Reviewerly/exHarmony huggingface dataset:
Hybrid Dual and Meet-LWE Attack

The Learning with Errors (LWE) problem is one of the most prominent problems in lattice-based cryptography. Many practical LWE-based schemes, including Fully Homomorphic encryption (FHE), use sparse ternary secret for the sake of efficiency. Several (hybrid) attacks have been proposed that benefit from such sparseness, thus researchers believe the security of the schemes with sparse ternary secrets is not well-understood yet. Recently, May [Crypto 2021] proposed an efficient meet-in-the-middle attack named Meet-LWE for LWE with ternary secret, which significantly improves Odlyzko’s algorithm. In this work, we generalize May’s Meet-LWE and then introduce a new hybrid attack which combines Meet-LWE with lattice dual attack. We implement our algorithm to FHE-type parameters of LWE problem and compare it with the previous hybrid dual attacks. The result shows that our attack outperforms other attacks in a large range of parameters. We note that our attack has no impact on the LWE-based schemes in the PQC Standardization held by NIST as their secrets are not sparse and/or ternary.
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction

Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning. We introduce a novel method based on a hierarchy built on 1-nearest neighbor graphs in the original space which is used to preserve the grouping properties of the data distribution on multiple levels. The core of the proposal is an optimization-free projection that is competitive with the latest versions of t-SNE and UMAP in performance and visualization quality while being an order of magnitude faster at run-time. Furthermore, its interpretable mechanics, the ability to project new data, and the natural separation of data clusters in visualizations make it a general purpose unsupervised dimension reduction technique. In the paper, we argue about the soundness of the proposed method and evaluate it on a diverse collection of datasets with sizes varying from 1 K to 11M samples and dimensions from 28 to 16K. We perform comparisons with other state-of-the-art methods on multiple metrics and target dimensions high-lighting its efficiency and performance. Code is available at https://github.com/koulakis/h-nne
FUTURE: A Lightweight Block Cipher Using an Optimal Diffusion Matrix

In this work, we present FUTURE, a new 64-bit lightweight SPN-based block cipher. FUTURE encrypts data in a single clock cycle with a very low implementation cost compared to other block ciphers in unrolled fashion. The advantage of an unrolled implementation is that there are no sequential elements, such as registers, in the implementation and hence no clock. While designing FUTURE in a completely unrolled fashion, the goal was to keep the implementation costs low along with minimal latency. Security is the most essential aspect of a cryptographic primitive. However, in addition to security, an essential secondary criterion for a lightweight cryptographic primitive is efficient implementation in hardware and software. Most lightweight block ciphers refrain from the use of MDS matrices in the round function, and as a result, they need more rounds for full encryption. Using MDS matrices in a lightweight block cipher is a challenging task due to its high implementation cost. The lightweight block cipher FUTURE overcomes this challenge by judiciously choosing a very lightweight MDS matrix, which is a composition of 4 sparse matrices. We also use a lightweight cryptographically significant Sbox which is a composition of 4 Sboxes.
The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing

Machine learning is transforming the video editing industry. Recent advances in computer vision have leveled-up video editing tasks such as intelligent reframing, rotoscoping, color grading, or applying digital makeups. However, most of the solutions have focused on video manipulation and VFX. This work introduces the Anatomy of Video Editing, a dataset, and benchmark, to foster research in AI-assisted video editing. Our benchmark suite focuses on video editing tasks, beyond visual effects, such as automatic footage organization and assisted video assembling. To enable research on these fronts, we annotate more than 1.5M tags, with relevant concepts to cinematography, from 196176 shots sampled from movie scenes. We establish competitive baseline methods and detailed analyses for each of the tasks. We hope our work sparks innovative research towards underexplored areas of AI-assisted video editing. Code is available at: https://github.com/dawitmureja/AVE.git .
What Matters For Meta-Learning Vision Regression Tasks?

Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This paper makes two main contributions that help understand this barely explored area. First, we design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation of unprecedented complexity in the meta-learning domain for computer vision. To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization. Finally, we (iii) provide some insights and practical recommendations for training meta-learning algorithms on vision regression tasks. Second, we propose the addition of functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion. The experimental results show that the results of prior work are misleading as a consequence of a poor choice of the loss function as well as too small meta-training sets. Specifically, we find that CNPs outperform MAML on most tasks without fine-tuning. Furthermore, we observe that naive task augmentation without a tailored design results in underfitting.
Real time embedded system for object detection using deep learning

Blind people face difficulty in practicing their daily life safely because they cannot know the objects surrounding them and this exposes them to many dangers. Given that the number of blind people around the world is a significant number, this research deals to build an intelligent system that helps the blind people to know the objects surrounding them in an internal environment where it is formed. The system consists of two parts, the first part is the software, which depends on the use of one of the deep learning algorithms and its name is YOLO (You Only Look Once). This algorithm was chosen because of its high speed and accuracy, and this is what this group of people needs, which the research aims to help them as much as possible. The algorithm was trained on a ready-made dataset called COCO (Common Objects in COntex) Dataset which It is used for the purposes of discovering objects and detecting faces … etc. The other part of the system is the part of the hardware, which mainly depends on a fast and lightweight microprocessor, which is Raspberry Pi B3, in addition to the headphone and Raspberry Pi camera. After the images is taken by the Raspberry Pi Camera, these images are send to the Raspberry pi and the YOLO algorithm detect the objects in each image, and after the object is detected, it sends the output that is converted into sound via the head Phone to the person and thus can avoid the things surrounding them.
BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking

Estimating human motion from video is an active research area due to its many potential applications. Most state-of-the-art methods predict human shape and posture estimates for individual images and do not leverage the temporal information available in video. Many "in the wild" sequences of human motion are captured by a moving camera, which adds the complication of conflated camera and human motion to the estimation. We therefore present BodySLAM, a monocular SLAM system that jointly estimates the position, shape, and posture of human bodies, as well as the camera trajectory. We also introduce a novel human motion model to constrain sequential body postures and observe the scale of the scene. Through a series of experiments on video sequences of human motion captured by a moving monocular camera, we demonstrate that BodySLAM improves estimates of all human body parameters and camera poses when compared to estimating these separately.
On Local Distributions in Graph Signal Processing

Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distributed linear operations, whose output depends only on the local neighborhood of each node. Moreover, a graph filter's output can be computed separately at each node by carrying out repeated exchanges with immediate neighbors. Graph filters can be compactly written as polynomials of a graph shift operator (typically, a sparse matrix description of the graph). This has led to relating the properties of the filters with the spectral properties of the corresponding matrix -- which encodes global structure of the graph. In this work, we propose a framework that relies solely on the local distribution of the neighborhoods of a graph. The crux of this approach is to describe graphs and graph signals in terms of a measurable space of rooted balls. Leveraging this, we are able to seamlessly compare graphs of different sizes and coming from different models, yielding results on the convergence of spectral densities, transferability of filters across arbitrary graphs, and continuity of graph signal properties with respect to the distribution of local substructures.
Spatio-Temporal Deformable Attention Network for Video Deblurring

The key success factor of the video deblurring methods is to compensate for the blurry pixels of the mid-frame with the sharp pixels of the adjacent video frames. Therefore, mainstream methods align the adjacent frames based on the estimated optical flows and fuse the alignment frames for restoration. However, these methods sometimes generate unsatisfactory results because they rarely consider the blur levels of pixels, which may introduce blurry pixels from video frames. Actually, not all the pixels in the video frames are sharp and beneficial for deblurring. To address this problem, we propose the spatio-temporal deformable attention network (STDANet) for video delurring, which extracts the information of sharp pixels by considering the pixel-wise blur levels of the video frames. Specifically, STDANet is an encoder-decoder network combined with the motion estimator and spatio-temporal deformable attention (STDA) module, where motion estimator predicts coarse optical flows that are used as base offsets to find the corresponding sharp pixels in STDA module. Experimental results indicate that the proposed STDANet performs favorably against state-of-the-art methods on the GoPro, DVD, and BSD datasets.
Contrastive Learning Ideas in Underwater Terrain Image Matching

