Reviewerly/exHarmony huggingface dataset:
Effects of virtual reality usage on kappa angle, accommodation, pupil, depth perception, and examination of the relationship of these parameters with discomfort perception

Objective: This study aims to examine the effects of virtual reality (VR) usage on the eyes and investigate the parameters responsible&#x0D; for the subsequent discomfort sensation.&#x0D; Materials and Methods: This prospective study enrolled 20 healthy volunteers who were engaged in a 10-minute VR game session.&#x0D; Refractive errors, kappa angles, phoria presence, accommodative responses, and scotopic, mesopic, and photopic pupillometry values&#x0D; were recorded before and after using VR. A Virtual Reality Sickness Questionnaire (VRSQ) was applied to assess discomfort, and the&#x0D; relation with evaluated parameters was investigated.&#x0D; Results: Twenty volunteers (mean age 29.80±0.57 years) included 11 females (55%) and 9 males (45%). The mean spherical equivalent&#x0D; refractive error was – 1.94±0.28 diopters and 5 (25%) volunteers had phoria. Average kappa angles were 0.23±0.02 mm (x-axis) and&#x0D; 0.11±0.01 mm (y-axis). Post-VR, the median [(interquartile range (IQR)] stereopsis decreased from 30 (30-60) to 60 (60-60) arc&#x0D; seconds (P
Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters

In a prior report (Raju et al., 2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 75 Hz were the signal and data above 75 Hz were noise. Here, we compare five filters in their ability to preserve signal and remove noise. We compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. All filters were evaluated on 216 fixation segments (256 samples), from nine subjects. Mean frequency response profiles and amplitude spectra for all five filters are provided. Also, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. We also report on the effect of these filters on temporal autocorrelation.
Visual Replacements: Cross-Language Domain-Specific Representations in Structured Editors

To help developers work at the level of abstraction of their choice, some editors offer to replace parts of source code with domain-specific representations (DSRs). Typically, such replacements are language-specific and optimized to limited use-cases. In this paper, we extend a general-purpose structured editor with visual replacements, a mechanism to replace code of any programming language with DSRs. A visual replacement consists of language-dependent queries to map arbitrary syntax trees and a language-independent DSR of differing abstraction and interactivity, ranging from simple images to graphical user interfaces that modify source code. Our extension synchronizes source code and DSR automatically, while ensuring that language support such as autocompletion and error checking work even inside a replacement. We demonstrate the use and applicability of the replacement mechanism in three case studies.
programmingLanguage as Language;

Programming languages are languages --- “unnatural” languages because they are constructed explicitly; “formal” languages because they rely on mathematical notations and are described mathematically; “machine” languages because they are used to communicate with machines. Above all, programming languages are “human” languages. Programs in programming languages are spoken and read and written and designed and debugged and debated by humans, supported by human communities and forming those communities in turn. Langauge implementations, being programs themselves, are likewise designed and debugged and debated by humans.
Creating disinformation: Archiving fake links on the Wayback Machine viewed through the lens of routine activity theory

This case study uses routine activity theory to contextualise the method used by an external bad actor to create fake links within the Internet Archive for the Web site Yourbrainonporn.com. It then discusses the social media campaign which occurred two years later using screenshots of these fake links accessed via the Wayback Machine to defame the site owner. An organised disinformation campaign on social media began attacking the site owner of Yourbrainonporn.com (a pornography recovery Web site) for allegedly, accidentally, posting evidence on his own site of him searching for and hosting hardcore pornography. In fact, the list of purportedly incriminating links did not point to any content, but the defamers’ intentions seemed to have always been to set up a smear campaign against a particular site and its author. Options are discussed for the Internet Archive to provide improved guardianship and to educate the public to minimise harm from this type of social media attack based on screenshots of fake URLs.
Improve children’s literacy with the reading aloud method

Children who lack motivation and interest in learning and reading often demonstrate apathy and are easily discouraged, leading to a lack of concentration on their educational pursuits. As a result, these students face challenges in their academic journey. Therefore, it is crucial to make concerted efforts to enhance children's literacy by implementing the reading aloud approach, which can positively influence their overall growth and development in the Rusunawa Ciseureuh Purwakarta community. The primary objective of this program is achieved through the utilization of the reading-aloud technique. The anticipated outcomes of this program encompass increased children's motivation and interest in literacy, which will be supported by supplementary elements such as incorporating enjoyable learning techniques, engaging in gardening activities, decorating pots, and providing incentives for participating children.
Gledaj (TV) i uči? Važnost (utjelovljenog) kulturnog kapitala u objašnjenju klasične i digitalne medijske pismenosti

