Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Transmitter for Quantum Communication Based on Faint Pulse Laser

The advent of quantum computers marks the beginning of the quantum 2.0 era. Unlike classical computers, quantum computers have faster and more efficient computational abilities, enabling them to break classical encryption such as the RSA (Rivest-Shamir-Adlemar) algorithm in a matter of minutes. To address this threat, a quantum communication system is required, which possesses quantum properties as well. The urgency of developing an independent and autonomous quantum communication system is crucial for a country. In this research, a transmitter for quantum communication based on faint pulse laser is developed. A weakened laser diode is used as the photon source. The laser diode weakening is achieved using a laser driver that generates short-duration pulse signals. By combining AND and XOR logic gates in such a way that when given a square wave input, the output of the logic gate system can produce short-duration pulses. The information in this communication system consists of randomly implemented keys, realized using the myRIO board and serving as a subsystem for random port selection. The designed laser driver produces pulse signals with a width of 128 nanoseconds and an amplitude of 1.5168 V. Additionally, the random port selection subsystem has been successfully designed using the myRIO board. Random numbers from ‘0’ to ‘3’ are generated uniformly. From 100 observation samples, an expected value of 1.445 and a variance of 0.911 are obtained. In the subsequent research, observations of single photon and the generated keys will be conducted.
Simulating Early Childhood Drawing Behaviors under Physical Constraints Using Reinforcement Learning

The ability to draw is a skill that children naturally develop over time. The interaction between a child's body and the environment plays a crucial role in this developmental process, yet there has been limited research exploring how physical properties of these interactions influence drawing development. We address this issue through a simulation study using a novel system: a reinforcement learning drawing agent within a simulated physical environment of a pen and a canvas. We demonstrate that acquired drawing behaviors can diverge based solely on the difference in physical configurations, particularly viscous resistance and that drawing patterns resembling those of young children can emerge. Also, we show that appropriate physical setups are important for an agent to efficiently learn drawing. These findings imply that when discussing how children's drawing behaviors develop, it is preferable to take the physical characteristics of the drawing environment into account. Moreover, our proposed system can be a new tool to investigate the development of children's drawing behaviors.
Using Zero-Knowledge Proof in Privacy-Preserving Networks

The role of digital identity systems in today’s cyber infrastructure is pivotal to providing secure access to online services. Privacy-preserving mechanisms, however, are becoming more important as cyber threats develop. Especially in large networks, zero-knowledge proofs (ZKPs) are an effective way to enhance privacy in digital identity systems. When used during authentication, they protect the privacy of user data by verifying knowledge to another entity without unveiling the actual data. A ZKP can also reduce identity theft risks, mitigate man-in-the-middle attacks, and enhance security when integrated into privacy-preserving networks. In this paper, we examine the role that ZKPs play in privacy-preserving networks and the possibility of using them in this regard, in order to gain insight into their application in digital identity systems.
Free-Hand Gesture Recognition Using Conv3D-Networks with Cross Stitch Units for Multi-Modal Data

Free-hand gesture recognition is a challenging task that has applications in various domains, including human-computer interaction, virtual reality, and gaming. In this paper, we propose a method for free-hand gesture recognition using Convolutional 3D networks with Cross Stitch Units for multimodal data. Our approach combines the intermediate representations learned by Convolutional 3D networks from the RGB and depth modalities using Cross Stitch Units, which allows the network to learn more robust and effective features for the task. We evaluate our approach on the publicly available Multi-Modal Hand Gesture Dataset (MMHGD). The MMHGD consists of a high number of samples of six free-hand gesture classes. It offers four modalities: RGB images, 3D point clouds, acceleration data recorded by a device on the users wrist, and audio data. First, we set a baseline by performing uni-modal gesture recognition on each of the two modalities (RGB and 3D) using a state-of-the-art Convolutional 3D network architecture. Then we show that free-hand gesture recognition can be further improved by using Cross Stitch Units to fuse both modalities. We show that uni-modal gesture recognition using Convolutional 3D networks already offers an acceptable gesture recognition accuracy. Using Cross Stitch Units for multimodal fusion can further improve the achieved gesture recognition accuracy and thus lead to even more reliable results.
A Systematic Review of Noise Types, Denoising Methods, and Evaluation Metrics in Images

