Reviewerly/exHarmony huggingface dataset:
Enabling metadesign through an exploration of misinterpretation: Design process verification focusing on the role of objects in the actual production process

This case study analyses the role that writing and verbal communication plays when using objects in the collaborative metadesign process. It is common for co-design practices to be introduced in the ideation and other early stages of the design process. In these processes achieving consensus among participants may be critically important. Our research shows that an appropriate physical object can help not only in the initial phase of the process but also in the later stages of designing and production. In this article, we focused on the co-design process carried out by a Japanese craftsman and a group of designers and explored how the artefacts created along the way affected the process of communication. By examining misunderstandings and consensus between the participants, we learnt more about the possible role of objects in bridging the different viewpoints. The results of this study will contribute to the active introduction of metadesign methods in later design phases and, thus, improve the inclusiveness within metadesign projects.
LAAP: Lightweight Anonymous Authentication Protocol for IoT Edge Devices Based on Elliptic Curve

The massive heterogeneous devices and open channels of the Internet of Things (IoT) lead to low efficiency and privacy leakage in the authentication process, which brings great challenges to identity authentication. This paper focuses on the anonymous authentication between the IoT edge device and the cloud server. In this work, we first propose a novel lightweight anonymous authentication protocol (LAAP) to meet security and efficiency requirements. Especially, the proposed protocol uses dynamic pseudonyms to prevent the traceable attacks caused by fixed identity identification and also uses symmetric encryption to optimize the server’s search for anonymous device information, and the time complexity is reduced from <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <mi>n</mi> </mrow> </mfenced> </math> to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <mn>1</mn> </mrow> </mfenced> </math> . Then, the formal security analysis and informal security analysis are provided to prove the security of the proposed protocol. Finally, extensive numerical results indicate that the proposed LAAP protocol is superior to the benchmarks in terms of computing overhead and communication overhead, while the storage overhead is consistent with the lowest level among other protocols.
Preparation of Modified Carbon Nanotube Polyvinylidene Fluoride Composite Film and Its Application in Packaging Design

Sustainable design does not emphasize the safety of the ecosystem, but builds a systematic innovation process, taking into account customer needs, environmental benefits, social benefits, and corporate development. This article is based on the preparation of a modified carbon nanotube polyvinylidene fluoride composite film to analyze its application value in packaging design, aiming at analyzing the performance of the modified carbon nanotube and the polyvinylidene fluoride composite film during the preparation process, and applying its various characteristics to the process of packaging design optimization concept. The article is mainly divided into two parts: preparing modified carbon nanotubes and preparing polyvinylidene fluoride composite films. Among them, the photomechanical properties of carbon nanotubes, the relationship between the composition of the composite material and the dielectric properties, the dielectric response and relaxation behavior, the hydrophilicity of the composite membrane, and the membrane separation technology were discussed, and the carbon nanotubes were constructed. Incorporating carbon nanotubes into carbon fiber hybrid materials, and combining carbon nanotubes with polymer materials, works extremely well. The structure model is prepared, the molecular pore size, hydrophilicity, etc. are recorded, and surface modification, chemical modification, blending modification, and other methods are applied to analyze and evaluate the data. The experimental results show that in the application of modified carbon nanotubes and polyvinylidene fluoride composite films and packaging design, the interactive function of toughening modification increases 2.44 points; through enhancement modification, packaging design reduction increases by 2.5 points; and through the compatibilization modification, the functionality of the packaging design has increased by 1.88 points.
Predicting Substance Use Treatment Failure with Transfer Learning

Transfer learning, which involves repurposing a trained model on a related task, may allow for better predictions with substance use data than models that are trained using the target data alone. This approach may also be useful for small clinical datasets. The current study examined a method of classifying substance use treatment success using transfer learning. Transfer learning was used to classify data from a nationwide database. We trained a convolutional neural network on a heroin use treatment dataset, then trained and tested on a smaller opioid use treatment dataset. We compared this model with a baseline model that did not benefit from transfer learning, and a tuned random forest (RF). The goal was to see if model weights transfer across related substances and from large to small datasets. The transfer model outperformed the RF model and baseline model. These findings suggest leveraging the power of large datasets for transfer learning may be an effective approach in predicting substance use disorder (SUD) treatment outcomes. It is possible to achieve a score that performs better than RF using transfer learning.
Enabling In-Network Floating-Point Arithmetic for Efficient Computation Offloading

