Reviewerly/exHarmony huggingface dataset:
Improvement of Object Detection Based on Faster R-CNN and YOLO

The development of artificial intelligence technology has been greatly assisted by object detection. The object detector like you-only-look-once (YOLO) v2 can detect an object in real-time and also with good accuracy. However, except for the lower computation cost and faster speed, the single-stage detector YOLO v2 is not as good as the two-stage detectors like Faster R-CNN in terms of accuracy; more improvement is needed to increase the accuracy. This paper uses the Kalman filter to fuse Faster R-CNN and YOLO v2 to obtain better detection accuracy. The results from Faster R-CNN are served as observation due to its better accuracy, while that from YOLO v2 as state variables. Experiment is carried out in video samples containing vehicle images. The results show that the fusion of the two algorithms by using the Kalman filter can provide better object detection.
Spatial-Temporal Genetic-Based Attention Networks for Short-Term Photovoltaic Power Forecasting

Photovoltaic (PV) output power is significantly random and fluctuating due to its sensitivity to meteorological factors, making PV power forecasting a big challenge. Accurate short-term PV power forecasting plays a crucial role for the stable operation and maintenance management of PV systems. To achieve this target, the paper proposes a novel Spatial-Temporal Genetic-based Attention Networks (STGANet), which consists of a spatial-temporal module (STM) and a genetic-based attention module (GAM). STM serves to predict the missing solar irradiance to support the generation forecast, and contains a graph convolutional neural network to learn the spatial and temporal dependencies between historical meteorological data, while using dilated convolution as the non-linear part to simplify the network structure. The GAM efficiently explores for potential relationships in input features with attentional mechanism and uses genetic-based operation and LSTM which takes forecasting error as reference to find global optimal solutions and to avoid getting trapped in local optimal solutions. The model is verified through comparative experiment with several benchmark models using a real-world historical meteorological dataset and a power generation dataset of PV plants in southeastern China. The results have illustrated that the proposed model can provide better prediction performance in PV systems.
Confidential machine learning on untrusted platforms: a survey

Abstract With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.
A Novel Chaotic Image Encryption Algorithm Based on Latin Square and Random Shift

To realize the safe transmission of images, a chaotic image encryption algorithm based on Latin square and random shift is proposed. The algorithm consists of four parts: key generation, pixel scrambling, pixel replacement, and bit scrambling. Firstly, the key is generated from the plain image to improve the sensitivity of the encryption method. Secondly, each pixel in each row of the image matrix is moved cyclically to the right, in turn, to change the position of the image pixel and realize pixel position scrambling. Then, a 256-order Latin square matrix composed of a chaotic sequence is used as a lookup table, and the replacement coordinates are calculated based on the image pixel value and the chaotic sequence value, replacing the corresponding coordinate elements in the image matrix. Finally, decompose the bitplane of the image matrix and combine it into two-bit matrices, scramble the two bit matrices, respectively, with the Latin square matrix, recombine the scrambled two-bit matrices, and convert them into decimal to obtain the ciphertext image. In the proposed encryption method, all the Latin square matrices used are generated by chaotic sequences, further enhancing the complexity of the generated Latin square matrix and improving the algorithm’s security. Experimental results and security analysis show that the proposed algorithm has good security performance and is suitable for image encryption.
A New and Direct R-Value Measurement Method of Sheet Metal Based on Multi-Camera DIC System

More and more attention has been given in the field of mechanical engineering to a material’s R-value, a parameter that characterizes the ability of sheet metal to resist thickness strain. Conventional methods used to determine R-value are based on experiments and an assumption of constant volume. Because the thickness strain cannot be directly measured, the R-value is currently determined using experimentally measured strains in the width, and loading directions in combination with the constant volume assumption, to determine the thickness strain indirectly. This paper provides an alternative method for determining the R-value without any assumptions. This method is based on the use of a multi-camera DIC system to measure strains in three directions simultaneously. Two sets of stereo-vision DIC measurement systems, each comprised of two GigE cameras, are placed on the front and back sides of the sample. Use of the double-sided calibration strategy unifies the world coordinate system of the front and back DIC measurement systems to one coordinate system, allowing for the measurement of thickness strain and direct calculation of R-value. The Random Sample Consensus (RANSAC) algorithm is used to eliminate noise in the thickness strain data, resulting in a more accurate R-value measurement.
Lean integration of IT security and data privacy governance aspects into product development in agile organizations

This article deals with the design of a product development-specific framework to support lean and adequate governance. This framework is based on layers of product-specific standards and regulations. The layers can be merged into a specific set to address the demands of a product to fit the state-of-the-art requirements of its domain. For the product domain, specific layers are presented with examples from IT security and data privacy for the software development phase. The approach is generic and can be extended to other domains like finance services or embedded products and their life-cycle phases.
Review on Image Recoloring Methods for Efficient Naturalness by Coloring Data Modeling Methods for Low Visual Deficiency

