Reviewerly/exHarmony huggingface dataset:
Semantic-aware self-supervised depth estimation for stereo 3D detection

Besides the 3D object supervision, the auxiliary disparity supervision is usually indispensable when training a stereo-based 3D object detector. The disparity supervision is either transformed from LiDAR points or generated from pre-trained models. However, the former suffers from the high cost and over-sensitivity to airborne particles of LiDAR devices, and the latter from the limited cross-dataset transferability of contemporary stereo matching models. To alleviate those problems, we propose a self-supervision framework for stereo-based 3D detection that relies on neither LiDARs nor external models. A Depth-based Self-supervision (DSelf) is proposed to unify the coordinate spaces of self-supervised losses and detection into a 3D space. However, the DSelf supervision is dense compared with the sparse LiDAR points, which introduces redundancy and irrelevancy into the stereo features. A Semantic-Aware Sampler (SASampler) is proposed to address the problems by an unbalanced sampling of foreground and background pixels. Combining our SASampler and DSelf supervision, the resultant detector (named S3D) achieves state-of-the-art detection results without explicit disparity supervisions.
Visual and textual explainability for a biometric verification system based on piecewise facial attribute analysis

The decisions behind the mechanics of a biometric verification system based on Machine Learning (ML) are difficult to comprehend. Although there is now well-established research in various fields of application, such as health or justice, the use of ML-based methods is accompanied by a lack of confidence that results in their limited use. The explainability of a ML system and the comprehension of what lies behind its prediction is one of the numerous characteristics that define “trust” in these systems. Over the years, face-based biometric authentication has been the subject of extensive research in both academia and industry. However, existing biometric authentication systems still have problems regarding accuracy, robustness and, explainability. Still lacking in the literature is a comprehensive examination of the use of post-hoc explainability techniques for such systems. Cognitive neuroscience has always been interested in the method by which people perceive faces; local elements such as the nose, eyes, and mouth are critical to the perception and recognition of a face. In this work, starting from this assumption, we propose a framework of visual and textual explainability based on the parts of a face by analyzing them with respect to the facial attributes reported in the CelebA dataset. The primary objective is to be able to explain why two pictures of different subjects are distinct. This is done by sinthesizing pairs of images that illustrate how dissimilar the various parts of the face under investigation are and incisive and direct textual explanations of the distinguishing features are generated. A further study analyzes an interpretable mapping between the semantic space of the text and the space of the image.
Ensuring AI For Science is Science: Making Randomness Portable

Science is a practice of systematically studying something and offering data and evidence to reach a conclusion. With first principles simulations, basic physics are used to model some phenomena leading to consistent, repeatable results. With an incomplete physics model or models too complex or costly to run for a given task, AI or ML are being used to estimate what the missing physics would be if we could meet our goals with a first principles approach. Our work has been exploring how to ensure ML is capable of offering a science level of consistency so we can trust our science applications incorporating ML models. Our earlier work examined the impact of pseudorandom numbers on model quality. For this study, we have examined the pseudo-random number generation algorithms used to seed essentially all ML algorithms to ensure that model generation can be performed by other scientists to achieve identical results.
Localizing UX Advocacy and Accountability: Using Personas to Amplify User Agency

Purpose: This article documents ongoing UX research to develop a grant-funded mobile safety app for recreational boaters. The article presents a workflow for designers to align user advocacy with organizational accountability through the use of personas. Each year, numerous boating safety concerns and incidents go unreported. User research into this context shows that recreational boaters want a mobile app that helps them enjoy boating trips while remaining safe.&lt;br/&gt; Method: We reviewed best practices from literature, analyzed interviews with 141 stakeholders, and then discussed findings using personas to amplify user agency as part of a Lean UX workflow for the development of a mobile app that balances users' goals with organizational accountability.&lt;br/&gt; Results: Representative groups of boaters want features that help them with navigation, charting, and communication. These features would help alleviate pain points and enable goals having to do with not getting lost, avoiding hazards, and communicating trip progress to audiences onshore. In addition, the personas we have developed will help us communicate to the development team behind the app to explain how they can develop features that accommodate users' needs.&lt;br/&gt; Conclusion: Though personas are limited as to how well they represent actual users, if used properly within a design process they are a powerful tool for amplifying user agency so that resulting apps achieve user adoption. As part of a Lean UX workflow, personas are a useful tool in tailoring products and services to user needs while ensuring organizational accountability to those needs.
Multi-party co-signature scheme based on SM2

