Reviewerly/exHarmony huggingface dataset:
Exploiting Symmetry in Variational Quantum Machine Learning

Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inductive bias relevant to the learning task. However, precious little is known about guiding principles for the construction of suitable parametrizations. In this work, we holistically explore when and how symmetries of the learning problem can be exploited to construct quantum learning models with outcomes invariant under the symmetry of the learning task. Building on tools from representation theory, we show how a standard gateset can be transformed into an equivariant gateset that respects the symmetries of the problem at hand through a process of gate symmetrization. We benchmark the proposed methods on two toy problems that feature a non-trivial symmetry and observe a substantial increase in generalization performance. As our tools can also be applied in a straightforward way to other variational problems with symmetric structure, we show how equivariant gatesets can be used in variational quantum eigensolvers.
Dual stream network with attention mechanism for photovoltaic power forecasting

The operations of renewable power generation systems highly depend on precise Photovoltaic (PV) power forecasting, providing significant economic, and environmental advantages for energy efficient buildings and urban energy systems. However, precise PV power forecasting, particularly, solar power is more challenging due to solar energy intermittence, instability, and randomness. These challenges hinder the integration of PV into smart grids, where accurate power forecasting is a promising solution in this direction, providing effective planning and management services. Therefore, in this work, we introduce a dual-stream network for accurate PV forecasting. The proposed network parallelly learns spatial patterns using convolutional network and temporal representations via sequential learning algorithm. These features are then integrated together to form a single, yet representative feature vector used as an input to self-attention mechanism to further select the optimal features for PV power forecasting. To the best of our knowledge, the proposed dual stream network with advanced features selection mechanism is a pioneering approach for time series analysis, narrowed towards PV power forecasting. We derive our network after a series of experimentations involving solo and hybrid models, resulting in higher forecasting accuracy against state-of-the-art models.
Avatar-mediated experience in the metaverse: The impact of avatar realism on user-avatar relationship

This study aims to explore the role of avatars in mediating users with the metaverse environment. This study applies the construal level theory and source attractiveness model to understand the impact of avatar realism on the interaction between users and their avatars. Computer-simulated experiences were tested in realistic and abstract avatar conditions using serial mediation analysis. It was found that the user-avatar relationship and the intention to use the avatar were both increased by avatar realism. Specifically, psychological distance and social presence serially mediated these effects. Furthermore, the mediation of psychological distance and social presence was moderated by user familiarity with avatars. This study identifies the role of psychological distance to the user-and-avatar dyad in the metaverse by examining the multi-layered psychological mechanism behind the virtual interaction and relationship. It also provides implications for developing metaverse platforms by considering avatar characteristics and user familiarity.
Fashion and the metaverse: Clarifying the domain and establishing a research agenda

In recent years, the metaverse has garnered significant attention as a term referring to a network of 3D virtual worlds that integrate elements of both physical and digital worlds. Fashion brands have begun exploring the metaverse as a new marketing platform, which is expected to bring about substantial changes in the fashion and retail industry. However, a lack of consensus on the nature of the metaverse and its impact on the fashion industry currently exists, and limited academic research is available on the metaverse's influence on fashion brands' marketing strategies and brand experiences. To address this gap, this study employs a thematic analysis approach on trade journals and industry articles that cover fashion brands' metaverse strategies. Through this analysis, the study provides a typology of current marketing strategies of fashion brands in the metaverse. Based on these empirical findings, this research proposes a theoretical framework that explains how different metaverse strategies affect different dimensions of brand equity. Finally, this study offers research directions for fashion brands' metaverse strategies by presenting an integrated framework that synthesizes the key insights from our research findings.
Continuous transfer of neural network representational similarity for incremental learning

The incremental learning paradigm in machine learning has consistently been a focus of academic research. It is similar to the way in which biological systems learn, and reduces energy consumption by avoiding excessive retraining. Existing studies utilize the powerful feature extraction capabilities of pre-trained models to address incremental learning, but there remains a problem of insufficient utilization of neural network feature knowledge. To address this issue, this paper proposes a novel method called Pre-trained Model Knowledge Distillation (PMKD) which combines knowledge distillation of neural network representations and replay. This paper designs a loss function based on centered kernel alignment to transfer neural network representations knowledge from the pre-trained model to the incremental model layer-by-layer. Additionally, the use of memory buffer for Dark Experience Replay helps the model retain past knowledge better. Experiments show that PMKD achieved superior performance on various datasets and different buffer sizes. Compared to other methods, our class incremental learning accuracy reached the best performance. The open-source code is published athttps://github.com/TianSongS/PMKD-IL.
FUTR3D: A Unified Sensor Fusion Framework for 3D Detection

Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Existing multi-modal 3D detection models usually involve customized designs depending on the sensor combinations or setups. In this work, we propose the first unified end-to-end sensor fusion framework for 3D detection, named FUTR3D, which can be used in (almost) any sensor configuration. FUTR3D employs a query-based Modality-Agnostic Feature Sampler (MAFS), together with a transformer decoder with a set-to-set loss for 3D detection, thus avoiding using late fusion heuristics and post-processing tricks. We validate the effectiveness of our framework on various combinations of cameras, low-resolution LiDARs, high-resolution LiDARs, and Radars. On NuScenes dataset, FUTR3D achieves better performance over specifically designed methods across different sensor combinations. Moreover, FUTR3D achieves great flexibility with different sensor configurations and enables low-cost autonomous driving. For example, only using a 4-beam LiDAR with cameras, FUTR3D (58.0 mAP) surpasses state-of-the-art 3D detection model [41] (56.6 mAP) using a 32-beam LiDAR. Our code is available on the ${\text{project page}}$.
Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction

Camouflaged object detection (COD) aims to address the tough issue of identifying camouflaged objects visually blended into the surrounding backgrounds. COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries. Existing approaches to this problem have developed various techniques to mimic the human visual system. Albeit effective in many cases, these methods still struggle when camouflaged objects are so deceptive to the vision system. In this paper, we propose the FEature Decomposition and Edge Reconstruction (FEDER) model for COD. The FEDER model addresses the intrinsic similarity of foreground and background by decomposing the features into different frequency bands using learnable wavelets. It then focuses on the most informative bands to mine subtle cues that differentiate foreground and background. To achieve this, a frequency attention module and a guidance-based feature aggregation module are developed. To combat the ambiguous boundary problem, we propose to learn an auxiliary edge reconstruction task alongside the COD task. We design an ordinary differential equation-inspired edge reconstruction module that generates exact edges. By learning the auxiliary task in conjunction with the COD task, the FEDER model can generate precise prediction maps with accurate object boundaries. Experiments show that our FEDER model significantly outperforms state-of-the-art methods with cheaper computational and memory costs. The code will be available at https://github.com/ChunmingHe/FEDER.
Design of a Modified Transformer Architecture Based on Relative Position Coding

Abstract Natural language processing (NLP) based on deep learning provides a positive performance for generative dialogue system, and the transformer model is a new boost in NLP after the advent of word vectors. In this paper, a Chinese generative dialogue system based on transformer is designed, which only uses a multi-layer transformer decoder to build the system and uses the design of an incomplete mask to realize one-way language generation. That is, questions can perceive context information in both directions, while reply sentences can only output one-way autoregressive. The above system improvements make the one-way generation of dialogue tasks more logical and reasonable, and the performance is better than the traditional dialogue system scheme. In consideration of the long-distance information weakness of absolute position coding, we put forward the improvement of relative position coding in theory, and verify it in subsequent experiments. In the transformer module, the calculation formula of self-attention is modified, and the relative position information is added to replace the absolute position coding of the position embedding layer. The performance of the modified model in BLEU, embedding average, grammatical and semantic coherence is ideal, to enhance long-distance attention.
Review on 2D and 3D MRI Image Segmentation Techniques

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. &lt;/P&gt;&lt;P&gt; Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.
Survey of Fuzzy Min–Max Neural Network for Pattern Classification Variants and Applications

Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many different real-world applications. Artificial neural networks and fuzzy logic are two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the hybrid models, the fuzzy min-max (FMM) neural network has been proven to be a premier model for undertaking pattern classification problems. While FMM is useful in terms of its capability of online learning, it suffers from several limitations in the learning procedure. Therefore, over the past years, researchers have proposed numerous improvements to overcome the limitations of the original FMM model. This paper carries out a comprehensive survey of the developments conducted on the FMM model for pattern classification. In order to assist recent researchers in selecting the most suitable FMM variant and to provide proper guidance for future developments, this study divides the variants of FMM into two main board categories, namely FMM variants with and without contraction. This division facilitates understanding of the developments conducted by researchers on the original FMM neural network, as well as provides the scope to identify the limitations that still exist in the FMM models. This paper also summarizes the use of FMM and its variants in solving different benchmark and real-world problems. Finally, the possible future trends are highlighted.
Fault Detection of Insulators Using Second-order Fully Convolutional Network Model

As the traditional methods of insulator fault detection rely on the low-level feature extraction of images and classifier design, it is difficult to achieve fault detection of insulator for images with complex background. To address this issue, a fault detection method using second-order full convolutional network (SOFCN) is proposed in this paper. Firstly, the first-order FCN is used to learn the image features to segment insulator areas from images with complex background. Secondly, the mathematical morphology reconstruction operation is used to improve the segmentation result to get the accurate localization of the insulator areas. Finally, the FCN network is used again to detect the insulator fault and obtain the fault region. Experiments show that the proposed SOFCN is not only able to obtain accurate insulator region, but also able to effectively suppress the interference of noninsulator region. Compared to the conventional methods, the proposed SOFCN obtains higher recognition accuracy without feature extraction and the selection of a classifier. Moreover, the computational complexity of the proposed method is low. Furthermore, compared to the classical CNN and FCN segmentation methods, the proposed SOFCN can effectively suppress complex background interference to improve the accuracy of insulator fault detection.
Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations

Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.
Recycling: Semi-Supervised Learning With Noisy Labels in Deep Neural Networks