Recent research has discovered new applications for object tracking and identification by simulating the colour distribution of a homogeneous region. The colour distribution of an object is resilient when it is subjected to partial occlusion, scaling, and distortion. When rotated in depth, it may remain relatively stable in other applications. The challenging task in image recoloring is the identification of the dichromatic color appearance, which is remaining as a significant requirement in many recoloring imaging sectors. This research study provides three different vision descriptions for image recoloring methods, each with its own unique twist. The descriptions of protanopia, deuteranopia, and tritanopia may be incorporated and evaluated using parametric, machine learning, and reinforcement learning techniques, among others. Through the use of different image recoloring techniques, it has been shown that the supervised learning method outperforms other conventional methods based on performance measures such as naturalness index and feature similarity index (FSIM).
Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning

Commonsense is defined as the knowledge that is shared by everyone.However, certain types of commonsense knowledge are correlated with culture and geographic locations and they are only shared locally.For example, the scenarios of wedding ceremonies vary across regions due to different customs influenced by historical and religious factors.Such regional characteristics, however, are generally omitted in prior work.In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models' ability to understand cultural and geo-location-specific commonsense.In particular, we study two state-of-the-art Vision-and-Language models, VisualBERT and ViLBERT trained on VCR, a standard multimodal commonsense benchmark with images primarily from Western regions.We then evaluate how well the trained models can generalize to answering the questions in GD-VCR.We find that the performance of both models for non-Western regions including East Asia, South Asia, and Africa is significantly lower than that for Western region.We analyze the reasons behind the performance disparity and find that the performance gap is larger on QA pairs that: 1) are concerned with culture-related scenarios, e.g., weddings, religious activities, and festivals; 2) require high-level geo-diverse commonsense reasoning rather than low-order perception and recognition.
Token-Based Insurance Solutions on Blockchain

With the rising demand for protection against new risks such as loss of digital assets, novel insurance services and products emerge. In particular, token-based insurance solutions on blockchain transform the insurance business by providing cover for new risks and streamlined, (semi-)automated underwriting and claim processes. In the chapter, we present a general framework of token-based insurance solutions, delegating their fundamental building blocks that include core roles, main tokens and assets, as well as key processes and operations. We describe three major token-based insurance solutions in the market and compare them in terms of native token functionality, tokenized cover types, claim assessment process and capital model. Based on the discussion of the general framework and concrete examples of token-based insurance solutions, we summarize their advantages and point out their drawbacks. We conclude that despite being at a nascent stage, the token-based insurance space bears the promise to unseat the incumbent players with more use cases being explored and more user activities.
Automated conceptual model clustering: a relator-centric approach

In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, domain experts must be able to understand and reason with their content. In other words, these models need to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages on the rich semantics of ontology-driven conceptual models (ODCM). In particular, we propose a formal notion of Relational Context to guide the automated clusterization (or modular breakdown) of conceptual models. Such Relational Contexts capture all the information needed for understanding entities "qua players of roles" in the scope of an objectified (reified) relationship (relator). The paper also presents computational support for automating the identification of Relational Contexts and this modular breakdown procedure. Finally, we report the results of an empirical study assessing the cognitive effectiveness of this approach.
Exploring Differential-Based Distinguishers and Forgeries for ASCON

Automated methods have become crucial components when searching for distinguishers against symmetric-key cryptographic primitives. While MILP and SAT solvers are among the most popular tools to model ciphers and perform cryptanalysis, other methods with different performance profiles are appearing. In this article, we explore the use of Constraint Programming (CP) for differential cryptanalysis on the Ascon authenticated encryption family (first choice of the CAESAR lightweight applications portfolio and current finalist of the NIST LWC competition) and its internal permutation. We first present a search methodology for finding differential characteristics for Ascon with CP, which can easily find the best differential characteristics already reported by the Ascon designers. This shows the capability of CP in generating easily good differential results compared to dedicated search heuristics. Based on our tool, we also parametrize the search strategies in CP to generate other differential characteristics with the goal of forming limited-birthday distinguishers for 4, 5, 6 and 7 rounds and rectangle attacks for 4 and 5 rounds of the Ascon internal permutation. We propose a categorization of the distinguishers into black-box and non-black-box to better differentiate them as they are often useful in different contexts. We also obtained limited-birthday distinguishers which represent currently the best known distinguishers for 4, 5 and 6 rounds under the category of non-black-box distinguishers. Leveraging again our tool, we have generated forgery attacks against both reduced-rounds Ascon-128 and Ascon-128a, improving over the best reported results at the time of writing. Finally, using the best differential characteristic we have found for 2 rounds, we could also improve a recent attack on round-reduced Ascon-Hash.
Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor Network

