Reviewerly/exHarmony huggingface dataset:
Multi-instance Randomness Extraction and Security Against Bounded-Storage Mass Surveillance

Consider a state-level adversary who observes and stores large amounts of encrypted data from all users on the Internet, but does not have the capacity to store it all. Later, it may target certain “persons of interest” in order to obtain their decryption keys. We would like to guarantee that, if the adversary’s storage capacity is only (say) $$1\%$$ of the total encrypted data size, then even if it can later obtain the decryption keys of arbitrary users, it can only learn something about the contents of (roughly) $$1\%$$ of the ciphertexts, while the rest will maintain full security. This can be seen as an extension of incompressible cryptography (Dziembowski CRYPTO’06, Guan, Wichs and Zhandry EUROCRYPT’22) to the multi-user setting. We provide solutions in both the symmetric key and public key setting with various trade-offs in terms of computational assumptions and efficiency. As the core technical tool, we study an information-theoretic problem which we refer to as “multi-instance randomness extraction”. Suppose $$X_1, \ldots , X_t$$ are correlated random variables whose total joint min-entropy rate is $$\alpha $$ , but we know nothing else about their individual entropies. We choose t random and independent seeds $$S_1,\ldots ,S_t$$ and attempt to individually extract some small amount of randomness $$Y_i = \textsf{Ext}(X_i;S_i)$$ from each $$X_i$$ . We’d like to say that roughly an $$\alpha $$ -fraction of the extracted outputs $$Y_i$$ should be indistinguishable from uniform even given all the remaining extracted outputs and all the seeds. We show that this indeed holds for specific extractors based on Hadamard and Reed-Muller codes.
Searching for ELFs in the Cryptographic Forest

Extremely Lossy Functions (ELFs) are families of functions that, depending on the choice during key generation, either operate in injective mode or instead have only a polynomial image size. The choice of the mode is indistinguishable to an outsider. ELFs were introduced by Zhandry (Crypto 2016) and have been shown to be very useful in replacing random oracles in a number of applications. One open question is to determine the minimal assumption needed to instantiate ELFs. While all constructions of ELFs depend on some form of exponentially-secure public-key primitive, it was conjectured that exponentially-secure secret-key primitives, such as one-way functions, hash functions or one-way product functions, might be sufficient to build ELFs. In this work we answer this conjecture mostly negative: We show that no primitive, which can be derived from a random oracle (which includes all secret-key primitives mentioned above), is enough to construct even moderately lossy functions in a black-box manner. However, we also show that (extremely) lossy functions themselves do not imply public-key cryptography, leaving open the option to build ELFs from some intermediate primitive between the classical categories of secret-key and public-key cryptography. (The full version can be found at https://eprint.iacr.org/2023/1403 .)
Non-Interactive Anonymous Router with Quasi-Linear Router Computation

Anonymous routing is an important cryptographic primitive that allows users to communicate privately on the Internet, without revealing their message contents or their contacts. Until the very recent work of Shi and Wu (Eurocrypt’21), all classical anonymous routing schemes are interactive protocols, and their security rely on a threshold number of the routers being honest. The recent work of Shi and Wu suggested a new abstraction called Non-Interactive Anonymous Router (NIAR), and showed how to achieve anonymous routing non-interactively for the first time. In particular, a single untrusted router receives a token which allows it to obliviously apply a permutation to a set of encrypted messages from the senders. Shi and Wu’s construction suffers from two drawbacks: 1) the router takes time quadratic in the number of senders to obliviously route their messages; and 2) the scheme is proven secure only in the presence of static corruptions. In this work, we show how to construct a non-interactive anonymous router scheme with sub-quadratic router computation, and achieving security in the presence of adaptive corruptions. To get this result, we assume the existence of indistinguishability obfuscation and one-way functions. Our final result is obtained through a sequence of stepping stones. First, we show how to achieve the desired efficiency, but with security under static corruption and in a selective, single-challenge setting. Then, we go through a sequence of upgrades which eventually get us the final result. We devise various new techniques along the way which lead to some additional results. In particular, our techniques for reasoning about a network of obfuscated programs may be of independent interest.
Sign Language Dataset for Automatic Motion Generation

Several sign language datasets are available in the literature. Most of them are designed for sign language recognition and translation. This paper presents a new sign language dataset for automatic motion generation. This dataset includes phonemes for each sign (specified in HamNoSys, a transcription system developed at the University of Hamburg, Hamburg, Germany) and the corresponding motion information. The motion information includes sign videos and the sequence of extracted landmarks associated with relevant points of the skeleton (including face, arms, hands, and fingers). The dataset includes signs from three different subjects in three different positions, performing 754 signs including the entire alphabet, numbers from 0 to 100, numbers for hour specification, months, and weekdays, and the most frequent signs used in Spanish Sign Language (LSE). In total, there are 6786 videos and their corresponding phonemes (HamNoSys annotations). From each video, a sequence of landmarks was extracted using MediaPipe. The dataset allows training an automatic system for motion generation from sign language phonemes. This paper also presents preliminary results in motion generation from sign phonemes obtaining a Dynamic Time Warping distance per frame of 0.37.
Online Crime in the Metaverse

