Reviewerly/exHarmony huggingface dataset:
Attention Boosted Deep Networks For Video Classification

Video classification can be performed by summarizing image contents of individual frames into one class by deep neural networks, e.g., CNN and LSTM. Human interpretation of video content is influenced by the attention mechanism. In other words, video class can be more attentively decided by certain information than others. In this paper, we propose to integrate the attention mechanism into deep networks for video classification. The proposed framework employs 2D CNN networks with ImageNet pretrained weights to extract features of video frames that are then fed to a bidirectional LSTM network for video classification. An attention block has been developed that can be added after the LSTM network in the proposed framework. Several different 2D CNN architectures have been tested in the experiments. The results with respect to two publicly available datasets have demonstrated that integrating attention can boost the performance of deep networks in video classification compared to not applying the attention block. We also found out that applying attention to the LSTM outputs on the VGG19 architecture provides the highest classification accuracy in the proposed framework.
A Study of Friend Abuse Perception in Facebook

Social networks like Facebook provide functionality that can expose users to abuse perpetrated by their contacts. For instance, Facebook users can often access sensitive profile information and timeline posts of their friends and also post abuse on the timeline and news feed of their friends. In this article, we introduce AbuSniff, a system to identify Facebook friends perceived to be abusive or strangers and protect the user by restricting the access to information for such friends. We develop a questionnaire to detect perceived strangers and friend abuse. We train supervised learning algorithms to predict questionnaire responses using features extracted from the mutual activities with Facebook friends. In our experiments, participants recruited from a crowdsourcing site agreed with 78% of the defense actions suggested by AbuSniff, without having to answer any questions about their friends. When compared to a control app, AbuSniff significantly increased the willingness of participants to take a defensive action against friends. AbuSniff also increased the participant self-reported willingness to reject friend invitations from strangers and abusers, their awareness of friend abuse implications, and their perceived protection from friend abuse.
End-to-End Video Saliency Detection via a Deep Contextual Spatiotemporal Network

As an interesting and important problem in computer vision, learning-based video saliency detection aims to discover the visually interesting regions in a video sequence. Capturing the information within frame and between frame at different aspects (such as spatial contexts, motion information, temporal consistency across frames, and multiscale representation) is important for this task. A key issue is how to jointly model all these factors within a unified data-driven scheme in an end-to-end fashion. In this article, we propose an end-to-end spatiotemporal deep video saliency detection approach, which captures the information on spatial contexts and motion characteristics. Furthermore, it encodes the temporal consistency information across the consecutive frames by implementing a convolutional long short-term memory (Conv-LSTM) model. In addition, the multiscale saliency properties for each frame are adaptively integrated for final saliency prediction in a collaborative feature-pyramid way. Finally, the proposed deep learning approach unifies all the aforementioned parts into an end-to-end joint deep learning scheme. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.
Automatic 3D Character Reconstruction from Frontal and Lateral Monocular 2D RGB Views

We present an automatic animatable 3D character reconstruction method from frontal and lateral RGB pictures of the person to reconstruct. We propose a method where, after obtaining a 3D reconstruction from a frontal view, we adapt the template of the Skinned Multi-Person Linear Model (SMPL) to it. This frontal modified SMPL is then used in a second step as an input to reconstruct the character from the lateral view, thus obtaining a correct person reconstruction in all dimensions. Our method enables the creation of 3D animatable characters focusing on two main aspects: shape and texture. The obtained results show that the proposed method achieves an accurate automatic 3D character reconstruction from only two views that enhances current methods while offering a reproducible work-flow.
VR Training for Railway Wagons Maintenance: architecture and implementation

The problems of supporting the required professional skills of railway wagons maintenance workers are regarded in this article. It is proposed to design a simulator based on virtual reality to test the skills of a car inspector in accordance with the instructions. The requirements for the simulator, its architecture and the architecture of the software simulator, platforms and implementation tools are considered. The paper provides features of the implemented prototype of the simulator based on HTC Vive and Unity platform for training skills of car inspection by the inspector and their further verification by the instructor in accordance with the regulations illustrated by a 3D model of the wagon with the fault and the tools of the inspector. Analysis of the simulator prototype's feasibility showed its sufficient ergonomic adequacy to the actual process of wagons maintenance and identified a number of implementation problems that will be taken into account in further research and development.
Enhancing Bilevel Optimization for UAV Time-Optimal Trajectory using a Duality Gap Approach

