Reviewerly/exHarmony huggingface dataset:
Entangled photon-pair source using a wedge-shaped nonlinear crystal

Entangled photon pairs constitute a crucial resource in quantum technologies. The current technique for preparing photon pairs involves performing spontaneous parametric down-conversion (SPDC) in bulk nonlinear crystals. The increased prominence of quantum technologies has led to a growing interest in high performance entangled photon-pair sources. However, the birefringence walk-off caused by nonlinear crystals affects the quality of the entangled photon pairs generated in SPDC. This letter presents an innovative approach for producing entangled photon pairs using a wedge-shaped nonlinear crystal to compensate for the birefringence walk-off. The proposed scheme features a basic entanglement source path, which can be applied to other methods of preparing entangled photon pairs. An experiment demonstrated that this method can produce high-quality entangled photon pairs with a high degree of entanglement, paving the way for the development of practical quantum communication, sensing, and computation.
An efficient permutation approach for SbPN-based symmetric block ciphers

Abstract It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained devices. Therefore, an efficient hardware design approach that requires some specific hardware resource may not be efficient if implemented in software. Substitution bit Permutation Network based ciphers such as PRESENT and GIFT are efficient, lightweight cryptographic hardware design approaches. These ciphers introduce confusion and diffusion by employing a 4 × 4 static substitution box and bit permutations. The bit-wise permutation is realised by simple rerouting, which is most cost-effective to implement in hardware, resulting in negligible power consumption. However, this method is highly resource-consuming in software, particularly for large block-sized ciphers, with each single-bit permutation requiring multiple sub-operations. This paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding feature. The conventional permutation using bit rotation and the proposed approach have been implemented, analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development Environment. The real-time performance comparison between conventional and the proposed approaches in terms of memory (RAM/ROM) footprint, power, energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation scenarios. The proposed approach substantially reduces execution time, energy and power consumption for both PRESENT and GIFT ciphers, thus demonstrating the efficiency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers.
One-Class Fake Speech Detection Based on Improved Support Vector Data Description

With the development of deep neural synthesis methods, speech forgery techniques based on text-to-speech (TTS) and voice conversion (VC) pose a serious threat to auto speaker verification (ASV) systems. Some studies show that the attack success rate of deep synthetic speech on ASV systems can reach about 90%. Existing detection methods improve the detection generalization of known forgery methods by a lot of training data, but the detection effect and robustness against unknown methods are poor. We propose an anti-spoofing scheme based on one-class classification for detecting unknown synthetic. We implement deep support data description to capture the feature of bonafide speech. An autoencoder structure is introduced to enhance the detection performance. The proposed method is only trained on native speech, which reduces reliance on large amounts of fake speech. Our method achieves an equal error rate of 8.10% on the evaluation set of ASVspoof 2019 challenge and outperforms other state-of-the-art methods. In the generalization test, the proposed method can reach the equal error rate of 15% on “In-the-wild” dataset and 23% on FoR dataset, which is lower than that of other advanced algorithms.
A novel path generation method for robotic measurement with local pruning and collision-free adjustment

Abstract Robotic measurement is an essential task during downtime in the nuclear power underwater environment. However, the narrow operating space of the underwater nuclear environment brings about an enormous challenge about the efficiency and safety of robotic measurement. This paper proposes a novel path-generation method with local pruning and collision-free adjustment. The proposed method increases the distance between the path and obstacles and reduces the number of collision detections. This core idea is to calculate extreme value points and path feature points as well as the collision-free adjustment for a smooth path. Firstly, the path is divided into subintervals where interval’s endpoints are the extreme value points. Next, the path points are distinguished by path feature points in each subinterval. A two-level connection strategy is proposed to connect the path points among path feature points within the constraints of curvature and density. Then, the smooth path is adjusted to avoid obstacles. The use of extreme value points and path feature points contributes to increasing the efficiency of path generation and improving the safety of the robotic measurement. Simulations and practical experiments are carried out to validate the feasibility of the proposed method over another method. Path safety index (PSI) is defined to measure the distance between path and obstacles. The efficiency of path generation is improved by up to 101% and PSI is improved by up to 20.75%.
Programmable photonic chips and applications

