Reviewerly/exHarmony huggingface dataset:
An AI-based Approach for Grading Students’ Collaboration

Soft skills (such as communication and collaboration) are rarely addressed in programming courses, mostly because they are difficult to teach, assess, and grade. A quantitative, modular, AI-based approach for assessing and grading students' collaboration has been examined in this article. The pedagogical underpinning of the approach includes a pedagogical framework and a quantitative soft skill assessment rubric, which have been adapted and used in an extracurricular Java programming course. The objective was to identify pros and cons of using different AI methods within this approach when it comes to assessing and grading collaboration in group programming projects. More specifically, fuzzy rules and several machine learning methods (ML onward) have been examined to see which one would yield the best results regarding performance, interpretability/explainability of recommendations, and feasibility/practicality. The data used for training and testing span four academic years, and the results suggest that almost all of the examined AI methods, when used within the proposed AI-based approach, can provide adequate grading recommendations as long as teachers cover other aspects of the assessment not covered by the rubrics: code quality, plagiarism, and project completion. The fuzzy-rule-based method requires time and effort to be spent on (manual) creation and tuning of fuzzy rules and sets, whereas the examined ML methods require lesser initial investments but do need historical data for training. On the other hand, the fuzzy-rule-based method can provide the best explanations on how the assessment/grading was made—something that proved to be very important to teachers.
DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation

Deep learning approaches achieve prominent success in 3D semantic segmentation. However, collecting densely annotated real-world 3D datasets is extremely time-consuming and expensive. Training models on synthetic data and generalizing on real-world scenarios becomes an appealing alternative, but unfortunately suffers from notorious domain shifts. In this work, we propose a Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and context gaps caused by different sensing mechanisms and layout placements across domains. Our DODA encompasses virtual scan simulation to imitate real-world point cloud patterns and tail-aware cuboid mixing to alleviate the interior context gap with a cuboid-based intermediate domain. The first unsupervised sim-to-real adaptation benchmark on 3D indoor semantic segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 8 popular Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA approaches by over 13% on both 3D-FRONT $$\rightarrow $$ ScanNet and 3D-FRONT $$\rightarrow $$ S3DIS. Code is available at https://github.com/CVMI-Lab/DODA .
DeepRide: Dashcam Video Description Dataset for Autonomous Vehicle Location-Aware Trip Description

Video description is one of the most challenging task in the combined domain of computer vision and natural language processing. Captions for various open and constrained domain videos have been generated in the recent past but descriptions for driving dashcam videos have never been explored to the best of our knowledge. With the aim to explore dashcam video description generation for autonomous driving, this study presents DeepRide: a large-scale dashcam driving video description dataset for location-aware dense video description generation. The human-described dataset comprises visual scenes and actions with diverse weather, people, objects, and geographical paradigms. It bridges the autonomous driving domain with video description by textual description generation of the visual information as seen by a dashcam. We describe 16,000 videos (40 seconds each) in English employing 2,700 man-hours by two highly qualified teams with domain knowledge. The descriptions consist of eight to ten sentences covering each dashcam video’s global features and event features in 60 to 90 words. The dataset consists of more than 130K sentences, totaling approximately one million words. We evaluate the dataset by employing location aware vision-language recurrent transformer framework to elaborate on the efficacy and significance of the visio-linguistics research for autonomous vehicles. We provided base line results to evaluate the dataset by employing three existing state-of-the-art recurrent models. The memory augmented transformer performed superior due to its highly summarized memory state for visual information and the sentence history while generating the trip description. Our proposed dataset opens a new dimension of diverse and exciting applications, such as self-driving vehicle reporting, driver and vehicle safety, inter-vehicle road intelligence sharing, and travel occurrence reports.
Part-Wise Topology Graph Convolutional Network for Skeleton-Based Action Recognition

