Reviewerly/exHarmony huggingface dataset:
Time’s a Thief of Memory

ARM TrustZone is a system-on-chip security solution that provides hardware guarantees to isolate the untrusted applications running in the normal world from sensitive computation and data by placing them in the secure world. In a multi-tenant scenario, such isolation is paramount to protect tenants from each other and is guaranteed by partitioning resources (memory, peripherals, etc.) between the tenants. Several third-party defence mechanisms add to this isolation through techniques like statically whitelisting communication channels. Consequently, two tenants cannot communicate with each other except using two legitimate channels: (1) shared memory and (2) legitimate API calls. However, we show that seemingly simple covert channels can be created to break this isolation and create a third illegitimate channel. We use simple thread counters and TrustZone configurations to demonstrate a break in Trustzone’s isolation through a non-root, cross-core, cross-user, bidirectional (secure to normal world and vice-versa), cross-world covert channel on OP-TEE implementation on ARM TrustZone that, by design, places no limit on channel capacity. Our channel bypasses the established defence mechanisms (which mainly target cache occupancy measurements and performance monitoring units) in ARM TrustZone, achieves a maximum bandwidth of 130 KBps with a 2.5% error rate, and allows arbitrary code execution in the secure world leading to denial-of-service attacks.
Analyzing the Leakage Resistance of the NIST’s Lightweight Crypto Competition’s Finalists

We investigate the security of the NIST Lightweight Crypto Competition’s Finalists against side-channel attacks. We start with a mode-level analysis that allows us to put forward three candidates (Ascon, ISAP and Romulus-T) that stand out for their leakage properties and do not require a uniform protection of all their computations thanks to (expensive) implementation-level countermeasures. We then implement these finalists and evaluate their respective performances. Our results confirm the interest of so-called leveled implementations (where only the key derivation and tag generation require security against differential power analysis). They also suggest that these algorithms differ more by their qualitative features (e.g., two-pass designs to improve confidentiality with decryption leakage vs. one-pass designs, flexible overheads thanks to masking vs. fully mode-level, easier to implement, schemes) than by their quantitative features, which all improve over the AES and are quite sensitive to security margins against cryptanalysis.
Comparing Key Rank Estimation Methods

Recent works on key rank estimation methods claim that algorithmic key rank estimation is too slow, and suggest two new ideas: replacing repeat attacks with simulated attacks (PS-TH-GE rank estimation), and a shortcut rank estimation method that works directly on distinguishing vector distributions (GEEA). We take these ideas and provide a comprehensive comparison between them and a performant implementation of a classical, algorithmic ranking approach, as well as some earlier work on estimating distinguisher distributions. Our results show, in contrast to the recent work, that the algorithmic ranking approach outperforms GEEA, and that simulation based ranks are unreliable.
Manipulated Face Detection and Localization Based on Semantic Segmentation

In this paper, we propose a novel manipulated face detection and localization approach, which simultaneously detect manipulated face images and videos and locate the manipulated regions at semantic-level. To do this, we design a multi-branch autoencoder composed of four types of modules, including feature encoder, shared decoder, semantic decoder, and classification network. The feature encoder extracts latent feature from the input face image. The shared decoder obtains structure feature from the latent feature. The four semantic decoders decode structure feature into four different semantic prediction masks, respectively. The classification network outputs the semantic prediction labels based on the latent feature. Finally, the manipulation prediction label and manipulation prediction mask of the input face image can be generated with the semantic prediction labels and semantic prediction masks. Extensive experiments show that our approach can effectively detect and locate manipulated face images and videos at semantic-level, even under cross-manipulation, cross-dataset, and cross-compression scenarios.
EB-FedAvg: Personalized and Training Efficient Federated Learning with Early-Bird Tickets

