Reviewerly/exHarmony huggingface dataset:
Mixed Quantization Enabled Federated Learning to Tackle Gradient Inversion Attacks

Federated Learning (FL) enables collaborative model building among a large number of participants without the need for explicit data sharing. But this approach shows vulnerabilities when gradient inversion attacks are applied to it. FL models are at higher risk in the event of a gradient inversion attacks, which has a higher success rate in retrieving sensitive data from the model gradients, due to the presence of communication in their inherent architecture. The most alarming thing about this gradient inversion attack is that it can be performed in such a covert way that it does not hamper the training performance while the attackers backtrack from the gradients to get information about the raw data. Some of the common existing approaches proposed to prevent data reconstruction in the context of FL are adding noise with differential privacy, homomorphic encryption, and gradient pruning. These approaches suffer from some major drawbacks, including a tedious key generation process during encryption with an increasing number of clients, a significant performance drop, and difficulty in selecting a suitable pruning ratio. As a countermeasure, we propose a mixed quantization enabled FL scheme, and we empirically show that issues addressed above can be resolved. In addition, our approach can ensure more robustness as different layers of the deep model are quantized with different precisions and quantization modes. We empirically proved the validity of our defense method against both the iteration based and recursion based gradient inversion attacks and evaluated the performance of our proposed FL framework on three benchmark datasets and found out that our approach outperformed the baseline defense mechanisms.
FashionVQA: A Domain-Specific Visual Question Answering System

Humans apprehend the world through various sensory modalities, yet language is their predominant communication channel. Machine learning systems need to draw on the same multimodal richness to have informed discourses with humans in natural language; this is particularly true for systems specialized in visually-dense information, such as dialogue, recommendations, and search engines for clothing. To this end, we train a visual question-answering (VQA) system to answer complex natural language questions about apparel in fashion photoshoot images. The key to the successful training of our VQA model is the automatic creation of a visual question-answering dataset with 168 million samples from item attributes of 207 thousand images using diverse templates. The sample generation employs a strategy that considers the difficulty of the question-answer pairs to emphasize challenging concepts. We see that using the same transformer for encoding the question and decoding the answer, as in language models, achieves maximum accuracy, showing that visual language models (VLMs) make the optimal visual question-answering systems for our dataset. The accuracy of the best model surpasses the human expert level. Our approach for generating a large-scale multimodal domain-specific dataset provides a path for training specialized models capable of communicating in natural language. The training of such domain-expert models, e.g., our fashion VLM model, cannot rely solely on the large-scale general-purpose datasets collected from the web.
Electromagnetic Information Theory in Phase-Space: A Quantum Tunnelling Approach

We describe a mathematical physics approach to electromagnetic information theory. The approach is based on quantum tunnelling theories that are formulated in a ray dynamical phase-space. Starting from a scattering model of waves transmitted across two holographic surfaces, we show that the eigenvalues corresponding to communication modes supported by the surfaces can be approximated as tunnelling rates. The related phase-space calculations provide simplified formulas of degrees of freedom for diversity in the near-field. The approach results in rigorous, physics-based framework for general characterisation of surface communications in free-space and confined environments.
Adaptive Human-Centric Video Compression for Humans and Machines

We propose a novel framework to compress human-centric videos for both human viewing and machine analytics. Our system uses three coding branches to combine the power of generic face-prior learning with data-dependent detail recovery. The generic branch embeds faces into a discrete code space described by a learned high-quality (HQ) codebook, to reconstruct an HQ baseline face. The domain-adaptive branch adjusts reconstruction to fit the current data domain by adding domain-specific information through a supplementary codebook. The task-adaptive branch derives assistive details from a low-quality (LQ) input to help machine analytics on the restored face. Adaptive weights are introduced to balance the use of domain-adaptive and task-adaptive features in reconstruction, driving trade-offs among criteria including perceptual quality, fidelity, bitrate, and task accuracy. Moreover, the proposed online learning mechanism automatically adjusts the adaptive weights according to the actual compression needs. By sharing the main generic branch, our framework can extend to multiple data domains and multiple tasks more flexibly compared to conventional coding schemes. Our experiments demonstrate that at very low bitrates we can restore faces with high perceptual quality for human viewing while maintaining high recognition accuracy for machine use.
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge

