Reviewerly/exHarmony huggingface dataset:
Neural network assisted quantum state and process tomography using limited data sets

In this study we employ a feed-forward artificial neural network (FFNN) architecture to perform tomography of quantum states and processes obtained from noisy experimental data. To evaluate the performance of the FFNN, we use a heavily reduced data set and show that the density and process matrices of unknown quantum states and processes can be reconstructed with high fidelity. We use the FFNN model to tomograph 100 two-qubit and 128 three-qubit states which were experimentally generated on a nuclear magnetic resonance (NMR) quantum processor. The FFNN model is further used to characterize different quantum processes including two-qubit entangling gates, a shaped pulsed field gradient, intrinsic decoherence processes present in an NMR system, and various two-qubit noise channels (correlated bit flip, correlated phase flip and a combined bit and phase flip). The results obtained via the FFNN model are compared with standard quantum state and process tomography methods and the computed fidelities demonstrates that for all cases, the FFNN model outperforms the standard methods for tomography.
FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid

Protecting data privacy is paramount in the fields such as finance, banking, and healthcare. Federated Learning (FL) has attracted widespread attention due to its decentralized, distributed training and the ability to protect the privacy while obtaining a global shared model. However, FL presents challenges such as communication overhead, and limited resource capability. This motivated us to propose a two-stage federated learning approach toward the objective of privacy protection, which is a first-of-its-kind study as follows: (i) During the first stage, the synthetic dataset is generated by employing two different distributions as noise to the vanilla conditional tabular generative adversarial neural network (CTGAN) resulting in modified CTGAN, and (ii) In the second stage, the Federated Probabilistic Neural Network (FedPNN) is developed and employed for building globally shared classification model. We also employed synthetic dataset metrics to check the quality of the generated synthetic dataset. Further, we proposed a meta-clustering algorithm whereby the cluster centers obtained from the clients are clustered at the server for training the global model. Despite PNN being a one-pass learning classifier, its complexity depends on the training data size. Therefore, we employed a modified evolving clustering method (ECM), another one-pass algorithm to cluster the training data thereby increasing the speed further. Moreover, we conducted sensitivity analysis by varying Dthr, a hyperparameter of ECM at the server and client, one at a time. The effectiveness of our approach is validated on four finance and medical datasets.
Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques

This paper describes the participation of team QUST in the SemEval2023 task 3. The monolingual models are first evaluated with the under-sampling of the majority classes in the early stage of the task. Then, the pre-trained multilingual model is fine-tuned with a combination of the class weights and the sample weights. Two different fine-tuning strategies, the task-agnostic and the task-dependent, are further investigated. All experiments are conducted under the 10-fold cross-validation, the multilingual approaches are superior to the monolingual ones. The submitted system achieves the second best in Italian and Spanish (zero-shot) in subtask-1.
Editable User Profiles for Controllable Text Recommendation

Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts through retrieval given user-interacted documents and learns personalized representations of the concepts based on user documents. This concept based user profile is then leveraged to make recommendations. The design of our model affords control over the recommendations through a number of intuitive interactions with a transparent user profile. We first establish the quality of recommendations obtained from LACE in an offline evaluation on three recommendation tasks spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we validate the controllability of LACE under simulated user interactions. Finally, we implement LACE in an interactive controllable recommender system and conduct a user study to demonstrate that users are able to improve the quality of recommendations they receive through interactions with an editable user profile.
ITportrait: Image-Text Coupled 3D Portrait Domain Adaptation

Domain adaptation of 3D portraits has gained more and more attention. However, the transfer mechanism of existing methods is mainly based on vision or language, which ignores the potential of vision-language combined guidance. In this paper, we propose an Image-Text multi-modal framework, namely Image and Text portrait (ITportrait), for 3D portrait domain adaptation. ITportrait relies on a two-stage alternating training strategy. In the first stage, we employ a 3D Artistic Paired Transfer (APT) method for image-guided style transfer. APT constructs paired photo-realistic portraits to obtain accurate artistic poses, which helps ITportrait to achieve high-quality 3D style transfer. In the second stage, we propose a 3D Image-Text Embedding (ITE) approach in the CLIP space. ITE uses a threshold function to self-adaptively control the optimization direction of images or texts in the CLIP space. Comprehensive experiments prove that our ITportrait achieves state-of-the-art (SOTA) results and benefits downstream tasks. All source codes and pre-trained models will be released to the public.
Approximation of Nonlinear Functionals Using Deep ReLU Networks