With the burgeoning growth of the metaverse and online virtual environments, new security challenges have been introduced that require careful exploration and mitigation. An increasing proportion of human interactions and transactions now take place in these digital spaces, making it essential to protect users and ensure the safety and integrity of virtual worlds. This chapter explores three dimensions of this issue. First, through a study of the types of crimes that occur in these environments, to gain a holistic understanding of the cybercrime technoscape. Second, the authors use a two-pronged approach to increase the safety of the metaverse by targeting both potential perpetrators and victims. This is achievable by identifying indicators that may be used to detect potential perpetrators or victims. Thirdly and finally, strategies and techniques to make these online communities safer are suggested.
Experimental and quantitative evaluation of frequency modulation caused by Doppler effect around high-speed moving sound source

High-speed train noise remains a wayside environmental issue. For accurate noise prediction, the characteristics of a moving sound source must be revealed. In this work, the frequency modulation of sound waves emitted from a high-speed moving sound source was experimentally investigated. In the experiment, the sound field around a running train model emitting a 40 kHz pure tone was measured by an optical measurement technique, parallel phase-shifting interferometry, which can visualize instantaneous sound fields. For quantitative evaluation, a lens distortion correction was also developed and adopted for the visualization results. From the measured result of a sound source moving at a running speed of 280 km/h, the frequency modulation, known as the Doppler effect, was observed, and the measured frequency agreed well with the theoretical values.
Enhancing Security of Web-Based IoT Services via XSS Vulnerability Detection

The Internet of Things (IoT) technology is experiencing significant growth and integration into various aspects of daily life. With the rising number of connected devices, diverse security challenges are emerging as substantial threats to IoT. Cross-Site Scripting (XSS) is one of the major security risks in web services and so is within the application layer of IoT. Many existing web applications remain susceptible to XSS vulnerabilities. In this paper, we propose an XSS detection scheme aimed at enhancing the security of IoT, particularly concerning web application services. To achieve this, we developed a framework for combining symbolic execution and dynamic taint analysis to provide a comprehensive security assessment. Our objective is to increase the ratio of vulnerability detection while avoiding false alarms and keeping the required analysis time as minimal. To realize our idea, we have defined an instrumentation scheme for taint analysis and concolic executions and automated the process of vulnerability detection for a web application. Our framework is capable of pinpointing the precise locations of security vulnerabilities and the exact input datasets at risk of XSS threats. Subsequently, the detected flaws can be easily removed. The experimental results demonstrate the validity of the proposed scheme. We achieved a detection rate of XSS threats of 90.62% using a test set of SecuriBench Micro and 69.11% using OWASP while showing 0% false positives.
Systematic Literature Review: Utilization of E-learning Moodle in Schools

Online education and learning frequently use the Moodle Learning Management System (LMS). The purpose of this study was to ascertain how E-learning and moodle were used. The SLR (Systematic Literature Review) method was the research methodology used in this study. By cataloguing all publications wit
Navigating the Nexus: Legal and Economic Implications of Emerging Tech-nologies

This research delves into the multifaceted interplay among emerging technologies, legal frameworks, and economic landscapes, highlighting the complexities, challenges, and opportunities within this nexus. The study unfolds the rapid evolution of emerging technologies, showcasing their transformative potential across industries while uncovering the intricate relationship between technological advancements and the adaptation of legal structures. It elucidates how the pace of innovation often outstrips regulatory capacities, leading to gaps and uncertainties in legal frameworks. Moreover, conflicts between economic objectives and ethical considerations underscore the need for integrative approaches that balance innovation with responsible practices. Successful integration of legal and economic factors is exemplified by businesses prioritizing ethical conduct, fostering consumer trust, and enhancing market reputation. Legislative initiatives worldwide reflect efforts to adapt regulations, focusing on data privacy, cybersecurity, and ethical AI development. Anticipated growth projections in technology sectors signal substantial economic opportunities but necessitate adaptive legal frameworks. Policy recommendations advocate for agile regulations, investments in education, international cooperation, and sustainable economic strategies to navigate these complexities responsibly. Ultimately, the research emphasizes collaborative efforts, innovative thinking, and ethical responsibility as crucial pillars in steering the course of emerging technologies for the collective benefit of society
Avoiding excessive AI service agent anthropomorphism: examining its role in delivering bad news

