Reviewerly/exHarmony huggingface dataset:
KPointer: Keep the code pointers on the stack point to the right code

Affected by vulnerabilities, the control data on the stack is easily destroyed, which provides the most convenient conditions for code reuse attacks (CRAs). The operating system (OS) does not impose strict restrictions on the control flow paths. It allows instructions to jump to any location in the same address space. The OS will prevent code execution if and only if an execution error occurs. However, attackers can use stack overflow to accurately tamper with the control data on the stack and avoid execution errors. Although canary technology has been widely adopted, it turns out that this method can be bypassed. The traditional shadow stack technology can only protect the backward control flow and is invalid for the forward control flow. In contrast, the defense effect of the control flow integrity methods is better. Unfortunately, they either cannot get rid of the source code dependence on the protected objects, or cannot provide high-precision instruction boundaries. All these problems make it difficult to eliminate the CRAs based on stack overflow. Faced with these problems, this paper proposes a new security method KPointer. It filters the vulnerable data by tracking the overwriting operation to the stack data. Next, these data will be tracked to locate the jump instructions related to them. Finally, we use new security strategies to determine whether the current instruction is illegal. Experiments and analysis show that KPointer has a good protection effect on the CRAs based on stack overflow. It does not depend on the source code of the protected objects and only introduces 2.7% performance overhead to the CPU.
A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning

Meta-learning aims to teach the machine how to learn. Embedding model-based meta-learning performs well in solving the few-shot problem. The methods use an embedding model, usually a convolutional neural network, to extract features from samples and use a classifier to measure the features extracted from a particular stage of the embedding model. However, the feature of the embedding model at the low stage contains richer visual information, while the feature at the high stage contains richer semantic information. Existing methods fail to consider the impact of the information carried by the features at different stages on the performance of the classifier. Therefore, we propose a meta-learning method based on adaptive feature fusion and weight optimization. The main innovations of the method are as follows: firstly, a feature fusion strategy is used to fuse the feature of each stage of the embedding model based on certain weights, effectively utilizing the information carried by different stage features. Secondly, the particle swarm optimization algorithm was used to optimize the weight of feature fusion, and determine each stage feature’s weight in the process of feature fusion. Compared to current mainstream baseline methods on multiple few-shot image recognition benchmarks, the method performs better.
Performance Modeling of Hyperledger Fabric 2.0

Hyperledger Fabric has become one of the most widely used consortium blockchain frameworks with the ability to execute custom smart contracts. Performance modeling and network evaluation are necessary for performance estimation and optimization of the Fabric blockchain platform. The compatibility and effectiveness of existing performance modeling methods must be improved. For this reason, we proposed a compatible performance modeling method using queuing theory for Fabric considering the limited transaction pool. Taking the 2.0 version of Fabric as a case, we have established the model for the transaction process in the Fabric network. By analyzing the two-dimensional continuous-time Markov process of this model, we solved the system stationary equation and obtained the analytical expressions of performance indicators such as the system throughput, the system steady-state queue length, and the system's average response time. We collected the required parameter values through the official test suite. An extensive analysis and simulation was performed to verify the accuracy and the effectiveness of the model and formula. We believe that this method can be extended to a wide range of scenarios in other blockchain systems.
Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems (IATS).Despite the IATS's benefits, security remains a significant challenge.Blockchain technology has grown in popularity as a means of implementing safe, dependable, and decentralised independent IATS systems, allowing for more utilisation of legacy IATS infrastructures and resources, which is especially advantageous for crowdsourcing technologies.Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development.In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems, this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching (BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details.Additionally, the deep belief network (DBN) model is used to select suitable vehicles and goods for transportation.Additionally, the chaotic krill herd technique is used to tune the DBN model's hyperparameters.The performance of the BDL-IATS technique is validated, and the findings are inspected under a variety of conditions.The simulation findings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.
A typed continuation-passing translation for lexical effect handlers