In recent years, functional neural networks have been proposed and studied in order to approximate nonlinear continuous functionals defined on $L^p([-1, 1]^s)$ for integers $s\ge1$ and $1\le p<\infty$. However, their theoretical properties are largely unknown beyond universality of approximation or the existing analysis does not apply to the rectified linear unit (ReLU) activation function. To fill in this void, we investigate here the approximation power of functional deep neural networks associated with the ReLU activation function by constructing a continuous piecewise linear interpolation under a simple triangulation. In addition, we establish rates of approximation of the proposed functional deep ReLU networks under mild regularity conditions. Finally, our study may also shed some light on the understanding of functional data learning algorithms.
Explanation Strategies for Image Classification in Humans vs. Current Explainable AI

Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. In image classification, we found that humans adopted more explorative attention strategies for explanation than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations diagnostic for inferring class labels, whereas the other involved explorative scanning with more visual explanations rated higher for effectiveness. Interestingly, XAI saliency-map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans differ in information and strategy use for explanations, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.
Are Visual Recognition Models Robust to Image Compression?

Reducing the data footprint of visual content via image compression is essential to reduce storage requirements, but also to reduce the bandwidth and latency requirements for transmission. In particular, the use of compressed images allows for faster transfer of data, and faster response times for visual recognition in edge devices that rely on cloud-based services. In this paper, we first analyze the impact of image compression using traditional codecs, as well as recent state-of-the-art neural compression approaches, on three visual recognition tasks: image classification, object detection, and semantic segmentation. We consider a wide range of compression levels, ranging from 0.1 to 2 bits-per-pixel (bpp). We find that for all three tasks, the recognition ability is significantly impacted when using strong compression. For example, for segmentation mIoU is reduced from 44.5 to 30.5 mIoU when compressing to 0.1 bpp using the best compression model we evaluated. Second, we test to what extent this performance drop can be ascribed to a loss of relevant information in the compressed image, or to a lack of generalization of visual recognition models to images with compression artefacts. We find that to a large extent the performance loss is due to the latter: by finetuning the recognition models on compressed training images, most of the performance loss is recovered. For example, bringing segmentation accuracy back up to 42 mIoU, i.e. recovering 82% of the original drop in accuracy.
Learnings from Data Integration for Augmented Language Models

One of the limitations of large language models is that they do not have access to up-to-date, proprietary or personal data. As a result, there are multiple efforts to extend language models with techniques for accessing external data. In that sense, LLMs share the vision of data integration systems whose goal is to provide seamless access to a large collection of heterogeneous data sources. While the details and the techniques of LLMs differ greatly from those of data integration, this paper shows that some of the lessons learned from research on data integration can elucidate the research path we are conducting today on language models.
A Survey on Recent Teacher-student Learning Studies

Knowledge distillation is a method of transferring the knowledge from a complex deep neural network (DNN) to a smaller and faster DNN, while preserving its accuracy. Recent variants of knowledge distillation include teaching assistant distillation, curriculum distillation, mask distillation, and decoupling distillation, which aim to improve the performance of knowledge distillation by introducing additional components or by changing the learning process. Teaching assistant distillation involves an intermediate model called the teaching assistant, while curriculum distillation follows a curriculum similar to human education. Mask distillation focuses on transferring the attention mechanism learned by the teacher, and decoupling distillation decouples the distillation loss from the task loss. Overall, these variants of knowledge distillation have shown promising results in improving the performance of knowledge distillation.
Fast polynomial arithmetic in homomorphic encryption with cyclo-multiquadratic fields

