Reviewerly/exHarmony huggingface dataset:
Deep Learning for Spectrum Awareness and Covert Communications via Unintended RF Emanations

We present a deep learning-based spectrum sensing and covert communication framework for unintended (side-channel) electromagnetic emanations. Electronic devices release unintentional RF emissions (without using any RF transmitter) depending on their processing activities and these emissions can be captured at the clock frequency and its harmonics. First, we train a deep neural network, in particular, a convolutional neural network (CNN), to detect RF emanations from a microcontroller (in particular, Arduino Uno R3). Through over-the-air (OTA) experiments, we show that the CNN that is trained with the input of signals received at a software-defined radio (SDR) can reliably detect RF emanations at a range of up to 10 feet, while the performance of conventional energy detector remains limited. Second, we demonstrate how to encode RF emanations due to different programs running on a microcontroller and generate frequency shift keying (FSK) modulated signals without using any RF transmitter. The conventional scheme of quadrature demodulation can decode signals communicated at a range of up to 9 inches. On the other hand, we show that a CNN that is trained with RF emanation data collected with an SDR can decode the signals encoded over RF emanations with higher reliability and extend the communication range up to 4 feet. These capabilities are promising to support emerging systems such as Internet of Things (IoT) with novel applications including covert communications, low-power device monitoring and sensing, and energy-efficient communications.
Development of education and science in the historical industry in the conditions of transformations

The development of the historical field is an important aspect of modern European education and science. The study of the transformations of this important part of the modern humanities, comparison with the Ukrainian and Eastern European realities is a goal of our article. The work is based on the analysis of modern specialized literature and the use of scientific research methods: analysis and synthesis, induction, and deduction. The comparative method shows the peculiarities of the development of the historical branch in the EU countries (in particular, in Latvia). In modern historical education, the principles of consolidation prevail. European historical science is characterized by the importance of international conferences that promote consolidation. Ukrainian historical science has its successes and problems, it can also be compared with European, in particular Latvian (although Latvia was also part of the USSR).
Computational Imaging for Simultaneous Image Restoration and Super-Resolution Image Reconstruction of Single-Lens Diffractive Optical System

Diffractive optical elements (DOEs) are difficult to apply in natural scenes imaging covering the visible bandwidth-spectral due to their strong chromatic aberration and the decrease in diffraction efficiency. Advances in computational imaging make it possible. In this paper, the image quality degradation model of DOE in bandwidth-spectral imaging is established to quantitatively analyze its degradation process. We design a DDZMR network for a single-lens diffractive lens computational imaging system, which can simultaneously perform image restoration and image super-resolution reconstruction on degraded images. The multimodal loss function was created to evaluate the reconstruction of the diffraction imaging degradation by the DDZMR network. The prototype physical prototype of the single-lens harmonic diffraction computational imaging system (SHDCIS) was built to verify the imaging performance. SHDCIS testing showed that optical chromatic aberration is corrected by computational reconstruction, and the computational imaging module can interpret an image and restore it at 1.4 times the resolution. We also evaluated the performance of the DDZMR model using the B100 and Urban100 datasets. Mean Peak Signal to Noise Ratio (PSNR)/Structural Similarity (SSIM) were, respectively, 32.09/0.8975 and 31.82/0.9247, which indicates that DDZMR performed comparably to the state-of-the-art (SOTA) methods. This work can promote the development and application of diffractive imaging systems in the imaging of natural scenes in the bandwidth-spectrum.
Synthetic Data -- what, why and how?

This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The article is intended for a non-technical audience, though some formal definitions have been given to provide clarity to specialists. This article is intended to enable the reader to quickly become familiar with the notion of synthetic data, as well as understand some of the subtle intricacies that come with it. We do believe that synthetic data is a very useful tool, and our hope is that this report highlights that, while drawing attention to nuances that can easily be overlooked in its deployment.
Participatory Cultures of Digital Games: The Double-Edged Sword of Being a Reddit Community Moderator

