Reviewerly/exHarmony huggingface dataset:
Microcomputed tomography for discriminating between different forming techniques in ancient pottery: New segmentation method and pore distribution recognition

Abstract Microcomputed tomography is a valuable tool for studying ancient ceramics technology. Analysing pottery 3‐D images is a challenging issue, the data being extremely noisy and heterogeneous. Quantitative criteria are introduced for the characterisation of a previously unrecognised pottery building method, the Spiralled Patchwork Technology (SPT). An analytical protocol has been implemented that applies to 3‐D reconstructions of ceramic sherds and integrates automatic segmentation of porous systems and shape recognition using Hough transform. It enables discriminating between SPT and other techniques for vessels manufacture, and opens up many perspectives for independent characterisation of ancient technical gestures in ceramic technology.
Communication-Efficient Private Information Acquisition: Multicasting via Crowding

This paper focuses on the way to protect privacy of clients requesting datasets stored in data servers while keeping communication efficiency. To this end, we introduce a novel communication-efficient and privacy protecting framework termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">crowded information acquisition (CIA)</i> , well suited to a large number of clients scenario. We investigate the CIA under various conditions addressing possible communication scenarios. Contrary to the conventional belief, the results claim that a large number of clients demanding private services can enhance the privacy protection while providing low latency services and generating a small amount of traffic.
Towards Practical Cybersecurity Mapping of STRIDE and CWE — a Multi-perspective Approach

Cybersecurity practitioners seek to prevent software vulnerabilities during the whole life-cycle of systems. Threat modeling which is done on the system design phase is an efficient way for securing systems; preventing system flaws is easier and more efficient than patching the security of the system later on. Therefore, many Secure Software Development methods include threat modeling as an integral part of the methodology. STRIDE is a popular threat modeling method used by many practitioners. Threat modelers using the STRIDE method work with abstract threat categories, and would benefit learning about the information about current system weaknesses and vulnerabilities. The information is available on the weakness and vulnerability databases (such as the CWE and the CVE). To our knowledge, there exists no mapping between the STRIDE threats and the actual weaknesses and vulnerabilities listed on the databases, thus hindering the effectiveness of the threat modeling and the DevSecOps and Secure Software Development Life Cycle methods as a whole. This work attempts to bridge the gap by exploring possible mappings between the STRIDE threats and the CWE weaknesses, with the goal of improving the cybersecurity processes from end to end. The paper explores three different approaches for mapping the STRIDE to the CWE weakness database, and discusses the findings. The paper concludes that the mapping between the STRIDE and the CWE “Technical Impact” and “Scope” elements of the CWE entries is the most prominent for the mapping. Paper also shows that other mappings were challenged by the different conceptual backgrounds between the threats and the weaknesses. The paper also discusses the challenges caused by the inherent vagueness of the items within the frameworks and the CWE and CVE databases, causing that the mappings to these databases remain largely as a manual tasks, which should be carried out by the domain experts.
Distributed training for accelerating metalearning algorithms

The lack of large amounts of training data diminishes the power of deep learning to train models with a high accuracy. Few shot learning (i.e. learning using few data samples) is implemented by Meta-learning, a learn to learn approach. Most gradient based metalearning approaches are hierarchical in nature and computationally expensive. Metalearning approaches generalize well across new tasks by training on very few tasks; but require multiple training iterations which lead to large training times.
Implicit Aspect-Based Opinion Mining and Analysis of Airline Industry Based on User-Generated Reviews

Abstract Mining opinions from reviews has been a field of ever-growing research. These include mining opinions on document level, sentence level and even aspect level. While explicitly mentioned aspects from user-generated texts have been widely researched, very little work has been done in gathering opinions on aspects that are implied and not explicitly mentioned. Previous work to identify implicit aspects and opinion was limited to syntactic-based classifiers or other machine learning methods trained on restaurant dataset. In this paper, the present is a novel study for extracting and analysing implicit aspects and opinions from airline reviews in English. Through this study, an airline domain-specific aspect-based annotated corpus, and a novel two-way technique that first augments pre-trained word embeddings for sequential with stochastic gradient descent optimized conditional random fields (CRF) and second using machine and ensemble learning algorithms to classify the implied aspects is devised and developed. This two-way technique resolves double-implicit problem, most encountered by previous work in implicit aspect and opinion text mining. Experiments with a hold-out test set on the first level i.e., entity extraction by optimized CRF yield a result of ROC-AUC score of 96% and F 1 score of 94% outperforming few baseline systems. Further experiments with a range of machine and ensemble learning classifier algorithms to classify implied aspects and opinions for each entity yields a result of ROC-AUC score ranging from 71 to 94.8% for all implied entities. This two-level technique for implicit aspect extraction and classification outperforms many baseline systems in this domain.
Making a game with PoseNet, a pose estimator model