Purpose The aim of this paper is twofold. First, it seeks to understand how different forms of anthropomorphism, namely verbal and visual, can enhance or detract from the subjective well-being of consumers and their co-creation behaviors whilst collaborating with artificial intelligence (AI) service agents. Second, it seeks to understand if AI anxiety and trust in message, function as primary and secondary consumer appraisals of collaborating with AI service agents. Design/methodology/approach A conceptual model is developed using the theories of the uncanny valley and cognitive appraisal theory (CAT) with three hypotheses identified to guide the experimental work. The hypotheses are tested across three experimental studies which manipulate the level of anthropomorphism of AI. Findings Results demonstrate that verbal and visual anthropomorphism can assist consumer well-being and likelihood of co-creation. Further, this relationship is explained by the mediators of anxiety and trust. Originality/value The empirical results and theorizing suggest verbal anthropomorphism should be present (absent) and paired with low (high) visual anthropomorphism, which supports the “uncanny valley” effect. A moderated mediation relationship is established, which confirms AI anxiety and trust in a message as mediators of the AI service agent anthropomorphism-consumer subjective well-being/co-creation relationship. This supports the theorizing of the conceptual model based on the “uncanny valley” and CAT.
DMA controller design based on SHA-1 dual channel improvement algorithm

In order to make direct memory access (DMA) high-speed transmission while ensuring the security and integrity of data, the traditional Secure Hash Algorithm (SHA) is improved from the algorithm model and hardware architecture level by adopting the methods of data synchronization preprocessing and multi-block multi-output, and a dual-channel design with instant data comparison and data error correction functions adapted to high-speed DMA is achieved. The simulation results show that the maximum clock frequency of the DMA controller is 120 MHz and the throughput rate is up to 3.8 Gbps. The improved comparison channel can output the message summary at an interval of 0.56 μs, whereas the traditional algorithm needs to wait five times as long as the data handling time. The error correction channel also implements multi-block output and error block indexing. The results show that the improved dual-channel design based on the SHA algorithm can be highly adapted to DMA controllers and has good application prospects.
Live Demonstration: AI-Assisted Magnetic Skin tracker for Speech Recognition

Speech-sound disorder (SSD) afflicted people can have any combination of difficulties with perception, articulation/motor production, and phonotactics, that may impact their speech intelligibility and acceptability, thus finding challenging to communicate with the public. As a result, many patients suffer from frustration, isolation, and depression. Natural-verbal communication for SSD people is now more feasible than ever thanks to advancements in wearable artificial skins and machine learning. An Assistive Magnetic Skin System (AM2S) is proposed to enable SSD afflicted people to communicate with their mouths. Using magnetic field sensors integrated into Magnetphones, the system reads the movement of the mouth by tracking the movement of magnetic skin patches attached next to the bottom lip. The measured magnetic field signals data is then processed using a Fine k-Nearest Neighbor (KNN) classifier model. The classified data can be exported verbally on speakers, or visually on a display. AM2S successfully identifies the full English alphabets with average success rate of 94.96%.
A COMPARATIVE STUDY OF DIFFERENT ARTIFICIAL INTELLIGENCE MODELS AND RESPONSE SURFACE METHODOLOGY FOR HEPTACHLOR REMOVAL USING FE/CU NANOPARTICLES

Water is a basic and essential resource in the human body. Every structure in the body including cells, tissues and organs needs water to work properly. Hence, without food, humans can last up to several weeks, but just a few days without water. Meanwhile, before water is consumed by a human’s body, harmful impurities such as heptachlor which is a highly toxic organochlorine compound in the water must be removed. To remove heptachlor from the wastewater, the adsorption using the bimetallic iron/cupper (Fe/Cu) nanoparticles can be a solution. However, the effectiveness of the elimination of heptachlor using the Fe/Cu nanoparticles could be affected by environmental factors including pH, adsorbent dosage, contact time, initial adsorbate concentration, and stirring rate. Response surface methodology (RSM) is widely used to correlate these factors with the heptachlor removal efficiency to achieve performance optimisation. However, the artificial intelligence models may perform better than RSM to optimise the heptachlor removal process. Therefore, this study aims to compare the performance of different artificial intelligence models with RSM for heptachlor removal using Fe/Cu nanoparticles. These different artificial intelligence models include principal component regression (PCR), artificial neural network (ANN), locally weighted kernel partial least square regression (LW-KPLSR), partial least square regression (PLSR), and least-square support vector regression (LSSVR). Based on the obtained results, the LW-KPLSR model performed better than other artificial intelligence models and RSM. Its root means square error, and mean absolute error are around 159% to 3,297% lower than other models and RSM. Moreover, its coefficient of determination which is so-called R2 is the highest among others. These results denote that LW-KPLSR is more convincing as compared to RSM to predict optimum performance of heptachlor removal.
Title Pending 761