Abstract Volunteer community moderators are at the centre of a multitude of conflicting views in the media and society, while being continually exposed to inappropriate content and risking their mental wellbeing. On Reddit.com, their specific interest in a topic drives them to volunteer for moderation responsibilities to nurture the participa-tory community; however, this puts them in the uncertain position of being neither a member of the community nor a representative of the social media platform tied to the community. By adopting Nico Carpentier’s concept of participation as a site of political–ideological negotiation, this article explores the precarious conditions and expectations of community moderators around digital games after the 2020 COVID-19 struggles, with more people turning to social media, which increases demand for platform moderation. The study is based on semi-structured interviews with community moderators associated with online media communities (subreddits) of different games on the social media platform Reddit. It draws two conclusions: Firstly, the field of subreddit moderation involves multiple actors, such as content creators, content generators, moderators, and game studios. Here, moderators hold an assumed absolute power in two processes of decision-making: (1) ensure that content abides by the sitewide rules and subreddit specific rules; and (2) promote content that is sensitive to the subreddit’s cultural context. In decision-making moments, the tools that are afforded to moderators from the Reddit platform put moderators at a disadvantage, as they are forced to implement their decisions using negative reinforcement as opposed to achieving change in the subreddit through positive reinforcement. Secondly, community moderators exist within a participatory process of checks and balances, making any moderation actions a double-edged sword where any intervention by moderators can result in backlash or disagreement from content creators and content generators, and material advantages largely determine actors’ involvement in these participatory processes.
Three-Pass Identification Scheme Based on MinRank Problem with Half Cheating Probability

In Asiacrypt 2001, Courtois proposed the first three-pass zero-knowledge identification (ID) scheme based on the MinRank problem. However, in a single round of Courtois' ID scheme, the cheating probability, i.e., the success probability of the cheating prover, is 2/3 which is larger than half. Although Courtois also proposed a variant scheme which is claimed to have half cheating probability, its security is not formally proven and it requires another hardness assumption on a specific one-way function and that verifier always generates challenges according to a specific non-uniform distribution. In this paper, we propose the first three-pass zero-knowledge ID scheme based on the MinRank problem with the cheating probability of exactly half for each round, even with only two-bit challenge space, without any additional assumption. Our proposed ID scheme requires fewer rounds and less total average communications costs compared to Curtois' under the same security level against impersonation.
Generative Adversarial Neural Operators

We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences and engineering are known to have many types of data that are sampled from infinite-dimensional function spaces, where classical finite-dimensional deep generative adversarial networks (GANs) may not be directly applicable. GANO generalizes the GAN framework and allows for the sampling of functions by learning push-forward operator maps in infinite-dimensional spaces. GANO consists of two main components, a generator neural operator and a discriminator neural functional. The inputs to the generator are samples of functions from a user-specified probability measure, e.g., Gaussian random field (GRF), and the generator outputs are synthetic data functions. The input to the discriminator is either a real or synthetic data function. In this work, we instantiate GANO using the Wasserstein criterion and show how the Wasserstein loss can be computed in infinite-dimensional spaces. We empirically study GANO in controlled cases where both input and output functions are samples from GRFs and compare its performance to the finite-dimensional counterpart GAN. We empirically study the efficacy of GANO on real-world function data of volcanic activities and show its superior performance over GAN.
The Impact of Dynamic Capabilities on Knowledge Management in Kurdistan Region of Iraq

There are different factors that contribute and affect Knowledge Management such as the management system, teamwork, and dynamic capabilities within the organization. The aim of this study is to investigate dynamic capabilities of a firm and their impact on knowledge management at various companies in the Kurdistan Region of Iraq. Furthermore, it is aimed to examine the effects of employee experience on dynamic capabilities and knowledge management. Driven from the results, it was observed that organizational dynamics is one of the significant indicators of knowledge management. Secondly, the findings have revealed that experience is an important moderator between organizational dynamics and knowledge management.
Analysis of tik-tok video‘s competitive factors using a comprehensive multilevel fuzzy evaluation method