This paper aims to identify whether dimensions of cultural capital affect traditional and media literacy by using survey data collected in 2022 as part of the project Medijsko obrazovanje je važno (N=1033). Using Bourdieu's theoretical framework, three dimensions of cultural capital were operationalised as independent variables: objectified (number of books in the household), embodied (taste in TV programmes), and institutionalised (level of education) cultural capital. Traditional media literacy was operationalised as critical reading of media messages, while digital media literacy was operationalised as creating online digital content. Both of these variables were used as dependent variables in the analysis. Following principal component analysis (PCA), hierarchical regression analysis was conducted to establish whether cultural capital has explanatory power for both dimensions of media literacy. The results point to the statistically significant role of objectified cultural capital in explaining traditional media literacy, although that explanation’s statistical power remains questionable. Digital media literacy exhibited no statistically significant connection with all three cultural capital dimensions. However, additional analysis points to the relevance of digital skills as a more potent factor in explaining traditional and digital media literacy. Overall, digital capital represents a more relevant distinction factor than users’ classic cultural capital in a contemporary media environment. The implications of this data point to the conclusion that media literacy is primarily shaped through various forms of digital capital, although the role of television should not be neglected when developing future programmes in media literacy.
BIDS: Blockchain and Intrusion Detection System Coalition for Securing Internet of Medical Things Networks

The benefits of the Internet of Medical Things (IoMT) in providing seamless healthcare to the world are at the forefront of technological advancement. However, security concerns of any IoMT systems are high since they threaten to compromise personal information of patients and can even cause health hazards. Researchers are exploring the use of various techniques to ensure a high level of security of IoMT systems. One key concern is that the computing power of any Internet of Things (IoT) device is relatively low, hence mechanisms that require low computational power are appropriate for designing Intrusion Detection Systems (IDS). In this research work, a blockchain IDS coalition is proposed for securing IoMT networks and devices. The blockchain ledger is compact and uses less processing resources. Additionally, the ledger requires less communication overhead. The cryptographic hashes in the suggested architecture ensure complete data secrecy and integrity between parties who are trusted and those who are untrustworthy. Peer-to-peer networks in both central and cluster networks are also included in this work for complete decentralization. The proposed model can counter various attacks, including Denial of Service (DoS), anonymity attacks, impersonation attacks, Man-In-The-Middle (MITM), and Cross-Site Scripting (XSS). The proposed method achieved an F1- score as high as 100% and reported an AUC value of over 99%.
A Construction of Three Party Post Quantum Secure Authenticated Key Exchange Using Ring Learning With Errors and ECC Cryptography

A three-party authenticated key exchange protocol enables two entities to agree on a session key with the help of a dedicated server over public channel. Shor’s algorithm is a big threat for existing authenticated key exchange protocols. Lattice based cryptography plays a very important role in designing authentication and key agreement secure against the threat of quantum attacks. However, it is not easy job to design quantum resistant password based three party protocol due to the high demand for security requirements and the limited resources nature of mobile devices. In this article, we have proposed a new post quantum three party key exchange based on ring learning with errors assumption. This protocol is motivated from Islam et al.’s authenticated key exchange protocol. Their protocol is not secure against stolen smartcard attack, password guessing attack, and can’t provide user anonymity. The anonymous communication is a big requirement for practical applications like e-healthcare services/smart vehicular communication. This paper also contains the performance analysis of the proposed protocol along with other relevant protocols.
SCBM: A Hybrid Model for Vietnamese Visual Question Answering