Programmable switches are recently used for accelerating data-intensive distributed applications. Some computational tasks, traditionally performed on servers in data centers, are offloaded into the network on programmable switches. These tasks may require the support of on-the-fly floating-point operations. Unfortunately, programmable switches are restricted to simple integer arithmetic operations. Existing systems circumvent this restriction by converting floats to integers or relying on local CPUs of switches, incurring extra processing delayed and accuracy loss. To address this gap, we propose NetFC, a table-lookup method to achieve on-the-fly in-network floating-point arithmetic operations nearly without accuracy loss. Specifically, NetFC utilizes logarithm projection and transformation to convert the original huge table enumerating all operands and results into several much smaller tables that can fit into the data plane of programmable switches. To cope with the table inflation problem on 32-bit floats, we also propose an approximation method that further breaks the large tables into smaller ones. In addition, NetFC leverages two optimizations to improve accuracy and reduce on-chip memory consumption. We use both synthetic and real-life datasets to evaluate NetFC. The experimental results show that the average accuracy of NetFC is above 99.9% with only 448KB memory consumption for 16-bit floats and 99.1% with 496KB memory consumption for 32-bit floats. Furthermore, we integrate NetFC into two distributed applications and two in-network telemetry systems to show its effectiveness in further improving the performance.
Validation of a set of compact and low-cost Stokes lenses aimed at refractive astigmatism measurement for optometric practice

We describe the construction and characterization of three Stokes lenses that can be used in optometric practice to measure different ranges of refractive astigmatism using subjective routines. The proposed set of Stokes lenses is assembled from Risley prism mounts coming from a Nidek phoropter in which the optical prisms are replaced by three different sets of cylindrical lenses ( ± 0.75, ± 1.25, and ± 1.75 D) coming from a trial lens case to cover a different astigmatism range (1.5, 2.5, and 3.5 D in modulus, respectively). We present the experimental characterization of the three Stokes lenses with the power vector notation and use an automatic lensmeter (Topcon CL-300) for dioptric power measurement. The measured dioptric power profiles are in concordance with the theoretical predictions, and statistical testing reveals no differences between measured and theoretical values for the three lenses, neither in spherical nor in cylindrical components (p ≥ 0.05 for all cases). Bland–Altman plots show very good agreement between measured and theoretical values with limits of agreement below 0.12 D for both M and J components for the three lenses. These Stokes lenses can be easily constructed from regular Risley prism mounts, so they can be used in almost any phoropter model for refractive error compensation.
Efficient Inverse Model for Cloud Computing and Information Based on the Symmetric Random Matrix

With the continuous development of cloud storage technology, cloud storage has become a new way of storage for both enterprises and individuals. The cloud environment can support users to share data, but it can lead to some malicious users accessing or modifying data by illegal means, so access control is an important way to protect user data. In this paper, we propose a traceable attribute encryption scheme based on signature authentication and based on the information efficient inverse model of an asymmetric random matrix, which can trace the user who leaks information. A user attribute revocation scheme based on improved attribute encryption is also proposed, which enables data owners to reduce computational overhead. In cloud storage access control, the workload of the data owner increases exponentially when facing a high number of user attribute revocation problems, which are jointly decided by the data owner and the authorization center. An error proportional allocation (EPA) calculation method is developed to achieve an optimal estimation of system parameters in the lattice cipher scheme; experimental results show that the scheme has the advantages of shorter parameters, efficient computation, and lower storage load, proving that the scheme is resistant to IND-SAS-CPA under the standard model-based assumption of determining LWE semantic security against IND-SAS-CPA attacks; and it is used in a cloud file sharing (CFS) service framework, which can make sensitive cloud data free from the risk of privacy leakage.
Technology Based on Interactive Theatre Performance Production and Performance Platform

