Reviewerly/exHarmony huggingface dataset:
Comparative Analysis on Classical Meta-Metric Models for Few-Shot Learning

Few-shot learning are methods and scenarios learned from a small amount of labeled data. While recent meta-metric learning methods have made significant progress, there are still questions about what is the key point of these methods and how they work. To address these problems, in this paper, we evaluate the effects of different parts in classical models. To be specific, we 1) use four typical networks AlexNet, VGG16, GoogLeNet, and ResNet50 to replace the original feature extraction part in the Matching Network, Prototypical Network, and Relation Network, and compare the best results with 17 state-of-the-art meta-metric learning algorithms. 2) fix the feature extraction part of the Matching Network, Prototypical Network and Relation Network, and change the similarity measurement part of each into L1, L2, Cosine. 3) conduct above three models on datasets of different granularity. The experimental results show that for all models evaluated, the addition of non-pretrained networks will make the classification results worse, which shows that it is easy to overfit when using deep networks for few-shot learning. Changes in similarity measurement methods have a significant impact on results, which shows the importance to choose a suitable measurement. Moreover, there are differences in performance on different granularity datasets.
An Architecture for Multi-chain Business Process Choreographies

An increasing number of organizations employ blockchain technology in their business process landscapes, especially when dealing with inter-organizational choreographies. Due to complex requirements with regard to data security and privacy in practice, however, no singular blockchain captures all use cases. Blockchains optimized for various levels of risk tolerance and confidentiality coexist in multi-chain environments, posing severe architectural challenges for blockchain-based Business Process Management Systems (BPMSs). Current state-of-the-art approaches lack the global perspective necessary, and focus on single-blockchain environments. In this paper, we alleviate these issues by developing a general architecture for multi-chain BPMSs for choreographies. We show the feasibility of our architecture by a prototypical implementation, and discuss future challenges using a concrete case study.
Defining and Evaluating Fair Natural Language Generation

Our work focuses on the biases that emerge in the natural language generation (NLG) task of sentence completion. In this paper, we introduce a mathematical framework of fairness for NLG followed by an evaluation of gender biases in two state-of-the-art language models. Our analysis provides a theoretical formulation for biases in NLG and empirical evidence that existing language generation models embed gender bias.
Linear Complexity Private Set Intersection for Secure Two-Party Protocols

In this paper, we propose a new private set intersection (PSI) protocol with bi-oblivious data transfer that computes the following functionality. The two parties ( $$P_1$$ and $$P_2$$ ) input two sets of items (X and Y, respectively) and one of the parties ( $$P_2$$ ) outputs $$f_i(b_i)$$ for each $$y_i \in Y$$ , where $$b_i$$ is 0 or 1 depending on the truth value of $$y_i {\mathop {\in }\limits ^{?}} X$$ and $$f_i$$ is defined by the other party ( $$P_1$$ ) as taking 1-bit input and outputting the party’s ( $$P_1$$ ’s) data to be transferred. This functionality is generally required when the PSI protocol is used as a part of a larger secure two-party secure computation such as threshold PSI or any function of the whole intersecting set in general. Pinkas et al. presented a PSI protocol at Eurocrypt 2019 for this functionality, which has linear complexity only in communication. While there are PSI protocols with linear computation and communication complexities in the classical PSI setting where the intersection itself is revealed to one party, to the best of our knowledge, there is no PSI protocol, which outputs a function of the membership results and satisfies linear complexity in both communication and computation. We present the first PSI protocol that outputs only a function of the membership results with linear communication and computation complexities. While creating the protocol, as a side contribution, we provide a one-time batch oblivious programmable pseudo-random function based on garbled Bloom filters. We also implemented our protocol and provide performance results.
Multi-level and multi-scale horizontal pooling network for person re-identification

Person re-identification (Re-ID) is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Despite recent remarkable progress, person re-identification methods are either subject to the power of feature representation, or give equal importance to all examples. To mitigate these issues, we introduce a simple, yet effective, Multi-level and Multi-scale Horizontal Pooling Network (MMHPN) for person re-identification. Concretely, our contributions are three-fold:1) we take partial feature representation into account at different pooling scales and different semantic levels so that various partial information is obtained to form a robust descriptor; 2) we introduce a Part Sensitive Loss (PSL) to reduce the effect of easily classified partition to facilitate training of the person re-identification network, 3) we conduct extensive experimental results using the Market-1501, DukeMTMC-reID and CUHK03 datasets and achieve mAP scores of 83.4%, 75.1% and 65.4% respectively on these challenging datasets.
AN ANALYSIS OF THE NEW CHALLENGES FACING CYBER SECURITY EXPERTISE

