Reviewerly/exHarmony huggingface dataset:
Fringe Projection Method for 3D High-Resolution Reconstruction of Oil Painting Surfaces

The fringe projection (FP) method is an outstanding tool for reconstructing painted surfaces. This technique, which has been used for conservation and digitization, does not damage the artwork and can reach sub-millimeter accuracy. To carry out this type of analysis, it is necessary to achieve the most accurate measurements possible. Measuring the precision that a projector-camera-object arrangement can achieve is a complex task. In this paper, we show an experimental method used to measure the accuracy of this technique with instrumentation within the reach of most conservation laboratories. The method consists of capturing, as a reference model, a stepped cylindrical Nylamid® pyramid, as a construction whose shape, size, and manufacturing accuracy are known with high precision. The pyramid has eight well-defined steps, which are fashioned with an accuracy more exact than that of the fringe projection method. The height of each step was measured, obtaining the mean and variance of the height measurements fitted to a Gaussian distribution. In this work, we show the measured heights of the steps, obtained by varying the period of the fringes. The smallest detectable step height was less than 44.1 µm; however, this was obtained with a variance in the order of the step height. The smallest detectable step height with a small variance was 0.1008 mm. In addition to this accuracy measurement, a qualitative evaluation of a painting was carried out, finding the presence of possible superimposed thin layers, fabric, and microcracks, which commonly occur in the drying and aging processes. Further research would provide an experimental measurement of the method’s accuracy and its variance as essential for obtaining a confidence criterion that could then be applied to the model of the painting’s surface.
A framework for understanding and evaluating news infographics

There is a lack of definitional rigour for journalism infographics that allows ambiguity in their evaluation and research. This study is an attempt to remedy this imprecision to understand how the form differs from other visual modes of communication, and to more clearly establish criteria for its analysis. We argue that a news infographic is grounded on three essential criteria—integration, abstraction, and extension. We test the validity of our framework by applying it to case studies of infographics recognized for their excellence. We discuss how our criteria represent an elaboration of a two-part concept of appearance and explanation, and suggest ways in which the framework can be used to judge existing infographics and to guide the creation of future infographics for the news.
Pengembangan Bahan Ajar Berbasis Blended Learning pada Mata Kuliah Ilmu Pendidikan Prodi Pendidikan Agama Islam

The background of this research is that the limitations of teaching materials in the subject of Education tend to be carried out manually. The formulation of the problem in this study is that is it feasible for blended Learning-based teaching materials in the Educational Sciences course to be used in the PAI Study Program. This study aims to produce products in the form of Blended Learning-based teaching materials in PAI Education Study Program subjects. This research uses development research methods, carried out through ten stages using the Dick and Carey model. To find out the feasibility of the product being developed, quantitative data is needed in the form of response scores about product quality through a questionnaire validated by media experts and material experts and student responses. The results of the study showed that teaching materials based on blended learning through the Macromedia Flash 8 program in the subject of Education in the first semester of the PAI study program on the components of education had a “very good” feasibility category which was based on the percentage gain using the Likert scale. The results of validation and trials include (1) the validation of the material expert said that the final percentage obtained from the material and linguistic aspects is 89.09% with very good criteria, (2) Validation of media experts stated that the final percentage obtained from material and display aspects was 100,00% with very good criteria, (3) Individual trials stated that the final percentage obtained from material and display aspects is 78.95% with good criteria (4) Small group trials state that the final percentage obtained is 90,67% with very good criteria (5) and field trials state that the final percentage obtained is 91,40% with very good criteria.single paragraph of abstract, about 150-250 words without indentation of the first line with 1 spaces and 11-point. We strongly encourage authors to use the following style of structured abstracts, but without heading: (1) Background: place the gap analysis of this study (about 1-2 sentences) and highlight the purposes of the study; (2) Methods: briefly describe the main methods applied, including samples/participants, instruments, collecting data technique, and analyzing data technique; (3) Results: summarizing the article’s main findings, should be in line with the purposes of the study; (4) Conclusion. The abstract should be an objective representation of the article. It must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.
Minerva Surgical, Inc. v. Hologic, Inc. <i>United States Court of Appeals of the Federal Circuit, 2023 59 F.4th 1371</i>

Biotechnology Law ReportVol. 42, No. 2 Minerva Surgical, Inc. v. Hologic, Inc. United States Court of Appeals of the Federal Circuit, 2023 59 F.4th 1371Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri-Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:6 Apr 2023https://doi.org/10.1089/blr.2023.29306.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 2Apr 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Minerva Surgical, Inc. v. Hologic, Inc. United States Court of Appeals of the Federal Circuit, 2023 59 F.4th 1371.Biotechnology Law Report.Apr 2023.108-114.http://doi.org/10.1089/blr.2023.29306.cipPublished in Volume: 42 Issue 2: April 6, 2023Online Ahead of Print:March 30, 2023PDF download
Adaptive event-triggered control for uncalibrated visual servoing of robot manipulators