Effect handlers are a language feature which enjoys popularity in academia and is also gaining traction in industry. Programs use abstract effect operations and handlers provide meaning to them in a delimited scope. Each effect operation is handled by the dynamically closest handler. Using an effect operation outside of a matching handler is meaningless and results in an error. A type-and-effect system prevents such errors from happening. Lexical effect handlers are a recent variant of effect handlers with a number of attractive properties. Just as with traditional effect handlers, programs use effect operations and handlers give meaning to them. But unlike with traditional effect handlers, the connection between effect operations and their handler is lexical. Consequently, they typically have different type-and-effect systems. The semantics of lexical effect handlers as well as their implementations use multi-prompt delimited control. They rely on the generation of fresh labels at runtime, which associate effect operations with their handlers. This use of labels and multi-prompt delimited control is theoretically and practically unsatisfactory. Our main result is that typed lexical effect handlers do not need the full power of multi-prompt delimited control. We present the first CPS translation for lexical effect handlers to pure System F. It preserves well-typedness and simulates the traditional operational semantics. Importantly, it does so without requiring runtime labels. The CPS translation can be used to study the semantics of lexical effect handlers as well as as an implementation technique.
The Effects of Business Strategy and Organizational Culture of Korean Companies on Market Satisfaction: The Case of the African Market

The purpose of our study was to investigate the relationship between business strategy, organizational culture, and market satisfaction. This study focused on Korean companies entering Africa and companies wishing to enter Africa and analyzed the satisfaction of companies with the African market focusing on internal environmental conditions. In terms of data, we collected 183 usable samples from companies. In order to analyze the data, we used structural equation modeling using AMOS 25.0. Based on the results, first, we can see that there is a positive relationship between business strategy and organizational culture. Moreover, there is also a positive relationship between business strategy and market satisfaction. Lastly, we found a positive relationship between organizational culture and market satisfaction. These study results give the following implications in establishing investment strategies for Korean companies entering Africa or willing to enter, which are in the midst of macroscopic environmental changes.
Relating to Soil: Chromatography as a Tool for Environmental Engagement

Due to the ongoing environmental crisis, there is an increased interest in technologies that strengthen relations to the environment. This pictorial contributes to a broader discussion in HCI on how technologies could create a different understanding of and relationship to the more-than-human world. It focuses on soil care practices, and how current (limited) capacities of digital sensing could be complemented with soil chromatography – a qualitative chemical test method for visually assessing soil health. The pictorial is based on a series of workshops conducted in an urban community farm. The discussion focuses on how interactive technologies may support the process of conducting and interpreting soil chromatography and what it means to study, care for, and design with the more-than-human.
The nature of security: A conceptual framework for integral-comprehensive modeling of IT security and cybersecurity

Cybersecurity is a broadly defined concept comprising security for many different types of elements. Dealing with cybersecurity is a multidimensional problem, and the damage generated by cyberattacks can be very diverse. Reports about cybersecurity show recurrent problems, or increasing on their frequency of appearance, with no clear approach for solving them. Existing models deal with cybersecurity in several different but general ways, and results are not better. Consequently, managing cybersecurity deserves consideration of a new approach. Our approach is based on the nature of security. Security services are modeled around three basic security concepts, namely isolation, interaction, and representation. With these three concepts, a cybersecurity development starts with security objectives for overcoming the cybersecurity challenges, and also has a security representation to achieve integral and comprehensive security results. We propose an architecture-based security conceptual framework having three components, namely a system representation model kind, a security representation model kind, and a security process model kind, to accomplish the security process for a system. The security process is fully guided and supported with security objectives from the beginning to the end. The framework proposes several models, based on data structures for representing the system, the security, and the process itself. The models are scalable to represent systems of any size, from tiny to huge technology infrastructures, and with support for automation of the security process. The scope of the framework is the security of IT systems and cybersecurity, including information, software, virtual resources, hardware, IT devices, money, people, and other related physical objects being represented digitally. The framework was developed while creating a university cloud infrastructure, and consolidated while supporting the security of several national wide software and infrastructure applications for digital signature in Costa Rica. We aim to provide a new and innovative way for doing cybersecurity, by directly targeting the actual security requirements; with a simple, systemic, structured and potentially automated security process, and for achieving integral and comprehensive security solutions.
A virtual meter-based visualization tool to present energy flows in multiple zone variable air volume air handling unit systems

