Reviewerly/exHarmony huggingface dataset:
Estimation of solar irradiation and optimum tilt angles for south-facing surfaces in the United Arab Emirates: a case study using PVGIS and PVWatts

The tilt angle is a crucial factor for the optimization of solar Photovoltaic panels and solar thermal col-lectors performance. Therefore in this present investigation, a mathematical model is built using MATLAB Program, in order to determine the optimum tilting angle and the solar irradiation falling on a surface facing south in the United Arab Emirates (UAE) for different periods. Furthermore, PVGIS and PVWatts programs are also applied in order to validate the established mathematical model. Simulation results reveal that when using a mono-crystalline module with 5-kWp peak power monthly variation of the tilt angle for all UAE emirates can produce about 15% solar energy higher than horizontal solar panels. However, seasonal and semiannual adjustments could produce nearly 14% more energy if the tilt angle of the solar panels changed at least two times in a year (semiannually). It is worthy to mention that the yearly fixed optimum angle of 23 degrees for UAE gives about 8% energy gain higher than a horizontal solar panel.
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality

Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects. We approach the task by modeling the video as tokens that represent the multi-scale semantic concepts in the video. We propose COMPOSER, a Multiscale Transformer based architecture that performs attention-based reasoning over tokens at each scale and learns group activity compositionally. In addition, prior works suffer from scene biases with privacy and ethical concerns. We only use the keypoint modality which reduces scene biases and prevents acquiring detailed visual data that may contain private or biased information of users. We improve the multiscale representations in COMPOSER by clustering the intermediate scale representations, while maintaining consistent cluster assignments between scales. Finally, we use techniques such as auxiliary prediction and data augmentations tailored to the keypoint signals to aid model training. We demonstrate the model’s strength and interpretability on two widely-used datasets (Volleyball and Collective Activity). COMPOSER achieves up to $$+5.4\%$$ improvement with just the keypoint modality (Code is available at https://github.com/hongluzhou/composer .).
Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning

We propose a novel Parallel Monte Carlo tree search with Batched Simulations (PMBS) algorithm for accelerating long-horizon, episodic robotic planning tasks. Monte Carlo tree search (MCTS) is an effective heuristic search algorithm for solving episodic decision-making problems whose underlying search spaces are expansive. Leveraging a GPU-based large-scale simulator, PMBS introduces massive parallelism into MCTS for solving planning tasks through the batched execution of a large number of concurrent simulations, which allows for more efficient and accurate evaluations of the expected cost-to-go over large action spaces. When applied to the challenging manipulation tasks of object retrieval from clutter, PMBS achieves a speedup of over 30× with an improved solution quality, in comparison to a serial MCTS implementation. We show that PMBS can be directly applied to real robot hardware with negligible sim-to-real differences. Supplementary material, including video, can be found at https://github.com/arc-l/pmbs.
Multi-Input Quadratic Functional Encryption: Stronger Security, Broader Functionality

Multi-input functional encryption, MIFE, is a powerful generalization of functional encryption that allows computation on encrypted data coming from multiple different data sources. In a recent work, Agrawal, Goyal, and Tomida (CRYPTO 2021) constructed MIFE for the class of quadratic functions. This was the first MIFE construction from bilinear maps that went beyond inner product computation. We advance the state-of-the-art in MIFE, and propose new constructions with stronger security and broader functionality.
Complex Backdoor Detection by Symmetric Feature Differencing

Many existing backdoor scanners work by finding a small and fixed trigger. However, advanced attacks have large and pervasive triggers, rendering existing scanners less effective. We develop a new detection method. It first uses a trigger inversion technique to generate triggers, namely, universal input patterns flipping victim class samples to a target class. It then checks if any such trigger is composed of features that are not natural distinctive features between the victim and target classes. It is based on a novel symmetric feature differencing method that identifies features separating two sets of samples (e.g., from two respective classes). We evaluate the technique on a number of advanced attacks including composite attack, reflection attack, hidden attack, filter attack, and also on the traditional patch attack. The evaluation is on thousands of models, including both clean and trojaned models, with various architectures. We compare with three state-of-the-art scanners. Our technique can achieve 80-88% accuracy while the baselines can only achieve 50-70% on complex attacks. Our results on the TrojAI competition rounds 2–4, which have patch backdoors and filter backdoors, show that existing scanners may produce hundreds of false positives (i.e., clean models recognized as trojaned), while our technique removes 78-100% of them with a small increase of false negatives by 0-30%, leading to 17-41% overall accuracy improvement. This allows us to achieve top performance on the leaderboard.
Towards Understanding Adversarial Robustness of Optical Flow Networks

