Reviewerly/exHarmony huggingface dataset:
Massive Superpoly Recovery with Nested Monomial Predictions

Determining the exact algebraic structure or some partial information of the superpoly for a given cube is a necessary step in the cube attack – a generic cryptanalytic technique for symmetric-key primitives with some secret and public tweakable inputs. Currently, the division property based approach is the most powerful tool for exact superpoly recovery. However, as the algebraic normal form (ANF) of the targeted output bit gets increasingly complicated as the number of rounds grows, existing methods for superpoly recovery quickly hit their bottlenecks. For example, previous method stuck at round 842, 190, and 892 for Trivium, Grain-128AEAD, and Kreyvium, respectively. In this paper, we propose a new framework for recovering the exact ANFs of massive superpolies based on the monomial prediction technique (ASIACRYPT 2020, an alternative language for the division property). In this framework, the targeted output bit is first expressed as a polynomial of the bits of some intermediate states. For each term appearing in the polynomial, the monomial prediction technique is applied to determine its superpoly if the corresponding MILP model can be solved within a preset time limit. Terms unresolved within the time limit are further expanded as polynomials of the bits of some deeper intermediate states with symbolic computation, whose terms are again processed with monomial predictions. The above procedure is iterated until all terms are resolved. Finally, all the sub-superpolies are collected and assembled into the superpoly of the targeted bit. We apply the new framework to Trivium, Grain-128AEAD, and Kreyvium. As a result, the exact ANFs of the superpolies for 843-, 844- and 845-round Trivium, 191-round Grain-128AEAD and 894-round Kreyvium are recovered. Moreover, with help of the Möbius transform, we present a novel key-recovery technique based on superpolies involving all key bits by exploiting the sparse structures, which leads to the best key-recovery attacks on the targets considered.
Multiple Object Tracking in Deep Learning Approaches: A Survey

Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.
Cross-Epoch Learning for Weakly Supervised Anomaly Detection in Surveillance Videos

Weakly Supervised Anomaly Detection (WSAD) in surveillance videos is a complex task since usually only video-level annotations are available. Previous work treated it as a regression problem by giving different scores on normal and anomaly events. However, the widely used mini-batch training strategy may suffer from the data imbalance between these two types of events, which limits the model's performance. In this work, a cross-epoch learning (XEL) strategy associated with a hard instance bank (HIB) is proposed to introduce additional information from previous training epochs. Two new losses are proposed for XEL to achieve a higher detection rate as well as a lower false alarm rate of anomaly events. Moreover, the proposed XEL can be directly integrated into any existing WSAD framework. Experimental results of three XEL embedded models have shown promising AUC improvement (3%~7%) on two public datasets, surpassing the state-of-the-art methods. Our code is available at: https://github.com/sdjsngs/XEL-WSAD.
Challenges and Common Solutions in Smart Contract Development

Smart contracts are a promising means of formalizing and reliably enforcing agreements between entities using distributed ledger technology (DLT).Research has revealed that a significant number of smart contracts are subject to programming flaws, making them vulnerable to attacks and leading to detrimental effects, such as asset loss.Researchers and developers call for a thorough analysis of challenges to identify their causes and propose solutions.To respond to these calls, we conducted two literature reviews and diverse expert interviews and synthesized scattered knowledge on challenges and solutions.We identified 29 challenges (e.g., code visibility, code updateability, and encapsulation) and 60 solutions (e.g., gas limit specification, off-ledger computations, and shadowing).Moreover, we developed 20 software design patterns (SDPs) in collaboration with smart contract developers.The SDPs help developers adjust their programming habits and thus support them in their daily development practices.Our results provide actionable knowledge for smart contract developers to overcome the identified challenges and offer support for comparing smart contract integration concepts across three fundamentally different DLT protocols (i.e., Ethereum, EOSIO, and Hyperledger Fabric).Moreover, we support developers in becoming aware of peculiarities in smart contract development and the resulting benefits and drawbacks.
Introduction to generation, manipulation and characterization of optical quantum states