Energy data estimated by virtual meters provide operational staff with an opportunity to improve building energy performance. Interactive visualization of virtual metered energy serves as a tool that can be used to identify energy consumption patterns and gain insights that can support operational decisions. This paper introduces a novel interactive visualization tool that applies visual analytics principles in developing a standalone application that illustrates equipment-level virtual metered energy trends. Users can interact with these visualizations via a user interaction panel designed to support operational decisions. Interviews with building operation professionals in two facility management companies are conducted to gain insights into visualization requirements that would assist decision-making processes. The use of the visualization tool is demonstrated using operational data collected from a commercial building located in Ottawa, Canada. The data are used to train inverse greybox models representing heat transfer mechanisms in an air handling unit and a variable air volume zone. Optimal values for model parameters are fed into the visualization tool to develop virtual meters that estimate the heat added or extracted by the air handling unit and the heat added by zone-level radiant heaters. The virtual metered energy is presented through a visualization interface that allows user interaction via processes including select, filter, explore, and compare. The potential of the visualization tool for assisting decision-making processes and improving building energy performance is demonstrated through illustrative examples.
Hardware implementation of the Haar 2D discrete wavelet transform with an application to image watermarking

This paper presents a hardware implementation of the lifting 2D discrete wavelet transform of Haar for real-time image applications. The hardware architecture is designed using Xilinx System Generator software. Then, it is implemented on the FPGA-ZYNQ ZC 020 which results in low resources utilization and high performance. In FPGA-ZYNQ, the proposed hardware 2D DWT of Haar can achieve a frequency of 271.37 MHz with a throughput of 8683.84 Mbps. In addition, an application of the hardware lifting 2D DWT of Haar for image watermarking is presented. The algorithm inserts a binary watermark in the high-frequency band HL after two-level of the wavelet decomposition. The experimental results show good results.
Experimental and numerical damage characterization of glass/polypropylene multidirectional laminates under quasi-static loading condition

Polypropylene (PP) as a commodity thermoplastic, when reinforced with continuous glass fibers provides cost-effective composites. However, the low glass transition temperature and fabrication induced semi-crystalline morphology of PP set challenges for the experimental characterization especially when the quantification of damage mechanisms in multidirectional laminates is concerned. This paper is aimed at performing in-situ experimental observation, quantification and theoretical modeling of damage mechanisms in glass fiber reinforced polypropylene multidirectional laminates subjected to uniaxial quasi-static loading conditions. Two Stereo Digital Image Correlation systems (3D-DIC) are applied to measure the full-field strains and quantify the extent of damage mechanisms from the specimen's edge. The effects of ply thickness, off-axis ply orientation and location on damage initiation and growth are studied by testing different lay-ups. To validate the experimental measurements, a recent physics-based modeling technique is implemented that can predict the evolution of damage modes as well as their effects on the laminate properties. Good agreements are observed between the experimental measurements and simulation results which verify the accuracy of both analyses.
Adaptive Color Image Encryption Scheme Based on Multiple Distinct Chaotic Maps and DNA Computing

In this paper, we propose an adaptive encryption scheme for color images using Multiple Distinct Chaotic Maps (MDCM) and DNA computing. We have chosen three distinct chaotic maps, including a 2D-Henon map, a Tent map, and a Logistic map, to separately encrypt the red, green, and blue channels of the original image. The proposed scheme adaptively modifies the parameters of the maps, utilizing various statistical characteristics such as mean, variance, and median of the image to be encrypted. Thus, whenever there is a change in the plain image, the secret keys also change. This makes the proposed scheme robust against the chosen and known plaintext attacks. DNA encoding has also been used to add another layer of security. The experimental analysis of the proposed scheme shows that the average value of entropy is approximately eight, the Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) are 99.61% and 33%, respectively, and correlation coefficients close to zero, making the scheme not only reliable but also resilient against many attacks. Moreover, the use of low-dimensional maps reduces the computational costs of the scheme to a large extent.
Practical Secret Image Sharing Based on the Chinese Remainder Theorem

Compared with Shamir’s original secret image sharing (SIS), the Chinese-remainder-theorem-based SIS (CRTSIS) generally has the advantages of a lower computation complexity, lossless recovery and no auxiliary encryption. However, general CRTSIS is neither perfect nor ideal, resulting in a narrower range of share pixels than that of secret pixels. In this paper, we propose a practical and lossless CRTSIS based on Asmuth and Bloom’s threshold algorithm. To adapt the original scheme for grayscale images, our scheme shares the high seven bits of each pixel and utilizes the least significant bit (LSB) matching technique to embed the LSBs into the random integer that is generated in the sharing phase. The chosen moduli are all greater than 255 and the share pixels are in the range of [0, 255] by a screening operation. The generated share pixel values are evenly distributed in the range of [0, 255] and the selection of (k,n) threshold is much more flexible, which significantly improves the practicality of CRTSIS. Since color images in RGB mode are made up of three channels, it is easy to extend the scheme to color images. Theoretical analysis and experiments are given to validate the effectiveness of the proposed scheme.
RiverGame - a game testing tool using artificial intelligence