In the study, an adaptive event-triggered control strategy is proposed for image-based visual servoing of eye-to-hand robot manipulators, where the camera used does not need to be calibrated, and the dynamics behavior of manipulator is considered in design and analysis. To address the uncertainty in camera parameters and the nonlinearity in robot dynamics, and accommodate the errors between the real-time control signals and the piecewise-constant event-triggered control signals, a new and novel robust adaptive estimation approach is developed, and well fused with visual feedback control design so that the boundedness of all closed-loop signals, and the asymptotic convergence of image error towards zero are established simultaneously. Besides rigorous proof in theory, the obtained results are also confirmed by comparative simulation tests.
Mapping state transition susceptibility in quantum annealing

Quantum annealing is a novel type of analog computation that aims to use quantum mechanical fluctuations to search for optimal solutions of Ising problems. Quantum annealing in the transverse field Ising model, implemented on D-Wave devices, works by applying a time dependent transverse field, which puts all qubits into a uniform state of superposition, and then applying a Hamiltonian over time which describes a user programmed Ising problem. We present a method which utilizes two control features of D-Wave quantum annealers, reverse annealing and an h-gain schedule, to quantify the susceptibility, or the distance, between two classical states of an Ising problem. The starting state is encoded using reverse annealing, and the second state is encoded on the linear terms of problem Hamiltonian. An h-gain schedule is specified which incrementally increases the strength of the linear terms, thus allowing a quantification of the h-gain strength required to transition the anneal into a specific state at the final measurement. By the nature of quantum annealing, the state tends towards global minima and therefore we restrict the second classical state to a minimum solution of the given Ising problem. This susceptibility mapping, when enumerated across all initial states, shows in detail the behavior of the quantum annealer during reverse annealing. The procedure is experimentally demonstrated on three small test Ising's which were embedded in parallel on the D-Wave Advantage_system4.1. Analysis of the state transition mapping shows detailed characteristics of the reverse annealing process including intermediate state transition paths, which are visually represented as state transition networks.
Interrupt Stack Protection for Linux Kernel in Hardware Virtualization Layer of ARM64 Architecture

Kernel security is of paramount importance in computer systems. As the number of vulnerabilities in the kernel continues to grow, computer systems security risks are increasing. To prevent the kernel interrupt stack from being attacked, researchers provide discussion over complete hypervisor supervision and kernel co-layer security domain techniques. Complete hypervisor supervision brings a heavy overhead and co-layer security domain techniques cannot achieve privilege-level isolation. We focus on memory-based security threats in kernel security vulnerabilities, protecting the kernel at a higher level by using virtualization technology. Compared with the existing work, our implementation method achieves a small performance loss to protect the interrupt stack. We have implemented our system on openEuler operating systems and Phytium processors. Although the deployment of protection code will result in increased kernel interrupt latency and processor overhead, experimental verification shows that the overall system overhead is acceptable.
CAGCN: Causal attention graph convolutional network against adversarial attacks

Have you ever been exposed to advertising accounts on social networks or distributed denial-of-service (DDoS) attacks? These attacks occur as intrusions in a network. Recently, several studies have demonstrated the vulnerability of graph convolutional networks (GCNs). In other words, given an abnormal graph with perturbations from a normal graph, the performance of GCNs drops significantly. To solve this problem, we propose a causal attention graph convolutional network (CAGCN). We design a causal graph where given data are affected by an attack and utilize the causal mechanism on GCNs to cut off the bias. Specifically, we use two types of attention, node attention (NoA) and neighbor attention (NeA), and demonstrate the robustness of our model, which does not significantly degrade the performance of the model as the attack becomes stronger. In addition, to show that the causal mechanism works well for robust learning, we apply the causal mechanism to the previous study and compared it.
THE PHENOMENON OF POST-INTERNET ART: ARTISTIC STRATEGIES AND PLACE IN THE FIELD OF CONTEMPORARY ART

The study is devoted to the phenomenon of post-Internet art, its main representatives and key works, and a description of their characteristics. In contrast to the practices of net art, post-Internet art is a “return to the material”: artists create objects inspired by the network and often bearing the “mark” of network aesthetics. However, these objects have physical characteristics; they evoke a tactile sensation in the viewer, serving as a spontaneous affirmation of materiality in our increasingly virtualised world. Post-Internet art is a little-studied area of contemporary art; the emergence of this trend is associated with the development of net-art practices (network art), which have offered new forms of interaction with viewers who got the opportunity to join the artistic process as full participants. The term “post-Internet” should be understood not as a situation “after” the Internet (as if the Internet “ended”) but as a situation after some significant event that changed the usual course of things. “Post-Internet” is a situation after the Internet has ceased to be perceived in a romantic way, as a space of anonymity and permissiveness, “undeveloped land” and is now understood as part of an online-offline “bundle” - with appropriate communication channels, creation of commercial platforms, digital currencies and etc., that is, as part of vernacular practices (everyday experience). From the point of view of its orientation, post-Internet art deals with understanding the consequences of the influence of Internet practices on contemporary art and culture. Net art artists created such works that were intended for the Internet, that is, used the possibilities of the Internet as a means of expression (they studied the structure of the Internet, the features of the code, the specifics of certain browsers, programs, etc.), whereas post-Internet artists seem to “bring” digital objects from the digital environment into the material world and use both online and offline formats for reflection on the effects of almost ubiquitous networking. In the 2010s, the Internet was no longer perceived as a “new” medium; it became more difficult to talk about post-Internet art as a specific movement in art since more and more aspects of our daily life are structurally determined by the World Wide Web, including art as a whole. Of course, the Internet continues to interest artists as a kind of socio-cultural state; however, discourses related to the Internet start to be increasingly woven into a wider range of issues - digital capitalism, ecology, social issues, etc. Therefore, the “marking” of art created under the influence of the Internet or referring to it in one way or another as “post-Internet art” is becoming less and less common, which allows us to speak about the specific temporal boundaries of post-Internet art as an art movement.
MP-CLF: An effective Model-Preserving Collaborative deep Learning Framework for mitigating data leakage under the GAN