The distinctive nature of the intrinsic features of underwater terrain images, along with their variability of intensity and texture due to the complex environment in which they are acquired, poses great challenges to manual feature-based template-matching methods. In this paper, we propose a novel data-driven framework for template matching of underwater terrain images. This combines the idea of contrastive learning with the template-matching process, allowing self-supervised end-to-end training without the need for additional data annotation. The resulting positive and negative sample comparisons improve the discriminatory ability of the model. Specifically, we achieve data enhancement by simulating grayscale and texture differences between images to improve the anti-interference capability of the model, and we propose a patch-based sample-extraction strategy to extract positive and negative samples. To further improve the discriminative ability for features among patches, we propose a feature-fusion module based on an attention mechanism. This improves the level of discrimination of the features to be matched by fusing the contextual features of patches. In addition, we solve the problem of matching multi-scale templates by introducing a masking mechanism. We performed detailed ablation experiments on the components in the framework to verify their effectiveness. We also compared the performance of our model with other state-of-the-art template-matching methods. The experimental results demonstrate that our proposed method has superior matching performance when compared to its competitors.
An exploratory study of reactions to bot comments on GitHub

The widespread use of bots to support software development makes social coding platforms such as GitHub a particularly rich source of data for the study of human-bot interaction. Software development bots are used to automate repetitive tasks, interacting with their human counterparts via comments posted on the various discussion interfaces available on such platforms. One type of interaction supported by GitHub involves reacting to comments using predefined emoji. To investigate how users react to bot comments, we conducted an observational study comprising 54 million GitHub comments, with a particular focus on comments that elicited the laugh reaction. The results from our analysis suggest that some reaction types are not equally distributed across human and bot comments and that a bot's design and purpose influence the types of reactions it receives. Furthermore, while the laugh reaction is not exclusively used to express laughter, it can be used to convey humor when a bot behaves unexpectedly. These insights could inform the way bots are designed and help developers equip them with the ability to recognize and recover from unanticipated situations. In turn, bots could better support the communication, collaboration, and productivity of teams using social coding platforms.
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification

The main question we address in this paper is how to scale up visual recognition of unseen classes, also known as zero-shot learning, to tens of thousands of categories as in the ImageNet-21K benchmark. At this scale, especially with many fine-grained categories included in ImageNet-21K, it is critical to learn quality visual semantic representations that are discriminative enough to recognize unseen classes and distinguish them from seen ones. We propose a Hierarchical Graphical knowledge Representation framework for the confidence-based classification method, dubbed as HGR-Net. Our experimental results demonstrate that HGR-Net can grasp class inheritance relations by utilizing hierarchical conceptual knowledge. Our method significantly outperformed all existing techniques, boosting the performance by 7% compared to the runner-up approach on the ImageNet-21K benchmark. We show that HGR-Net is learning-efficient in few-shot scenarios. We also analyzed our method on smaller datasets like ImageNet-21K-P, 2-hops and 3-hops, demonstrating its generalization ability. Our benchmark and code are available at https://kaiyi.me/p/hgrnet.html .
RayTran: 3D Pose Estimation and Shape Reconstruction of Multiple Objects from Videos with Ray-Traced Transformers

We propose a transformer-based neural network architecture for multi-object 3D reconstruction from RGB videos. It relies on two alternative ways to represent its knowledge: as a global 3D grid of features and an array of view-specific 2D grids. We progressively exchange information between the two with a dedicated bidirectional attention mechanism. We exploit knowledge about the image formation process to significantly sparsify the attention weight matrix, making our architecture feasible on current hardware, both in terms of memory and computation. We attach a DETR-style head [9] on top of the 3D feature grid in order to detect the objects in the scene and to predict their 3D pose and 3D shape. Compared to previous methods, our architecture is single stage, end-to-end trainable, and it can reason holistically about a scene from multiple video frames without needing a brittle tracking step. We evaluate our method on the challenging Scan2CAD dataset [3], where we outperform (1) state-of-the-art methods [15, 34, 35, 39] for 3D object pose estimation from RGB videos; and (2) a strong alternative method combining Multi-View Stereo [17] with RGB-D CAD alignment [4].
A Task-Agnostic Regularizer for Diverse Subpolicy Discovery in Hierarchical Reinforcement Learning

