Reviewerly/exHarmony huggingface dataset:
Soil CT image quality enhancement via an improved super-resolution reconstruction method based on GAN

Computed tomography (CT) is an effective instrument to characterize the internal structure of soil. However, the resolution of soil CT images is often limited by the physical properties of the scanner and the imaging protocol, which can lead to difficulties in accurately characterizing fine-scale features of soil. At present, the super-resolution reconstruction results of CT images often have the problems of blurred feature boundaries and low image quality, causing inaccuracies in the analysis of soil structure. Therefore, this study developed an improved super-resolution reconstruction method based on generative adversarial networks (SRLGAN) to accurately reconstruct high-resolution CT images from low-resolution CT images and assist in digital soil descriptions. SRLGAN utilized a lightweight CNN super-resolution reconstruction module as the generator model, which can improve the reconstruction image quality while reducing the number of trainable parameters and inference time. Meanwhile, a closed-loop loss function was employed to reduce input image information loss and improve method generalization. Compared with traditional reconstruction methods and deep learning methods, the proposed SRLGAN method showed superior performance with a higher peak signal-to-noise ratio (PSNR) of 45.869 dB and a structural similarity index (SSIM) of 0.992. Particularly, the PSNR was 9.95% and 4.49% higher than the best-performing traditional method (Bicubic) and deep learning method (SRGAN), respectively. Furthermore, compared to the SRGAN, trainable parameters and inference time of the SRLGAN have decreased by 77.84% and 57.86%, respectively, which indicates the highest degree of lightweighting. This study demonstrated that SRLGAN can generate high-quality, high-resolution soil CT images that can be used for subsequent segmentation and 3D structural analysis, offering an intelligent approach to understanding the internal soil structure.
RSLC-Deeplab: A Ground Object Classification Method for High-Resolution Remote Sensing Images

With the continuous advancement of remote sensing technology, the semantic segmentation of different ground objects in remote sensing images has become an active research topic. For complex and diverse remote sensing imagery, deep learning methods have the ability to automatically discern features from image data and capture intricate spatial dependencies, thus outperforming traditional image segmentation methods. To address the problems of low segmentation accuracy in remote sensing image semantic segmentation, this paper proposes a new remote sensing image semantic segmentation network, RSLC-Deeplab, based on DeeplabV3+. Firstly, ResNet-50 is used as the backbone feature extraction network, which can extract deep semantic information more effectively and improve the segmentation accuracy. Secondly, the coordinate attention (CA) mechanism is introduced into the model to improve the feature representation generated by the network by embedding position information into the channel attention mechanism, effectively capturing the relationship between position information and channels. Finally, a multi-level feature fusion (MFF) module based on asymmetric convolution is proposed, which captures and refines low-level spatial features using asymmetric convolution and then fuses them with high-level abstract features to mitigate the influence of background noise and restore the lost detailed information in deep features. The experimental results on the WHDLD dataset show that the mean intersection over union (mIoU) of RSLC-Deeplab reached 72.63%, the pixel accuracy (PA) reached 83.49%, and the mean pixel accuracy (mPA) reached 83.72%. Compared to the original DeeplabV3+, the proposed method achieved a 4.13% improvement in mIoU and outperformed the PSP-NET, U-NET, MACU-NET, and DeeplabV3+ networks.
CANARY: An Adversarial Robustness Evaluation Platform for Deep Learning Models on Image Classification

The vulnerability of deep-learning-based image classification models to erroneous conclusions in the presence of small perturbations crafted by attackers has prompted attention to the question of the models’ robustness level. However, the question of how to comprehensively and fairly measure the adversarial robustness of models with different structures and defenses as well as the performance of different attack methods has never been accurately answered. In this work, we present the design, implementation, and evaluation of Canary, a platform that aims to answer this question. Canary uses a common scoring framework that includes 4 dimensions with 26 (sub)metrics for evaluation. First, Canary generates and selects valid adversarial examples and collects metrics data through a series of tests. Then it uses a two-way evaluation strategy to guide the data organization and finally integrates all the data to give the scores for model robustness and attack effectiveness. In this process, we use Item Response Theory (IRT) for the first time to ensure that all the metrics can be fairly calculated into a score that can visually measure the capability. In order to fully demonstrate the effectiveness of Canary, we conducted large-scale testing of 15 representative models trained on the ImageNet dataset using 12 white-box attacks and 12 black-box attacks and came up with a series of in-depth and interesting findings. This further illustrates the capabilities and strengths of Canary as a benchmarking platform. Our paper provides an open-source framework for model robustness evaluation, allowing researchers to perform comprehensive and rapid evaluations of models or attack/defense algorithms, thus inspiring further improvements and greatly benefiting future work.
Digital transformation in higher education in Vietnam today