Time-optimal trajectories for dynamic robotic vehicles are difficult to compute even for state-of-the-art nonlinear programming (NLP) solvers, due to nonlinearity and bang-bang control structure. This paper presents a bilevel optimization framework that addresses these problems by decomposing the spatial and temporal variables into a hierarchical optimization. Specifically, the original problem is divided into an inner layer, which computes a time-optimal velocity profile along a given geometric path, and an outer layer, which refines the geometric path by a Quasi-Newton method. The inner optimization is convex and efficiently solved by interior-point methods. The gradients of the outer layer can be analytically obtained using sensitivity analysis of parametric optimization problems. A novel contribution is to introduce a duality gap in the inner optimization rather than solving it to optimality; this lets the optimizer realize warm-starting of the interior-point method, avoids non-smoothness of the outer cost function caused by active inequality constraint switching. Like prior bilevel frameworks, this method is guaranteed to return a feasible solution at any time, but converges faster than gap-free bilevel optimization. Numerical experiments on a drone model with velocity and acceleration limits show that the proposed method performs faster and more robustly than gap-free bilevel optimization and general NLP solvers.
Shift-Invariant Convolutional Network Search

The development of Neural Architecture Search (NAS) makes Convolutional Neural Networks (CNN) more diverse and effective. But previous NAS approaches don't pay attention to the shift-invariant of CNN. Without the shift-invariant, convolutional network is not robust enough when input data is disturbed or damaged. Besides, taking accuracy as the only optimization goal of NAS cannot meet the increasingly diverse needs. In this paper, we propose Shift-Invariant Convolutional Network Search (SICNS). It uses one-shot NAS to search for shift-invariant convolutional network by incorporating the low-pass filter into the one-shot model. Furthermore, SICNS optimizes multiple indicators simultaneously through the multi-objective evolutionary algorithm. Through training one-shot model and evolving the architecture, we obtain convolutional networks which are robust and powerful on image classification task. Especially, our work can achieve 4.52% test error on CIFAR-10 with 0.7M parameters. And in case the input data are disturbed, the accuracy of searched network is 2.96% higher than network without low-pass filter.
Do Instance-level Review Diagrams Support Validation Processes of Cyber-Physical System Specifications

In the field of safety-critical systems, manual reviews are important to ensure high-quality software and to satisfy legal obligations. When applying model-based engineering approaches, no longer are only textual requirements specifications or software code under review, but also model-based specification artifacts like behavioral requirements models. As such behavioral specifications are typically documented on a type-level, errors concerning the interactions between multiple system instances can go unnoticed in manual reviews. This is particularly the case when multiple system instances of the same system type are interacting during runtime, which is typical for cyber-physical systems where networks of cyber-physical systems form dynamically to fulfill an overall purpose. In this paper, we report on a controlled experiment whose results indicate that instance-level review diagrams have -- compared to type-level diagrams - important positive effects on reviewing processes for behavioral specifications of cyber-physical systems. Specifically, the experiment provides empirical evidence that instance-level review diagrams are significantly more expressive and effective than type-level diagrams.
Detection of Similar Functions Through the Use of Dominator Information

The detection of code clones is an important technique for finding malware and malicious code. Many existing methods work on source code to detect code clones. Recent work in this area has started to focus on the analysis of compiled code to find malicious code. We introduce a new method to detect code clones using Java bytecode and control flow information from dominator trees. A prototype implementation was developed and compared with the state-of-the-art clone detector NiCad to evaluate the basic functionality of the method. First experiments have shown that the method can reliably find Type 1 and Type 2 clones and even find additional clones.
Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity

The effects of label noise and ambiguity are widespread, especially for subjective tasks such as bone age assessment (BAA). However, most existing BAA algorithms ignore these issues. We propose a robust framework for BAA supporting Tanner & Whitehouse 3 (TW3) method, which is clinically more objective and reproducible than Greulich & Pyle (GP) method, but has received less attention from the research community. Since the publicly available RSNA BAA dataset was annotated using GP method, we contribute additional TW3 annotations. We formulate TW3 BAA as an ordinal regression problem, and address both label noise and ambiguity with a two stage deep learning framework. The first stage focuses on correcting erroneous labels with ambiguity tolerated, while the latter stage introduces a module called Residual Context Graph (RCG) to conquer label ambiguity. Inspired by the way human experts handle ambiguity, we combine fine-grained local features with a graph based context. Experiments show the proposed framework outperforms previously reported TW3-based BAA systems by large margins. TW3 annotations of bone maturity levels for a portion of the RSNA BAA dataset will be made publicly available.
Study of digital textual watermarking distortions under Internet attacks in high resolution videos

