Reviewerly/exHarmony huggingface dataset:
A non-blind Watermarking Technique using Flexible Scaling Factor in Wavelet Transform

Discrete Wavelet Transformation based watermarking algorithm gives robust, secure and strong results in non-blind and semi blind applications. High coefficients are selected in wavelet transform and binary image embedded into cover image as a watermark using secret key. Most of embedding algorithms use scaling factor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> which is selected by try and use method. In this work, we use flexible scaling factor in different bands of the wavelet and also 8×8 blocks of the cover image to improve performance of the watermarking. Experimental results show that block and flexible scaling factor base Wavelet watermarking gives better peak signal-to-noise ratio (PSNR) values in Lena, Barbara, peppers and cameraman images. In addition to that, extracted logo has very high similarity ratio (SR) if compared to other Wavelet algorithms. Proposed algorithm is resist against the all common type of attacks.
Ownership Authentication and Tamper Detection in Digital Images via Zero-Watermarking

This paper presents a new zero-watermarking algorithm for digital images. The main challenge in every zero-watermarking algorithm is to create a highly capable, reliable, and distortion-free scheme. Unlike other zero-watermarking methods, our method not only focusing on ownership authentication, in the present proposal, we extend the capabilities of the zero-watermarking algorithm to detect tampering in digital images. Using a matching-pixel criterion in spatial domain in conjunction with a data hiding via bit replacing, compression and encryption, the algorithm obtains a zero-watermarking code used to authenticate the ownership and detect a tamper in the image. Experimental results show the effectiveness of the proposed method to detect tampering such as copy-move, copy-paste, text addition, constant average and cropping manipulations, as well as its robustness to prove the ownership authentication against these attacks.
Design-time exploration of voltage switching against power analysis attacks in 14 nm FinFET technology

Given their non-invasiveness and demonstrated effectiveness, power analysis attacks (PAAs) are concerning and to be accounted for in modern circuit design. That is especially relevant for technology-dependent verification of PAA countermeasure implementations. Prior art proposed various countermeasures against PAAs, including masking and hiding, voltage switching, noise injection, etc. Aside from the proven working principles of such countermeasures, it is important to understand that their effectiveness is primarily technology- and implementation-dependent. Hence, before deployment, especially for integrated circuits, such countermeasures require accurate circuit-level studies. This work investigates an industrial-grade 14 nm fin field-effect transistor (FinFET) technology at design-time in the context of PAAs. We leverage device-level measurement data from Intel high-volume manufacturing processes, build up accordingly well-characterized standard-cell libraries, and utilize a commercial-grade computer-aided design (CAD) flow for PAA evaluation at design-time. Our study is focused on (1) the effectiveness of voltage switching as a countermeasure, (2) the advanced encryption standard (AES) cipher as a representative circuit, and (3) the correlation power analysis (CPA) as an attack framework. We show that, to improve the resilience against the CPA attack in particular and to lower information leakage in general, specific voltage configurations are more promising than others for the 14 nm FinFET technology. • We study design-time technology evaluation for power side-channel attacks (PSCAs). • We focus on industrial-grade 14 nm fin field-effect transistor (FinFET) technology. • We use device-level measurement data from Intel high-volume manufacturing nodes. • We build up accordingly well-characterized standard-cell libraries. • We utilize a commercial-grade computer-aided design flow for PSCA evaluation. • We study voltage switching as countermeasure against correlation power analysis (CPA). • Resilience against CPA increases the more we switch, the larger the voltage difference. • But, information leakage is increasing for such configurations as well. • Thus, careful study of voltage differences, switching ratios is required. • Such study requires technology-accurate design-time evaluation, as proposed here. • Our findings are robust: we conduct batches of randomized-but-reproducible CPA runs. • Our findings are conservative: we use technology-accurate, zero-delay simulations. • Our findings serve as guidelines for secure design, e.g., for key refresh rates.
“Dark matter” of hunter‐gatherers archaeology: How many artifacts does a site lack?

