Reviewerly/exHarmony huggingface dataset:
Low-Complexity AES Architectures Resilient to Power Analysis Attacks

The advanced encryption standard (AES) is the current standard for symmetric-key cipher. To protect AES implementations from correlation power analysis (CPA) side-channel attacks (SCAs), many countermeasures have been proposed. However, existing approaches have large area overheads. This paper proposes two low-complexity techniques for AES to resist CPA attacks. By exploiting generalized dual ciphers, alternative conversions are utilized to substantially simplify all the involved constant matrix multiplications. Additionally, a multiplicative masking scheme utilizing simple constant multipliers is developed for AES designs used in resource-constraint applications. FPGA implementation results on a Xilinx XC7a200t device show that the proposed design with four Sboxes achieves 11.1% area reduction and 89.2% clock frequency increase compared to the best prior architecture without sacrificing CPA-attack resistance. In addition, the proposed multiplicative masking scheme not only keeps the resistance to CPA attacks, but also reduces the area by 52.6% and increases the clock frequency by 29.7% in a single-Sbox design compared to the previous scheme.
Extracting Key Information from Shopping Receipts by Using Bayesian Deep Learning via Multi-modal Features

This research presents a new key information extraction algorithm from shopping receipts. Specifically, we train semantic, visual and structural features through three deep learning methods, respectively, and formulate rule features according to the characteristics of shopping receipts. Then we propose a multi-class text classification algorithm based on multi-modal features using Bayesian deep learning. After post-processing the output of the classification algorithm, the key information we seek for can be obtained. Our algorithm was trained on a self-labeled Chinese shopping receipt dataset and compared with several baseline methods. Extensive experimental results demonstrate that the proposed method achieves optimal results on our Chinese receipt dataset.
Towards Energy-Optimized On-Board Computer Vision for Autonomous Underwater Vehicles

Autonomous underwater vehicles have increasing demands for computer vision capabilities. As an example, marine ecosystem observation will strongly benefit from real-time analysis of acquired images. However, the complexity of computer vision algorithms and the vast amount of data from still images or video induces serious challenges for the limited energy budget of the vehicles. We propose the extensive employment of field programmable gate array for the energy efficient implementation of on-board computer vision tasks. Our case study considers dedicated implementations for particle image velocimetry and stereo depth map estimation. The results show a substantial improvement in energy efficiency while maintaining attainable accuracy compared to a software reference model.
Coded Distributed Gaussian Process Regression

In this letter, we propose a coded load balancing method for distributed Gaussian process regression over heterogeneous wireless networks, where users with diverse computational and communications capabilities may offload excessive training data onto a computationally stronger central server to reduce collaborative processing times. The offloaded data are transformed using random Fourier feature mapping and encoded with a random orthogonal matrix to prevent transmission of raw data. The proposed method is particularly applicable to compute-intensive applications, where users operate with large datasets.
Equalization of the Visibility Loss between AR and Real Stimuli Sizes

The results of the empirical study based on an AR Landolt C vision were examined to compare real and virtual visibility as a function of stimulus size on an HMD (e.g. MS HoloLens 2). A counterbalanced within-subject-design study with n = 32 probands was conducted. The results show on one hand a visibility loss between real and immersive systems by the same size of representation. On the other hand, a transfer function can be derived to ensure comparable visibility between the systems. This transfer function can be used to compensate for the visibility loss as a design guideline for immersive applications. In approximation, the Weber-Fechner's relation can be applied on AR visibility versus size of stimuli.
To Answer or Not to Answer? Filtering Questions for QA Systems

Question answering (QA) systems are usually structured as strict conditional generators, which return an answer for every input question. Sometimes, however, the policy of always responding to questions may prove itself harmful, given the possibility of giving inaccurate answers, particularly for ambiguous or sensitive questions; instead, it may be better for a QA system to decide which questions should be answered or not. In this paper, we explore dual system architectures that filter unanswerable or meaningless questions, thus answering only a subset of the questions raised. Two experiments are performed in order to evaluate this modular approach: a classification on SQuAD 2.0 for unanswerable questions, and a regression on Pirá for question meaningfulness. Despite the difficulties involved in the tasks, we show that filtering questions may contribute to improve the quality of the answers generated by QA systems. By using classification and regression models to filter questions, we can get better control over the accuracy of the answers produced by the answerer systems.
Digital transformation of higher education in developed countries