Unsupervised lifelong learning refers to the ability to learn over time while memorizing previous patterns without supervision. Although great progress has been made in this direction, existing work often assumes strong prior knowledge about the incoming data (e.g., knowing the class boundaries), which can be impossible to obtain in complex and unpredictable environments. In this paper, motivated by real-world scenarios, we propose a more practical problem setting called online self-supervised lifelong learning without prior knowledge. The proposed setting is challenging due to the non-iid and single-pass data, the absence of external supervision, and no prior knowledge. To address the challenges, we propose Self-Supervised ContrAstive Life long LEarning without Prior Knowledge (SCALE) which can extract and memorize representations on the fly purely from the data continuum. SCALE is designed around three major components: a pseudo-supervised contrastive loss, a self-supervised forgetting loss, and an online memory update for uniform subset selection. All three components are designed to work collaboratively to maximize learning performance. We perform comprehensive experiments of SCALE under iid and four non-iid data streams. The results show that SCALE outperforms the state-of-the-art algorithm in all settings with improvements up to 3.83%, 2.77% and 5.86% in terms of kNN accuracy on CIFAR-10, CIFAR-100, and TinyImageNet datasets. We release the implementation at https://github.com/Orienfish/SCALE.
DeFlow: Self-supervised 3D Motion Estimation of Debris Flow

Existing work on scene flow estimation focuses on autonomous driving and mobile robotics, while automated solutions are lacking for motion in nature, such as that exhibited by debris flows. We propose DeFlow, a model for 3D motion estimation of debris flows, together with a newly captured dataset. We adopt a novel multi-level sensor fusion architecture and self-supervision to incorporate the inductive biases of the scene. We further adopt a multi-frame temporal processing module to enable flow speed estimation over time. Our model achieves state-of-the-art optical flow and depth estimation on our dataset, and fully automates the motion estimation for debris flows. Source code and dataset are available at project page.
MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods.
Utilizing Unsupervised Learning for Improving ML Channel State Feedback in Cellular Networks

Recent studies have shown that Machine Learning (ML) techniques can be used to compress Channel State Feedback (CSF) in order to reduce overhead; however, it is unclear if a single ML model will be adequate to generalize across all cellular deployments. This work explores a potential enhancement to ML CSF by studying whether multiple ML CSF models can achieve superior performance over a single model (with a fixed model size and architecture). The proposed approach uses unsupervised learning to partition wireless channels before training ML CSF models and is shown to provide a performance improvement, measured through end-to-end Normalized Mean Squared Error (NMSE) of the reconstructed CSF, in every investigated scenario for ML CSF and achieves a 1.9dB better overall NMSE than using a single ML CSF model. A separate potential approach for utilizing multiple ML CSF models is evaluated for reference by directly using channel model labels to partition the wireless channel before training ML CSF models. It is shown that this reference approach does not always lead to a performance improvement, highlighting the need for incorporating unsupervised learning in the creation of multiple ML CSF models.
Motion-state Alignment for Video Semantic Segmentation

In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges i.e., information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentation to keep both motion and state consistency. In the framework, we first construct a motion alignment branch armed with an efficient decoupled transformer to capture dynamic semantics, guaranteeing region-level temporal consistency. Then, a state alignment branch composed of a stage transformer is designed to enrich feature spaces for the current frame to extract static semantics and achieve pixel-level state consistency. Next, by a semantic assignment mechanism, the region descriptor of each semantic category is gained from dynamic semantics and linked with pixel descriptors from static semantics. Benefiting from the alignment of these two kinds of effective information, the proposed method picks up dynamic and static semantics in a targeted way, so that video semantic regions are consistently segmented to obtain precise locations with low computational complexity. Extensive experiments on Cityscapes and CamVid datasets show that the proposed approach outperforms state-of-the-art methods and validates the effectiveness of the motion-state alignment framework.
A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

