Reviewerly/exHarmony huggingface dataset:
The TAP free energy for high-dimensional linear regression

We derive a variational representation for the log-normalizing constant of the posterior distribution in Bayesian linear regression with a uniform spherical prior and an i.i.d. Gaussian design. We work under the "proportional" asymptotic regime, where the number of observations and the number of features grow at a proportional rate. Our representation holds when the variance of the additive noise is sufficiently large, which corresponds to a high-temperature condition in statistical physics. This rigorously establishes the Thouless–Anderson–Palmer (TAP) approximation arising from spin glass theory, and proves a conjecture of (In 2014 IEEE International Symposium on Information Theory (2014) 1499–1503 IEEE) in the special case of the spherical prior (at sufficiently high temperature).
GEIKD: Self-knowledge distillation based on gated ensemble networks and influences-based label noise removal

Self-distillation has gained widespread attention in recent years because it progressively transfers the knowledge in end-to-end training schemes within one network. However, self-distillation methods are susceptible to label noise hence leading to poor generalization performance. To address this problem, this paper proposes a novel self-distillation method, called GEIKD, which combines a gated ensemble self-teacher network and the influences-based label noise removal. Specifically, we design a gated ensemble self-teacher network composed of multiple teacher branches, which allows a gated fused knowledge based on a weighted bi-directional feature pyramid network. Moreover, we introduce influences estimation into the distillation process to quantify the effect of noisy labels on the distillation loss, and then reject the unfavorable instances as noisy labeled samples according to the calculated influences. Our influences-based label noise removal can be integrated with any existing knowledge distillation training schemes. The impact of noisy labels on knowledge distillation can be significantly alleviated by the proposed noisy instances removal with little extra training efforts. Experiments show that the proposed GEIKD method outperforms the state-of-the-art methods on CIFAR-100, TinyimageNet and fine-grained datasets CUB200, MIT-67, Stanford40 and FERC dataset, using clean data and data with noisy labels.
Attentive spatial-temporal contrastive learning for self-supervised video representation

Most existing self-supervised works learn video representation by using a single pretext task. A single pretext task, providing single supervision from unlabeled data, may neglect to describe the difference between spatial features and temporal features. The similar spatial features and temporal features may hinder distinguishing between two similar videos with different class labels. In this paper, we propose an attentive spatial–temporal contrastive learning network (ASTCNet), which learns self-attention spatial–temporal features by contrastive learning between multiple spatial and temporal pretext tasks. The spatial features are learned by multiple spatial pretext tasks, including spatial rotation, and spatial jigsaw. Each spatial feature is enhanced with spatial self-attention by learning the relation between patches. The temporal features are learned by multiple temporal pretext tasks, including temporal order, and temporal pace. Each temporal feature is enhanced with temporal self-attention by learning the relation between frames, and is enhanced by feeding the optical flow features into a motion module. To separate the spatial feature and the temporal feature learned in one video, we represent the video as different features for each pretext task, and design pretext task-based contrastive loss. The pretext task-based contrastive loss encourages the different pretext tasks to learn dissimilar features, and encourages the same pretext task to learn similar features. The pretext task-based contrastive loss can learn the discriminative features for each pretext task in one video. The experiments show that our method achieves state-of-the-art performance for self-supervised action recognition on the UCF101 dataset and the HMDB51 dataset.
Multistate transition dynamics by strong time-dependent perturbation in NISQ era

Abstract We develop a quantum computing scheme utilizing McLachlan variational principle in a hybrid quantum–classical algorithm to accurately calculate the transition dynamics of a closed quantum system with many excited states subject to a strong time-dependent perturbation. A systematic approach for optimal construction of a general N -state ansatz with unary N -qubit encoding is refined. We also use qubit efficient encoding in McLachlan variational quantum algorithm to reduce the number of qubits to <?CDATA ${\mathrm{log}}_{2}N$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>log</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> <mml:mi>N</mml:mi> </mml:math> , simultaneously diminishing depths of the quantum circuits. The significant reduction of the number of time steps is achieved by use of the second order marching method. Instrumental in obtaining high accuracy are adaptations of the circuits to include time-dependent global phase correction. We illustrated, tested and optimized our quantum computing algorithm on a set of 16 bound hydrogenic eigenstates exposed to a strong laser attosecond pulse. Results for transition probabilities are obtained with accuracy better than 1%, as established by comparison to the benchmark data. Use of interaction representation of the Hamiltonian reduces the noise accumulation while the quantum system evolves in time.
On Isotropy of Multimodal Embeddings