The high demand for AI services in conjunction with a dramatic chip shortage along with technology leaps such as 5/6G networks, quantum technologies, cybersecurity threats, and the CHIPS Act have resurrected a R&D push for next-generation semiconductor materials, devices, and information processing hardware capability. In this paper, we will present the latest advances in regarding programable photonics and chip, from devices to packaging and system architecture, bringing these innovations into the bigger picture of macro trends and national priorities such as Industry-4.0 and the recent U.S. CHIPS Act.
How Can We Design Privacy-Friendly Apps for Children? Using a Research through Design Process to Understand Developers' Needs and Challenges

Mobile apps used by children often make use of harmful techniques, such as data tracking and targeted advertising. Previous research has suggested that developers face several systemic challenges in designing apps that prioritise children's best interests. To understand how developers can be better supported, we used a Research through Design (RtD) method to explore what the future of privacy-friendly app development could look like. We performed an elicitation study with 20 children's app developers to understand their needs and requirements. We found a number of specific technical requirements from the participants about how they would like to be supported, such as having actionable transnational design guidelines and easy-to-use development libraries. However, participants were reluctant to adopt these design ideas in their development practices due to perceived financial risks associated with increased privacy in apps. To overcome this critical gap, participants formulated socio-technical requirements that extend to other stakeholders in the mobile industry, including parents and marketplaces. Our findings provide important immediate and long-term design opportunities for the HCI community, and indicate that support for changing app developers' practices must be designed in the context of their relationship with other stakeholders.
How Stated Accuracy of an AI System and Analogies to Explain Accuracy Affect Human Reliance on the System

AI systems are increasingly being used to support human decision making. It is important that AI advice is followed appropriately. However, according to existing literature, users typically under-rely or over-rely on AI systems, and this leads to sub-optimal team performance. In this context, we investigate the role of stated system accuracy by contrasting the lack of system information with the presence of system accuracy in a loan prediction task. We explore how the degree to which humans understand system accuracy influences their reliance on the AI system, by investigating numeracy levels and with the aid of analogies to explain system accuracy in a first of its kind between-subjects study (N=281). We found that explaining the stated accuracy of a system using analogies failed to help users rely on the AI systemappropriately (i.e., the tendency of users to rely on the system when the system is correct, or on themselves otherwise). To eliminate the impact of subjective attitudes towards analogy domains, we conducted a within-subjects study (N=248) where each participant worked on tasks with analogy-based explanations from different domains. Results from this second study confirmed that explaining stated accuracy of the system with analogies was not sufficient to facilitate appropriate reliance on the AI system in the context of loan prediction tasks, irrespective of individual user differences. Based on our findings from the two studies, we reason that the under-reliance on the AI system may be a result of users' overestimation of their own ability to solve the given task. Thus, although familiar analogies can be effective in improving the intelligibility of stated accuracy of the system, an improved understanding of system accuracy does not necessarily lead to improved system reliance and team performance.
PhotoFourier: silicon photonics joint transfer correlator for convolution neural network

Convolution Neural Networks are one of the pillars of the Machine Learning revolution over the last years. However, convolution operation is associated with a high computational cost. Here, we present a compact and high-speed solution to perform convolution leveraging optics on a chip, with the potential to reach over 350 TOPS/W.
"Thoughts &amp; Prayers" or " ❤️ &amp; 🙏 ": How the Release of New Reactions on CaringBridge Reshapes Supportive Communication in Health Crises

Following Facebook's introduction of the "Like" in 2009, CaringBridge (a nonprofit health journaling platform) implemented a "Heart" symbol as a single-click reaction affordance in 2012. In 2016, Facebook expanded its Like into a set of emotion-based reactions. In 2021, CaringBridge likewise added three new reactions: "Prayer", "Happy", and "Sad." Through user surveys (N=808) and interviews (N=13), we evaluated this product launch. Unlike Likes on mainstream social media, CaringBridge's single-click Heart was consistently interpreted as a simple, meaningful expression of acknowledgement and support. Although most users accepted the new reactions, the product launch transformed user perceptions of the feature and ignited major disagreement regarding the meanings and functions of reactions in the high stakes context of health crises. Some users found the new reactions to be useful, convenient, and reducing of caregiver burden; others felt they cause emotional harms by stripping communication of meaningful expression and authentic care. Overall, these results surface tensions for small social media platforms that need to survive amidst giants, as well as highlighting crucial trade-offs between the cognitive effort, meaningfulness, and efficiency of different forms of Computer-Mediated Communication (CMC). Our work provides three contributions to support researchers and designers in navigating these tensions: (1) empirical knowledge of how users perceived the reactions launch on CaringBridge; (2) design implications for improving health-focused CMC; and (3) concrete questions to guide future research into reactions and health-focused CMC.
"I don't know how to help with that" - Learning from Limitations of Modern Conversational Agent Systems in Caregiving Networks