Time for a fun application! Are you ready to pose? I hope so, because in this exercise your movements are the testing dataset.
Large-Scale Meta-Learning with Continual Trajectory Shifting

Meta-learning of shared initialization parameters has shown to be highly effective in solving few-shot learning tasks. However, extending the framework to many-shot scenarios, which may further enhance its practicality, has been relatively overlooked due to the technical difficulties of meta-learning over long chains of inner-gradient steps. In this paper, we first show that allowing the meta-learners to take a larger number of inner gradient steps better captures the structure of heterogeneous and large-scale task distributions, thus results in obtaining better initialization points. Further, in order to increase the frequency of meta-updates even with the excessively long inner-optimization trajectories, we propose to estimate the required shift of the task-specific parameters with respect to the change of the initialization parameters. By doing so, we can arbitrarily increase the frequency of meta-updates and thus greatly improve the meta-level convergence as well as the quality of the learned initializations. We validate our method on a heterogeneous set of large-scale tasks and show that the algorithm largely outperforms the previous first-order meta-learning methods in terms of both generalization performance and convergence, as well as multi-task learning and fine-tuning baselines.
Multiparameter mathematical models of the filtration problem of unstructured and structured fluids

The article is devoted to constructing a generalized mathematical model and the method of their solution of the process of filtration of fluids with various linear and nonlinear characteristics. The multiparameter model contains 13 mathematical models developed in due time by scientific research and included new mathematical models. The classification of these models is carried out in accordance with the laws of filtration, and they are confirmed by the results of numerical solutions. For unknown boundaries of disturbances, the application of the "shuttle" iteration method made it possible to reduce the number of iterations.
Poster: Shielding AppSPEAR - Enhancing Memory Safety for Trusted Application-level Security Policy Enforcement

This paper tackles the problem of memory-safe implementation of the AppSPEAR framework for application-level security policy enforcement. We contribute with a feasibility study that demonstrates the performance overhead of applying Rust's memory safety features on top of SGX trusted execution technology.
Multiparty protocol that usually shuffles

Abstract Multiparty computation is raising importance because its primary objective is to replace any trusted third party in the distributed computation. This work presents two multiparty shuffling protocols where each party, possesses a private input, agrees on a random permutation while keeping the permutation secret. The proposed shuffling protocols are based on permutation network, thereby data‐oblivious. The first proposal is ‐permute that permutes inputs in all possible ways. ‐permute network consists of layers, and in each layer there are gates. Our second protocol is ‐permute shuffling that defines a permutation set where , and the resultant shuffling is a random permutation . The ‐permute network contains less number of layers compare with ‐permute network. Let , the ‐permute network would define layers. The proposed shuffling protocols are unconditionally secure against malicious adversary who can corrupt at most parties. The probability that adversary can learn the outcome of ‐permute is upper bound by . Whereas, the probability that adversary can learn the outcome of ‐permute is upper bounded by , for some positive integer , and a recursive definition of . The protocols allow the parties to build quorums, and distribute the load among the quorums.
Delegation-based conversion from CPA to CCA-secure predicate encryption

In 2011, Yamada et al. proposed CPA to CCA-secure conversions for attribute-based encryption (ABE) based on the properties, verifiability and delegation. Later, the verifiability-based conversion w...
The Earth in Living Color: Monitoring Our Planet from Above

A new special collection invites papers on a new era of remote sensing missions and instruments that will provide insights into human and climate driven changes on planet Earth.
Design and Analysis of Configurable Ring Oscillators for True Random Number Generation Based on Coherent Sampling