Action recognition based on skeleton data has attracted extensive attention in computer vision. Graph convolutional network (GCN) has achieved remarkable performance by modeling the human skeleton as a spatial-temporal graph. The graph topology that dominates feature aggregation is the key for GCN to extract representative features. However, the previous models based on GCN mostly build skeleton topology that are naturally connected or adaptively shared, and lack the exploration of fine-grained relations of multi-level features. In this paper, we propose a novel Part-wise Topology Graph Convolution (PT-GC) for the task of skeleton action recognition. PT-GC first builds part-level topology with two modeling strategies, and then effectively aggregates multi-level joint features by combining global topology and part-level topology, which can accurately construct human topology. Finally, we adopt the two-stream architecture and combine PT-GC with a spatial-temporal modeling module to propose a powerful graph convolutional network named PT-GCN. On the two large-scale datasets, NTU RGB+D and NTU RGB+D 120, PT-GCN exhibits significant performance advantages, proving the effectiveness of our proposed method.
Super-Resolution of Digital Elevation Model with Local Implicit Function Representation

The Digital Elevation Model (DEM) is an important basic datas for analyzing and exploring the natural environment, hydrology, and geology. Since the cost of improving the DEM accuracy directly from the hardware device is high, it is envisaged to improve the DEM accuracy from the algorithm. Deep learning provides new research ideas for DEM super-resolution. Inspired by the use of implicit neural representation in image super-resolution, this paper combines implicit neural representation with DEM super-resolution, and proposes a DEM super-resolution algorithm based on implicit neural representation, which aims to convert low-resolution DEM (LRDEM) into high-resolution DEM (HRDEM). This paper presents the process of super-resolution of DEM data based on implicit neural representation. The experiment first maps the cropped normalized data to a high-dimensional space through Fourier transform, and then uses the encoder to extract features from the DEM data to generate a feature sequence. The decoder takes the feature sequence and DEM coordinates as input, and then predicts the coordinates of the DEM value. This paper trains and adjusts a super-resolution implicit neural expression model that generates DEM data through a large number of experiments, where the loss function is the norm loss of the predicted value and the true value. The experimental test results are compared with traditional interpolation methods, such as bicubic interpolation, nearest neighbor interpolation and bilinear interpolation. According to the evaluation index data of MSE and PSNR, arbitrary multiple super-resolution based on implicit neural expression can effectively solve the DEM super-resolution problem.
Characterizing Counterfactuals and Dependencies over (Generalized) Causal Teams

We analyze the causal-observational languages that were introduced in Barbero and Sandu (2018), which allow discussing interventionist counterfactuals and functional dependencies in a unified framework. In particular, we systematically investigate the expressive power of these languages in causal team semantics, and we provide complete natural deduction calculi for each language. Furthermore, we introduce a generalized semantics which allows representing uncertainty about the causal laws, and we analyze the expressive power and proof theory of the causal-observational languages over this enriched semantics.
H-ViT: Hybrid Vision Transformer for Multi-modal Vehicle Re-identification

Vehicle re-identification (ReID) is a critical technology in smart city and has drawn much attention. Many studies focus on single-modal (i.e., visible) vehicle re-identification, which are prone to be deteriorated under bad illumination conditions. Therefore, visible, near-infrared, and thermal-infrared multi-modal vehicle re-identification is worthy to study. This paper proposes a hybrid vision transformer (H-ViT) based multi-modal vehicle re-identification. The proposed H-ViT has two new modules: (1) modal-specific controller (MC) and (2) modal information embedding (MIE) structure. In the feature extraction process, the MC flexibly specifies modal-specific layers for different modal data and controls the sharing attribute of the position embedding to alleviate the difficulty brought by heterogeneous multi-modalities. The MIE structure learns inter- and intra-modal information to reduce feature deviations toward modal variations. Experimental results show that our H-ViT method achieves good performance on multi-modal vehicle re-identification datasets (i.e., RGBNT100 and RGBN300) by integrating MC and MIE modules, which are superior to existing algorithms.
Repairing SHACL Constraint Violations Using Answer Set Programming

The Shapes Constraint Language (SHACL) is a recent W3C recommendation for validating RDF graphs against shape constraints to be checked on target nodes of the data graph. The standard also describes the notion of validation reports for data graphs that violate given constraints, which aims to provide feedback on how the data graph can be fixed to satisfy the constraints. Since the specification left it open to SHACL processors to define such explanations, a recent work proposed the use of explanations in the style of database repairs, where a repair is a set of additions to or deletions from the data graph so that the resulting graph validates against the constraints. In this paper, we study such repairs for non-recursive SHACL, the largest fragment of SHACL that is fully defined in the specification. We propose an algorithm to compute repairs by encoding the explanation problem – using Answer Set Programming (ASP) – into a logic program, the answer sets of which correspond to (minimal) repairs. We then study a scenario where it is not possible to simultaneously repair all the targets, which may be often the case due to overall unsatisfiability or conflicting constraints. We introduce a relaxed notion of validation, which allows to validate a (maximal) subset of the targets and adapt the ASP translation to take into account this relaxation. Our implementation in Clingo is – to the best of our knowledge – the first implementation of a repair generator for SHACL.
Tight Preimage Resistance of the Sponge Construction