One of the important research areas in imaging is the formation of images, which plays an important role in many different applications, including surveillance, control, and security affairs. On the other hand, high spatial resolution is one of the most important factors for increasing image quality, but it increases the amount of storage memory. In face recognition systems, one of the existing challenges is maintaining the image recognition rate. Proposing a method that at least does not reduce detection rates would be very desirable. This article investigates how to compress facial images with high spatial resolution using innovative algorithms to reduce or even increase their accuracy as much as possible. In this article, meta-heuristic algorithms are used in a way that they are responsible for identifying the important and similar areas of matching macroblocks in the whole image segmentation. In the simulation and evaluation section, the facial images of the CIE and FEI databases have been examined as a selective study. The simulation results show the significant impact of the proposed methods using meta-heuristic algorithms in increasing the quality of PSNR and SSIM in contrast to the recognition efficiency. According to the proposed method, the larger the value of dividing the blocks, the better the average PSNR and SSIM. In general, depending on the type of application of the problem, there is a compromise to achieve the highest average PSNR or SSIM, using a genetic algorithm or gray wolf. The gray wolf algorithm, however, reaches its optimal answer much faster than the genetic algorithm.
Applications of Convolutional Neural Networks to Extracting Oracle Bone Inscriptions from Three-Dimensional Models

In recent years, high-fidelity three-dimensional (3D) oracle bone models (3D-OBMs) have received extensive attention from oracle bone experts due to their unparalleled reducibility to real oracle bone. In the research process of 3D-OBMs, the first procedure is to extract oracle bone inscriptions (OBIs) from the model to form individual oracle bone characters (OBCs). However, the manual extraction of OBIs is a time-consuming and labor-intensive task that relies heavily on oracle bone knowledge. To address these problems, we propose a texture-mapping-based OBI extractor (tm-OBIE), which leverages the symmetrical characteristics of the texture mapping process and is able to extract 3D-OBIs from 3D-OBMs saved as a wavefront file. The OBIs in the texture file were first located using a trained 2D object detector. After that, the 3D mesh area, where the OBIs are located, was obtained using an inverse texture mapping method. Thirdly, a specific 2D plane was fitted using the centroid of triangular faces in the flat regions of the mesh via a singular value decomposition (SVD) method. Finally, by measuring the distances between the triangle meshes and the fitted plane, the meshes of the 3D-OBIs were obtained. This paper verifies the feasibility of this method via experiments and analyzes the possibility of using the algorithm framework for extracting other ancient characters from their corresponding 3D models.
Students’ Anxiety in Learning English (A Study at the 8th Grade of SMPN 6 Mataram)

Anxiety has been expected to came failure in learning English. This study aimed to identify the levels of anxiety experienced by the students in learning English and what factors students believed contributed to their anxiety in learning English at the 8th grade of SMPN 6 Mataram. The method used in this study was qualitative, and the population consisted of 352 students, and 30 students (10%). were selected as the sample. Data collection techniques included questionnaires from Horwitz &amp; Cope (1986) and in-depth interviews from Price (1999). The results shows that no students were indicated as being very anxious and anxious levels, 14 participants (47%) were mildly anxious, 13 participants (43%) were at the relaxed level, and 3 participants (10%) were very relaxed. Students experienced class anxiety, which affected them negatively. Therefore, it was essential for English teachers to be aware of the anxiety among their students. It is also found that seven factors were found in this research, coming from both teachers and students. The anxiety factors from the teachers were teacher personality 3 participants (10%), speaking in front of the class 12 participants (40%) and incomprehensible input 7 participants (23%). While factors from students were student beliefs about English being difficult 22 participants (73%), ridiculed by peers 9 participants (30%), lack of preparation 12 participants (40%) and low self esteem 8 participants (27%). Apart from that, these factors were discussed in this study, deepening our understanding of foreign language anxiety.
Visualizing Synchronous Virtual Reference Service in Chandigarh Region Innovation and Knowledge Cluster (CRIKC) Institutions in India: Librarians’ Perception