Visual Question Answering (VQA) is a popular research topic that has gained attention from diverse fields like computer vision and natural language processing. While there are existing English foundation models that transfer well in downstream tasks like visual question answering, there is a lack of research in Vietnamese VQA (ViVQA). These attention-based models have shown high efficiency in generating spatial maps for relevant image regions or sentence parts, contributing to the success of these models. In this article, we propose a joint modeling approach for attention-based language and vision in ViVQA, called SCBM system, which achieves Accuracy, WUPS score 0.9, and WUPS score 0.0 of 0.6201, 0.6814, and 0.8719 on the ViVQA benchmark dataset, respectively. These results are twice as good as previous baselines (Co-attention combined with PhoW2V), opening up possibilities for further advancements in ViVQA. We also discuss the development path of ViVQA systems to achieve breakthroughs in this field.
A Real-Time 3-D Visual Detection-Based Soft Wire Avoidance Scheme for Industrial Robot Manipulators

Soft wires of robot operating tools often interfere with end-effector tasks in practical automated production scenarios. In order to avoid soft wires of industrial robot manipulators executing end-effector tasks, a real-time 3-D visual detection-based soft wire avoidance (3D-VDWA) scheme is proposed, which considers the soft wire detection and location, the soft wire avoidance, and the motion planning at the same time. The proposed 3D-VDWA includes three modules: 1) perception module; 2) motion planning module; and 3) soft wire avoidance module. The perception module is based on hue–saturation–value (HSV) range and depth information to detect and locate soft wires. The motion planning module is based on the method of the pseudo-inverse matrix to plan the path of the manipulator to the target position and orientation. The soft wire avoidance module is based on the Jacobian transpose method to dynamically avoid soft wire obstacles in the process of movement. Experiments demonstrate the effectiveness and the feasibility of the proposed scheme to solve the motion planning problem with soft wire avoidance of industrial manipulators for end-effector tasks.
Managing Networked IoT Assets Using Practical and Scalable Traffic Inference

The Internet has recently witnessed unprecedented growth of a class of connected assets called the Internet of Things (IoT). Due to relatively immature manufacturing processes and limited computing resources, IoTs have inadequate device-level security measures, exposing the Internet to various cyber risks. Prior research leveraged predictable patterns in IoT network traffic to develop inference models. However, they fall short of expectations in addressing practical challenges, preventing them from being deployed in production settings. This thesis identifies four practical challenges and develops techniques to address them which can help secure businesses and protect user privacy against growing cyber threats. My first contribution balances prediction gains against computing costs of traffic features for IoT traffic classification and monitoring. My second contribution addresses the challenges of measurement costs and data quality. I develop an inference method that uses stochastic and deterministic modeling to predict IoT devices in home networks from opaque and coarse-grained IPFIX flow data. Evaluations show that false positive rates can be reduced by 75% compared to related work without significantly affecting true positives. My third contribution focuses on the challenge of concept drifts by analyzing over six million flow records collected from 12 real home networks. Finally, my fourth contribution studies the resilience of machine learning models against adversarial attacks with a specific focus on decision tree-based models.
On Finding the Eigenvalues of the Matrix of Rotation Symmetric Boolean Functions

We consider the action on $\mathbb{F}_2^n$ by cyclic permutations ($\mathbb{Z}/n\mathbb{Z}$). Two elements $x, y\in \mathbb{F}_2^n$ are in the same orbit if they are cyclic shifts of each other. Cryptographic properties of rotation symmetric Boolean functions can be efficiently computed using the square matrix $_n\mathcal{A}$, the construction of which uses orbit representatives of the cyclic shifting action. In 2018, Ciungu and Iovanov proved that $_n\mathcal{A}^2=2^n\cdot I$, the identity matrix of dimension $g_n\times g_n$ where $g_n$ is the number of orbits. In this paper, we answer the open question of the precise number of positive and negative eigenvalues of $_n\mathcal{A}$.
LLMs as Potential Brainstorming Partners for Math and Science Problems

With the recent rise of widely successful deep learning models, there is emerging interest among professionals in various math and science communities to see and evaluate the state-of-the-art models' abilities to collaborate on finding or solving problems that often require creativity and thus brainstorming. While a significant chasm still exists between current human-machine intellectual collaborations and the resolution of complex math and science problems, such as the six unsolved Millennium Prize Problems, our initial investigation into this matter reveals a promising step towards bridging the divide. This is due to the recent advancements in Large Language Models (LLMs). More specifically, we conduct comprehensive case studies to explore both the capabilities and limitations of the current state-of-the-art LLM, notably GPT-4, in collective brainstorming with humans.
Security in Cryptocurrency