The removal of noise from images while retaining their quality and information is a crucial task in the field of image processing, and is commonly known as image denoising. As the use of digital images has become increasingly widespread in various fields, including medical imaging, astronomy, and microscopy, the problem of image noise has become more prominent. Image noise, which is any unwanted variation in brightness or color in an image, can significantly reduce the image quality and usefulness. To address this issue, various noise reduction techniques have been developed, such as filtering, wavelet denoising, and deep learning-based methods. However, selecting the appropriate denoising method for a specific type of noise and image can be challenging, and objective performance metrics are required to evaluate the effectiveness of different methods. Therefore, a thorough analysis of the various kinds of noise that can appear in digital images, the denoising techniques that are available, and the performance metrics that are used to assess them, is required. The goal of this review is to shed light on the importance of selecting the best denoising technique for the particular application at hand. This review will help researchers and practitioners choose the best denoising technique for their specific application by illuminating the benefits and drawbacks of various techniques.
Generating Synthetic Data from Large Language Models

Data collection for studying social phenomena is not only costly but is also, at best, a time-consuming and tedious task. Therefore, tools that may ease the task of data collection will speed up these studies and improve their efficiency. In this contribution, we argue that in some cases Large Language Models (LLMs) may serve as a tool to generate data for studying social phenomena. The rationale is that LLMs absorb a vast amount of data from various types and sources; and embed (an abstraction of) the data in models. Querying these models generates synthetic data that can be considered as a good approximation of the data on which they are trained. The methodological and practical issues involved in our rationale are discussed in this paper. By means of a use case, we illustrate how synthetic data can be generated (or collected) from GPT and how the data can be used for studying stereotypical views on social groups.
Hand Gesture Recognition-Based Control System Using Computer Vision for Transporter Robot

Robotics is a discipline that has experienced rapid development in recent decades. One of the main focuses in robotics development is the advancement of control systems. The primary objective is to create control systems that are more natural, easily understood, easily operated by humans, thereby enabling more intuitive interactions between humans and robots. To achieve this goal, research based on hand gesture recognition has been conducted by adapting computer vision methods as the control system for controlling a transporter robot. In this paper, computer vision methods are employed using the image processing framework called MediaPipe. MediaPipe is used to recognize and comprehend hand gesture movements, as well as display hand landmarks. The research utilizes two hand landmark functions: “Finger Up” for the right hand to control the wheel movement, and “Range Gesture” for the left hand to control the arm and gripper of the transporter robot. The testing results indicate that “Finger Up” function for the right hand successfully controls robot transporter’s wheels with a success rate of 100%. Meanwhile, “Range Gesture” function for the left hand effectively controls the transporter robot’s arm and gripper with a success rate of 97%, with a total average error of 3%.
The Importance of Growing Up: Progressive Growing GANs for Image Inpainting

In recent years, Generative Adversarial Networks (GANs) have proven to be a sophisticated approach for generative tasks in image processing, especially inpainting and image synthesis While most GAN approaches feature comparatively large networks, we introduce an approach to image inpainting using progressive growing GANs, which enables significantly reduced model sizes, faster convergence, and thus an overall more efficient training that is inspired by insights on the development of visual abilities in biological systems. We demonstrate the effectiveness and efficiency of our approach on Places, a comprehensive dataset encompassing a wide variety of images from diverse locations in the wild.
uSAP: An Ultra-Fast Stochastic Graph Partitioner

Stochastic graph partitioning (SGP) plays a crucial role in many real-world applications, such as social network analysis and recommendation systems. Unlike the typical combinatorial graph partitioning problem, SGP presents unique computational difficulties due to time-consuming sampling processes. To address this challenge, the recent HPEC launched the Stochastic Graph Partitioning Challenge (SGPC) to seek novel solutions from the high-performance computing community. Despite many SGP algorithms over the last few years, their speed-ups are not remarkable because of various algorithm limitations. Consequently, we propose uSAP, an ultra-fast stochastic graph partitioner to largely enhance SGP performance. uSAP introduces a novel strongly connected component-based initial block merging strategy to reduce the number of partitioning iterations significantly. To further improve the runtime and memory performance, uSAP adopts a dynamic batch parallel nodal block assignment algorithm and a dynamic matrix representation. We have evaluated uSAP on the 2022 official HPEC SGPC benchmarks. The results demonstrate the promising performance of uSAP on graphs of different sizes and complexities. For example, uSAP achieves 129.4x speed-up over the latest champion on a graph of 50K nodes.
Analysis of Sentiments in Low Resource Languages: Challenges and Solutions