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 2978-2986ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0890Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Linking Ontology, Epistemology And Research Methodology

The purpose of this paper is to offer insights that can help researchers to link ontology, epistemology and research methodology. This paper outlines the links among ontology, epistemology and research methodology by exploring ontological, epistemological and methodological perspectives in the research. It discusses how ontological and epistemological issues influence research methodology by providing a clear understanding for different research methodologies based on ontology and epistemology. Furthermore, attention is given to research aspects such as the elements of research process, research philosophy, research approach, research strategy, the choice of method, and research design.
Explainable Planning Using Answer Set Programming

In human-aware planning problems, the planning agent may need to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach to do so is called model reconciliation, where the planning agent tries to reconcile the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as an optimization problem, where the goal is to find a subset-minimal explanation that one can use to modify the model of the user such that the plan of the agent is also feasible and optimal to the user. This paper presents an algorithm for solving such problems using answer set programming.
Location privacy-preserving in online taxi-hailing services

Abstract Online taxi-hailing has become people’s most popular trip mode due to its convenience and low cost. However, it also poses a privacy threat to passengers and drivers, since the online taxi-hailing service providers are able to track their precise mobility trajectories. In addition, there is a certain time delay between the time of a passenger makes a request and the time of the driver arrives the passenger’s boarding position in current online taxi-hailing system. To solve these two problems, we present a new and efficient location privacy protection scheme based on the MinHash algorithm (LPPM). With the LPPM, the exact positions of passengers and drivers are generalized into a set of points of interest around them, and the distance between them is transformed into the similarity between the two sets. Thus a service provider can efficiently match passengers and drivers by using MinHash algorithm without revealing their specific location information. In this paper, we use mobile edge computing technology in the online taxi-hailing system to address the second challenge. It can speed up data processing, drivers can make decisions in advance and reduce the possibility of road congestion. Security analysis shows that LPPM has high security, and the final experimental results confirmed that LPPM is effective.
Simple Electromagnetic Analysis Against Activation Functions of Deep Neural Networks

From cloud computing to edge computing, the deployment of artificial intelligence (AI) has been evolving to fit a wide range of applications. However, the security over edge AI is not sufficient. Edge AI is computed close to the device and user, therefore allowing physical attacks such as side-channel attack (SCA). Reverse engineering the neural network architecture using SCA is an active area of research. In this work, we investigate how to retrieve an activation function in a neural network implemented to an edge device by using side-channel information. To this end, we consider multilayer perceptron as the machine learning architecture of choice. We assume an attacker capable of measuring side channel leakages, in this case electromagnetic (EM) emanations. The results are shown on an Arduino Uno microcontroller to achieve high quality measurements. Our experiments show that the activation functions used in the architecture can be obtained by a side-channel attacker using one or a few EM measurements independent of inputs. We replicate the timing attack in previous research by Batina et al., and analyzed it to explain how the timing behavior acts on different implementations of the activation function operations. We also prove that our attack method has the potential to overcome constant time mitigations.
Lyapunov-based Methods for Maximizing the Domain of Attraction

This paper investigates Lyapunov approaches to expand the domain of attraction (DA) of nonlinear autonomous models. These techniques had been examined for creating generic numerical procedures centred on the search of rational and quadratic Lyapunov functions. The outcomes are derived from all investigated methods: the method of estimation via Threshold Accepted Algorithm (TAA), the method of estimation via a Zubov technique and the method of estimation via a linear matrix inequality (LMI) optimization and genetic algorithms (GA). These methods are effective for a large group of nonlinear models, they have a significant ability of improvement of the attraction domain area and they are distinguished by an apparent propriety of direct application for compact and nonlinear models of high degree. The validity and the effectiveness of the examined techniques are established based on a simulation case analysis. The effectiveness of the presented methods is evaluated and discussed through the study of the renowned Van der Pol model.
Linear complementary pair of group codes over finite chain rings

