Reviewerly/exHarmony huggingface dataset:
Software Evaluation for Second Round Candidates in NIST Lightweight Cryptography

Lightweight cryptography algorithms are increasing in value because they can enhance security under limited resources. National Institute of Standards and Technology is working on standardising lightweight authenticated encryption with associated data. Thirty-two candidates are included in the second round of the NIST selection process, and their specifications differ with respect to various points. Therefore, for each algorithm, the differences in specifications are expected to affect the algorithm's performance. This study aims to facilitate the selection and design of those algorithms according to the usage scenarios. For this purpose, we investigate and compare the 32 lightweight cryptography algorithm candidates using specifications and software implementations. The results indicate that latency and memory usage depend on parameters and nonlinear operations. In terms of memory usage, a difference exists in ROM usage, but not in the RAM usage from our experiments using ARM platform. We also discovered that the data size to be processed efficiently differs according to the padding scheme, mode of operation, and block size.
One-Class Classification Using ℓp-Norm Multiple Kernel Fisher Null Approach

We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓp-norm regularisation (p ≥ 1) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.
Reference-Guided Large-Scale Face Inpainting With Identity and Texture Control

Face inpainting aims at plausibly predicting missing pixels of face images within a corrupted region. Most existing methods rely on generative models learning a face image distribution from a big dataset, which produces uncontrollable results, especially with large-scale missing regions. To introduce strong control for face inpainting, we propose a novel reference-guided face inpainting method that fills the large-scale missing region with identity and texture control guided by a reference face image. However, generating high-quality results under imposing two control signals is challenging. To tackle such difficulty, we propose a dual control one-stage framework that decouples the reference image into two levels for flexible control: High-level identity information and low-level texture information, where the identity information figures out the shape of the face and the texture information depicts the component-aware texture. To synthesize high-quality results, we design two novel modules referred to as Half-AdaIN and Component-Wise Style Injector (CWSI) to inject the two kinds of control information into the inpainting processing. Our method produces realistic results with identity and texture control faithful to reference images. To the best of our knowledge, it is the first work to concurrently apply identity and component-level controls in face inpainting to promise more precise and controllable results.
Metadata Analysis of Web Images for Source Authentication in Online Social Media

Camera mobility-based embedded devices now facilitate easy creation, manipulation, and sharing of digital content on social media. While doing so, the shareable misinformation or disinformation content may harm society. Such contents may be variously forwarded without verified integrity and authenticity of the source on Online Social Networks (OSN). Thus, it is inevitable to trace the epicenter and the kind of information being spread on social networks. In this paper, we have investigated the types of metadata linked with digital images and analyzed the different attributes that are susceptible to squandering the integrity of source authentication with the easy availability of online tools and mobile-based apps. Finally, we accentuate protecting the metadata through the watermarking technique to reveal a piece of important information in the digital forensic investigation.
Monogenic wavelet scattering network for texture image classification

The scattering transform network (STN) has a similar structure as that of a convolutional neural network. We propose a novel Monogenic Wavelet Scattering Network (MWSN) for 2D texture image classification through a cascade of monogenic wavelet filtering with nonlinear modulus and averaging operators by replacing the 2D Morlet wavelet filtering in the standard STN. Our MWSN can extract useful hierarchical and directional features with interpretable coefficients. Using the CUReT texture image database, we demonstrate the superior performance of our MWSN over the standard STN. This performance improvement can be explained by the natural extension of 1D analyticity to 2D monogenicity.
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization

Timeline summarization (TLS) is defined as a task for summarizing events in chronological order, which gives readers a comprehensive understanding of an evolutionary story. Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them, which significantly degrade the performance. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantages and obtain a globally optimal summary. In this paper, we present a {joint learning-based heterogeneous graph attention network for TLS (HeterTls), in which date selection and event detection are combined into a unified framework to improve the extraction accuracy and remove redundant sentences simultaneously. Our heterogeneous graph involves multiple types of nodes, the representations of which are iteratively learned across the heterogeneous graph attention layer. We evaluated our model on four datasets, and found that it significantly outperformed the current state-of-the-art baselines with regard to ROUGE scores and date selection metrics.
Universal Attack Against Automatic Modulation Classification DNNs Under Frequency and Data Constraints

