Reviewerly/exHarmony huggingface dataset:
Competencia digital y desarrollo humano en la era de la Inteligencia Artificial

In the age of Artificial Intelligence (AI), digital competence emerges as an essential pillar for human development. The article begins by exploring in detail what digital competence entails and how it has become a crucial skill in an increasingly digitized world. It then examines the profound transformation of society driven by AI, which has reshaped the economy, education and everyday life, creating new opportunities and challenges. It also delves into the convergence between digital competence and artificial intelligence, and highlights how these two areas influence each other, generating synergies and opportunities for technological progress and human development. Furthermore, it also highlights digital competence as a skill for human development and analyzes how the acquisition of digital skills can boost access to opportunities, inclusion and improved quality of life. Finally, strategies to foster digital competence and human development in the AI era are presented, and the importance of inclusive education, continuous learning, universal access to technology, socioemotional skills, ethical literacy, and cross-sector collaboration is emphasized.
Implementasi Malicious Traffic Untuk Mendeteksi Serangan Siber di SMK Muhammadiyah 1 Yogyakarta

The Covid-19 pandemic has had a positive impact including the increasing implementation of information technology in various fields including the education sector. One of them is the increasing use of mobile devices such as cell phones or tote computers that utilize wireless networks. Connecting devices via wireless networks has a serious impact on the potential threat of cyber attacks. The potential threat of cyberattacks on devices during the Covid-19 pandemic is increasing, this is indicated by 975 million anomalous traffic recorded by Badan Siber dan Sandi Negara (BSSN) in 2022. To identify these potential threats, a sensor is needed on the internal network. In this research, it is proposed to implement a cyber attack sensor to detect these potential threats. Based on network traffic monitoring, this research shows that there is a potential for data leakage originating from cell phones. Therefore, the potential for data leakage from cell phones or other devices needs to be evaluated to secure computer systems and networks.
Introducing the Toric Code

Abstract We introduce the toric code as an example of a quantum error correcting code. The system is a lattice of spins on a torus. We use a basis of spin up/down spins which we translate into diagrams of colored (or uncolored) edges of the lattice. We introduce commuting vertex and plaquette operators, and explain that particular eigenstates of these operators define our code-space which in the diagram language corresponds to sums over loop configuration. The code space decomposes into four orthogonal wavefunctions which are distinguished by the parity of the number of colored loops around each of the two cycles of the torus. Quantum information is stored in the coefficients of these four wavefunctions in superposition. We then turn to consider error processes. We discover that errors occur in pairs locally and are moved apart from each other by “strings” of additional error operators. We discover that it is easy to identify when physical errors have occurred and correct them before a logical error occurs. We extend the discussion to consider irregular “lattices” of spins and surfaces with arbitrary genus (and make the connection to the Shor Code). Finally we turn to consider the ZN generalization of the toric code.
Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.
Differential Privacy Preserving Quantum Computing via Projection Operator Measurements

Quantum computing has been widely applied in various fields, such as quantum physics simulations, quantum machine learning, and big data analysis. However, in the domains of data-driven paradigm, how to ensure the privacy of the database is becoming a vital problem. For classical computing, we can incorporate the concept of differential privacy (DP) to meet the standard of privacy preservation by manually adding the noise. In the quantum computing scenario, researchers have extended classic DP to quantum differential privacy (QDP) by considering the quantum noise. In this paper, we propose a novel approach to satisfy the QDP definition by considering the errors generated by the projection operator measurement, which is denoted as shot noises. Then, we discuss the amount of privacy budget that can be achieved with shot noises, which serves as a metric for the level of privacy protection. Furthermore, we provide the QDP of shot noise in quantum circuits with depolarizing noise. Through numerical simulations, we show that shot noise can effectively provide privacy protection in quantum computing.
The Market for Children’s Books and the Compensation of Illustrators in Soviet Russia in the 1920s and 1930s

In the USSR of the 1920s and 1930s, the democratization of children’s books was based on three phenomena: the boom in picture books along with extensive and intensive growth. The first part of this article traces the evolution of these phenomena in the economic environment and details their relation with editorial policies to uncover how the effort of democratization was intertwined with the definition of the Soviet children’s book. The second part of this study reveals how the contexts surrounding the popularization of children’s books defined the working conditions of illustrators and the ways of thinking about methods of remuneration. It examines the criteria on which the remuneration of graphic artists was based and how the amounts offered reflected their recognition. In conclusion, this article contributes to research devoted to the status of the artist in the USSR by showing how the recognition of the graphic artist evolved and how the system established in the 1930s laid the foundations for the illustrative activity of the 1960s, when certain visual artists saw it as a financial refuge.
Adamastor: a New Low Latency and Scalable Decentralized Anonymous Payment System

