Reviewerly/exHarmony huggingface dataset:
Adaptive Self-supervised Depth Estimation in Monocular Videos

In this work, we develop and evaluate two adaptive strategies to self-supervised depth estimation methods based on view reconstruction. First, we propose an adaptive consistency loss that extends the usage of minimum re-projection to enforce consistency on the pixel intensities, structure, and feature maps. Moreover, we evaluate two approaches to use uncertainty to weigh the error contribution in the input frames. Finally, we improve our model with a composite visibility mask. The results show that the adaptive consistency loss can effectively combine photometric, structure and feature consistency terms. Moreover, weighting the error contribution using uncertainty can improve the performance of a simpler version of the model, but cannot improve them model when all improvements are considered. Finally, our combined model achieves competitive results when compared to state-of-the-art methods.
Agile Powertrain Development: Considerations to Incorporate Agile Principles

Pressure to increase effectiveness and efficiency drives the product development process. The software industry has been using agile development approaches due to their advantages when coping with uncertainties and simultaneously increasing value. Therefore, the following work deals with the considerations of agile powertrain development. In order to identify important conditions for the incorporation of agile principles into powertrain development, semistructured interviews were conducted with experts from an engineering company. Having identified agile development challenges, advantages, and principles, the next step is comparing agile principles with the current principles of conventional powertrain development. Furthermore, a basic understanding of the powertrain development process regarding the incorporation of agile principles is established, the main attributes of powertrain development are investigated, and the key disciplines involved are identified. Finally, a model is created to assess whether the application of an agile or conventional development approach is more suitable.
Perspective error reduction in 2D Digital Image Correlation measurements by combination with Defocused Speckle Imaging

2D Digital Image Correlation (2D DIC) enables full-field in-plane displacement measurement by tracking the physical surface texture in the recorded images. However, because camera magnification is inversely proportional to object distance, points along the imaged surface can have different scales and appear to move at different rates. Surface curvature and out-of-plane motions may thus lead to perspective errors that manifest as apparent strains, limiting the method suitable only for flat objects that move purely in-plane. Defocused Speckle Imaging (DSI) is a related optical method where the movements of a laser-illuminated surface are determined by tracking the scattered interference speckle pattern. When the camera is in focus, the speckles appear to move at the same rate with the physical surface. However, camera defocus causes the speckle motion to deviate from the actual surface motion. Interestingly, the observed speckle motion magnitude increases with positive defocus distance, which is contrary to the perspective effects inherent to 2D DIC. Remarkably, with optimized geometric arrangement, 2D DIC and DSI have perspective errors that are equal in magnitude but with opposite signs. It is thus possible to compensate perspective errors by performing DIC and DSI measurements simultaneously and simply calculating the average of the two signals. This article derives the proposed compensation principle analytically using simple geometric framework. An experimental demonstration shows that the combination of DIC and DSI reduces magnification-dependent perspective errors by 73%, and errors due to object rigid-body rotation by 86%.
Double-Layer Simulation Model of Associative Reasoning

The article presents a model for the simulation of associative reasoning. This theme has been in the spotlight for a long time, mainly due to its influence on individuals' cognitive and decision-making processes because they significantly affect the individual knowledge base. This influence arises from the history of individual's perceptions, and due to that, significant differences between individuals must be taken into account. The model's main benefit is the selected way of representing knowledge. It takes the whole input perception (observation) and metadata about it as a basic associative processing unit. The model also respects the different importance of specific objects and concepts in perception. Concrete procedures for the implementation of associative processes are also described. The aim was to achieve a state when the model will simulate the dynamics of associative reasoning and specify the so-called knowledge context, which can then serve as a basis for subsequent decision-making processes. The paper describes in detail the structure of the model and some experiments that were performed with it.
How to Compare Adversarial Robustness of Classifiers from a Global Perspective

Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a given model. Point-wise measures for specific threat models are currently the most popular tool for comparing the robustness of classifiers and are used in most recent publications on adversarial robustness. In this work, we use robustness curves to show that point-wise measures fail to capture important global properties that are essential to reliably compare the robustness of different classifiers. We introduce new ways in which robustness curves can be used to systematically uncover these properties and provide concrete recommendations for researchers and practitioners when assessing and comparing the robustness of trained models. Furthermore, we characterize scale as a way to distinguish small and large perturbations, and relate it to inherent properties of data sets, demonstrating that robustness thresholds must be chosen accordingly. We hope that our work contributes to a shift of focus away from point-wise measures of robustness and towards a discussion of the question what kind of robustness could and should reasonably be expected. We release code to reproduce all experiments presented in this paper, which includes a Python module to calculate robustness curves for arbitrary data sets and classifiers, supporting a number of frameworks, including TensorFlow, PyTorch and JAX.
A Routine Activities Approach to Evidence-Based Risk Assessment: Findings From Two Simulated Phishing Attacks