Recent work demonstrated the lack of robustness of optical flow networks to physical patch-based adversarial attacks. The possibility to physically attack a basic component of automotive systems is a reason for serious concerns. In this paper, we analyze the cause of the problem and show that the lack of robustness is rooted in the classical aperture problem of optical flow estimation in combination with bad choices in the details of the network architecture. We show how these mistakes can be rectified in order to make optical flow networks robust to physical patch-based attacks. Additionally, we take a look at global white-box attacks in the scope of optical flow. We find that targeted white-box attacks can be crafted to bias flow estimation models towards any desired output, but this requires access to the input images and model weights. However, in the case of universal attacks, we find that optical flow networks are robust. Code is available at https://github.com/lmb-freiburg/understanding_flow_robustness.
Blockchain Technology Novel Prospective for Cloud Security

The Blockchain technology is a distributed ledger that has a record of data which comprises all of the information of transactions that have been performed and dispersed across a number of nodes that already exist in the network. All exchanges performed within the framework are confirmed by consensus means and if data is stored once that is never modified. The blockchain innovation mechanism is the most important innovation behind Bitcoin. It is still confronting security, information administration, compliance, reliability. Sellers offer their customers a set of generic items via the cloud. Moving forward, it has become a good choice as it offers pay-as-you-go services to customers. In business and other segments, the cloud has grown significantly with benefits such as rental services, integration resources, storage capabilities, and many more. It has a variety of safety issues despite its vitality, such as data breach, loss of sensitive data, and few others related to cloning, pooling of resources, etc. A significant amount of research was carried out recently that demonstrates the hazards associated with cloud services and the methodologies of cloud deployment to increase resource efficiency. The purpose of the study is to investigate a quick review on earlier contemplations centred on blockchain joining with the cloud in order to display their amazingness as a research target. This research paper aims to understand the risks so that basic security concerns are properly addressed in different cloud regions. The objective of the research is to reveal various security risks in cloud and network issues in order to impede threats in the cloud and facilitate risk reduction strategy for the researchers, end-users, and cloud providers for performing threat analysis. In this overview, the communication between blockchain and cloud is uncovered by joining blockchain with cloud computing.
Detection of Surface Defects in Solar Cells by Bidirectional-Path Feature Pyramid Group-Wise Attention Detector

Due to the multi-scale characteristics of defects and strong background interference, the automation of solar cell surface defect detection is still a challenge. To address this problem, this paper proposes a novel defect ohject detector called BPGA-Detector which consists of two parts: the Bidirectional-Path Feature Pyramid Network (BPFPN) and the Group-wise Attention Module (GAM). The Bidirectional-Path Feature Pyramid Network (BPFPN) combines multiscale features using a bidirectional-path feature fusion method that structured by connecting the bottom-up path feature pyramid network to the original FPN, preserving the characteristics of minor and weak flaws in the shallow layer. Furthermore, the Group-wise Attention Module (GAM) is elaborately designed to suppress the background disturbance and highlight the defect locations by connecting multi-layer contextuai features, which significantly improves the discriminant ability of small defects. Finally, the experimental results on a largescale solar cell dataset including 6263 images, 5763 of which are defective, demonstrate that the proposed method achieve superior detection performance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> up to 88.8%).
Defense of Advanced Persistent Threat on Industrial Internet of Things With Lateral Movement Modeling

Industrial Internet of Things (IIoT) is vulnerable to advanced persistent threat (APT). In this article, we study a scenario in which APT is launched to attack IIoT devices. Considering the APTs lateral movement, a node-level state evolution model is established to calculate the probability of every device in an IIoT system to be compromised by APT. Based on this, a Stackelberg game model is proposed for the APT attacker and defender, which can accurately describe the gaming process. An effective computational approach is developed to obtain the potential Stackelberg equilibrium strategy pair of the game. Extensive case studies and comparison studies are conducted to validate the effectiveness of the proposed method.
Time-Space Tradeoffs for Sponge Hashing: Attacks and Limitations for Short Collisions