Objective: The university library aims to provide university researchers with a trustworthy institutional repository for sharing data. The library sought CoreTrustSeal certification in order to measure the quality of data services in the institutional repository, and to promote researchers’ confidence when depositing their work.Methods: The authors served on a small team of library staff who collaborated to compose the certification application. They describe the self-assessment process, as they iterated through cycles of compiling information and responding to reviewer feedback.&amp;nbsp;Results: The application team gained understanding of data repository best practices, shared knowledge about the institutional repository, and identified areas of service improvements necessary to meet certification requirements. Based on the application and feedback, the team took measures to enhance preservation strategies, governance, and public-facing policies and documentation for the repository.Conclusions: The university library gained a better understanding of top-notch data services and measurably improved these services by pursuing and obtaining CoreTrustSeal certification.
ANÁLISE PRELIMINAR DA DISTRIBUIÇÃO DA RADIAÇÃO SOLAR GLOBAL HORIZONTAL NA CIDADE DE PORTO ALEGRE - RS

Solar energy has shown rapid and sustainable growth in Brazil over the last few years. The drop in costs of solar systems, regulation and government incentives has contributed to the increase of all types of solar energy applications, whether for energy, lighting, heating and other uses. In the same way, there is an increase in the demand for more detailed and qualified data regarding the behavior of solar radiation on the Earth's surface to improve the performance of projects that use this energy as their main source. Only information about the average annual or monthly values of solar energy available on the surface is insufficient for the correct modeling of large-scale systems or systems that use energy storage. This work aims to analyze the behavior of the distribution of horizontal daily global solar radiation in the city of Porto Alegre, Rio Grande do Sul (Latitude -30.05 and Longitude -51.16), through data collected between 2001 and 2014 by the automatic weather station (EMA) of the National Institute of Meteorology (INMET). From the treated, qualified, and analyzed horizontal global solar radiation data, information was obtained on the daily, monthly, seasonal and annual frequency distribution of the horizontal daily global radiation. The annual average value for the daily horizontal global solar radiation was 1533 kWh/m² with a standard deviation of 65.26 kWh/m² and a coefficient of variation equal to 4.25%. The monthly averages integrated from the daily values show maximum values in January (6.23 kWh/m²) and minimum values in June (2.16 kWh/m²).
Prediction of Indonesian presidential candidates in 2024 using sentiment analysis and text search on Twitter

This study aims to show whether Twitter analysis can predict and forecast candidates in the Indonesian presidential election in 2024. This study was conducted long before the election began. To reduce the gap and utopian attitude in analyzing, two forms of analysis were used at once, namely sentiment analysis and text searches on Twitter data. This study uses a quantitative approach with descriptive content analysis. The data was obtained from Twitter social media, with Twitter Search focusing on official accounts and topics surrounding the 2024 presidential election. The search and data collection first adjusted to the trend of poll results spread in online news. The trend resulting from the poll is used to adjust the names of candidates to be searched for on Twitter search. The analysis tool used also utilizes the Nvivo 12 Plus analysis software. This study succeeded in mapping out three potential candidates in the 2024 election, namely Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto. The mapping of potential candidates also has correspondence with the results of opinion polls in newspapers. From these findings, the information and data on Twitter help make predictions and an alternative to using the poll method. The drawback of this study lies in the limited use of time, so it is recommended that further research be carried out to collect and analyze similar data regularly until the election period. This may indicate that Twitter can predict earlier or better than polls.
Improved version of double-layered enclosure demo

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 Said Shakerin; Improved version of double-layered enclosure demo. Phys. Teach. 1 December 2023; 61 (9): 725. https://doi.org/10.1119/5.0169715 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 ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Temperature-independent almost perfect photon entanglement from quantum dots via the SUPER scheme

Entangled photon pairs are essential for quantum communication technology. They can be generated on-demand by semiconductor quantum dots, but several mechanisms are known to reduce the degree of entanglement. While some obstacles like the finite fine-structure splitting of the exciton states can currently be overcome, the excitation scheme itself can impair the entanglement fidelity. Here, we demonstrate that the swing-up of quantum emitter population (SUPER) scheme, using two red-detuned laser pulses applied to a quantum dot in a cavity, yields almost perfectly entangled photons. The entanglement remains robust against phonon influences even at elevated temperatures, due to decoupling of the excitation and emission process. With this achievement, quantum dots are ready to be used as entangled photon pair sources in applications requiring high degrees of entanglement up to temperatures of approximately 80 K.