Why do we need quantization to describe vision? What are the quadrature operators of the electromagnetic field? Is it possible to measure them? What are the characteristic functions useful for? In this brief tutorial we provide the theoretical tools needed to describe the generation, manipulation and characterization of optical quantum states and of the main passive (beam splitters) and active (squeezers) devices involved in experiments, such as the Hong-Ou-Mandel interferometer and the continuous-variable quantum teleportation. We also introduce the concept of operator ordering and the description of a system by means of the $p$-ordered characteristic functions. Then we focus on the quasi-probability distributions and, in particular, on the relation between the marginals of the Wigner function and the outcomes of the quadrature operator measurement. Finally, we introduce the balanced homodyne detection to measure the quadrature operator and the homodyne tomography as a tool for characterizing quantum optical states also in the presence of non-unit quantum efficiency.
Large-scale pinball twin support vector machines

Twin support vector machines (TWSVMs) have been shown to be effective classifiers for a range of pattern classification tasks. However, the TWSVM formulation suffers from a range of shortcomings: (i) TWSVM uses hinge loss function which renders it sensitive to dataset outliers (noise sensitivity). (ii) It requires a matrix inversion calculation in the Wolfe-dual formulation which is intractable for datasets with large numbers of features/samples. (iii) TWSVM minimizes the empirical risk instead of the structural risk in its formulation with the consequent risk of overfitting. This paper proposes a novel large scale pinball twin support vector machines (LPTWSVM) to address these shortcomings. The proposed LPTWSVM model firstly utilizes the pinball loss function to achieve a high level of noise insensitivity, especially in relation to data with substantial feature noise. Secondly, and most significantly, the proposed LPTWSVM formulation eliminates the need to calculate inverse matrices in the dual problem (which apart from being very computationally demanding may not be possible due to matrix singularity). Further, LPTWSVM does not employ kernel-generated surfaces for the non-linear case, instead using the kernel trick directly; this ensures that the proposed LPTWSVM is a fully modular kernel approach in contrast to the original TWSVM. Lastly, structural risk is explicitly minimized in LPTWSVM with consequent improvement in classification accuracy (we explicitly analyze the properties of classification accuracy and noise insensitivity of the proposed LPTWSVM). Experiments on benchmark datasets show that the proposed LPTWSVM model may be effectively deployed on large datasets and that it exhibits similar or better performance on most datasets in comparison to relevant baseline methods.
Vision-Based Autonomous Landing for Unmanned Aerial and Ground Vehicles Cooperative Systems

In this work, we consider a cooperative system in which UAVs perform long-distance missions with assistance of unmanned ground vehicles (UGVs) for battery charging. We propose an autonomous landing scheme for the UAV to land on a mobile UGV with high precision by leveraging multiple-scale Quick Response (QR)-codes for different altitudes. These QR-codes support the acquisition of the relative distance and direction between the UGV and UAV. As a result, the UGV is not required to report its accurate state to the UAV. In contrast to the conventional landing algorithms based on the global positioning system (GPS), the proposed vision-based autonomous landing algorithm is available for both outdoor and GPS-denied scenarios. To cope with the challenge on the moving platform landing, a quadratic programming (QP) problem is formulated to design the velocity controller by combining a control barrier function (CBF) and a control Lyapunov function (CLF). Finally, both extensive computer simulation and a prototype of the proposed system are shown to confirm the feasibility of the proposed system and landing scheme.
TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning

We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attention mask is used to identify reduced stability regions in the elevation map and is computed using channel and spatial attention modules and a novel reward function. We continuously compute and update a navigation cost-map that encodes the elevation information or the level-of-flatness of the terrain using the attention mask. We then generate locally least-cost waypoints on the cost-map and compute the final dynamically feasible trajectory using another DRL-based method. Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in uneven terrains. We observe an increase of 35.18% in terms of success rate and, a decrease of 26.14% in the cumulative elevation gradient of the robot's trajectory compared to prior navigation methods in high-elevation regions. We evaluate our method on a Husky robot in real-world uneven terrains (∼ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4m$</tex> of elevation gain) and demonstrate its benefits.
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?

Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark, in particular on the commonly reported validation split. This generated a distorted impression about the superiority of Pseudo-LiDAR-based (PL-based) approaches over methods working with RGB images only. Our first contribution consists in rectifying this view by pointing out and showing experimentally that the validation results published by PL-based methods are substantially biased. The source of the bias resides in an overlap between the KITTI3D object detection validation set and the training/validation sets used to train depth predictors feeding PL-based methods. Surprisingly, the bias remains also after geographically removing the overlap. This leaves the test set as the only reliable set for comparison, where published PL-based methods do not excel. Our second contribution brings PL-based methods back up in the ranking with the design of a novel deep architecture which introduces a 3D confidence prediction module. We show that 3D confidence estimation techniques derived from RGB-only 3D detection approaches can be successfully integrated into our framework and, more importantly, that improved performance can be obtained with a newly designed 3D confidence measure, leading to state-of-the-art performance on the KITTI3D benchmark.
Affordable But Not Cheap: A Case Study of the Effects of Two 3D-Reconstruction Methods of Virtual Humans

Realistic and lifelike 3D-reconstruction of virtual humans has various exciting and important use cases. Our and others’ appearances have notable effects on ourselves and our interaction partners in virtual environments, e.g., on acceptance, preference, trust, believability, behavior (the Proteus effect), and more. Today, multiple approaches for the 3D-reconstruction of virtual humans exist. They significantly vary in terms of the degree of achievable realism, the technical complexities, and finally, the overall reconstruction costs involved. This article compares two 3D-reconstruction approaches with very different hardware requirements. The high-cost solution uses a typical complex and elaborated camera rig consisting of 94 digital single-lens reflex (DSLR) cameras. The recently developed low-cost solution uses a smartphone camera to create videos that capture multiple views of a person. Both methods use photogrammetric reconstruction and template fitting with the same template model and differ in their adaptation to the method-specific input material. Each method generates high-quality virtual humans ready to be processed, animated, and rendered by standard XR simulation and game engines such as Unreal or Unity. We compare the results of the two 3D-reconstruction methods in an immersive virtual environment against each other in a user study. Our results indicate that the virtual humans from the low-cost approach are perceived similarly to those from the high-cost approach regarding the perceived similarity to the original, human-likeness, beauty, and uncanniness, despite significant differences in the objectively measured quality. The perceived feeling of change of the own body was higher for the low-cost virtual humans. Quality differences were perceived more strongly for one’s own body than for other virtual humans.
NGC: A Unified Framework for Learning with Open-World Noisy Data

The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance. There have been plenty of works concentrated on learning with indistribution (IND) noisy labels in the last decade, i.e., some training samples are assigned incorrect labels that do not correspond to their true classes. Nonetheless, in real application scenarios, it is necessary to consider the influence of out-of-distribution (OOD) samples, i.e., samples that do not belong to any known classes, which has not been sufficiently explored yet. To remedy this, we study a new problem setup, namely Learning with Open-world Noisy Data (LOND). The goal of LOND is to simultaneously learn a classifier and an OOD detector from datasets with mixed IND and OOD noise. In this paper, we propose a new graph-based framework, namely Noisy Graph Cleaning (NGC), which collects clean samples by leveraging geometric structure of data and model predictive confidence. Without any additional training effort, NGC can detect and reject the OOD samples based on the learned class prototypes directly in testing phase. We conduct experiments on multiple benchmarks with different types of noise and the results demonstrate the superior performance of our method against state of the arts.
DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks

We study involuntary micro-movements of both eyes, in addition to saccadic macro-movements, as biometric characteristic. We develop a deep convolutional neural network that processes binocular eye-tracking signals and verifies the viewer’s identity. In order to detect presentation attacks, we develop a model in which the movements are a response to a controlled stimulus. The model detects replay attacks by processing both the controlled but randomized stimulus and the ocular response to this stimulus. We acquire eye movement data from 150 participants, with 4 sessions per participant and conduct experiments on this new and legacy data sets with varying tracker precision and sampling rate. We observe that the model detects replay attacks reliably. For identification and identity verification, the model attains substantially lower error rates than prior work. We explore the relationships between training population size, training data volume, types of visual stimuli, number of training and enrollment sessions, interval between enrollment and probe sessions on one hand and the model performance on the other hand.
A free lunch from ViT: adaptive attention multi-scale fusion Transformer for fine-grained visual recognition

Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field. The vision transformer (ViT) achieves promising results on computer vision due to its attention mechanism. Nonetheless, with the fixed size of patches in ViT, the class token in deep layer focuses on the global receptive field and cannot generate multi-granularity features for FGVR. To capture region attention without box annotations and compensate for ViT shortcomings in FGVR, we propose a novel method named Adaptive attention multi-scale Fusion Transformer (AFTrans). The Selective Attention Collection Module (SACM) in our approach leverages attention weights in ViT and filters them adaptively to correspond with the relative importance of input patches. The multiple scales (global and local) pipeline is supervised by our weights sharing encoder and can be easily trained end-to-end. Comprehensive experiments demonstrate that AFTrans can achieve SOTA performance on three published fine-grained benchmarks: CUB-200-2011, Stanford Dogs and iNat2017.
Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection

The paper gives a brief overview of the three shared tasks organized at the PAN 2021 lab on digital text forensics and stylometry hosted at the CLEF conference. The tasks include authorship verification across domains, author profiling for hate speech spreaders, and style change detection for multi-author documents. In part the tasks are new and in part they continue and advance past shared tasks, with the overall goal of advancing the state of the art, providing for an objective evaluation on newly developed benchmark datasets.
Selective Dependency Aggregation for Action Classification

Video data are distinct from images for the extra temporal dimension, which results in more content dependencies from various perspectives. It increases the difficulty of learning representation for various video actions. Existing methods mainly focus on the dependency under a specific perspective, which cannot facilitate the categorization of complex video actions. This paper proposes a novel selective dependency aggregation (SDA) module, which adaptively exploits multiple types of video dependencies to refine the features. Specifically, we empirically investigate various long-range and short-range dependencies achieved by the multi-direction multi-scale feature squeeze and the dependency excitation. Query structured attention is then adopted to fuse them selectively, fully considering the diversity of videos' dependency preferences. Moreover, the channel reduction mechanism is involved in SDA for controlling the additional computation cost to be lightweight. Finally, we show that the SDA module can be easily plugged into different backbones to form SDA-Nets and demonstrate its effectiveness, efficiency and robustness by conducting extensive experiments on several video benchmarks for action classification. The code and models will be available at https://github.com/ty-97/SDA.
Deciding If and How to Use a COVID-19 Contact Tracing App: Influences of Social Factors on Individual Use in Japan

Contact tracing apps have been suggested as a promising approach towards containing viral spread during pandemics, yet their actual use in the COVID-19 pandemic has been low. While researchers have examined reasons for or against installing contact tracing apps, we have less understanding of their ongoing use and how they interact with everyday pressures related to work, communities, and mental well-being. Through a survey of 153 working people in Japan and 15 follow-up interviews, we investigated attitudes toward installing and using Japan's national contact tracing app, COCOA, and how these related to respondents' daily lives, work structures, and general attitudes about the pandemic. We found that motivations about installing the app differed from those related to ongoing usage. Specifically, we identified ways that people navigate uncertain norms of behaviour during the pandemic, and how people consider individual risks such as COVID-related stigmas, anxiety, and financial precarity when deciding if and how to use COCOA. In light of these, we discuss the tension between COCOA's design and desires to protect oneself by selective controlling disclosures. We note that perceived risks are closely tied to respondents' local contexts, and based on our analysis, we identify ways to address these challenges and tensions through design interventions at multiple scales.
Toward Impact-resilient Quadrotor Design, Collision Characterization and Recovery Control to Sustain Flight after Collisions

Collision detection and recovery for aerial robots remain a challenge because of the limited space for sensors and local stability of the flight controller. We introduce a novel collision-resilient quadrotor that features a compliant arm design to enable free flight while allowing for one passive degree of freedom to absorb shocks. We further propose a novel collision detection and characterization method based on Hall sensors, as well as a new recovery control method to generate and track a smooth trajectory after a collision occurs. Experimental results demonstrate that the robot can detect and recover from high-speed collisions with various obstacles such as walls and poles. Moreover, it can survive collisions that are hard to detect with existing methods based on IMU data and contact models, for example, when colliding with unstructured surfaces, or being hit by a moving obstacle while hovering.
Enabling spatio-temporal aggregation in Birds-Eye-View Vehicle Estimation

Constructing Birds-Eye-View (BEV) maps from monocular images is typically a complex multi-stage process involving the separate vision tasks of ground plane estimation, road segmentation and 3D object detection. However, recent approaches have adopted end-to-end solutions which warp image-based features from the image-plane to BEV while implicitly taking account of camera geometry. In this work, we show how such instantaneous BEV estimation of a scene can be learnt, and a better state estimation of the world can be achieved by incorporating temporal information. Our model learns a representation from monocular video through factorised 3D convolutions and uses this to estimate a BEV occupancy grid of the final frame. We achieve state-of-the-art results for BEV estimation from monocular images, and establish a new benchmark for single-scene BEV estimation from monocular video.