Federated learning is a well-known way to improve privacy in distributed machine learning. Its major goal is to learn a global model that provides good performance to the broadest number of participants. Statistical heterogeneity (also known as non-IID) and training efficiency are two key unresolved concerns in the rapidly developing field of technology. In this paper, we propose Early-Bird FedAvg (EB-FedAvg), a customized federated learning architecture with personalization and training effects based on Early-Bird Tickets. By applying for the early-bird tickets, each client learns an early-bird ticket network (i.e., a sub-network of the base model), and only these early-bird ticket networks are communicated between the server and clients. Instead of learning a shared global model as in traditional federated learning, each client learns a personalized model with EB-FedAvg; communication costs can be greatly reduced due to the compact size of the early-bird ticket network. Experiments on these datasets show that EB-FedAvg outperforms existing systems in personalization, training, and communication cost.
Forecasting Indonesia inflation using long short term memory method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation I. Gusti Bagus Ngurah Diksa, Heri Kuswanto, Kartika Fithriasari; Forecasting Indonesia inflation using long short term memory method. AIP Conference Proceedings 27 January 2023; 2540 (1): 080006. https://doi.org/10.1063/5.0105888 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Design android-based learning media using augmented reality technology to support ethnomathematics materials at junior high school

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Tri Rochmadi, Rino Richardo, Ahmad Anis Abdullah, Ariyadi Wijaya, Nurkhamid; Design android-based learning media using augmented reality technology to support ethnomathematics materials at junior high school. AIP Conference Proceedings 27 January 2023; 2540 (1): 070005. https://doi.org/10.1063/5.0105904 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Development of human digestive system e-module to improve students’ learning outcomes during pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ervin Setyantoko, Jan Hendriek Nunaki, Jeni Jeni, Insar Damopolii; Development of human digestive system e-module to improve students’ learning outcomes during pandemic. AIP Conference Proceedings 27 January 2023; 2540 (1): 020002. https://doi.org/10.1063/5.0105782 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Analysis of potential learning models to determine facts and concepts: Literature review

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Vivin Harlupi, Bambang Subali; Analysis of potential learning models to determine facts and concepts: Literature review. AIP Conference Proceedings 27 January 2023; 2540 (1): 020014. https://doi.org/10.1063/5.0105822 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Multiple period logit model using the maximum likelihood and Bayesian approach on data of breast cancer patients in C-Tech Laboratories Tangerang

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Nazmi Soraya, Santi Wulan Purnami, Jerry Dwi Trijoyo P., Edi Sukur; Multiple period logit model using the maximum likelihood and Bayesian approach on data of breast cancer patients in C-Tech Laboratories Tangerang. AIP Conference Proceedings 27 January 2023; 2540 (1): 080003. https://doi.org/10.1063/5.0113567 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The effect of learning using Google Classroom and Microsoft Teams on student’s learning motivations based on economic ability

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Haifa Azizzah, Slamet Santosa, Yudi Rinanto; The effect of learning using Google Classroom and Microsoft Teams on student’s learning motivations based on economic ability. AIP Conference Proceedings 27 January 2023; 2540 (1): 020026. https://doi.org/10.1063/5.0112464 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Prediction of rice growth phases with multitemporal Landsat-8 data using rotation forest multiclass method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Raditya Novidianto, Kartika Fithriasari, Heri Kuswanto; Prediction of rice growth phases with multitemporal Landsat-8 data using rotation forest multiclass method. AIP Conference Proceedings 27 January 2023; 2540 (1): 080007. https://doi.org/10.1063/5.0107155 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Card game “RECAME” to improve scientific literacy

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Zakia Yolanda, Murni Ramli, Dewi Puspita Sari; Card game “RECAME” to improve scientific literacy. AIP Conference Proceedings 27 January 2023; 2540 (1): 020013. https://doi.org/10.1063/5.0108381 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
ARTIFICIAL INTELLIGENCE AS A COMMON HERITAGE OF MANKIND