We discuss the advantages and limitations of cyclotomic fields to have fast polynomial arithmetic within homomorphic encryption, and show how these limitations can be overcome by replacing cyclotomic fields by a family that we refer to as cyclo-multiquadratic. This family is of particular interest due to its arithmetic efficiency properties and to the fact that the Polynomial Learning with Errors (PLWE) and Ring Learning with Errors (RLWE) problems are equivalent for it. Likewise, we provide exact expressions for the condition number for any cyclotomic field, but under what we call the twisted power basis. As a tool for our result, we obtain refined polynomial upper bounds for the condition number of cyclotomic fields with up to 6 different primes dividing the conductor. From a more practical side, we also show that for this family, swapping between NTT and coefficient representations can be achieved at least twice faster than for the usual cyclotomic family.
Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating frequency of output signals. The hybrid effects of the time delays, Markov chain, and protocol parameters are apparently reflected in the co-designed estimator which can be solved by a combination of comprehensive matrix inequalities.
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation

Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level scene understanding by segmenting all pixels and associating objects in a video. Current solutions can be categorized into online and near-online approaches. Evolving over the time, each category has its own specialized designs, making it nontrivial to adapt models between different categories. To alleviate the discrepancy, in this work, we propose a unified approach for online and near-online VPS. The meta architecture of the proposed Video-kMaX consists of two components: within clip segmenter (for clip-level segmentation) and cross-clip associater (for association beyond clips). We propose clip-kMaX (clip k-means mask transformer) and HiLA-MB (Hierarchical Location-Aware Memory Buffer) to instantiate the segmenter and associater, respectively. Our general formulation includes the online scenario as a special case by adopting clip length of one. Without bells and whistles, Video-kMaX sets a new state-of-the-art on KITTI-STEP and VIPSeg for video panoptic segmentation, and VSPW for video semantic segmentation. Code will be made publicly available.
Artifact magnification on deepfake videos increases human detection and subjective confidence

The development of technologies for easily and automatically falsifying video has raised practical questions about people's ability to detect false information online. How vulnerable are people to deepfake videos? What technologies can be applied to boost their performance? Human susceptibility to deepfake videos is typically measured in laboratory settings, which do not reflect the challenges of real-world browsing. In typical browsing, deepfakes are rare, engagement with the video may be short, participants may be distracted, or the video streaming quality may be degraded. Here, we tested deepfake detection under these ecological viewing conditions, and found that detection was lowered in all cases. Principles from signal detection theory indicated that different viewing conditions affected different dimensions of detection performance. Overall, this suggests that the current literature underestimates people's susceptibility to deepfakes. Next, we examined how computer vision models might be integrated into users' decision process to increase accuracy and confidence during deepfake detection. We evaluated the effectiveness of communicating the model's prediction to the user by amplifying artifacts in fake videos. We found that artifact amplification was highly effective at making fake video distinguishable from real, in a manner that was robust across viewing conditions. Additionally, compared to a traditional text-based prompt, artifact amplification was more convincing: people accepted the model's suggestion more often, and reported higher final confidence in their model-supported decision, particularly for more challenging videos. Overall, this suggests that visual indicators that cause distortions on fake videos may be highly effective at mitigating the impact of falsified video.
Reflected Diffusion Models

Score-based diffusion models learn to reverse a stochastic differential equation that maps data to noise. However, for complex tasks, numerical error can compound and result in highly unnatural samples. Previous work mitigates this drift with thresholding, which projects to the natural data domain (such as pixel space for images) after each diffusion step, but this leads to a mismatch between the training and generative processes. To incorporate data constraints in a principled manner, we present Reflected Diffusion Models, which instead reverse a reflected stochastic differential equation evolving on the support of the data. Our approach learns the perturbed score function through a generalized score matching loss and extends key components of standard diffusion models including diffusion guidance, likelihood-based training, and ODE sampling. We also bridge the theoretical gap with thresholding: such schemes are just discretizations of reflected SDEs. On standard image benchmarks, our method is competitive with or surpasses the state of the art without architectural modifications and, for classifier-free guidance, our approach enables fast exact sampling with ODEs and produces more faithful samples under high guidance weight.
Credit Card Fraud Detection: Smote Technique to Improve Model Performance

