Reviewerly/exHarmony huggingface dataset:
A Bag-of-Prototypes Representation for Dataset-Level Applications

This work investigates dataset vectorization for two dataset-level tasks: assessing training set suitability and test set difficulty. The former measures how suitable a training set is for a target domain, while the latter studies how challenging a test set is for a learned model. Central to the two tasks is measuring the underlying relationship between datasets. This needs a desirable dataset vectorization scheme, which should preserve as much discriminative dataset information as possible so that the distance between the resulting dataset vectors can reflect dataset-to-dataset similarity. To this end, we propose a bag-of-prototypes (BoP) dataset representation that extends the image-level bag consisting of patch descriptors to dataset-level bag consisting of semantic prototypes. Specifically, we develop a codebook consisting of K prototypes clustered from a ref-erence dataset. Given a dataset to be encoded, we quan-tize each of its image features to a certain prototype in the codebook and obtain a K -dimensional histogram. Without assuming access to dataset labels, the BoP representation provides rich characterization of the dataset semantic distribution. Furthermore, BoP representations cooperate well with Jensen-Shannon divergence for measuring dataset-to-dataset similarity. Although very simple, BoP consistently shows its advantage over existing representations on a series of benchmarks for two dataset-level tasks.
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training

Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on VisDial data via supervised learning or leveraged pretraining on related vision-and-language datasets. This paper presents a semi-supervised learning approach for visually-grounded dialog, called Generative Self-Training (GST), to leverage unlabeled images on the Web. Specifically, GST first retrieves in-domain images through out-of-distribution detection and generates synthetic dialogs regarding the images via multimodal conditional text generation. GST then trains a dialog agent on the synthetic and the original Vis-Dial data. As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1.2M → 12.9M QA data). For robust training of the synthetic dialogs, we also propose perplexity-based data selection and multimodal consistency regularization. Evaluation on Vis-Dial v1.0 and v0.9 datasets shows that GST achieves new state-of-the-art results on both datasets. We further observe the robustness of GST against both visual and textual adversarial attacks. Finally, GST yields strong performance gains in the low-data regime. Code is available at https:/github.com/gicheonkang/gst-visdial.
NIPQ: Noise proxy-based Integrated Pseudo-Quantization

Straight-through estimator (STE), which enables the gradient flow over the non-differentiable function via approximation, has been favored in studies related to quantization-aware training (QAT). However, STE incurs unstable convergence during QAT, resulting in notable quality degradation in low precision. Recently, pseudo-quantization training has been proposed as an alternative approach to updating the learnable parameters using the pseudo-quantization noise instead of STE. In this study, we propose a novel noise proxy-based integrated pseudo-quantization (NIPQ) that enables unified support of pseudo-quantization for both activation and weight by integrating the idea of truncation on the pseudo-quantization framework. NIPQ updates all of the quantization parameters (e.g., bit-width and truncation boundary) as well as the network parameters via gradient descent without STE instability. According to our extensive experiments, NIPQ outperforms existing quantization algorithms in various vision and language applications by a large margin.
Level-S<sup>2</sup>fM: Structure from Motion on Neural Level Set of Implicit Surfaces

This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> fM, which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and the radiance fields from the established key-point correspondences. Our novel formulation poses some new challenges due to inevitable two-view and few-view configurations in the incremental SfM pipeline, which complicates the optimization of coordinate MLPs for volumetric neural rendering with unknown camera poses. Nevertheless, we demonstrate that the strong inductive basis conveying in the 2D correspondences is promising to tackle those challenges by exploiting the relationship between the ray sampling schemes. Based on this, we revisit the pipeline of incremental SfM and renew the key components, including two-view geometry initialization, the camera poses registration, the 3D points triangulation, and Bundle Adjustment, with a fresh perspective based on neural implicit surfaces. By unifying the scene geometry in small MLP networks through coordinate MLPs, our Level-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> fM treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3D points, reject the outliers in correspondences via querying SDF, and refine the estimated geometries by NBA (Neural BA). Not only does our Level-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> fM lead to promising results on camera pose estimation and scene geometry reconstruction, but it also shows a promising way for neural implicit rendering without knowing camera extrinsic beforehand.
Evaluation of the Gilbert–Varshamov Bound using Multivariate Analytic Combinatorics