Artificial intelligence technologies today employ techniques known as machine learning and deep learning, which apply datasets to a suitable mathematical or statistical technique known as an algorithm. This in turn produces a model that can be employed to predict an outcome, given a new set of data that was previously unseen by the model. The principle of common heritage of mankind, which has originated in the 19th century, promotes the concept that humanity as a whole has rights and responsibilities over territories or outer space. This study aims to advance the concept of treating the components of artificial intelligence as an intellectual common in the form of a common heritage of mankind, in order to promote the discovery and the development of more novel artificial intelligence applications for the benefit of people around the world. This work employs a mix of legal doctrinal research related to intellectual property law and conceptual theoretical discussion. The potential application of open access and open data licensing is discussed. The history of the common heritage of mankind is covered, and the potential benefit of recognizing basic artificial intelligence components as a common heritage of mankind is explored. Finally, a novel method for implementing this idea is proposed. This work is significant in advancing a method to liberate certain artificial intelligence technologies from intellectual property rights protection, in order to promote greater experimentation and the development of artificial intelligence applications for the greater good of humanity.
Increase Security of Image Encryption Depends on Change Pixel Location

In any communication, security is the most important issue in today’s world. The information security has be come one of the most signify can tproblemsin data communication. Lots of datasecurity and data hiding algorithmshavebeendevelopedinthelastdecade. Cryptography and steganography are the two major techniques for secret communication. In this paper, the secret text is first encryptedbyusingAESwhichhasverygood performanceandisamostpowerfultechnique. Now this encrypted text is embedded using steganography. Our proposed system gives threestages of security for secret. The main aim of proposed method to increase security of embedding and extraction phase using AESencryption,steganography and change pixel location. In order to evaluate performance the proposed algorithm performs series of tests. These tests include, PSNR and MSE.
Physical Anti-copying Semi-robust Random Watermarking for QR Code

Recently, QR code has been applied in anti-counterfeiting scenarios, where a unique QR code is attached for a specific item. However, such a QR code-based anti-counterfeiting solution cannot resolve the physical illegal copying issue. The genuine QR code can be physically replicated by scanning and printing. In this work, we propose a physical anti-copying semi-robust randomly watermarking system for QR code. Specifically, the authentic and counterfeit channels a QR code experiences are investigated first. By exploiting the distortion characteristics between two channels, we devise a randomly watermark embedding system, where the watermark bit is embedded via modulating the relationship between two carefully selected transformed coefficients. Finally, to obtain a valid and recognizable binary QR code image, a random binarization procedure is applied, and the regions originally belonging to the white module are erased. The final resultant watermark appears as white-dot pattern resides the black module of QR code, which is robust to the authentic print-scan but fragile to the physically illegal copying. Experimental results demonstrate the effectiveness of the proposed watermarking system. This work makes the first step towards exploring semi-robust watermarking for combating physically illegal copying.
Adaptive Despread Spectrum-Based Image Watermarking for Fast Product Tracking

With the digitalization of the physical markets, the number of users engaging in e-commerce and shopping online is rapidly increasing. An important application area for digital watermarking is the product tracking scenario. For product tracking scenario, watermarking can be used to provide both product links and copyright protection, so the robustness and extraction efficiency of watermarking are the most important metrics. The auto-convolution function (ACNF) based watermarking scheme is the latest image watermarking that achieves the most comprehensive robustness. However, ACNF watermarking is not resilient in the case of Gauss noise and average filtering. Besides, ACNF watermarking focuses only on robustness and ignores extraction efficiency, and the low efficiency of watermark extraction leads to unpleasant user experience. In this paper, we propose an adaptive despread spectrum-based image watermarking for fast product tracking. For watermark embedding, we construct a low-frequency watermark signal in the spatial domain to enhance the robustness to signal processing attacks. In watermark extraction, our scheme uses discrete wavelet transform (DWT) for image dimensionality reduction and adaptively watermark despread spectrum according to the wavelet decomposition level, which can achieve accurate and fast extraction of watermark. The experimental results demonstrate that our proposed watermarking scheme has superior robustness and extraction efficiency than the existing methods under the same imperceptibility.
Pose Estimation for Nonlinear Visual Servo Control of an Omnidirectional Platform

