Reviewerly/exHarmony huggingface dataset:
The Geometry of Feature Space in Deep Learning Models: A Holistic Perspective and Comprehensive Review

As the field of deep learning experiences a meteoric rise, the urgency to decipher the complex geometric properties of feature spaces, which underlie the effectiveness of diverse learning algorithms and optimization techniques, has become paramount. In this scholarly review, a comprehensive, holistic outlook on the geometry of feature spaces in deep learning models is provided in order to thoroughly probe the interconnections between feature spaces and a multitude of influential factors such as activation functions, normalization methods, and model architectures. The exploration commences with an all-encompassing examination of deep learning models, followed by a rigorous dissection of feature space geometry, delving into manifold structures, curvature, wide neural networks and Gaussian processes, critical points and loss landscapes, singular value spectra, and adversarial robustness, among other notable topics. Moreover, transfer learning and disentangled representations in feature space are illuminated, accentuating the progress and challenges in these areas. In conclusion, the challenges and future research directions in the domain of feature space geometry are outlined, emphasizing the significance of comprehending overparameterized models, unsupervised and semi-supervised learning, interpretable feature space geometry, topological analysis, and multimodal and multi-task learning. Embracing a holistic perspective, this review aspires to serve as an exhaustive guide for researchers and practitioners alike, clarifying the intricacies of the geometry of feature spaces in deep learning models and mapping the trajectory for future advancements in this enigmatic and enthralling domain.
FAMM: Facial Muscle Motions for Detecting Compressed Deepfake Videos Over Social Networks

As a face manipulation technique, the misuse of Deepfakes poses potential threats to the state, society, and individuals. Several countermeasures have been proposed to reduce the negative effects produced by Deepfakes. Current detection methods achieve satisfactory performance in dealing with uncompressed videos. However, videos are generally compressed when spread over social networks because of limited bandwidth and storage space, which generates compression artifacts and the detection performance inevitably decreases. Hence, how to effectively identify compressed Deepfake videos over social networks becomes a significant problem in video forensics. In this paper, we propose a facial-muscle-motions-based (FAMM) framework to solve the problem of compressed Deepfake video detection. Specifically, we first locate faces from consecutive frames and extract landmarks from the face images. Then, continuous facial landmarks are utilized to construct facial muscle motion features by modeling the five sensory and face regions. Finally, we fuse the diverse forensic knowledge using Dempster-Shafer theory and provide the final detection results. Furthermore, we demonstrate the effectiveness of FAMM through analyzing mutual information, compression procedure, and facial landmarks for compressed Deepfake videos. Theoretical analyses illustrate that compression does not affect facial muscle motion feature construction and the differences in designed features exist between the real and Deepfake videos. Extensive experimental results conclude that the proposed method outperforms the state-of-the-art methods in detecting compressed Deepfake videos. More importantly, FAMM achieves comparable detection performance on compressed videos that are over real-world social networks.
Deep Progressive Asymmetric Quantization Based on Causal Intervention for Fine-Grained Image Retrieval

In the field of computer vision, fine-grained image retrieval is an extremely challenging task due to the inherently subtle intra-class object variations. In addition, the high-dimensional real-valued features extracted from large-scale fine-grained image datasets slow the retrieval speed and increase the storage cost. To solve above issues, existing fine-grained image retrieval methods mainly focus on finding more discriminative local regions for generating discriminative and compact hash codes, which achieve limited fine-grained image retrieval performance due to the large quantization errors and the confounding granularities and context of discriminative parts, i.e., the correct recognition of fine-grained objects mainly attribute to the discriminative parts and their context. To learn robust causal features and reduce the quantization errors, we propose a deep progressive asymmetric quantization (DPAQ) method based on causal intervention to learn compact and robust descriptions for fine-grained image retrieval task. Specifically, we introduce a structural causal model to learn robust casual features via causal intervention for fine-grained visual recognition. Subsequently, we design a progressive asymmetric quantization layer in the feature embedding space, which can preserve the semantic information and reduce the quantization errors sufficiently. Finally, we incorporate both the fine-grained image classification and retrieval tasks into an end-to-end deep learning architecture for generating robust and compact descriptions. Experimental results on several fine-grained image retrieval datasets demonstrate that the proposed DPAQ method performs the best for fine-grained image retrieval task and surpasses the state-of-the art fine-grained hashing methods by a large margin.
Big data mining on trace element geochemistry of sphalerite