This paper discusses the mechanisms of cryptocurrency, the idea of using security in the system, and the popularity of it. To begin, the authors provide a background on cryptocurrency and how it works. The authors understand that while most people may be familiar with the concept, they may not know how it works. Next, the authors discuss the security of cryptocurrency in-depth within the paper. The authors also provide examples of attacks on cryptocurrency systems to show the vulnerabilities within the system. Lastly, the authors discuss the popularity of the system to further express the need for security in cryptocurrency.
LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation

With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number of training resources or learn motions that are precisely aligned with template videos. It is non-trivial to balance a trade-off between the degree of generation freedom and the resource costs for video generation. In our study, we present a few-shot-based tuning framework, LAMP, which enables text-to-image diffusion model Learn A specific Motion Pattern with 8~16 videos on a single GPU. Specifically, we design a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning. The well-developed text-to-image techniques can provide visually pleasing and diverse content as generation conditions, which highly improves video quality and generation freedom. To capture the features of temporal dimension, we expand the pretrained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers and modify the attention blocks to the temporal level. Additionally, we develop an effective inference trick, shared-noise sampling, which can improve the stability of videos with computational costs. Our method can also be flexibly applied to other tasks, e.g. real-world image animation and video editing. Extensive experiments demonstrate that LAMP can effectively learn the motion pattern on limited data and generate high-quality videos. The code and models are available at https://rq-wu.github.io/projects/LAMP.
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms

Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive to small changes in their environment. This presents a serious concern for the real world deployment of MARL algorithms, where the testing environment may slightly differ from the training environment. In this work we show that we can gain robustness by controlling a policy's Lipschitz constant, and under mild conditions, establish the existence of a Lipschitz and close-to-optimal policy. Based on these insights, we propose a new robust MARL framework, ERNIE, that promotes the Lipschitz continuity of the policies with respect to the state observations and actions by adversarial regularization. The ERNIE framework provides robustness against noisy observations, changing transition dynamics, and malicious actions of agents. However, ERNIE's adversarial regularization may introduce some training instability. To reduce this instability, we reformulate adversarial regularization as a Stackelberg game. We demonstrate the effectiveness of the proposed framework with extensive experiments in traffic light control and particle environments. In addition, we extend ERNIE to mean-field MARL with a formulation based on distributionally robust optimization that outperforms its non-robust counterpart and is of independent interest. Our code is available at https://github.com/abukharin3/ERNIE.
Extracting Quantum Dynamical Resources: Consumption of Non-Markovianity for Noise Reduction

Noise is possibly the most formidable challenge for quantum technologies. As such, a great deal of effort is dedicated to developing methods for noise reduction. One remarkable achievement in this direction is dynamical decoupling; it details a clear set of instructions for counteracting the effects of quantum noise. Yet, the domain of its applicability remains limited to devices where exercising fast control is possible. In practical terms, this is highly limiting and there is a growing need for better noise reduction tools. Here we take a significant step in this direction, by identifying the crucial ingredients required for noise suppression and the development of methods that far outperform traditional dynamical decoupling techniques. Using resource theoretic methods, we show that the key resource responsible for the efficacy of dynamical decoupling, and related protocols, is non-Markovianity (or temporal correlations). Using this insight, we then propose two methods to identify optimal pulse sequences for noise reduction. With an explicit example, we show that our methods enable a more optimal exploitation of temporal correlations, and extend the timescales at which noise suppression is viable by at least two orders of magnitude. Importantly, the corresponding tools are built on operational grounds and are easily implemented in the current generation of quantum devices.
BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling

The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme that enables efficient distributed data-parallel training on sequences of different sizes with minimal overhead. By using this scheme we were able to reduce the padding amount by more than 100$x$ while not deleting a single frame, resulting in an overall increased performance on both training time and Recall in our experiments.
Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation

To explore how humans can best leverage LLMs for writing and how interacting with these models affects feelings of ownership and trust in the writing process, we compared common human-AI interaction types (e.g., guiding system, selecting from system outputs, post-editing outputs) in the context of LLM-assisted news headline generation. While LLMs alone can generate satisfactory news headlines, on average, human control is needed to fix undesirable model outputs. Of the interaction methods, guiding and selecting model output added the most benefit with the lowest cost (in time and effort). Further, AI assistance did not harm participants' perception of control compared to freeform editing.