Sentiment analysis, which enables the automatic identification and classification of sentiments conveyed in textual data, has emerged as a major topic of study in Natural Language Processing (NLP). While analyzing sentiment for high-resource languages has made significant advancements, low-resource languages have received relatively little attention. This study attempts to investigate the challenges and potential solutions associated with sentiment analysis in low-resource languages. This paper also suggests some cutting-edge strategies to increase sentiment analysis's precision and applicability in less-resource languages. The fusion of linguistic expertise and advanced methodologies paves the way for a deeper understanding of low-resource language sentiments.
Blockchain-Enabled SAGIN Communication for Disaster Prediction and Management

The Sixth Generation (6G) network will rely on the integration and cooperation of a plethora of communication and networking systems. Space-air-ground integrated networks (SA-GIN) which integrate satellites, aerial networks, and terrestrial communication, can provide seamless and continuous delivery of services and applications with high levels of performance. While SAGIN provides promising solutions for data collection and communication, it poses security concerns given that it may allow untrusted devices to be part of the cooperative system. In this paper we introduce a blockchain-enabled SAGIN framework that authenticates both devices and the data collected by SAGIN participants. Federated Learning (FL) is supported by the framework to enable network distribution and accurate real-time data analysis. As a proof-of-concept, the solution considers a use-case for earthquake event prediction and monitoring. Satellite sensors are used to collect data and identify the presence of thermal anomalies used for earthquake forecasting. Furthermore, a post-earthquake monitoring technique is used which relies on Unmanned Aerial Vehicles (UAVs) to provide access to remote places in order to collect high quality images. The collected images are then processed locally on UAV swarms or through edge devices for real-time decision making. An experimental study is conducted on models and approaches needed to increase prediction accuracy for earthquake events.
Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play

Infants' ability to recognize and categorize objects develops gradually. The second year of life is marked by both the emergence of more semantic visual representations and a better understanding of word meaning. This suggests that language input may play an important role in shaping visual representations. However, even in suitable contexts for word learning like dyadic play sessions, caregivers utterances are sparse and ambiguous, often referring to objects that are different from the one to which the child attends. Here, we systematically investigate to what extent caregivers' utterances can nevertheless enhance visual representations. For this we propose a computational model of visual representation learning during dyadic play. We introduce a synthetic dataset of ego-centric images perceived by a toddler-agent that moves and rotates toy objects in different parts of its home environment while “hearing” caregivers' utterances, modeled as captions. We propose to model toddlers' learning as simultaneously aligning representations for 1) close-in-time images and 2) co-occurring images and utterances. We show that utterances with statistics matching those of real caregivers give rise to representations supporting improved category recognition. Our analysis reveals that a small decrease/increase in object-relevant naming frequencies can drastically impact the learned representations. This affects the attention on object names within an utterance, which is required for efficient visuo-linguistic alignment. Overall, our results support the hypothesis that caregivers' naming utterances can improve toddlers' visual representations.
Frequency Monitoring Interface Design and System using HackRF

Kominfo as a regulator in communication technology, especially in the distribution of frequency usage, always monitors the frequency circulating in the field. However, Indonesia’s vast territory makes some areas outside urban areas difficult to reach. Therefore, a portable frequency monitoring system will be created. This prototype was created to maximize the monitoring system in the form of storage, processing, and visualization. Therefore, a dashboard interface that is integrated with Hack RF will be created with historical features and good visualization. This feature is needed with the aim that there are no errors or violations in the use of frequencies circulating in open spaces. The frequency monitoring system created will be accessible from any device in the same network as the website GUI system and integrated with SDR HackRF. In the GUI we will be able to listen to the sound according to the frequency we set, and the most important thing is to know the three main aspects of the spectrum, namely frequency, bandwidth, and gain. These three aspects will then be inputted from the dashboard into the database which will then be compared with the legal frequency data prototype. The results of the comparison will generate an alert system using the telegram bot.
Toward Understanding Bugs in Swift Programming Language

Swift programming language has been widely used in IOS application development and has formed a perfect Apple development ecosystem due to its syntactic simplicity and functionality. However, as a complex programming language, Swift inevitably has problems, which may cause the program to fail to run normally. In this paper, we empirically analyze the ones in the Swift language. We collected 7446 bugs and 2749 revisions and manually analyzed the root causes of 180 bugs. We found that defects in Swift are unevenly distributed in components and source files; the test cases are small in size, and the complexity and workload of defect fixing are not significant; the symptoms of defects manifest themselves in various forms, but mainly in the form of Crash; and the root causes of defects are mostly semantic bugs. The conclusions drawn based on the findings are helpful for the development, testing, maintenance, and application of the Swift programming language.
Real-World Robot Control and Data Augmentation by World-Model Learning from Play