The cryptographic sponge is a popular method for hash function design. The construction is in the ideal permutation model proven to be indifferentiable from a random oracle up to the birthday bound in the capacity of the sponge. This result in particular implies that, as long as the attack complexity does not exceed this bound, the sponge construction achieves a comparable level of collision, preimage, and second preimage resistance as a random oracle. We investigate these state-of-the-art bounds in detail, and observe that while the collision and second preimage security bounds are tight, the preimage bound is not tight. We derive an improved and tight preimage security bound for the cryptographic sponge construction. The result has direct implications for various lightweight cryptographic hash functions. For example, the NIST Lightweight Cryptography finalist Ascon-Hash does not generically achieve $$2^{128}$$ preimage security as claimed, but even $$2^{192}$$ preimage security. Comparable improvements are obtained for the modes of Spongent, PHOTON, ACE, Subterranean 2.0, and QUARK, among others.
Blockchain Based New E-voting Protocol System without Trusted Tallying Authorities

Voting is a vital aspect of human democracy, and efforts have been made throughout civilization to enhance the mechanisms, processes, and methodologies used to vote in a way that is transparent, verifiable, and accessible. Blockchain technology has been significantly widely used to solve multidimensional difficulties in various application sectors including healthcare, banking, electronic voting, etc., in recent decades. In this article, we propose a new e-voting protocol system which is based on blockchain technology without trusted tallying authorities. One of the most challenging challenges in arranging elections is gaining the faith of all voters in the counting process. The current election system frequently results in the recounting of votes cast, raising issues about the authenticity of many of the votes cast. There have even been instances where the opposition has expressed questions due to a lack of transparency in the vote verification and tallying process, there are concerns regarding the election’s overall validity. The current study offers a new voting mechanism which is based on blockchain technology to address this issue. The new protocol allows each voter to rate each candidate by giving different scores to them, instead of voting for a single candidate. Before being submitted, every vote is encoded using a different encryption method. The new protocol assures the validity of the votes cast during the counting procedure and maintains anonymity at the same moment. For the relatively large election, our proposed protocol will be flexible and feasible because the overhead of counting ballots is linear.
FingerprintNet: Synthesized Fingerprints for Generated Image Detection

While recent advances in generative models benefit the society, the generated images can be abused for malicious purposes, like fraud, defamation, and false news. To prevent such cases, vigorous research is conducted on distinguishing the generated images from the real ones, but challenges still remain with detecting the unseen generated images outside of the training settings. To overcome this problem, we analyze the distinctive characteristic of the generated images called 'fingerprints,' and propose a new framework to reproduce diverse types of fingerprints generated by various generative models. By training the model with the real images only, our framework can avoid data dependency on particular generative models and enhance generalization. With the mathematical derivation that the fingerprint is emphasized at the frequency domain, we design a generated image detector for effective training of the fingerprints. Our framework outperforms the prior state-of-the-art detectors, even though only real images are used for training. We also provide new benchmark datasets to demonstrate the model's robustness using the images of the latest anti-artifact generative models for reducing the spectral discrepancies.
A High-Speed FPGA-Based Hardware Implementation for Leighton-Micali Signature