Due to the emerging current globalization trends (Industry 4.0., Digitization, Artificial Intelligence, and Disruptive Technologies) and the unprecedented accompanying social challenges, new network media solutions are becoming increasingly powerful.The short video industry will have more opportunities, while market competition will be increasingly stronger.The competition between enterprises is essentially the competition of core competitiveness.This paper focuses on Tik-Tok (in China Douyin) short video and it studies the core competence of Douyin short video from four aspects: international development ability, cultural communication ability, technology research and development ability and profit generation ability.Starting from the introduction and analysis of core competence theories, the short video industry and particularly Douyin short video will be overviewed, including its current business environment.Multilevel fuzzy comprehensive evaluation was used to test the core competitiveness of Douyin short video.The final aim is to obtain specific measures in order to enhance its core competitiveness through the above methods including future challenges (how to strictly distinguish between entertainment and trading, the homogeneity of video recommendation categories, network security and supervision).Besides that, at the same time, its social, and business opportunities (new realization mode, obtaining better traffic, creating more social value to promote economic development, knowledge popularization) will be analysed.
The Creative Indifference of a “Shifter of Registers” (Epochenverschleppe) – Gregor Von Rezzori

The body of the article goes on to highlight the problem of Gregor von Rezzori’s extravagant, lavish and idiosyncratic style entwined with his innate exquisite irony and scepticism, sometimes sarcasm, which can be inferred from his stance of indifference to social norms and trends.In the given context, the most expressive and illustrious are Rezzori’s novels “Oedipus Prevails at Stalingrad” (1954) and “On My Own Traces” (1997), which reflect the author’s ambitious venture into his autobiographical mythologizing from a perspective of the category of time and shed the light on how the artist’s negative experience can be turned into creative indifference. Of particular value, for our research, is the convergence of timeless themes and the ideological intertextuality created in two different surroundings, and provoked by fateful historical events. Rezzori’s irony of his own search for self-identity, a distinguishing feature for postmodern poetics, is one of the most critical topics to perceive the author’s personal experience. Transforming his childhood memories of his lost homeland into the imaginative artistic style, the author succeeded in creating a mythical topos, derived from the personal author’s mythologies (his autobiographical fiction “An Ermine in Czernopol” serves as an excellent example). It should be articulated, that pervasive irony and self-irony are key features of the author’s style. With his novel “Oedipus at Stalingrad”, Rezzori prior to postmodernism in art, foreshadowed his conceptual principles, philosophical fundamentals and worldview. In time, his fiction “On My Own Traces” picked up the threads of the ideas of “Oedipus” quintessence. To conclude, Rezzori’s mystical experiences of his childhood are preserved in his fiction like the most precious treasures that reveal the childish fragile soul seeking for its recognition. To grasp Rezzori’s philosophy, we should realize the author’s key concept – the category of time, which enables him to recognize himself as “Epochenverschlepper”: in his fiction, time lines are blurred, to some extent they become meaningless and functionally typify human history continuity. Consequently, in periods when everything seems rickety and unsettled: the past, the present, and the unpredictable future, the author dares to delve into in-depth psychology of human feelings and knowing thy-self.
NukaBot: Research and Design of a Human-Microbe Interaction Model

We describe the design, implementation and on going evaluation processes of “NukaBot”, a system built to realize Human to Microbe Interaction, aimed to assist production of fermented food. Our system senses, records and analyzes in real time the fermentation process inside a “nukadoko”, a traditional method to produce vegetable pickles in Japan that involves a highly complex network of microbes. The flora of a nukadoko mainly consists of lactic acid bacteria, yeasts and gram-negative bacteria. The novelty of our system lies in providing an intuitive user interface that lets its user chat with a virtual persona attributed to the nukadoko. The NukaBot thus enables non-specialists to discern the complex dynamics of the microbial communities within a nukadoko in daily situations.
A Question Answering System over Chronic Diseases and Epigenetics Knowledge

Medical records describe patients’ health conditions and help experts to decide on treatments. The scientific biomedical knowledge can improve the prevention and treatment of diseases and promote innovation and discovery in health. However, healthcare professionals may have difficulty in searching for relevant scientific information due to lack time and constant literature update. The present work proposes a Question Answering (Q&amp;A) architecture to support a more focused search for information about chronic diseases. A user question in natural language initiates the search for answering and promoting knowledge such as a learning healthcare system. To evaluate the system, we employ a reference collection on epigenetics and chronic disease and calculate performance measures like precision, recall and F-measure.
The Impact of Institutional Quality on Financial Inclusion