Nowadays, as noisy labels, also known as erroneous labels, are becoming increasingly ubiquitous in daily life, it is of great importance to be able to learn from them. This paper efficiently uses semi-supervised learning to highlight a method for utilizing all training data. The proposed algorithm comprises two stages: In the first stage, a small portion of training data regarded as clean samples is selected by the small-loss criteria with a proposed rejection ratio in a mini-batch manner. The proposed rejection ratio is determined by the number of classes regardless of the noise ratio. In the second stage, semi-supervised learning is used to update the parameters of the network with selected clean samples (labeled data) and the remaining noisy samples (unlabeled data) using the Renyi entropy regularization for low-density separation among classes. The proposed algorithm is believed to have the capability to train deep neural network robustly to noisy labels, although noise ratio is unknown. The experimental results considering the MNIST and CIFAR-10 datasets confirm that the proposed algorithm achieves the best accuracy among sample-selection methods.
Robust Performance Metrics for Authentication Systems

Research has produced many types of authentication systems that use machine learning.However, there is no consistent approach for reporting performance metrics and the reported metrics are inadequate.In this work, we show that several of the common metrics used for reporting performance, such as maximum accuracy (ACC), equal error rate (EER) and area under the ROC curve (AUROC), are inherently flawed.These common metrics hide the details of the inherent tradeoffs a system must make when implemented.Our findings show that current metrics give no insight into how system performance degrades outside the ideal conditions in which they were designed.We argue that adequate performance reporting must be provided to enable meaningful evaluation and that current, commonly used approaches fail in this regard.We present the unnormalized frequency count of scores (FCS) to demonstrate the mathematical underpinnings that lead to these failures and show how they can be avoided.The FCS can be used to augment the performance reporting to enable comparison across systems in a visual way.When reported with the Receiver Operating Characteristics curve (ROC), these two metrics provide a solution to the limitations of currently reported metrics.Finally, we show how to use the FCS and ROC metrics to evaluate and compare different authentication systems.
MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool

In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages.We further improve this model by using features from the original word for every normalization candidate.For comparison with future work, we propose the bundling of seven datasets in six languages to form a new benchmark, together with a novel evaluation metric which is particularly suitable for cross-dataset comparisons.MoNoise reaches a new state-of-art performance for six out of seven of these datasets.Furthermore, we allow the user to tune the 'aggressiveness' of the normalization, and show how the model can be made more efficient with only a small loss in performance.The online demo can be found on:
Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions

Causal inference is known to be very challenging when only observational data are available. Randomized experiments are often costly and impractical and in instrumental variable regression the number of instruments has to exceed the number of causal predictors. It was recently shown in Peters, Bühlmann and Meinshausen (2016) (J. R. Stat. Soc. Ser. B. Stat. Methodol. 78 947–1012) that causal inference for the full model is possible when data from distinct observational environments are available, exploiting that the conditional distribution of a response variable is invariant under the correct causal model. Two shortcomings of such an approach are the high computational effort for large-scale data and the assumed absence of hidden confounders. Here, we show that these two shortcomings can be addressed if one is willing to make a more restrictive assumption on the type of interventions that generate different environments. Thereby, we look at a different notion of invariance, namely inner-product invariance. By avoiding a computationally cumbersome reverse-engineering approach such as in Peters, Bühlmann and Meinshausen (2016), it allows for large-scale causal inference in linear structural equation models. We discuss identifiability conditions for the causal parameter and derive asymptotic confidence intervals in the low-dimensional setting. In the case of nonidentifiability, we show that the solution set of causal Dantzig has predictive guarantees under certain interventions. We derive finite-sample bounds in the high-dimensional setting and investigate its performance on simulated datasets.
Reverse JPEG Compatibility Attack

A novel steganalysis method for JPEG images is introduced that is universal in the sense that it reliably detects any type of steganography as well as small payloads. It is limited to quality factors 99 and 100. The detection statistic is formed from the rounding errors in the spatial domain after decompressing the JPEG image. The attack works whenever, during compression, the discrete cosine transform is applied to integer-valued signal. Reminiscent of the well-established JPEG compatibility steganalysis, we call the new approach the “reverse JPEG compatibility attack.” While the attack is introduced and analyzed under simplifying assumptions using reasoning based on statistical signal detection, the best detection in practice is obtained with machine learning tools. Experiments on diverse datasets of both grayscale and color images, five steganographic schemes, and with a variety of JPEG compressors demonstrate the universality and applicability of this steganalysis method in practice.
Performance evaluation of K-means clustering on Hadoop infrastructure

Today we are living with the extensive volume of information which is developing at a very fast pace. Clustering is the process of group similar kinds of information. The serial k-means clustering method takes a large amount of computational time when it is applied to enormous data sets which are large in size of tera and petabyte. In this paper, we evaluated the performance of the K-means algorithm in different ways like K-mean simple (using java codes on MapReduce), K-means (using java codes on MapReduce) with IEC (Initial Equidistant Centres), K-mean on Mahout (using Machine learning library) and K-mean on Spark (Machine learning library) on static IP address data sets over the MapReduce framework. In addition to this we also compare the K-mean simple and K-mean (IEC) on different iteration level. Outcome shows on the better performance on the basis of time on different infrastructure and it also defines the behaviour of K-means algorithms on the basis of centroids and different iteration levels.