Two-party collaborative signature scheme is an important cryptographic technology for user authentication and data integrity protection when using mobile devices for financial and securities transactions. However, the two-party collaboration scheme has the following shortcomings: firstly, it is not flexible enough, and it requires the collaborating parties to be secure and trusted; secondly, the two-party collaboration security still needs to be improved. Once a hacker obtains the signature private key and collaborative identity of a mobile device, it can construct a legitimate two-party collaborative signature. Third, the application scenario of two-party co-signature is limited and cannot meet the application scenario of multi-device co-signature. For this reason, this paper designs a multi-party collaborative signature scheme based on SM2 digital signature algorithm in the standard "SM2 Elliptic Curve Public Key Cryptography" of GM/T003-2012. This scheme consists of multiple (more than 2) participants to jointly generate the signature group public key and valid signature in an interactive manner, while ensuring that each user cannot know the signature key other than their own during the signing process. We implement this scheme based on the GMP library. The experimental results show that this scheme is not only flexible but also more secure and trustworthy to meet the application scenario of multi-device collaborative signing. In addition, the time for multiple participants to construct signatures in this scheme is similar, and the time for signature verification is less different from that of the original SM2 signature.
Quantum classifiers for domain adaptation

Transfer learning (TL), a crucial subfield of machine learning, aims to accomplish a task in the target domain with the acquired knowledge of the source domain. Specifically, effective domain adaptation (DA) facilitates the delivery of the TL task where all the data samples of the two domains are distributed in the same feature space. In this paper, two quantum implementations of the DA classifier are presented with quantum speedup compared with the classical DA classifier. One implementation, the quantum basic linear algebra subroutines-based classifier, can predict the labels of the target domain data with logarithmic resources in the number and dimension of the given data. The other implementation efficiently accomplishes the DA task through a variational hybrid quantum-classical procedure.
High Throughput PRESENT Cipher Hardware Architecture for the Medical IoT Applications

The Internet of Things (IoT) is an intelligent technology applied to various fields like agriculture, healthcare, automation, and defence. Modern medical electronics is also one such field that relies on IoT. Execution time, data security, power, and hardware utilization are the four significant problems that should be addressed in the data communication system between intelligent devices. Due to the risks in the implementation algorithm complexity, certain ciphers are unsuitable for IoT applications. In addition, IoT applications are also implemented on an embedded platform wherein computing resources and memory are limited in number. Here in the research work, a reliable lightweight encryption algorithm with PRESENT has been implemented as a hardware accelerator and optimized for medical IoT-embedded applications. The PRESENT cipher is a reliable, lightweight encryption algorithm in many applications. This paper presents a low latency 32-bit data path of PRESENT cipher architecture that provides high throughput. The proposed hardware architecture has been implemented and tested with XILINX XC7Z030FBG676-2 ZYNQ FPGA board 7000. This work shows an improvement of about 85.54% in throughput with a reasonable trade-off over hardware utilization.
Merchant Recommender System Using Credit Card Payment Data

As the size of the domestic credit card market is steadily growing, the marketing method for credit card companies to secure customers is also changing. The process of understanding individual preferences and payment patterns has become an essential element, and it has developed a sophisticated personalized marketing method to properly understand customers’ interests and meet their needs. Based on this, a personalized system that recommends products or stores suitable for customers acts to attract customers more effectively. However, the existing research model implementing the General Framework using the neural network cannot reflect the major domain information of credit card payment data when applied directly to store recommendations. This study intends to propose a model specializing in the recommendation of member stores by reflecting the domain information of credit card payment data. The customers’ gender and age information were added to the learning data. The industry category and region information of the settlement member stores were reconstructed to be learned together with interaction data. A personalized recommendation system was realized by combining historical card payment data with customer and member store information to recommend member stores that are highly likely to be used by customers in the future. This study’s proposed model (NMF_CSI) showed a performance improvement of 3% based on HR@10 and 5% based on NDCG@10, compared to previous models. In addition, customer coverage was expanded so that the recommended model can be applied not only to customers actively using credit cards but also to customers with low usage data.
Multi-Layer Decomposition and Synthesis of HDR Images to Improve High-Saturation Boundaries