Over 36 years of implementing renovation and integration, Vietnamese higher education has achieved important results, contributing to the socio-economic development of Vietnam. From the philosophical approach, social philosophy, philosophy of education, through the research and analysis of documents of previous studies and other related documents, the article analyzes and clarifies the basic content related to digital transformation in higher education in Vietnam today. The result shows that to adapt to the changes and impacts of science and technology and the 4.0 industrial revolution, the Government of Vietnam has determined to develop digital transformation in general and higher education in particular. The process of applying digital transformation in higher education in the current stage of Vietnam today also encounters barriers and challenges, including Conditions for ensuring the quality and the quality of the assessment; The process of completing legal documents and internal regulations of higher education institutions; Habits of educational administrators, lecturers, and employees in higher education institutions; The data obtained by higher education institutions is sometimes inaccurate and unreliable, forcing leaders in higher education institutions to make strategic forecasts and implement new educational measures to be able to receive continuous, fast, accurate and structured data; System adaptability; The process of approaching digital transformation at local higher education institutions faces many difficulties; A comprehensive change from reallocation of job responsibilities, goals, strategies, roles in higher education institutions; The establishment of a digital culture in the foundation. The article also offers solutions to develop digital transformation in higher education in Vietnam today. The study also points out the basic solutions to develop digital transformation in higher education in Vietnam today, which are expressed through raising awareness and determination about digital transformation, considering human the center, selecting and applying technology, and improving the legal system, policies, and regulations on digital transformation in higher education.
Migratory Perception in Edge-Assisted Internet of Vehicles

Autonomous driving technology heavily relies on the accurate perception of traffic environments, mainly through roadside cameras and LiDARs. Although several popular and robust 2D and 3D object detection methods exist, including R-CNN, YOLO, SSD, PointPillar, and VoxelNet, the perception range and accuracy of an individual vehicle can be limited by blocking from other vehicles or buildings. A solution is to harness roadside perception infrastructures for vehicle–infrastructure cooperative perception, using edge computing for real-time intermediate features extraction and V2X networks for transmitting these features to vehicles. This emerging migratory perception paradigm requires deploying exclusive cooperative perception services on edge servers and involves the migration of perception services to reduce response time. In such a setup, competition among multiple cooperative perception services exists due to limited edge resources. This study proposes a multi-agent reinforcement learning (MADRL)-based service scheduling method for migratory perception in vehicle–infrastructure cooperative perception, utilizing a discrete time-varying graph to model the relationship between service nodes and edge server nodes. This MADRL-based approach can efficiently address the challenges of service placement and migration in resource-limited environments, minimize latency, and maximize resource utilization for migratory perception services on edge servers.
User Experience Evaluation of a Virtual Reality Tool Used for 3D Modelling in Industrial Design Education: A Study in the Indian Context

Virtual reality (VR) technology has recently been adopted by educators for use in the classroom. Currently, this educational model includes not only lectures with teachers in the online classroom but also practical sessions using online platforms. Few studies have explored the potential of pedagogical approaches to implementing VR in the classroom for the purpose of design education. The focus of this paper was to study the learning experiences of the 3D visualisation of products among industrial design students through the strategic implementation of virtual reality technology. A within-subjects comparative study was conducted to measure cognitive workload and engagement and enjoyment, while a 3D modelling task was given using two different set-ups (conventional 3D software versus VR-based software). The statistical results show that the NASA-TLX score was lower in the case of the VR-based 3D modelling exercise compared to the conventional 3D software-based exercise. On the other hand, the mean values were higher for the engagement and enjoyment and usability scores, which means that the VR-based experience for 3D modelling was better than the traditional modelling experience using conventional software. Hence, there are possibilities to implement VR-based 3D modelling tools for online industrial design education for 3D visualisation in the near future.
A Simple Framework for Scene Graph Reasoning with Semantic Understanding of Complex Sentence Structure

A rapidly expanding multimedia environment in recent years has led to an explosive increase in demand for multimodality that can communicate with humans in various ways. Even though the convergence of vision and language intelligence has shed light on the remarkable success over the last few years, there is still a caveat: it is unknown whether they truly understand the semantics of the image. More specifically, how they correctly capture relationships between objects represented within the image is still regarded as a black box. In order to testify whether such relationships are well understood, this work mainly focuses on the Graph-structured visual Question Answering (GQA) task which evaluates the understanding of an image by reasoning a scene graph describing the structural characteristics of an image in the form of natural language together with the image. Unlike the existing approaches that have been accompanied by an additional encoder for scene graphs, we propose a simple yet effective framework using pre-trained multimodal transformers for scene graph reasoning. Inspired by the fact that a scene graph can be regarded as a set of sentences describing two related objects with a relationship, we fuse them into the framework separately from the question. In addition, we propose a multi-task learning method that utilizes evaluating the grammatical validity of questions as an auxiliary task to better understand a question with complex structures. This utilizes the semantic role labels of the question to randomly shuffle the sentence structure of the question. We have conducted extensive experiments to evaluate the effectiveness in terms of task capabilities, ablation studies, and generalization.
A CASE STUDY ON MIGRATING TOWARDS FUNCTIONALLY SAFE ZONAL ARCHITECTURE USING MBSE