The spread of digital technologies in all dimensions within modern society became possible as a result of the 4th Industrial Revolution. It leads to qualitative changes in all significant areas, including higher education, and makes its digital transformation inevitable. At the initial stage the introduction of digital technologies into educational activities was an initiative of some Higher education institutions, but now this process acquires a systemic character in most countries with developed educational systems. The governments of these countries adopt the general digitalization strategies, as well as strategies for digital transformation of science and higher education. The article focuses on tertiary education. The digital transformation of higher education is associated with the development of digital services, infrastructure modernization, data management and human resources. The paper identifies trends in education digital development. The authors carried out the ranking assessment of countries’ potential for digital technologies development. For the ranking six key indicators in twelve countries with the most developed education systems located on different continents were taking into consideration.
ICDT: Incremental Context Guided Deliberation Transformer for Image Captioning

Image Captioning is a task to generate descriptions for given images. Most encoder-decoder methods suffer from lacking the ability to correct the mistakes in predicted word. Though current deliberation motivated models can refine the generated text, they use single level image features throughout two stages. Due to the insufficient image information provided for the second-pass, deliberation action is ineffective in some cases. In this paper, we propose Incremental Context Guided Deliberation Transformer, namely ICDT, which consists of three modules, including: 1) Incremental Context Encoder, 2) Raw Caption Decoder and 3) Deliberation Decoder. Motivated by human writing habits in daily life, we treat the process of generating a caption as a deliberation procedure. The Raw Caption Decoder in first-pass constructs a draft sentence and then the Deliberation Decoder in second-pass polishes it to a better high-quality caption. In particular, for image encoding process, we design an Incremental Context Encoder that can provide cumulative encoded context based on different levels of image features for the deliberation procedure. Our encoder makes image features at different levels play specific roles in each decoding pass, instead of being simply fused and fed into the model for training. To validate the performance of the ICDT model, we evaluate it on the MSCOCO dataset. Compared with both Transformer-based models and deliberation-motivated models, our ICDT improves the state-of-the-art results and reaches 81.7% BLEU-1, 40.6% BLEU-4, 29.6% METEOR, 59.7% ROUGE and 134.6% CIDEr.
Evaluating Atoms of Confusion in the Context of Code Reviews

Code review is a popular software engineering practice. Success of code reviews can be threatened by confusion experienced by code reviewers. For instance, on the one hand, research has studied the reasons for confusion in code reviews, and on the other hand, it also has analyzed source code patterns, so called "atoms of confusion", that have been shown to lead to misunderstanding in the lab setting. However, to the best of our knowledge, there is no research which tried to investigate the possible cause and effect relationship between atoms of confusion and confusion in code reviews. Another important aspect still not studied is how those atoms of confusion evolve across pull requests. In this emerging results paper, we report an exploratory case study to provide a deeper understanding of atoms of confusion, more specifically, whether atoms of confusion are related to confusion in code reviews and how they persist across pull requests. With the help of an existing tool for the detection of atoms of confusion, and a manual analysis of code reviews comments, we observed that statistical analysis did not show any relationship between atoms of confusion and presence of confusion comments in code reviews. Additionally, we found evidence that atoms of confusion are mostly not being removed in pull requests. Based on the results, we formulate hypotheses on atoms of confusion in the code review context, that should be confirmed or rejected by future studies.
Semi-Supervised Depth Estimation by Multi-Task Learning

Depth estimation is a historical problem in computer vision. It is essential to get accurate depth information from cameras to implement autopilot. Most of the early studies were based on multiple observation points. Therefore, monocular depth estimation was proposed to overcome this restriction, trying to estimate depth information of every pixel from a single image. In this paper, we propose a semi-supervised multitask model to predict depth map and semantic segmentation by making use of the implicit relation between the two tasks by sharing parameters. Moreover, our presented model is able to predict favorable depth map against the state-of-the-art on the KITTI benchmark. The code we used to train and evaluate our models is available at: https://github.com/fyhfly/Multi-task-Depth-Estimation.
Glow-Mind: An Input/Output Web System for Sharing Feelings by Pressing a Button

Uncomfortable silences can disrupt meetings and conferences. One of the reasons for these silences may be that participants are unable to share their intentions at the meeting, which makes it difficult to decide whether discussions should move on or go into greater depth. This study proposes a method for visualizing feelings and conveying them to participants in a meeting. Our design Glow-mind involves button systems in web browsers which can share feelings anonymously. This paper introduces the system requirements, tests the system, and sets out the results. To facilitate real-life meetings, we also consider a new user interface in which the size of each button is determined by the number of times it is pressed.
An Efficient FPGA Implementation for Real-Time and Low-Power UAV Object Detection