The determination of ore genesis is a main challenge in ore deposit research. Advanced and rapid analytical techniques have given rise to the accumulation of massive amounts of geoscientific data. Therefore, it is imperative to conduct data mining on ore geochemical data to efficiently extract useful metallogenic information. In this contribution, 4095 sets of sphalerite trace element data from 86 deposits of different origins (VMS, MVT, porphyry, epithermal, SEDEX, and skarn) were compiled and analyzed. Factor analysis revealed the effects of physicochemical conditions on sphalerite trace element compositions. Specifically, the high Mn-Fe-Cu-Co-In but low As-Ga-Ge-Sb-Pb concentration of sphalerite is commonly related to decreasing pH or increasing temperature; high sulfur fugacity favors the entry of Fe and Co (but not Ga or Sn) into the sphalerite lattice; and high salinity causes enrichment in Cd-Ga-Ge-Mn, but depletion in Ag-Cu-In-Sb. Multivariate statistical analysis reveals that the Mn-Ge-Sn contents in sphalerite have good potential to differentiate among ore genetic types, the Mn-Ge-In are used to determine between magmatic-hydrothermal and non-magmatic-hydrothermal deposits. Furthermore, machine learning models demonstrate high accuracy of sphalerite trace element data in distinguishing ore types, i.e., 93.02 % (random forest) and 92.82 % (gradient boosting), and its reliability was validated by receiver operating characteristics. Additionally, the blind tests by machine learning on sphalerite trace elements indicate that the Qingshuitang deposit (in the Qin-Hang metallogenic belt, South China), have been an MVT deposit, which is also supported by its low-temperature and high-salinity fluids, wallrock alterations (esp. bariteization), and the obvious age distinction between mineralization and local magmatism. This study highlights that machine learning and multivariate statistical analysis on sphalerite trace-element data can differentiate metallogenic origin and conditions.
Boosted federated learning based on improved Particle Swarm Optimization for healthcare IoT devices

As healthcare data becomes increasingly available from various sources, including clinical institutions, patients, insurance companies, and pharmaceutical industries, machine learning (ML) services are becoming more significant in healthcare-facing domains. Therefore, it is imperative to ensure the integrity and reliability of ML models to maintain the quality of healthcare services. Particularly due to the growing need for privacy and security, healthcare data has resulted in each Internet of Things (IoT) device being treated as an independent source of data, isolated from other devices. Moreover, the limited computational and communication capabilities of wearable healthcare devices hinder the applicability of traditional ML. Federated Learning (FL) is a paradigm that maintains data privacy by storing only learned models on a server and advances with data from scattered clients, making it ideal for healthcare applications where patient data must be safeguarded. The potential of FL to transform healthcare is significant, as it can enable the development of new ML-powered applications that can enhance the quality of care, lower costs, and improve patient outcomes. However, the accuracy of current Federated Learning aggregation methods suffers greatly in unstable network situations due to the high volume of weights transmitted and received. To address this issue, we propose an alternative approach to Federated Average (FedAvg) that updates the global model by gathering score values from learned models primarily utilized in Federated Learning, using an improved version of Particle Swarm Optimization (PSO) called FedImpPSO. This approach boosts the robustness of the algorithm in erratic network conditions. To further enhance the speed and efficiency of data exchange within a network, we modify the format of the data clients send to servers using the FedImpPSO method. The proposed approach is evaluated using the CIFAR-10 and CIFAR-100 datasets and a Convolutional Neural Network (CNN). We found that it yielded an average accuracy improvement of 8.14% over FedAvg and 2.5% over Federated PSO (FedPSO). This study evaluates the use of FedImpPSO in healthcare by training a deep-learning model over two case studies to evaluate the effectiveness of our approach in healthcare. The first case study involves the classification of COVID-19 using public datasets (Ultrasound and X-ray) and achieved an F1-measure of 77.90% and 92.16%, respectively. The second case study was conducted over the cardiovascular dataset, where our proposed FedImpPSO achieves 91.18% and 92% accuracy in predicting the existence of heart diseases. As a result, our approach demonstrates the effectiveness of using FedImpPSO to improve the accuracy and robustness of Federated Learning in unstable network conditions and has potential applications in healthcare and other domains where data privacy is critical.
Leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems

This work compares two approaches to provide metacognitive interventions and their impact on preparing students for future learning across Intelligent Tutoring Systems (ITSs). In two consecutive semesters, we conducted two classroom experiments: Exp. 1 used a classic artificial intelligence approach to classify students into different metacognitive groups and provide static interventions based on their classified groups. In Exp. 2, we leveraged Deep Reinforcement Learning (DRL) to provide adaptive interventions that consider the dynamic changes in the student's metacognitive levels. In both experiments, students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that adaptive DRL-based interventions closed the metacognitive skills gap between students. In contrast, static classifier-based interventions only benefited a subset of students who knew how to use BC in advance. Additionally, our DRL agent prepared the experimental students for future learning by significantly surpassing their control peers on both ITSs.
An aggregation of aggregation methods in computational pathology

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions.
Real-time decoding for fault-tolerant quantum computing: progress, challenges and outlook

Abstract Quantum computing is poised to solve practically useful problems which are computationally intractable for classical supercomputers. However, the current generation of quantum computers are limited by errors that may only partially be mitigated by developing higher-quality qubits. Quantum error correction (QEC) will thus be necessary to ensure fault tolerance. QEC protects the logical information by cyclically measuring syndrome information about the errors. An essential part of QEC is the decoder, which uses the syndrome to compute the likely effect of the errors on the logical degrees of freedom and provide a tentative correction. The decoder must be accurate, fast enough to keep pace with the QEC cycle (e.g. on a microsecond timescale for superconducting qubits) and with hard real-time system integration to support logical operations. As such, real-time decoding is essential to realize fault-tolerant quantum computing and to achieve quantum advantage. In this work, we highlight some of the key challenges facing the implementation of real-time decoders while providing a succinct summary of the progress to-date. Furthermore, we lay out our perspective for the future development and provide a possible roadmap for the field of real-time decoding in the next few years. As the quantum hardware is anticipated to scale up, this perspective article will provide a guidance for researchers, focusing on the most pressing issues in real-time decoding and facilitating the development of solutions across quantum, nano and computer science.
DISC: Learning from Noisy Labels via Dynamic Instance-Specific Selection and Correction

Existing studies indicate that deep neural networks (DNNs) can eventually memorize the label noise. We observe that the memorization strength of DNNs towards each instance is different and can be represented by the confidence value, which becomes larger and larger during the training process. Based on this, we propose a Dynamic Instance-specific Selection and Correction method (DISC) for learning from noisy labels (LNL). We first use a two- view-based backbone for image classification, obtaining confidence for each image from two views. Then we propose a dynamic threshold strategy for each instance, based on the momentum of each instance's memorization strength in previous epochs to select and correct noisy labeled data. Benefiting from the dynamic threshold strategy and two-view learning, we can effectively group each instance into one of the three subsets (i.e., clean, hard, and purified) based on the prediction consistency and discrepancy by two views at each epoch. Finally, we employ different regularization strategies to conquer subsets with different degrees of label noise, improving the whole network's robustness. Comprehensive evaluations on three controllable and four real-world LNL benchmarks show that our method outperforms the state-of-the-art (SOTA) methods to leverage useful information in noisy data while alleviating the pollution of label noise. Code is available at https://github.com/JackYFL/DISC.
Quantum computing and industrial information integration: A review

Originally a part of the study of physics, quantum mechanics is fast becoming an interdisciplinary field. Quantum computing offers the potential to reach the higher levels of accuracy and security associated with a much faster speedup. Many different approaches have been proposed to building a quantum computer. Seeking the best solution for quantum computers is the mission of both practitioners and researchers. Quantum mechanics is a creative technique that has the potential to redefine information-related disciplines. Unique properties of quantum superposition and entanglement will enable powerful computational scale at a faster and more efficient speed than classical computers, and quantum computing is attracting attention for implementation across broader industries. This study attempts to depict the roadmaps of quantum computing and industrial information integration as they move into the future, based on reviewing the extant popular literature.
Stringent bounds on HWW and HZZ anomalous couplings with quantum tomography at the LHC