Due to the rapid progress made in quantum computers, modern cryptography faces great challenges. Many digital signature schemes that have resistance to quantum computing are studied and standardized by several influential international organizations. The Leighton-Micali signature (LMS) protocol, one of the hash-based signature schemes, is standardized by both the Internet Engineering Task Force (IETF) and the National Institute of Standards and Technology (NIST) due to its well-studied security and relatively small signature size. However, the heavy computation load and high latency of LMS limits its practical applications. In this paper, for the first time, we propose a full hardware implementation of LMS to accelerate all the three stages: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$key~generation$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$signature~generation$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$verification$ </tex-math></inline-formula> . Considering the scalability requirement and the characteristic of the parameter sets of LMS, we extract the coarse-grained basic logic, a hash group, and build a reconfigurable architecture for all available parameters by carefully designing the parallelism degree while achieving low latency and high hardware utilization efficiency. Then, we devise a fusion architecture for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$key~generation$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$signature~generation$ </tex-math></inline-formula> based on the hash group module. Moreover, for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$signature~verification$ </tex-math></inline-formula> stage, we propose a separate architecture by applying the hash group module along with an efficient depth-first Merkle tree module. We code our designs with Verilog language in parameterized style and implement them on a Xilinx XCVU7P FPGA platform. The experimental results show that significant improvements are obtained for different parameter sets by the proposed designs when compared to state-of-the-art works.
Bioeconomy Sustainability Index: A Fuzzy Cognitive Mapping Approach

Abstract There is no one unique parameter with value which can represent bioeconomy sustainability and development. However, many different parameters can be used to describe the economic, social, environmental technological aspects of bioeconomy. The paper present combined approach with using multi criteria decision analysis (MCDA) and fuzzy cognitive map (FCM) analysis methods for bioeconomy sustainability index. Study describe methodology of fuzzy cognitive map developing, including expert involving, obtained data from survey combination to bioeconomy indicators and categories. The weight of selected FCM components were normalized using MCDA method. The critical points of the applied method and opportunities for improvement have been identified. The role of respondents in the results is also considered.
Breast Ultrasound Tumor Detection Based on Active Learning and Deep Learning

Early breast cancer screening and diagnosis policy plays a significance role in reducing breast cancer mortality, which is the most common malignant tumor for women. Therefore, its accuracy and efficiency are very important. To cover these challenges in mass breast screening and diagnosis, including varied ultrasound image quality from different equipments, expensive professional annotation, we propose a novel method based on active learning and convolution neural networks for selecting more informative images and tumor detection, respectively. Firstly, we verify the effectiveness of active learning in the application of our breast ultrasound data. Secondly, we select the informative images from the origin training set using the Multiple Instance Active Learning (MIAL) with One-Shot Path Aggregation Feature Pyramid Network (OPA-FPN) structure. Through this way, we effectively balance the ratio of hard samples and simple samples in the origin training set. Finally, we train the model based on EfficientDet with specific and valid parameters for our breast ultrasound data. Through the corresponding ablation experiment, it is verified that the model trained on the selected dataset by combining MIAL with OPA-FPN exceeds the origin model in the metrics about sensitive, specificity and F1-score. Meanwhile, while keeping the corresponding metrics approximately the same, the confidence of inference images from the new model is higher and stable.
A Hierarchical Clustering Federated Learning System Based on Industry 4.0

This study proposes using dendrogram clustering as the basis to construct a federated learning system for A.I. model parameter updating. The authors adopted a private blockchain to accelerate downloads of the latest parameters corresponding to the computation results of an A.I. model. This study reduced the computational complexity of the backend server with the A.I. model to elevate backend server performance. Furthermore, the authors propose a hash function to determine whether the machines added new training data. The experimental results revealed that the proposed method could reduce the computational complexity of federated learning and that private blockchains can be applied to ensure parameter confidentiality and completeness. In summary, this research uses software computing methods to save machine learning data transmission, reduce network load, and provide privacy protection for parameter data without updating existing production equipment so that small-cost enterprises can import Industry 4.0.
Privacy-preserving cooperative online matching over spatial crowdsourcing platforms

With the continuous development of spatial crowdsourcing platform, online task assignment problem has been widely studied as a typical problem in spatial crowdsourcing. Most of the existing studies are based on a single-platform task assignment to maximize the platform's revenue. Recently, cross online task assignment has been proposed, aiming at increasing the mutual benefit through cooperations. However, existing methods fail to consider the data privacy protection in the process of cooperation and cause the leakage of sensitive data such as the location of a request and the historical data of cooperative platforms. In this paper, we propose Privacy-preserving Cooperative Online Matching (PCOM), which protects the privacy of the users and workers on their respective platforms. We design a PCOM framework and provide theoretical proof that the framework satisfies the differential privacy property. We then propose two PCOM algorithms based on two different privacy-preserving strategies. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.