Reviewerly/exHarmony huggingface dataset:
Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)

Deep Learning (DL) techniques help software developers thanks to their ability to learn from historical information which is useful in several program analysis and testing tasks (e.g., malware detection, fuzz testing, bug-finding, and type-checking). DL-based software systems are also increasingly adopted in safety-critical domains, such as autonomous driving, medical diagnosis, and aircraft collision avoidance systems. In particular, testing the correctness and reliability of DL-based systems is paramount, since a failure of such systems would cause a significant safety risk for the involved people and/or environment. The 4th International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023) was co-located with the 45th International Conference on Software Engineering (ICSE), with the goal of targeting research at the intersection of software engineering and deep learning and devise novel approaches and tools to ensure the interpretability and dependability of software systems that depends on DL components.
Tool Support for Architectural Pattern Selection and Application in Cloud-Centric Service-Oriented IDEs

Architectural patterns are high-level design guidelines and principles for designing the architecture of software systems. They play a crucial role laying the foundations to the organization and structure of software systems and have high impact on their quality and success both in terms of engineering and business aspects. Deciding for a concrete software architecture design requires careful analysis of several factors regarding the software system including system characteristics, constraints, required quality attributes and any trade-offs made along with the design decision; and hence is often not trivial. This paper presents a framework for architectural pattern selection and application that (i) supports the decision-making process of choosing an appropriate architectural pattern as well as (ii) the organization of the software structure based on the chosen pattern in an automated fashion when integrated within IDEs. In particular, the paper presents how this framework is implemented and integrated within an innovative open source cloud-native integration development environment.
Software Engineering Education for the Next Generation:SEENG 2023 Workshop Report

The 5th International Workshop on Software Engineering Education for the Next Generation was held on May 16, 2023 in Melbourne, Australia. The workshop was part of the 45th International Conference on Software Engineering. It specifically supported the general theme of "Educating the Next Generation of Software Engineers". Building on its predecessors, the workshop used a highly interactive format, structured around eight short paper presentations to generate discussion topics, an activity to select the most interesting topics, and structured breakout sessions. This enabled the participants to discuss the most interesting topics in detail. Participants presented the results of the breakout sessions using mind maps.
The world’s first completed copyright case of NFT works: Shenzhen Golden Idea Cultural and Creative Co., Ltd. v Hangzhou Bigverse Technology Co., Ltd.

Shenzhen Golden Idea Cultural and Creative Co., Ltd. v Hangzhou Bigverse Technology Co., Ltd. is the world’s first completed case about copyright infringement of non-fungible token (NFT) works in which a final judgment has been rendered. In this case, the Chinese court explored many legal issues related to NFT works based on an in-depth investigation of NFTs’ transaction process and business model. First, it clarified the legal nature of NFT works. Second, it made explicit judgments on the rights and obligations of related parties such as users, internet platforms, NFT distributors and copyright owners. Third, it clarified the duty of care and legal responsibilities of NFT trading platforms. Lastly, it affirmed the measure of disconnecting the link and blackholing the address of the uploaded NFT information to maximize the effect of stopping the dissemination of infringing content.
Cognitive Architecture for Process industries

This paper introduces a Cross-Sectorial Big Data Processing platform which provides tools for the semantic modelling of the data analytical processes and for the automatic generation of data analysis scripts for solving the described problems. The main contribution of this paper is the cognitive component for the automatic extraction of the task definition from the narrative description of the problem based on the Large Language Models (LLMs). We have evaluated the proposed method on five problems from the different domains and found that the automatic extraction of the task definition can have promising results that can be applied to full-automatic data analytics.
Modular Monoliths the way to Standardization

In resent years monolith architecture gains once again a lot of popularity, in order to reduce costs and time compared to more complicated architectures. Taking into account the advantages of micro-service, and trying to embed some of them to monolith architectures, we come to the creation of modular monoliths. This type of design can be consider quite new, and so there isn’t yet a specific architecture design that someone could follow if they wish to use it. In this paper we present an architectural design and an implementation strategy for modular monoliths. To evaluate the usefulness of this architecture, we have conducted a study, validating the design and its implementation. In this study 12 architects from different companies took part, expressing some concerns regarding the feasibility in bigger project but also giving an overall positive feedback for the design.
Scalable and Portable Federated Learning Simulation Engine