This paper presents Adamastor, a new low latency and scalable decentralized anonymous payment system, which is an extension of Ring Confidential Transactions (RingCT) that is compatible with consensus algorithms that use Delegated Proof of Stake (DPoS) as a defense mechanism against Sybil attacks. Adamastor also includes a new Decoy Selection Algorithm (DSA) that can be of independent interest, called SimpleDSA, a crucial aspect of protocols that use ring signatures to anonymize the sender. SimpleDSA offers security against homogeneity attacks and chain analysis. Moreover, it enables the pruning of spent outputs, addressing the issue of perpetual output growth commonly associated with such schemes. Adamastor is implemented and evaluated using the Narwhal consensus algorithm, demonstrating significantly lower latency compared to Proof of Work based cryptocurrencies. Adamastor also exhibits ample scalability, making it suitable for a decentralized and anonymous payment network.
G-MEMP: Gaze-Enhanced Multimodal Ego-Motion Prediction in Driving

Understanding the decision-making process of drivers is one of the keys to ensuring road safety. While the driver intent and the resulting ego-motion trajectory are valuable in developing driver-assistance systems, existing methods mostly focus on the motions of other vehicles. In contrast, we focus on inferring the ego trajectory of a driver's vehicle using their gaze data. For this purpose, we first collect a new dataset, GEM, which contains high-fidelity ego-motion videos paired with drivers' eye-tracking data and GPS coordinates. Next, we develop G-MEMP, a novel multimodal ego-trajectory prediction network that combines GPS and video input with gaze data. We also propose a new metric called Path Complexity Index (PCI) to measure the trajectory complexity. We perform extensive evaluations of the proposed method on both GEM and DR(eye)VE, an existing benchmark dataset. The results show that G-MEMP significantly outperforms state-of-the-art methods in both benchmarks. Furthermore, ablation studies demonstrate over 20% improvement in average displacement using gaze data, particularly in challenging driving scenarios with a high PCI. The data, code, and models can be found at https://eth-ait.github.io/g-memp/.
Recommender systems may enhance the discovery of novelties

Recommender systems are vital for shaping user online experiences. While some believe they may limit new content exploration and promote opinion polarization, a systematic analysis is still lacking. We present a model that explores the influence of recommender systems on novel content discovery. Surprisingly, analytical and numerical findings reveal these techniques can enhance novelty discovery rates. Also, distinct algorithms with similar discovery rates yield varying opinion polarization outcomes. Our approach offers a framework to enhance recommendation techniques beyond accuracy metrics.
Quantum cryptography - A modern approach

In Our daily life we are getting very much dependent on Internet or Virtual Reality. They are nothing but an illusion of a Cyberspace. It has effect on our every aspect of life, like Education, Sports, and Health etc. We are using the term Cryptography which is the science of using mathematics to encrypt and decrypt data. We had used various Encryption standards in back days namely, DES, AES, RSA, Elliptical AES etc. But nowadays we are using Quantum Cryptography for this. It is nothing but use of Physics (mainly Quantum Physics) for Encrypting the data. In this Research paper we will mainly focus on the new approaches that come in the path of Quantum Cryptography. We will also view some interaction of Quantum Cryptography in future Internet services. Quantum computing is yet to be explored but still the explored part has the capabilities to counter the “alien” hacking protocols if tested over any System. Older System were using protocols like RSA, AES, DES even some of them are more capable than our need, but the use of QC will not only secure the systems but there are major chances that eavesdropper got caught while eavesdropping. Also QC is a pathway to secure communication. This paper includes the review of older state of Quantum Cryptography which will give us information about QKD algorithms and its protocol and we will also overview on its risk and security features. We will also provide a glimpse of current various works going in various Educational Institutions.
The Impact of Electronic Marketing on Sustaining Entrepreneurial Viability in Major Restaurants in the Kingdom of Saudi Arabia in the Context of the Covid-19