To assess the efficacy of routine activity theory (RAT) for explaining phishing victimization and guide evidence-based policy, we launched two phishing attacks via a university Listserv ( N = 25,875). The first email offered access to a pdf file; the second offered free concert tickets. Several interesting findings emerged demonstrating phishing victimization results from network users’ routine behaviors. Students were significantly less likely to open the phishing email sharing a pdf but more likely to open the email offering free concert tickets. Moreover, students were mor e likely to click the malicious link embedded within the phishing email in both studies, often using mobile devices. Conversely, employees were more likely to click the link while connected to the university network, thus exposing the network to greater levels of risk. Finally, the email offering concert tickets was opened at a frequency more than double the email containing the pdf. Theoretical and policy implications are discussed.
Sets of Semantic Superposed graphs: the new theory that enriches the graph theory

Graph theory has been since long time very helpful either in science or in real life. Since it has been introduced, it was considered like a crossroad in science. Many problems are computer science, in logistics, in physics, in electrical engineering, in automatic science were all inspired by graph theory. Moreover, sone algorithms such as the shortest path are omnipresent within our life. That is why nobody could ignore the importance of graph theory. Nowadays, the graph theory, as is, could not answer correctly to our new needs. In my point, for the current graph theory to answer much better to our new problem-solving requirements, the theory must include two more main pillars: the semantic factor and the superimposition factor. In this paper we introduce the all-new graph theory: the semantic superposed graph theory.
Research on blood type card image automatic recognition algorithm

ABO blood type automatic recognition can realize the automatic detection of blood type, effectively improve the speed and accuracy of blood type analysis, and has a wide range of clinical applications. In this paper, an automatic blood type recognition algorithm based on image processing is designed. Firstly, the image of blood type card is preprocessed, including image enhancement and median filtering. Then, the micro column tubules are segmented by template matching. Then, the threshold analysis is carried out, and the gray image is transformed into a binary image. Finally, the distribution of red blood cell aggregates in micro column tubules is identified, and determine the ABO blood type of the sample. The experimental results show that the algorithm can effectively segment the micro column tubules on the blood type card, realize the effective recognition of red blood cell distribution, and complete the effective interpretation of blood group.
Exploiting Pseudo-locality of Interchange Distance

String metrics play an important role in various computational tasks. In this paper, we focus on a property shared by a subset of string rearrangement metrics, called pseudo-locality. This subset includes, for example, the swap, interchange and parallel-interchange distances. Intuitively, operators of pseudo-local string metrics have a bounded effect on the Hamming distance between the string prior to the operation and after it. The goal of this paper is to examine how to exploit pseudo-locality in order to transform tools derived for the Hamming distance in order to derive tools for other pseudo-local metrics. Specifically, we demonstrate such a way to exploit pseudo-locality of the interchange distance combined with additional techniques to derive: 1. The first efficient approximate nearest-neighbour (ANN) search data structure for the interchange distance, which is \(\mathcal {NP}\)-hard to compute for general strings even for binary alphabets. 2. The first linear time algorithm to compute approximate pattern matching with interchanges, which is a vast improvement from the \(\varTheta (nm)\) known algorithm.
End-to-End Transferable Anomaly Detection via Multi-Spectral Cross-Domain Representation Alignment