In spite of unique advantages like higher recognition accuracy and better generalization capability, automatic modulation classification (AMC)-oriented deep neural networks (ADNNs) are still vulnerable to adversarial examples (AEs). Recent results revealed that an attacker can easily fool ADNNs through adding a small and imperceptible perturbation to the original signal. Among different AE generation methods, universal adversarial perturbation (UAP) has unique characteristics, including input agnostic and shift invariance. However, applying UAP directly to RF signals faces three main challenges, i.e., perturbation neutralization, high perceptibility, and dependency of original signals. In this backdrop, a novel UAP under frequency and data constraints (UAP-FD) attack is put forward for solving these problems in this article. First, an individual perturbation is filtered based on the representation visualization algorithm to counter the neutralization problem in perturbation integration. Second, the high-frequency components in the integrated UAP is eliminated through signal decomposition and reconstruction for promoting the imperceptibility. Third, a proxy signal generation method is proposed to help UAP-FD adapt to data-free black-box settings. A series of experiments is conducted to evaluate the aggressiveness and imperceptibility of UAP-FD attack in different settings on a public data set. Results show that, compared with the existing proposal, UAP-FD has a 40% higher fooling rate, and it can reduce the accuracy of the ADNN model from 83% to 9% while maintaining a good imperceptibility and shift-invariance property. In addition, UAP-FD is applied to real-world captured signals over the transmission channel; and it can reduce the model accuracy from 98.3% to 12.5%.
Topic Integrated Opinion-Based Drug Recommendation With Transformers

Information from online platforms is vast, with health related data remaining largely unexplored for the purpose of developing a sentiment-based recommendation model. Though state-of-the-art models such as transformers are being researched in this domain, the model configuration has not been diligently investigated, particularly for deriving quality input for sentiment classification by inlaying contextual embeddings and significant sequence segments. A topic modeling and transformer-based model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topicT-AttNN</i> ) with LSTM and attention mechanism is proposed in this study for classifying sentiments from drug reviews on three aspects and overall opinion. The sentiment score thus obtained is used as a measure for identifying user-advocated drugs for a condition. The proposed model outperforms baselines for all the aspects with higher test accuracy and F1-scores, with the highest F1-score recorded as 0.9585. The results indicate the significance of LSTM and attention layers for identifying words in documents based on the dominance and the competence of the transformer unit in extracting specific context of words in reviews. With this work, we propose that the transformer architecture can be further enhanced with deep learning techniques by contriving potent layers to form the most optimal framework.
Finding the Number of Clusters Using a Small Training Sequence

In clustering the training sequence (TS), K-means algorithm tries to find empirically optimal representative vectors that achieve the empirical minimum to inductively design optimal representative vectors yielding the true optimum for the underlying distribution.In this paper, the convergence rates on the clustering errors are first observed as functions of β -α , where β is the training ratio that relates the training sequence size to the number of representative vectors, and α is a non-negative constant.From the convergence rates, we can observe the training performance for a finite TS size.If the TS size is relatively small, errors occur in finding the number of clusters.In order to reduce the errors from small TS sizes, a compensation constant (1 -β -α ) -1 for the empirical errors is devised based on the rate analyses and a novel algorithm for finding the number of clusters is proposed.The compensation constant can be applied to other clustering applications especially when the TS size is relatively small.
Information Architects: Using Community-Engaged and Qualitative Methods to Design a Technology-Based Nutrition and Cancer Risk Intervention for Rural Adults

Nearly half of all cancer deaths are preventable through modification or avoidance of key risk factors. As such, there is a growing urgency to identify effective, low-resource, and scalable technologies that support clinical care and patient self-management of health behaviors.Informed by theories of cognitive load and user-centered design approaches, we develop a culturally tailored, multicomponent digital intervention to engage rural adults between 50-73 years old with their personalized nutrition risk factors for colorectal cancer (CRC) prevention.A total of 48 adults tested a Virtual Health Assistant (VHA) prototype during focus groups in individual think-aloud interviews to facilitate iterative adaptations to a web-based CRC prevention intervention. Qualitative data was analyzed to identify user needs and preferences related to information and with a focus on avoiding cognitive overload.The VHA serves as a conceptual pre-training for users helping them understand CRC prevention key concepts and engendering motivation to act on the promoted behavior. A website was identified as a strategy to fill information gaps and present actionable information, after the VHA interaction. Cognitive load reducing strategies were used including segmenting where information is presented in learner-controlled segments rather than continuously.Findings indicate potential benefits of designing CRC prevention information technologies with the rural older adults. Integrating patient-centered needs before launching health information web content will be important as the rapid growth of telemedicine aims to reach traditionally marginalized and underserved populations. Theoretically informed considerations for potential adverse outcomes (eg, information overload) are discussed.
X-Ray Tomography Crystal Characterization: Automatic 3D Segmentation