Abstract The increasing market demands in recent times are driving complexity in machines and their control architectures. To satisfy these demands, their architecture needs frequent updates and upgrades which in turn require significant efforts from architects. This paper presents a case study on architecture migration in automotive industry using Model Based System Engineering (MBSE) which will lend a helping hand in successfully rearchitecting any complex system. The Automotive industry is currently undergoing a massive transformation towards software defined vehicles driven by Connected, Autonomous, Shared and Electrification (C.A.S.E). This is possible only with the support of a robust and flexible vehicle Electrical and Electronics (EE) architecture. Taking the C.A.S.E driven transformation into consideration, the present domain control architecture is migrating towards zonal architecture. This paper presents RAZA (Rearchitecting Approach for Zonal Architecture) which is based on the INCOSE defined technical process and ISO/IEC/IEEE 15288:2015 Systems and software engineering — System life cycle processes.
Moving towards a Server‐Zone Vehicle Architecture with MBSE at Continental

Abstract The automotive industry is currently experiencing a major transition towards a revolutionary vehicle architecture concept known as Server‐Zone architecture. As a result, complexity increases exponentially and, in order to better deal with it, new development approaches like Model Based Systems Engineering (MBSE) are required. This paper focuses on how MBSE can support the migration towards a server‐zone vehicle architecture, using a real‐life example from Continental. Along with this example, the authors provide modeling insights into the application of Continental's MBSE framework.
Linking Swedish Learning Materials to Exercises through an AI-Enhanced Recommender System

As an integral part of AI-enhanced learning, a content recommender automatically filters and recommends relevant learning materials to the learner or the instructor in a learning system. It can effectively help instructors in pedagogical practices and support students in self-regulated learning. Content recommendation technologies and applications have been studied extensively, however, the SOTA technologies have not adequately adapted to the education domain and there is very limited research on how different models and solutions can be applied in the Swedish context and for multiple subjects. In this paper, we develop a text similarity-based content recommender system. Specifically, given a quiz, we automatically recommend supportive learning resources as a reference to the answer and link back to the textbook sections where the examined knowledge points reside. We present a generic method for Swedish educational content recommendations using the most representative models, evaluate and analyze in multi-dimensions such as model types, pooling methods, subjects etc. The best results are obtained by Sentence-BERT (SBERT) with max paragraph-level pooling, outperforming traditional Natural Language Processing (NLP) models and knowledge graph-based models, obtaining on average 95% in Recall@3 and 82% in MRR, and outstanding in dealing with texts containing symbols, equations or calculations. This research provides empirical evidence and analysis, and can be used as a guidance when building a Swedish educational content recommender.
Angular resolution of radiation characteristics required to reproduce uttered speech in all three-dimensional directions

For applications in six-degrees-of-freedom (6DoF) content reproduction, this study investigates the angular resolution of radiation characteristics required to reproduce uttered speech in all three-dimensional (3D) directions as an angle at which humans are unable to perceive a difference in the comprehensive audio quality change between adjacent radiation directions. The radiation characteristics of uttered Japanese speech for female and male speakers are measured in three cross-sections, namely the horizontal, median, and frontal planes. The measurements are obtained using a spherical jig for 3D microphone arrangement. A subjective evaluation is conducted to clarify the impact of the difference in the radiation directions on the audio quality of uttered speech in each cross-section. Finally, a statistical analysis of the experimental results shows that the required angular resolutions are 30° relative to the horizontal plane, 30° relative to the median plane, and 60° relative to the frontal plane. Therefore, it is concluded that an angular resolution of at least 30° is required to reproduce uttered speech in all 3D directions.
Special Delivery: Programming with Mailbox Types