Federated learning (FL) is one of the most promising approaches to ensure privacy in the application of data-driven techniques to sensitive information. However, the implementation of such approaches in a production environment is still an important challenge. In this paper, we present a scalable, portable, hardware-independent, model-agnostic FL Simulation Engine (FLSE) with the aim of easing the job of researchers who want to train FL models to be deployed in production environments. The FLSE offers a tool that can be used both standalone or embedded within a larger architecture, it can be deployed seamlessly and allows concurrent, scalable, and highly available V&V assessment support for FL models. The tool allows researchers to understand the behaviour, in terms of metric performance, of their proposed models in production scenarios, allowing a boost in trustworthiness towards ethical AI.
On the Containerization and Orchestration of RISC-V architectures for Edge-Cloud computing

Containerization technologies, orchestration systems and open hardware architectures, such as RISC-V, are crucial as the foundation of open digital infrastructures for the computing continuum - the seamless distribution of data and computation across platforms with heterogeneous capabilities. However, it is unknown how containerization technologies and orchestration systems, such as Kubernetes, would impact performance in new architectures based on RISC-V. This work aims to address this question and introduces KubeEdge-V, an orchestration platform for RISC-V systems. We define the minimum components required to support the basic features of the containerization and orchestration platforms: network plug-ins, container runtimes and computational/networking requirements , and we evaluated KubeEdge-V performance on a prototype of a distributed computing system based on SiFive processors called Monte Cimone. Finally, the paper compares the performance of KubeEdge-V on SiFive processors with an equivalent system based on ARM architecture featuring the same power envelope.
A Lattice-Based Key Exchange Protocol Over NTRU-NIP

We introduce NTRU-NIP-KE, a novel non-invertible polynomial (NTRU-NIP)-based key exchange protocol that is immune to quantum attacks. NTRU-NIP offers a broad collection of polynomials from which to choose throughout the key generation process. An additional advantage of the proposed key exchange protocol is the impossibility of a lattice attack using the shortest vector algorithm.
Evaluation of the SEBAL and SAFER models in the estimation of the actual evapotranspiration and crop coefficients of sugarcane in Colombia

Estimating actual evapotranspiration (ETa) and crop coefficients (Kc) is crucial for efficient water management in sugarcane crops in Colombia. Because the implementation of water use policies in agriculture has made water management strategies increasingly demanding, the use of surface energy modeling tools such as the Surface Energy Balance Algorithm for Land (SEBAL) and the Simple Algorithm for Evapotranspiration Retrieving (SAFER) has gained importance. These tools not only estimate the amount of water based on evapotranspiration for each satellite-image pixel, but also determine Kc values along with information gathered by weather stations. This study aimed to estimate the ETa and Kc of sugarcane using the SEBAL and SAFER algorithms. Thirteen central pivots located in the sugarcane-growing region of the Cauca river valley were studied for three consecutive crop cycles between 2018 and 2021. Analyses were performed on Landsat 8 satellite images for both modeling algorithms. Reference evapotranspiration (ETo) data from 37 weather stations of the Colombian sugar sector were also interpolated to determine Kc values. SEBAL-based analysis results for ETa showed maximum values of 4.2 mm.day<sup>-1</sup> and minimum values of 1.02 mm.day<sup>-1</sup>, while SAFER yielded maximum values of 3.8 mm.day<sup>-1</sup> and minimum values of 1.02mm.day<sup>-1</sup>. SEBAL-based analysis results for Kc indicated maximum values of 0.9 and minimum values of 0.2, while those of SAFER indicated maximum values of 0.7 and minimum values of 0.1. Although the ETa and Kc values estimated using these models were lower compared with those of FAO 56 and should be calibrated using a direct method, these models showed potential to be applied in water management programs for Colombia’s Cauca river valley.
An Intelligent Technique for Initial Distribution of Genetic Algorithms

The need to find the global minimum in multivariable functions is a critical problem in many fields of science and technology. Effectively solving this problem requires the creation of initial solution estimates, which are subsequently used by the optimization algorithm to search for the best solution in the solution space. In the context of this article, a novel approach to generating the initial solution distribution is presented, which is applied to a genetic optimization algorithm. Using the k-means clustering algorithm, a distribution based on data similarity is created. This helps in generating initial estimates that may be more tailored to the problem. Additionally, the proposed method employs a rejection sampling algorithm to discard samples that do not yield better solution estimates in the optimization process. This allows the algorithm to focus on potentially optimal solutions, thus improving its performance. Finally, the article presents experimental results from the application of this approach to various optimization problems, providing the scientific community with a new method for addressing this significant problem.
Framing Networked Learning