Understanding the structural parameters of crystals during crystal growth is essential for the pharmaceutical and chemical industries. This study proposes a new method for 3D images of crystals obtained with micro X-ray computed tomography. This method aims to improve the crystal segmentation compared to the watershed methods. It is based on plane recognition at the surface of the crystals. The obtained segmentation is evaluated on a synthetic image and by considering the recognized particle number and convexity. The algorithm applied to three samples (potassium alum, chromium alum, and copper sulfate) reduced oversegmentation by 87% compared to watershed based on ultimate erosion while keeping the convexity of the recognized particle.
GPR: A Python implementation of an extremely simple classifier based on fuzzy logic and gene expression programming

In this work, we present a Python-based implementation of an extremely simple classifier (GPR), which combines gene expression programming (GEP) features and the algebraic representation of the ’if-then’ fuzzy rules theory of the Takagi–Sugeno fuzzy inference system. Generated fuzzy metarules are highly interpretable and suitable for many applications. We provide an open-source Python implementation of the GPR algorithm to enable the use of the algorithm without any commercial software tools and open access to the research community. We also added enhancements to improve the readability and interpretability of the rules.
Design and analysis of high-performance real-time image dehazing using convolutional neural and generative adversarial networks

Optical imaging sensors suffer from distortions caused by atmospheric particles such as dust, mist, fog, haze, and smoke, resulting in degradation of object detection and recognition. To circumvent these issues, image dehazing is an essential preprocessing stage for various real time applications. Several conventional dehazing methods rely on the haze formation model that are inherently dependent on a large number of variables, requiring huge computational burden on the processor. This severely affects the dehazing performance and also restricts real time processing. To overcome these issues, this work deals with an end-to-end real time dehazing architecture based on light weight Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). Proposed depthwise seperable and residual (DSR) block has been used instead of convolution layers that significantly lowered the parameters and computations. Furthermore, sigmoid and bilateral ReLu activation functions have been exploited to prevent oversaturation of dehazed images. The proposed model achieves significant enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for both synthetic and real-world hazy images, when compared to other architectures such as dark channel prior (DCP) and DehazeNet. The performance outcome of CNN and GAN based dehazing architectures are analyzed and compared.
TALK: Tracking Activities by Linking Knowledge

Dependable and accurate monitoring of elderly at home becomes crucial to limit both the costs and human efforts of following up elderly for establishing a healthy care system. Human Activity Recognition (HAR) tools, based on sensors installed in smart homes, will become an important tool to provide useful information to the caregiver when something happens in the house of an elderly and care is required. The current available detection tools either exist out of interpretable knowledge-driven techniques or scalable data-driven ones. In this paper, a hybrid methodology that combines both approaches is designed and evaluated to Track Activities by Linking Knowledge (TALK). Both sensor data and their link to the relevant domain knowledge about where those sensors are installed, the performed activities that occur, and how the household is constructed, are generalized in a specific knowledge graph (KG) structure to represent continuous events. The interpretable knowledge graph embedding technique Instance Neighboring using Knowledge (INK) is then used to transform these events inside the KG to a tabular format, which can be used by any traditional machine learning classifier to create a HAR tool. The TALK methodology is evaluated on two HAR datasets and shows (a) that TALK outperforms both traditional automated data-driven as well as knowledge-driven techniques in terms of predictive performance, and (b) how TALK can be easily used in a more out of lab environment. All these results and the interpretable aspects show that TALK can become an important tool to monitor elderly in their homes efficiently, effectively and with less intrusive techniques.
High-performance optoelectronics for integrated photonic neural networks

Integrated optoelectronic devices represents a fundamental building block of hardware accelerators for photonics neural networks. Nanophotonic electro-optic modulators and detectors have significant performance advantages in power efficiency, communication bandwidth, and parallelism compared to conventional free-space photonics. Here, we present strategies and experimental validations of novel high-performance nanophotonic opto-electronic devices, involving heterogeneous integration of emerging materials into silicon photonic integrated circuits to exploit new functionality and device-scaling laws for efficient and ultrafast modulators, detectors, and photonic nonvolatile memory. The optoelectronic implementations of neural networks are demonstrated which significantly extends the spectrum of information processing capabilities.
Open source variational quantum eigensolver extension of the quantum learning machine for quantum chemistry