Analytic combinatorics in several variables refers to a suite of tools that provide sharp asymptotic estimates for certain combinatorial quantities. In this paper, we apply these tools to determine the Gilbert–Varshamov (GV) bound for the sticky insertion and the constrained-synthesis channel.
Private Set Union Based Approach to Enable Private Federated Submodel Learning

We consider the federated submodel learning (FSL) problem and propose an approach where clients are able to update the central model information theoretically privately. Our approach is based on private set union (PSU), which is further based on multi-message symmetric private information retrieval (MM-SPIR). With our scheme, the server does not learn anything further than the subset of submodels updated by the clients: the server does not know which client updated which submodel(s), or anything about the local client data. In comparison to the state-of-the-art private FSL schemes of Jia-Jafar and Vithana-Ulukus, our scheme does not require noisy storage of the model at the databases; and in comparison to the secure aggregation scheme of Zhao-Sun, our scheme incorporates the creation of the required client-side common randomness via random symmetric private information retrieval (RSPIR) and one-time pads. Our system is initialized with a replicated storage of submodels and a sufficient amount of common randomness in two databases at the server-side. The protocol starts with a common randomness generation (CRG) where the two databases establish common randomness at the client-side (FSL-CRG phase). Next, the clients utilize the established client-side common randomness to have the server determine privately the union of indices of submodels to be updated collectively by the clients (FSL-PSU phase). Then, the two databases broadcast the current versions of the submodels in the set union to clients. The clients update the submodels based on their local data. Finally, the clients use a variation of FSL-PSU to write the updates back to the databases privately (FSL-write phase). Our proposed private FSL scheme achieves low communication cost, and is also robust against client dropouts, client late-arrivals, and database drop-outs.
Teacher-generated spatial-attention labels boost robustness and accuracy of contrastive models

Human spatial attention conveys information about the regions of visual scenes that are important for performing visual tasks. Prior work has shown that the information about human attention can be leveraged to benefit various supervised vision tasks. Might providing this weak form of supervision be useful for self-supervised representation learning? Addressing this question requires collecting large datasets with human attention labels. Yet, collecting such large scale data is very expensive. To address this challenge, we construct an auxiliary teacher model to predict human attention, trained on a relatively small labeled dataset. This teacher model allows us to generate image (pseudo) attention labels for ImageNet. We then train a model with a primary contrastive objective; to this standard configuration, we add a simple output head trained to predict the attention map for each image, guided by the pseudo labels from teacher model. We measure the quality of learned representations by evaluating classification performance from the frozen learned embeddings as well as performance on image retrieval tasks (see supplementary material). We find that the spatial-attention maps predicted from the contrastive model trained with teacher guidance aligns better with human attention compared to vanilla contrastive models. Moreover, we find that our approach improves classification accuracy and robustness of the contrastive models on ImageNet and ImageNet-C. Further, we find that model representations become more useful for image retrieval task as measured by precision-recall performance on ImageNet, ImageNet-C, CIFAR10, and CIFAR10-C datasets.
EMT-NAS: Transferring architectural knowledge between tasks from different datasets

The success of multitask learning (MTL) can largely be attributed to the shared representation of related tasks, allowing the models to better generalise. In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights. However, the joint training of weighting parameters on multiple related tasks may lead to performance degradation, known as negative transfer. To address this issue, this work proposes an evolutionary multitasking neural architecture search (EMT-NAS) algorithm to accelerate the search process by transferring architectural knowledge across multiple related tasks. In EMT-NAS, unlike the traditional MTL, the model for each task has a personalised network architecture and its own weights, thus offering the capability of effectively alleviating negative transfer. A fitness re-evaluation method is suggested to alleviate fluctuations in performance evaluations resulting from parameter sharing and the mini-batch gradient descent training method, thereby avoiding losing promising solutions during the search process. To rigorously verify the performance of EMT-NAS, the classification tasks used in the empirical assessments are derived from different datasets, including the CIFAR-10 and CIFAR-100, and four MedMNIST datasets. Extensive comparative experiments on different numbers of tasks demonstrate that EMT-NAS takes 8% and up to 40% on CIFAR and MedMNIST, respectively, less time to find competitive neural architectures than its single-task counterparts.
Sentiment Analysis of Using ChatGPT in Education