Recently, high dynamic range (HDR) imaging has been used in many fields such as display, computer graphics, and digital cameras. Various tone mapping operators (TMOs) are used for effective HDR imaging. TMOs aim to express HDR images without loss of information and natural images on existing display equipment. In this paper, to improve the color distortion that occurs during tone mapping, multi-layer decomposition-based color compensation and global color enhancement of the boundary region are proposed. Multi-layer decomposition is used to preserve the color information of the input image and to find the area where color distortion occurs. Color compensation and enhancement are especially used to improve the color saturation of the border area, which is degraded due to color distortion and tone processing. Color compensation and enhancement are processed in IPT color space with excellent hue linearity to improve effective performance by reducing interference between luminance and chrominance components. The performance of the proposed method was compared to the existing methods using naturalness, distortion, and tone-mapped image quality metrics. The results show that the proposed method is superior to the existing methods.
Abstractive Summary of Public Opinion News Based on Element Graph Attention

The summary of case–public opinion refers to the generation of case-related sentences from public opinion information related to judicial cases. Case–public opinion news refers to the judicial cases (intentional homicide, rape, etc.) that cause large public opinion. The public opinion news in these cases usually contains case element information such as the suspect, victim, time, place, process, and sentencing of the case. In the multi-document summary of case–public opinion, due to the problem of information cross and information redundancy between different documents under the same case, in order to generate a concise and smooth summary, this paper proposes an abstractive summary model of case–public opinion based on the attention of a case element diagram. Firstly, multiple public opinion documents in the same case are split into paragraphs, and then the paragraphs and case elements are coded based on the transformer method to construct a heterogeneous graph containing paragraph nodes and case element nodes. Finally, in the decoding process, the two-layer attention mechanism is applied to the case element node and paragraph node, so that the model can effectively solve the redundancy problem in summary generation.
Analyzing Geographic Questions Using Embedding-based Topic Modeling

Recently, open-domain question-answering systems have achieved tremendous progress because of developments in large language models (LLMs), and have successfully been applied to question-answering (QA) systems, or Chatbots. However, there has been little progress in open-domain question answering in the geographic domain. Existing open-domain question-answering research in the geographic domain relies heavily on rule-based semantic parsing approaches using few data. To develop intelligent GeoQA agents, it is crucial to build QA systems upon datasets that reflect the real users’ needs regarding the geographic domain. Existing studies have analyzed geographic questions using the geographic question corpora Microsoft MAchine Reading Comprehension (MS MARCO), comprising real-world user queries from Bing in terms of structural similarity, which does not discover the users’ interests. Therefore, we aimed to analyze location-related questions in MS MARCO based on semantic similarity, group similar questions into a cluster, and utilize the results to discover the users’ interests in the geographic domain. Using a sentence-embedding-based topic modeling approach to cluster semantically similar questions, we successfully obtained topic models that could gather semantically similar documents into a single cluster. Furthermore, we successfully discovered latent topics within a large collection of questions to guide practical GeoQA systems on relevant questions.
Career in Geology: An Educational Project in Geosciences for the Enhancement of Student Learning in STEM Disciplines

For about 15 years, Italian universities have been experiencing a shortage in the choice of degree programs concerning Earth Sciences, in accordance with the national trend of disaffection to STEM disciplines, both for educational reasons and because of problems in the labor market. To counter this phenomenon, in 2016, the Ministry launched the National Geology Plan (NGP) through which universities interacted directly with educational institutions in their territory. Although the numbers were small, the increase in enrollment was encouraging until the 2018/2019 academic year, before the COVID-19 pandemic. The local project “GEOLAB-UNISANNIO” of the University of Sannio in Benevento is trying to implement an educational program related to STEM fields. The authors believe that either all undergraduates or the public in general would benefit from improved Geoscience education. Accordingly, the authors, as the scientific leaders of an educational program (“Career in Geology”) under the GEOLAB–UNISANNIO project, foresee the positive effects on students’ basic scientific knowledge and academic careers of this type of approach in students’ retaining. This educational program is illustrated in its articulations with the results obtained (field experiences, production of scientific reports and posters, documentaries, general satisfaction).
A Deep Learning Approach to Predict Chronological Age