The asynchronous and unidirectional communication model supported by mailboxes is a key reason for the success of actor languages like Erlang and Elixir for implementing reliable and scalable distributed systems. While many actors may send messages to some actor, only the actor may (selectively) receive from its mailbox. Although actors eliminate many of the issues stemming from shared memory concurrency, they remain vulnerable to communication errors such as protocol violations and deadlocks. Mailbox types are a novel behavioural type system for mailboxes first introduced for a process calculus by de’Liguoro and Padovani in 2018, which capture the contents of a mailbox as a commutative regular expression. Due to aliasing and nested evaluation contexts, moving from a process calculus to a programming language is challenging. This paper presents Pat, the first programming language design incorporating mailbox types, and describes an algorithmic type system. We make essential use of quasi-linear typing to tame some of the complexity introduced by aliasing. Our algorithmic type system is necessarily co-contextual, achieved through a novel use of backwards bidirectional typing, and we prove it sound and complete with respect to our declarative type system. We implement a prototype type checker, and use it to demonstrate the expressiveness of Pat on a factory automation case study and a series of examples from the Savina actor benchmark suite.
Calculating Compilers for Concurrency

Choice trees have recently been introduced as a general structure for defining the semantics of programming languages with a wide variety of features and effects. In this article we focus on concurrent languages, and show how a codensity version of choice trees allows the semantics for such languages to be systematically transformed into compilers using equational reasoning techniques. The codensity construction is the key ingredient that enables a high-level, algebraic approach. As a case study, we calculate a compiler for a concurrent lambda calculus with channel-based communication.
Combinator-Based Fixpoint Algorithms for Big-Step Abstract Interpreters

Big-step abstract interpreters are an approach to build static analyzers based on big-step interpretation. While big-step interpretation provides a number of benefits for the definition of an analysis, it also requires particularly complicated fixpoint algorithms because the analysis definition is a recursive function whose termination is uncertain. This is in contrast to other analysis approaches, such as small-step reduction, abstract machines, or graph reachability, where the analysis essentially forms a finite transition system between widened analysis states. We show how to systematically develop sophisticated fixpoint algorithms for big-step abstract interpreters and how to ensure their soundness. Our approach is based on small and reusable fixpoint combinators that can be composed to yield fixpoint algorithms. For example, these combinators describe the order in which the program is analyzed, how deep recursive functions are unfolded and loops unrolled, or they record auxiliary data such as a (context-sensitive) call graph. Importantly, each combinator can be developed separately, reused across analyses, and can be verified sound independently. Consequently, analysis developers can freely compose combinators to obtain sound fixpoint algorithms that work best for their use case. We provide a formal metatheory that guarantees a fixpoint algorithm is sound if its composed from sound combinators only. We experimentally validate our combinator-based approach by describing sophisticated fixpoint algorithms for analyses of Stratego, Scheme, and WebAssembly.
The Verse Calculus: A Core Calculus for Deterministic Functional Logic Programming

Functional logic languages have a rich literature, but it is tricky to give them a satisfying semantics. In this paper we describe the Verse calculus, VC, a new core calculus for deterministic functional logic programming. Our main contribution is to equip VC with a small-step rewrite semantics, so that we can reason about a VC program in the same way as one does with lambda calculus; that is, by applying successive rewrites to it. We also show that the rewrite system is confluent for well-behaved terms.
Generic Programming with Extensible Data Types: Or, Making Ad Hoc Extensible Data Types Less Ad Hoc

We present a novel approach to generic programming over extensible data types. Row types capture the structure of records and variants, and can be used to express record and variant subtyping, record extension, and modular composition of case branches. We extend row typing to capture generic programming over rows themselves, capturing patterns including lifting operations to records and variations from their component types, and the duality between cases blocks over variants and records of labeled functions, without placing specific requirements on the fields or constructors present in the records and variants. We formalize our approach in System R𝜔, an extension of F𝜔 with row types, and give a denotational semantics for (stratified) R𝜔 in Agda.
A General Fine-Grained Reduction Theory for Effect Handlers

Effect handlers are a modern and increasingly popular approach to structuring computational effects in functional programming languages. However, while their traditional operational semantics is well-suited to implementation tasks, it is less ideal as a reduction theory. We therefore introduce a fine-grained reduction theory for deep effect handlers, inspired by our existing reduction theory for shift0, along with a standard reduction strategy. We relate this strategy to the traditional, non-local operational semantics via a simulation argument, and show that the reduction theory preserves observational equivalence with respect to the classical semantics of handlers, thus allowing its use as a rewriting theory for handler-equipped programming languages -- this rewriting system mostly coincides with previously studied type-based optimisations. In the process, we establish theoretical properties of our reduction theory, including confluence and standardisation theorems, adapting and extending existing techniques. Finally, we demonstrate the utility of our semantics by providing the first normalisation-by-evaluation algorithm for effect handlers, and prove its soundness and completeness. Additionally, we establish non-expressibility of the lift operator, found in some effect-handler calculi, by the other constructs.