Embeddings, i.e., vector representations of objects, such as texts, images, or graphs, play a key role in deep learning methodologies nowadays. Prior research has shown the importance of analyzing the isotropy of textual embeddings for transformer-based text encoders, such as the BERT model. Anisotropic word embeddings do not use the entire space, instead concentrating on a narrow cone in such a pretrained vector space, negatively affecting the performance of applications, such as textual semantic similarity. Transforming a vector space to optimize isotropy has been shown to be beneficial for improving performance in text processing tasks. This paper is the first comprehensive investigation of the distribution of multimodal embeddings using the example of OpenAI’s CLIP pretrained model. We aimed to deepen the understanding of the embedding space of multimodal embeddings, which has previously been unexplored in this respect, and study the impact on various end tasks. Our initial efforts were focused on measuring the alignment of image and text embedding distributions, with an emphasis on their isotropic properties. In addition, we evaluated several gradient-free approaches to enhance these properties, establishing their efficiency in improving the isotropy/alignment of the embeddings and, in certain cases, the zero-shot classification accuracy. Significantly, our analysis revealed that both CLIP and BERT models yielded embeddings situated within a cone immediately after initialization and preceding training. However, they were mostly isotropic in the local sense. We further extended our investigation to the structure of multilingual CLIP text embeddings, confirming that the observed characteristics were language-independent. By computing the few-shot classification accuracy and point-cloud metrics, we provide evidence of a strong correlation among multilingual embeddings. Embeddings transformation using the methods described in this article makes it easier to visualize embeddings. At the same time, multiple experiments that we conducted showed that, in regard to the transformed embeddings, the downstream tasks performance does not drop substantially (and sometimes is even improved). This means that one could obtain an easily visualizable embedding space, without substantially losing the quality of downstream tasks.
A CP-ABE Scheme Based on Lattice LWE and Its Security Analysis

In recent years, the research on ciphertext-policy attribute-based encryption (CP-ABE) scheme design based on learning with errors (LWE) has been a challenging problem, and many researchers have made a lot of attempts at it. At EUROCRYPT 2021, Datta, Komargodski, and Waters proposed the first provably secure direct CP-ABE construction that supports NC1 circuit access structures. Improving on their work, we propose a CP-ABE scheme. Technically, we use the new lattice two-stage sampling technique of Lai, Liu, and Wang (EUROCRYPT 2021) in the key generation phase instead of the extended trapdoor sampling technique. In this way, we obtain a CP-ABE scheme, which is at least as secure as the original scheme under the same conditions, and has a shorter ciphertext; we provide an innovative design idea for designing CP-ABE schemes only based on LWE, although this is only a partial theoretical work.
De-Selection Bias Recommendation Algorithm Based on Propensity Score Estimation

There are various biases present in recommendation systems, and recommendation results that do not consider these biases are unfair to users, items, and platforms. To address the problem of selection bias in recommendation systems, in this study, the propensity score was utilized to mitigate this bias. A selection bias propensity score estimation method (SPE) was developed, which takes into account both user and item information. This method accurately estimates the user’s choice tendency by calculating the degree of difference between the user’s selection rate and the selected preference of the item. Subsequently, the SPE method was combined with the traditional matrix decomposition-based recommendation algorithms, such as the latent semantic model (LFM) and the bias singular value model (BiasSVD). The propensity score was then inversely weighted into the loss function, creating a recommendation model that effectively eliminated selection bias. The experiments were carried out on the public dataset MovieLens, and root mean square error (RMSE) and mean absolute error (MAE) were selected as evaluation indicators and compared with two baseline models and three models with other propensity score estimation methods. Overall, the experimental results demonstrate that the model combined with SPE achieves a minimum increase of 2.00% in RMSE and 2.97% in MAE compared to its baseline model. Moreover, in comparison to other propensity score estimation methods, the SPE method effectively eliminates selection bias in the scoring data, thereby enhancing the performance of the recommendation model.
An ASP Framework for the Refinement of Authorization and Obligation Policies