Anomaly detection (AD) aims to distinguish abnormal instances from what is defined as normal, which strongly correlates with the safe and robust applications of machine learning. A well-performed anomaly detector often relies on the training on massive labeled data, while it is of high cost to annotate data in practice. Fortunately, this dilemma can be solved by transferring the knowledge of a label-rich dataset (source domain) to assist the learning on the label-scarce dataset (target domain), which is known as domain adaptation in transfer learning. In this paper, we propose a Multi-spectral Cross-domain Representation Alignment (MsRA) method for the anomaly detection in the domain adaptation setting, where we can only access normal source data and limited normal target data. Specifically, MsRA first constructs multi-spectral feature representations by fusing different frequency components of the original features, which mitigates the information scarcity due to limited target training data by capturing richer input pattern information. Then we employ the adversarial training strategy to learn domain-invariant features and force the features of normal data to be more compact by the center clustering. Finally, the distance of each sample to the prototype of normal class can be used as its anomaly score, where the prototype is the center of both source and target data. In this way, we achieve anomaly detection in an end-to-end manner, without two-stage training for feature extraction and anomaly detection. Comprehensive experiments on cross-domain anomaly detection benchmarks validate the effectiveness of MsRA.
Multi-exposure remote sensing image HDR synthesis technology based on spaceborne DSP

Since traditional high dynamic range (HDR) imaging technology cannot be directly applied to satellites, in the field of remote sensing imaging, a technology for directly generating HDR remote sensing images on the satellite has been explored for a long time. Based on the above requirements, this paper proposes a multi-exposure remote sensing image fusion algorithm based on spaceborne DSP TMS320C6678 (C6678) to realize this technology. Firstly, three low dynamic range images of overexposure, normal exposure and underexposure are generated by the method of fast continuous shooting by the satellite camera, and the acquired multi-exposure remote sensing images are transmitted to the satellite DSP C6678 for processing. Then the multi-exposure remote sensing image fusion algorithm built in DSP will be used to quickly generate high-quality and high-signal-to-noise HDR images. This technology greatly improves the image information acquisition capabilities of remote sensing satellites and fills the gap in the application of spaceborne HDR synthesis technology.
Towards a Pragmatic Interoperability on the MIDAS Middleware

Nowadays, many organizations store and publish their data and services based on the Cloud Computing paradigm. In this scenario, cloud consumers access these resources anytime and anywhere. Software as a Service (SaaS) and Data as a Service are examples of cloud services. While DaaS delivers and manages data on-demand, SaaS is a delivery model of applications in a cloud environment. However, the vast amount of social data and applications enable different formats of DaaS, such as non-structured (e.g., text), semi-structured (e.g., JSON), and structured format (e.g., Relational Database). The lack of standardization makes users dependent on a system due to the lack of interoperability among different providers. Interoperability is heterogeneous systems' ability to communicate transparently, and it is classified into syntactic, semantic, and pragmatic levels. Middleware for SaaS and DaaS (MIDAS) is a solution to provide interoperability among cloud services. Although the latest version of MIDAS promotes a semantic approach, pragmatic aspects are not addressed. This paper enhances MIDAS to provide pragmatic interoperability in a cloud environment. Our approach presents the necessary elements that MIDAS must consider to provide pragmatic interoperability among cloud services. We conduct a set of experiments to validate our pragmatic MIDAS. We evaluate the overhead of our approach, the correctness of our novel MIDAS, and the effort to implement the MIDAS middleware with dynamic pragmatic information. Results evidence that our approach is towards pragmatic interoperability among cloud services.
Privacy Amplification With Tamperable Memory via Non-Malleable Two-Source Extractors

We extend the classical problem of privacy amplification to a setting where the active adversary, Eve, is also allowed to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully corrupt</i> the internal memory (which includes the shared randomness, and local randomness tape) of one of the honest parties, Alice and Bob, before the execution of the protocol. We require that either one of Alice or Bob detects tampering, or they agree on a shared key that is indistinguishable from the uniform distribution to Eve. We obtain the following results: 1) we give a privacy amplification protocol via low-error non-malleable two-source extractors with one source having low min-entropy. In particular, this implies the existence of such (non-efficient) protocols; 2) we show that even slight improvements to the state-of-the-art explicit non-malleable two-source extractors would lead to explicit low-error, low min-entropy two-source extractors, thereby resolving a long-standing open question. This suggests that obtaining (information-theoretically secure) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit</i> non-malleable two-source extractors for (1) might be hard; 3) we present explicit constructions of low-error, low min-entropy non-malleable two-source extractors in the CRS model of (Garg, Kalai, Khurana, Eurocrypt 2020), assuming either the quasi-polynomial hardness of DDH or the existence of nearly-optimal collision-resistant hash functions; 4) we instantiate our privacy amplification protocol with the above mentioned non-malleable two-source extractors in the CRS model, leading to explicit, computationally-secure protocols. This is not immediate from (1) because in the computational setting we need to make sure that, in particular, all randomness sources remain samplable throughout the proof. This requires upgrading the assumption of quasi-polynomial hardness of DDH to sub-exponential hardness of DDH.We emphasize that each of the first three results can be read independently.
Training Stable Graph Neural Networks Through Constrained Learning