The development of Internet of Things (IoT) communication technology has accelerated the data transmission between IoT devices, thus facilitating collaborative data processing based on the cloud, such as collaborative deep learning. The collaborative deep learning framework allows local devices to cooperate on training models without sharing private data, which resolves the contradiction of the availability and privacy of data. However, the emergence of the Generative Adversarial Network (GAN) attack has shown that poorly protected local data is vulnerable to being learned by adversaries. In this paper, we aim to address the threat GAN attacks pose to collaborative deep learning. We propose a Model-Preserving Collaborative deep Learning Framework, called MP-CLF, which can effectively resist the GAN attack. Based on fully connected neural network learning, MP-CLF employs a matrix blinding technology to break the local modeling of the GAN attack by blinding specific model parameters and trainers’ data, which is easily implementable and has strong security. Besides, MP-CLF builds a user partition model pre-training to improve training quality and strengthen model protection. Using the MNIST dataset and Fashion-MNIST dataset, we experimentally demonstrate that MP-CLF can completely resist the GAN attack with good computational efficiency.
A multi-scale graph embedding method via multiple corpora

Graph embedding aims at learning continuous vector representations for graphs which is crucial for graph analytics. Natural Language Process (NLP)-based graph embedding methods build corpus for graph data by treating substructures as words and then use NLP models to learn graph embeddings. However, the size difference and data redundancy among substructures are less explored in the built corpora. To mitigate this problem, we propose an unsupervised multi-scale graph embedding method. To be a specific, we first build multiple graph corpora for a graph dataset, where each corpus only contains substructures of specific granularity. Then, we extend a document embedding model to each graph corpus to obtain graph embeddings of different scales. At last, we obtain the final multi-scale embedding of a graph by pooling its multiple embeddings. Comprehensive experiments on real graph datasets indicate that the proposed method obtains competitive results with state-of-the-arts, and is superior to some classic graph kernels and graph embedding methods on six out of ten benchmark datasets.
Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics

This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models.
Toward Privacy-Preserving Directly Contactable Symptom-Matching Scheme for IoT Devices

The development of IoT devices has driven technological advancements across industries, especially in healthcare. IoT devices have brought many conveniences to patients, such as symptom matching, the real-time acquisition of health data, and online diagnosis. However, the development of the Internet of Things also brings security and privacy challenges, which have attracted the attention of many scholars. In symptom matching, patients can communicate with patients similar to themselves through symptom matching, exchange treatment experiences, and encourage each other. However, matching in plaintext will pose a huge threat to user privacy, such as discrimination, which in turn affects job hunting, etc. Therefore, this paper proposes a symptom-matching scheme for IoT devices based on the Diffie–Hellman key agreement. Specifically, we construct and formally define the Switching Threshold Label Private Set Intersection (STLPSI) protocol based on the Diffie–Hellman key agreement and apply it for medical symptom matching. Our protocol can not only set the threshold of the same symptoms, but also patients who meet the threshold can obtain one another’s contact information. Furthermore, our scheme does not rely on any trusted third parties. Through security analysis and experiments, our scheme is shown to be effective in preserving privacy during symptom matching.
Service selection model based on user intention and context

The internet interconnects billions of objects and services. Real-world services are offered by entities with various functions communicating with one another. The selection of services based on their functionality is a complex process because as the number of services rises, so does the number of services that offer the same functionality. Quality of service (QoS) can be a criterion for selecting a suitable service. However, QoS' relative significance fluctuates due to changing user preferences, and users may exhibit various behaviors depending on their contexts and intentions. Defining a user's preference based on user intentions and context is similarly challenging; nonetheless, scholars have paid little attention to this topic. This study provides a new model for service selection based on user intentions and context. The model dynamically selects the appropriate set of QoS with their importance to specify a user preference for various behaviors. The issue of assessing user preference to select the desired service is resolved by calculating the QoS importance based on the user's behavior history and context. The study proposed a dynamic K-Skyline method to optimize a search space and a multi-criteria decision-making technique to select and rank services efficiently. A case study and an experiment demonstrating the proposed model are presented, in which real-world datasets are utilized. The experimental results of the proposed model validate the model's efficiency and robustness.