True Random Number Generators (TRNGs) are indispensable in modern cryptosystems. Unfortunately, to guarantee high entropy of the generated numbers, many TRNG designs require a complex implementation procedure, often involving manual placement and routing. In this work, we introduce, analyse, and compare three dynamic calibration mechanisms for the COherent Sampling ring Oscillator based TRNG: GateVar , WireVar , and LUTVar , enabling easy integration of the entropy source into complex systems. The TRNG setup procedure automatically selects a configuration that guarantees the security requirements. In the experiments, we show that two out of the three proposed mechanisms are capable of assuring correct TRNG operation even when an automatic placement is carried out and when the design is ported to another Field-Programmable Gate Array (FPGA) family. We generated random bits on both a Xilinx Spartan 7 and a Microsemi SmartFusion2 implementation that, without post processing, passed the AIS-31 statistical tests at a throughput of 4.65 Mbit/s and 1.47 Mbit/s, respectively.
Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people's perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features.
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees

This paper addresses the problem of model-free reinforcement learning for Robust Markov Decision Process (RMDP) with large state spaces. The goal of the RMDP framework is to find a policy that is robust against the parameter uncertainties due to the mismatch between the simulator model and real-world settings. We first propose the Robust Least Squares Policy Evaluation algorithm, which is a multi-step online model-free learning algorithm for policy evaluation. We prove the convergence of this algorithm using stochastic approximation techniques. We then propose Robust Least Squares Policy Iteration (RLSPI) algorithm for learning the optimal robust policy. We also give a general weighted Euclidean norm bound on the error (closeness to optimality) of the resulting policy. Finally, we demonstrate the performance of our RLSPI algorithm on some standard benchmark problems.
Dense Fusion Network with Multimodal Residual for Sentiment Classification

In this paper, we propose a deep dense fusion network with multimodal residual (DFMR) to integrate multimodal information including language, acoustic speeches, and visual images for sentiment analysis. DFMR exploits a dense fusion (DF) block to fuse the multimodal features obtained by modality-specific sequence networks, which is achieved by modelling their unimodal, bimodal and trimodal interactions jointly. Instead of concatenating the multimodal features directly, DF block conducts fusion for any two paired modalities firstly, and the fused information will be integrated with the other modalities subsequently. Furthermore, DFMR stacks multiple DF blocks to capture high-level semantic information conveyed by the multimodal representations. In particular, DFMR adopts a multimodal residual (MR) block to integrate the modality-specific features and fused features in each DF blocks, to avoid forgetting the multi-aspect information and alleviate gradient vanishing during stacking. Extensive experiments conducted on four public benchmark datasets show that DFMR outperforms eleven state-of-the-art baselines.
Implementation of Auditable Blockchain Voting System with Hyperledger Fabric

An efficient democratic process requires a quick, fair and fraud-free election process. Many electronic-based voting systems have been developed to fulfil these requirements but there are still unsolved issues with transparency, privacy and data integrity. The development of distributed ledger technology called blockchain creates the potential to solve these issues. This technology’s rapid advancement resulted in numerous implementations, one of which is Hyperledger Fabric, a secure enterprise permissioned blockchain platform. In this paper, the implementation of an Auditable Blockchain Voting System in Hyperledger Fabric is presented to showcase how various platform components can be used to facilitate electronic voting and improve the election process.
Cross-lingual Supervision Improves Unsupervised Neural Machine Translation

We propose to improve unsupervised neural machine translation with cross-lingual supervision (CUNMT), which utilizes supervision signals from high resource language pairs to improve the translation of zero-source languages.Specifically, for training En-Ro system without parallel corpus, we can leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model.Simple and effective, CUNMT significantly improves the translation quality with a big margin in the benchmark unsupervised translation tasks, and even achieves comparable performance to supervised NMT.In particular, on WMT'14 En-Fr tasks CUNMT achieves 37.6 and 35.18 BLEU score, which is very close to the large scale supervised setting and on WMT'16 En-Ro tasks CUNMT achieves 35.09BLEU score which is even better than the supervised Transformer baseline.
Smart Visualization of Mixed Data

In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Verdolini 2020) and robust clustering. The resulting groups are visualized via MDS maps and characterized through an analysis of several graphical outputs. The methodology is illustrated on a real dataset related to European COVID-19 numerical health data, as well as the policy and restriction measurements of the 2020–2021 COVID-19 pandemic across the EU Member States. The results show similarities among countries in terms of incidence and the management of the emergency across several waves of the disease. With the proposed methodology, new smart visualization tools for analyzing mixed data are provided.
End-to-End Automatic Speech Recognition: Its Impact on the Workflowin Documenting Yoloxóchitl Mixtec