The main purpose of this chapter is to explore the idea of conceptualising networked learning, not as a kind of human activity, but as a way of viewing human activities. This idea is explored by outlining four metaphors of society that are named the biosphere, the distorted reality, the community, and the market. The metaphors are used as foundations for four different frames that can be used when studying or design for networked learning. Each metaphor introduces its own system of concepts, and the corresponding frame consequently directs attention in a certain way and raises certain kinds of questions. It is suggested that explicating frames in this way could help practitioners and researchers to make their approach to networked learning visible and to move beyond what has been called the impasse of ideology in the field of networked learning, since it may encourage discussions about different knowledge interests and objectives. It is also noted that by combining several frames, it may be possible to approach networked learning in a more thoughtful, nuanced, and well-balanced way. The hope with this is to promote collaborations and connections between the field of networked learning and other fields.
The method of using the online course “Creative Thinking through Learning Elementary Maths” in the Mathematics teacher training system

Abstract The article offers the method of using the online “Creative Thinking through Learning Elementary Maths” and the results of checking the effectiveness of the implementation of this course in the mathematics teacher training system. The research substantiates the possibility of three methodological options for combining educational materials of the online course with the study of the educational discipline “Elementary Mathematics”. The methodological options are chosen depending on the number of credits assigned to the study of the elementary mathematics course at the university. Practical tasks of the course were focused on the organization of students’ activities (recognition, classification, solving and creation) with Rich tasks of three levels of complexity. The results of control tests in Elementary Mathematics give grounds for concluding about the effectiveness of the developed method.
High-Pass-Kernel-Driven Content-Adaptive Image Steganalysis Using Deep Learning

Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to assess the strength of steganography schemes. In this paper, a new steganalysis scheme is presented to detect stego-images. Predefined kernels guide the set of a conventional convolutional layer, and the tight cohesion provides completely guided training. The learning rate of convolutional layers with predefined kernels is higher than the global learning rate. The higher learning rate of the convolutional layers with predefined kernels assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels. The Leaky ReLU layer is exploited against the ReLU layer to boost the detection performance. Transfer learning is applied to enhance detection performance. The deep network weights are initialized using the weights of the trained network from high-payload stego-images. The strength of the proposed scheme is verified on the HILL, Mi-POD, S-UNIWARD, and WOW content-adaptive steganography schemes. The results are overwhelming and better than the existing steganalysis schemes.
Artificial intelligence in a modernizing science and technology education: a textual narrative synthesis in the COVID-19 era

Abstract The COVID-19 pandemic shifted the mode of learning to online. This gave the artificial intelligence (AI) developers the reason to increase its usage and production to every detail of the teaching learning process. This synthesis paper was intended to capture these new technologies in the last five years. Using the textual narrative analysis approach and course design triangle model, this paper looked into the AI integration in the field of science and technology education. A total of 15 studies were analyzed and synthesized coming from different journal hosting platforms such as Google Scholar, ERIC, Elsevier, etc. The results have shown that there is indeed a wider utilization of AI in various aspects of the teaching and learning process. These were seen in the learning activities, presentation translators, marking, feedbacking, predicting average score, and intelligent tutoring system. These AI technologies have proven to positively affect the learning of the students and helped the teachers along the process. Furthermore, there is a need to totally synthesize these AI systems which can be beneficial to a wider technical audience.
Digitalization of geographic higher education: problems and prospects

Abstract The article addresses the relevant issue of today – digitalization of geographic higher education. The future of geographic education in the digital world depends on the use of digital technologies, which are dramatically changing approaches to teaching geography. The generalization of scientific literature and the analysis of pedagogical experience allow the authors to define the concept digitalization of geographic. The article explores some challenges in the implementation of digital technologies in geographic disciplines in higher education: narrowing geographic education digitalization to the use of various gadgets; the application of general purpose digital technologies; underestimation of GIS technologies during the study of geography; occasional use of VR, AR technologies and mobile applications of geographic and cartographic content; the lack of educational and methodological support and proper technological availability. The authors pay special attention to the prospects for the development of geographic education digitalization, which are envisioned in the introduction of “Digitalization of geographic education” into the curriculum of future geography teachers training; the use of high-quality free educational resources (ArcGIS Online, QGIS); the creation of own geo-information platforms (“Digital Geography”); and updating of the material and technical base of the scientific research laboratory on the methodology of teaching geo-information technologies.
Structure-Aware Low-Rank Adaptation for Parameter-Efficient Fine-Tuning