Drama refers to a comprehensive art that realizes the purpose of narrative through language, movement, dance, music, puppets, and other forms. From the pre-Qin period⟶the Han and Wei periods⟶the Tang, Song, and Jin periods⟶the Yuan Dynasty⟶the late Yuan Ming and Qing periods⟶modern times, the art of opera has been welcomed and recognized by a vast audience since its birth, but it is difficult to inherit the art of Chinese opera. The fundamental reason is that there is no good platform for performance. It should be said that since the beginning of the performing arts, there has also been academic thinking on the practice of this artistic creation, so in the academic field of drama, there is actually drama performance science. Due to the changes of the times, Chinese drama art cannot keep up with the pace of the Internet. Therefore, this article proposed a technical research based on the interactive drama performance production and performance platform. This article mainly talks about face recognition technology and its algorithm, and applies it to theatrical performances and productions. In the following experiments, this technology is also used to create a network platform, and its user experience evaluation effect is good. The theatrical performances and productions of young people were also scored, and the final experimental results showed that: at the minimum required level, which is at least three (or more) dramatic materials in the class, scores are high at both levels. Among them, 94.4% of the classes in the first-level kindergarten and 86.1% of the classes in the second-level kindergarten meet this standard. Most of the two levels are qualified, that is to say, in the performance area, at least three people can perform at the same time, which is enough to show that theatrical performance has been passed down.
GAAT: Group Adaptive Adversarial Training to Improve the Trade-Off Between Robustness and Accuracy

Adversarial training is by far one of the most effective methods to improve the robustness of deep neural networks against adversarial examples. However, the trade-off between robustness and accuracy is still a challenge in adversarial training. Previous methods used adversarial examples with a fixed perturbation budget or specific perturbation budgets for each example, which is inefficient in improving the trade-off and lacks the ability to control the trade-off flexibly. In this paper, we show that the largest element of logit, [Formula: see text], can roughly represent the minimum distance between an example and its neighboring decision boundary. Thus, we propose group adaptive adversarial training (GAAT) that divides the training dataset into several groups based on [Formula: see text] and develops a binary search algorithm to determine the group perturbation budgets for each group. Using the group perturbation budgets to perform adversarial training can fine-tune the trade-off between robustness and accuracy. Extensive experiments conducted on CIFAR-10 and ImageNet-30 show that our GAAT can achieve a more perfect trade-off than TRADES, MMA, and MART.
“I’m not this Person”: Racism, content moderators, and protecting and denying voice online

Much scholarship across the humanities and social sciences seek to shed light on the intersection of far-right politics and social media platforms. Yet, scholars tend to focus on racist actors and the ideological underpinnings of platform policies while the contingencies that shape the experiences of content reviewers who make decisions about racist content remain underexamined. This article fills this gap by exploring such contingencies from a linguistic anthropological perspective. Drawing on Facebook moderators’ stories, I illustrate the factors adjacent to, and beyond, ideology that animate the adjudication of racist hate speech.
Analysis of Legal Issues of the Crime of Endangering Public Safety Based on Data Mining Algorithm

In order to improve the analysis effect of legal issues of the crime of endangering public safety, this paper analyzes the legal issues of the crime of endangering public safety based on the actual mining algorithm and uses numerical methods to qualitatively study the evolution behavior across phase intervals. Moreover, this paper improves the algorithm based on the actual needs of legal mining. When solving the eigenvalues of the given Hamiltonian’s duration equation, the zero-energy solution can be found in the case of open boundaries. In addition, this paper applies the improved algorithm proposed in this paper to the mining of criminal law legal issues of crimes against public safety, assigns a dynamic IP to each process, and builds a model based on this. Through the above simulation research, it can be seen that the legal problem analysis model of the crime of endangering public safety based on the data mining algorithm proposed in this paper has a good legal data mining effect.
Modeling and Analysis Method of National Fitness Big Data for Basketball Projects Based on a Multivariate Statistical Model

How to start from the fitness needs of people and effectively improve the precision of the supply of public fitness services everyone is an important issue that needs to be solved first at the current stage. This requires us to proceed from the reality, conduct accurate research, and find a method that can match the current problem. In this paper, taking basketball projects in national fitness as an example, by introducing a proposition about the development of small basketball events, the corresponding big data modeling and analysis methods are studied. The research methods and research objectives involved in this paper are based on the relevant parameters of the multivariate statistical model. First, the article introduces the calculation principle of the multiple linear regression model. We introduce the concept of variance inflation factor involved in this principle and carry out the modeling and analysis of big data based on this variable. In order to illustrate the application effect of big data in this kind of research, this paper introduces three different big data technologies, including immune selection optimization algorithm, particle swarm optimization algorithm, and Elman neural network, to predict and analyze the variance inflation factor (VIF) corresponding to the small basketball project. The analysis results show that the Elman network exhibits certain advantages in terms of computing convergence time. And, as the number of calculation steps increases, the superiority of the Elman network is more obvious. As far as the prediction performance is concerned, the square of the correlation coefficient corresponding to the immune selection optimization algorithm is the largest and the sum of the squares of the residuals is the smallest, showing superior prediction performance.
The Need for Harmonised and Specialised Global Legislation to Address the Growing Spectre of Cybercrime