Financial inclusion (FI) is considered crucial in poverty reductionand promoting economic status.However the FI varies greatly across countries.This study explores the determinants of financial inclusion particularly the effect of institutions, both legal and political, on financial inclusion (FI).The study uses data of 64 former colonies and employs 2SLS, DOLS and FMOLS to examine the causal effect of institutions on financial inclusion.The study found that legal and political institutions significantly affect the degree of FI across countries.The study suggests that establishing strong institutions can help countries achieve higher levels of financial inclusion.
Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis (Preprint)

<sec> <title>UNSTRUCTURED</title> Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards. </sec>
Risk Assessment for Cyber Security of Manufacturing Systems: A Game Theory Approach

This paper presents a novel approach using game theory to assess the risk likelihood in manufacturing systems quantifiably. Cybersecurity is a pressing issue in the manufacturing sector. Nevertheless, managing the risk in cybersecurity has become a critical challenge for modern manufacturing enterprises. In risk management thinking, the first step is to identify the risk, then validate it, and lastly, consider responses to the risk. If the risk is below the security risk appetite of the manufacturing system, it could be accepted. However, if it is above the risk appetite, the system should appropriately respond by either avoiding, transferring, or mitigating the risk. The validation of the risk in terms of severity and likelihood of the threat, however, is challenging because the later component is hard to quantify. In this paper, Failure Modes and Effects Analysis (FMEA) method is modified by employing game theory to quantitatively assess the likelihood of cyber-physical security risks. This method utilizes the game theory approach by modeling the rivalry between the attacker and the system as a game and then try to analyze it to find the likelihood of the attacker’s action. We first define players of the game, action sets, and the utility function. Major concerns of cyber security issues in the manufacturing area are carefully considered in defining the cost function composed of defense policy, loss in production, and recovery. A linear optimization model is utilized to find a mixed-strategy Nash Equilibrium, which is the probability of choosing any action by the attacker also known as the likelihood of an attack. Numerical experiments are presented to further illustrate the method. Forecasting the attacker’s behavior enables us to assess the cybersecurity risk in a manufacturing system and thereby be more prepared with plans of proper responses.
3D Skeleton Action Recognition for Security Improvement

Most of action recognition methods allow achieving high action recognition accuracy, but only after processing the entire video sequence, however, for security issues, it is primordial to detect dangerous behavior occurrence as soon as possible allowing early warnings.In this paper, we present a human activity recognition method using 3D skeleton information, recovered by an RGB-D sensor by proposing a new descriptor modeling the dynamic relation between 3D locations of skeleton joints expressed in Euclidean distance and spherical coordinates between the normalized joints, A PCA dimension reduction is used to remove noisy information and enhance recognition accuracy while improving calculation and decision time.We also study the accuracy of the proposed descriptor calculated on limited few first frames and using limited skeleton joint number, to perform early action detection by exploring several classifiers.We test this approach on two datasets, MSR Daily Activity 3D and our own dataset called INDACT.Experimental evaluation shows that the proposed approach can robustly classify actions outperforming state-of-the-art methods and maintain good accuracy score even using limited frame number and reduced skeleton joints.
Detecting audio adversarial examples for protecting speech-to-text transcription neural networks

With the increasing use of deep learning techniques in real-world applications, their vulnerabilities have received significant attention from deep-learning researchers and practitioners. In particular, adversarial examples for deep neural networks and protection methods against them have been well-studied in recent years because they have serious vulnerabilities that threaten safety in the real-world. Audio adversarial examples, which are targeted attacks, are designed such that the deep neural network-based speech-to-text systems misunderstand input voice sound. In this study, we propose a new protection method against audio adversarial examples. The proposed protection method is based on a sandbox approach, where an input voice sound is checked in the system to determine if it is an audio adversarial example. To evaluate the proposed protection method, we used actual audio adversarial examples created on deep speech, which is a typical speech-to-text transcription neural network. The experimental results show that our protection method can detect audio adversarial examples with high accuracy.