This paper describes three open access Yoloxóchitl Mixtec corpora and presents the results and implications of end-to-end automatic speech recognition for endangered language documentation.Two issues are addressed.First, the advantage for ASR accuracy of targeting informational (BPE) units in addition to, or in substitution of, linguistic units (word, morpheme, morae) and then using ROVER for system combination.BPE units consistently outperform linguistic units although the best results are obtained by system combination of different BPE targets.Second, a case is made that for endangered language documentation, ASR contributions should be evaluated according to extrinsic criteria (e.g., positive impact on downstream tasks) and not simply intrinsic metrics (e.g., CER and WER).The extrinsic metric chosen is the level of reduction in the human effort needed to produce high-quality transcriptions for permanent archiving.
Improving novelty detection by self-supervised learning and channel attention mechanism

Purpose In novelty detection, the autoencoder based image reconstruction strategy is one of the mainstream solutions. The basic idea is that once the autoencoder is trained on normal data, it has a low reconstruction error on normal data. However, when faced with complex natural images, the conventional pixel-level reconstruction becomes poor and does not show the promising results. This paper aims to provide a new method for improving the performance of novelty detection based autoencoder. Design/methodology/approach To solve the problem that conventional pixel-level reconstruction cannot effectively extract the global semantic information of the image, a novel model with the combination of attention mechanism and self-supervised learning method is proposed. First, an auxiliary task, reconstruct rotated image, is set to enable the network to learn global semantic feature information. Then, the channel attention mechanism is introduced to perform adaptive feature refinement on the intermediate feature map to optimize the correspondingly passed feature map. Findings Experimental results on three public data sets show that the proposed method has potential performance for novelty detection. Originality/value This study explores the ability of self-supervised learning methods and attention mechanism to extract features on a single class of images. In this way, the performance of novelty detection can be improved.
Diversity-Aware Batch Active Learning for Dependency Parsing

While the predictive performance of modern statistical dependency parsers relies heavily on the availability of expensive expert-annotated treebank data, not all annotations contribute equally to the training of the parsers.In this paper, we attempt to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning (AL).In particular, we investigate whether enforcing diversity in the sampled batches, using determinantal point processes (DPPs), can improve over their diversity-agnostic counterparts.Simulation experiments on an English newswire corpus show that selecting diverse batches with DPPs is superior to strong selection strategies that do not enforce batch diversity, especially during the initial stages of the learning process.Additionally, our diversityaware strategy is robust under a corpus duplication setting, where diversity-agnostic sampling strategies exhibit significant degradation.
PatchNet: a tiny low-light image enhancement net

Underexposed images are usually low in brightness and contrast, which degrade the performance of many computer vision algorithms. To solve the problem of overexposing areas that tend to be normal while recovering dark areas in low-light image enhancement tasks, we propose an image-to-patch enhancement model and design a lightweight convolutional neural network called PatchNet. Specifically, the new enhancement model indirectly enhances the network by introducing a patch image, which preserves the incremental information from the low-light image to the normal image. The incremental information is fused with the input image to recover the dark areas while protecting the normal areas of the image. Extensive experiments on real datasets demonstrate the advantages of our method over state-of-the-art methods in subjective feeling and objective evaluation. Our method has achieved better results in restoring details and the adjustment of brightness. By comparing to other methods, our method is more efficient.
Babel Fees via Limited Liabilities

Custom currencies (ERC-20) on Ethereum are wildly popular, but they are second class to the primary currency Ether. Custom currencies are more complex and more expensive to handle than the primary currency as their accounting is not natively performed by the underlying ledger, but instead in user-defined contract code. Furthermore, and quite importantly, transaction fees can only be paid in Ether. In this paper, we focus on being able to pay transaction fees in custom currencies. We achieve this by way of a mechanism permitting short term liabilities to pay transaction fees in conjunction with offers of custom currencies to compensate for those liabilities. This enables block producers to accept custom currencies in exchange for settling liabilities of transactions that they process. We present formal ledger rules to handle liabilities together with the concept of babel fees to pay transaction fees in custom currencies. We also discuss how clients can determine what fees they have to pay, and we present a solution to the knapsack problem variant that block producers have to solve in the presence of babel fees to optimise their profits.