In this paper, an efficient real-time hardware accelerator based on Field Programmable Gate Array (FPGA) is proposed for unmanned aerial vehicle (UAV) object detection. We first analyze the iSmart3-SkyNet (a popular UAV object detection network), using the roofline model. Then, a series of optimization strategies are proposed for low power and real-time UAV object detection based on FPGA. Stackable shared PE and regulable loop count improve the computing roof and the utilization of computing resources. Channel augmentation is used to increase the memory bandwidth, and improve the computing efficiency for shallow layers. Regulable Loop Count reduces unnecessary computation, and pre-load workflow improves the overall parallelism of heterogeneous systems. The results show that our accelerator (SEUT) achieves 78.6 frames per second and 0. 068J per image with 0.73 Intersection over Union for object detection. Source code will be available at https://github.com/aicerl/accob.
A New Method to Measure the Distance of Two Evidence Bodies

Evidence theory is an effective uncertainty reasoning method. But when evidence system is highly conflict, D-S combination rule will generate a counter-intuitive result. There are many ways to resolve this situation. One category of modified methods is to weight the evidence bodies of evidence system again. These methods use Jousselme distance, Pignistic probability transformation, information entropy, interval distance and so on. By analyzing these methods, we give a new method to measure the distance of two evidence bodies and then give the weight of evidences bodies by using the new distance. Final example shows the validity of our proposed distance.
Style-Based Attentive Network for Real-World Face Hallucination

Real-world face hallucination is a challenging image translation problem. There exist various unknown transformations in real-world LR images that are hard to be modeled using traditional image degradation procedures. To address this issue, this paper proposes a novel pipeline, which consists of a style Variational Autoencoder (styleVAE) and an SR network incorporated with an attention mechanism. To get real-world-like low-quality images paired with the HR images, we design the styleVAE to transfer the complex nuisance factors in real-world LR images to the generated LR images. We also use mutual information estimation (MI) to get better style information. In addition, both global and local attention residual blocks are proposed to learn long-range dependencies and local texture details, respectively. It is worth noticing that styleVAE is presented in a plug-and-play manner and thus can help to improve the generalization and robustness of our SR method as well as other SR methods. Extensive experiments demonstrate that our method is effective and generalizable both quantitatively and qualitatively.
Going Beyond Accuracy: Interpretability Metrics for CNN Representations of Physiological Signals

Though deep neural networks such as convolutional neural networks (CNNs) have achieved high predictive accuracy on many tasks in health domain, the lack of explanation and reasoning behind the predictions can cause adverse consequences in many “high stakes” applications such as predicting mortality in ICU. Existing popular explanation methods, including GradCam, LIME, SHAP and so on, are designed for image and text data, but not for physiological data in the form of time series like electrocardiograms (ECG). Moreover, these methods can only generate interpretable visualizations for qualitative evaluations while cannot satisfy the need for quantitative evaluations. To fill this gap, we introduce a novel generalized framework for quantitatively explaining CNN representations for physiological signals called ENPhyS. Specifically, inspired by visual concepts (e.g. parts, objects and scenes), we first define physiological concepts which are drawn from existing clinical knowledge (e.g. P wave in ECG). ENPhys further measures the interpretability of CNN by the alignment between a hidden CNN unit and each physiological concept. Finally, we propose novel interpretability metrics —Consistency, Confidence, Completeness and C-Score —to quantify the interpretability of CNNs from different perspectives. We evaluate our metrics on three state-of-the-art CNN models and on three types of physiological signals. Results from user study suggest that our metrics are in line with human intuition of interpretability.
Identifying Security Requirements for Smart Grid Components: A Smart Grid Security Metric

The most vital requirement for the electric power system as a critical infrastructure is its security of supply. In course of the transition of the electric energy system, however, the security provided by the N-1 principle increasingly reaches its limits. The IT/OT convergence changes the threat structure significantly. New risk factors, that can lead to major blackouts, are added to the existing ones. The problem, however, the cost of security optimizations are not always in proportion to their value. Not every component is equally critical to the energy system, so the question arises, "How secure does my system need to be?". To adress the security-by-design principle, this contribution introduces a Security Metric (SecMet) that can be applied to Smart Grid architectures and its components and deliver an indicator for the "Securitisation Need" based on an individual risk assessment.
Multi-scale Local Region Relation Attention in Convolutional Neural Networks for Facial Action Unit Intensity Prediction