Graph Neural Networks (GNN) rely on graph convolutions to learn features from network data. GNNs are stable to different types of perturbations of the underlying graph, a property that they inherit from graph filters. In this paper we leverage the stability property of GNNs as a typing point in order to seek for representations that are stable within a distribution. We propose a novel constrained learning approach by imposing a constraint on the stability condition of the GNN within a perturbation of choice. We showcase our framework in real world data, corroborating that we are able to obtain more stable representations while not compromising the overall accuracy of the predictor.
Parallel Composition of Weighted Finite-State Transducers

Finite-state transducers (FSTs) are frequently used in speech recognition. Transducer composition is an essential operation for combining different sources of information at different granularities. However, composition is also one of the more computationally expensive operations. Due to the heterogeneous structure of FSTs, parallel algorithms for composition are suboptimal in efficiency, generality, or both. We propose an algorithm for parallel composition and implement it on graphics processing units. We benchmark our parallel algorithm on the composition of random graphs and the composition of graphs commonly used in speech recognition. The parallel composition scales better with the size of the input graphs and for large graphs can be as much as 10 to 30 times faster than a sequential CPU algorithm.
Channel Replay Aided Modulation Classification of Underwater Acoustic Communication Signals

Modulation classification or recognition of underwater acoustic (UWA) communication signals faces great difficulties due to the unpredictable characteristics of underwater acoustic channels as well as lack of training data. As traditional feature-based methods work poorly in such scenarios, resurgent artificial neural networks are gaining more attentions in the area of automatic modulation classification. Deep learning, however, demands large amount of training data for good recognition performance. To address the problem of insufficient training data, we propose to adopt the channel replay technique. With this technique, channel realizations with the same statistical characteristics as the measured underwater acoustic channel, can be generated as much as one needs. Training and validation data sets can then be generated with the replayed channels. A residual network (ResNet) is adopted to achieve simultaneously feature extraction and modulation recognition. It is shown the proposed ResNet not only classifies artificial test data but also experimental data collected in an at-sea UWA communication trail.
Use of Hybrid ECC to enhance Security and Privacy with Data Deduplication

Cloud computing is one of the most growing technologies in recent technological developments. It offers many applications like it can store big data by taking no or little amount of cost. Cloud services offer Infrastructure as a service (IaaS), Software as a service (SaaS) and Platform as a service (PaaS). Cloud computing is also used to take backup for data. Most of the big companies are now moving towards cloud services like google Facebook amazon Flipkart and many more. Day by day data size is being increased and charges are also increasing. Since cloud computing is best known technology it's more vulnerable to get attacked by attackers and data might be hacked or can be monitored or even can be altered over cloud space. This is the main reason why cloud companies need to take responsibility for our data. This is the main challenge for companies to make our data secured and safe over their data centers. This paper has proposed an enhanced hybrid ECC based encryption so that data is stored on the data center in encrypted form so that no one can understand the original content even though it gets monitored overcloud. This paper used CRT (Chinese remainder theorem) for generating a key for elliptical curve cryptography (ECC), and with ECC, this research work has proposed to add extra encryption with arithmetic encryption and after that, the data deduplication checking has been done by using cosine similarity so that no duplicated data is stored on the cloud. Finally, the advantages of our proposed system with known algorithms are discussed.
Noise-Resistant Optical Implementation of Analogue Neural Networks

Analogue artificial neural networks are widely considered as promising computational models that more closely imitate the information processing capabilities of the human brain compared to digital neural networks. The significant computation power and the much reduced power consumption per operation make the analogue implementation of neural networks very attractive. There is an active research on artificial neural networks (ANNs) implementation using both analogue photonic and electronic hardware [1] – [4] . However, compared to digital realisations the conventional analogue systems are more sensitive to the noise that is inevitably present in practical implementations [2] , [3] . Noise properties in ANNs have been studied both in the electronic and photonic domains. However, photonic ANNs are much less investigated compared to the electronic implementations, for which some training techniques have been proposed to enhance ANNs robustness against noise [1] , [4] .