Sponge hashing is a novel alternative to the popular Merkle-Damgård hashing design. The sponge construction has become increasingly popular in various applications, perhaps most notably, it underlies the SHA-3 hashing standard. Sponge hashing is parametrized by two numbers, r and c (bitrate and capacity, respectively), and by a fixed-size permutation on $$r+c$$ bits. In this work, we study the collision resistance of sponge hashing instantiated with a random permutation by adversaries with arbitrary S-bit auxiliary advice input about the random permutation that make T online queries. Recent work by Coretti et al. (CRYPTO ’18) showed that such adversaries can find collisions (with respect to a random c-bit initialization vector) with advantage $$\varTheta (ST^2/2^c + T^2/ 2^{r})$$ . Although the above attack formally breaks collision resistance in some range of parameters, its practical relevance is limited since the resulting collision is very long (on the order of T blocks). Focusing on the task of finding short collisions, we study the complexity of finding a B-block collision for a given parameter $$B\ge 1$$ . We give several new attacks and limitations. Most notably, we give a new attack that results in a single-block collision and has advantage $$\begin{aligned} \varOmega \left( \left( \frac{S^{2}T}{2^{2c}}\right) ^{2/3} + \frac{T^2}{2^r}\right) . \end{aligned}$$ In certain range of parameters (e.g., $$ST^2>2^c$$ ), our attack outperforms the previously-known best attack. To the best of our knowledge, this is the first natural application for which sponge hashing is provably less secure than the corresponding instance of Merkle-Damgård hashing. Our attack relies on a novel connection between single-block collision finding in sponge hashing and the well-studied function inversion problem. We also give a general attack that works for any $$B\ge 2$$ and has advantage $$\varOmega ({STB}/{2^{c}} + {T^2}/{2^{\min \{r,c\}}})$$ , adapting an idea of Akshima et al. (CRYPTO ’20). We complement the above attacks with bounds on the best possible attacks. Specifically, we prove that there is a qualitative jump in the advantage of best possible attacks for finding unbounded-length collisions and those for finding very short collisions. Most notably, we prove (via a highly non-trivial compression argument) that the above attack is optimal for $$B=2$$ in some range of parameters.
Resistance of Ascon Family Against Conditional Cube Attacks in Nonce-Misuse Setting

Ascon family is one of the finalists of the National Institute of Standards and Technology (NIST) lightweight cryptography standardization process.The family includes three Authenticated Encryption with Associated Data (AEAD) schemes: Ascon-128 (primary), Ascon-128a, and Ascon-80pq.In this paper, we study the resistance of the Ascon family against conditional cube attacks in nonce-misuse setting, and present new state-and key-recovery attacks.Our attacks recover the full state and the secret key of Ascon-128a when reduced to 7 out of 8 rounds of Ascon-permutation for the encryption phase, with 2 117 data and 2 116.2 time.These are the best known attack results for Ascon-128a as far as we know, while violating the data limit 2 64 imposed by designers.We also show that the partial state information of Ascon-128 can be recovered with 2 44.8 data.Finally, by assuming that the full state information of Ascon-80pq was recovered by Baudrin et al.'s attack, we show that the 160-bit secret key of Ascon-80pq can be recovered with 2 128 time.Although our attacks do not invalidate designers' security claim.those allow us to understand the security of Ascon in nonce-misuse setting.
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment

Although deep neural networks have achieved reasonable accuracy in solving face alignment, it is still a challenging task, specifically when dealing with facial images, under occlusion, or extreme head poses. Heatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for face alignment. CBR methods require less computer memory, though their performance is less than HBR methods. In this paper, we propose an Adaptive Coordinate-based Regression (ACR) loss to improve the accuracy of CBR for face alignment. Inspired by the Active Shape Model (ASM), we generate Smooth-Face objects, a set of facial landmark points with fewer variations compared to the ground truth landmark points. We then introduce a method to estimate the level of difficulty in predicting each landmark point for the network by comparing the distribution of the ground truth landmark points and the corresponding Smooth-Face objects. Our proposed ACR Loss can adaptively modify its curvature and the influence of the loss based on the difficulty level of predicting each landmark point in a face. Accordingly, the ACR Loss guides the network toward more challenging points than easier points, which improves the accuracy of the face alignment task. Our extensive evaluation shows the capabilities of the proposed ACR Loss in predicting facial landmark points in various facial images.
Auxiliary Classifier based Residual RNN for Image Captioning

Image captioning aims to generate a description of visual contents with natural language automatically. This is useful in several potential applications, such as image understanding and virtual assistants. With recent advances in deep neural networks, natural and semantic text generation has been improved in image captioning. However, maintaining the gradient flow between neurons in consecutive layers becomes challenging as the network gets deeper. In this paper, we propose to integrate an auxiliary classifier in the residual recurrent neural network, which enables the gradient flow to reach the bottom layers for enhanced caption generation. Experiments on the MSCOCO and VizWiz datasets demonstrate the advantage of our proposed approach over the state-of-the-art approaches in several performance metrics.
Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy. However, it is not necessarily true since the object detector can hardly distinguish between class-agnostic knowledge and class-specific knowledge automatically without explicit modeling. In this work we propose to learn three types of class-agnostic commonalities between base and novel classes explicitly: recognition-related semantic commonalities, localization-related semantic commonalities and distribution commonalities. We design a unified distillation framework based on a memory bank, which is able to perform distillation of all three types of commonalities jointly and efficiently. Extensive experiments demonstrate that our method can be readily integrated into most of existing fine-tuning based methods and consistently improve the performance by a large margin.
Coarse-To-Fine Incremental Few-Shot Learning

Different from fine-tuning models pre-trained on a large-scale dataset of preset classes, class-incremental learning (CIL) aims to recognize novel classes over time without forgetting pre-trained classes. However, a given model will be challenged by test images with finer-grained classes, e.g., a basenji is at most recognized as a dog. Such images form a new training set (i.e., support set) so that the incremental model is hoped to recognize a basenji (i.e., query) as a basenji next time. This paper formulates such a hybrid natural problem of coarse-to-fine few-shot (C2FS) recognition as a CIL problem named C2FSCIL, and proposes a simple, effective, and theoretically-sound strategy Knowe: to learn, freeze, and normalize a classifier’s weights from fine labels, once learning an embedding space contrastively from coarse labels. Besides, as CIL aims at a stability-plasticity balance, new overall performance metrics are proposed. In hat sense, on CIFAR-100, BREEDS, and tieredImageNet, Knowe outperforms all recent relevant CIL or FSCIL methods.
RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to address multiple design objectives, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoHNAS</i> , a novel NAS framework that jointly optimizes for adversarial-robustness and hardware-efficiency of DNNs executed on specialized hardware accelerators. Besides the traditional convolutional DNNs, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoHNAS</i> additionally accounts for complex types of DNNs such as Capsule Networks. For reducing the exploration time, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoHNAS</i> analyzes and selects appropriate values of adversarial perturbation for each dataset to employ in the NAS flow. Extensive evaluations on multi - Graphics Processing Unit (GPU) - High Performance Computing (HPC) nodes provide a set of Pareto-optimal solutions, leveraging the tradeoff between the above-discussed design objectives. For example, a Pareto-optimal DNN for the CIFAR-10 dataset exhibits 86.07% accuracy, while having an energy of 38.63 mJ, a memory footprint of 11.85 MiB, and a latency of 4.47 ms.
Class-Agnostic Object Detection with Multi-modal Transformer

What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and novel objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. For the first time in literature, we demonstrate that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on the observation that existing MViTs do not include multi-scale feature processing and usually require longer training schedules, we develop an efficient MViT architecture using multi-scale deformable attention and late vision-language fusion. We show the significance of MViT proposals in a diverse range of applications including open-world object detection, salient and camouflage object detection, supervised and self-supervised detection tasks. Further, MViTs can adaptively generate proposals given a specific language query and thus offer enhanced interactability. Code: https://git.io/J1HPY .