Abstract The extra‐site activities of hunter‐gatherers and artifact curation between functionally different camps raise the issue of items a site lacks. This issue is usually addressed using the fractal properties of raw material. The model presented in this paper simultaneously considers the qualitative and quantitative (exponential) properties of missing artifacts. Our model was tested on Federmesser and Swiderian materials from the site of Lubrza 10, Western Poland. The obtained results confirm the utility of this model application to comparative analysis of assemblages coming from different sites and their structural units and identification of their function.
Multimodal Blended Teaching Mode of College English in the Background of “Big Data”

With the rapid development of China’s social economy, the status of China in the international community is getting higher and higher English, as an international communicative language, has been widely used in various fields. The teaching of English in colleges and universities aims to lay a solid foundation for students’ future study and work. The purpose of this paper is to study the methods of multimodal teaching mode of college English based on the rapid development of the current big data era. Under the background of big data, college English teaching should also make corresponding changes and innovations, pay attention to the instrumentality and practice of English language, and carry out multi-mode teaching, so as to further meet the teaching needs of college English and students’ learning needs. Therefore, this article mainly explores the multimodal teaching mode of college English under the background of big data, and improves the effect of English. Finally, the study of university English multimodal teaching mode is analyzed by chart, which helps to improve the quality of college English teaching. The experimental results show that college English teachers should actively introduce big data into classroom teaching, give full play to the value and function of the big data era, combine the characteristics of students’ own growth, design scientific teaching links and teaching content, and promote the development of students’ comprehensive ability.
KHV: KVM-Based Heterogeneous Virtualization

A KVM (Kernel-based Virtual Machine) is subject to the complexity of the Linux kernel and the difficulty and cost of safety certification; thus, it is not popularized in embedded high-reliability scenarios. This paper proposes a KVM-based Heterogeneous Virtualization (KHV), which is independent of hardware virtualization (KVM mandatory virtualization), follows the principle of static partitioning, localizes the hypervisor, and inherits the KVM software ecosystem. KHV balances the demands of static partitioning and flexible sharing in the embedded system. The paper implemented KHV on the RISC-V Xuantie C910 CPU-based SoC and conducted a performance comparison with KVM. The experiment shows that KHV is 50% smaller than KVM in terms of fluctuation, and KHV makes the guest OS have the same performance as the bare-metal OS in scheduler benchmarks, whereas KVM dropped an average of 28%.
Do-It-Yourself Recommender System: Reusing and Recycling With Blockchain and Deep Learning

Due to aggressive urbanization (with population size), waste increases exponentially, resulting in environmental damage.Even though it looks challenging, such an issue can be controlled if we can reuse them.To handle this, in our work, we design a machine learning and blockchainoriented system that identifies the waste objects/products and recommends to the user multiple 'Do-It-Yourself' (DIY) ideas to reuse or recycle.Blockchain records every transaction in the shared ledger to enable transaction verifiability and supports better decision-making.In this study, a Deep Neural Network (DNN) trained on about 11700 images is developed using ResNet50 architecture for object recognition (training accuracy of 94%).We deploy several smart contracts in the Hyperledger Fabric (HF) blockchain platform to validate recommended DIY ideas by blockchain network members.HF is a decentralized ledger technology platform that executes the deployed smart contracts in a secured Docker container to initialize and manage the ledger state.The complete model is delivered on a web platform using Flask, where our recommendation system works on a web scraping script written using Python.Fetching DIY ideas using web-scraping takes nearly 1 second on a desktop machine with an Intel Core-i7 processor with 8 cores, 16 GB RAM, installed with Ubuntu 18.04 64-bit operating system, and Python 3.6 package.Further, we evaluate blockchain-based smart contracts' latencies and throughput performances using the hyperledger caliper benchmark.To the best of our knowledge, this is the first work that integrates blockchain technology and deep learning for the DIY recommender system.
Numeral Tense Detection in Chinese Financial News

Time information is a very important dimension in information space, which can be shown as tense expressions in natural language. Meanwhile, numerals play an important role in financial texts, which is the embodiment of fine-grained information, and most of financial events contain numerals. We have observed that Chinese does not express the tense of texts intuitively at the lexical level of verbs, but through some adverb or auxiliary tense operators, and there has not further research on numeral tense in Chinese financial texts yet. However, the tense of numerals in the texts is very crucial for the financial fields which pays attention to time series. Therefore, to assist Chinese tense understanding in financial texts, in this paper, we propose a novel task for numeral tense detection in Chinese financial fields. We firstly annotate a numeral tense dataset based on Chinese finance news texts, named CFinNumTense, which defines the numeral tense categories into “past tense”, “future tense”, “static state” and “time”, and then conduct Chinese finance numeral tense detection task on CFinNumTense. We employ RoBERTa (Robustly Optimized BERT Pretraining Approach) pre-trained model as the embedding layer and use four baseline models, i.e., FNN (Feedforward Neural Network), TextCNN (Text Convolutional Neural Networks), RNN (Recurrent Neural Networks) and BiLSTM (Bi-directional Long Short-Term Memory), to detect numeral tenses, respectively. In the ablation experiments, we design NE (Numeral Encoding) to improve the information on target numeral in the texts, and design an auxiliary learning model based on BiLSTM. Experiments show that the multitask learning of target numeral tense detection and tense operator extraction can strengthen the understanding ability of target numeral tense in Chinese financial texts.
Understanding Dropout for Graph Neural Networks