Abstract This paper introduces a framework for assisting policy authors in refining and improving their policies. In particular, we focus on authorization and obligation policies that can be encoded in Gelfond and Lobo’s $\mathscr{AOPL}$ language for policy specification. We propose a framework that detects the statements that make a policy inconsistent, underspecified, or ambiguous with respect to an action being executed in a given state. We also give attention to issues that arise at the intersection of authorization and obligation policies, for instance when the policy requires an unauthorized action to be executed. The framework is encoded in Answer Set Programming.
Blockchain in Higher Education: A Secure Traceability Architecture for Degree Verification.

College fraud has become a global phenomenon due to the increasing demand for diplomas in the labor market. Credential fraud is a multi-billion-dollar industry, and no country is immune to this problem. Almost every employer has or will encounter diplomas produced through the fraudulent activities of students or officials of legitimate institutions or issued by fake institutions. Because the accreditation process and the university's internal data systems are opaque to employers, employers must trust intermediaries involved in the accreditation process, such as students, faculty, and university officials, to ensure the legitimacy of the accomplishments claimed in the credentials. Due to this trust issue, there is always a risk of error and fraudulent activity leading to the creation and presentation of fake diplomas. An emerging technology called blockchain promises to build trustless and transparent systems where we do not have to trust any intermediary or central authority to conduct and record transactions between two strangers. Blockchain technology can help develop a trustless distributed digital platform through a peer-to-peer consensus network open to all to innovate their own trustless business models. It builds integrity and trust among strangers. People from different industries are using blockchain technology to develop applications to enable trustless transactions and protect systems and data from fraudulent activities This paper aims to explore how blockchain technology can be used to establish a certificate system that operates without intermediaries, such as students, teachers, and university officials. By leveraging the potential of blockchain technology, the system can be made trustless, and fraudulent activities within the certificate system can be prevented.
Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach

Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model's grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.
Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise

Abstract This paper presents twin-hyperspheres of resisting noise for binary classification to imbalanced data with noise. First, employing the decision of evaluating the contributions created by points for the training of the hyperspheres, then the label density estimator is introduced into the fuzzy membership to quantize the provided contributions, and finally, unknown points can be assigned into corresponding classes. Utilizing the decision, the interference created by the noise hidden in the data is suppressed. Experiment results show that when noise ratio reaches 90%, classification accuracies of the model are 0.802, 0.611 on the synthetic datasets and UCI datasets containing Gaussian noise, respectively. Classification results of the model outperform these of the competitors, and these boundaries learned by the model to separate noise from majority classes and minority classes are superior to these learned by the competitors. Moreover, efforts gained by the proposed density fuzzy are effectiveness in noise resistance; meanwhile, the density fuzzy does not rely on specific classifiers or specific scenarios.
Analysis and Improvement of the Effect of Listening Anxiety on College English Teaching Based on Neural Network

Listening is an important form of language input, especially for students.Language learning should be mainly based on auditory input, so listening plays a very important role in the process of language learning.Listening is one of the language skills most likely to cause anxiety in foreign language learners.In order to better analyze the impact of listening anxiety on the effect of college English teaching, we propose an analysis and improvement method based on a neural network model.The goal of this paper is to utilize a neural network model for analyzing the various factors that influence psychological anxiety of college students and to formulate targeted and directional countermeasures for the causes of this problem.We propose a neural network-based model for the English listening anxiety classification and recognition.Based on this method, we expect to effectively reduce the anxiety of college students in college English listening learning, so as to improve the quality of English listening teaching.Specifically, firstly, we design a questionnaire to collect data on students, such as anxiety level, reasons, difficulty of listening materials, classroom atmosphere, emotion.Secondly, we utilize the text material obtained from the questionnaire as training data for the model, and we design a text processing and transformation method.Thirdly, we design a feature extraction and anxiety classification method using a neural network model.Finally, through experiments with students in different classes, we have confirmed that the method proposed in this paper demonstrates excellent performance in classifying and recognizing English anxiety.
Face-voice based multimodal biometric authentication system via FaceNet and GMM