The purpose of this research is to examine to what extent the Corona pandemic affected the biggest restaurants in the Kingdom of Saudi Arabia in terms of sales, marketing client relations, generating profits, and elevating the leadership level. The goal of the study is to find out how electronic marketing may be used to boost these regions during the pandemic and what effect it might have on the popularity of important restaurants in the Kingdom of Saudi Arabia. To achieve its goals, the study used a descriptive-analytical methodology. The needed information from respondents was collected as part of the research design using a survey questionnaire. With the use of the descriptive-analytical methodology, the data can be systematically and impartially examined to reveal information about the variables being studied. The study examined the performance of e-marketing in the restaurant sector during the COVID-19 pandemic. Respondents from two cities in Saudi Arabia who were largely male, aged 25 to 35, and had post-graduate education made up the sample. According to the study, e-marketing helped restaurants gain a competitive advantage, greatly increase profits, develop a good market share, and significantly attract consumers. It also made it easier to receive and handle customer complaints. The study's findings, which show a substantial difference in the responses from those who strongly agreed with the claims, are statistically significant. The importance of this paper lies in its analysis of how restaurants in the Kingdom of Saudi Arabia utilized marketing mix methods while they were closed during the Covid-19 outbreak by investing in e-marketing. The study offers insights into how firms can adapt and innovate during a crisis to sustain client engagement and sales by examining the efficacy of different techniques. The study's results may also be helpful for scholars and officials who want to comprehend how the epidemic has damaged the region's restaurant industry economically and figure out how to help impacted companies.
Dual-Material Metastructure for Simultaneous Filtering and Focusing of Incoherent White Light

Microlenses integrated with color filters continue to be of great interest for sensors, light-emitting diodes, three-dimensional (3D) imaging, and 3D display applications. Many techniques have been investigated to reduce the thickness and the size of the total devices, and microlenses with hybrid functions have been developed. However, traditional hybrid microlenses usually work under coherent illumination due to the single material used and the lack of effective diffraction elements for incoherent illumination. Here, we demonstrate a new flat optics device based on a dual-material 3D metastructure that achieves simultaneous color filtering and focusing under spatially incoherent white light illumination. Our device comprises a nanocone array fabricated by 3D direct laser writing at the interface with a Fresnel metalens based on a typical two-dimensional (2D) material: graphene oxide-reduced graphene oxide. The unique spatial arrangement of the nanocone array enables us to double the acceptable spatial coherence of the illumination compared with conventional nanopillar-based structural color. The chemical and optical stabilities of the metalens under the laser fabrication conditions allow for the successful integration with the nanocone array (∼200 nm diameter) and diffraction-limited focusing (within 50 μm). The dual-material 3D metastructure enables simultaneous filtering and focusing for selected wavelengths within a distance of 20 μm under incoherent white light illumination. Our results show promise for expanding the degrees of freedom of light manipulation in flat optics and the implementation of hybrid and multifunctional photonic devices in CCD and CMOS-compatible, high-resolution light field prints for 3D display.
AI Regulation and the Protection of Source Code

Abstract Artificial intelligence (AI) has been rapidly developing since the mid-20th century. Today, AI is deployed in many aspects of our daily lives and is increasingly ubiquitous. At the same time, concerns about AI, including discrimination, privacy, and security, have prompted calls for greater regulation. To this end, regulators may seek access to AI’s source code (the human-readable program for the software). However, many trade agreements contain provisions prohibiting source code disclosure as a condition for market access in the relevant countries. This article considers the tension between AI regulation and these provisions. In situations where such provisions apply, for AI regulation to remain compliant, we argue that regulators can only require access to source code in exceptional cases where the general exceptions in the General Agreement on Trade in Services apply.
Assessment innovation using quizizz paper mode in PAI learning

With the development of the times, more and more advanced information technologies have emerged. One sign of the advancement of information technology is the use of various innovative applications, especially in the educational aspect. Most educators still do not understand the existence of this innovative application, this is the driving reason behind this research. This study aims to describe the use of Paper Mode Quizizz innovation in PAI learning assessment. The approach in this study is a qualitative approach using the literature study method. Data collection techniques by collecting and analyzing from various articles and images and video tutorials. The quizizz application is a game-based learning application by conducting assessments using quizzes to make it more fun because there are elements of games in it. Paper Mode Quizizz is one of the features of the Quizizz platform in the form of quizzes using paper that can be done by students without using a smartphone device. The result of this study is that Paper Mode Quizizz as a learning assessment media can be an alternative for educators so that assessments can run interestingly, interactively, and meaningfully. In addition, this innovation is also a learning medium in the form of educational games that can increase student participation in learning.
Adaptive Distributional Security for Garbling Schemes with $${\mathcal {O}({{|x|}})}$$ Online Complexity