While commercial conversational agents (CA) (i.e. Google assistant, Siri, Alexa) are widely used, these systems have limitations in error-handling, flexibility, personalization and overall dialogue management that are amplified in care coordination settings. In this paper, we synthesize and articulate these limitations through quantitative and qualitative analysis of 56 older adults interacting with a commercial CA deployed in their home for a 10 week period. We look at the CA as a compensatory technology in an older adult's care network. We argue that the CA limitations are rooted in the rigid cue-and-response style of task-oriented interactions common in CAs. We then propose a redesign for CA conversation flow to favor flexibility and personalization that is nonetheless viable within the limitations of current AI and machine learning technologies. We explore design tradeoffs to better support the usability needs of older adults compared to current design optimizations driven by efficiency and privacy goals.
"How Do You Quantify How Racist Something Is?": Color-Blind Moderation in Decentralized Governance

Volunteer moderators serve as gatekeepers for problematic content, such as racism and other forms of hate speech, on digital platforms. Prior studies have reported volunteer moderators' diverse roles in different governance models, highlighting the tensions between moderators and other stakeholders (e.g., administrative teams and users). Building upon prior research, this paper focuses on how volunteer moderators moderate racist content and how a platform's governance influences these practices. To understand how moderators deal with racist content, we conducted in-depth interviews with 13 moderators from city subreddits on Reddit. We found that moderators heavily relied on AutoMod to regulate racist content and racist user accounts. However, content that was crafted through covert racism and "color-blind'' racial frames was not addressed well. We attributed these challenges in moderating racist content to (1) moderators' concerns of power corruption, (2) arbitrary moderator team structures, and (3) evolving forms of covert racism. Our results demonstrate that decentralized governance on Reddit could not support local efforts to regulate color-blind racism. Finally, we discuss the conceptual and practical ways to disrupt color-blind moderation.
Towards Intersectional Moderation: An Alternative Model of Moderation Built on Care and Power

Shortcomings of current models of moderation have driven policy makers, scholars, and technologists to speculate about alternative models of content moderation. While alternative models provide hope for the future of online spaces, they can fail without proper scaffolding. Community moderators are routinely confronted with similar issues and have therefore found creative ways to navigate these challenges. Learning more about the decisions these moderators make, the challenges they face, and where they are successful can provide valuable insight into how to ensure alternative moderation models are successful. In this study, I perform a collaborative ethnography with moderators of r/AskHistorians, a community that uses an alternative moderation model, highlighting the importance of accounting for power in moderation. Drawing from Black feminist theory, I call this "intersectional moderation." I focus on three controversies emblematic of r/AskHistorians' alternative model of moderation: a disagreement over a moderation decision; a collaboration to fight racism on Reddit; and a period of intense turmoil and its impact on policy. Through this evidence I show how volunteer moderators navigated multiple layers of power through care work. To ensure the successful implementation of intersectional moderation, I argue that designers should support decision-making processes and policy makers should account for the impact of the sociotechnical systems in which moderators work.
Image Captioning - A Deep Learning Approach Using CNN and LSTM Network

An image caption generator is a system that uses artificial intelligence and computer vision to analyze an image and generate a written description or a caption. The caption gives a brief description which accurately aligns with the content of the image. The various elements within the image are recognized and interpreted using deep learning techniques. The process of using datasets to train the model to assign English language labels or descriptors to an image is known as image tagging. Tagging helps identify an image and its description for easier search and retrieval in the future. In our research, a new model is suggested that utilizes an encoder-decoder architecture to generate appropriate and grammatically correct captions for images. This model employs methods from both image analysis and natural language processing/generation to examine and characterize pictures. The goal is to generate accurate captions that precisely convey the content of the images. We utilize a particular deep learning architecture called VGG16 in this method. VGG16 is a Convolutional Neural Network (CNN) that has demonstrated exceptional performance in image recognition. The VGG16 architecture is used as the encoding layer to extract important features from the image. After the VGG16 model processes the image, the results are fed into an LSTM (Long Short-Term Memory), a type of recurrent neural network, which then predicts or generates a textual description of the image, one word at a time. For generating accurate captions, it is trained on a set of labelled images and their corresponding captions called the Flickr8k Captions dataset. This dataset is used to provide the model with the ground truth captions. After the training phase is completed, the model creates descriptions for a group of test images. These generated captions are then compared to the actual captions present in the test dataset. The comparison is done using a metric called the BLEU score, which is a measure of the accuracy of the generated captions. The effectiveness of the model is determined based on this score.
Turtl: IPFS Based Decentralized File Sharing System