With the growing scale of pre-trained language models (PLMs), full parameter fine-tuning becomes prohibitively expensive and practically infeasible. Therefore, parameter-efficient adaptation techniques for PLMs have been proposed to learn through incremental updates of pre-trained weights, such as in low-rank adaptation (LoRA). However, LoRA relies on heuristics to select the modules and layers to which it is applied, and assigns them the same rank. As a consequence, any fine-tuning that ignores the structural information between modules and layers is suboptimal. In this work, we propose structure-aware low-rank adaptation (SaLoRA), which adaptively learns the intrinsic rank of each incremental matrix by removing rank-0 components during training. We conduct comprehensive experiments using pre-trained models of different scales in both task-oriented (GLUE) and task-agnostic (Yelp and GYAFC) settings. The experimental results show that SaLoRA effectively captures the structure-aware intrinsic rank. Moreover, our method consistently outperforms LoRA without significantly compromising training efficiency.
Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion Classification

An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices, such as wristbands, are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection and activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with a convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users.
Did John Stuart Mill Write ‘On Social Freedom’?

During his final years, John Stuart Mill reportedly attempted to update the argument of On Liberty (1859). Published posthumously in 1907, ‘On Social Freedom’ represents the initial, unrefined draft of his reworked ideas. This article argues that John Stuart Mill was not the author of ‘On Social Freedom’. First, we revisit the question of the essay’s authorship traditionally: the emphasis is on the essay’s content and the historical context of the mid-twentieth-century debate on Mill as its author. We trace the disagreement to two broad reactions to Mill’s thought. Ultimately, the question of whether the manuscript’s substantial divergence from J. S. Mill’s renowned works is enough to refute his authorship depends on one’s interpretation of Mill as a systematic philosopher. Second, we tackle this task non-traditionally: the focus shifts to the tools of computer-assisted authorship identification and the use of machine learning (ML) techniques. Once we establish some key ideas, methods, and limitations of this field of studies, we present our attempts at a computer-assisted solution to the puzzle. The results of our experiments, using ML techniques, corroborate the conclusions reached via the traditional route.
FenceTalk: Exploring False Negatives in Moving Object Detection

Deep learning models are often trained with a large amount of labeled data to improve the accuracy for moving object detection in new fields. However, the model may not be robust enough due to insufficient training data in the new field, resulting in some moving objects not being successfully detected. Training with data that is not successfully detected by the pre-trained deep learning model can effectively improve the accuracy for the new field, but it is costly to retrieve the image data containing the moving objects from millions of images per day to train the model. Therefore, we propose FenceTalk, a moving object detection system, which compares the difference between the current frame and the background image based on the structural similarity index measure (SSIM). FenceTalk automatically selects suspicious images with moving objects that are not successfully detected by the Yolo model, so that the training data can be selected at a lower labor cost. FenceTalk can effectively define and update the background image in the field, reducing the misjudgment caused by changes in light and shadow, and selecting images containing moving objects with an optimal threshold. Our study has demonstrated its performance and generality using real data from different fields. For example, compared with the pre-trained Yolo model using the MS COCO dataset, the overall recall of FenceTalk increased from 72.36% to 98.39% for the model trained with the data picked out by SSIM. The recall of FenceTalk, combined with Yolo and SSIM, can reach more than 99%.
Vehicle color recognition using instance segmentation

This study presents a method for vehicle color recognition using instance segmentation, a powerful tool for image analysis. Vehicle color information is a crucial part of city surveillance, however, extracting the color is a challenging task due to high levels of occlusion and the reflective nature of the vehicles. This method uses a state-of-the-art instance segmentation algorithm to differentiate overlapped objects and then selects informative pixels in terms of the object color. The results indicate taking advantage of an instance segmentation method increases color recognition performance.