Building generalist robots that can perform well in complex environments requires high generalization ability. Imitation learning and reinforcement learning are two promising approaches, but they have a tradeoff between data collection cost and data richness. To overcome this tradeoff, we focus on “play data” collected by humans teleoperating a robot out of curiosity. Specifically, we propose a framework for real-world robot control and data augmentation based on world-model learning from play data. We demonstrate that our framework can be used to train robots to perform goal-conditioned object-manipulation tasks in the real world. Furthermore, we show that imagination in the world model can be used to create novel combinations of actions that were not included in the original play data. These findings suggest that further learning from the augmented data may enable robots to acquire a higher generalization ability.
CHSTS: Chameleon Hash based on Schnorr threshold signature on Editable Blockchain

Blockchain's immutability, while a core feature, can pose challenges in cases involving sensitive information or compliance with legal regulations, hindering its development. Many subsequent works designed editable blockchains based on the chameleon hash function proposed by H. Krawczyk et al. to solve the above problems. However, existing editable blockchain solutions lack security and are inefficient. To address the security and efficiency issues, we introduce the novel concept of Chameleon hashing based on Schnorr threshold signatures (CHSTS). The CHSTS scheme has double trapdoors, the publisher of the message owns the long-term trapdoor, and the editorial committee owns the ephemeral trapdoor. Message publishers can collaborate with the editorial committee to modify their published information, preventing the exposure of private data, facilitating oversight during the editing process, and enabling the tracking of malicious users. We have designed an updatable ephemeral trapdoor based on Schnorr signatures, effectively avoiding the inefficiencies associated with bilinear pairing. Experimental results show that our CHSTS scheme has a significant advantage in computational cost.
Identifying Key Aspects of How People Experience Co-creative System: In Reference to Weavesilk Application

In the evolving landscape of co-creative systems, an intersection of human creativity and artificial intelligence (AI), this research aims to uncover the subjective experiences of individuals engaged in human-AI, human-human, and machine-machine interactions. While co-creative systems have made significant strides, understanding the nuances of user experiences remains an understudied area. This study bridges the gap by systematically exploring the key aspects of user experiences and their influence on the development and efficacy of co-creative systems. The multifaceted nature of co-creative systems enables users to explore, create, and solve problems across domains, such as art, poetry, humor, robotics, and computer games. To grasp the significance of user experiences within these systems, it is imperative to consider diverse modes of interaction human-human collaboration, human-AI engagement, and machine-machine collaboration, each contributing distinct challenges and opportunities to the user experience. A critical research gap emerges when considering the technical capabilities of co-creative systems in comparison to the exploration of user experiences. This research seeks to address this imbalance by delving into the personal narratives of participants who engage with co-creative systems. By employing a qualitative approach and thematic analysis, we reveal invaluable insights into user experiences. The diverse participant pool, representing regions including India, Hong Kong, Nigeria, and Sri Lanka, enriches this exploration by offering a comprehensive understanding of user perspectives. The thematic analysis uncovers recurring themes that encapsulate the dimensions of user experiences, covering both positive and negative aspects. Key themes include the sharing of workloads on tasks, the systematic navigation of steps in the creative process, and the mechanics of user interaction with the system. These insights bridge the divide between theoretical research and practical application, offering actionable implications for the development of more user-centric co-creative systems. This research holds significant relevance to the conference, as it addresses pivotal questions in the co-creative systems domain and contributes to the ongoing discourse on the role of these systems in fostering human-AI collaboration and driving innovation across various domains. Focusing on user experiences paves the way for co-creative systems that are not only technically advanced but also engaging and transformative.
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments

Model-based next state prediction and state value prediction are slow to converge. To address these challenges, we do the following: i) Instead of a neural network, we do model-based planning using a parallel memory retrieval system (which we term the slow mechanism); ii) Instead of learning state values, we guide the agent's actions using goal-directed exploration, by using a neural network to choose the next action given the current state and the goal state (which we term the fast mechanism). The goal-directed exploration is trained online using self-supervised learning, via action selection given any start and goal state experienced in the trajectory obtained during hippocampal replay. Empirical studies show that our proposed method has a 91.9% solve rate across 100 episodes in a dynamically changing grid world, significantly outperforming state-of-the-art actor critic mechanisms such as PPO (61.2%), TRPO (26.1%) and A2C (23.9%), as well as replay buffer methods such as DQN (4.9%). Ablation studies demonstrate that both fast and slow mechanisms are crucial, and increasing both the depth and breadth of memory retrieval improves performance. We posit that the future of Reinforcement Learning (RL) will be to model goals and sub-goals for various tasks, and plan it out in a goal-directed memory-based approach.