This paper presents a study on the use of the Chat Generative Pretrained Transformer (ChatGPT) in education. In this work, we propose a sentiment analysis model of tweets related to the use of the ChatGPT in education. The purpose of this research is to identify common sentiments, topics, and perspectives that are expressed towards ChatGPT in the education field based on the data collected from Twitter. Twitter was used to collect 11830 tweets about the use of ChatGPT in education. Topics and emotions expressed in the tweets were extracted using NLP algorithms and organized into distinct groups. Also, the most frequent words in the positive and negative opinion words are determined. The findings of the paper indicate that most tweets about ChatGPT are either positive or neutral, with a small percentage expressing negative sentiments. In addition, the study analyzes the sentiments expressed in tweets about the employment of ChatGPT in education using four different classifiers: Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). According to the results, the SVM classifier has the highest accuracy of 81.4 percent.
Tunable Convolutions with Parametric Multi-Loss Optimization

Behavior of neural networks is irremediably determined by the specific loss and data used during training. However it is often desirable to tune the model at inference time based on external factors such as preferences of the user or dynamic characteristics of the data. This is especially important to balance the perception-distortion trade-off of ill-posed image-to-image translation tasks. In this work, we propose to optimize a parametric tunable convolutional layer, which includes a number of different kernels, using a parametric multi-loss, which includes an equal number of objectives. Our key insight is to use a shared set of parameters to dynamically interpolate both the objectives and the kernels. During training, these parameters are sampled at random to explicitly optimize all possible combinations of objectives and consequently disentangle their effect into the corresponding kernels. During inference, these parameters become interactive inputs of the model hence enabling reliable and consistent control over the model behavior. Extensive experimental results demonstrate that our tunable convolutions effectively work as a drop-in replacement for traditional convolutions in existing neural networks at virtually no extra computational cost, outperforming state-of-the-art control strategies in a wide range of applications; including image denoising, deblurring, super-resolution, and style transfer.
Universal Compression of High Dimensional Gaussian Vectors with James-Stein shrinkage

We study universal compression of n i.i.d. copies of a k−variate Gaussian random vector, when the mean is an unknown vector in an Euclidean ball of ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> , and the covariance is known. We adopt the high dimensional scaling k = Θ(n) to bring out a compression perspective on the inadmissibility of unbiased estimates of a k−variate Gaussian (when k ≥ 3), in particular focusing on the optimal unbiased Maximum Likelihood estimate. We use arguments based on the redundancy-capacity theorem to show that the redundancy of a universal compressor in this high dimensional setting must be lower bounded as Θ(n). We show that natural compression schemes based on the Maximum Likelihood estimate of the mean have suboptimal Θ(n log n) redundancy, but a scheme based on the James-Stein biased estimate of the mean incurs redundancy that is also Θ(n).
Elderly Helper Detection based on R2-yolov5

In order to assess the independent walking ability of elderly people, a system for assessing the independent walking ability of elderly people using yolov5 target detection was developed, and the accuracy of the detection was improved with a modified R2-yo1ov based on yolov5 by replacing the residual structure bottleneck in its unique C3 structure with the Res2net residual module, and by channel-wise and activation function optimization in terms of channels and activation functions. In order to enhance the information transfer between network layers, the upper and lower feature layers are fused to improve the detection effect. The experimental results show that the map of R2-yo1ov5 tested on the elderly helper dataset can reach 96.7%, which is 1.8% higher than the original yolov5 network, and the detection effect of support class is improved by 5.5%, which is a significant improvement in the detection effect and can meet the requirements of the detection scenario.
Approximation Methods for Enumerating Sequences for Probabilistic Amplitude Shaping

We consider approximation of N(n, E) and N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> (n, E), the number of sequences of ASK symbols of length n with energy equal to E and at most equal to E, respectively. These quantities are often encountered in the context of constellation shaping, such as in shell mapping. We provide implementation techniques for our approximation methods. Numerical evaluation shows that approximations based on the techniques are extremely tight even at lengths as short as 16. Our presented approximation formulas for such quantities, theoretically justified, in conjunction with the implementation techniques, establish a new directional and theoretical foundation that can be applied to applications such as probabilistic shaping.
Binary MDS Array Codes with Flexible Array Dimensions and Their Fast Encoding