Linear complementary dual (LCD) codes and linear complementary pair (LCP) of codes over finite fields have been intensively studied recently due to their applications in cryptography, in the context of side channel and fault injection attacks. The security parameter for an LCP of codes (C, D) is defined as the minimum of the minimum distances d(C) and $$d(D^\bot )$$ . It has been recently shown that if C and D are both 2-sided group codes over a finite field, then C and $$D^\bot $$ are permutation equivalent. Hence the security parameter for an LCP of 2-sided group codes (C, D) is simply d(C). We extend this result to 2-sided group codes over finite chain rings.
Collaboration between Law Enforcement Agencies in Combating Cybercrime: Implications of a Taiwanese Case Study about ATM Hacking

An increasing number of cybercrimes has presented new global challenges to law enforcement agencies that traditionally operate within designated geographical jurisdictions and patrol territories. The borderless nature of cyberspace has brought substantial opportunities—both legal and illegal—to its users, and many local law enforcement agencies have encountered motivated offenders taking advantage of the globally connected Internet and causing damage locally and transnationally. This study examines a high-profile case of European criminals who hacked into a Taiwanese financial institution—First Commercial Bank (FCB)—and programmed its ATMs to “spit out” cash netting the thieves $2.6 million US dollars in 2016 summer. Before the incident of FCB, this European criminal group committed more than a hundred similar ATMs hackings, victimizing dozens of financial institutions across several European countries, and profiting over one billion Euros. FCB is the only case revealing specific details about the modus operandi of ATM hacking thus far, in addition to disclosing reactions from law enforcement. By analyzing qualitative data collected from different branches of law enforcement involved in the investigations, this unique case study underscores the importance of national-local law enforcement collaboration in fighting transnational cybercrime. Empirical implications are particularly valuable in the law enforcement context of “turf jealousies” when defending homeland security.
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency

Abstract Accurate real depth annotations are difficult to acquire, needing the use of special devices such as a LiDAR sensor. Self-supervised methods try to overcome this problem by processing video or stereo sequences, which may not always be available. Instead, in this paper, we propose a domain adaptation approach to train a monocular depth estimation model using a fully-annotated source dataset and a non-annotated target dataset. We bridge the domain gap by leveraging semantic predictions and low-level edge features to provide guidance for the target domain. We enforce consistency between the main model and a second model trained with semantic segmentation and edge maps, and introduce priors in the form of instance heights. Our approach is evaluated on standard domain adaptation benchmarks for monocular depth estimation and show consistent improvement upon the state-of-the-art. Code available at https://github.com/alopezgit/DESC .
A Novel Solution Based on Scale Invariant Feature Transform Descriptors and Deep Learning for the Detection of Suspicious Regions in Mammogram Images.

Deep learning methods have become popular for their high-performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem of the scarcity of public datasets. Some investigations to assess transfer learning knowledge inference abilities in the context of mammogram screening and possible combinations with unsupervised techniques are in progress.We propose a novel technique for the detection of suspicious regions in mammograms that consist of the combination of two approaches based on scale invariant feature transform (SIFT) keypoints and transfer learning with pretrained CNNs such as PyramidNet and AlexNet fine-tuned on digital mammograms generated by different mammography devices. Preprocessing, feature extraction, and selection steps characterize the SIFT-based method, while the deep learning network validates the candidate suspicious regions detected by the SIFT method.The experiments conducted on both mini-MIAS dataset and our new public dataset Suspicious Region Detection on Mammogram from PP (SuReMaPP) of 384 digital mammograms exhibit high performances compared to several state-of-the-art methods. Our solution reaches 98% of sensitivity and 90% of specificity on SuReMaPP and 94% of sensitivity and 91% of specificity on mini-MIAS.The experimental sessions conducted so far prompt us to further investigate the powerfulness of transfer learning over different CNNs and possible combinations with unsupervised techniques. Transfer learning performances' accuracy may decrease when the training and testing images come out from mammography devices with different properties.
A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass

Deep Learning (DL) is a high capable machine learning algorithm with the detailed analysis abilities on images. Although DL models achieve very high classification performances, the applications are trending on using and fine-tuning pre-trained DL models by transfer learning due to the dependence on the number of data, long train time, employments in modeling the most meaningful architecture. In this chapter, we proposed own pruned and simple DL architectures on ROIs extracted from mammography to classify cancer-normal using Convolutional Neural Network (CNN) and Deep Autoencoder (Deep AE) models, which are the most popular DL algorithms. Breast Cancer, which occurs as a result of developing of normal breast tissue to a tumour, is one of the deadliest diseases according to WHO reports. The detection of cancerous mass at early stages is one of the decisive step to start the treatment process. Mammography images are the most effective and simplest way of the diagnosis of breast cancer. Whereas early diagnosis of breast cancer is a hard process due to characteristics of mammography, the computer-assisted diagnosis systems have ability to perform a detailed analysis for a complete assessment. The aim of this study is proposing a robust cancer diagnosis model with a light-weighted DL architecture and comparing the efficiency of the dense layer with the Deep AE kernels against CNN. The ROIs from mammography images were fed into the DL algorithms and the achievements were evaluated. The proposed Deep AE architecture reached the classification performance rates of 95.17%, 96.81%, 93.65%, 93.38%, 96.95%, and 0.972 for overall accuracy, sensitivity, specificity, precision, NPV, and AUROC, respectively.
Security Awareness Training for the Workforce: Moving Beyond "Check-the-Box" Compliance

Security awareness training requirements set a minimum baseline for introducing security practices to an organization's workforce. But is simple compliance enough to result in behavior change?
Equipping Public-Key Cryptographic Primitives with Watermarking (or: A Hole Is to Watermark)

Program watermarking enables users to embed an arbitrary string called a mark into a program while preserving the functionality of the program. Adversaries cannot remove the mark without destroying the functionality. Although there exist generic constructions of watermarking schemes for public-key cryptographic (PKC) primitives, those schemes are constructed from scratch and not efficient. In this work, we present a general framework to equip a broad class of PKC primitives with an efficient watermarking scheme. The class consists of PKC primitives that have a canonical all-but-one (ABO) reduction. Canonical ABO reductions are standard techniques to prove selective security of PKC primitives, where adversaries must commit a target attribute at the beginning of the security game. Thus, we can obtain watermarking schemes for many existing efficient PKC schemes from standard cryptographic assumptions via our framework. Most well-known selectively secure PKC schemes have canonical ABO reductions. Notably, we can achieve watermarking for public-key encryption whose ciphertexts and secret-keys are constant-size, and that is chosen-ciphertext secure. Our approach accommodates the canonical ABO reduction technique to the puncturable pseudorandom function (PRF) technique, which is used to achieve watermarkable PRFs. We find that canonical ABO reductions are compatible with such puncturable PRF-based watermarking schemes.
Unidirectional transmission of single photons under nonideal chiral photon-atom interactions

Single-photon transport in nonideal chiral photon-atom interaction structures generally contains information backflow and thus limits the capabilities to transfer information between distant emitters in cascaded quantum networks. Here, in the nonideal chiral case, a $V$-type atom coupled to a waveguide is proposed to realize completely unidirectional transmission of the single photons in a superposition state of different frequencies. A microwave field is introduced to drive the two excited states of the atom and results in photon conversion between two transitions. By adjusting the Rabi frequency and the phase of the external driving field, the transport behaviors of incident photons with specific frequencies can be optimized to complete transmission or reflection. Based on the constructive interferences between photons from different traveling paths, the transmission probabilities of the specific-frequency photons could be enhanced. Due to photon conversions with compensating the dissipations and ensuring the complete destructive interference, the ideal unidirectional transmission contrast can be maintained to $\ifmmode\pm\else\textpm\fi{}1$, even when the atom has dissipations into the nonwaveguide modes.
MvMM-RegNet: A New Image Registration Framework Based on Multivariate Mixture Model and Neural Network Estimation

Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training. However, intensity-based metrics can be misleading when the assumption of intensity class correspondence is violated, especially in cross-modality or contrast-enhanced images. Moreover, existing learning-based registration methods are predominantly applicable to pairwise registration and are rarely extended to groupwise registration or simultaneous registration with multiple images. In this paper, we propose a new image registration framework based on multivariate mixture model (MvMM) and neural network estimation. A generative model consolidating both appearance and anatomical information is established to derive a novel loss function capable of implementing groupwise registration. We highlight the versatility of the proposed framework for various applications on multimodal cardiac images, including single-atlas-based segmentation (SAS) via pairwise registration and multi-atlas segmentation (MAS) unified by groupwise registration. We evaluated performance on two publicly available datasets, i.e. MM-WHS-2017 and MS-CMRSeg-2019. The results show that the proposed framework achieved an average Dice score of \(0.871\pm 0.025\) for whole-heart segmentation on MR images and \(0.783\pm 0.082\) for myocardium segmentation on LGE MR images (Code is available from https://zmiclab.github.io/projects.html).