppliers. Further information asymmetries may not only exacerbate this loss of trust, but also spark distrust and lead to an overall feeling of ambivalence. Eventually, ambivalent customers may feel inclined to switch suppliers. To prevent this domino effect, electricity suppliers must eliminate ambivalence by increasing customer trust and reducing customer distrust. Here, we discuss how these challenges can be met with a customer loyalty program built on blockchain technology. We developed the program following a Design Science Research approach that facilitated refinement in four iteration and evaluation cycles. Our results indicate that the developed customer loyalty program restores trust, reduces distrust, and resolves customer ambivalence by providing four features: improved customer agency, sufficient and verifiable information, appropriate levels of usability, and unobstructed data access.
Intermediate-Task Transfer Learning with BERT for Sarcasm Detection

Sarcasm detection plays an important role in natural language processing as it can impact the performance of many applications, including sentiment analysis, opinion mining, and stance detection. Despite substantial progress on sarcasm detection, the research results are scattered across datasets and studies. In this paper, we survey the current state-of-the-art and present strong baselines for sarcasm detection based on BERT pre-trained language models. We further improve our BERT models by fine-tuning them on related intermediate tasks before fine-tuning them on our target task. Specifically, relying on the correlation between sarcasm and (implied negative) sentiment and emotions, we explore a transfer learning framework that uses sentiment classification and emotion detection as individual intermediate tasks to infuse knowledge into the target task of sarcasm detection. Experimental results on three datasets that have different characteristics show that the BERT-based models outperform many previous models.
Probabilistic time series forecasting with deep non‐linear state space models

Abstract Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series observations. It is a widespread challenge in various tasks, such as risk management and decision making. To investigate temporal patterns in time series data and predict subsequent probabilities, the state space model (SSM) provides a general framework. Variants of SSM achieve considerable success in many fields, such as engineering and statistics. However, since underlying processes in real‐world scenarios are usually unknown and complicated, actual time series observations are always irregular and noisy. Therefore, it is very difficult to determinate an SSM for classical statistical approaches. In this paper, a general time series forecasting framework, called Deep Non‐linear State Space Model (DNLSSM), is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series data. We fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series data. In particular, the unscented Kalman filter (UKF) is adopted to calculate marginal likelihoods and update distributions recursively for non‐linear functions. After that, a non‐linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive definitive. Therefore, the authors enhance the tolerance of UKF to round‐off errors and manage to combine UKF and deep neural networks. In this manner, the DNLSSM effectively models non‐linear correlations between observed time series data and underlying dynamic processes. Experiments in both synthetic and real‐world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.