Information security has become an inseparable aspect of the field of information technology as a result of advancements in the industry. Authentication is crucial when it comes to dealing with security. A user must be identified using biometrics based on certain physiological and behavioral markers. To validate or establish the identification of an individual requesting their services, a variety of systems require trustworthy personal recognition schemes. The goal of such systems is to ensure that the offered services are only accessible by authorized users and not by others. This case study provides enhanced accuracy for multimodal biometric authentication based on voice and face hence, reducing the equal error rate. The proposed scheme utilizes the Gaussian mixture model for voice recognition, FaceNet model for face recognition and score level fusion to determine the identity of the user. The results reveal that the proposed scheme has the lowest equal error rate in comparison to the previous work.
Improved Vehicle Detection Using Weather Classification and Faster R-CNN with Dark Channel Prior

Recent advancements in artificial intelligence have led to significant improvements in object detection. Researchers have focused on enhancing the performance of object detection in challenging environments, as this has the potential to enhance practical applications. Deep learning has been successful in image classification and target detection and has a wide range of applications, including vehicle detection. However, object detection models trained on high-quality images often struggle to perform well under adverse weather conditions, such as fog and rain. In this paper, we propose an improved vehicle detection method using weather classification and a Faster R-CNN with a dark channel prior (DCP). The proposed method first classifies the weather within the image, preprocesses the image using the dark channel prior (DCP) based on the classification result, and then performs vehicle detection on the preprocessed image using a Faster R-CNN. The effectiveness of the proposed method is shown through experiments with images in various weather conditions.
Understanding Antecedents That Affect Customer Evaluations of Head-Mounted Display VR Devices through Text Mining and Deep Neural Network

Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors are expected to influence customer evaluations. Therefore, the present study aims to: (1) analyze customer reviews of hands-on HMD VR devices, provided with new user experience (UX), using text mining, and artificial neural network techniques; (2) comprehensively examine variables that affect user evaluations of VR devices; and (3) suggest major implications for the future development of VR devices. The research procedure consisted of four steps. First, customer reviews on HMD VR devices were collected from Amazon.com. Second, candidate variables were selected based on a literature review, and sentiment scores were extracted. Third, variables were determined through topic modeling, in-depth interviews, and a review of previous studies. Fourth, an artificial neural network analysis was performed by setting customer evaluation as a dependent variable, and the influence of each variable was checked through feature importance. The results indicate that feature importance can be derived from variables, and actionable implications can be identified, unlike in general sentiment analysis.
Surprise-based JND estimation for perceptual quantization in H.265/HEVC codecs

Just noticeable distortion (JND), reflecting the perceptual redundancy directly, has been widely used in image and video compression. However, the human visual system (HVS) is extremely complex and the visual signal processing has not been fully understood, which result in existing JND models are not accurate enough and the bitrate saving of JND-based perceptual compression schemes is limited. This paper presents a novel pixel-based JND model for videos and a JND-based perceptual quantization scheme for HEVC codecs. In particular, positive and negative perception effects of the inter-frame difference and the motion information are analyzed and measured with an information-theoretic approach. Then, a surprise-based JND model is developed for perceptual video coding (PVC). In our PVC scheme, the frame-level perceptual quantization parameter (QP) is derived on the premise that the coding distortion is infinitely close to the estimated JND threshold. On the basis of the frame-level perceptual QP, we determine the perceptual QP for each coding unit through a perceptual adjustment function to achieve better perceptual quality. Experimental results indicate that the proposed JND model outperforms existing models significantly, the proposed perceptual quantization scheme improves video compression efficiency with better perceptual quality and lower coding complexity.