Reviewerly/exHarmony huggingface dataset:
Efficient Physical Layer Key Agreement for FlexRay Networks

The FlexRay protocol provides deterministic and fault-tolerant communication for automotive applications with stringent requirements regarding reliability and real-time performance. But the lack of intrinsic security mechanisms makes FlexRay vulnerable to spoofing and DoS attacks while existing features are not sufficient to assure reliable communication in the presence of adversaries. Since adding cryptography mandates for secret shared keys, providing secure key establishment techniques is critical in assuring the intended security objectives. In this paper we propose a backward compatible key agreement scheme for FlexRay communication based on intrinsic characteristics of the FlexRay physical layer. In particular, we determine that the symbol window is suitable for implementing a covert channel based on physical properties of the bus which allows us to bootstrap a secure session key between two nodes or to re-enforce existing shared keys. We evaluate and discuss the performance of software-based implementations of the proposed mechanism on two different automotive embedded platforms relying only on the application level.
Feature Augmented Memory with Global Attention Network for VideoQA

Recently, Recurrent Neural Network (RNN) based methods and Self-Attention (SA) based methods have achieved promising performance in Video Question Answering (VideoQA). Despite the success of these works, RNN-based methods tend to forget the global semantic contents due to the inherent drawbacks of the recurrent units themselves, while SA-based methods cannot precisely capture the dependencies of the local neighborhood, leading to insufficient modeling for temporal order. To tackle these problems, we propose a novel VideoQA framework which progressively refines the representations of videos and questions from fine to coarse grain in a sequence-sensitive manner. Specifically, our model improves the feature representations via the following two steps: (1) introducing two fine-grained feature-augmented memories to strengthen the information augmentation of video and text which can improve memory capacity by memorizing more relevant and targeted information. (2) appending the self-attention and co-attention module to the memory output thus the module is able to capture global interaction between high-level semantic informations. Experimental results show that our approach achieves state-of-the-art performance on VideoQA benchmark datasets.
Fusing and refining convolutional neural network models for assembly action recognition in smart manufacturing

Assembly carries paramount importance in manufacturing. Being able to support workers in real time to maximize their positive contributions to assembly is a tremendous interest of manufacturers. Human action recognition has been a way to automatically analyze and understand worker actions to support real-time assistance for workers and facilitate worker–machine collaboration. Assembly actions are distinct from activities that have been well studied in the action recognition literature. Actions taken by assembly workers are intricate, variable, and may involve very fine motions. Therefore, recognizing assembly actions remains a challenging task. This paper proposes to simply use only two wearable devices that respectively capture the inertial measurement unit data of each hand of workers. Then, two convolutional neural network models with an identical architecture are independently trained using the two sources of inertial measurement unit data to respectively recognize the right-hand and the left-hand actions of an assembly worker. Classification results of the two convolutional neural network models are fused to yield a final action recognition result because the two hands often collaborate in assembling operations. Transfer learning is implemented to adapt the action recognition models to subjects whose data have not been included in dataset for training the models. One operation in assembling a Bukito three-dimensional printer, which is composed of seven actions, is used to demonstrate the implementation and assessment of the proposed method. Results from the study have demonstrated that the proposed approach effectively improves the prediction accuracy at both the action level and the subject level. Work of the paper builds a foundation for building advanced action recognition systems such as multimodal sensor-based action recognition.
Task-based visual analytics for interactive multiobjective optimization

We study how visual interaction techniques considered in visual analytics can be utilized when implementing interactive multiobjective optimization methods, where a decision maker iteratively participates in the solution process.We want to benefit from previous research and avoid re-inventing ideas.Our aim is to widen awareness and increase the applicability of interactive methods for solving real-world problems.As a concrete approach, we introduce seven high-level tasks that are relevant for interactive methods.These high-level tasks are based on low-level tasks proposed in the visual analytics literature.In addition, we give an example on how the high-level tasks can be implemented and demonstrate this in the context of a real-world multiobjective optimization problem related to wastewater treatment plant operation.Finally, we make recommendations for implementations of interactive methods.We conclude that task-based visual analytics can help in implementing interaction between human decision makers and interactive multiobjective optimization methods.
Real-world Cross-modal Retrieval via Sequential Learning