Facial Action Unit (FAU) intensity can describe the degree of change in the appearance of a specific location on the face and can be used for the analysis of human facial behavior. Due to the subtle changes in FAU, FAU intensity prediction still faces great challenges. Previous works using attention mechanisms for FAU intensity prediction either simply crop the FAU regions or directly use attention mechanisms to obtain local representations of FAUs, but these methods do not capture FAU intensity features at different scales and locations well. In addition, the dependencies between FAUs also contain important information. In this paper, we propose a multi-scale local-region relational attention model based on convolutional neural networks (CNN) for FAU intensity prediction. Specifically, we first reflect the relationship between FAUs by adjusting the luminance values of face images to capture local features with pixel-level relationships. Then, we use the introduced multi-scale local area relational attention model to extract the local attention latent relational features of FAU. Finally, we combine local attention potential relationship features, facial geometry information, and deep global features captured using an autoencoder to achieve robust FAU intensity prediction. The method is evaluated on the public benchmark dataset DISFA, and experimental results show that our method achieves comparable performance to state-of-the-art methods and validates the effectiveness of a multi-scale local-region relational attention model for FAU intensity prediction.
Instance Contour Adjustment via Structure-Driven CNN

Instance contour adjustment is desirable in image editing, which allows the contour of an instance in a photo to be either dilated or eroded via user sketching. This imposes several requirements for a favorable method in order to generate meaningful textures while preserving clear user-desired contours. Due to the ignorance of these requirements, the off-the-shelf image editing methods herein are unsuited. Therefore, we propose a specialized two-stage method. The first stage extracts the structural cues from the input image, and completes the missing structural cues for the adjusted area. The second stage is a structure-driven CNN which generates image textures following the guidance of the completed structural cues. In the structure-driven CNN, we redesign the context sampling strategy of the convolution operation and attention mechanism such that they can estimate and rank the relevance of the contexts based on the structural cues, and sample the top-ranked contexts regardless of their distribution on the image plane. Thus, the meaningfulness of image textures with clear and user-desired contours are guaranteed by the structure-driven CNN. In addition, our method does not require any semantic label as input, which thus ensures its well generalization capability. We evaluate our method against several baselines adapted from the related tasks, and the experimental results demonstrate its effectiveness.
A Framework for Normalizing Activations as Functions

We propose a framework to describe normalizing-activations-as-function methods in Algorithm 4.1. We divide the normalizing-activations-as-function framework into three abstract processes: normalization area partitioning (NAP), normalization operation (NOP), and normalization representation recovery (NRR). We consider the more general mini-batch (of size m) activations in a convolutional layer $${{{\textbf {{\textsf {X}}}}}}\in \mathbb {R}^{d \times m \times h \times w}$$ , where d, h and w are the channel number, height and width of the feature maps, respectively. NAP transforms the activations $${{{\textbf {{\textsf {X}}}}}}$$ into $${\boldsymbol{X}}\in \mathbb {R}^{S_1 \times S_2}$$ , where $$S_2$$ indexes the set of samples used to compute the estimators. NOP denotes the specific normalization operation (see main operations in Sect. 1.1 ) on the transformed data $${\boldsymbol{X}}$$ . NRR is used to recover the possible reduced representation capacity.
Deployment Optimization of Spotlights for Visual Coverage in Pure Dark Indoor Scene

In this article, a deployment method of spotlights is studied for visual coverage task in pure dark indoor scene. To characterize the illumination performance of target in visual sensor, a criterion called illumination strength of spotlight with combining the diffuse reflection and specular reflection on the basis of distance/angle illumination attenuation of light source as well as shading is proposed. Then, an optimization problem is formulated for a number of spotlights with the aim of covering the target with a desired luminance value in camera network as uniformly as possible. An elimination genetic algorithm possessing faster convergence rate and better convergence performance than standard genetic algorithm is utilized to solve the optimization problem. Finally, numerical simulations and experiments are conducted to validate the proposed deployment method of multiple spotlights for visual coverage task in pure dark indoor scene.
Research on the Embedded Mathematical Model of Artificial Intelligence Measurement

In view of the unclear concept and weak basic theory of artificial intelligence, the bottleneck of basic measurement technology has not been solved. The relationship between AI metrics and basic cognitive quantity is extremely complex, so it is challenging and uncertain to scientifically construct its functional relationship. This article starts from revealing the internal mechanism, measurement principle and essential characteristics of artificial intelligence, the embedded physical laws are characterized, and the measurement model theory based on intelligence entropy is proposed. Multi-channel exploration of artificial intelligence measurement traceability methods provides ideas for forming the relationship model between artificial intelligence measurement indicators and benchmark quantities. The design verification method and approach of artificial intelligence measurement are established to ensure the reproducibility, accuracy and consistency of artificial intelligence measurement method and data.