Multimedia content transmitted through the unprotected channels include several types of information, such as text messages, audio, images, and video sequences. Each type of embedded information can be distorted under various attacks with unknown parameters. In this paper, we study a special issue of textual watermarking distortions in videos under the simulated intentional and accidental attacks. Three approaches for textual messages embedding are exploited considering text as the sequence of binary codes, ordinary image, and barcode image. The experiments were conducted using test video sequences with high resolution compressed by x264 (H.264/AVC) codec in order to obtain the dependences between the robustness, capacity, and imperceptibility for blind watermarking schemes. We formulate the recommendations for hiding of textual information depending on a goal of embedding.
Radar as a Teacher: Weakly Supervised Vehicle Detection using Radar Labels

It has been demonstrated that the performance of an object detector degrades when it is used outside the domain of the data used to train it. However, obtaining training data for a new domain can be time consuming and expensive. In this work we demonstrate how a radar can be used to generate plentiful (but noisy) training data for image-based vehicle detection. We then show that the performance of a detector trained using the noisy labels can be considerably improved through a combination of noise-aware training techniques and relabelling of the training data using a second viewpoint. In our experiments, using our proposed process improves average precision by more than 17 percentage points when training from scratch and 10 percentage points when fine-tuning a pre-trained model.
Intelligent NPP Operators Support Systems:Flexible Modeling Software Package and Digital Twins

Design of NPP (nuclear power plant) safety important systems (relating to a critical information infrastructure) is basing on observing the regulatory documents and proving reliable plant performance during all its life-cycle. An aspect of the increased requirements for reliable and safe NPP APCS (automated process control system) performance is the necessity of perfecting NPP operator information support systems. An approach is considering, under which a core of the operator information support system is a flexible modeling software package that can be used as a digital shadow or digital twin in the dependence of solved tasks. This approach applies to a wide range of tasks of upper-level systems of NPP APCS.
En-VStegNET: Video Steganography using spatio-temporal feature enhancement with 3D-CNN and Hourglass

Learning Spatio-temporal features has shown improved performance on tasks involving video analysis using deep learning, and the deep learning community has used these features to solve a varied variety of problems. Video steganography is one such problem where learning these features for a video can help improve the performance of steganography. Steganography is the practice of concealing confidential information, to protect the information from an adversary, into an ordinary cover message in a way that the cover message does not seem suspicious to the adversary. Recent deep-learning-based steganography methods have proven to improve the secrecy and capacity of steganography over traditional techniques. In this paper, we propose a novel state-of-the-art deep 3D-CNN architecture with enhancement feature learning for full video steganography. The proposed model outperforms the current state-of-the-art methods for full video steganography both qualitatively and quantitatively. We have validated our model by comparing it with new as well as traditional steganography techniques, on quality and different statistical metrics, namely, PSNR, SSIM, APD, VIF at the frame, and video level. Moreover, to check the undetectability of our model, we have subjected our model to detection by steganalysis tools like SRNet. Results of fine-tuning classifiers, like ResNet and Inception-v3, to detect steganographic messages from ordinary messages maintains our model's undetectability and accuracy.
Building and maintaining a third-party library supply chain for productive and secure SGX enclave development

The big data industry is facing new challenges as concerns about privacy leakage soar. One of the remedies to privacy breach incidents is to encapsulate computations over sensitive data within hardware-assisted Trusted Execution Environments (TEE). Such TEE-powered software is called secure enclaves. Secure enclaves hold various advantages against competing for privacy-preserving computation solutions. However, enclaves are much more challenging to build compared with ordinary software. The reason is that the development of TEE software must follow a restrictive programming model to make effective use of strong memory encryption and segregation enforced by hardware. These constraints transitively apply to all third-party dependencies of the software. If these dependencies do not officially support TEE hardware, TEE developers have to spend additional engineering effort in porting them. High development and maintenance cost is one of the major obstacles against adopting TEE-based privacy protection solutions in production.
A Constant-Time Algorithm of CSIDH Keeping Two Points