Quantum Chemistry (QC) is one of the most promising applications of Quantum Computing. However, present quantum processing units (QPUs) are still subject to large errors. Therefore, noisy intermediate-scale quantum (NISQ) hardware is limited in terms of qubits counts and circuit depths. Specific algorithms such as Variational Quantum Eigensolvers (VQEs) can potentially overcome such issues. We introduce here a novel open-source QC package, denoted Open-VQE, providing tools for using and developing chemically-inspired adaptive methods derived from Unitary Coupled Cluster (UCC). It facilitates the development and testing of VQE algorithms. It is able to use the Atos Quantum Learning Machine (QLM), a general quantum programming framework enabling to write, optimize and simulate quantum computing programs. Along with Open-VQE, we introduce myQLM-Fermion, a new open-source module (that includes the key QLM ressources that are important for QC developments (fermionic second quantization tools etc...). The Open-VQE package extends therefore QLM to QC providing: (i) the functions to generate the different types of excitations beyond the commonly used UCCSD ans{\"a}tz;(ii) a new implementation of the "adaptive derivative assembled pseudo-Trotter method" (ADAPT-VQE), written in simple class structure python codes. Interoperability with other major quantum programming frameworks is ensured thanks to myQLM, which allows users to easily build their own code and execute it on existing QPUs. The combined Open-VQE/myQLM-Fermion quantum simulator facilitates the implementation, tests and developments of variational quantum algorithms towards choosing the best compromise to run QC computations on present quantum computers while offering the possibility to test large molecules. We provide extensive benchmarks for several molecules associated to qubit counts ranging from 4 up to 24.
Semantic-embedding Guided Graph Network for cross-modal retrieval

Many methods focus on aligning image regions with the corresponding text fragments, and ignore that images contain fragments that cannot be expressed by texts. To fully express the information of images and prevent the performance degradation caused by fine-grained information deviating from the core meaning of images, we propose Semantic-embedding Guided Graph Network (SGGN) for cross-modal retrieval. It learns the detail representations of each modality, with an integrated semantic information, by guiding local fragments to capture the internal correlation of cross-modal data and effectively convey the information. To further bridge the semantic gap between different modalities, SGGN uses adversarial network to play a game, and uses graph aggregation network to absorb complementary information of neighbor samples. We evaluate our approach on two datasets. Our method (based on R@10) achieves 97.2% on Flickr30k dataset. On MS-COCO dataset, it reaches 99.2% using 1K test set and 92.0% using 5K test set.
Design of a finite impulse response filter for rapid single-flux-quantum signal processors based on stochastic computing

Rapid-Single-Flux-Quantum (RSFQ) circuit technology is well known for its low power consumption and latency, which enables digital signal processing up to tens of GHz. As a fundamental digital filter, the Finite Impulse Response (FIR) filter has wide applications in communication systems. A design of an FIR filter based on RSFQ circuit technology is proposed. However, the FIR filter consumes large amounts of adders and multipliers. Based on Stochastic Computing (SC) theory with which adder and multiplier are much simpler, the hardware cost of FIR filter is dramatically reduced. A novel stochastic number generator (SNG), a stochastic-to-binary converter (SBC), and the FIR filter were designed and verified via logic simulation with a target frequency of 10 GHz. The results indicated the FIR filter performs correct operations. The proposed FIR filter consists of 2255 Josephson junctions (JJs) without wiring cells (i.e., Josephson Transmission Lines (JTLs), Passive Transmission Lines (PTLs)), which is acceptable, making it possible to be used in RSFQ digital signal processors.
Text Pin: Improving text selection with mode-augmented handles on touchscreen mobile devices

Text selection is a required step prior to performing many text editing tasks like copy or cut. Text selection relying on adjustment handles is a widely adopted method on touchscreen mobile devices. However, this method has limitations like slow and unconformable interactions when dragging handles and poor performance of selecting non-adjacent text for editing as one. To address these limitations, this study proposes Text Pin, a novel technique that specifies the two ends of intended text by finger tapping rather than finger dragging. We conducted three pilot studies to design Text Pin and an experiment to evaluate its performance. Experimental results show Text Pin (M = 11.49 s) was generally faster than the default handle-based technique on Android (M = 14.95 s) by 23.14% and a gesture-based technique (Fix & Slide) (M = 14.41 s) by 20.26% while having similar selection accuracy (Text Pin vs the default technique vs Fix & Slide: 0.95 vs 0.94 vs 0.95). Our study consequently demonstrates the potential of Text Pin as a suitable alternative to existing handle-based text selection techniques on touchscreen mobile devices.