AbstractThe present study aims to highlight the librarians’ perception pertaining to the implementation of Virtual Reference Service (VRS) in the Chandigarh Region Innovation and Knowledge Cluster Institutions (CRIKC). The study is based on a sample drawn from librarians of the CRIKC libraries. To assess and evaluate the librarians’ perception regarding the feasibility of VRS, a questionnaire was prepared and distributed among 19 library professionals of CRIKC libraries. The reason for choosing the questionnaire was to know the status of virtual reference service as well as librarians’ perception regarding the feasibility of VRS in CRIKC libraries. The findings suggest that the major challenge faced by librarians in providing VRS is the need for an additional budget for staffing, followed by an increased staff workload and a lack of staff training facilities. However, despite these challenges, the librarians perceive VRS to be highly effective in promoting the optimum use of the library’s collection and resources, improving staff efficiency, and promoting information literacy. From the users’ perspective, remote access to online assistance and faster access to information were found to be the most effective features of VRS. Additionally, personalized service, 24/7 availability, and the ability to save chat sessions for later use were also perceived as beneficial by most of the librarians. These results highlight the potential benefits of implementing web 2.0-enabled VRS in academic libraries, despite the challenges involved. It is essential to allocate a sufficient budget for staffing and provide staff training facilities to overcome the challenges faced in implementing VRS successfully. Furthermore, by improving user satisfaction and promoting information literacy, VRS can prove to be an effective tool for enhancing library services and resources.Keywords: Virtual reference servicedigital reference servicesynchronous virtual reference service Chandigarh region innovation and knowledge clusterCRIKC Conflict of interestThe authors have no conflicts of interest to report.Additional informationNotes on contributorsA SinhababuDr. Atasi Sinhababu Assistant Librarian, Central Library, Amity University Punjab, Mohali. IT City Rd, Sector 82, Alpha Block D, JLPL Industrial Area, Punjab 140306, India. Email asinhababu@pb.amity.eduS. KumarDr. Shiv KumarAssistant Professor, Panjab University, Chandigarh, 160014, India. Email. shivpuchd@pu.ac.in
Robustness of energy landscape controllers for spin rings under coherent excitation transport

Abstract The design and analysis of controllers to regulate excitation transport in quantum spin rings presents challenges in the application of classical feedback control techniques to synthesize effective control and generates results in contradiction to the expectations of classical control theory. This paper examines the robustness of controllers designed to optimize the fidelity of an excitation transfer to uncertainty in system and control parameters. We use the logarithmic sensitivity of the fidelity error as the robustness measure, drawing on the classical control analog of the sensitivity of the tracking error. Our analysis shows that quantum systems optimized for coherent transport demonstrate significantly different correlation between error and the log-sensitivity depending on whether the controller is optimized for readout at an exact time T or over a time-window T ± Δ /2.
Improving Earthquake Early Warning Initial Peak Ground Motion Magnitude Estimation with Station Corrections: A Case Study Using the P-Alert Network in Taiwan

ABSTRACTEarthquake early warning systems assess the size of an earthquake based on initial ground motion and provide warnings before the arrival of large waves. We employ the iterative regression to simultaneously estimate the station correlations and the corresponding linear relations for magnitude. We report the standard error reductions of the magnitude estimation based on the initial peak P-wave acceleration (Pa), velocity (Pv), and displacement (Pd) as 14.52%, 7.63%, and 7.58%, respectively. Factor analysis of the stationary correction reveals its correlation with several factors. This study shows that station correction can help to improve the precision of magnitude estimation in the future.KEYWORDS: Earthquake Early WarningStatistical LearningGround MotionGround Motion Authors’ ContributionsT.C. H developed the theoretical formalism, conducted the analysis, and wrote the paper. Y.M. W supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.Disclosure StatementNo potential conflict of interest was reported by the author(s).Data Availability StatementThe strong-motion waveform records from the P-Alert network can be downloaded at http://palert.earth.sinica.edu.tw/db/(last accessed July 2022).Additional informationFundingOur work was supported by the Ministry of Science and Technology (MOST) of Taiwan under MOST 106-2116-M-002-019-MY3 and 109-2116-M-002-030-MY3. Our work was also supported by the Research Center of Future Earth of the National Taiwan University under grant number 107L901002 and financially supported by the NTU Research Center for Future Earth from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. The authors thank Mr. Benjamin Ming Yang and Mr. Chia-Hao Yeh for their help to access to Vs30 and Z1.0 EGDT database for TSMIP.
Should You Put an Emoticon on Your Flag? How Subliminal Visual Stimuli Can Change Political Opinions

Abstract Political objects, like national flags, arouse emotions even when they are subliminal stimuli. Through two experiments that show subliminal stimuli to the subjects, this article analyzes if those emotions are positive or negative ones—that is to say, if they enhance an inclusive reaction or an excluding one. Besides, the article compares the intensity of the flag’s emotional effect with that of the emoticons, which are figures intended to represent emotional reactions or states. Findings confirm that the Spanish flag, as a subliminal stimulus, influences the opinions of the participants. However, it has had a lower effect than that obtained in previous, similar experiments. Emoticons produce a much more intense type of response than flags. The experiments (n = 85; n = 126) enlarge the knowledge about the emotional effects of political objects of nonconscious perception. Besides, we present an original methodological contribution: the use of emoticons to alter political views.