Cross-modal retrieval is playing an increasingly important role in our daily life with the explosive growth of multimedia data. However, its learning paradigm under real-life environments is less studied, and most existing approaches are developed in the pre-desired settings (e.g., unchanging modalities and explicitly modal-aligned samples). Inspired by the recent achievement in the field of cognition mechanism on how the human brain acquires knowledge, we present a new sequential learning method for real-world cross-modal retrieval. In this method, a unified model is maintained to capture the common knowledge of various modalities but are learned in a sequential manner such that it behaves adaptively according to the evolving distribution of different modalities, and needs no laborious alignment operations among multimodal data before learning. Furthermore, we reformulate the objective of optimization-based meta-learning and propose a novel meta-learning method to overcome the catastrophic forgetting encountered in sequential learning. Extensive experiments are conducted on four popular image-text multimodal datasets and a five-modal dataset, showing that our method achieves state-of-the-art cross-modal retrieval performance without explicit modal-alignment.
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent

We prove the first superpolynomial lower bounds for learning one-layer neural networks with respect to the Gaussian distribution using gradient descent. We show that any classifier trained using gradient descent with respect to square-loss will fail to achieve small test error in polynomial time given access to samples labeled by a one-layer neural network. For classification, we give a stronger result, namely that any statistical query (SQ) algorithm (including gradient descent) will fail to achieve small test error in polynomial time. Prior work held only for gradient descent run with small batch sizes, required sharp activations, and applied to specific classes of queries. Our lower bounds hold for broad classes of activations including ReLU and sigmoid. The core of our result relies on a novel construction of a simple family of neural networks that are exactly orthogonal with respect to all spherically symmetric distributions.
On the Reliability of Test Collections for Evaluating Systems of Different Types

As deep learning based models are increasingly being used for information retrieval, a major challenge is to ensure the availability of test collections for measuring their quality. Test collections are usually generated based on pooling results of various retrieval systems, but until recently this did not include deep learning systems. This raises a major challenge for reusable evaluation: Since deep learning based models use external resources (e.g. word embeddings) and advanced representations when compared to traditional methods, they may return different types of relevant document that were not identified in the original pooling. If so, test collections constructed using traditional methods could lead to biased and unfair evaluation results for deep learning systems. This paper uses simulated pooling to test the fairness and reusability of test collections, showing that especially when shallow pools (e.g. depth-10 pools) are used, pooling based on traditional systems only may lead to biased evaluation of deep learning systems.
Implementation of Supersingular Isogeny-Based Diffie-Hellman and Key Encapsulation Using an Efficient Scheduling

Isogeny-based cryptography is one of the promising post-quantum candidates mainly because of its smaller public key length. Due to its high computational cost, efficient implementations are significantly important. In this paper, we have proposed a high-speed FPGA implementation of the supersingular isogeny Diffie-Hellman (SIDH) and key encapsulation (SIKE). To this end, we have adapted the algorithm of finding optimal large-degree isogeny computation strategy for hardware implementations. Using this algorithm, hardware-suited strategies (HSSs) can be devised. We have also developed a tool to schedule field arithmetic operations efficiently using constraint programming. This tool enables reducing the latency of SIDH and SIKE subroutines by up to 14% at NIST's highest security level, i.e., using the SIKEp751 parameter set. We have also improved the latency of field inversion, the most costly field operation in SIDH, by 23% using the Montgomery ladder technique. We have provided constant-time implementations of SIDH and SIKE on Virtex-7 using SIKEp751 utilizing 6 and 8 prime field multipliers to resemble the previous work. Experimental results show that using 8 multipliers SIDH and SIKE encapsulation and decapsulation can be performed in 24.66 ms and 24.10 ms, which is 1.37 and 1.12 times faster than the latest corresponding FPGA implementations, respectively.
Fractional Data Distillation Model for Anomaly Detection in Traffic Videos

Timely automatic detection of anomalies like road accidents forms the key to any intelligent traffic monitoring system. In this paper, we propose a novel Fractional Data Distillation model for segregating traffic anomaly videos from a test dataset, with a precise estimation of the start time of the anomalous event. The model follows a similar approach to that of the typical fractional distillation procedure, where the compounds are separated by varying the temperature. Our model fractionally extracts the anomalous events depending on their nature as the detection process progresses. Here, we employ two anomaly extractors namely Normal and Zoom, of which former works on the normal scale of video and the latter works on the magnified scale on the videos missed by the former, to separate the anomalies. The backbone of this segregation is scanning the background frames using the YOLOv3 detector for spotting possible anomalies. These anomaly candidates are further filtered and compared with detection on the foreground for matching detections to estimate the start time of the anomalous event. Experimental validation on track 4 of 2020 AI City Challenge shows an s4 score of 0.5438, with an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 0.7018.
Supporting Dynamic Graphs and Temporal Entity Deletions in the LDBC Social Network Benchmark's Data Generator