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We evaluate the costs of our implementation and that of Meyer et al. in terms of the number of operations on a finite prime field. Our evaluation shows that our constant-time implementation of CSIDH reduces the calculation cost by 28% compared with the implementation by Mayer et al. We also implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152 million clock cycles, which is about 29% faster than that of Meyer et al. and confirms the above reduction ratio in our cost evaluation.
The Machine Vision Measurement Module of the Modularized Flexible Precision Assembly Station for Assembly of Micro- and Meso-Sized Parts

The machine vision measurement module is indispensable for the Modularized Flexible Precision Assembly Station (MFPAS), which is a fully automatic assembly system being developed at Dalian University of Technology (DUT). MFPAS consists of basic and additional modules, and are expected to be flexible, expandable, and re-configurable to adapt to a variety of parts with a large size range, requiring the machine vision measurement module to be able to achieve accurate measurement of position, as well as orientation of the parts with different size scale. An automatic zooming vision system was set up for evaluation and final integration in MFPAS. Pixel equivalent, principal point and orientation deviation of images were analyzed and experimentally studied using different magnifications of the lens. A new template with circular patterns of different diameters was designed for zoom-lens calibration. The experiments show that the measurement error caused by the variation of the pixel equivalent, principal point and orientation is estimated under 10 μm without online calibration. When high accuracy is required, online calibration can be employed during assembly. The evaluation results of the vision system with or without on-line calibration were given for a better trade-off between accuracy and efficiency during assembly.
Possibility and Impossibility Results for Receiver Selective Opening Secure PKE in the Multi-challenge Setting

Public key encryption (PKE) schemes are usually deployed in an open system with numerous users. In practice, it is common that some users are corrupted. A PKE scheme is said to be receiver selective opening (RSO) secure if it can still protect messages transmitted to uncorrupted receivers after the adversary corrupts some receivers and learns their secret keys. This is usually defined by requiring the existence of a simulator that can simulate the view of the adversary given only the opened messages. Existing works construct RSO secure PKE schemes in a single-challenge setting, where the adversary can only obtain one challenge ciphertext for each public key. However, in practice, it is preferable to have a PKE scheme with RSO security in the multi-challenge setting, where public keys can be used to encrypt multiple messages. In this work, we explore the possibility of achieving PKE schemes with receiver selective opening security in the multi-challenge setting. Our contributions are threefold. First, we demonstrate that PKE schemes with RSO security in the single-challenge setting are not necessarily RSO secure in the multi-challenge setting. Then, we show that it is impossible to achieve RSO security for PKE schemes if the number of challenge ciphertexts under each public key is a priori unbounded. In particular, we prove that no PKE scheme can be RSO secure in the k-challenge setting (i.e., the adversary can obtain k challenge ciphertexts for each public key) if its secret key contains less than k bits. On the positive side, we give a concrete construction of PKE scheme with RSO security in the k-challenge setting, where the ratio of the secret key length to k approaches the lower bound 1.
A*-Based Similarity Assessment of Semantic Graphs

The similarity assessment of graphs is a fundamental problem that is particularly challenging if efficiency is of core importance. In this paper, we focus on a similarity measure for semantically labeled graphs whose labels are composed in an object-oriented manner. The measure is based on A* search and is particularly suited for case-based reasoning as it can be combined with knowledge-intensive local similarity measures and outputs similarities and corresponding mappings usable for explanation and adaptation. However, particularly for large graphs, the search space must be pruned to improve efficiency of A* search at the cost of sacrificing global optimality. We address this issue and present complementary improvements of the measure, which we systematically evaluate for the similarity assessment of semantic workflow graphs. The experimental results demonstrate that the new measure considerably reduces the computation time and memory consumption while increasing the accuracy.
A Model-Free Approach to Meta-Level Control of Anytime Algorithms

Anytime algorithms offer a trade-off between solution quality and computation time that has proven to be useful in autonomous systems for a wide range of real-time planning problems. In order to optimize this trade-off, an autonomous system has to solve a challenging meta-level control problem: it must decide when to interrupt the anytime algorithm and act on the current solution. Prevailing meta-level control techniques, however, make a number of unrealistic assumptions that reduce their effectiveness and usefulness in the real world. Eliminating these assumptions, we first introduce a model-free approach to meta-level control based on reinforcement learning and prove its optimality. We then offer a general meta-level control technique that can use different reinforcement learning methods. Finally, we show that our approach is effective across several common benchmark domains and a mobile robot domain.