Graph neural network (GNN) has demonstrated superior performance on graph learning tasks. GNN captures the data dependencies via message passing amid neural networks. Hence the prediction of a node label can utilize information from its neighbors in a graph. Dropout is a regularization as well as an ensemble method for convolutional neural network (CNN), which has been carefully studied. However, there are few existing works that focused on dropout schemes for GNN. Although GNN and CNN share similar model architecture, both with convolutional layers and fully connected layers, the input data structure for GNN and CNN are different and convolution operation differs. This suggests the dropout schemes for CNN should not be directly applied to GNN without a good understanding of the impact. In this paper, we divide the existing dropout schemes for GNN into two categories: (1) dropout on feature maps and (2) dropout on graph structure. Based on the drawbacks of current GNN dropout models, we propose a novel layer compensation dropout and a novel adaptive heteroscadestic Gaussian dropout, which can be applied to any type of GNN models and outperforms their corresponding baselines in shallow GNNs. Then an experimental study shows Bernoulli dropout generalize better while Gaussian dropout is slightly stronger in transductive performance. At last, we theoretically study how different dropout schemes mitigate over-smoothing problems and experimental results shows that layer compensation dropout allows a GNN model to maintain or slightly improve its performance as the GNN model adds more layers while all the other dropout models suffer from performance degradation when GNN goes deep.
Enhancing Query Answer Completeness with Query Expansion based on Synonym Predicates

Community-based knowledge graphs are generated following hybrid approaches, where human intelligence empowers computational methods to effectively integrate encyclopedic knowledge or provide a common understanding of a domain. Existing community-based knowledge graphs represent essential sources of knowledge for enhancing the accuracy of data mining, information retrieval, question answering, and multimodal processing. However, despite the enormous effort conducted by contributing communities, community-based knowledge graphs may be incomplete and integrate duplicated data and metadata. We tackle the problem of enhancing query answering against incomplete community-based knowledge graphs by proposing an efficient query processing approach to estimate answer completeness and increase the results. It assumes that community-based knowledge graphs comprise synonym predicates that complement knowledge graph triples required to raise query answering completeness. The aim is proposing a novel query expansion method based on synonym predicates identified using embeddings built on a knowledge graph. Our preliminary analysis shows that our approach improves query answer completeness. However, queries can be expanded with some similar predicates which do not lead to complete answers. This shows that more work is required for query expansion with the minimum synonym predicates that maximize answer completeness.
Sequence-Based Extractive Summarisation for Scientific Articles

This paper presents the results of research on supervised extractive text summarisation for scientific articles. We show that a simple sequential tagging model based only on the text within a document achieves high results against a simple classification model. Improvements can be achieved through additional sentence-level features, though these were minimal. Through further analysis, we show the potential of the sequential model relying on the structure of the document depending on the academic discipline which the document is from.
Diachronic Analysis of Time References in News Articles

Time expressions embedded in text are important for many downstream tasks in NLP and IR. They have been, for example, utilized for timeline summarization, named entity recognition, temporal information retrieval, question answering and others. In this paper, we introduce a novel analytical approach to analyzing characteristics of time expressions in diachronic text collections. Based on a collection of news articles published over a 33-years’ long time span, we investigate several aspects of time expressions with a focus on their interplay with publication dates of containing documents. We utilize a graph-based representation of temporal expressions to represent them through their co-occurring named entities. The proposed approach results in several observations that could be utilized in automatic systems that rely on processing temporal signals embedded in text. It could be also of importance for professionals (e.g., historians) who wish to understand fluctuations in collective memories and collective expectations based on large-scale, diachronic document collections.
A bridge-based algorithm for simultaneous primal and dual defects compression on topologically quantum-error-corrected circuits

Topological quantum error correction (TQEC) using the surface code is among the most promising techniques for fault-tolerant quantum circuits. The required resource of a TQEC circuit can be modeled as a space-time volume of a three-dimensional diagram by describing the defect movement along the time axis. For large-scale complex problems, it is crucial to minimize the space-time volume for a quantum algorithm with a reasonable physical qubit number and computation time. Previous work proposed an automated tool to perform bridge compression on a large-scale TQEC circuit. However, the existing automated bridging compression is only for dual defects and not for primal defects. This paper presents an algorithm to perform bridge compression on primal and dual defects simultaneously. In addition, the automatic compression algorithm performs initialization/measurement simplification and flipping to improve the compression. Compared with the state-of-the-art work, experimental results show that our proposed algorithm can averagely reduce space-time volumes by 47%.
Timed speculative attacks exploiting store-to-load forwarding bypassing cache-based countermeasures