In this short paper, we will provide a new explicit construction of binary MDS array codes with triple parities from their parity-check matrices, which contains array codes with array number 8 (8 bits=1 byte). In addition, to demonstrate the applicability of our MDS array codes, we present an effective decoding method aimed at the erased errors. Furthermore, a fast encoding algorithm of our extended MDS array codes is also explored, whose computational complexity is 2 XORs per bit when their code lengths approach infinity.
Periodic Properties of Commutative Polynomials Defined by Fourth Order Recurrence Relations with Two Variables Over Z<sub>2</sub> <sup>k</sup>

In constructing public-key cryptosystems, commutative property is an essential characteristic. Taking advantage of the commutative property of Chebyshev polynomials, a publickey cryptosystem over the residue class ring ${\mathbb{Z}_{{2^k}}}$ has been introduced, which can be implemented very efficiently. Unfortunately, however, the cryptosystem is broken using knowledge of the periodic properties of Chebyshev polynomials. Although commutative polynomials with two variables can be candidates for a public-key cryptosystem instead of Chebyshev polynomials, characteristics of the polynomials should be discussed carefully. In this study, we analyzed some properties of commutative polynomials with two variables over ${\mathbb{Z}_{{2^k}}}$. More precisely, the degree period and the condition for permutation polynomial are given theoretically and verified experimentally. Based on the derived properties, a security evaluation of a key-exchange protocol using the polynomials is also discussed.
Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields

Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with crossmodal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets. Our project pages are available at: https://github.com/intelpro/CBMNet
Private Read Update Write (PRUW) With Heterogeneous Databases

We investigate the problem of private read update write (PRUW) with heterogeneous storage constrained databases in federated submodel learning (FSL). In FSL a machine learning model is divided into multiple submodels based on different types of data used to train it. A given user downloads, updates and uploads the updates back to a single submodel of interest, based on the type of user’s local data. With PRUW, the process of reading (downloading) and writing (uploading) is carried out such that information-theoretic privacy of the updating submodel index and the values of updates is guaranteed. We consider the practical scenario where the submodels are stored in databases with arbitrary (heterogeneous) storage constraints, and provide a PRUW scheme with a storage mechanism that utilizes submodel partitioning and encoding to minimize the communication cost.
The Optimal Rate of MDS Variable Generation

A collection of K random variables are called (K,n)-MDS if any n of the K variables are independent and determine all remaining variables. In the MDS variable generation problem, K users wish to generate variables that are (K,n)-MDS using a randomness variable owned by each user. We show that to generate 1 bit of (K,n)-MDS variables for each n ∈ {1,2, ⋯ ,K}, the minimum size of the randomness variable at each user is 1+1/2+ ⋯ +1/K bits.
Optimal Second-Order Rates for Quantum Information Decoupling

In this paper, we consider the standard quantum information decoupling, in which Alice aims to decouple her system from the environment by discarding some of her systems. To achieve an ε-decoupling with trace distance as the error criterion, we establish a one-shot characterization for the largest size of the remainder system in terms of the conditional (1 − ε)-hypothesis-testing entropy. When the underlying system is independent and identically prepared, our result leads to the matched second-order rate as well as the moderate deviation rate.
Deep Privacy based Face Anonymization for Smart Cities

Interest in privacy has increased due to the public’s increased attention given to it by the introduction of the EU’s GDPR. The number of images containing identifiable features has multiplied dramatically in an increasingly digital world where data is gathered on a large scale through surveillance systems, smartphones, cameras, etc. In order to protect our privacy, it is essential to look into methods that can anonymize individuals in real time before the digital data is stored. We look into two state-of-the-art face detectors and consider how they perform in real time. In addition, we consider multiple methods for anonymizing individuals in the loop and how it affects the resulting image. The performance is based on the WiderFace benchmark, including easy, medium, and hard subsets.
Security Bounds for Bitcoin Under Network Delay

We improve security-latency bounds of Nakamoto consensus by analyzing the race between adversarial and honest chains in three different phases: pre-mining, confirmation and post-confirmation. We find the probability distribution of the length of the adversarial chain and the rigged adversarial chain under jumper models during the confirmation interval. We analyze certain properties of this race to model pre-mining and post-confirmation phases with random walks that provide tighter bounds than existing results. Combining all three phases provides novel upper and lower bounds for blockchains with small λΔ.