Social networking sites have been with us since the early 2000s, and now online social media networking platforms have revolutionized the way we connect and communicate with each other. These social networking sites expanded exponentially, social media giants like Facebook, Instagram, Twitter, and Snapchat. Due to this there's a massive influx of personal information that has become available online and stored in the cloud. But most of these companies use centralized databases and, in centralized there's a risk of data breaches. It's important for individuals to be aware of risks associated with social media and to take steps to protect their personal information online. So, through this approach is to propose a blockchain based web app which would help in storing, sharing, or uploading files in a secured and decentralized way. Because the entire system is based on blockchain, IPFS is more secure and safer than traditional centralized database systems. Via IPFS, users' data is more secure, data breaches are also prevented, and there is a very small possibility that the user content ID will leak. Because the content ID is encrypted using a hashing algorithm each time a user uploads content, the system improves security and user privacy through end-to-end encryption and decentralization. User data privacy is improved by encryption using a hashing method.
Parallel Computing and Deep Learning-Based Techniques for Colorization of Visual Media

The goal of visual media colorization, which is a basic and fundamental subject in computer graphics, is the addition of color to a monotone source image in order to get a vivid bright-colored result. Currently, artists around the world must manually decide the colors using editing software. This study intends to automate this process of converting a black-and-white medium into a colored one. In this research work, Deep Learning for visual media colorization with the help of parallel computing is proposed, with a simple colorization method in which the model detects objects and imparts color to the input feed based on the trained dataset. A model will be trained based on CNN to automatically convert grayscale video feed into the colored video feed.
An Image Encryption Model Using Hyper Chaotic Map Dependent Grey-Intelligent Optimization

With the rapid growth of technologies and scientific innovations, information sharing was made digital, like text messages, file sharing, and pictures and audio messages especially, digital images have been found widely in economics, politics, education, and some other important fields. Methods to securely as well as secretly transmit and store digital images, particularly those that contain private information, have thus become an important concern in those domains. Image encryption technology is one to ensure the safety of image information, as it provides a pattern that is effective for people in transferring and receiving securely over a network. In this research, the Hyperchaotic map image encryption algorithm is proposed based on Grey-intelligent optimization. The optimal range of chaotic parameters in the model offers the secret image excellent security as well as with hyper-hybrid mapping, chaos encryption is used to encrypt the secret image. The proposed method achieved an improvement of 18.08% for MSE, 3.47% for PSNR, 18.55% for RMSE, and 0.33% for SSIM, demonstrating the model's efficacy and excellence of the research.
An Exploratory Analysis of Cloud Security Models in Social Networks

Social networks have seen a rapid increase in usage and popularity over the past decade, with billions of users worldwide engaging in various platforms. As these platforms continue to store and manage massive amounts of user data, concerns about data security and privacy have grown. This research study addresses the central research question of how existing cloud security models address the unique security challenges associated with social networks and explores the improvements that can be made to enhance protection against threats. This research study examines the current cloud security models utilized by social networks and explores their effectiveness in addressing the unique security challenges posed by these platforms. This research study also identifies the areas for improvement and proposes potential solutions to enhance the security of social networks.
Chilling Tales: Understanding the Impact of Copyright Takedowns on Transformative Content Creators

Though current conversations around automated content moderation often focus on newer contexts for this technology such as harassment or misinformation, one other important use case is copyright detection systems such as YouTube's Content ID. This context for content removals online has some unique properties such as its relationship to legal requirements (Section 512 of the Digital Millennium Copyright Act that governs copyright notice-and-takedown), and provides a longstanding example of the intersection between policy, online platform design, and algorithms. Based on qualitative analysis of survey data, this paper examines the impact of DMCA takedowns and automated copyright-related content removals on a community of content creators who primarily share transformative works, remixed content that makes use of copyrighted material. Our data reveals patterns of chilling effects related to prior experiences and perceptions of policies and process. Based on these findings we make recommendations for platform policy and design, education, and advocacy, and discuss the implications of our findings for current and proposed laws.