Garbling schemes allow to garble a circuit C and an input x such that C(x) can be computed while hiding both C and x. In the context of adaptive security, an adversary specifies the input to the circuit after seeing the garbled circuit, so that one can pre-process the garbling of C and later only garble the input x in the online phase. Since the online phase may be time-critical, it is an interesting question how much information needs to be transmitted in this phase and ideally, this should be close to $${|x|}$$ . Unfortunately, Applebaum, Ishai, Kushilevitz, and Waters (AIKW, CRYPTO 2013) show that for some circuits, specifically PRGs, achieving online complexity close to $${|x|}$$ is impossible with simulation-based security, and Hubáček and Wichs (HW, ITCS 2015) show that online complexity of maliciously secure 2-party computation needs to grow with the incompressibility entropy of the function. We thus seek to understand under which circumstances optimal online complexity is feasible despite these strong lower bounds. Our starting point is the observation that lower bounds (only) concern cryptographic circuits and that, when an embedded secret is not known to the adversary (distinguisher), then the lower bound techniques do not seem to apply. Our main contribution is distributional simulation-based security (DSIM), a framework for capturing weaker, yet meaningful simulation-based (adaptive) security which does not seem to suffer from impossibility results akin to AIKW. We show that DSIM can be used to prove security of a distributed symmetric encryption protocol built around garbling. We also establish a bootstrapping result from DSIM-security for $$\text {NC}^0$$ circuits to DSIM-security for arbitrary polynomial-size circuits while preserving their online complexity.
AI Based Video Processing using OO

The increase in digital video material has increased the requirement for efficient video processing methods. Conventional techniques of video processing are sometimes time-consuming and reliant on substantial user intervention, resulting in decreased efficiency and precision. Video processing based on Artificial Intelligence (AI) is developing as a feasible alternative to conventional approaches. In this study, we investigate the application of Object-Oriented AI concepts to video processing. Our method utilizes object identification and recognition algorithms based on deep learning to recognize and track objects in video streams. In addition, we illustrate the efficacy of our method by applying it to a variety of actual video processing settings
Enhancing Bank Service Popularity Through Digital Innovation: Strategies for Customer-Centric Technology Integration

The paper explores the possible strategies to increase the use of digital tools in modern banking. The conceptual study proposes four strategies banks should focus on to be competitive and satisfy diverse customer needs. The primary focus is enhancing the customer experience through digital tools, such as online payments, credit requests, and bill payments while acknowledging the security and technical limitations with which users may have issues—the strategies banks can utilize have a customer-centric approach that includes user-friendly design and personalization. The technology focus would encompass technological advancements with security features like encryption and biometric verification and integration with broader services. Financial product innovation and effective marketing strategies are also essential for increasing the use of digital banking tools. Blockchain and AI, integrated marketing, and customer relationship management should be significant points of emphasis for the banks to serve the business and consumer markets effectively.
Algebraic Attacks on Round-Reduced Rain and Full AIM-III

Picnic is a NIST PQC Round 3 Alternate signature candidate that builds upon symmetric primitives following the MPC-in-the-head paradigm. Recently, researchers have been exploring more secure/efficient signature schemes from conservative one-way functions based on AES, or new low-complexity one-way functions like Rain (CCS 2022) and AIM (CCS 2023 and Round 1 Additional Signatures announced by NIST PQC). The signature schemes based on Rain and AIM are currently the most efficient among MPC-in-the-head-based schemes, making them promising post-quantum digital signature candidates. However, the exact hardness of these new one-way functions deserves further study and scrutiny. This work presents algebraic attacks on Rain and AIM for certain instances, where one-round Rain can be compromised in $$2^{n/2}$$ for security parameter $$n\in \{128,192,256\}$$ , and two-round Rain can be broken in $$2^{120.3}$$ , $$2^{180.4}$$ , and $$2^{243.1}$$ encryptions, respectively. Additionally, we demonstrate an attack on AIM-III (which aims at 192-bit security) with a complexity of $$2^{186.5}$$ encryptions. These attacks exploit the algebraic structure of the power function over fields with characteristic 2, which provides potential insights into the algebraic structures of some symmetric primitives and thus might be of independent interest.