Fashion is more than Paris runways. Fashion is about how people express their interests, identity, mood, and cultural influences. Given an inventory of candidate garments from different categories, how to assemble them together would most improve their fashionability? This question presents an intriguing visual recommendation challenge to automatically create capsule wardrobes. Capsule wardrobe generation is a complex combinatorial problem that requires the understanding of how multiple visual items interact. The generative process often needs fashion experts to manually tease the combinations out, making it hard to scale.
Comparing type systems for deadlock freedom

Message-passing software systems exhibit non-trivial forms of concurrency and distribution; they are expected to follow intended protocols among communicating services, but also to never “get stuck”. This intuitive requirement has been expressed by liveness properties such as progress or (dead)lock freedom and various type systems ensure these properties for concurrent processes. Unfortunately, very little is known about the precise relationship between these type systems and the classes of typed processes they induce. This paper puts forward the first comparative study of different type systems for message-passing processes that guarantee deadlock freedom. We compare two classes of deadlock-free typed processes, here denoted L and K. The class L stands out for its canonicity: it results from Curry-Howard interpretations of classical linear logic propositions as session types. The class K, obtained by encoding session types into Kobayashi's linear types with usages, includes processes not typable in other type systems. We show that L is strictly included in K, and identify the precise conditions under which they coincide. We also provide two type-preserving translations of processes in K into processes in L.
Structure-Enhanced Pop Music Generation via Harmony-Aware Learning

Pop music generation has always been an attractive topic for both musicians and scientists for a long time. However, automatically composing pop music with a satisfactory structure is still a challenging issue. In this paper, we propose to leverage harmony-aware learning for structure-enhanced pop music generation. On the one hand, one of the participants of harmony, chord, represents the harmonic set of multiple notes, which is integrated closely with the spatial structure of music, the texture. On the other hand, the other participant of harmony, chord progression, usually accompanies the development of the music, which promotes the temporal structure of music, the form. Moreover, when chords evolve into chord progression, the texture and form can be bridged by the harmony naturally, which contributes to the joint learning of the two structures. Furthermore, we propose the Harmony-Aware Hierarchical Music Transformer (HAT), which can exploit the structure adaptively from the music, and make the musical tokens interact hierarchically to enhance the structure in multi-level musical elements. Experimental results reveal that compared to the existing methods, HAT owns a much better understanding of the structure and it can also improve the quality of generated music, especially in the form and texture.
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning

Federated learning (FL) is a prevailing distributed learning paradigm, where a large number of workers jointly learn a model without sharing their training data. However, high communication costs could arise in FL due to large-scale (deep) learning models and bandwidth-constrained connections. In this paper, we introduce a communication-efficient algorithmic framework called CFedAvg for FL with non-i.i.d. datasets, which works with general (biased or unbiased) SNR-constrained compressors. We analyze the convergence rate of CFedAvg for non-convex functions with constant and decaying learning rates. The CFedAvg algorithm can achieve an $\mathcal{O}\left( {1/\sqrt {mKT} + 1/T} \right)$ convergence rate with a constant learning rate, implying a linear speedup for convergence as the number of workers increases, where K is the number of local steps, T is the number of total communication rounds, and m is the total worker number. This matches the convergence rate of distributed/federated learning without compression, thus achieving high communication efficiency while not sacrificing learning accuracy in FL. Furthermore, we extend CFedAvg to cases with heterogeneous local steps, which allows different workers to perform a different number of local steps to better adapt to their own circumstances. The interesting observation in general is that the noise/variance introduced by compressors does not affect the overall convergence rate order for non-i.i.d. FL. We verify the effectiveness of our CFedAvg algorithm on three datasets with two gradient compression schemes of different compression ratios.
Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation

• Breast nodule classification (benign vs. malignant) on two-dimensional ultrasound images initially marked as BI-RADS 3 and 4 yields an area under the receiver operating characteristic curve of 0.67. • A mask region-based convolutional neural network can be implemented to solve the classification problem. • Despite high variability in original ultrasound images, the neural network can discriminate between characteristics based solely on image features. The purpose of this study was to create a deep learning algorithm to infer the benign or malignant nature of breast nodules using two-dimensional B-mode ultrasound data initially marked as BI-RADS 3 and 4. An ensemble of mask region-based convolutional neural networks (Mask-RCNN) combining nodule segmentation and classification were trained to explicitly localize the nodule and generate a probability of the nodule to be malignant on two-dimensional B-mode ultrasound. These probabilities were aggregated at test time to produce final results. Resulting inferences were assessed using area under the curve (AUC). A total of 460 ultrasound images of breast nodules classified as BI-RADS 3 or 4 were included. There were 295 benign and 165 malignant breast nodules used for training and validation, and another 137 breast nodules images used for testing. As a part of the challenge, the distribution of benign and malignant breast nodules in the test database remained unknown. The obtained AUC was 0.69 (95% CI: 0.57–0.82) on the training set and 0.67 on the test set. The proposed deep learning solution helps classify benign and malignant breast nodules based solely on two-dimensional ultrasound images initially marked as BIRADS 3 and 4.
Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups

Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate.Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively.Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes.DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps.DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.