Recently, researchers have turned their focus to predicting the age of people since numerous applications depend on facial recognition approaches. In the medical field, Alzheimer's disease mainly depends on patients' ages. Multiple methods have been implemented and developed to predict age. However, these approaches lack accuracy because every image has unique features, such as shape, pose, and scale. In Saudi Arabia, Vision 2030, concerning the quality of life, is one of the twelve initiatives that were launched recently. The health sector has gained increasing attention as the government has introduced age-based policies to improve the health of its elderly residents. These residents are urgently advised to vaccinate against COVID-19 based on their age. In this paper, proposing a practical, consistent, and trustworthy method to predict age is presented. This method uses the color intensity of eyes and a Convolutional Neural Network (CNN) to predict age in real time based on the ensemble of CNN. A segmentation algorithm is engaged since the approach takes its input from a video stream or an image. This algorithm extracts data from one of the essential parts of the face: the eyes. This part is also informative. Several experiments have been conducted on MATLAB to verify and validate results and relative errors. A Kaggle website dataset is utilized for ages 4 to 59. This dataset includes over 270,000 images, and its size is roughly 2 GB. Consequently, the proposed approach produces ±8.69 years of Mean Square Error (MSE) for the predicted ages. Lastly, a comparative evaluation of relevant studies and the presented algorithm in terms of accuracy, MSE, and Mean Absolute Error (MAE) is also provided. This evaluation shows that the approach developed in the current study outperforms all considered performance metrics since its accuracy is 97.29%. This study found that the color intensity of eyes is highly effective in predicting age, given the high accuracy and acceptable MSE and MAE results. This indicates that it is helpful to utilize this methodology in real-life applications.
FUSSL: Fuzzy Uncertain Self Supervised Learning

Self supervised learning (SSL) has become a very successful technique to harness the power of unlabeled data, with no annotation effort. A number of developed approaches are evolving with the goal of outperforming supervised alternatives, which have been relatively successful. Similar to some other disciplines in deep representation learning, one main issue in SSL is robustness of the approaches under different settings. In this paper, for the first time, we recognise the fundamental limits of SSL coming from the use of a single-supervisory signal. To address this limitation, we leverage the power of uncertainty representation to devise a robust and general standard hierarchical learning/training protocol for any SSL baseline, regardless of their assumptions and approaches. Essentially, using the information bottleneck principle, we decompose feature learning into a two-stage training procedure, each with a distinct supervision signal. This double supervision approach is captured in two key steps: 1) invariance enforcement to data augmentation, and 2) fuzzy pseudo labeling (both hard and soft annotation). This simple, yet, effective protocol which enables cross-class/cluster feature learning, is instantiated via an initial training of an ensemble of models through invariance enforcement to data augmentation as first training phase, and then assigning fuzzy labels to the original samples for the second training phase. We consider multiple alternative scenarios with double supervision and evaluate the effectiveness of our approach on recent baselines, covering four different SSL paradigms, including geometrical, contrastive, non-contrastive, and hard/soft whitening (redundancy reduction) baselines. We performed extensive experiments under multiple settings to show that the proposed training protocol consistently improves the performance of the former baselines, independent of their respective underlying principles.
TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation

Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications. However, current solutions are particurly computationally demanding, especially due to the wide usage of spatio-temporal 3D convolutions. We observe that, while different model architectures achieve similar performance on benchmarks, visual variations between predicted saliency maps are still significant. Inspired by this intuition, we propose a lightweight model that employs multiple simple heterogeneous decoders and adopts several practical approaches to improve accuracy while keeping computational costs low, such as hierarchical multi-map knowledge distillation, multi-output saliency prediction, unlabeled auxiliary datasets and channel reduction with teacher assistant supervision. Our approach achieves saliency prediction accuracy on par or better than state-of-the-art methods on DFH1K, UCF-Sports and Hollywood2 benchmarks, while enhancing significantly the efficiency of the model.
SD-Conv: Towards the Parameter-Efficiency of Dynamic Convolution

Dynamic convolution achieves better performance for efficient CNNs at the cost of negligible FLOPs increase. However, the performance increase can not match the significantly expanded number of parameters, which is the main bottleneck in real-world applications. Contrastively, mask-based unstructured pruning obtains a lightweight network by removing redundancy in the heavy network. In this paper, we propose a new framework, Sparse Dynamic Convolution (SD-CONV), to naturally integrate these two paths such that it can inherit the advantage of dynamic mechanism and sparsity. We first design a binary mask derived from a learnable threshold to prune static kernels, significantly reducing the parameters and computational cost but achieving higher performance in Imagenet-1K. We further transfer pretrained models into a variety of downstream tasks, showing consistently better results than baselines. We hope our SD-Conv could be an efficient alternative to conventional dynamic convolutions.