Many data processing pipelines operate on highly-connected data sets that can be efficiently modelled as graphs. These graphs are rarely static, but rather change rapidly and often exhibit dynamic, temporal, or streaming behaviour. During the last decade, numerous graph benchmarks have been proposed, which cover a significant portion of the features required in practical use cases. However, whilst these benchmarks often contain some update operations, none of them include complex deletions, which makes it challenging to test the performance of graph processing systems under such operations. To address this limitation, we have extended the LDBC Social Network Benchmark (SNB) by introducing lifespan attributes for the creation and deletion dates of its entities. We have defined constraints for selecting these dates from intervals that ensure that the graph always satisfies the cardinality constraints prescribed by the schema and other semantic constraints of the social network domain. We have implemented the proposed lifespans in the SNB generator.
Towards the Perceptual Quality Enhancement of Low Bit-rate Compressed Images

In this paper, a low bit-rate compressed image quality enhancement framework is presented. A recent image/video coding method and a deep learning based quality enhancement method are integrated to improve the perceptual quality of compressed images. The proposed architecture is designed to reduce the coding artifact and restore the blurred texture details. The experimental results presents that the proposed framework yields a 33% improvement in the Perceptual Index score which is consistent with visual evaluation on a sample of results.
Image Quality Assessment Without Reference By Mixing Deep Learning-Based Features

Images are often distorted by some necessary treatments (capture, compression, transmission, etc..) that can affect the perceptual quality. To evaluate the impact of these treatments, a plethora of metrics have been developed in the literature. In this paper, we propose an efficient blind method to estimate the quality of 2D-images based on the selection of relevant patches through the saliency information and a Convolutional Neural Network (CNN). Saliency information was here used to focus on regions that highly impact the subjective scores. Three CNN models were individually evaluated and compared (AlexNet, VGG19 and ResNet50). A pairwise combination of deep learning-based feature vectors was then applied to estimate the quality. Different pooling strategies were employed (Concatenation, Element-wise operation and Bilinear Pooling). Our method was compared to the state-of the-art using two common datasets (LIVE-P2 and CSIQ). The results obtained showed the efficiency of our approach and its generalization ability.
A Benchmark of Light Field View Interpolation Methods

Light field view interpolation provides a solution that reduces the prohibitive size of a dense light field. This paper examines state-of- the-art light field view interpolation methods with a comprehensive benchmark on challenging scenarios specific for interpolation tasks. Each method is analyzed in terms of their strengths and weaknesses in handling different challenges. We find that large disparities in a scene are the main source of challenge for the light field view interpolation methods. We also find that a basic backward warping based on the depth estimation from optical flow provides comparable performance against usually complex learning-based methods.
Large Scale Vehicle Re-Identification by Knowledge Transfer from Simulated Data and Temporal Attention

Automated re-identification (re-id) of vehicles is the foundation of many traffic analysis applications across camera networks, e.g.vehicle tracking, counting, or traffic density and flow estimation.The re-id task is made difficult by variations in lighting, viewpoint, image quality and similar vehicle models and colors that can occur across the network.These influences can cause a high visual appearance variation for the same vehicle while different vehicles may appear near identical under similar conditions.However, with a growing number of available datasets and well crafted deep learning models, much progress has been made.In this work we summarize the results of our participation in the NVIDIA AI City Challenge 2020 for vehicle reid.We address the re-id task by relying on well-proven design choices from the closely related person re-id literature.In addition to this, we explicitly address viewpoint and occlusions variation.The former is addressed by incorporating vehicle viewpoint classification results into our matching distance.The required viewpoint classifier is trained predominantly on simulated data and we show that it can be applied to real-world imagery with minimal domain adaptation.We address occlusion by relying on temporal attention scores which emphasize video frames that contain minimal occlusion.Finally, we further boost re-id accuracy by applying video-based re-ranking and an ensemble of complementary models.Our models, code, and simulated data is available at https://github.com/corner100/2020-aicitychallenge-IOSB-VeRi.
Domain Fingerprints for No-Reference Image Quality Assessment

Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and then used as the unique characteristics to identify the degradation sources and assess the quality of the image. To this end, we design a new domain-aware architecture, which enables simultaneous determination of both the distortion sources and the quality of an image. With the distortion in an image better characterized, the image quality can be more accurately assessed, as verified by extensive experiments, which show that the proposed DA-IQA performs better than almost all the compared state-of-the-art NR-IQA methods.
L2R²: Leveraging Ranking for Abductive Reasoning

The abductive natural language inference task (αNLI) is proposed to evaluate the abductive reasoning ability of a learning system. In the αNLI task, two observations are given and the most plausible hypothesis is asked to pick out from the candidates. Existing methods simply formulate it as a classification problem, thus a cross-entropy log-loss objective is used during training. However, discriminating true from false does not measure the plausibility of a hypothesis, for all the hypotheses have a chance to happen, only the probabilities are different. To fill this gap, we switch to a ranking perspective that sorts the hypotheses in order of their plausibilities. With this new perspective, a novel L2R2 approach is proposed under the learning-to-rank framework. Firstly, training samples are reorganized into a ranking form, where two observations and their hypotheses are treated as the query and a set of candidate documents respectively. Then, an ESIM model or pre-trained language model, e.g. BERT or RoBERTa, is obtained as the scoring function. Finally, the loss functions for the ranking task can be either pair-wise or list-wise for training. The experimental results on the ART dataset reach the state-of-the-art in the public leaderboard.
Evaluation of Saccadic Scanpath Prediction: Subjective Assessment Database and Recurrent Neural Network Based Metric

In recent years, predicting the saccadic scanpaths of humans has become a new trend in the field of visual attention modeling. Given various saccadic algorithms, determining how to evaluate their ability to model a dynamic saccade has become an important yet understudied issue. To our best knowledge, existing metrics for evaluating saccadic prediction models are often heuristically designed, which may produce results that are inconsistent with human subjective assessment. To this end, we first construct a subjective database by collecting the assessments on 5,000 pairs of scanpaths from ten subjects. Based on this database, we can compare different metrics according to their consistency with human visual perception. In addition, we also propose a data-driven metric to measure scanpath similarity based on the human subjective comparison. To achieve this goal, we employ a long short-term memory (LSTM) network to learn the inference from the relationship of encoded scanpaths to a binary measurement. Experimental results have demonstrated that the LSTM-based metric outperforms other existing metrics. Moreover, we believe the constructed database can be used as a benchmark to inspire more insights for future metric selection.
-MABE: Distributed Multilevel Attribute-Based EMR Management and Applications

Current systems used by medical institutions for the management and transfer of Electronic Medical Records (EMRs) can be vulnerable to security and privacy threats. In addition, these systems are centralized, often lack interoperability, and give patients limited or no access to their own EMRs. In this article, we propose a novel distributed data sharing scheme that applies the security benefits of blockchain to address these concerns. We deploy smart contracts on Ethereum blockchain and utilize a distributed storage system to alleviate the dependence on the record-generating institutions to manage and share patient records. To preserve privacy of patient records, we implement our smart contracts as a method to allow patients to verify attributes prior to granting access rights. Our proposed scheme also facilitates selective sharing of medical records among staff members that belong to different levels of a hierarchical institution. We provide extensive security, privacy, and evaluation analyses to show that our proposed scheme is both efficient and practical.
Fail-Operational Automotive Software Design Using Agent-Based Graceful Degradation

Ensuring fail-operational behavior is critical to enable autonomous driving. With the absence of a driver as a fallback in a failure scenario it will not be sufficient to use state-of-the-art fail-safe approaches. Here, instead of costly hardware redundancy, graceful-degradation can be used by repurposing the allocated resources of non-critical applications for safety-critical applications. However, solving the mapping problem with a state-of-the-art design-time analysis leads to semi-static solutions, where the mapping is fixed and the task activation is chosen at run-time. Therefore, such solutions are unsuited for future automotive architectures that will be highly customizable and which will include frequent software updates. In this paper we introduce and analyze the effectiveness of an agent-based approach that finds application mappings at run-time, ensures the fail-operational behaviour of safety-critical applications by using graceful degradation, and reconfigures itself after ECU failures. Our results indicate that the number of tolerated ECU failures until a safety-critical application fails can be significantly improved without adding any redundant hardware resources.