In this study credit card fraud detection problem was solved with two machine learning models. Programming language is Python. Dataset is obtained from Kaggle. It is the dataset of September 2013 for European cardholders. The total number of transactions is 284,807. However, only 492 of them are fraud transactions. This means that more than 99% of them are genuine transactions and 0.17% are fraud. Therefore, it was found that the dataset is quite unbalanced. As a first step, exploratory data analysis has performed. Then different machine learning algorithms has applied on this dataset. These models are Random Forest model and Decision Trees model. According to the findings, random forest model works better than decision trees model. However, this dataset has class imbalance problem. If the imbalance problem is solved, models perform better. The reason is that the relevant model predicts the label with higher importance given to genuine transactions and became more accurate. In this study, SMOTE (The Synthetic Minority Oversampling Technique) technique is used to overcome this problem. As explained before, these two machine learning models tested and compared. Random forest model has performed better. Then, random forest model has chosen. According to the results obtained, random forest model has performed better after applying the SMOTE technique.
Capacity Abuse Attack of Deep Learning Models Without Need of Label Encodings

In recent years, Machine Learning (ML) models, especially deep learning models, have become commodities. In this context, data centers which hold a lot of data often buy ML models from ML model providers, train them on their data locally and use the trained models to provide intelligent services. Existing work has shown that there is a risk of data leakage, which could cause incalculable consequences. Even under the black-box condition, there are still some attacks that can steal the private data held by data centers, and the Capacity Abuse Attack (CAA) is the state-of-the-art attack method. CAA attackers steal the training data by labeling malicious samples with the data to be stolen. However, the label encodings are usually mapped into other output forms such as categories, and it is impossible for the adversary to know the mapping relationship between the form outputted by the trained model and the label encodings. Without the mapping relationship, CAA becomes invalid. Aiming at the limitation of CAA, this study proposes a novel practical attack method, i.e., Capacity Abuse Attack II (CAAII), which can find the mapping relationship between the output in the arbitrary form returned by the trained model and the values of the stolen data. Experiments are conducted on MNIST, Fashion-MNIST, and CIFAR10 datasets, and experimental results show that no matter what forms are returned by the model, our attack method can always find the mapping relationship and successfully steals the training data.
Globally and Locally Optimized Pannini Projection for High Fov Rendering of 360° Images

To render a spherical (360º or omnidirectional) image on planar displays, a 2D image - called as viewport - must be obtained, by projecting a sphere region on a plane, according to the user’s viewing direction and a predefined field of view (FoV). However, any sphere to plan projection introduces geometric distortions, such as object stretching and/or bending of straight lines, which intensity increases with the considered FoV. In this paper, a fully automatic content-aware projection is proposed, aiming to reduce the geometric distortions when high FoVs are used. This new projection is based on the Pannini projection, whose parameters are firstly globally optimized according to the image content, followed by a local conformality improvement of relevant viewport objects. A crowdsourcing subjective test showed that the proposed projection is the most preferred solution among the considered state-of-the-art sphere to plan projections, producing viewports with a more pleasant visual quality.
Content Restructures Thought

This paper argues that the conventions surrounding content encourage certain ways of paying attention in media ecologies. The author uses the concept of attention as a new materialist rhetorical lens to understand writing-as-content and proposes two additional attributes that complement Dush’s (2015) definition of content as a metaphor for writing. The attention-related qualities of scannability and selectabilty primarily determine whether a text is more content-like or less content-like. Writing-as-content represents a paradigm shift in the way that we approach writing and communication on a large scale, with not always desirable effects. This article discusses how in particular the areas of interpersonal communication and education are being affected by this shift and considers ways to respond going forward.
Expert Interviews of Drivers, Barriers and Social Considerations for Ai Adoption in Scm

This study explored the challenges and recommendations for implementing Artificial Intelligence (AI) in Supply Chain Management (SCM). The experts identified several drivers for AI adoption in SCM, including increased efficiency, improved decision-making, and reduced costs. However, they highlighted several barriers to AI adoption, such as data quality and management issues, resistance to change, and lack of understanding and trust in AI. To overcome these barriers and ensure successful AI implementation, companies should involve all stakeholders, focus on data quality and management, and ensure the AI solution integrates with existing processes and workflows. In addition, companies should also avoid common mistakes when implementing AI, such as neglecting the importance of explainability and transparency in AI decision-making, underestimating the importance of involving all stakeholders, and rushing into large-scale implementation without conducting small-scale pilot projects. By following the recommendations and avoiding common mistakes, companies can harness the benefits of AI in SCM while minimising risks and challenges.