This paper developed a nonlinear visual servo control using the reverse kinematic model of the omnidirectional platform with mecanum wheels for trajectory tracking using visual feedback for pose estimation based on binary markers. Control actions are sent from the computer to the platform using a wireless communication channel (Zigbee Protocol). The experimental results obtained show that the errors converge asymptotically to values close to zero, demonstrating that the proposed controller works correctly and responds adequately to external disturbances.
Failure Rate in University Students: Analysis of its Variation in the Transition from Face-to-Face Education to Virtual Education

The article presents an exploratory and descriptive study of the failure rate of students of the professional school of mechanical and electrical engineering of a public university in Peru. The purpose is to identify if the level of failure has experienced any positive or negative variation when moving from the context of face-to-face education to virtual education, due to the state of health emergency. This study makes it possible to demonstrate an initial reference state, relevant for decision-making at the level of university academic management, through strategies that will increase the approval rate of this professional school. The results obtained indicate that the level of failure of the students has experienced a positive variation when moving from the face-to-face context to the virtual one, due to the increase in the average rate of failure from 25% to 34%. The results are equivalent to 242 students failed during the context of face-to-face education, and 438 students failed during the context of virtual education. In addition, it is visualized that the specialty subjects, where there are the largest number of failed students, are linked to the area of electrical engineering. Being the subject of Electrical Machines I, the one with the highest failure rate, with a total average of 43% during the context of face-to-face education and 48% during virtual education.
Disrupting the Stock Market: Stocks Gone Crypto

Financial technology development is disrupting industries throughout the world. More and more companies are embracing some type of these innovative technologies because their executives believe that those technologies have value-increasing potential. But do investors share their point of view? Do investors react uniformly to the blockchain-related announcements globally? The purpose of this research is to determine, what is investors’ reaction to such changes in a corporate strategy. Investors’ reaction to corporate signals and announcements is best explored with the event study methodology. We focus our analysis on the large-cap publicly traded companies from the Central and Eastern Europe, more specifically, on the three companies from technology sector which are among the largest companies from this region. Our results show that there is a difference in the reaction between the investors in the emerging and developed markets. Cumulative abnormal returns are significant, but the investor reaction in Poland and the Czech Republic is positive, while investors in developed market’s reaction is a negative one.
Leveraging Open Banking: Challenges and Opportunities

The main goal of this research is to examine the application of innovative open banking models and services through the lens of the end users. Many countries have already started the process of writing regulations for open banking and its assorted services. These regulations are often written before the technology is entirely embraced by its intended users, and for this reason acceptance studies are of crucial importance in ascertaining the user expectations and the intended scope of their use. For this purpose, we present an UTAUT-2 based acceptance study which deals with user acceptance of open banking coupled with other notable technologies such as blockchain. By enabling open banking through the blockchain, many benefits could be achieved, primarily when dealing with trust and privacy of user data. These benefits are expected to play an influential part in raising the acceptance levels of open banking as a whole. Through the analysis of the model, we have identified several notable influential factors that can play a large part in the behavioral intention of users, and as such can be used as a guideline for the development of new and innovative open banking services.
A Medical Privacy Protection Model Based on Threshold Zero-Knowledge Protocol and Vector Space

Recent years have witnessed the rapid development of technologies such as the Internet and the Internet of Things, which have driven the development of all walks of life including economics, medicine, and so on. In the healthcare industry, big data has become a core asset in transforming personal care, and public health, and has been officially incorporated into the national development strategy as a national basic strategic resource. However, privacy security and leakage through medical big data become a serious issue in the healthcare industry, resulting in exploiting details specific to a disease, identity theft, destruction of databases, etc. This study proposes a privacy protection model for medical· data based on threshold zero-knowledge protocol and vector space. The model uses a threshold zero-knowledge protocol to verify the user's identity from the data source. After the verification, the system will vectorize its historical access records to calculate the correlation between their access requests and historical access records. The experimental results show that the model can issue the access permission accurately and reliably, as well as reduce the misjudgment of the system.