Modern advances in technology have made the world a global village, and the impact of this on almost all spheres of life and society is phenomenal. The rapid growth of information and virtual technology in most spheres of life and society as well as the interconnection of computers through super highways and international computer networks have made cybercrime more diverse, more dangerous, more global and more challenging to fight. The ubiquity of the internet and the connectivity of virtually every computer to the global community hold ramifications that have yet to be determined. Cybercrime does not require physical proximity between the victim and the perpetrator for the commission of the offence. Cybercrime is unbounded, in that the victim and the perpetrator can be in different cities, states, or even countries. Deterring and punishing cybercriminals require an international legal framework to investigate and prosecute cybercrime.
Light Field Local Surface Feature Detection

A light field local surface features detection method is proposed based on the scale space established on the moving frame determined by the depth information. Experimental results show more detected features than SIFT and LiFF.
Calligraphy Character Detection Based on Deep Convolutional Neural Network

Calligraphy (the special art of drawing characters with a brush specially made by the Chinese) is an integral part of Chinese culture, and detecting Chinese calligraphy characters is highly significant. At present, there are still some challenges in the detection of ancient calligraphy. In this paper, we are interested in the calligraphy character detection problem focusing on the calligraphy character boundary. We chose High-Resolution Net (HRNet) as the calligraphy character feature extraction backbone network to learn reliable high-resolution representations. Then, we used the scale prediction branch and the spatial information prediction branch to detect the calligraphy character region and categorize the calligraphy character and its boundaries. We used the channel attention mechanism and the feature fusion method to improve the detection effectiveness in this process. Finally, we pre-trained with a self-generated calligraphy database and fine-tuned with a real calligraphy database. We set up two groups of ablation studies for comparison, and the comparison results proved the superiority of our method. This paper found that the classification of characters and character boundaries has a certain auxiliary effect on single character detection.
Supply chain challenges in public schools in the uMzinyathi district, KwaZulu-Natal

Background: The South African Schools Act No. 84 of 1996 states that all learners should be provided with a high-quality education and in this way lays a strong foundation for the development of their talents and capabilities. The education sector faces many challenges that undermine the quality of education, particularly in public schools. Unless these challenges are identified and addressed, learners’ education will continue to be impacted negatively.Objectives: This study investigated supply chain challenges in public schools in the uMzinyathi district and explored strategies for managing and addressing these challenges so as to benefit the public education sector as a whole.Method: The study is exploratory, and a case study approach was used. Data were collected through semistructured interviews with 20 stakeholders in the Department of Education. The stakeholders were selected by means of a nonprobability purposive sampling method. The collected data were analysed using thematic analysis.Findings: The findings revealed that the main supply chain challenges centre around three themes, namely procurement, service delivery and logistics and demand. These challenges negatively impact on learners’ education.Conclusion: The contribution of this study is twofold. Firstly, it adds to the existing body of literature by identifying the challenges experienced in the country’s public education sector, and secondly, the findings and recommendations could be useful in order to address the supply chain challenges faced by public schools for stakeholders in the South African public education sector.
A blockchain-based conditional privacy-preserving authentication scheme for edge computing services

Edge computing is a new paradigm, especially suitable for Internet of Things (IoT) applications, which require low latency, location awareness and mass connections. It surely makes up for some shortcomings in cloud computing in terms of energy consumption and real-time analysis areas. However, it is still a great challenge to guarantee data security and privacy protection in edge computing environment. In this paper, we focus on the topic of authentication in edge computing and propose a conditional privacy-preserving authentication scheme based on blockchain, which guarantees user anonymity, conditional traceability as well as multi-factor security. The scheme uses elliptic curve cryptography (ECC) and does not use bilinear pairing operations. The key materials are stored on the blockchain and managed by the smart contracts. Formal security proof and heuristic security analysis show that the protocol satisfies desired security requirements and resists against most kinds of conventional attacks. Moreover, the performance comparison shows that the proposed scheme is more efficient in computation and communication than previous related schemes.