The automatic subpolicy discovery approach in hierarchical reinforcement learning (HRL) has recently achieved promising performance on sparse reward tasks. This accelerates transfer learning and unsupervised intelligent creatures while eliminating the domain-specific knowledge constraint. Most previously developed approaches are demonstrated to suffer from collapsing into the situation where one subpolicy dominates the whole task, since they cannot ensure the diversity of different subpolicies. In contrast, this article proposes a task-agnostic regularizer (TAR) for learning diverse subpolicies in HRL. Specifically, we first formulate the discovery of diverse subpolicies as a trajectory inference problem and then propose a corresponding information-theoretic objective to encourage diversity. Subsequently, considering computability, we instantiate the objective as two simplifications for discrete and continuous action spaces. We extensively evaluate the proposed diversity-driven regularizer on three HRL task domains: 1) meta reinforcement learning; 2) hierarchical policy learning in the option framework; and 3) unsupervised subpolicy discovery. The extensive results obtained show that our TAR approach can improve upon the state-of-the-art performance on all three HRL domains without modifying any existing hyperparameters, indicating the wide applicability and robustness of our approach.
BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention

Increasingly, convolution neural network (CNN) based super resolution models have been proposed for better reconstruction results, but their large model size and complicated structure inhibit their real-time hardware implementation. Current hardware designs are limited to a plain network and suffer from lower quality and high memory bandwidth requirements. This paper proposes a super resolution hardware accelerator with hardware efficient pixel attention that just needs 25. 9K parameters and simple structure but achieves 0. 38dB better reconstruction images than the widely used FSRCNN. The accelerator adopts full model block wise convolution for full model layer fusion to reduce external memory access to model input and output only. In addition, CNN and pixel attention are well supported by PE arrays with distributed weights. The final implementation can support full HD image reconstruction at 30 frames per second with TSMC 40nm CMOS process.
Improving the Transient Times for Distributed Stochastic Gradient Methods

We consider the distributed optimization problem where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> agents, each possessing a local cost function, collaboratively minimize the average of the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> cost functions over a connected network. Assuming stochastic gradient information is available, we study a distributed stochastic gradient algorithm, called exact diffusion with adaptive stepsizes (EDAS) adapted from the Exact Diffusion method [1] and NIDS [2] and perform a non-asymptotic convergence analysis. We not only show that EDAS asymptotically achieves the same network independent convergence rate as centralized stochastic gradient descent (SGD) for minimizing strongly convex and smooth objective functions, but also characterize the transient time needed for the algorithm to approach the asymptotic convergence rate, which behaves as <inline-formula><tex-math notation="LaTeX">$K_{T}=\mathcal {O}(\frac{n}{1-\lambda _{2}})$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$1-\lambda _{2}$</tex-math></inline-formula> stands for the spectral gap of the mixing matrix. To the best of our knowledge, EDAS achieves the shortest transient time when the average of the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> cost functions is strongly convex and each cost function is smooth. Numerical simulations further corroborate and strengthen the obtained theoretical results.
Can Cross-Domain Term Extraction Benefit from Cross-lingual Transfer?

Automatic term extraction (ATE) is a natural language processing task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. In this paper, we experiment with XLM-RoBERTa to evaluate the abilities of cross-lingual and multilingual versus monolingual learning in the cross-domain ATE task. The experiments are conducted on the ACTER corpus covering four domains (Corruption, Wind energy, Equitation, and Heart failure) and three languages (English, French, and Dutch) and on the RSDO5 Slovenian corpus, covering four additional domains (Biomechanics, Chemistry, Veterinary, and Linguistics). Regarding the ACTER test set, the cross-lingual and multilingual models boost the performance in F1-score by up to 5% if the term extraction task excludes the extraction of named entity terms (ANN version) and 3% if including them (NES version) compared to the monolingual setting. By adding an extra Slovenian corpus into the training set, the multilingual model demonstrates a significant improvement in terms of Recall, which, on average, increases by 18% in the ANN version and 13% in the NES version compared with the monolingual setting. Furthermore, our methods defeat state-of-the-art (SOTA) approaches with approximately 2% higher F1-score on average for the ANN version in English and Dutch, and the NES version in French. Regarding the RSDO5 test set, our monolingual approach proves to have consistent performance across all the train-validation-test combinations, achieving an F1-score above 61%. These results are a good indication of the potential in cross-lingual and multilingual language models not only for term extraction but also for other downstream tasks. Our code is publicly available at https://github.com/honghanhh/ate-2022 .