A bstract Quantum tomography provides the full reconstruction of the density matrix of a state. We use it to study the Higgs boson decay into weak gauge bosons. Anomalous couplings beyond the Standard Model can be constrained by means of observables easily defined in terms of the polarization density matrix. We describe a strategy based on three observables that together provide the most stringent limits. Two of these observables are linked to the entanglement between the polarizations of the two gauge bosons, the other is based on CP-odd combinations of one momentum and two polarizations. We find for the Z channel that this strategy offers, already with the available LHC data, limits competitive with the best available bounds. We argue that the inclusion of these observables in routine experimental analyses can lead to more stringent global fit limits.
Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application With Blockchain-Based Authentication

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block.
L2CS-Net : Fine-Grained Gaze Estimation in Unconstrained Environments

Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. The performance of existing appearance-based gaze methods remains unsatisfactory due to the uniqueness of eye appearance, lightning conditions, and the diversity of head pose and gaze directions. In this paper, we propose a novel multi-loss two-branch CNN architecture (L2CS-Net) to explicitly learn the discriminative features for each gaze angle by predicting each gaze angle using a separate fully connected layer and loss function. In addition, we introduce a new multi-loss gaze function that consists of combined classification and regression losses to further enhance the model performance. We perform gaze classification utilizing a softmax layer along with cross-entropy loss. To obtain fine-grained predictions, we calculate the expectation of the gaze-class probabilities followed by a Mean Squared Error (MSE) loss. We evaluated our model with two popular datasets collected with unconstrained settings. Our proposed model achieves state-of-the-art performance on the MPIIGaze and Gaze360 datasets, respectively. We make our code open source at https://github.com/Ahmednull/L2CS-Net.
Conceptual Basis of the Lexicon in Machine Translation

This chapter describes the organization and content of lexical information used by unitran, an interlingual machine translation system. Lexical word entries have two levels of description: The first is an underlying form called lexical conceptual structure that is derived from hierarchically organized primitives, and the second is a mapping from this representation to a corresponding syntactic structure. The interaction between these two levels of lexical description is crucial for the fulfillment of two tasks, lexical selection (i.e., word choice) and syntactic realization (i.e., generation of syntactic structure), both of which are necessary for the resolution of a number of different source-language/target-language divergences. The two levels of lexical description and the tasks of lexical selection and syntactic realization are described. In addition, the approach described here is compared to others that have considered the problem of syntactic divergence resolution.
Assessing the Contribution of Representation to Results

In this paper, we make a methodological point concerning the contribution of the representation of the output of a neural network model when using the model to compare to human error performance. We replicate part of Dell, Juliano & Govindjee’s work on modeling speech errors using recurrent networks (Dell et al., 1993). We find that 1) the error patterns reported by Dell et al. do not appear to remain when more networks are used; and 2) some components of the error patterns that are found can be accounted for by simply adding Gaussian noise to the output representation they used. We suggest that when modeling error behavior, the technique of adding noise to the output representation of a network should be used as a control to assess to what degree errors may be attributed to the underlying network.
Experimental Study of Stroke Shortcuts for a Touchscreen Keyboard with Gesture-Redundant Keys Removed

We present experimental results for two-handed typing on a graphical Qwerty keyboard augmented with linear strokes for Space, Backspace, Shift, and Enter---that is, swipes to the right, left, up, and diagonally down-left, respectively. A first study reveals that users are more likely to adopt these strokes, and type faster, when the keys corresponding to the strokes are removed from the keyboard, as compared to an equivalent stroke-augmented keyboard with the keys intact. A second experiment shows that the keys-removed design yields 16% faster text entry than a standard graphical keyboard for phrases containing mixed-case alphanumeric and special symbols, without increasing error rate. Furthermore, the design is easy to learn: users exhibited performance gains almost immediately, and 90% of test users indicated they would want to use it as their primary input method.