In this paper, we propose a novel class of speculative attacks, called Timed Speculative Attacks (TSA), that does not depend on the state changes in the cache memory. Instead, it makes use of the timing differences that occur due to store-to-load forwarding. We propose two attack strategies - Fill-and-Forward utilizing correctly speculated loads, and Fill-and-Misdirect using mis-speculated load instructions. While Fill-and-Forward exploits the shared store buffers in a multi-threaded CPU core, the Fill-and-Misdirect approach exploits the influence of rolled back mis-speculated loads on subsequent instructions. As case studies, we demonstrate a covert channel using Fill-and-Forward and key recovery attacks on OpenSSL AES and Romulus-N Authenticated Encryption with Associated Data scheme using Fill-and-Misdirect approach. Finally, we show that TSA is able to subvert popular cache-based countermeasures for transient attacks.
DFVNet: Real-Time Disguised Face Verification

A face is arguably the most conspicuous biometric for recognizing a person, yet its identification can be an abstruse task if the subject tries to impersonate someone else using different disguises. In this work, we present DFVNet, a deep learning framework to help identify disguised faces in real-time with greater accuracy. The proposed architecture employs three neural networks to achieve its goal: the first one does real-time image matting on the given image dataset to extract the image foreground without the use of a trimap at 63 FPS, the second one uses a spatial fusion convolutional network to extract 18 facial key-points whose heatmap prognosis are coincided using optical flow, which are then processed and their relative angles used as input in the third one i.e. the deep neural network for subject identification. Our method mitigates the anomalies induced by the disguises, outperforming the previously used methods for the same.
Code smell analysis in cloned Java variants

Families of software products are usually created using opportunistic reuse (clone-and-own) in which products are cloned and adapted to meet new requirements, user preferences, or non-functional properties. Opportunistic reuse brings short-term benefits, e.g., reduced time-to-market, whereas creating long-term drawbacks, e.g., the need of changing multiple variants for any maintenance and evolution activity. This situation is even worse when the individual products have poor design or implementation choices, the so-called code smells. Due to their harmfulness to software quality, code smells should be detected and removed as early as possible. In a family of software products, the same code smell must be identified and removed in all variants where it is are present. Identifying instances of similar code smells affecting different variants has not been investigated in the literature yet. This is the case of the Apo-Games family, which has the challenge of identifying the flaws in the design and implementation of cloned games. To address this challenge, we applied our inconsistency and repair approach to detect and suggest solutions for six types of code smells in 19 products of the Apo-games family. Our results show that a considerable number of smells were identified, most of them for the long parameter list and data class types. The number of the same smells identified in multiple variants ranged between 2.9 and 20.2 on average, showing that clone-and-own may lead to the replication of code smells in multiple products. Lastly, our approach was able to generate between 4.9 and 28.98 repair alternatives per smell on average.
Behavioral customization of state machine models at ESO

For the development of the Extremely Large Telescope, the European Southern Observatory uses state machines to define life cycles and basic behavior of control software components. To provide certain degrees of freedom, the component life cycles need to be customizable, but to remain compatible, they must also conform to specific standard behavior. High customization causes difficulties in maintenance and may also lead to incompatible solutions. On the other hand, the introduction of strict compatibility requirements may increase maintainability but it also make the system less flexible. To avoid spending a significant portion of the Assembly, Integration, and Verification phase in integration hell, finding the right balance between variability and compatibility early enough is important.
Improving the customization of software product lines through the definition of local features

Variability in software product lines (SPL) is mostly described with feature models. In basic feature models, the selection of a feature for a particular product determines whether or not the feature is present in the product in a global manner. Even though there are cardinality-based feature models that allow a subset of features to be specified a number of times for each product, it is not possible to customize each instance of the feature with specific details for different elements of the product.
Accuracy- and consistency-aware recommendation of configurations

Constraint-based configurators support users in deciding which components and features should be included in a configuration. Due to the increasing size and complexity of configurable products and services, recommender systems are used to personalize the interaction with configurators. Since basic recommendation approaches such as collaborative filtering do not take into account constraints between variable values, recommendations can induce inconsistencies between user requirements and the underlying configuration knowledge base. In this paper, we introduce a constraint-based configuration approach that integrates the results of model-based collaborative filtering (e.g., implemented as feed forward neural network) into constraint solving in such a way that the solver (configurator) is able to determine consistency-preserving and user-relevant configurations.