As is the case with any very complex and interactive software, many video games are released with various minor or major issues that can potentially affect the user experience, cause security issues for players, or exploit the companies that deliver the products. To test their games, companies invest important resources in quality assurance personnel who usually perform the testing mostly manually. The main goal of our work is to automate various parts of the testing process that involve human users (testers) and thus to reduce costs and run more tests in less time. The secondary goal is to provide mechanisms to make test specification writing easier and more efficient. We focus on solving initial real-world problems that have emerged from several discussions with industry partners. In this paper, we present RiverGame, a tool that allows game developers to automatically test their products from different points of view: the rendered output, the sound played by the game, the animation and movement of the entities, the performance and various statistical analyses. We also address the problem of input priorities, scheduling, and directing the testing effort towards custom and dynamic directions. At the core of our methods, we use state-of-the-art artificial intelligence methods for analysis and a behavior-driven development (BDD) methodology for test specifications. Our technical solution is open-source, independent of game engine, platform, and programming language.
Anywear Academy: A Larp-based Camp to Inspire Computational Interest in Middle School Girls

This paper presents a case study of designing and running a Larp (live action role play)-based summer camp in which middle school-age girls create social wearables, toward building computational and design skills, interest, and self-efficacy. Our design draws upon prior evidence that edu-larps can address the identity gap for underrepresented groups in STEM. The focus on creation of social wearables built using E-textiles builds on existing larp practices that use costuming as a method for establishing identity as well as for providing a platform campers can use to enhance their dramatic spectacles. Our findings will be of interest to those working in the areas of informal learning of computation through Arduino and another small device programming, as well as those interested in the intersection of larp and technology design practices, and edu-larp.
SW: A weighted space division framework for imbalanced problems with label noise

Imbalanced data learning is a ubiquitous challenge in data mining and machine learning. In particular, the ubiquity and inevitability of noise can exacerbate severe performance degradation. The synthetic minority oversampling technique (SMOTE) and its variants have been proposed. The core ideas of these variants are emphasizing the specific area or combining it with different noise filters; they introduce additional parameters that are difficult to optimize or rely on specific noise filters. Furthermore, SMOTE-based methods randomly select the nearest neighbor samples and perform random interpolation to synthesize new samples without considering the impact of the sample space’s chaotic degree. In this study, a framework called SW is proposed, which performs weighted sampling by calculating the sample space’s chaos. It is a general, robust and adaptive framework that copes with noisy imbalanced datasets and combines various oversampling algorithms to improve their performances. In the SW framework, the complete random forest (CRF) is introduced to divide the sample space and adaptively assign weights to distinguish and filter noisy and outlier samples. When synthesizing a new sample, the SW framework selects the seed samples’ neighbors and calculates the informed position using the derived weights, bringing the new sample closer to the safe area. Experimental results on 16 benchmark datasets and eight classic classifiers with eight pairs of representative oversampling algorithms demonstrate the SW framework’s effectiveness. The SW framework improves significantly in high-noise situations. In particular, SW-kmeans-SMOTE improved by approximately 5 % on average across all the metrics. Code and framework are available at https://github.com/dream-lm/SW_framework .
Recognition and Classification of Ship Images Based on SMS-PCNN Model

In the field of ship image recognition and classification, traditional algorithms lack attention to the differences between the grain of ship images. The differences in the hull structure of different categories of ships are reflected in the coarse-grain, whereas the differences in the ship equipment and superstructures of different ships of the same category are reflected in the fine-grain. To extract the ship features of different scales, the multi-scale paralleling CNN oriented on ships images (SMS-PCNN) model is proposed in this paper. This model has three characteristics. (1) Extracting image features of different sizes by parallelizing convolutional branches with different receptive fields. (2) The number of channels of the model is adjusted two times to extract features and eliminate redundant information. (3) The residual connection network is used to extend the network depth and mitigate the gradient disappearance. In this paper, we collected open-source images on the Internet to form an experimental dataset and conduct performance tests. The results show that the SMS-PCNN model proposed in this paper achieves 84.79% accuracy on the dataset, which is better than the existing four state-of-the-art approaches. By the ablation experiments, the effectiveness of the optimization tricks used in the model is verified.